Fig. 1. SKF and Linde engineers discussing plans at the Suzhou plant.
4Predictive maintenance benefits overall
maintenance strategy for gas plants in China.
Take a condition-based approach to maintenance
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he Linde Group is a world leading gases and engineering company operating in more than 100 countries. With such large and widespread operations it is no surprise that
maintenance activities are well run and that the company is no stranger to advanced monitoring techniques and equipment. In USA and UK particularly, but also elsewhere
in the world, on-line machine monitoring has been used for a number of years at Linde plants. And, at its Shanghai headquarters it has a large and impressive ‘remote operations centre’ where it monitors and tracks the process operations of all it’s major gas plants in China 24 hours a day. But Henry Aung-Kyi, Linde Reliability Engineering Manager for Greater China also knows that the key to successful maintenance operations is the right balance of techniques, systems and methodologies. An experienced and knowledgeable engineer, Aung-Kyi, is convinced of the value of highly sophisticated on-line monitoring system – for critical machinery- but, for some of the plant equipment at his gas plants, this level of
technology and cost is not needed for optimum management of his company’s resources. And he has, for a long time, believed in the value of predictive maintenance (PdM), even if there is a price to pay in terms of time to acquire the skills and train the workforce. Many process and manufacturing plants
operate preventive maintenance activities (PM). PM activities are usually time based, with machine adjustments and replacements parts being made on a routine basis without regard of the operational performance or condition of the major piece of equipment or its component parts. However, predictive maintenance (PdM) is a ‘condition’ based approach that builds a picture of the anticipated condition of a component or piece of equipment based on trend data taken while the equipment is in service. Such data is mostly vibration analysis data, but can include lubricant analysis, temperature measurements etc. Based on this trend data, and applying principles of statistical process control, an engineer can predict when is the best time to carry out maintenance activity. This allows him to plan work when it is most cost effective with a full
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