If you had 3 months’ warning of every major failure in your plant over the past year, how much would you have saved? For some people, this question triggers thoughts about avoiding expensive process interruptions. For others, reduced spares costs or secondary damage caused by the original failures. Many reflect on how they might have been able to reduce their excessive planned maintenance workload. Common to all however is an agreement that the potential savings from predictive maintenance are very substantial indeed.

So why doesn’t everybody do it? Despite the fact that reliable predictive maintenance technology has been available for decades, less than 1 percent of the companies that could benefit have used it. For most people, implementing a predictive maintenance programme through condition monitoring has just been too expensive and complicated to make practical sense.

A new generation of highly-intelligent but simple to use systems is changing the way many people think about predictive maintenance. The Artesis MCM (Motor Condition Monitor) is a small instrument providing complete predictive maintenance cover for a complete electric motor driven system, including the driven equipment. Requiring no special sensors, Artesis MCM takes its inputs from the supply cables to the motor and monitors condition using advanced mathematical modelling. It can be installed in a motor control cabinet in less than an hour, trains itself in a few days, and provides automated diagnosis of a very wide range of mechanical and electrical faults.

The Artesis Plant Condition Monitor (PCM) provides similar cover for generator and alternator systems, and is equally simple to set up and use.

The Artesis system has proved a very cost-effective solution not only for traditional users of predictive maintenance, but also in those who have found previous technologies inappropriate – utilities, food processing and building services for example. They can now enjoy the benefits of predictive maintenance with a fraction of the effort and cost of traditional approaches.