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Reliability Based Maintenance detects potential equipment failures

Emerson Process Management : 28 January, 2007  (Company News)
UK COAL has implemented a Reliability Based Maintenance program utilizing Emerson Process Management's Machinery Health Management technologies. In the year 2005, the program prevented equipment failures which would have resulted in a potential production loss of over $1.8 million USD. Emerson's Machinery Health technologies, including the AMS Suite: Machinery Health Manager predictive maintenance software application, are a key element of the PlantWeb digital plant architecture.
AMS Machinery Manager combines predictive maintenance techniques with analysis tools to enable easy and accurate assessment of machinery health. The AMS application will document, trend and communicate all details of machinery health using vibration analysis, infrared thermography, oil analysis, ultrasonics, and motor diagnostics. The comprehensive results enable UK COAL to identify potential failures, and schedule equipment replacement into non-production time.

UK COAL is Britain's largest producer of coal, supplying around 10% of the country's energy needs for electricity generation. The company currently has seven deep mines located in central and northern England, and five active surface mine sites. The main business drivers are safety and availability.

Using tools and techniques that are proven in other industries that rely on rotating machinery and conveyor systems, UK COAL has significantly reduced equipment failures, improved safety and plant availability, while in turn avoiding costs and lost production.

Vibration monitoring at UK COAL had been carried out by a third party organization, but was moved in-house when the RBM program was implemented. UK COAL uses Emerson's CSI 2120 Machinery Health Analyzers for the collection and analysis of vibration data, CSI 5100 Machinery Health Oil Analyzers to perform oil analysis, and AMS Machinery Manager software to analyze and report results.

UK COAL's engineers are also using the Emerson equipment to perform vibration checks on newly overhauled equipment. For example, a 400 horsepower gearbox that had been overhauled was found to have a high degree of backlash, and corrections were made. Had the excessive backlash not been detected before the gearbox was installed, premature wear would have been inevitable, reducing the service life of the gearbox. Since the gearbox was to be fitted in an extremely awkward location, two miles underground, quality of the gearbox rebuild was paramount. A failure in service could have resulted in a lost production of $200,000, and a repair cost of $6000.

Bearing monitoring provides another example of savings from predictive maintenance. Bearing failures cause eighty percent of underground fires, but often there is little detectable rise in temperature until the bearing has failed, and by then it may be too late. Using Emerson's AMS Machinery Manager software, the condition monitoring department at UK COAL's Rossington mine detected a bearing fault on the non-drive end of a conveyor drive motor. The motor was changed out and stripped down during non-production time, and severe damage was noticed on both inner and outer bearing raceways. Had this unit failed during operation, it is estimated that four hours of production would have been lost, equating to $160,000. There would also have been potential human cost if the bearing had overheated and caused a fire.

Emerson's Machinery Health Management solutions are key elements of the PlantWeb digital plant architecture. PlantWeb integrates process automation and asset management in a network of predictive intelligence. From a solid foundation of intelligent plant assets, the digital network of the PlantWeb architecture delivers predictive diagnostic information that is actionable by Emerson's systems and software. In addition, Emerson's comprehensive PlantWeb services help users extract the full value from their technology investments, resulting in improved process availability, increased productivity, and reduced equipment cost-of-ownership.
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