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Choosing the Most Suitable Predictive Maintenance Sensor

-Chris Murphy, Applications Engineer, Analog Devices

­­­­­­Introduction

Condition-based monitoring (CbM) involves monitoring of machines or assets using sensors to measure the current state of health. Predictive maintenance (PdM) involves a combination of techniques such as CbM, machine learning, and analytics to predict upcoming machine or asset failures. When monitoring the health of a machine, it is critically important to select the most suitable sensors to ensure faults can be detected, diagnosed, and even predicted. There are many sensors currently used to sense and detect faults, in rotating machinery and their loads, with the end goal of avoiding unplanned downtime. Ranking each sensor is difficult as PdM techniques are applied to a multitude of rotating machines (motors, gears, pumps, and turbines) and nonrotating machines (valves, circuit breakers, and cables).

Many industrial motors are designed to work up to 20 years in continuous production applications such as chemical and food processing plants and power generation facilities, but some motors do not reach their projected lifetime.1 This could be due to insufficient operation of the motor, insufficient maintenance programs, lack of investment in PdM systems, or not having a PdM system in place at all. PdM enables maintenance teams to schedule repairs and avoid unplanned downtime. Early prediction of machine faults through PdM can also help maintenance engineers identify and repair motors running inefficiently, enabling increased performance, productivity, asset availability, and lifetime.


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