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The Convergence of Predictive and Preventative Maintenance for Mill Reliability
The Convergence of Predictive and Preventative Maintenance for Mill Reliability
Gerdau was looking to reduce their annual maintenance spend while also improving productivity, thus targeting margin improvements within their manufacturing operations. 
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Predicting Rare Failures in Hydro Turbines
Predicting Rare Failures in Hydro Turbines
Utility companies that operate hydro turbines have a vested interest in performing regular maintenance to prevent unexpected failures. Most maintenance occurs on a scheduled basis where the asset is taken offline, inspected, and repaired proactively if needed. Hydro turbine units are highly reliable, meaning that few examples of unplanned downtime exist. However, these failures are very costly to their operators.Given the sensitivity operators have to unplanned downtime, many have equipped turbines and generators with sensors and platforms to collect valuable performance information in real-time. But because there are so few historical hydro failures to compare against, rich streaming data and legacy statistics-based analysis are not very accurate at predicting true failure events. In fact, they often create more problems by overloading monitoring teams with benign false positives that result in unnecessary downtime to evaluate. This begs the question: Can artificial intelligence help maintenance teams extract more value out of their data?
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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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Vale Fertilizantes Saves $1.4M in Production Losses with Predix Asset Performanc
Vale Fertilizantes Saves $1.4M in Production Losses with Predix Asset Performanc
Reducing production lossesIn 2013, the company identified a need in the maintenance and operation of its acid nitric plant to reduce production losses and improve annual production. Vale noticed there was a gap in nitric acid production from 2011 to 2012 and discovered that three pieces of equipment were responsible for the main losses, including two weak acid condensers and a compressor discharge air cooler. The condenser’s losses were due to thickness loss, lack of availability of the spare condenser, and shell cracking.With a production loss above 14,000 tons in 15 months, Vale aimed to reduce annual loss by 10,000 tons by August 2015. 
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Automotive manufacturer increases productivity for cylinder-head production by 2
Automotive manufacturer increases productivity for cylinder-head production by 2
IBM
Daimler AG was looking for a way to maximize the number of flawlessly produced cylinder-heads at its Stuttgart factory by making targeted process adjustments. The company also wanted to increase productivity and shorten the ramp-up phase of its complex manufacturing process.
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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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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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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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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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Driving peak performance with comprehensive support coverage from IBM
Driving peak performance with comprehensive support coverage from IBM
IBM
With more than 11,000 employees at 94 locations across India, leading commercial vehicle manufacturer VECV needs seamless, responsive technical support to ensure high-availability IT operations. The company sought services from a trusted IT provider capable of simplifying coverage for its multi-vendor environment, accelerating issue resolution for end-users and structuring an effective governance framework for vendor management.
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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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Minimizing downtime by engaging IBM Services – Technology Support
Minimizing downtime by engaging IBM Services – Technology Support
IBM
Simplifying maintenanceHana Financial Group had recently consolidated the infrastructure and resources of 11 of its affiliates at a local IBM data centre. However, the business was left with more than 100 service and maintenance contracts that needed to be reviewed and renewed periodically. These contracts also involved 100 separate bills that Hana Financial Group had to manage. Managing such a large volume of bills was cumbersome and sometimes resulted in late payments. The group wanted to improve efficiency and eliminate the overhead involved with managing these contracts by consolidating its heterogeneous IT systems and data storage systems under more consistent processes.
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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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4G Power Status Monitoring Alarm Used in Chicken Farm
养鸡场使用的4G电源状态监视警报基于4G无线网络通信,用于养鸡场以监控电源故障,缺相,温度,湿度并现场驱动频闪警报器。
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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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Gas Pipeline Improves Station Efficiency and Drives Revenue with DataRPM
Gas Pipeline Improves Station Efficiency and Drives Revenue with DataRPM
In the capital-intensive oil and gas industry, businesses rely heavily on expensive assets that are deployed in harsh environmental conditions. From a drilling point in the sea to an intermediate station in the desert, the dynamic environmental conditions at each point along the long line affect the performance of the assets deployed along the line. The systems that are used to support these mission-critical assets must also be highly reliable, responsive and secure.One company that operated a long-distance gas pipeline encountered numerous challenges with the overall efficiency of its pipeline, ranging from sub-optimal usage to wastage of natural resources. Even with the optimal equipment and setup, the wide array of variables in operating conditions combined with the sheer distance covered by the pipeline made running the business difficult.In this case, there were 22 injection stations along the length of the pipeline, operating under very disparate conditions with different efficiencies. This made it difficult to identify the interdependent effectiveness of these injection stations, despite having a large data set on various parameters at each injection substation. Even a single instance of failure could cost the company hundreds of thousands of dollars in lost revenue as well as any additional costs for repairs that had to be made.The company was spending $5 million per mile of pipeline annually in corrective maintenance. Along with this, the loss of revenue due to the undelivered material was estimated at $250 million. With energy prices dropping, the loss in revenue directly reduced the bottom line of the company. With the clock ticking and revenue dipping, building a perfect efficiency improvement model became a top priority.
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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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Temperature and humidity monitoring solution
温湿度监控解决方案血液站属于医疗卫生机构,负责收集和储存血液并将血液提供给临床或血液制品生产单位。因此,血液站中心的血液非常宝贵。温度和湿度环境对于确保血液在血液站中的安全非常重要。
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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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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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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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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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