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1,193 case studies
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Continuous condition monitoring pays off at a large power utility
Continuous condition monitoring pays off at a large power utility
A large power utility in Hawaii was looking for more frequent condition monitoring on their Balance of Plant (BOP) generation assets. They had experienced significant equipment failures that occurred between their scheduled quarterly walkaround condition monitoring routes.
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How a major player in the oil & gas industry decreased downtime
How a major player in the oil & gas industry decreased downtime
Sean Simon is the SVP of Operations at CIG Logistics, where sand is transloaded and stored for third parties in the oil and gas industry. Before looking into CMMS solutions, his team spent three years trying to manage their maintenance operations with a paper-based system, leaving them with the major issue of not being able to gather or access data. “There’s no way to mine paper. There was no daily summary, no way of tying together comments or keywords.” As a result, trying to track and schedule preventive maintenance was nearly impossible. “It was like owning a car in the 1950s. You had to try to remember the last time you did something and guess at the maintenance that needed to be done in the future”.
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Scalable Predictive Maintenance in INSEE
Scalable Predictive Maintenance in INSEE
SCCC had committed to running a showcase Digital Factory for the ASEAN region and had already invested heavily in smart factory equipment and sensors. They required a predictive maintenance system that would leverage their existing investments and integrate with their SAP PM maintenance system.
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Predictive Analytics Solution for Off Highway Equipment
Predictive Analytics Solution for Off Highway Equipment
The client wanted to reduce downtime and production losses by effectively prioritizing maintenance activities and proactively replacing components before failure.
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CN Helped Pine Printshop with a Responsive and Top-notch E-commerce Portal
To digitally expand their business, Pine Printshop was looking for a catalog-based site that would help people buy ready-made products (e.g. apparels, board pins, stickers, etc.) and even allow customers to personalize their own t-shirts, caps, and hoodies.
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ArcelorMittal condition monitoring
ArcelorMittal condition monitoring
ArcelorMittal’s rotating assets often operate in harsh environments. A conveyor at the company’s hot strip mill in Ghent, Belgium moves plates of sizzling hot steel along the production process. In conditions like these, traditional proximity-based technologies like vibration and acoustic analysis fail: the sensors can’t handle the high temperatures.“In the steel industry, assets frequently operate in conditions that are not hospitable to sensitive sensor technologies,” says Andy Roegis, ArcelorMittal’s industrial digitalization manager for northern Europe. “The conveyor on our hot strip mill is a critical part of the production process, but it’s virtually impossible to use manual or vibration-based techniques to assess its condition.”
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Automatic monitoring of acoustic emission saves catastrophic failure
Automatic monitoring of acoustic emission saves catastrophic failure
Traditional measurement tools are ineffective when it comes to slowly rotating equipment. There are faults like Bearing Failure, Ring Plugging, Gear Tooth Crack and many more which can lead to the shutdown of machines. 1 minute of downtime cost the company $10. RingPluggingis a very common issue which Pinnacle Pellet was facing very frequently due to diverse feed quality into the machines. Product ring plugging can be detected as sound levels increase in specific roller bearings.
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Wireless Condition Monitoring Predicts Failure Of Calendar Roll Gearbox
Wireless Condition Monitoring Predicts Failure Of Calendar Roll Gearbox
The calendar machine run by a motor has a shaft mounted gearbox connected to the roller. This gearbox allows maximum paper load and feeds the paper with reduced speed to the roller. In spite of scheduled preventive maintenance, it was observed that gearbox used to fail frequently. The rise in vibrations leading to the eventual failure of gearbox adversely affected the quality of the paper. Monitoring the gearbox was thus vital and critical.
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Large-scale Implementation of Wireless Predictive Maintenance
Large-scale Implementation of Wireless Predictive Maintenance
In 2016, Arizona Public Service (APS) decided to enter the California ISO (CAISO) market, which allows them to sell power into the California market. One of their key assets was Sundance, a 420 MW unmanned peaker plant located 50 miles outside Phoenix. The entry into the CA energy market meant that starts tripled and run hours doubled almost immediately at the plant. They started looking for wireless Predictive Maintenance (PdM) system because the running hours were typically when no one was on site, which meant that traditional forms of PdM were not possible. Typically, a specialist would collect vibration and other condition data on equipment, but it had to be taken during operation, and it was difficult to get personnel out to the site.“Reliability was foremost on our minds,” commented Don Lamontagne, Supervisor of Equipment Reliability Engineering. “We faced huge loss of potential revenue, as well as fines if we weren’t able to generate power when it’s needed.”
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Predictive Maintenance For Connected Vehicles
Predictive Maintenance For Connected Vehicles
By 2025, Transport for London will have to meet strict emission-control regulations. This means buying and operating new fleets of hybrid or fully electric, zero-emission buses. As a consequence, many Original Equipment Manufacturers (OEMs) and operators will have to develop new technologies to help them get-to-market fast enough to meet demand.
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Predictive maintenance in Schneider Electric
Predictive maintenance in Schneider Electric
Schneider Electric Le Vaudreuil factory in France is recognized by the World Economic Forum as one of the world’s top nine most advanced “lighthouse” sites, applying Fourth Industrial Revolution technologies at large scale. It was experiencing machine-health and unplanned downtime issues on a critical machine within their manufacturing process. They were looking for a solution that could easily leverage existing machine data feeds, be used by machine operators without requiring complex setup or extensive training, and with a fast return on investment.
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Predictive maintenance of medical devices based on years of experience and advan
Predictive maintenance of medical devices based on years of experience and advan
Failure prediction by human operators requires advanced skills, and the limited number of experts cannot monitor all MRI systems around the world. "Corrective maintenance" for repairs after breakdowns has also become inevitable.
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Improving Refinery Safety and Efficiency with AI at the Edge
Improving Refinery Safety and Efficiency with AI at the Edge
Historically, Texmark's operations and maintenance teams monitored equipment manually and through wired sensors at a significant cost of dollars and man-hours each year in inspecting equipment on site.Wanting to act quickly and execute on this multi-year plan, Texmark turned to Deloitte to deliver an end-to-end IIoT implementation in late 2017. Over the course of the next two years, Deloitte coordinated the following partners for the project:1. National Instruments to outfit pumps with sensors that collect operational data2. Hewlett Packard Enterprises to enable deployment at the edge3. OSIsoft PI to aggregate the sensor data from the NI sources4. SparkCognition™ to predict impending pump failures5.Flowserve to help develop the model and data flow architecture 6. PTC ThingWorx to create a mobile-supported interface
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Identifying Vane Failure From Combustion Turbine Data
Identifying Vane Failure From Combustion Turbine Data
In late 2015, a deployed combustion turbine experienced a row two vane failure, which caused massive secondary damage to the compressor, resulting in nearly two months of downtime and up to $30M in repairs costs and lost opportunity. This failure, though rare, is representative of typical catastrophic events that are very difficult to catch. Though the onsite plant operations team had been monitoring the asset, this specific failure mode was previously unknown and very nuanced, and existing alarms did not have enough information for SMEs to properly diagnose it in time.The OEM decided to evaluate SparkCognition’s predictive analytics solution, SparkPredict®, with the following objectives:1. Demonstrate the ability to detect and distinguish operational and anomalous online steady-state conditions based on blind data provided from the turbine.2. Provide additional insights about the key contributing factors to the underlying anomalies.3. Provide a UI that interfaces to live streaming data from the asset.
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Detecting Cavitation And High Vane Pass Frequency For Pumps
Detecting Cavitation And High Vane Pass Frequency For Pumps
The Condensate Cooling Water (CCW) pump, one of the critical pumps in maintaining steadystate operations, is a horizontal vane pump operating at up to 1650 m3/hr with a discharge pressure of 9 MPa (62 psi) at 986 rpm. Each day this pump is offline costs the plant $250,000 in lost revenue and each failure costs tens of thousands of dollars to execute an unplanned repair. Thus, Larsen & Toubro (L&T) really needed a predictive maintenance solution to detect faults at an early stage and provide a reliable prediction of Remaining Useful Life (RUL)
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Smart Ccondition Monitoring Saves USD 30 Thousand In A Forging Unit
Smart Ccondition Monitoring Saves USD 30 Thousand In A Forging Unit
The 1000 Ton main forging press had a 75 HP motor and fed a trimming machine. The motor pulley combination was situated on top of the Press at a height of about 15 feet thereby reducing its access for routine maintenance. The company found difficulty ensuring constant uptime of the Press.
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Wireless Predictive Maintenance to Fix a Dated Walk-Around Program
Wireless Predictive Maintenance to Fix a Dated Walk-Around Program
C&W Services was using a manual condition monitoring program at one of its leading life sciences’ client up until last year. At best, data was collected manually every 30 days, even on the most critical machines, using a handheld data logger. After the data collection, all of the data analysis had to be outsourced to a third party for analysis. This approach has several limitations:1. Unplanned Downtime2. Shortage of Manpower3. Safety and Access to Machines4. Inconsistent Readings Collected by Manual Processes
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Saving millions by avoiding expensive downtime for hydraulic fracturing equipmen
Saving millions by avoiding expensive downtime for hydraulic fracturing equipmen
To extract shale oil and gas, specialized equipment is used to fracture rock via a process called hydraulic fracturing (or “fracking”). To do this efficiently, users must know when their equipment needs maintenance. If the equipment stops working while in the well, millions of dollars are lost due to downtime and logistics. Additionally, our client, a major oilfield equipment company, needed a way to make their product stand out. They wanted to accomplish this by providing oil and gas software solutions but had no idea on how to develop and deliver software in the cloud.
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Anaren Microwave Implements their manufacturing CMMS
Anaren Microwave Implements their manufacturing CMMS
Like many organizations, Anaren had a homegrown work order application that had basic asset management functionality. “It was menu driven, so quite cumbersome,” explained Bill, “reporting was limited and it still relied heavily on paper transactions and records. We looked at our business needs going forward and decided this was one area that could be modernized.” On launching the Manufacturing CMMS project, Bill, the business analyst of the company identified three major areas for improvement:1. Improve efficiency by eliminating paper.2. Improve the control of preventive maintenance. 3. Improve inventory management.
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Sentilo Terrassa (Smart City Open Data)
Sentilo Terrassa (Smart City Open Data)
Terrassa City was in need to ameliorate their information and communication flow between municipal managers, in order to generate new services to its citizens. The City Council was missing an internal management platform of the municipal services, and wanted to initiate a Smart City strategy to solve this issue, along with bringing value to all parties involved (municipality, businesses, citizens and other local entities). 
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Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance
Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance
The extrusion and other machines at Mondi’s plant are large and complex, measuring up to 50 meters long and 15 meters high. Each machine is controlled by up to five programmable logic controllers (PLCs), which log temperature, pressure, velocity, and other performance parameters from the machine’s sensors. Each machine records 300–400 parameter values every minute, generating 7 gigabytes of data daily.Mondi faced several challenges in using this data for predictive maintenance. First, the plant personnel had limited experience with statistical analysis and machine learning. They needed to evaluate a variety of machine learning approaches to identify which produced the most accurate results for their data. They also needed to develop an application that presented the results clearly and immediately to machine operators. Lastly, they needed to package this application for continuous use in a production environment.
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USD 1.2 Million Saved On A Forging Press Line By Predicting Cutter Life
USD 1.2 Million Saved On A Forging Press Line By Predicting Cutter Life
The major press line in the company has a circular saw machine which cuts metal rods with precision in predefined lengths for further heating in the furnace. The cut pieces are then fed to the forging line to make automotive components. The length and perpendicularity of the cut pieces are crucial to obtain a good quality forging.It was observed that circular saw failed to maintain the precise length and perpendicularity while cutting the metal rods leading to heavy rejections. This was a serious concern and routine preventive maintenance was unable to overcome it.
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Condition Based Maintenance - “Indispensable” Sensor Technology
Condition Based Maintenance - “Indispensable” Sensor Technology
At the corporation’s Mizushima plant near Okayama, pH measurement during the neutralization of strong acids is closely monitored. For this important measurement Tsutomu Ishikawa and Naoto Ogura, engineers at the plant’s Instrumentation and Engineering Department were not satisfied with the performance of the pH sensors they were using.For this reason, sensors were regularly exchanged at the plant to minimize the chance of failure in the process. But the operation cost was high and Mr. Ishikawa and Mr. Ogura needed a better solution. Specifically, they required to know in advance when a pH sensor would need to be cleaned, calibrated or replaced: “We wanted to grasp how deposits forming on the pH sensors would affect the timing of sensor maintenance and exchange.”
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