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Abstract

Many advanced techniques have been developed for diagnosis of machine faults caused by vibration. They are effective if the inspected vibration is well isolated from interference caused by vibrations from adjacent components. However, the components of manufacturing machines are numerous, small, and packed closely together. Thus the signal collected by a sensor is the aggregate of vibrations from all nearby components. This, coupled with noise, makes it nearly impossible to detect the anomalous vibration generated by a particular component, especially those generated by small defective components. Recently, new signal processing methods, such as blind source separation (BSS) and blind equalization (BE), have been proposed to separate or recover the aggregated vibrations so that each source of vibration can be correctly identified. In this paper, a comparison study is presented. Some widely used BSS and BE algorithms have been compared to evaluate their performance in the separation of mechanical vibrations. Both simulated signals and real vibrations generated by industrial machines were used to verify the effectiveness of BSS and BE. Their deficiencies have also been identified and improvements are proposed in the paper, so that they could be effectively applied in the fault diagnosis of complex manufacturing machines.

Keywords

Blind Source Separation Blind Equalization Blind Deconvolution Machine Fault Diagnosis

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