Detection and Diagnosis of Centrifugal Pump Bearing Faults Based on the Envelope Analysis of Airborne Sound Signals

As key components in centrifugal pumps rolling bearings work to reduce friction and maintain the impeller rotor in correct alignment with stationary parts under the action of radial and transverse loads. Effective fault detection of bearings allows appropriate preventive action to be taken timely, where required, and enhances performance operation. To develop an easy implementation and yet effective method for detecting and diagnosing pump bearing faults, the focus of this study is on utilising airborne sound signals which can be acquired more remotely and at lower cost, compared with vibration based methods which needs high numbers of sensors for monitoring a pump system. However, acoustic signals are much noisy, and it is difficult to detect machine faults using conventional signal processing methods such as time domain features, where the results have a limited and weak fault signatures. Thus, a more advanced signal processing technique: the envelope spectrum is adopted to establish accurate diagnostic fault patterns. The evaluating results show that the proposed method is effective and accurate to enhance the amplitudes at bearing characteristic frequencies, allowing diagnostic information to be extracted reliably, which also makes the Root Mean Square (RMS) of the envelope signals give a full separation between faulty and healthy cases over a wide range of pump operation, outperforming the vibration signals.