Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Abstract

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

“…For non-parametric methods, the kernel function is utilized to estimate the PDF [44,45]. Given the N independent observations {A 1 , A 2 , …, A t } of the random variable x with a PDF of p(a), the kernel estimator of the PDF is defined as equation (8):…”

“…Signal-processing-based methods are often employed to diagnose the RBCF by extracting or separating impulsive features of faults from the raw vibration signals, which can provide a guide for engineers or experts to diagnose the compound fault [8]. In industrial environments, the feature information can be obscured by heavy background noise from measurement systems.…”

Gearbox Compound Fault Diagnosis in Edge-IoT Based on Legendre Multiwavelet Transform and Convolutional Neural Network

“…Due to different types of faults coupled together, non-stationarity and a large amount of noise, it is very difficult to effectively extract the most valuable fault characteristics from the raw data by using the existing methods [8]. If the specific fault category can be accurately recognized and predicted in the edge-IoT context, then the huge losses caused by the fault should be effectively avoided [9]. Thus, it is significantly meaningful to develop a high accuracy fault diagnosis method for the gearbox compound faults under a strong noise environment.…”