A review on the application of blind deconvolution in machinery fault diagnosis

Fault diagnosis is of significance for ensuring the safe and reliable operation of machinery equipment. Due to the heavy noise and interference, it is difficult to detect the fault directly from the measured signal. Hence, signal processing techniques that can achieve feature extraction, signal denoising, and fault identification are the most common tools in the field. Blind deconvolution methods (BDMs), as one of the most classic methods, have been studied extensively and applied fully for machinery fault diagnosis. Up to now, plenty of publications about the studies and applications of BDMs for machinery fault diagnosis have been presented to academic journals, technical reports, and conference proceedings. This paper intends to survey and summarize the current progress of BDMs applied in machinery fault diagnosis, as well as provides a comprehensive review of BDMs from history to state-of-the-art methods and finally to research prospects. Firstly, the theoretical background and brief history of BDMs are introduced. Secondly, the modified BDMs are classified to review their basic principles. After that their merits and limitations as well as the performance analysis are summarized. Thirdly, the research and application on machinery fault detection using BDMs are overviewed. Finally, the prospects of BDMs in machinery fault diagnosis are discussed.

Automated summary

Fault diagnosis is crucial for the safe operation of machinery equipment. Signal processing techniques, especially blind deconvolution methods, play a significant role in feature extraction, signal denoising, and fault identification. The use of blind deconvolution methods in machinery fault diagnosis has been extensively studied and applied, with a focus on historical background, principles, merits, limitations, performance analysis, research, and applications.