THE ANALYSIS OF POWER SUPPLY SIGNALS BY INCLUDING PHASE EFFECTS FOR MACHINE FAULT DIAGNOSIS

Substantial efforts have been devoted to developing Condition Monitoring techniques to provide timely preventative measures for ensuring a safe and cost-effective operation of electromechanical systems. High investment of installation and implementation in using conventional condition monitoring techniques such as vibration based monitoring makes it difficult to be used in most industries such as petrochemical processing, food and drinking processes, paper mills and so on where large number of motor drives are used but operational profits are very limited. To overcome the shortages of vibration based monitoring, this project focuses on developing condition monitoring techniques based on electrical signal analysis which can offer great savings as electric signatures that can monitor a large system are generally available in most motor drives. However, fault signatures in electrical signatures such as instantaneous current and voltage signals are very weak and contaminated by noise. To enhance the signatures, this study has focused on using two more advanced signal processing approaches: 1) Modulation signal bispectrum analysis, which enhances the modulation and suppresses random noise by including phase linkages.

2) Instantaneous phase quantities including conventional instantaneous power factor and a novel instantaneous phase of voltage and current which highlights instantaneous phase changes through a summation of instantaneous phases in current and voltage signals. It has the ability of enhancing the phase components that are of the same phases in both voltage and current signals, and also cancel out any random components to a great extent, producing more diagnostic information. These two approaches emphasis the use of phase information along with that of amplitudes and frequency in a signal that is based on in most previous methods in the condition monitoring fields.

Based on a general electromechanical system comprising of a AC motor, a gearbox and a DC generator, it firstly explored the characteristics of the signatures by modelling and simulation studies, which lead to that faults in a sensorless Variable speed drive system can produce combined amplitude and frequency modulation effects in both current and voltage signals fed to the AC motor. Moreover, the modulating frequencies and levels are closely associated with the rotational frequencies of the gearbox and fault severity respectively, which become more significant at higher load conditions.

Experimental evaluations have found that these two proposed methods allow common faults in the downstream gearbox including gear tooth breakage, oil shortage and excessive bearing clearances to be detected and diagnosed under high load conditions, showing the effectiveness and accuracy of these two new approaches. Furthermore, the results show that the electrical signature analysis is capable of detecting and diagnosing different faults in sensorless variable speed drive systems. Instantaneous phase of voltage and current has been shown to provide more consistent and accurate separation between the three different faults under different loads. The use of the modulation signal bispectrum analysis succeed to provide an improved, accurate and reliable diagnostic with the power signal providing the best means of detecting and determining fault severity with good separation between fault levels.