The Blueprint for Industrial Transformation: Building a Strong Data Foundation with AWS IoT SiteWise
AWS IoT SiteWise is a managed service that makes it easy to collect, organize, and analyze data from industrial equipment at scale, helping customers make better, data-driven decisions. Our customers such as Volkswagen Group, Coca-Cola İçecek, and Yara International have used AWS IoT SiteWise to build industrial data platforms that allow them to contextualize and analyze Operational Technology (OT) data generated across their plants, creating a global view of their operations and businesses. In addition, our AWS Partners such as Embassy of Things (EOT), Tata Consulting Services (TCS) Edge2Web, TensorIoT, and Radix Engineering have made AWS IoT SiteWise the foundation for purpose-built applications that enable use cases such as predictive maintenance and asset performance monitoring. Through these engagements with customers and partners, we have learned that the main obstacles in scaling digital transformation initiatives include project complexity, infrastructure costs, and time to value.
With newly added APIs, AWS IoT SiteWise now allows you to bulk import, export, and update industrial asset model metadata at scale from diverse systems such as data historians, other AWS accounts, or – in the case of AWS Independent Software Vendors (ISV) Partners – their own industrial data modeling tools.
To collect real-time data from equipment, AWS IoT SiteWise provides AWS IoT SiteWise Edge, software created by AWS and deployed on premises to make it easy to collect, organize, process, and monitor equipment at the edge. With SiteWise Edge, customers can securely connect to and read data from equipment using industrial protocols and standards such as OPC-UA. In collaboration with AWS Partner Domatica, we recently added support for an additional 10 industrial protocols including MQTT, Modbus, and SIMATIC S7, diversifying the type of data that can be ingested into AWS IoT SiteWise from equipment, machines, and legacy systems for processing at the edge or enriching your industrial data lake. By ingesting data to the cloud with sub-second latency, customers can use AWS IoT SiteWise to monitor hundreds of thousands of high-value assets across their industrial operations in near real time.
China Mobile, Haier & Huawei Digitalize Production Material Management
Adopting open-source Industrial IoT software
Siloed solutions and ad-hoc efforts to tap into the fourth industrial revolution by funding one-time AI/ML and digitalisation projects in manufacturing fell short of their promises. Enterprises did not address the fundamental challenges behind the lagging security, updates and maintenance in industrial hardware, but only focused on applying the latest technologies. Legacy install bases and a lack of standardisation prevented industrial transformation from occurring. To fully reap the benefits of Industry 4.0, the industrial factory has to close the gaps between Operational Technology (OT) and IT. The convergence between the two domains calls for a transition from legacy stacks with closed standards and interfaces to modern IT solutions and the embrace of open-source software.
Intel embraces SDN to modernize its chip factories
The US chip giant has implemented software-defined networking in its semiconductor manufacturing plants, moving the tech beyond the data center and into a vertical seeking to benefit from zero-downtime machine connectivity. But as part of Intel’s expansive plans to upgrade and build a new generation of chip factories in line with its Integrated Device Manufacturing (IDM) 2.0 blueprint, unveiled in 2021, the Santa Clara, Calif.-based semiconductor giant opted to implement SDN within its chip-making facilities for the scalability, availability, and security benefits it delivers.
Aside from zero downtime, moving to Cisco’s Application Centric Infrastructure (ACI) enabled Intel to solve the increasingly complex security challenges associated with new forms of connectivity, ongoing threats, and software vulnerabilities. The two companies met for more than a year to plan and implement for Intel’s manufacturing process security and automation technology that had been used only in data centers. The collaboration with Cisco enables ACI to be deployed for factory floor process tools, embedded controllers, and new technologies such as IoT devices being introduced into the factory environment, according to Intel.
Intel has deployed SDN in roughly 15% of its factories to date and will continue to migrate existing Ethernet-based factories to SDN. For new implementations, Intel has chosen to use open source Ansible playbooks and scripts from GitHub to accelerate its move to SDN.
🧠 Data Driven Optimization - AI, Analytics IIoT and Oden Technologies
If you can predict that offline quality test in real time, so that you know, in real time, that you’re making good products, it reduces the risk to improve the process in real time. We actually use that type of modeling to then prescribe the right set points for the customer to reach whatever outcome they want to achieve. If they want to lower the cost, lower the material consumption and lower energy consumption, increase the speed, then we actually give them the input parameters that they need to use in order to get a more efficient output.
And then the last step, which is more exploratory, which we’re working on now is also generating work instructions for the operators, kind of like an AI support system for the operator. Because still, and we recognize this, the big bottleneck for a lot of manufacturers is talent. Talent is very scarce, it’s very hard to hire a lot of people that can perform these processes, especially when they say that it’s more of an art than a science. We can lower the barrier to entry for operators to become top performers, through recommendations, predictions and generative AI for how to achieve high performance. By enabling operators to leverage science more than art or intuition, we can really change the game in terms of how we make things.
IOTA Data Preservation Implementation for Industrial Automation and Control Systems
Blockchain 3.0, an advanced iteration of blockchain technology, has emerged with diverse applications encompassing various sectors such as identity authentication, logistics, medical care, and Industry 4.0/5.0. Notably, the integration of blockchain with industrial automation and control systems (IACS) holds immense potential in this evolving landscape. As industrial automation and control systems gain popularity alongside the widespread adoption of 5G networks, Internet of Things (IoT) devices are transforming into integral nodes within the blockchain network. This facilitates decentralized communication and verification, paving the way for a fully decentralized network. This paper focuses on showcasing the implementation and execution results of data preservation from industrial automation and control systems to IOTA, a prominent distributed ledger technology. The findings demonstrate the practical application of IOTA in securely preserving data within the context of industrial automation and control systems. The presented numerical results validate the effectiveness and feasibility of leveraging IOTA for seamless data preservation, ensuring data integrity, confidentiality, and transparency. By adopting IOTA’s innovative approach based on Directed Acyclic Graph (DAG), the paper contributes to the advancement of blockchain technology in the domain of Industry 4.0/5.0.
It's All About Narrowband IoT
NB-IoT technology is designed to meet everything that traditional cellular technology has fallen short of, and that is precisely why it is recognized as a technology of the future. 3GPP has identified NB-IoT to be a less expensive option than LTE-M, with the added benefits of extensive range, longevity, and ability to support a large number of devices over just 200 kHz of the spectrum. This means that it is rapidly getting popular among a wide variety of devices, ranging from storage units and wind turbines to smoke detectors and smart parking systems. NB-IoT provides deeper building penetration than LTE-M, which is achieved by low bitrates. It also offers better link budgets for NB-IoT.
NB-IoT has a very narrow bandwidth of 200KHz, and therefore the data peaks at around 250kbps. This feature comes useful in scenarios where lower amounts of data are required to be transmitted at infrequent intervals, mainly over short distances. Because it is designed to operate at low speed, the low power consumption is stressed as a major advantage associated with it. NB-IoT employs two power optimization techniques called PSM(Power Saving Mode) and eDRX (Extended Discontinuous Reception).
ABB and China Telecom unveil joint digitalization and industrial IoT lab
ABB and China Telecom unveil a joint digitalization and industrial IoT laboratory in Hangzhou, China. The collaboration between ABB Measurement & Analytics China Technology Center and China Telecom’s Internet of Things subsidiary E Surfing IoT will focus on developing end-to-end industrial IoT solutions for industrial companies based in China.
As part of the collaboration, ABB and E Surfing IoT will explore avenues for technology integration and industrial application of new technologies as well as new directions for next-generation industrial IoT solutions. The two teams will focus on comprehensive digital solutions that incorporate ABB sensor technology, China Telecom’s 5G network, industrial IoT and connectivity technology, as well as cloud computing.
Raising Productivity With the Latest Connected Factory Standards
One example is YSUP-LINK, which is a part of Yamaha’s YSUP production support system. It uses REST to allow surface-mount equipment like a YSP10 automated printer and the latest YRM20 mounters in a production line to be connected to various third-party MES or other Industry 4.0 applications. It also handles controlling and collecting information from equipment in the production line and connects to intelligent component storage, as well as supporting the possibility to connect with third-party equipment and software in the future.
How BigQuery helps Leverege deliver business-critical enterprise IoT solutions at scale
Leverege IoT Stack is deployed with Google Kubernetes Engine (GKE), a fully managed kubernetes service for managing collections of microservices. Leverege uses Google Cloud Pub/Sub, a fully managed service, as the primary means of message routing for data ingestion, and Google Firebase for real-time data and user interface hosting. For long-term data storage, historical querying and analysis, and real-time insights , Leverege relies on BigQuery.
BigQuery allows Leverege to record the full volume of historical data at a low storage cost, while only paying to access small segments of data on-demand using table partitioning. For each of these examples, historical analysis using BigQuery can help identify pain points and improve operational efficiencies. They can also do so with both public datasets and private datasets. This means an auto wholesaler can expose data for specific vehicles, but not the entire dataset (i.e., no API queries). Likewise, a boat engine manufacturer can make subsets of data available to different end users.
Maersk embraces edge computing to revolutionize supply chain
Gavin Laybourne, global CIO of Maersk’s APM Terminals business, is embracing cutting-edge technologies to accelerate and fortify the global supply chain, working with technology giants to implement edge computing, private 5G networks, and thousands of IoT devices at its terminals to elevate the efficiency, quality, and visibility of the container ships Maersk uses to transport cargo across the oceans.
“Two to three years ago, we put everything on the cloud, but what we’re doing now is different,” Laybourne says. “The cloud, for me, is not the North Star. We must have the edge. We need real-time instruction sets for machines [container handling equipment at container terminals in ports] and then we’ll use cloud technologies where the data is not time-sensitive.”
Laybourne’s IT team is working with Microsoft to move cloud data to the edge, where containers are removed from ships by automated cranes and transferred to predefined locations in the port. To date, Laybourne and his team have migrated about 40% of APM Terminals’ cloud data to the edge, with a target to hit 80% by the end of 2023 at all operated terminals. Maersk has also been working with AI pioneer Databricks to develop algorithms to make its IoT devices and automated processes smarter. The company’s data scientists have built machine learning models in-house to improve safety and identify cargo. Data scientists will some day up the ante with advanced models to make all processes autonomous.
Control systems evolve to meet enterprise and operational needs
For decades, selection of plant control and reliability technologies was frequently a matter of convenience. Individual plants across an organization selected technology based on price and local technical preference, often resulting in a wide variety of technologies across the enterprise. At the time, such decisions were convenient, reliable, and cost-effective. However, new, modern technologies — coupled with the need for increased sustainability and market agility — have changed the paradigm, driving a shift in the way engineers design automation solutions.
As the many layers of the Purdue model of industrial engineering have flattened in the cloud and edge computing age, connectivity has become more important. Today, forward-thinking process manufacturers are making automation decisions with an enterprise IT mindset, moving away from a collection of local systems to a single system that is deployed everywhere. In doing so, they unlock the capacity for improved data democratization, fleet optimization, and improved personnel productivity.
Everactive Releases First Development Kit, Opening Batteryless IoT to All
Everactive, the maker of category-defining batteryless Internet of Things (IoT) systems, is releasing its first development kit to allow third-party developers to build their own IoT products without the constraints of batteries. The company’s self-powered hardware and managed network is purpose-built to acquire and deliver the dense physical-world data that will usher in an age of hyperscale IoT. Opening up that technology will allow IoT developers to easily create the scalable, sustainable, and data-rich products long demanded by their customers.
The development kit includes two of Everactive’s patented batteryless IoT devices, each with a comprehensive sensor suite that simultaneously measures temperature, humidity, pressure, and triaxial acceleration. Utilizing a low-light indoor photovoltaic harvester as the sole power source, these devices can measure and wirelessly transmit data down to every 15 seconds. The ability to wirelessly deliver such robust data exclusively from harvested energy represents a significant breakthrough for designers and engineers whose IoT projects have struggled to scale. The development kit will allow developers to better understand and experiment with the power of energy harvesting technology for IoT applications.
Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning
Automated health monitoring, including anomaly/fault detection, is an absolutely necessary attribute of any modern industrial system. Problems of this sort are usually solved through algorithmic processing of data from a great number of physical sensors installed in various equipment. A broad range of ML-based and statistical techniques are used here. An important common parameter that defines the practical complexity and tractability of the problem is the dimensionality of the feature vector generated from the signals of the sensors.
While there is a great variety of methods and techniques described in ML and statistical literature, it is easy to go in the wrong direction when trying to solve problems for industrial systems with a large number of IoT sensors. The seemingly “obvious” and stereotypical solutions often lead to dead-ends or unnecessary complications when applied to such systems. Here we generalize our experience and delineate some potential pitfalls of the stereotypical approaches. We also outline quite a general methodology that helps to avoid such traps when dealing with IoT data of high dimension. The methodology rests on two major pillars: hierarchical decomposition and one-class learning. This means that we try to start health monitoring from the most elementary parts of the whole system, and we learn mainly from the healthy state of the system.
Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
Modern manufacturing, transportation, and energy companies routinely operate thousands of machines and perform hundreds of quality checks at different stages of their production and distribution processes. Industrial sensors and IoT devices enable these companies to collect comprehensive real-time metrics across equipment, vehicles, and produced parts, but the analysis of such data streams is a challenging task.
We start with a discussion of how the health monitoring problem can be converted into standard machine learning tasks and what pitfalls one should be aware of, and then implement a reference Vertex AI pipeline for anomaly detection. This pipeline can be viewed as a starter kit for quick prototyping of IoT anomaly detection solutions that can be further customized and extended to create production-grade platforms.
The State of Industrial Security in 2022
This report shows nearly all — 94% — of organizations have experienced at least one security incident, which likely impacted their industrial IoT infrastructure. These incidents had significant impact on organizations, with 87% of them reporting their operations were impacted for one day or more. The incidents involved a wide range of attacks, with web application, malicious external hardware/removable media, and distributed denial of service attacks being the most frequent.
The Importance of M2M for Manufacturing
One way manufacturing manages the incredible amount of data to leverage the power of IoT analytics is through Machine-to-Machine (M2M) technology. M2M offers a way to increase the power and utility of a cloud-based analytics platform while addressing many challenges brought about by the amount of data produced.
Sensor Noise and Straightforward Software Techniques To Reduce It
Sensor telemetry is at the heart of IoT. But while it can lead to amazing insights, it can also be noisy and inconsistent. There are two main sources of the problem. First, all sensors have hardware limitations and only measure to a certain degree of accuracy, with sequential readings having some amount of variance. (We call this variation in sensor readings, “sensor noise”.) Second, even if a sensor could measure with perfect accuracy and precision, the world itself that the sensor is measuring still presents variation; for instance, an IR distance sensor is affected by sunlight.
We can accept noise and inconsistency as a reality of IoT, but we can also take reasonable steps to reduce them. For instance, is there more accurate hardware available? Are there adjustable gain, sensitivity, positioning, or other calibrations to make on our sensors? Can we reduce environmental factors? Should we average out multiple readings over time? In many cases, these basic steps are enough to allow the data of interest to stand out.
Heineken’s Event-Driven Connectivity Strategy
To understand the scope of this connectivity project, it’s important to realize that Heineken runs more than 3,500 applications globally, connecting them with more than 5,000 interfaces. ERP systems in use across the company include SAP, Oracle’s JD Edwards, and Microsoft Dynamics, as well as the Hybris and Virto e-commerce platforms, Salesforce customer relationship management, and various manufacturing execution and invoicing systems.
Groeneweg adds that, with its new event-driven system in place, Heineken can now deploy scalable “plug-and-play” technologies quickly to take advantage of timely business insights at scale. To explain this, Groeneweg offers an example involving the introduction of a new global invoice management application. Before the implementation of Heineken’s event-driven system, multiple point-to-point integrations would need to be built to embed the new application into the company’s IT landscape. “We would have to connect it to at least 20 applications to get master data, ERP data, customer data, etc.,” says Groeneweg. “With the event-driven approach, we just point the chatbot to the right topics and queues where the data is already available from all the source systems it needs to access. No integration work is required at all.”
How KAMAX connected their industrial machines to AWS in hours instead of weeks
Every manufacturing customer these days is talking about Industry 4.0, digital transformation, or AI/ML, but these can be daunting topics for manufacturers. Historically, connecting industrial assets to the cloud has been a large and complicated undertaking. Older assets increase the complexity, leaving many manufacturers with legacy equipment stalled at the starting gate. KAMAX, a player for cold forming parts in the sector of steel processing, shows that it is not only possible to transform, but can be easy when working with the right partners. KAMAX wanted a fully managed shop floor solution to acquire data from industrial equipment, process the data and make it available fast, to improve their operational efficiency. KAMAX employed their subsidiary and digital incubator, nexineer digital, Amazon Web Services (AWS) and CloudRail to help. This Industrial IoT collaboration increased manufacturing efficiency and effectiveness within their plants by automating and optimizing traditionally manual tasks, increasing production capacity, and optimizing tool changeover times (planned downtimes) of machines. This solution helped KAMAX realize quantifiable time savings of 2.5% – 3.5%.
SDRs for IIoT, RF data, and manufacturing control
The flexibility of SDR platforms allows each individual wireless link to be customized according to its operating conditions. It also allows a broad variety of digital signal processing techniques such as frequency hopping and modulation techniques to be implemented with ease. In addition, use of software-based components in SDRs helps to shorten the cycle of developing and evaluating new radio protocols used in Industry 4.0.
Introducing Snap Signal : Hardware and Software for your IIoT Evolution
What’s Cognitive Manufacturing? Why Should It Matter To You?
The whole complex ecosystem of industries requires integration of various data systems. It is not just the sensor data system that needs retrofication. As many systems are analogue, there exist multiple interfaces because of various proprietary and automation systems such as DCS, SCADA, Historian, and PLC systems. With multiple protocols a simplification of this ecosystem can be done by customisation, bringing data from all the heterogeneous processes to a big data platform, understanding the business processes and gaps, and applying the predictive and prescriptive analytics.
Manufacturing Shifts To AI Of Things
Preventive maintenance is an important part of smart manufacturing, but this is just the beginning. AIoT can be deployed in many different areas in a factory to further increase productivity. For example, it can be used for incoming inspection. Traditionally, the quality control department performs sample inspection. Instead of inspecting 100% of the components used to build a device, only a sample — say 10% — will be audited. With the installation of a 3D HD camera, AIoT can inspect 100% of the components and screen out defective parts at an early stage. Additionally, a robotic arm can pick out defective components or those of different colors and/or shapes, further reducing reject rates.
AIoT also can be used to improve worker safety, resulting in lower worker compensation payments. For example, a warehouse can be equipped with AIoT cameras to ensure only authorized workers wearing appropriate safety equipment can enter the warehouse.
How to Build Scalable Data and AI Industrial IoT Solutions in Manufacturing
Unlike traditional data architectures, which are IT-based, in manufacturing there is an intersection between hardware and software that requires an OT (operational technology) architecture. OT has to contend with processes and physical machinery. Each component and aspect of this architecture is designed to address a specific need or challenge, when dealing with industrial operations.
The Databricks Lakehouse Platform is ideally suited to manage large amounts of streaming data. Built on the foundation of Delta Lake, you can work with the large quantities of data streams delivered in small chunks from these multiple sensors and devices, providing ACID compliances and eliminating job failures compared to traditional warehouse architectures. The Lakehouse platform is designed to scale with large data volumes. Manufacturing produces multiple data types consisting of semi-structured (JSON, XML, MQTT, etc.) or unstructured (video, audio, PDF, etc.), which the platform pattern fully supports. By merging all these data types onto one platform, only one version of the truth exists, leading to more accurate outcomes.
Why we invested in ZEDEDA: visibility, control, and security for the distributed edge
Traditionally, the deployment and remote management of Industrial Internet of Things (IIoT) at the edge has been difficult and expensive. One of the most significant hurdles faced by enterprise users today is the cost and lead time needed to update what are often proprietary legacy solutions. In the current environment, the majority of IIoT solution providers offer highly customized, siloed solutions for customers in specific verticals. As a result, these verticalized solutions tend to be fragmented and often are expensive to update and maintain.
We invested in ZEDEDA because the company has proven it can remain a horizontal play across multiple industrial verticals. ZEDEDA was founded in 2016 as a vertical solution provider. But in 2019, the company made the decision to open source its flagship product, EVE-OS.
Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices
Researchers are working to reduce the size and complexity of the devices that these algorithms can run on, all the way down to a microcontroller unit (MCU) that’s found in billions of internet-of-things (IoT) devices. An MCU is memory-limited minicomputer housed in compact integrated circuit that lacks an operating system and runs simple commands. These relatively cheap edge devices require low power, computing, and bandwidth, and offer many opportunities to inject AI technology to expand their utility, increase privacy, and democratize their use — a field called TinyML.
Koch Ag & Energy High Value Digitalization Deployments Leverages AWS
This application uses existing plant sensors, Monitron sensors, Amazon Lookout and SeeQ software to implement predictive maintenance on more complex equipment. The goal accomplished was successfully implementing predictive maintenance requires data from thousands of sensors to gain a clear understanding of unique operating conditions and applying machine learning models to achieve highly accurate predictions. In the past modeling equipment behavior and diagnosis issues requiring significant investment in time money inhabiting scaling this capability across all assets.
IBM’s vision of the connected factory
As far as our software is concerned, we are providing solutions for specific use cases that can deliver the quick wins that manufacturers are looking for. We have a solution called Maximo Application Suite which can monitor equipment effectiveness, asset health, asset performance, and visual inspection. And these kind of quick wins can already be delivered as part of a standard product. We are also working with customers in the field on things which are not necessarily already coded in the software. Something else which IBM brings to the table is that we are open source.
Apollo Tyres Moves to AWS to Build Smart, Connected Factories
Apollo Tyres needed to upgrade its infrastructure to develop new ways of engaging with fleet operators, tyre dealers, and consumers, while delivering tyres and services efficiently at competitive prices. The company’s first step was to create a data lake on AWS, which centrally stores Apollo Tyres’ structured and unstructured data at scale. This data lake provides the foundation for an integrated data platform, which enables Apollo Tyres’ engineers around the world to collaborate in developing cloud-native applications and improve enterprise-wide decision making. The integrated data platform enables Apollo Tyres to innovate new products and services, including energy-efficient tires and remote warranty fulfillment.
Using blockchain to share and monetize telecoms assets
Weaver Labs will be the open telecommunications partner in the Track & Trust project, which aims to deliver a scalable, cost-efficient communications platform and network combining satellite, IoT mesh and blockchain components, serving mostly supply chain use cases. The end solution will be a modular product that will provide a plug and play communication network that allows for end-to-end tracking of the supply chain. This will start from the initial supply of goods/aid and extend all the way to the last-mile shipments, even when limited or no telecommunication infrastructure is available.
The Long-range Disruption of Industrial IoT LoRaWAN Networks
This blog post from the Nozomi Networks Labs team investigates attacks against a low-power radio frequency WAN technology that is widely used in industrial IoT networks. Our research focused on the viability of discovering the transmission frequency of the IoT network, and jamming the signal to disrupt network communication. Although there are some practical limitations to the attack scenario we investigated, we clearly determined that there are potential attack vectors that should be considered as technology matures.
Machine Monitoring Becomes Simpler And More Affordable Than Ever
What makes all this possible is a new application of a simple technology—the current transformer, essentially an amperage meter. As Dunford explains, maintenance engineers have used these small, inexpensive devices for decades to detect, for example, when a machine starts drawing excess power, possibly indicating a need for maintenance or even an impending malfunction.
Guidewheel uses the same information to detect when a machine is running or stopped, how long it has been running or not, and the number and period of cyclical operations. In the case of continuous operations such as extrusion, the level of current draw can be correlated with production rate.
Is Clip A ‘Slack’ For Factories?
Clip aims to bring data gathering and analytics, information sharing, and collaboration onto a single platform. The system connects all intelligent industrial equipment in a production facility, together with workers who can access all information and adjust operations through computers and portable devices.
It’s an ambitious undertaking, one that requires guaranteeing a very high degree of interoperability to ensure that people, machines and processes can communicate with each other seamlessly, and that all key systems such as Material Requirements Planning (MRP), Enterprise Resource Planning (ERP) and others can directly access up-to-date information from machines and processes. This higher level of automation, if implemented right, can unlock a new level of efficiency for manufacturing companies.
Condition Monitoring via LoRaWAN
LoRaWAN can be a good choice when key factors become particularly important. These factors include wide areas of coverage on an operating site with different buildings, low cost of infrastructure and operation, use of an established standard, and a large number of users and providers. However, LoRaWAN is less suitable for transmitting large amounts of data due to the low bandwidth, the associated low data transmission rate, and the duty cycle regulations in the 868 MHz range. For this reason, additional sensors with embedded AI algorithms are required for sophisticated monitoring applications.
Best practices in IIoT-based predictive maintenance
A key component of the FDT 3.0 standard is the FDT Server built around a core server, which provides a center point for a wide range of client and server interactions. It includes an OPC UA server providing access to device type manager (DTM) data with authenticated OPC UA clients and a web server enabling the use of web user interfaces on remotely connected, browser-based clients and other mobile devices such as smart phones, tablets and PCs. The solution also supports the use of apps that improve workforce productivity and plant availability.
“The latest industry trends center around advanced data analytics, digital twins and cloud computing. The FDT 3.0 standard supports these solutions by delivering network and device information to enable improved diagnostics and predictive analytics. The technology provides a tool to not only monitor and predict asset health, but also remotely configure and manage assets for the highest level of reliability.”
Global Lighthouse Network: Unlocking Sustainability through Fourth Industrial Revolution Technologies
The Global Lighthouse Network is a community of production sites and other facilities that are world leaders in the adoption and integration of the cutting-edge technologies of the Fourth Industrial Revolution (4IR). Lighthouses apply 4IR technologies such as artificial intelligence, 3D-printing and big data analytics to maximize efficiency and competitiveness at scale, transform business models and drive economic growth, while augmenting the workforce, protecting the environment and contributing to a learning journey for all-sized manufacturers across all geographies and industries.
Before the Flood: How Technology Is Helping Build Water Resilience Around the Globe
At Veolia Water Technologies—a division of global water, waste, and energy management giant Veolia—the company’s developers are working on new ways to prepare cities for the inevitable. They’re applying digital and IoT technologies and predictive analytics to build water-resilience management techniques such as flood modeling, sustainable drainage design, clean water distribution, and resource optimization.
This is why railway communications needs great network design
A solid network design is the foundation to deliver on stringent performance requirements associated with mission-critical railway communications and to deliver on consumer expectations, which remain unchanged regardless of being at home or sitting on a train moving at 500km/h.
Network design has the potential to identify the optimal site locations to deliver the target performance at the best TCO, but its complexity cannot be overlooked. While cell planning tools exist, operating them for the right outcome is not trivial and requires highly skilled experts connected to a global knowledge base to keep up to date with the latest industry developments and realize the potential of 5G-based FRMCS.
Secure device onboarding for manufacturing supply chain
FDO 1.0 can offer many benefits for manufacturers that have industrial and enterprise devices. It’s also useful with multi-ecosystem applications and services and helps streamline distributor sales. Other benefits for manufacturers include:
- Zero-touch onboarding: It can integrate with existing zero-touch solutions.
- Speed and security: It is designed to onboard with IoT devices in less than a minute, which is up to 20 times faster than it would have been for a manual installer.
- Hardware flexibility: It is designed to be hardware-agnostic and work with any microcontroller or computer processor.
- Cloud flexibility: As with hardware, it is flexible and can work with the internet and on-premise.
- Late binding: This reduces costs and complexity in the supply chain by providing a single SKU for all customers.
Late binding, in particular, is a key aspect of the process, Kerslake said. “Late binding reduces costs and complexity in supply chain, providing a single device SKU for all customers instead of making unique SKUs and creating a mess of things.”
Energy Harvesting Startups Could Power Some IoT Dreams
Removing batteries from the industrial equation cuts costs and reduces the hours that people spend replacing them. Using batteryless equipment in industrial and consumer settings could also greatly reduce the number of batteries that are thrown into landfills around the globe. It is estimated that 3 billion batteries a year are discarded in the U.S. alone!
For instance, Everactive argues that if you were to deploy 10,000 battery-powered industrial IoT sensors across your facility to transmit real-time data about the health of your machinery, over time your team would be replacing around 2,000 batteries a year. Many of these sensors would be located in difficult-to-reach areas, further increasing the time and expense needed to replace the chemical cells.
Aerospace, Defense and Industry 4.0
“Designing for manufacturability, modeling the production environment, and then producing our products with a minimum of duplicated effort—this can give us the breakthroughs in speed and affordability that the A&D environment needs in a time of limited budgets and rapidly changing threats,” explains Daughters. “These technologies are an essential component to our ‘digital thread’ across the product life cycle. They give us the ability to simulate tradeoffs between capability, manufacturability, complexity, materials and cost before transitioning to the physical world.”
“In a nutshell, I4.0 involves leveraging technology to better serve the world,” says Matt Medley, industry director for A&D manufacturing at IFS, a multinational enterprise software company. “More than just collecting and processing mounds of data via sensors and the Industrial Internet of Things (IIoT), I4.0 is turning data into actionable intelligence to not only drive efficiency and grow profits, but to also help companies be good stewards of our natural resources and local communities. Aerospace and defense companies whose enterprise software can keep pace with developments like additive manufacturing, AI, digital twins, and virtual and augmented reality (V/AR) are the ones that will thrive in an increasingly digital 4.0 era.”
Inside Schneider Electric’s Smart Factory
According to Clayton, the goal of Schneider Electric’s IIoT initiative in Lexington is to boost efficiency and overall market competitiveness by introducing technologies that modernize and reinvent the control, monitoring and management processes of the plant.
It’s part of Schneider Electric’s global effort to digitally transform its factories and distribution centers. The 183-year-old company’s supply chain encompasses nearly 300 factories and logistics centers in more than 40 countries. Most of those facilities use the same IIoT technology that the company offers to its customers.
“These facilities are core to [our] Tailored Sustainable Connected Supply Chain 4.0 program, which creates a customized, sustainable and end-to-end connected supply chain across the plan, procurement, make, customer and sustain domains,” explains Clayton.
Manure Spreading goes High-Tech with IIoT
Manure spreaders have a tandem hydraulic pump. One pump drives the beater system at the backend that spreads, or applies, the product onto the field. A hydraulically driven end gate, or tailgate, opens up to allow the product out the backend, and the system also has a hydraulically driven variable speed floor.
An essential function of the control system is to monitor the torque load on the beater. With the beater requiring the highest horsepower load, it is crucial to use a pressure control, essentially a torque control, to keep the entire operation under maximum load the drive line can handle. For example, if the operator is driving the floor too fast, which increases the pressure, the control system will stop the floor or slow it down accordingly based on the load that you would see on that beater.
PLCs improve predictive maintenance
There is no doubt PLC technology is already strongly established on the plant floor. However, by embedding IT protocols, Cloud connectivity, and security features into today’s PLCs, it is possible to gather data that may have existed idly and use it to provide a much stronger idea as to what condition devices and machines are in to prevent unplanned downtime.
Integrating Falkonry with Azure IoT
Falkonry Clue applies advanced analytics to multivariate time-series data to discover meaningful patterns. This valuable operational data is supplied to Clue’s powerful AI engine by leveraging Microsoft Azure’s IoT infrastructure. Clue is designed to fit seamlessly into Azure’s reference architecture thereby easing the integration process.
Connecting the plant to the cloud, the Azure IoT Hub acts as a bi-directional communications brain for all connected IoT devices – managing data transfers, updates, setting up credentials for every device, and defining access control policies. These connected devices include OPC UA enabled sources such as most SCADA systems that support the MQTT protocol for data transfer.
Optimizing manufacturing processing and quality management with digital twins, IIoT
The application of IIoT and digital twin technologies in production process and quality management in steel production processes with the following characteristics:
- Integrate process design data, quality specification data, equipment operational real time data, quality measurement data into a holistic end-to-end closed-loop system, enabling comprehensive online monitoring and analytics of production process and supporting product quality traceability.
- Combine digital twin and Industrial Internet technology seamlessly into a holistic platform to support such an application.
- Enable digital twin for both equipment and product alike, dynamically bind product digital twins with equipment digital twins to enabling product process and quality online tracking, monitoring and traceability.
- Combine online data and analytic technologies with Lean management and Six Sigma concepts and best practice for production process and quality management, creating a digital Lean capability.
Davey Textiles Shows Digital Transformation Can Be Affordable and Effective
If something interrupted operations, the Uptake Fusion’s Downtime Tracker sent an alert to the operator. Due to the noise levels on the floor, the solution sent the alert via Twitter, ensuring operators could be notified directly through their hearing protection devices.
The company could also now visualize production data to examine trends and anomalies for products, days, shifts, equipment, room locations, and other key variables. They now had new insight into causes of lost production, enabling them to eliminate issues that undermined operational optimization. Uptake Fusion also managed all of this using a single-pane view, minimizing user complexity.
How Companies Oversee IoT Device Management
Companies have been using Internet of Things (IoT) devices for a long time, from agricultural companies monitoring weather and crop conditions to industrial companies tracking the output and safety within manufacturing facilities.
IoT can accelerate processes by giving companies real-time data and visibility, but having a lot of devices can be a maintenance and security headache. In those cases, it’s even more important to have the right procedures in place for IoT management.
Getting Industrial About The Hybrid Computing And AI Revolution
Beyond Limits is applying such techniques as deep reinforcement learning (DRL), using a framework to train a reinforcement learning agent to make optimal sequential recommendations for placing wells. It also uses reservoir simulations and novel deep convolutional neural networks to work. The agent takes in the data and learns from the various iterations of the simulator, allowing it to reduce the number of possible combinations of moves after each decision is made. By remembering what it learned from the previous iterations, the system can more quickly whittle the choices down to the one best answer.
Seeq Accelerates Chemical Industry Success with AWS
Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, today announced agreements with two of the world’s premier chemical companies: Covestro and allnex. These companies have selected Seeq on Amazon Web Services (AWS) as their corporate solution, empowering their employees to improve production and business outcomes.
The Machine Economy is Here: Powering a Connected World
In combination with the real-time data produced by IoT, blockchain, and ML applications are disrupting B2B companies across various industries from healthcare to manufacturing. Together, these three fundamental technologies create an intelligent system where connected devices can “talk” to one another. However, machines are still unable to conduct transactions with each other.
This is where distributed ledger technology (DLT) and blockchain come into play. Cryptocurrencies and smart contracts (self-executing contracts between buyers and sellers on a decentralized network) make it possible for autonomous machines to transact with one another on a blockchain.
Devices participating in M2M transactions can be programmed to make purchases based on individual or business needs. Human error was a cause for concern in the past; machine learning algorithms provide reliable and trusted data that continue to learn and improve — becoming smarter each day.
The Autonomous Factory: Innovation through Personalized Production at Scale
Personalized products are in high demand these days. Meeting this demand is leading companies to increasingly automate their production processes and even make parts of it autonomous. However, this approach presents a trade-off: with increasing personalization comes increasing complexity. Therefore, companies need to decide on the expedient extents and levels of automation to be implemented in their factories. Two strategies that may help along the way: 1. Limited implementation in selected areas. 2. Co-creation with trusted partners.
IIoT builds new bridges to new adventures
Engenuity Inc. in Conroe, Tex., provides control automation and data integration for oil and gas and other industries, and recently found deficiencies in validation pressure testing of blowout preventers (BOP) and well-control devices. Because pressure tests are needed every few weeks for regulatory compliance, executed and recorded manually over several hours, and can cost up to $6 per second to run in offshore valve arrays, testing can cost millions of dollars per year. To reduce these expenses, Engenuity collaborated with clients like Shell International Exploration and Production Co., and developed automated, hydrostatic, test execution and reporting solutions, which use Opto 22’s groov Edge Programmable Industrial Controller (EPIC) for process control, automatic notification, and process history storage and replication.
Evolution of Machine Autonomy in Factory Transactions
So while we’ve not completely entered the age of the machine economy, defined as a network of smart, connected, and self-sufficient machines that are economically independent and can autonomously execute transactions within a market with little to no human intervention, we are getting close.
The building blocks to create the factory of the future are here, including the Internet of Things (IoT), artificial intelligence (AI), and blockchain. This trifecta of technology has the potential to disrupt the industrial space, but it needs to be connected with a few more things, such as digital twin technology, mobile robots, a standardized way for machines to communicate, and smart services, like sharing machine capacity in a distributed ecosystem.
“The biggest obstacle is culture,” said IIC’s Mellor. “The average age of the industrial plant is 19 years. These are huge investments that last for decades. The organizations that run these facilities are very cautious. Even a 0.5% chance of failure can cost millions of dollars.”
Detect: Monitoring, Identifying, and Responding to Industrial Cyber Threats
A harsh reality of cybersecurity is that even the most state-of-the art protective controls cannot fully eliminate risk. As such, the ability to detect, investigate, and respond to potential threats quickly and effectively when they do surface is imperative.
The purpose of detecting threats is to mitigate those which pose risk to your organization. But to determine which threats to focus on, your team needs the ability to make sense of what’s being detected within your organization’s industrial environment. This is another area where the sheer size and complexity of enterprise industrial networks comes into play; without the right capabilities in place, security personnel can be flooded with an overwhelming barrage of alerts that do little to inform risk-mitigation decisions.
Cloud-based app for micro-breweries
When the yeast consumes the sugar to produce alcohol: That’s when the flavour is developed. It’s when beer becomes beer. Australian craft brewers are passionate about brewing, not industrial operational technology, yet Leonie Wong and Rex Chen from the MindSphere team still managed to make the data work for them; they want to always land the perfect brew and waste not a single drop.
In this market, Deacam, an Australian original equipment manufacturer (OEM), which provides automated brewing equipment and solutions to microbreweries, was looking to differentiate itself. Leonie Wong, responsible for Vertical Sales for Food & Beverage for Siemens Australia, and Solution Architect Rex Chen met with Deacam and their customers, the microbreweries themselves.
How Edge Analytics Can Help Manufacturers Overcome Obstacles Associated with More Equipment Data
Big data is transforming a variety of sectors, ushering them into the era of Industry 4.0. However, having access to raw data and knowing what to do with it are at completely different ends of the digitalization spectrum. To help manufacturers understand, and overcome, some of the challenges associated with smart manufacturing, Martin Thunman, CEO and co-founder of leading low-code platform for streaming analytics, automation and integration for industrial IoT, Crosser shares his insight.
OPC-UA: the Universal Language of Industry 4.0
Forgive the obscene title of this article, for implying OPC-UA is nothing but a simple communication protocol is a great injustice. Indeed, OPC-UA encompasses this, but also so much more. It is a living, breathing, specification: one that outlines an information-centric architecture that is interwoven with security systems-systems which permeate a definitive rule-set for device modelling and communication.
At its essence, OPC-UA is a platform-independent, machine-to-machine communications architecture that focuses on providing an object-oriented approach to modelling data.
Application Layer Protocol Options for M2M and IoT Functionality
With adoption of Internet of Things (IoT) and Industry 4.0 functions, devices are increasingly connected via industrial protocols. What’s more, today’s machine to machine (M2M) communications are rapidly standardizing on these protocols. Complicating matters is that IoT protocols don’t describe a single application-layer protocol, as several standards are in operation. So while early IoT implementations used standard internet protocols, there are also dedicated IoT protocols now available.
Modeling communication structures and identifying the right protocol for a particular application can be daunting. This article outlines what various protocols do as well as the options available for these protocols — so design engineers can more easily select the most suitable to integrate.
FactoryEye - a Dynamic Industry 4.0 Solution for North American Manufacturers
Gaining an Edge on Line Control
Edge control provides access to real time OEE and information visualization that changes the value calculation. With edge control, end-users can easily tie together existing equipment, other legacy controllers and new external sensing. The combined raw data can be analyzed at the edge to generate information needed by operators to take fast informed action, and it is the foundation for more advanced production line integration, with the ultimate goal of insight-driven and adaptive operation.
Intel Accelerates AI for Industrial Applications
The human eye can correct for different lighting conditions easily. However, images collected by camera can naturally vary in intensity and contrast if background lighting varies as well. We’ve seen scale challenges observed by factories trying to deploy AI for defect detection based on the exact same hardware, software and algorithm deployed on different machines on the factory floor. Sometimes it took months for factory managers and data scientists to find out why they were getting great results on one machine with high accuracy, low false positive and false negative rates, while on the next machine over the AI application would crash.
Total Cost of Ownership Guide: No-Code App Platforms vs Traditional MES
You’ve found a no-code, IIoT native application platform that can replace your MES partially or fully. You are excited about augmenting human workflows, flexible deployments, and continuous improvements — but you have to do your due diligence and prove ROI.
We get it! No-Code App Platforms are new to the Industrial and Manufacturing technology landscape. Even though they were developed for a different era, Manufacturing Execution Systems (MES) are a tried and tested means of coordinating, executing, and tracking manufacturing processes.
Integrated intelligent technologies optimize yield and increase profits for rice millers
The digitally connected technology provides mill operators with the insights they need to correctly adjust solution settings. Over time, the intelligent system is capable of adjusting autonomously. Where millers were once left to take corrective action after an incident occurred, they can now prevent costly reprocessing steps and proactively manage the entire process. With these advances, the miller can optimize operating costs, quality and yield, all of which have a direct impact on the profit of the mill.
Progress Continues on Industrial Open Source Software
The Eclipse Foundation, an independent, not-for-profit corporation created to foster a vendor-neutral approach to open source innovation in the Industrial Internet of Things (IIoT) space, has released its 2020 annual community report.
The Eclipse Foundation foresees the open source ecosystem continuing to expand as companies become more software-centric, noting that “digitalization is the single biggest industry trend in the world today.”
Industrial DataOps: Unlocking Data and Analytics for Industry 4.0
As an approach to data analytics, DataOps is all about reducing the time to high-accuracy analyses using automation, statistical process control, and agile methodologies so that manufacturers are able to use the data they collect quicker and with a higher degree of confidence.
The role of DataOps in Industry 4.0 is to take all of the info created and collected by machines, like IIoT devices, and effectively condense them into refined, usable business “fuel” to drive decision-making, rather than be left to sit in a data warehouse, unexamined.
How Augmented Reality Became a Serious Tool for Manufacturing
Making monsters appear in games like Pokémon Go is not the only application for augmented reality these days. Industry is using the technology too, harnessing CAD data for training workers, standardizing workflows, and enabling collaboration.
A manufacturer's guide to scaling Industrial IoT
Despite tailwinds from declining compute power costs and improvements in IIoT integration, connectivity, and platform usability and management, few manufacturers have successfully scaled up their IIoT-enabled use cases in a way that achieves significant operational or financial benefits.
To understand the key enablers behind IIoT-based value capture at scale, we drew on our field work and extensive research of those companies successfully scaling IIoT to offer manufacturers ready-to-use guidance on strategically orienting their business, organization, and technology toward IIoT success.
IoT Supply Chain Vulnerability Poses Threat to IIoT Security
Most companies that construct products with the aid of IIoT-based operations are likely to keep close tabs on the supply chain that provides a predictable stream of raw materials and services that allows them to crank out products and keep the business humming.
But a second, underlying supply chain receives less scrutiny. And if the security of that supply chain is somehow compromised, business could grind to a halt.
That overlooked supply chain delivers the components that build out an IIoT infrastructure. The purchaser of those devices is at the end of the supply chain that — from a security perspective — lacks sufficient transparency into the chain. In fact, it would be a challenge to track the origins of the internal elements that comprise the delivered IIoT devices.
Building effective IoT applications with tinyML and automated machine learning
The convergence of IoT devices and ML algorithms enables a wide range of smart applications and enhanced user experiences, which are made possible by low-power, low-latency, and lightweight machine learning inference, i.e., tinyML.
Facilitating IoT provisioning at scale
Whether you’re looking to design a new device or retrofitting an existing device for the IoT, you will need to consider IoT provisioning which brings IoT devices online to cloud services. IoT provisioning design requires decisions to be made that impact user experience and security for both network commissioning and credential provisioning mechanisms which configure digital identities, cloud end-points, and network credentials so that devices can securely connect to the cloud.
Industrial automation unites the best of OT and IT
As operational and information technology roles progressively overlap in the industrial automation space, a hybrid operational technology/information technology (OT/IT) solution becomes increasingly necessary.
Precision of Digital Twin Data Models Hold Key to Success
As the industrial sector turns to digital twin technology for operational efficiency, digital twin data model accuracy is key to success of digital replicas.