Industrial Information Hub and Senseye Predictive Maintenance
How the Water Industry Is Transforming Submersible Pump Maintenance With AI
A technology called electrical signature analysis (ESA) makes reliable, remote monitoring of submerged pumps possible. ESA measures the current and voltage being supplied to the motor driving the pump. Because it captures the electrical signals, ESA sensors are current and voltage probes that are installed in the motor control cabinet. This bypasses the need to install sensors on, or even near, submerged pumps.
One solution utility operators are increasingly deploying to improve the energy efficiency of pumps are variable frequency drives (VFDs). VFDs are a type of motor controller that drive an electric motor by varying the supply frequency. By matching fluctuating load and demand requirements, organizations can operate pumps more efficiently, saving energy and extending their lifetime.
Smart Trac: AI-Assisted for Predictive/Condition-Based Vibration Monitoring
igus fixed flange bearings become intelligent
The new fixed flange bearings are currently in the prototype phase. However, they have already convinced one pilot customer: the Société National des Chemins de Fer Luxembourgeois (CFL). Luxembourg’s national railway company operates a 200-metre washing system that cleans local and long-distance trains every day. In the past, gearbox malfunctions on the washing trolley repeatedly led to costly system failures. The operator therefore abandoned lubricated standard ball bearings and instead used the networked spherical balls from igus in the UC bearing housings. The advantage is that the spherical balls made of high-performance plastics are corrosion-free, resist chemicals and enable low-friction and maintenance-free dry operation thanks to incorporated solid lubricants.
Predictive Maintenance 3D Simulation Use Case
Why CP Kelco chose to partner with Tractian
🧠 Toyota and Generative AI: It’s Here, and This is How We’re Using It
Toyota’s initial goal in 2016 was to engineer a resilient cloud safety system, and that led to the development of Safety Connect, a service powered by Drivelink from software company Toyota Connected North America (TCNA). The Safety Connect service is designed to leverage key data points from the vehicle to identify when a collision has occurred and send an automatic notification to call center agents. Should the driver become unconscious, telematics information can provide a more complete picture of the situation, enabling agents to contact authorities faster when it’s needed most.
Vehicle maintenance has also been a focus of AI-driven enhancements. Connected vehicles have hundreds of sensors, and we have been using data from these vehicles to build machine learning models for the most common maintenance items, including batteries, brakes, tires, and oil, and are currently investigating dozens of other components, using daily streaming data from millions of connected and consented vehicles. This suite of predictive maintenance models will help make customers aware of potential maintenance needs prior to component failures, so they can enjoy more reliable mobility experiences.
Predictive Maintenance for Semiconductor Manufacturers with SEEQ powered by AWS
There are challenges in creating predictive maintenance models, such as siloed data, the offline nature of data processing and analytics, and having the necessary domain knowledge to build, implement, and scale models. In this blog, we will explore how using Seeq software on Amazon Web Services can help overcome these challenges.
The combination of AWS and Seeq pairs a secure cloud services platform with advanced analytics innovation. Seeq on AWS can access time series and relational data stored in AWS data services including Amazon Redshift, Amazon DynamoDB, Amazon Simple Storage Service (S3), and Amazon Athena. Once connected, engineers and other technical staff have direct access to all the data in those databases in a live streaming environment, enabling exploration and data analytics without needing to go through the steps to extract data and align timestamps whenever more data is required. As a result, monitoring dashboards and running reports can be set to auto generate and are easily shared among groups or sites. This enables balancing machine downtimes and planning ahead for maintenance without disrupting schedules or compromising yields.
Bosch Rexroth Predictive Analytics @ TSR Recycling GmbH
AI Keeps Assembly Conveyor Rolling
The overhead conveyor is the backbone of the plant. It handles almost 1,250 cars per day during a three-shift operation. There is no back-up equipment, so failure is not an option. The conveyor’s parts are exposed to relatively high forces, causing them to wear in a relatively short time. Given that the conveyor is several meters above the floor, it is difficult to access for inspection.
ŠKODA engineers developed the Magic Eye to continuously monitor the condition of the conveyor’s moving parts without the need for maintenance personnel to climb ladders and physically do the job.
Six cameras are mounted on the conveyor frame at strategic locations to monitor the condition of various conveyor elements. Rapid assessment of each trolley’s condition is carried out as the conveyor is running. Images collected by the camera are transmitted via WiFi to a central database, where they are analyzed by artificial intelligence algorithms. The software detects wear by comparing each new image of the trolley with previously collected images. If an anomaly is detected, the software sends an alert to maintenance personnel, who can fix the trolley before it can create unexpected downtime.
Improved GRU prediction of paper pulp press variables using different pre-processing methods
Predictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector.Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units’ conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different.
DuPont + Augury: Driving Innovation with Predictive Maintenance
Which machines can I monitor with predictive maintenance?
Monitoring rotating equipment is the best way to provide advanced warning of machine faults. Rotating equipment is nearly ubiquitous in today’s production facilities, appearing in critical machines like pumps, extractors, compressors, ventilators, and mixers. Most machines are powered by electric motors—themselves rotating equipment. These may transmit power directly, or through belt drives or gearboxes—more rotating equipment. From there, we often find even more rotating equipment, such as fans or impellers.
Moving parts will all, eventually, break. AiSight’s predictive maintenance solution monitors the indicators of breakdowns. Early intervention is best—our analysis of faults finds that 71% of machine fault alerts are caused by relatively minor issues such as dirt accumulation, looseness, and inadequate lubrication. These are simple issues to fix, but cause serious damage when not detected early. And these are only the beginning of what we monitor; our predictive maintenance solution identifies imbalances, misalignments, looseness, bearing defects, friction, gearbox problems, belt drive problems, cavitation, and impacts. How our solution detects and identifies problems depends on the machine.
Achieving World-Class Predictive Maintenance with Normal Behavior Modeling
Central to the normal behavior modeling (NBM) concept is an algorithm known as an autoencoder, shown in Figure 1. Over time, the autoencoder’s input layer ingests a continuous stream of quantitative data from equipment sensors (temperature, pressure, etc.). This data is then fed to a hidden layer (of which there are typically several), where it gets compressed. Numerical weights (a value between 0 and 1) are then applied to each node, with the goal of eventually reproducing the input values at the output layer.
The principal purpose of NBM is to define the normal state of a complex system and then proactively identify instances where the system is operating outside of normal with sufficient advance warning to allow maintenance or repair actions to take place to avoid revenue loss, repair costs, and safety compromises that typically come with such failures.
auton-survival: An Open-Source Package for Regression, Counterfactual Estimation, Evaluation
Real-world decision-making often requires reasoning about when an event will occur. The overarching goal of such reasoning is to help aid decision-making for optimal triage and subsequent intervention. Such problems involving estimation of Times-to-an-Event frequently arise across multiple application areas, including, predictive maintenance. Reliability engineering and systems safety research involves the use of remaining useful life prediction models to help extend the longevity of machinery and equipment by proactive part and component replacement.
Discretizing time-to-event outcomes to predict if an event will occur is a common approach in standard machine learning. However, this neglects temporal context, which could result in models that misestimate and lead to poorer generalization.
Developing AI Predictive Maintenance Models
Use Machine Learning to Implement Effective Predictive Maintenance
The Power of Predictive Maintenance
“Getting to the level of predictive maintenance is an evolutionary process for manufacturers, regardless of their specialty,” notes Will Healy III, global business strategy manager at Balluff Inc. “Right now, there is great interest in retrofitting equipment with sensors to perform condition monitoring as a means to implement predictive maintenance. The next step is using equipment with integrated smart sensors and artificial intelligence. These technologies also enable prescriptive maintenance, which uses machine learning to help companies specifically adjust their operating conditions for desired production outcomes.”
One of the first robotic predictive maintenance applications of the IIoT occurred several years ago in the auto industry when General Motors teamed up with Cisco and FANUC America Corp. to launch a zero downtime program. Called ZDT, the predictive analytics service identifies potential failures so engineers and plant managers can schedule maintenance and repairs. This prevents unexpected breakdowns during production, thereby saving manufacturers time and money. According to Tuohy, the ZDT program has proven to be quite successful over the last several years. He says that about 30,000 robots worldwide are connected to the system.
A deep transfer learning method for monitoring the wear of abrasive belts with a small sample dataset
According to the analysis of displacement data, a new method for the prediction of abrasive belt wear states using a multiscale convolutional neural network based on transfer learning is proposed. Initially, first-order difference preprocessing is ingeniously performed on displacement data. Then, the network parameters of the model are obtained by pretraining the fault dataset and are directly transferred or fine-tuned according to the preprocessed displacement data. Finally, the preprocessed displacement data corresponding to different abrasive belt wear states are accurately classified. This method verifies the application of transfer learning between cross-domain data in industry and resolves the contradiction between the large sample size required for deep learning and the difficulty of obtaining a large amount of sample data in actual production. The experimental results show that this method can accurately predict the wear status of abrasive belts, with an average prediction accuracy of 93.1%. This method has the advantages of low cost and easy operation, and can be applied to guide the replacement time of abrasive belts in production.
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.
MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings
Rolling bearings are important components of industrial rotating machinery and equipment. The prediction of the remaining useful life (RUL) of rolling bearings is of great significance for improving the safety of the machine, reducing the economic and property losses caused by the failure of the bearings. However, for the task of predicting the RUL of rolling bearings, the information of the past time and the future time are as important as the information of the current time. In order to make better use of the extracted features for RUL prediction of rolling bearings, this paper has proposed a novel deep learning framework of multi-scale long-term recurrent convolutional network with wide first layer kernels and residual shrinkage building unit (MSWR-LRCN). The major difference from the previous deep neural network is that our new network organically combines the attention mechanism with multi-scale feature fusion strategy, and improves the anti-noise ability of the entire network. In addition, moving average (MA) method and a polynomial fitting model are also used, which help predict the RUL of rolling bearings effectively. The results show that this method has improved the prediction accuracy compared with the existing methods.
Predictive Monitoring: Gas Turbines Demo
A pressing case for predictive analytics at MacLean-Fogg
Metform chose to focus specifically on the AMP50XL’s drive train because “that was the area where we saw the biggest opportunity for improvement.” While they’d previously been gathering data from the machine for predictive-maintenance use, the old process was neither efficient nor of adequate detail, they realized. “From a data collection standpoint, there was a lot of spreadsheets, a lot of handwritten notes, a lot of tribal knowledge,” Delk said. “We wanted to make sure we could gather that information and put it into context as we were analyzing the equipment.”
“We’re able to monitor the machine health, see in real time how the machine is doing and see a signal of a problem before it becomes a major problem. We have a long way to go in terms of learning how to better use the system and gain further confidence in the system, but at this point, I’m really pleased with the progress we made. I’m anxious to expand this to the other nine Hatebur presses.”
Can Boston Dynamics’ Robots Spot And Stretch Make It Profitable?
Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review
Failure prediction is the task of forecasting whether a material system of interest will fail at a specific point of time in the future. This task attains significance for strategies of industrial maintenance, such as predictive maintenance. For solving the prediction task, machine learning (ML) technology is increasingly being used, and the literature provides evidence for the effectiveness of ML-based prediction models. However, the state of recent research and the lessons learned are not well documented. Therefore, the objective of this review is to assess the adoption of ML technology for failure prediction in industrial maintenance and synthesize the reported results. We conducted a systematic search for experimental studies in peer-reviewed outlets published from 2012 to 2020. We screened a total of 1,024 articles, of which 34 met the inclusion criteria.
Industry 4.0 and the Automotive Industry
“It takes about 30 hours to manufacture a vehicle. During that time, each car generates massive amounts of data,” points out Robert Engelhorn, director of the Munich plant. “With the help of artificial intelligence and smart data analytics, we can use this data to manage and analyze our production intelligently. AI is helping us to streamline our manufacturing even further and ensure premium quality for every customer. It also saves our employees from having to do monotonous, repetitive tasks.”
One part of the plant that is already seeing benefits from AI is the press shop, which turns more than 30,000 sheet metal blanks a day into body parts for vehicles. Each blank is given a laser code at the start of production so the body part can be clearly identified throughout the manufacturing process. This code is picked up by BMW’s iQ Press system, which records material and process parameters, such as the thickness of the metal and oil layer, and the temperature and speed of the presses. These parameters are related to the quality of the parts produced.
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.
Forecast Anomalies in Refrigeration with PySpark & Sensor-data
A refrigeration has four important components: Compressor, Condenser Fan, Evaporator Fan & Expansion Valve. Loosely speaking, together they try to keep the pressure at a reasonable level so as to maintain the temperature within (Remember, PV = nRT). In Walmart, we collect sensor data for all of these components (eg. pressure, fan speed, temperature) at a 10 minutes interval along with metrics like if the system is in defrost or not, compressor is locked out or not etc. We also capture outside air temperature as it impacts the condenser fan speed and in turn, the temperature.
The objective is to minimize the number of malfunctions and suggest probable resolutions of the same to save time. So, we leveraged this telemetry information in order to forecast anomalies in temperature, which would help in prioritizing issues and be proactive rather than reactive.
The Cost of Unplanned Downtime for Refineries
According to the American Institute of Chemical Engineers (AlChE), the cost of missed production for a U.S. refinery with an average-sized fluid catalytic cracking unit of 80,000 barrels per day will range from $340,000 a day at profit margins of $5 per barrel, to $1.7 million a day at profit margins of $25 per barrel, based on a conservative estimate. A single, unplanned shutdown that lasts hours can lead to the release of a year’s worth of emissions into the atmosphere, according to John Hague, Aspen Technology Inc.
One type of innovative inspection process is Rapid Ultrasonic Gridding (aka RUG), which creates data-rich visual grid maps that identify areas of corrosion and other damage mechanisms. It is 10 times faster than traditional gridding and competing methods. In most situations, the operator can quickly make the decision of whether to proceed with maintenance measures to resolve the issue, or to return the inspected asset to operation.
How and Why Pharmaceutical Manufacturers Are Applying Artificial Intelligence
“Opportunities to reduce manufacturing costs exist across all stages of the product lifecycle. Advanced analytics can reveal those opportunities, allowing pharma companies to take informed action to save money,” said Richard Porter, global director, pharmaceuticals, at AspenTech. “Whether using multivariate analytics to identify process degradation and its impact on quality or predicting final product quality to reduce lab testing lag times, these techniques offer pharmaceutical companies a competitive advantage.”
A purified water system at a pharmaceutical manufacturing facility.“The company tried to avoid batch losses—with each batch valued between $250,000-$300,000—as frequent shutdowns to replace the seals limited capacity,” said Porter. “As the company needed to ramp up capacity, it purchased two additional mills. Adopting Aspen Mtell, which connects to OPC UA supported devices, for predictive maintenance allowed the company to reduce supply chain disruptions from seal replacements and cut lifecycle maintenance costs by 60%. In addition, the company reduced capital expenditures and associated lifecycle maintenance costs by 50%.”
SKF uses cloud to offer new business models
The idea is simple: Instead of buying industrial bearings – whether for conveyor belts, pumps, crushers, paper machines, steel or pulp mills and railway bogies – SKF’s customers pay for uninterrupted rotation services. Under SKF’s Rotating Equipment Performance service, customers pay a fixed fee, which covers the provision of bearings, seals, lubrication and condition monitoring.
On the topic of payment: For many manufacturing operations, the argument for XaaS is that payments fall under operational expenditures (OPEX), thus leaving capital expenditure (CAPEX) budgets intact for the big, essential investments. When a contract is drawn up the parties agree on targets, which could be machine production level, uptime or other KPIs. Digitalization is essential for delivery and to ensure the promised uptime.
Aside from detecting failures before they happen, data evaluation is essential for selecting the right rotation services. SKF can measure the rotating equipment performance and from the data recognize whether the solution it has proposed is meeting its customers’ needs. If not, adjustments can be made to provide the best solution possible.
Colgate-Palmolive Focuses on Machine Health to Improve Supply Chain Operations
Colgate-Palmolive is feeding this wireless sensor data into Augury’s machine health software platform. Pruitt pointed out that this enables Colgate-Palmolive’s machine data to be compared with machine data from more than 80,000 other machines connected to the Augury platform around the world.
“That massive analytical scale brings us insights on how to optimize the performance of equipment and make ever-smarter choices on how and where we deploy it,” Pruitt said. “What’s possible only gets more compelling as this AI solution harnesses more data to create better health outcomes for our machines and our business.”
Providing a specific example of how Augury’s Machine Health system has helped Colgate-Palmolive, Pruitt noted that the system’s AI detected rising temperatures in the drive of a tube maker and alerted the plant team. “Upon inspection, they discovered a problem with the motor’s water cooling system,” he said. “By getting it quickly resolved, we prevented the drive from failing due to overheating, which would’ve stopped the tube production line and incurred replacement costs. We figure the savings at 192 hours of downtime and an output of 2.8 million tubes of toothpaste, plus $12,000 for a new motor and $27,000 in variable conversion costs.”
Predictive Maintenance ROI: A $432 Billion Boost To The World’s Leading Manufacturers
By extrapolating our findings across Fortune Global 500 (FG500) industrial companies, we’ve calculated that these companies are losing 3.3 million hours in production time annually to unscheduled downtime and taking a near $1 trillion financial hit - equivalent to 8% of their annual revenues.
From the returns seen from our clients, we estimate that the widespread use of advanced, AI-driven machine-health monitoring and Predictive Maintenance could save FG500 manufacturers 1.7 million production hours a year and deliver a 4% productivity boost worth $432 billion.
Machine Learning Keeps Rolling Bearings on the Move
Rolling bearings are essential components in automated machinery with rotating elements. They come in many shapes and sizes, but are essentially designed to carry a load while minimizing friction. In general, the design consists of two rings separated by rolling elements (balls or rollers). The rings can rotate can rotate relative to each other with very little friction.
The ability to accurately predict the remaining useful life of the bearings under defect progression could reduce unnecessary maintenance procedures and prematurely discarded parts without risking breakdown, reported scientists from the Institute of Scientific and Industrial Research and NTN Next Generation Research Alliance Laboratories at Osaka University.
The scientists have developed a machine learning method that combines convolutional neural networks and Bayesian hierarchical modeling to predict the remaining useful life of rolling bearings. Their approach is based on the measured vibration spectrum.
Google Cloud and Seagate: Transforming hard-disk drive maintenance with predictive ML
At Google Cloud, we know first-hand how critical it is to manage HDDs in operations and preemptively identify potential failures. We are responsible for running some of the largest data centers in the world—any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services. In the past, when a disk was flagged for a problem, the main option was to repair the problem on site using software. But this procedure was expensive and time-consuming. It required draining the data from the drive, isolating the drive, running diagnostics, and then re-introducing it to traffic.
That’s why we teamed up with Seagate, our HDD original equipment manufacturer (OEM) partner for Google’s data centers, to find a way to predict frequent HDD problems. Together, we developed a machine learning (ML) system, built on top of Google Cloud, to forecast the probability of a recurring failing disk—a disk that fails or has experienced three or more problems in 30 days.
Amazon Lookout For Equipment – Predictive Maintenance Is Now Mature
Amazon Lookout for Equipment is designed for maintainers, not data scientists, and it comes from a place of knowledge. Incorporating expertise and insight gleaned from maintaining its own assets, Amazon aims to make it as easy as possible for users to get started and begin seeing value, addressing potential issues around usability and configurability.
In terms of technical abilities, it currently only covers simple assets like motors, conveyors, and servos – essentially, the kind of assets Amazon itself uses. It doesn’t yet monitor more sophisticated assets like robots or CNC machinery, although, in time, I do not doubt that these, too, will also be covered. As it stands, though, it will be competent for a lot of standard factory equipment.
Pushing The Frontiers Of Manufacturing AI At Seagate
Big data, analytics and AI are widely used in industries like financial services and e-commerce, but are less likely to be found in manufacturing companies. With some exceptions like predictive maintenance, few manufacturing firms have marshaled the amounts of data and analytical talent to aggressively apply analytics and AI to key processes.
Seagate Technology, an over $10B manufacturer of data storage and management solutions, is a prominent counter-example to this trend. It has massive amounts of sensor data in its factories and has been using it extensively over the last five years to ensure and improve the quality and efficiency of its manufacturing processes.
Sensor Fusion: The Swiss Army Knife of Digitalization
With the proper communication protocols and network architecture in place, smart sensor technology and the data it provides can be the bulwark on which digital transformation is built.
If industrial control systems are the brains of a plant, then sensors are its eyes and ears. Simply put, without sensors there would be nothing for SCADA, DCS, or PLCs to respond to. That’s why increasingly intelligent or ‘smart’ sensors packing more onboard processing power, the ability to monitor new variables, and digital communication capabilities are playing such an important role in helping plant operators and enterprise level planners alike to see better and respond to problems with more finesse.
More Fleets Are Utilizing Remote Diagnostics
A growing number of motor carriers are taking advantage of remote diagnostics to streamline vehicle maintenance and reduce downtime, but fleets must know how to filter through the data to use this information effectively.
Remote diagnostics services, which are available through all major truck manufacturers and various component and technology suppliers, enable fleet managers to monitor engine fault codes and maintenance issues while the truck is on the road.
A Case of Applying AI to an Ethylene Plant
Unexpected equipment failures or maintenance may result in unscheduled plant shutdowns in continuously operating petrochemical plants such as ethylene plants. To avoid this, the operation status needs to be continuously monitored. However, since troubles in plants have various causes, it is difficult for human workers to precisely grasp the plant status and notice the signs of unexpected failures and need for maintenance. To solve this problem, we worked with a customer in an ethylene plant and developed a solution based on AI analysis. Using AI analysis based on customer feedback, we identified several factors from numerous sensor parameters and created an AI model that can grasp the plant status and detect any signs of abnormalities. This paper introduces a case study of AI analysis carried out in an ethylene plant and the new value that AI technology can offer to customers, and then describes how to extend the solution business with AI analysis.
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.