Single Feature Extraction Method of Bearing Fault Signals Based on Slope Entropy

Shi, Erna

doi:10.1155/2022/6808641

Cited by 5 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike the above entropies, slope entropy (SlopEn) is a new entropy estimator proposed based on symbolic patterns and magnitude information [ 17 ] and has been applied in the underwater acoustic field and medical field many times [ 18 , 19 , 20 , 21 ]. In 2022, SlopEn was introduced into the field of bearing fault diagnosis for the first time, and experimental results showed that, compared with PE and DE, SlopEn could better extract fault information [ 22 ]. However, all the above-mentioned entropy-based bearing fault diagnosis methods suffer from two defects: (i) the methods extract only the fault information of the low-frequency component for the bearing signal, and (ii) there is the problem of threshold selection for SlopEn, and the thresholds usually need to be optimized using an optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm

Tang

Huang

et al. 2023

Sensors

View full text Add to dashboard Cite

Slope entropy (SlopEn) has been widely applied in fault diagnosis and has exhibited excellent performance, while SlopEn suffers from the problem of threshold selection. Aiming to further enhance the identifying capability of SlopEn in fault diagnosis, on the basis of SlopEn, the concept of hierarchy is introduced, and a new complexity feature, namely hierarchical slope entropy (HSlopEn), is proposed. Meanwhile, to address the problems of the threshold selection of HSlopEn and a support vector machine (SVM), the white shark optimizer (WSO) is applied to optimize both HSlopEn and an SVM, and WSO-HSlopEn and WSO-SVM are proposed, respectively. Then, a dual-optimization fault diagnosis method for rolling bearings based on WSO-HSlopEn and WSO-SVM is put forward. We conducted measured experiments on single- and multi-feature scenarios, and the experimental results demonstrated that whether single-feature or multi-feature, the WSO-HSlopEn and WSO-SVM fault diagnosis method has the highest recognition rate compared to other hierarchical entropies; moreover, under multi-features, the recognition rates are all higher than 97.5%, and the more features we select, the better the recognition effect. When five nodes are selected, the highest recognition rate reaches 100%.

show abstract

Section: Introductionmentioning

confidence: 99%

A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm

Tang

Huang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…It was also a valuable feature in the machine learning ECG signal classification [ 20 ]. The method has been used for other types of signals, such as bearing fault signals [ 21 ] and ship-radiated noise signals [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Monotonic Complexity of Stochastic Model of the Channel Gating Dynamics

Machura

Wawrzkiewicz-Jałowiecka

Richter

et al. 2023

Entropy

View full text Add to dashboard Cite

The simple model of an ionic current flowing through a single channel in a biological membrane is used to depict the complexity of the corresponding empirical data underlying different internal constraints and thermal fluctuations. The residence times of the channel in the open and closed states are drawn from the exponential distributions to mimic the characteristics of the real channel system. In the selected state, the dynamics are modeled by the overdamped Brownian particle moving in the quadratic potential. The simulated data allow us to directly track the effects of temperature (signal-to-noise ratio) and the channel’s energetic landscape for conformational changes on the ionic currents’ complexity, which are hardly controllable in the experimental case. To accurately describe the randomness, we employed four quantifiers, i.e., Shannon, spectral, sample, and slope entropies. We have found that the Shannon entropy predicts the anticipated reaction to the imposed modification of randomness by raising the temperature (an increase of entropy) or strengthening the localization (reduction of entropy). Other complexity quantifiers behave unpredictably, sometimes resulting in non-monotonic behaviour. Thus, their applicability in the analysis of the experimental time series of single-channel currents can be limited.

show abstract

“…In 2019, SlEn was first proposed by David Cuesta-Frau, and successively applied it to the classification of electroencephalographic (EEG) records and electromyogram (EMG) records [27], classify the activity records of patients with bipolar disorder [28], and the features extraction of fever time series [29]. Then SlEn is also used to extract the features of ship radiated noise signals [30] and bearing fault signals [31].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

Gao

2022

Fractal Fract

View full text Add to dashboard Cite

Slope entropy (SlEn) is a time series complexity indicator proposed in recent years, which has shown excellent performance in the fields of medical and hydroacoustics. In order to improve the ability of SlEn to distinguish different types of signals and solve the problem of two threshold parameters selection, a new time series complexity indicator on the basis of SlEn is proposed by introducing fractional calculus and combining particle swarm optimization (PSO), named PSO fractional SlEn (PSO-FrSlEn). Then we apply PSO-FrSlEn to the field of fault diagnosis and propose a single feature extraction method and a double feature extraction method for rolling bearing fault based on PSO-FrSlEn. The experimental results illustrated that only PSO-FrSlEn can classify 10 kinds of bearing signals with 100% classification accuracy by using double features, which is at least 4% higher than the classification accuracies of the other four fractional entropies.

show abstract

Single Feature Extraction Method of Bearing Fault Signals Based on Slope Entropy

Cited by 5 publications

References 45 publications

A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm

A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm

Non-Monotonic Complexity of Stochastic Model of the Channel Gating Dynamics

Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

Contact Info

Product

Resources

About