Cost to Limit Uptake of Predictive Maintenance

July 7, 2008

Predictive-maintenance systems are expected to continue to be limited to use in high-cost production machinery, as the expense of such systems will prevent widespread adoption for the foreseeable future,


Predictive-maintenance systems are expected to continue to be limited to use in high-cost production machinery, as the expense of such systems will prevent widespread adoption for the foreseeable future, according to a report by Frost & Sullivan (www.frost.com). The study, titled Advances in Intelligent and Predictive Maintenance Systems, finds that while predictive maintenance systems are generally designed by experienced professionals, they are also available as turnkey solutions. Yet still the cost of such systems remains a significant obstacle to end-user uptake.

Ultimately, Frost & Sullivan says the economic benefit of predictive maintenance systems becomes evident when evaluating the return on investment, as end-users can save significant expense by reducing downtime and machinery maintenance costs.

Predictive maintenance systems use various sensors to record physical parameters, such as vibration, temperature, and pressure. The parameters are then logged and compared to historical data from the machine in order to check for variations. For example, a change in the vibration signature could indicate a bearing malfunction. This prompts the personnel to trace and replace the faulty component.

According to Frost & Sullivan, rapid improvements in sensors have also facilitated the creation of more sensitive maintenance systems. In fact, MEMS technology has aided the development of solid-state sensors, which perform better and are cheaper than conventional sensors.

MEMS sensors’ production relies on the same fabrication process as microprocessors and the evolution of microprocessor fabrication that has resulted currently in unachievable capabilities.

Frost says the availability of tremendous computing power has enabled the addition of processing power to units not just centrally, but also locally. The local processing units and minimal storage will allow the comparison of data from sensors with benchmark values. Moreover, with processing power increasing with every generation, monitoring systems have become even more sophisticated, while holding at a steady price point.

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