Optimized statistical parameters of acoustic emission signals for monitoring of rolling element bearings

Abstract

Acoustic emission (AE) signal generated from artificial defects in rolling element bearings are investigated using experimental measurements in this paper. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, faults diagnostics, defect size calculation, and prognostics. In this paper, the effect of defect size, operating speed, and loading conditions on statistical parameters of AE signals, using design of experiment method, have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings.

Early sub-surface fault detection in rolling element bearing using acoustic emission signal based on a hybrid parameter of energy entropy and deep autoencoder

“…Plastic deformation and fracture associated with the nucleation and growth of cracks represent the primary mechanisms of the sources releasing the elastic strain energy associated with AE transients [2]. In contrast with the vibration signal, the sources generating AE signals are characterised by a much wider frequency range (100 kHz and 1 MHz) [3], which does not overlap significantly with low-frequency mechanical vibration signals caused by imbalance or misalignment of machine components [4,5]. A great deal of evidence has been accumulated, suggesting that AE parameters can reveal the faults in rotating equipment before they show up in the vibration acceleration range.…”

“…Therefore, the relevance of the involved parameters strongly affects the performance of the detectors. The conventional AE features extracted from AE waveforms include, but are not limited to, AE hit parameters such as counts, duration, rise time, counts to peak, amplitude, etc [2,19,20], as well as statistical parameters/features such as root mean square (RMS), kurtosis, crest factor, skewness, etc [5,7,21], defined in the time domain. In addition, multiple signal processing techniques involve spectral decomposition techniques, such as Fourier transformation [22], wavelet analysis [23][24][25], variational mode decomposition [26], etc, to assess the AE signal in the frequency and time-frequency domains.…”

Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology Gradient Spectrum Entropy

“…Li et al [30] proposed a three-dimensional lumped-parameter nonlinear dynamic model for compound planetary gear set, which takes into consideration time-varying mesh stiffness (TVMS), mesh phase relations, and gear chipping defects. Other fault damage degree identification methods have also been proposed to realize quantitative fault diagnosis [31][32][33][34][35][36][37][38][39][40][41], which offer better results; however, drawbacks, such as lower identification accuracy, longer identification time, and multiple faults with different fault severity degrees, remain.…”

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

“…Concurrent fault diagnosis method [19][20][21]: The concurrent fault diagnosis method is a traditional monitoring diagnosis method, where all the tests are completed before applying the diagnosis procedure. The whole diagnosis process only needs one cycle of testing and diagnosis procedure.…”

“…Analyze the system and build its dependency model based on the information flows or the multisignal flow graphs model [12,25]. After that, the dependency matrix in the form of Equation 3can be built based on the reachability algorithm [26] or experimental design method [21,27].…”