Machine Health

Assembly Line

Detecting dangerous gases to improve safety and reduce emissions

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Topics: Nondestructive Test, Machine Health

Vertical: Petroleum and Coal

Organizations: Emerson

The primary advantage of differential optical absorption spectroscopy is its scalability. Two elements are required: a calibrated light source tuned to emit a specific wavelength, and a receiver able to read the same wavelength. In some cases, the receiver must also read a reference source for comparison. The two elements can be within the same housing to function as a point detector, but the source and receiver can also be separated, sending a beam across an open path, looking for a cloud of the target gas to move into its field of view.

Read more at Plant Engineering

Yokogawa and Mitsubishi Heavy Industries to Undertake AI-enabled Robot System Project for the Nippon Foundation - DeepStar Joint Research & Development Program

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Topics: Autonomous Mobile Robot, Machine Health

Organizations: Yokogawa, Mitsubishi, Nippon Foundation

The aim of this project is to develop an automatic inspection system that utilizes robots to identify and predict hazards in offshore facilities. The use of a wide variety of robots to enable unmanned operations and thereby reduce the risk of performing inspections on offshore platforms has long been considered; however, the centralized coordination of individual robots is complex as it requires the management of multiple systems and the data that they acquire. Yokogawa has already been engaged in the research and development of a robot service platform that centralizes the management of multiple robots and seamlessly links them with existing control systems. Leveraging the findings of this R&D, this project will build a communications infrastructure and robot system that is well suited for the environment found on offshore platforms, and utilize an AI application to convert for use in offshore platform operations the image and sound data acquired by robots.

Read more at Yokogawa Press Releases

Detecting low-flow cavitation using predictive maintenance system SAM4

Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers

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Topics: Cloud Computing, Machine Health

Organizations: Google, Litmus Automation

The new manufacturing solutions from Google Cloud give manufacturing engineers and plant managers access to unified and contextualized data from across their disparate assets and processes.

Manufacturing Data Engine is the foundational cloud solution to process, contextualize and store factory data. The cloud platform can acquire data from any type of machine, supporting a wide range of data, from telemetry to image data, via a private, secure, and low cost connection between edge and cloud. With built-in data normalization and context-enrichment capabilities, it provides a common data model, with a factory-optimized data lakehouse for storage.

Manufacturing Connect is the factory edge platform co-developed with Litmus Automation that quickly connects with nearly any manufacturing asset via an extensive library of 250-plus machine protocols. It translates machine data into a digestible dataset and sends it to the Manufacturing Data Engine for processing, contextualization and storage. By supporting containerized workloads, it allows manufacturers to run low-latency data visualization, analytics and ML capabilities directly on the edge.

Read more at Google Cloud Blog

Common challenges to machine health and ways to overcome them

Dual Linear Phased Array Corrosion Mapping

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Topics: Machine Health, Nondestructive Test

Organizations: Gecko Robotics

Asset health is paramount to the efficient and safe operation of facilities producing energy and manufactured goods. Ultrasonic corrosion mapping is a non-destructive testing (NDT) technique that uses data from ultrasonic measurements to map material thickness across a piece of equipment, such as tanks, pipes, and pressure vessels. The data is used to graph corrosion on the equipment for easy visual interpretation. Currently, there are a number of tools available to complete corrosion mapping inspections. However, one automated dual linear phased array technique offers increased productivity, accuracy, and data density over other methods.

Read more at Gecko Robotics

Detecting Corrosion and Erosion in Horizontal Boiler Tube Assemblies

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Topics: Nondestructive Test, Machine Health

Organizations: Gecko Robotics

Boilers play an essential role in improving the efficiency of thermal power generation. Three boiler sections, economizer, superheater, and reheater, are tightly bundled tube assemblies inherent to the process by maintaining high temperature feedwater and steam that drives the steam turbine and generator. Tube assemblies can be vertical or horizontal, but the focus of this article are assemblies in the horizontal configuration. Because of the curved design, depth of tubing, location, and contents they are subject to a variety of corrosion and erosion mechanisms that can result in failure and unplanned outages.

The susceptibility for failure in a tube assembly is further exacerbated by inadequate inspection methods for detecting or predicting corrosion and erosion damage. However, specialized robot-based NDT techniques, such as Rapid Ultrasonic Gridding (RUG), offer unparalleled coverage and data compared to traditional methods, giving owner/operators the confidence that their equipment can operate optimally.

Read more at Gecko Robotics Blog

Using digital twin for cost-efficient wind turbines

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Author: Nobuo Namura

Topics: digital twin, machine health

Organizations: Hitachi

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.

Read more at Hitachi Industrial AI Blog

Augury Raises $180M To Become One of the First Industry 4.0 Unicorns

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Topics: machine health, funding event

Organizations: Augury

The new investment and valuation is a validation of the emerging Machine Health category, of which Augury is the pioneer and leader. Machine Health uses the Internet of Things and Artificial Intelligence to predict and prevent industrial machine failures and improve machine performance. Machine Health allows manufacturers to reduce downtime, increase production capacity and productivity, optimize the cost of industrial asset care and accelerate their digital transformation.

Augury’s customers include some of the world’s top manufacturers, including Colgate-Palmolive, PepsiCo, Hershey’s, ICL and Roseburg. The company’s Machine Health solutions deliver an ROI of 3x-10x for customers, with programs paying for themselves within months.

Read more at Augury

Augury Becomes a Unicorn But Machine Health is Just Getting Started

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Topics: machine health

Organizations: Augury

Augury went into overdrive in 2021. Our revenue grew 150% and our team doubled as we made our 100-millionth machine recording. We saved one customer a million pounds of snacks and another 2.8 million tubes of toothpaste. We are helping our customers make medicines, produce clean water, and deliver so many products that make our life better, from diapers to construction materials, snack foods to vaccines. With this new funding we can continue to expand globally, innovate in Augury’s core manufacturing market and step into new ones.

Read more at Augury Blog

Machine Monitoring Becomes Simpler And More Affordable Than Ever

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Author: @mattnaitove

Topics: IIoT, machine health

Organizations: Guidewheel

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.

Read more at Plastics Technology

A pressing case for predictive analytics at MacLean-Fogg

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Topics: metal forming, predictive maintenance, machine health

Vertical: Fabricated Metal

Organizations: MacLean-Fogg, Predictronics

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.”

Read more at Plant Engineering

The Cost of Unplanned Downtime for Refineries

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Topics: predictive maintenance, machine health

Vertical: Petroleum and Coal

Organizations: Gecko Robotics

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.

Read more at Gecko Robotics Blog

Sensor-based leakage detection in vacuum bagging

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Authors: Anja Haschenburger, Niklas Menke, Jan Stuve

Topics: machine health, failure analysis

A majority of aircraft components are nowadays manufactured using autoclave processing. Essential for the quality of the component is the realization of an airtight vacuum bag on top of the component to be cured. Several ways of leakage detection methods are actually used in industrial processes. They will be dealt with in this paper. A special focus is put on a new approach using flow meters for monitoring the air flow during evacuation and curing. This approach has been successfully validated in different trials, which are presented and discussed. The main benefit of the method is that in case of a leakage, a defined limit is exceeded by the volumetric flow rate whose magnitude can be directly correlated to the leakage’s size and position. In addition, the potential of this method for the localization of leakages has been investigated and is discussed.

Haschenburger, A., Menke, N. & Stüve, J. Sensor-based leakage detection in vacuum bagging. Int J Adv Manuf Technol (2021).

Read more at Springer

How SparkCognition Improved Production Efficiency for a Beverage Manufacturer

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Topics: machine health, energy consumption

Vertical: Beverage

Organizations: SparkCognition

We developed seven new deep learning models to detect anomalies in resource consumption, machine status/health, and overall efficiency. (As always with a Total Plant solution, these models were tailored to the specific data, technical context, and business goals and strategies of the client.)

Once developed, the models were deployed into our AI platform for execution and KPI-driven reporting. Another key new function we delivered: predictive analysis, to anticipate problems before they occur, based on patterns detected in current and historical data, and notify the beverage manufacturer in time to take preventative action.

Finally, the results of the AI-powered analysis were delivered via a configurable dashboard that provides at-a-glance insight into the plant’s efficiency, including new KPIs reflecting water usage, water balance, power consumption, heat generation, and waste levels. This information can also now be streamed whenever, wherever, and to whomever the manufacturer requires, now or in the future.

Read more at SparkCognition

Colgate-Palmolive Focuses on Machine Health to Improve Supply Chain Operations

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Author: David Greenfield

Topics: predictive maintenance, machine health

Vertical: Chemical

Organizations: Colgate-Palmolive, Augury

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.”

Read more at AutomationWorld

Predictive Maintenance ROI: A $432 Billion Boost To The World’s Leading Manufacturers

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Author: Niall Sullivan

Topics: predictive maintenance, machine health

Organizations: Senseye

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.

Read more at Senseye Blog