Bearing faults are one of the most common inducements for machine failures. Therefore, it is very important to perform bearing fault diagnosis reliably and rapidly. However, it is fundamental but difficult to extract impulses buried in heavy background noise for bearing fault diagnosis. In this paper, a novel adaptive enhanced sparse period-group lasso (AdaESPGL) algorithm for bearing fault diagnosis is proposed. The algorithm is based on the enhanced sparse group lasso (ESGL) penalty proposed in this paper to promote the sparsity within and across groups of the impulsive feature of bearing faults. Moreover, a periodic prior is embedded and updated dynamically through each iteration of the optimization procedure. Additionally, we form a deterministic rule about how to set the parameters adaptively. The main advantage over conventional sparse representation methods is that AdaESPGL is parameter-free (forming a deterministic rule) and rapid (extracting the impulsive information directly from the time domain). Finally, the performance of AdaESPGL is verified through series of numerical simulations and the diagnosis of a motor bearing. Results demonstrate its superiority in extracting periodic impulses under comparison with other state-of-the-art methods.

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.