Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

Fuzzy entropy (FE) can be regarded as an effective measure for nonlinear characterization of rolling element bearing (REB) health condition by quantifying the complexity of vibration signals. However, during continuous monitoring operation under heavy noise, transient impulses corresponding to a REB fault get submerged under unnecessary random noise components. As a consequence, FE algorithm not only fails to detect a REB fault at the earliest point of inception but also performs poorly in monitoring the development of the incepted fault in an efficient manner. Aiming at solving the aforementioned limitations of FE in continuous monitoring of REB health, background noise associated with collected vibration signals is eliminated by weighting the corresponding square envelope signal. Due to the utilization of weighted squared envelope signal, the proposed measure is termed as weighted square envelope-based FE (WSEFE). One simulated case and two different run-to-failure experimental cases are used for validation. The comparison results demonstrate that the proposed WSEFE not only overcomes the limitations of original FE but also performs better than conventional permutation entropy and advanced FE-based measure multiscale FE (MFE) in continuous monitoring of REB health.

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“…Based on Equations (14) and (15), weighted squared envelope signal WSE S REB [ n ] can be calculated as Hou et al 32 :…”

Section: Wsefe For Continuous Reb Health Monitoringmentioning

confidence: 99%

“…where, Const . represents a positive value and can be calculated as Hou et al. 32 The detailed calculation process of Const . is shown in Appendix A.…”

Section: Wsefe For Continuous Reb Health Monitoringmentioning

confidence: 99%

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

Noman

Wen

et al. 2023

Structural Health Monitoring

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“…where τ represents slip coefficient which is related to shifted percentage [25]. In our case, τ is set to 0.065.…”

Section: Family Extension Of Sparsity Measuresmentioning

confidence: 99%

“…In the following subsections, we will further evaluate the performance of the designed health metrics in three aspects. 1) First Fault Occurrence Time (FFOT) Determination and Threshold Analysis: Hou et al [25] analyzed the healthy state of the first to 300-th samples of the dataset and used the Lilliefors test to ensure that the historical health data followed the Gaussian distribution in their previous work. Therefore, 3σ rule can be used to establish statistical thresholds for the detection of the first failure occurrence time and to determine upper and lower thresholds for the division of health and fault states.…”

Section: Validation and Analysis Based On Two Run-to-failure Bearing ...mentioning

confidence: 99%

Research on Sparsity Measures for Rotating Machinery Health Monitoring

Cao¹

2022

Preprint

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Machine health management is one of the main research contents of PHM technology, which aims to monitor the health states of machines online and evaluate degradation stages through real-time sensor data. In recent years, classic sparsity measures such as kurtosis, Lp/Lq norm, pq-mean, smoothness index, negative entropy, and Gini index have been widely used to characterize the impulsivity of repetitive transients. Since smoothness index and negative entropy were proposed, the sparse properties have not been fully analyzed. The first work of this paper is to analyze six properties of smoothness index and negative entropy. In addition, this paper conducts a thorough investigation on multivariate power average function and finds that existing classical sparsity measures can be respectively reformulated as the ratio of multivariate power mean functions (MPMFs). Finally, a general paradigm of index design are proposed for the expansion of sparsity measures family, and several newly designed dimensionless health indexes are given as examples. Two different run-to-failure bearing datasets were used to analyze and validate the capabilities and advantages of the newly designed health indexes. Experimental results prove that the newly designed health indexes show good performance in terms of monotonic degradation description, first fault occurrence time determination and degradation state assessment.

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“…To date, several moment-based values were reported to measure the damage degree of rotating machinery to fault spectrum sparsity or envelope spectra exhibiting discrete peaks at corresponding fault frequencies. In particular, kurtosis (also related to 1 / 2 norms [10]) measures the spikiness of a vibration signal [11], better estimating the resonance frequency band of bearing impulsive failures [12,13]. However, kurtosis is sensitive to impulsive noise structure, so that it tends to decrease in value when the frequency of the impulses increases or impulses overlap [14].…”

Section: Introductionmentioning

confidence: 99%

Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed

et al. 2021

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Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

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Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

Cited by 52 publications

References 36 publications

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

Research on Sparsity Measures for Rotating Machinery Health Monitoring

Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed

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