Fault Diagnosis of Rotating Machinery using Improved Entropy Measures
Creators: name: Huo, Zhiqiang
Publisher: name: University of Lincoln
Year: 2023

description: Fault diagnosis of rotating machinery is of considerable significance to ensure high reliabilityand safety in industrial machinery. The key to fault diagnosis consists in detecting potentialincipient fault presence, recognizing fault patterns, and identifying degrees of failures inmachinery. The process of data-driven fault diagnosis method often requires extractinguseful feature representations from measurements to make diagnostic decision-making.Entropy measures, as suitable non-linear complexity indicators, estimate dynamic changesin measurements directly, which are challenging to be quantified by conventional statisticalindicators. Compared to single-scale entropy measures, multiple-scale entropy measureshave been increasingly applied to time series complexity analysis by quantifying entropyvalues over a range of temporal scales. However, there exist a number of challenges intraditional multiple-scale entropy measures in analyzing bearing signals for bearing faultdetection. Specifically, a large majority of multiple-scale entropy methods neglect high?frequency information in bearing vibration signal analysis. Moreover, the data length oftransformed multiple signals is greatly reduced as scale factor increases, which can introduceincoherence and bias in entropy values. Lastly, non-linear and non-stationary behaviors ofvibration signals due to interference and noise may reduce the diagnostic performance oftraditional entropy methods in bearing health identification, especially in complex industrialsettings.This dissertation proposes a novel multiple-scale entropy measure, named AdaptiveMultiscale Weighted Permutation Entropy (AMWPE), for extracting fault features associatedwith complexity change in bearing vibration analysis. A new scale-extraction mechanism -adaptive Fine-to-Coarse (F2C) procedure - is presented to generate multiple-scale time seriesfrom the original signal. It has advantages of extracting low- and high-frequency informationfrom measurements and generating improved multiple-scale time series with a hierarchicalstructure. Numerical evaluation is carried out to study the performance of the AMWPEmeasure in analyzing the complexity change of synthetic signals. Results demonstrated thatthe AMWPE algorithm could provide high consistency and stable entropy values in entropyestimation. It also presents high robustness against noise in analyzing noisy bearing signals incomparison with traditional entropy methods. Additionally, a new bearing diagnosis methodis put forth, where the AMWPE method is applied for entropy analysis and a multi-classsupport vector machine classifier is used for identifying bearing fault patterns, respectively.Three experimental case studies are carried out to investigate the effectiveness of theproposed diagnosis method for bearing diagnosis. Comparative studies are presented tocompare the diagnostic performance of the proposed entropy method and traditional entropymethods in terms of computational time of entropy estimation, feature representation, anddiagnosis accuracy rate. Further, noisy bearing signals with different signal-to-noise ratiosare analyzed using various entropy measures to study their robustness against noise inbearing diagnosis. Additionally, the developed adaptive F2C procedure can be extended to avariety of entropy algorithms based on improved single-scale entropy method used in entropyestimation. In the combination of artificial intelligence techniques, the improved entropyalgorithms are expected to apply to machine health conditions and intelligent fault diagnosisin complex industrial machinery. Besides, they are suitable to evaluate the complexityand irregularity of other non-stationary signals measured from non-linear systems, such asacoustic emission signals and physiological signals.