… Rotating machinery comprises part of … rotating machinery with regards to its quality,
performance and intelligence. The rotor, one of the most important components in rotating …

… This paper proposes a weighted impulse (WI) index to evaluate the performance of the
adaptive general scale transformation SR (AGSTSR) in rotating machinery fault diagnosis and …

… Unsupervised learning-based diagnosis methods, which can reduce the … fault diagnosis
easier in big data environments, have been widely applied in rotating machinery fault diagnosis. …

… , the fault data come from different machines. Therefore, on some occasions, fault diagnosis
is a … Specifically, the fault diagnosis problem has been treated as a similarity metric-learning …

… These problems affect the efficiency of diagnosis tasks badly. Therefore, this article proposes
an adaptive broad learning system (ABLS) for fault diagnosis of rotating machinery. In the …

… In this article, we presented a novel TFA technique for the fault diagnosis of rotating
machinery. The technique employed a fixed point iteration algorithm to solve the diffused TFR …

… Fault diagnosis methods for rotating machinery have always … used to extract fault features
or classify fault features obtained by … solve the fault diagnosis problems of rotating machinery, …

… fault detection and diagnosis of rotating machinery machines … widely used in the fault diagnosis
of the rotating machinery. In … its applications in fault diagnosis of rotating machinery in this …

… DL techniques have been predominantly used in the fault diagnosis of rotating machinery.
They can be trained either in a supervised or unsupervised manner or in other learning …

… signals are usually dominated by rotating frequency harmonics, and the … rotating machinery
fault feature extraction, because of its capability in intuitive spectral representation of rotating …