A Vibrational Signal Fault Diagnosis Rule Extraction Method Based on DST‐ACI Discriminant Criterion

Abstract

A fault diagnosis rule extraction method oriented to machine foot signal based on dynamic support threshold and association coefficient interestingness (DST-ACI) discriminant criterion is proposed in this paper. The new method includes three main innovations. First, the feature state coding method based on K-means clustering fully takes into account the imbalanced distribution of signal feature values due to the noise interference, and divide the signal feature values into several range intervals to generate the feature state code. Second, the frequent feature pattern mining method based on dynamic support threshold (DST) discriminant criterion can dynamically adjust support threshold according to the frequency of the feature states in each candidate pattern. Third, the fault diagnosis rule extraction method based on the association coefficient interestingness (ACI) discriminant criterion introduces a new metrics called ACI to evaluate the correlation between the pattern and the fault. Four types of fault simulation experiments were carried out, and the performance of the DST-ACI method was tested using the collected vibration signal. The results show that compared with the coding method based on equal-width discretization or equal-density discretization, the accuracy of the transactional dataset generated by the feature state coding method based on K-means clustering is higher. Compared with the frequent feature pattern mining method based on the constant support threshold criterion, the pattern mined by the DST-based criterion has generally higher support. Compared with the existing confidence-lift-based and confidence-improint-based fault diagnosis rule extraction frameworks, the positive correlation between the feature states and the fault type of the rules extracted based on the DST-ACI framework is generally stronger.

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors

“…GRU, as a variant of RNN, can alleviate, to a certain extent, the problem of gradient disappearance during the training process of traditional RNN 21 . Compared with LSTM, which has the problems of complex internal structure and excessive computation due to too many parameters, GRU replaces the input and forgetting gates with update gates on the basis of LSTM structure, reducing the complexity of the structure and the number of parameters, making it converge faster during the training process of the network 22 .…”

“…IBiGRU combines the advantages of bi-directional RNN and GRU and consists of forward and reverse GRUs stacked up and down, which can simultaneously input sequence data in forward and reverse order. Both forward and reverse GRUs are connected to the output layer, which can pass forward and backward information to the output layer at the same time, establish the connection between the current input and the forward and backward states, and better characterize the timing characteristics of the fault data 21 . The output of IBiGRU, y(t), is expressed as 23 where and are the outputs of the forward and backward hidden layers, is the weight matrix and is the bias term.…”

Fault Diagnosis of Crack on Gearbox Using Vibration-Based Approaches

“…During the past three decades, diagnostic techniques for the detection of gearbox defects have been intensively researched, including those reported by Dalpiaz et al [10], Ma et al [11], Mohammed et al [12], Saxena et al [13], Wu et al [14], and Li [15]. Rezaei et al [16] detected multicrack locations and lengths from transmission-error ratios.…”