Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …

Rotating machinery operates continuously for long periods of time under varying conditions
in actual industrial environments. The number of fault samples increases with equipment …

Recently, research on multisensor fault diagnosis under noisy signals has gained significant
attention. Due to various degrees of external interference and differences in sensor …

The motor is the power source of a mechanical system, which often operates under the
variable conditions make it prone to the failure, and the fault diagnosis is difficult. The …

Time–frequency (TF) analysis (TFA) is a valuable tool for capturing nonstationary features in
time-varying signals. However, for strongly amplitude-modulated and frequency-modulated …

Dictionary learning has emerged as an effective approach for data-driven fault diagnosis
due to its strong sparse representation ability. Nevertheless, the gathered vibration signals …

In pragmatic engineering milieus, rotating machinery mostly operates under normal
condition, leading to the long-tailed monitoring data distribution with far more normal than …

Abstract Models employing the continual learning (CL) paradigm hold promising potential
for application in rotating machinery fault diagnosis. These models allow the diagnostic …

Reciprocating mechanical vibration signals are characterised by multi-source impact
coupling and varied time-domain intervals. Different sub-impacts contain rich information …

Ramanujan Fourier mode decomposition (RFMD) is a novel non-stationary signal
decomposition method, which can decompose a complex signal into several components …