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 …

The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can
decompose signals into several narrow-band components, which is advantageous to …

Parameter-adaptive variational mode decomposition (VMD) has attenuated the dominant
effect of prior parameters, especially the predefined mode number and balancing parameter …

The vibration signal of faulty rolling bearing of rotating machine carries a large amount of
information reflecting its fault categories. However, compound fault features are easily mixed …

Bearing failure can cause hazardous effects on rotating machinery. The diagnosis of the
fault is very critical for reliable operation. The main steps for the machine learning process …

Partial faults of bearings trigger periodic vibration features, but the interference makes fault
diagnosis more difficult. A recursive sparse representation (RSR) algorithm is proposed to …

In the health monitoring of rotating machinery, there often coexists multiple fault sources.
Thus a multi-source compound fault signal will be excited and collected by sensors …

Among the most widespread systems in industrial plants are automated drive systems, the
key and most common element of which is the induction motor. In view of challenging …

Compound faults and their involved single faults often have severe overlap in traditional
feature spaces, and the strong background noise unavoidably exacerbates the degree of …

This paper employs a combined ensemble empirical mode decomposition (EEMD) and
singular value decomposition (SVD) technique to extract useful fault features from the …