A Fault Diagnosis Method for One‐Dimensional Vibration Signal Based on Multiresolution tlsDMD and Approximate Entropy

Abstract

Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

“…The similarity matrix à is used to characterize the system evolution process. In addition, the eigenvector of the operator A corresponds to the eigenvector of à , as shown in equation (24):…”

“…Therefore, the noise can be suppressed, but there is a problem of selecting truncated rank. Entropy DMD [24] calculates the entropy value of the feature mode generated by DMD decomposition, evaluates the disorder of the single-frequency mode, and selects the mode with lower disorder to reduce noise interference. However, entropy threshold selection is a problem, and the usefulness of the feature mode cannot be accurately measured.…”

A new rapid positioning strategy for the impact-point using dynamic mode decomposition and time difference of arrival *

“…Meanwhile, it has been found that DMD can be effectively applied to the noise reduction of one-dimensional vibration signals [50][51][52]. DMD is capable of extracting real and precise dynamic features from noisy data [51,53,54].…”

“…Meanwhile, it has been found that DMD can be effectively applied to the noise reduction of one-dimensional vibration signals [50][51][52]. DMD is capable of extracting real and precise dynamic features from noisy data [51,53,54]. There are two dominant solutions to debias the noise effect: (1) Forward/backward DMD (fbDMD), which eliminates the system deviation by averaging the data forward and backward the time [52,55]; (2) Total least-squares DMD (tlsDMD), which applies the total least squares algorithm [56].…”

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

“…The fault diagnostic technology can detect motor defects early in their development, allowing for prompt overhauls, saving time and money on fault repairs, and enhancing the economic advantages while avoiding production interruptions. Traditional fault diagnostic approaches need the artificial extraction of a considerable quantity of feature data, such as time domain features, frequency domain features, and time-frequency domain features [1][2][3], which adds to the fault diagnostic uncertainty and complexity. Traditional fault diagnosis methods are unable to meet the needs of the fault diagnosis in the context of big data due to the complex and efficient development of motors, which presents the data reflecting the operating status of motors with the characteristics of massive, diversified, 2 of 26 fast flowing speed, and low value density of "big data" [4][5][6].…”