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Smart Water Filtration Systems - Ayla Networks Industrial IoT Case Study
Smart Water Filtration Systems
Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.
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Predictive Maintenance for Industrial Chillers - SmartLog Industrial IoT Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
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Aircraft Predictive Maintenance and Workflow Optimization - SparkCognition Industrial IoT Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
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Predictive Maintenance Drives Smarter Fleet Management - Intel Industrial IoT Case Study
Predictive Maintenance Drives Smarter Fleet Management
Fleet managers are turning to predictive analytics to stay on top of maintenance and mitigate part failures before they happen. However, managing the large amount of new data generated by vehicle sensors is challenging.
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Predictive Maintenance for Gas Pipeline Compressors - Rovisys Industrial IoT Case Study
Predictive Maintenance for Gas Pipeline Compressors
CPG had a compression asset failure that interrupted service and had the potential to create customer dissatisfaction.
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Asset Management and Predictive Maintenance - Litmus Automation Industrial IoT Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
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Cutting-edge Predictive Analytics for HIROTEC Group - ThingWorx Industrial IoT Case Study
Cutting-edge Predictive Analytics for HIROTEC Group
Hirotec needed to ensure continuous operations and to minimize unplanned downtime in its manufacturing facilities. Unplanned downtime is costly and compromises Hirotec's ability to deliver its goods to customers on time.
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Cognitive Analytics for Oil and Gas - SparkCognition Industrial IoT Case Study
Cognitive Analytics for Oil and Gas
Oil and gas companies are having problems learning from the data to understand the different operational states and failure modes of assets, and uses this learning to provide adequate warning before failures occur so operators can plan for corrective actions thus optimizing their Operations and Maintenance budgets.
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Advanced Elastomer Systems Upgrades Production - Emerson Industrial IoT Case Study
Advanced Elastomer Systems Upgrades Production
In order to maintain its share of the international market for thermoplastic elastomers AES recently expanded its Florida plant by adding a new production line. While the existing lines were operating satisfactorily using a PROVOX distributed control system with traditional analog I/O, AES wanted advanced technology on the new line for greater economy, efficiency, and reliability. AES officials were anxious to get this line into production to meet incoming orders, but two hurricanes slowed construction.
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Automated Predicitive Analytics For Steel/Metals Industry - SORBA IoT Industrial IoT Case Study
Automated Predicitive Analytics For Steel/Metals Industry
Asset to be monitored: Wire Compactor that produces Steel RebarCustomer Faced The Following Challenges:Dependent upon machine uptime.Pressure cylinders within the compactor fail to control compression and speed causing problems in binding the coil.Equipment failure occurs in the final stage of production causing the entire line to stop, can you say bottleneck?Critical asset unequipped with sensors to produce data.
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Fusion Middleware Integration on Cloud for Pharma Major - Cognizant Industrial IoT Case Study
Fusion Middleware Integration on Cloud for Pharma Major
Customer wanted a real-time, seamless, cloud based integration between the existing on premise and cloud based application using SOA technology on Oracle Fusion Middleware Platform, a Contingent Worker Solution to collect, track, manage and report information for on-boarding, maintenance and off-boarding of contingent workers using a streamlined and Integrated business process, and streamlining of integration to the back-end systems and multiple SaaS applications.
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Mueller moves from preventive to predictive maintenance - Augury Industrial IoT Case Study
Mueller moves from preventive to predictive maintenance
Recognizing the recent advances in and affordability of predictive maintenance (PdM), the company decided to implement a PdM program in one of its facilities on a trial basis. Anticipating positive results, we hope to gradually pursue wider implementation.
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Pump Cavitation Detection - FogHorn Industrial IoT Case Study
Pump Cavitation Detection
Cavitation is a condition can occur in centrifugal pumps when there is a sudden reduction in fluid pressure. Pressure reduction lowers the boiling point of liquids, resulting in the production of vapor bubbles if boiling occurs. This is more likely to happen at the inlet of the pump where pressure is typically lowest. As the vapor bubbles move towards the outlet of the pump where pressure is higher, they rapidly collapse (return to a liquid state) resulting in shock waves that can damage pump components.
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Large Oil Producer Leverages Advanced Analytics Platform - C3 IoT Industrial IoT Case Study
Large Oil Producer Leverages Advanced Analytics Platform
Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.
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The Internet of Trains - Teradata Industrial IoT Case Study
The Internet of Trains
Train operators the world over are expected to work miracles, i.e. never to be late. So, with acute service and availability targets to meet, an efficient maintenance program is important. And data-enabled functionality is a must for Siemens. Reactive maintenance (after an incident) and routine, preventive maintenance with its visual inspections and scheduled exchange of components, are no longer enough. We’ve moved on to more cost-effective, condition-based, predictive maintenance. The actual condition of components is measured via the transfer and remote monitoring of diagnostic sensor data; data which is also used to analyse patterns and trends. This helps predict when a component is likely to fail, so it can be repaired before anything untoward happens. To ensure the commercial sustainability of this approach, Siemens needs to use and re-use existing data, creating a kind of ‘Internet of Trains’. Towards this end, they’re analysing sensor data in near real time, which means they can react very quickly, ensuring that customer transport services aren’t interrupted. “It is really difficult to define every issue before it impacts operations using only data from the trains”, Kress explains. However, recent success stories prove that everything is possible.
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Predictive Maintenance for Building Equipment  - C3 IoT Industrial IoT Case Study
Predictive Maintenance for Building Equipment
Unexpected failures of building equipment can result in significant problems for facility operators. For example, refrigeration system downtimes result in expensive loss of perishables for retailers, or drugs for pharmacies. HVAC system downtimes drive need for emergency maintenance activities, and result in reduced customer satisfaction.
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Oseco Case Study - Delivering Condition Monitoring with Seebo - Seebo Industrial IoT Case Study
Oseco Case Study - Delivering Condition Monitoring with Seebo
Oseco were completely new to Industry 4.0, Oseco needed to find a lean and quick method for successfully implementing condition monitoring.
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Unifying Predictive Analytics and Real-time Process Optimization for Oil & Gas - General Electric Industrial IoT Case Study
Unifying Predictive Analytics and Real-time Process Optimization for Oil & Gas
A leading oil & gas company and one of GE’s most trusted and innovative partners had no way of integrating independent equipment issue detection capabilities. The company was losing money as a result of unplanned downtime due to maintenance scheduling issues and outdated software systems. They believed that a well-designed combination of IT assets would generate stronger insight than they had, affording them the ability to monitor offshore equipment from an onshore facility using real-time insight and predictive analytics.
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Predictive maintenance in Schneider Electric - Senseye Industrial IoT Case Study
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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Porsche Announces Augmented Reality at Scale, Powered by Atheer - Atheer Industrial IoT Case Study
Porsche Announces Augmented Reality at Scale, Powered by Atheer
The usual practice for car repairs at a Porsche car dealership is to have a factory representative or regional engineer visit to help diagnose the problem, and sometimes a faulty assembly is shipped back to company HQ for damage analysis. All that costs time and money for customers and dealers alike. 
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Scalable Predictive Maintenance in Nissan - Senseye Industrial IoT Case Study
Scalable Predictive Maintenance in Nissan
With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.
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Condition Based Monitoring for Industrial Systems - Advantech B+B SmartWorx Industrial IoT Case Study
Condition Based Monitoring for Industrial Systems
A large construction aggregate plant operates 10 high horsepower Secondary Crusher Drive Motors and associated conveyor belts, producing 600 tons of product per hour. All heavy equipment requires maintenance, but the aggregate producer’s costs were greatly magnified any time that the necessary maintenance was unplanned and unscheduled. The product must be supplied to the customers on a tight time schedule to fulfill contracts, avoid penalties, and prevent the loss of future business. Furthermore, a sudden failure in one of the drive motors would cause rock to pile up in unwanted locations, extending the downtime and increasing the costs.Clearly, preventative maintenance was preferable to unexpected failures. So, twice each year, the company brought in an outside vendor to attach sensors to the motors, do vibration studies, measure bearing temperatures and attempt to assess the health of the motors. But that wasn’t enough. Unexpected breakdowns continued to occur. The aggregate producer decided to upgrade to a Condition Based Monitoring (CBM) sensor system that could continually monitor the motors in real time, apply data analytics to detect changes in motor behavior before they developed into major problems, and alert maintenance staff via email or text, anywhere they happened to be.A wired sensor network would have been cost prohibitive. An aggregate plant has numerous heavy vehicles moving around, so any cabling would have to be protected. But the plant covers 400 acres, and the cable would have to be trenched to numerous locations. Cable wasn’t going to work. The aggregate producer needed a wireless solution.
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Proactive Maintenance Saving Millions of Dollars -  Industrial IoT Case Study
Proactive Maintenance Saving Millions of Dollars
One of the world’s largest exploration and production companies was operating as leanly as possible given the prolonged slump in oil prices. As such, an operational parameter wasn’t effectively monitored and equipment failure went unnoticed in the machines.
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Vacuum Pump Solution Avoids Unplanned Downtime and Scrap Events - IoT Systems Industrial IoT Case Study
Vacuum Pump Solution Avoids Unplanned Downtime and Scrap Events
In order to scale up operations, loT Systems’ client needed a system to monitor vacuum pumps and provide managers with actionable data at near real-time speeds from anywhere, at any time, and on any device. The vacuum pumps are part of vertical furnaces that prepare semiconductor wafers at precise temperatures and pressures according to their clients’ exact specifications. If a pump was about to fail and a manager did not see the alarm, it could lead to a scrap event, costing the client revenue, delaying production with unplanned downtime, and increasing overall manufacturing costs. Managers could not see alarms in real time unless they physically walked around the floor, making predictive maintenance and preventing scrap events harder as the client scaled upwards.
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Predicting, Diagnosing and Reducing Equipment Failures - C3 IoT Industrial IoT Case Study
Predicting, Diagnosing and Reducing Equipment Failures
One of Europe’s largest integrated electric power companies was looking for analytics solutions to reliably forecast equipment failure and improve condition-based maintenance for its coal-fired power plant. With a diverse array of coal, oil, and gas/CCGT power plants, the utility’s more than 50GW worldwide generating portfolio has been under pressure to streamline global operations and reduce generating costs (both CapEx and operations /maintenance O&M expenses) by 7-10%.
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Tata Power Uses AVEVA PRiSM Predictive Asset Analytics Software - AVEVA Industrial IoT Case Study
Tata Power Uses AVEVA PRiSM Predictive Asset Analytics Software
- Avoid asset failures and reduce equipment downtime - Identify subtle changes in system and equipment behavior - Gain advanced warning of emerging equipment issues - Monitor the health and performance of critical assets fleet-wide in real time - Improve maintenance planning y Enable knowledge capture to optimize information sharing between plant personnels
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ArcelorMittal condition monitoring - Semiotic Labs Industrial IoT Case Study
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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SmartSignal Eliminated Risk of Equipment Failure -  Industrial IoT Case Study
SmartSignal Eliminated Risk of Equipment Failure
Given the extreme volatility in the Oil & Gas market, a global Oil & Gas company was operating as leanly as possible. As such, the company was unable to monitor hundreds of sensors for each turbine on a daily basis, causing equipment maintenance problems to go unnoticed.
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Reducing Unscheduled Downtime and Customer Efficiency - PTC Industrial IoT Case Study
Reducing Unscheduled Downtime and Customer Efficiency
PTC
Leica Microsystems attributes its success to providing innovative products and superior customer service. To extend its leadership position, the company began exploring a more proactive service approach for its line of confocal microscopes and tissue processors. The Leica Microsystems project team began searching for a global software that would allow for the shift from a reactive to proactive service company. Their initiative focused on downtime avoidance and the prediction of potential problems across the globe, targeting issue prevention. As a result, customers would not only benefit from minimal product downtime, but from faster service and increased productivity. To obtain approval and funding for the initiative, the team would need to prove to management that this service strategy shift would result in optimized instrument uptime and reduced costs of service.
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Big Data and Predictive Maintenance - Endian Industrial IoT Case Study
Big Data and Predictive Maintenance
Predictive maintenance refers to techniques that help determine the condition of in-service equipment in order to predict and/or optimize when maintenance should be performed. Predictive maintenance is one of the most important benefits of the Industry 4.0 revolution. 
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