scholarly journals Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

2019 ◽  
Vol 9 (8) ◽  
pp. 1681 ◽  
Author(s):  
Cui ◽  
Du ◽  
Yang ◽  
Xu ◽  
Song
Keyword(s):  
Rolling Bearing ◽  
Rolling Bearings ◽  
Outer Race ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Q Factors ◽  
The Impact ◽  
Fault Information ◽  
Fault Characteristic

Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

Fault diagnosis of rolling bearings based on multi-scale deep subdomain adaptation network

2022 ◽  
pp. 1-11
Author(s):  
Qin Zhou ◽  
Zuqiang Su ◽  
Lanhui Liu ◽  
Xiaolin Hu ◽  
Jianhang Yu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Receptive Fields ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Features ◽  
Diagnosis Method ◽  
Fault Information

This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.

A clustering K-SVD-based sparse representation method for rolling bearing fault diagnosis

2021 ◽  
Vol 63 (3) ◽  
pp. 160-167
Author(s):  
Qingwen Yu ◽  
Jimeng Li ◽  
Zhixin Li ◽  
Jinfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Representation Method ◽  
The Impact

It is challenging to extract weak impulse features from vibration signals corrupted by strong noise in mechanical fault diagnosis. Due to its simple calculation, fast convergence and easy implementation, K-singular value decomposition (K-SVD) has been widely used in rolling bearing fault diagnosis. However, it fails to consider the influence of noise and harmonics on atoms learning from impulse characteristics, which results in many irrelevant atoms, and then increases the difficulty of extracting the impulse features in bearing fault signals. Therefore, a clustering K-SVD-based sparse representation method is proposed in this paper and it is combined with the particle swarm optimisation (PSO)-based time-varying filter empirical mode decomposition (TVF-EMD) for rolling bearing fault diagnosis. The PSO-based TVF-EMD is developed to automatically decompose the original signal to eliminate the impact of noise and harmonics on the impulses in the signal. Then, the clustering K-SVD method is applied to perform dictionary learning on the sensitive component containing impulses to obtain a redundant dictionary of atoms with obvious impulse patterns. Finally, the orthogonal matching pursuit (OMP) algorithm is introduced to extract the fault features from rolling bearing vibration signals. The experimental results show that the proposed method can improve the robustness of the dictionary atoms to noise and achieve the extraction of rolling bearing fault features.

scholarly journals Vibration response of defect-ball-defect of rolling bearing with compound defects on both inner and outer races

2021 ◽  
Vol 1207 (1) ◽  
pp. 012006
Author(s):  
Wei Luo ◽  
Changfeng Yan ◽  
Junbao Yang ◽  
Yaofeng Liu ◽  
Lixiao Wu
Keyword(s):  
Rolling Bearing ◽  
Vibration Response ◽  
Radial Clearance ◽  
Rolling Bearings ◽  
Outer Race ◽  
Response Characteristics ◽  
Bearing Fault Diagnosis ◽  
Theoretical Criterion ◽  
Vibration Model ◽  
Rolling Elements

Abstract Aiming at the problem that the existing compound defects model of rolling bearings under radial load is difficult to reflect the actual contact between rolling elements and defects. A new model is proposed to accurately reflect the simultaneous or sequential contact between inner and outer race defects and rolling elements. Considering the coupled excitation between shaft and bearing and pedestal, time-varying displacement excitation, and radial clearance, a four degree-of-freedom vibration model of rolling bearing with compound faults on both inner and outer races is built. The vibration equations are calculated by the method of numerical way, and the model is verified by experiment. The vibration response characteristics of the Defect-Ball-Defect model are studied, which renders a theoretical criterion for bearing fault diagnosis.

scholarly journals The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Jianxi Du ◽  
Lingli Cui ◽  
Jianyu Zhang ◽  
Jin Li ◽  
Jinfeng Huang
Keyword(s):  
Frequency Band ◽  
Rolling Bearing ◽  
New Method ◽  
Bearing Faults ◽  
Outer Race ◽  
Optimal Analysis ◽  
Fault Features ◽  
Index Value ◽  
The Relationship ◽  
Fault Characteristic

This paper proposes a new method to realize the quantitative trend diagnosis of bearings based on Protrugram and Lempel–Ziv. Firstly, the fault features of original fault signals of bearing inner and outer race with different severity are extracted using Protrugram algorithm, and the optimal analysis frequency band is selected which reflects the fault characteristic. Then, the Lempel–Ziv complexity of the frequency band is calculated. Finally, the relationship between Lempel–Ziv complexity and fault size is obtained. Analysis results show that the severity of fault is proportional to the Lempel–Ziv complexity index value under different fault types. The Lempel–Ziv complexity showed different trend rules, respectively, in the inner and outer race, which realized the quantitative trend diagnosis of bearing faults.

scholarly journals A Novel Method for Extracting Maximum Kurtosis Component and Its Applications in Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Yonggang Xu ◽  
Zeyu Fan ◽  
Kun Zhang ◽  
Chaoyong Ma
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Frequency Bands ◽  
Bearing Fault Diagnosis ◽  
Important Index ◽  
Novel Method ◽  
Simulation Signal ◽  
Fault Information

Rolling bearing plays an important role in the overall operation of the mechanical system; therefore, it is necessary to monitor and diagnose the bearings. Kurtosis is an important index to measure impulses. Fast Kurtogram method can be applied to the fault diagnosis of rolling bearings by extracting maximum kurtosis component. However, the final result may disperse the effective fault information to different frequency bands or find wrong frequency band, resulting in inaccurate frequency band selection or misdiagnosis. In order to find the maximum component of kurtosis accurately, an algorithm of frequency band multidivisional and overlapped based on EWT (MDO-EWT) is proposed in this paper. This algorithm changes the traditional Fast Kurtogram frequency bands division method and filtering method. It builds the EWT boundaries based on the maximum kurtosis component in each iteration and finally obtains the maximum kurtosis component. Through the simulation signal and the rolling bearing inner and outer ring fault signals verification, it is proved that the proposed method has a good performance on accuracy and effectiveness.

Fault Diagnosis of Rolling Bearing Based on MED and Morphological Filtering

2014 ◽  
Vol 989-994 ◽  
pp. 3220-3223
Author(s):  
Shang Kun Liu ◽  
Gui Ji Tang ◽  
Bin Pang
Keyword(s):  
Fault Diagnosis ◽  
Background Noise ◽  
Amplitude Spectrum ◽  
Rolling Bearing ◽  
Minimum Entropy ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Morphological Filtering ◽  
Diagnosis Method ◽  
Fault Characteristic

One rolling bearing fault diagnosis method based on minimum entropy deconvolution (MED) and morphological filtering is proposed. Firstly, the strong background noise of rolling bearings is decreased by the MED method, then the morphological filtering which have different length of structure elements is designed and applied to the de-noised signal. Subsequently, bearing’s fault characteristic frequency is extracted by the amplitude spectrum analysis to the best morphological filtering component which have the maximum kurtosis. This method is used to analyze both a simulated bearing signal and an experimental inner ring fault signal, and the good result validated the effectiveness of this method.

scholarly journals An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 354 ◽  
Author(s):  
Shuting Wan ◽  
Bo Peng
Keyword(s):  
Random Forest ◽  
Integrated Approach ◽  
Rolling Bearing ◽  
Automatic Identification ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Recognition ◽  
Intelligent Recognition ◽  
Fault Information

Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

2021 ◽  
Author(s):  
Bo Fang ◽  
Hu Jianzhong ◽  
Cheng Yang ◽  
Yudong Cao ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Automatic Differentiation ◽  
Blind Deconvolution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Convex Optimization Problem ◽  
Bearing Fault Diagnosis ◽  
Filter Performance ◽  
Calculation Process

Abstract Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

High Resonance Component of Resonance-Based Sparse Decomposition Application in Extraction of Rolling Bearing Fault Information

2013 ◽  
Vol 753-755 ◽  
pp. 2290-2296 ◽  
Author(s):  
Wen Tao Huang ◽  
Yin Feng Liu ◽  
Pei Lu Niu ◽  
Wei Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Frequency Resolution ◽  
Sparse Decomposition ◽  
High Resonance ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Information Frequency ◽  
Fault Information

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise, resulting in the difficulties in analysis of early weak fault signal. This article introduces resonance-based signal sparse decomposition (RSSD) into rolling bearing fault diagnosis, and studies the fault information contained in high resonance component and low resonance component. This article compares the effect of the two resonance components to extract rolling bearing fault information in four aspects: the amount of fault information, frequency resolution of subbands, sensitivity to noise and immunity to autocorrelation processing. We find that the high resonance component has greater advantage in extraction of rolling bearing fault information, and it is able to indicate rolling bearing failure accurately.

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