Software : Operational Technology : IIoT
Hitachi provides solutions to customers in a range of sectors, including power/energy, industry/distribution/water and others. The Company operates in eight segments. The Information & Telecommunication Systems segment provides system integration, consulting, cloud service and others. The Social & Industrial Systems segment provides industrial equipment and plants, thermal power, nuclear power and natural energy power generation systems and others. The Electronic Systems & Equipment segment provides semiconductor manufacturing equipment and others. The Construction Machinery segment provides hydraulic excavators, wheel loaders and others. The High Functional Materials segment provides materials for semiconductors and displays and others. The Automotive Systems segment provides engine powertrain systems and others. The Smart Life & Ecofriendly Systems segment provides business air conditioners and others. The Others segment provides optical disk drive products and others.
🧑🏭🧠 Hitachi to use generative AI to pass expert skills to next generation
Japan’s Hitachi will utilize generative artificial intelligence to pass on expert skills in maintenance and manufacturing to newer workers, aiming to blunt the impact of mass retirements of experienced employees. The company will use the technology to generate videos depicting difficulties or accidents at railways, power stations and manufacturing plants and use them in virtual training for employees.
Hitachi already has developed an AI system that creates images based on 3D data of plants and infrastructure. It projects possible malfunctions – smoke, a cave-in, a rail buckling – onto an image of an actual rail track. This can also be done on images of manufacturing sites, including metal processing and assembly lines. Hitachi will merge this technology into a program for virtual drills that is now under development.
Hitachi Mining Excavators Factory Tour
Makersite secures $18M investment
We are happy to announce the completion of an $18M funding round. The investment is led by Hitachi Ventures, the global venture capital arm of Hitachi, Ltd., and Translink Capital, a Silicon Valley-based VC fund, with participation from KOMPAS, an EU-based venture capital fund, and seed investor Planet A.
When looking at the sustainability space right now, there’s a great buzz about reporting standards, ESG, Science Based Targets, GHG-Protocol, etc. But in 3-5 years’ time, no one will care about any now-implemented corporate reporting. What counts are the changes implemented across an organization. Makersite helps enterprises to take the right decisions today, not tomorrow. The investment will help us to grow our sales and marketing teams in Europe and the U.S along with increasing our delivery capacities. This way, the investment supports both our old and new customers.
Taranis Raises $40 Million Series D to Advance Crop Intelligence and Unlock Growth Opportunities for Agribusinesses
Taranis, the leading AI-powered crop intelligence provider, announced today that it has raised $40 million in Series D funding. The round was led by Inven Capital, a European climate tech fund, with participation from new investors Seraphim Space Investment Trust (‘SSIT’) and Farglory Group, and strong backing from existing investors: Vertex Growth, Viola Ventures, Vertex Ventures Israel, La Maison Partners, Hitachi Ventures, K3 Ventures, UMC Capital, OurCrowd, Micron Ventures, iAngels Ventures, Presidio Ventures (Sumitomo), Cavallo Ventures (Wilbur Ellis), Finistere Ventures, and Eyal Gura. This latest round brings Taranis’ total funding to $100 million.
Hitachi Acquires Key Industry 4.0 Systems Integrator – Flexware Innovation
Hitachi, Ltd. announced that it acquired Flexware Innovation, Inc. which has been a leading manufacturing Systems Integrator (SI) since 1996. Flexware Innovation was a strategic acquisition for Hitachi due to its focus on the TOTAL SEAMLESS SOLUTION that links “shop floor” and “top floor” with data and digital technology.
With this acquisition of Flexware Innovation, Hitachi will strengthen and enhance its business in the domain of MES (Manufacturing Execution Systems), SCADA (Supervisory Control and Data Acquisition), Software Development, Business Intelligence (BI), and ERP (Enterprise Resource Planning) implementation capabilities in North America, and accelerate the digitalization with JR Automation which engages in the robotic SI & automation. Further, through cooperation with Hitachi Vantara which has expertise in building and deploying Enterprise and Cloud applications, Hitachi will be able to provide TOTAL SEAMLESS SOLUTION from robotic SI & automation, MES, SCADA, BI, and ERP and help manufacturing leaders increase corporate value.
An integrated approach to optimising inter-warehouse transportation for more efficient supply chains
From our study, we proposed an algorithm to solve the integrated problem of identifying consolidation warehouses, optimising transshipment between consolidation warehouses, and scheduling the pickup and delivery of commodities to and from the consolidation points. The algorithm is based on a multi-armed bandit approach and iterates between solving the three problems WCP, SNDP, and PDP, until convergence to a global solution.
How can we help reduce plastic waste? Facilitating the use of recycled plastics using in-mold sensors to optimize the injection molding process
To use recycled materials with material properties that fluctuate from lot-to-lot, conventionally, a skilled operator made fine manual adjustments to the injection molding process conditions according to the material properties. As this is time consuming and requires experience, this has limited the type and amount of recycled materials used as manufacturers have sought to use recycled materials with consistent properties.
To address this issue, my colleagues and I conducted a study where we looked at how we could automatically optimize the process conditions and thereby contribute to quality, and presented our results at the 37th International Conference of the Polymer Processing Society (PPS-37) which was held in Fukuoka, Japan, from 11-15 April 2022. Below, I’d like to briefly share what we did.
Application of deep learning methods for more efficient water demand forecasting
In recent years, such predictions have also found wide application in near-optimal control operations of water networks. Water demand prediction is an active field, where different methods and techniques have been applied including conventional statistical methods and machine learning methods. Due to advancements in the field of sensing and IoT, an increasing amount of data is becoming available for water distribution systems, including water demand data. Therefore, we are seeing greater use of deep learning methods to develop models for water demand forecasting in recent years as deep learning methods can deal with seasonality as well as random patterns in the data, and provide accurate results compared to traditional methods.
We observed that the frequency of data, amount of data, and quality of data has an impact on the deep learning model accuracy. In CNN-LSTM, CNN effectively extracts the inherent characteristics of historical water consumption data such as seasonality, and LSTM can fully reflect the long-term historical process and future trend. Hence, water demand forecast predictions using CNN-LSTM produced a better result when compared to other single models such as GRU, MLP, CNN and LSTM.
Manufacturing line design configuration with optimized resource groups
Skilled line engineers spend several months designing a manufacturing line based on their experience. Optimization of the four design specifications from the viewpoint of productivity and equipment continuity is required for the line design process. However, these four design specifications are highly dependent on each other and the number of feasible combinations of the specifications is enormous and difficult to automate.
To solve these issues, our research introduces the concept of a resource group that enables a methodology to solve the four design items hierarchically and develops methods to quickly build new manufacturing lines in response to changes in product varieties and manufacturing fluctuations in a factory.
Operation planning method using convolutional neural network for combined heat and power system
The energy efficiency of a combined heat and power (CHP) can reach about 85%, whereas conventional thermal power plants operate only at 45% efficiency or lower. CHPs perform better mainly because the heat from generators can be used as a energy source to meet heat demands or power refrigerators to generate cold water, in other words the “waste” heat is used and not wasted. Therefore, a growing number of factories and commercial buildings are installing combined heat and power (CHP) systems that include various energy storage devices. To reduce the energy cost of CHPs, optimal operation plans to satisfy time-varying energy demands with minimum energy cost are required. However, conventional operation planning methods using optimized calculation have an issue with long computing time. Especially these days, operation plans need to be generated within a few minutes or even seconds to make up for output of renewable energy sources.
Action-limited, multimodal deep Q learning for AGV fleet route planning
In traditional operating models, a navigation system completes all calculations i.e., the shortest path planning in a static environment, before the AGVs start moving. However, due to constant incoming offers, changes in vehicle availability, etc., this creates a huge and intractable optimization problem. Meanwhile, an optimal navigation strategy for an AGV fleet cannot be achieved if it fails to consider the fleet and delivery situation in real-time. Such dynamic route planning is more realistic and must have the ability to autonomously learn the complex environments. Deep Q network (DQN), that inherits the capabilities of deep learning and reinforcement learning, provides a framework that is well prepared to make decisions for discrete motion sequence problems.
The Rapid Rise and Evolution of the Digital Twin
Digital twins have a well-established track record in the realm of high-end engineering, but the new technologies and trends will drive wider adoption and higher return on investment for digital twins. Jet-engine makers are veteran users of the technique to monitor performance and predict maintenance needs. For such complex and costly pieces of machinery, digital twins more than pay for themselves. Two new trends are underway that can make digital twins high-value propositions for more industries and applications: Sensor fusion and Access to data and compute.
A combinatorial auctions approach to capacity sharing in collaborative supply chains
Collaboration among multiple parties throughout the supply chain may improve resilience. In collaborative supply chains bilateral agreements between buyer and supplier are replaced or supplemented by multilateral agreements. Three major complications for multi-party collaboration in automotive supply chains arise. Existing bilateral contracts must be respected, procurement of parts needs to be bundled, and the delivery of parts is constrained temporally, to ensure available production capacity and minimise inventory.
Improving fault detection and isolation (FDI) in industrial networks using GCNN
Industrial networks of equipment are the backbone of resilient business operations. They are large-scale systems that consist of several interacting components. For example, water supply networks consist of connected components such as water tanks, pumps and pipes. Failure of any one component may disrupt the entire network, making it non-functional, and result in safety hazards and costly repairs. Thus, it is crucial to continuously monitor and maintain industrial networks to prevent any failure. Traditionally, monitoring such systems are focused on detecting faults on the level of a single component by considering the measurements generated by that component. These solutions are sub-optimal as they are independently applied to individual components without explicitly taking into consideration the dependency between the several components that co-exist in the network. Ignoring the interaction between components makes fault detection much more challenging. A fault in a component (say a leakage in a tank or a pipe) can affect the neighboring components. Therefore, designing a monitoring system without considering the network structure can degrade the diagnosis performance significantly. In order to solve this problem, my team and I looked at first modeling the industrial networks as weighted undirected graphs. The graph structure represents the connected components. We then used graph convolutional neural networks (GCNN) to detect and isolate faulty components in these systems. We applied our proposed method to a case study of a simulated water supply network and showed that GCNN outperforms traditional approaches for leakage detection.
Using digital twin for cost-efficient wind turbines
CBM of the wind turbine is usually conducted by monitoring vibration at many points on each component with dedicated sensors. Simply increasing the number of monitored points and components leads to an increase in monitoring cost. In our approach, the digital twin acts as virtual sensors for monitoring any component whose behavior can be simulated from a smaller number of sensors as input to the digital twin. Thus, CBM with the digital twin contributes to identifying critical turbines, components, and positions that need maintenance.
Smart apparel that evaluates workers' physical workload
One of the biggest challenges faced by society in developed countries is an aging society due to a declining birth rate. What this means is that there is or will be a serious labor shortage both in terms of overall numbers as well as those with expertise or experience. To overcome this shortage, older people or foreign workers are being increasingly employed to fill the gap. At such sites, accidents or injuries due to unfamiliar work may occur. Thus, it is important to understand how the physical workload is being handled in order to ensure a healthy and safe work environment. In this blog, I’d like to talk about the work that we are doing to quantitatively visualize strain and enable the most appropriate response based on the physical load.
In collaboration with Xenoma Inc., the German Research Center for Artificial Intelligence (DFKI) and its spin-off sci-track GmbH, Hitachi is pursuing the research and development of wearable AI technology that monitors workers physical load at all times in an effort to achieve solutions that improve worker safety and health in industrial fields.
Medicine piece-picking robot for Hitachi Transport System
Battery Resourcers Secures $70 Million in New Funding
Battery Resourcers, a vertically integrated lithium-ion battery recycling and manufacturing company, today announced the closing of its latest mid-round funding totalling $70 million. The company will use this latest funding round to advance and expand the industry’s most sustainable, cutting-edge closed loop material production technology that accepts mixed input of scrap batteries and end of life batteries to produce cathode material. In response to increased demand for sustainable battery production, Battery Resources will also expand commercial plants that will be operational in the U.S. and in Europe by the end of 2022.
Hitachi Ventures became the newest investor to join the world-class syndicate of strategic and financial investors already backing Battery Resourcers’ approach and technology. Existing investors include Orbia Ventures, Jaguar Land Rover’s InMotion Ventures, Doral Energy, At One Ventures, TDK Ventures and Trumpf Ventures.
Applying deep learning to sensor data to support workers in manufacturing
To achieve next-generation production systems and Multiverse Mediation with CPSs, 4M (huMan, Machine, Material, and Method) work transitions need to be clarified and used more accurately. However, traditional systems cannot detect deviations in manual procedures. To resolve these issues, we are developing a highly accurate detection technology for “human work”. Figure 2 shows the assembly cells considered in this study.
Compared to conventional approaches, we achieved a 15% reduction in product assembly time and a deviation detection leak of almost zero (more than 95% work identification accuracy). These results demonstrated the potential for our system to efficiently and effectively support manufacturing workers and contribute to greater efficiency and quality management in the assembly of complex equipment.
Fabs Drive Deeper Into Machine Learning
For the past couple decades, semiconductor manufacturers have relied on computer vision, which is one of the earliest applications of machine learning in semiconductor manufacturing. Referred to as Automated Optical Inspection (AOI), these systems use signal processing algorithms to identify macro and micro physical deformations.
Defect detection provides a feedback loop for fab processing steps. Wafer test results produce bin maps (good or bad die), which also can be analyzed as images. Their data granularity is significantly larger than the pixelated data from an optical inspection tool. Yet test results from wafer maps can match the splatters generated during lithography and scratches produced from handling that AOI systems can miss. Thus, wafer test maps give useful feedback to the fab.
Smart operation recommender system digitalizing OT knowledge to improve productivity
To contribute to resilient manufacturing systems through digitalization, it will be important to connect physical phenomena, OT knowledge and sensor data in parallel with developing AI to analyze and learn from the data. My colleagues and I developed an operation recommender system that can discriminate factors contributing to defects and recommend appropriate countermeasures. We will continue developing the technology to resolve issues in manufacturing by considering different manufacturing processes.
Digital twin for load monitoring of wind turbine blade
Recently, the lifetime extension of wind turbines has increasingly attracted attention as one way to reduce levelized cost of energy. To explain, generally, wind turbines are designed under the wind condition defined by design standards such as the International Electrotechnical Commission (IEC), however, real wind conditions do not always correspond to the design condition. Therefore, the actual lifetime of wind turbines can be extended when the real wind condition is less severe than the design condition. For the lifetime extension, however, it is important to have an accurate evaluation of remaining useful lifetime (RUL). To accurately evaluate RUL, we should know historical data of loads applied to a structure of wind turbine but unfortunately, often there are not enough sensors to provide a full set of data to evaluate the loads. Thus, while the simple solution would be to add more sensors for the load evaluation, this would defeat the purpose as it would entail additional costs, and thus reduce the goal of trying to reduce the levelized cost of energy through lifetime extension. So, the challenge is to accurately estimate the load from the sensor data available.
Influence estimation for generative adversarial networks
Expanding applications [1, 2] of generative adversarial networks (GANs) makes improving the generative performance of models increasingly crucial. An effective approach to improve machine learning models is to identify training instances that “harm” the model’s performance. Recent studies [3, 4] replaced traditional manual screening of a dataset with “influence estimation.” They evaluated the harmfulness of a training instance based on how the performance is expected to change when the instance is removed from the dataset. An example of a harmful instance is a wrongly labeled instance (e.g., a “dog” image labeled as a “cat”). Influence estimation judges this “cat labeled dog image” as a harmful instance when the removal of “cat labeled dog image” is predicted to improve the performance (Figure 1)
AI In Inspection, Metrology, And Test
“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”
That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”
Australian lidar firm Baraja raises $31 million in funding
Australian lidar firm Baraja said on Wednesday it had raised $31 million in a new funding round from investors including Blackbird Ventures and Hitachi Construction Machinery.
Baraja, which did not disclose its valuation after the round, said the funds will be used for hiring and to accelerate the development of its lidar technology.
Is maintenance worth the money? A data-driven answer
For many industrial and commercial operations, maintenance accounts for a large part of operating costs. For instance, maintenance costs range from 15% to 60% of the total production costs in manufacturing plants. In the airline industry, the 2014 global spend on maintenance, repair, and overhaul accounted for around 9% of the total operational costs, and this spend is estimated to reach 90 billion dollars in 2024. Even with maintenance cost being such a substantial part of the overall cost, maintenance managers have little visibility into whether maintenance expenditure is money well spent or not, i.e., whether the maintenance is effective or not. In this blog, I’d like to talk about a mathematical formulation of the maintenance effectiveness evaluation problem and a systematic way of solving it.
Knowledge base construction to improve voice-enabled AI in industrial settings
In this research, we focus on building a framework for constructing a KB of equipment components and their problems entities with “component
Equipment Health Indicator Learning using Deep Reinforcement Learning
We propose a machine learning based method to solve health indicator learning problem. Our key insight is that HIL can be modeled as a credit assignment problem which can then be solved using Deep Reinforcement Learning (DRL). The life of equipment can be thought as a series of state transitions from a state that is healthy at the beginning to a state that is completely unhealthy when it fails. Reinforcement learning learns from failures by naturally backpropagating the credit of failures into intermediate states.