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Bearings are reliable mechanical components which are designed according to known factors and should operate for their designed life. Nevertheless, in practice, various factors such as environmental and operating conditions may significantly reduce their service life, hence application of condition monitoring to bearings could achieve greater reliability. Condition based maintenance (CBM) systems are currently the main maintenance strategy for many applications due to their effectiveness in reducing maintenance costs and increasing reliability. However, trustworthy monitoring techniques are required to support the operation of the CBM system in tracking the condition of the bearing system, two types are widely used: acoustic emission (AE), and vibration analysis. This work presents a novel AE remote sensing system (AERSS) for rotating machinery which is able to provide a decision support feature using an intelligent approach to overcome any false alarms that may occur. It has been evaluated on an automotive bearing application, but could be adapted to monitor the behaviour of other transmission systems including those in aircraft, wind turbines, and industrial machinery. The study describes the design and operation of the online AERSS and demonstrates its ability to detect bearing defects.

A New Acoustic Emission Remote Sensing System; An Experimental Validation of Automotive Wheel Bearing Condition Monitoring