Application of modified culture Kalman filter in bearing fault diagnosis

Abstract Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified.

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Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

Shock and Vibration ◽

10.1155/2021/6687331 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jun He ◽

Xiang Li ◽

Yong Chen ◽

Danfeng Chen ◽

Jing Guo ◽

...

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Vibration Signal ◽

Rolling Bearing ◽

Learning Method ◽

Target Domain ◽

Source Domain ◽

Second Order Statistics ◽

Bearing Fault ◽

Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

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

Wireless Communications and Mobile Computing ◽

10.1155/2021/9790053 ◽

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.

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

Measurement Science and Technology ◽

10.1088/1361-6501/ac3fc7 ◽

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.753-755.2290 ◽

2013 ◽

Vol 753-755 ◽

pp. 2290-2296 ◽

Cited By ~ 2

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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Feature Extraction of Rolling Bearing Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.190-191.993 ◽

2012 ◽

Vol 190-191 ◽

pp. 993-997

Author(s):

Li Jie Sun ◽

Li Zhang ◽

Yong Bo Yang ◽

Da Bo Zhang ◽

Li Chun Wu

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Attribute Reduction ◽

Vibration Signal ◽

Rolling Bearing ◽

Mechanical Equipment ◽

Characteristic Parameters ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

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Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

Shock and Vibration ◽

10.1155/2016/9306205 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 27

Author(s):

Jie Tao ◽

Yilun Liu ◽

Dalian Yang

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Rolling Bearing ◽

Deep Belief Network ◽

Identification Accuracy ◽

Learning Ability ◽

Belief Network ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

The Individual

In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

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Rolling Bearing Fault Diagnosis Based on Second Generation Wavelet Denoising and Improved EEMD

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.2677 ◽

2014 ◽

Vol 556-562 ◽

pp. 2677-2680 ◽

Cited By ~ 2

Author(s):

Ling Jie Meng ◽

Jia Wei Xiang

Keyword(s):

Fault Diagnosis ◽

Second Generation ◽

Selection Criterion ◽

Vibration Signal ◽

Rolling Bearing ◽

Wavelet Denoising ◽

Intrinsic Mode Functions ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Second Generation Wavelet

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.

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Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽

10.3390/sym14010013 ◽

2021 ◽

Vol 14 (1) ◽

pp. 13

Author(s):

Jianpeng Ma ◽

Chengwei Li ◽

Guangzhu Zhang

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Information Fusion ◽

Rolling Bearing ◽

Fusion Method ◽

Rolling Bearings ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Evolution Analysis ◽

Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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Application of Balanced Multi-Wavelet in the Fault Diagnosis of Rolling Bearing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.226-228.210 ◽

2012 ◽

Vol 226-228 ◽

pp. 210-215

Author(s):

Sui Zheng Zhang ◽

Jian Yu Zhang ◽

Yang Yang

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Rolling Bearing ◽

Vanishing Moments ◽

One Dimensional ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Band Index ◽

Different Characteristics ◽

Better Than

Multi-wavelet has many excellent properties that single wavelet cannot satisfy simultaneously, such as symmetry, orthogonality, compact support and high vanishing moments etc. It contains several scaling functions and wavelet functions, which can make it match different characteristics of analyzed signal. Therefore, it is always used in bearing fault diagnosis. However, multi-wavelet is multi-dimensional and vibration signal is one-dimensional, so the 1-D vibration signal should be preprocessed before being decomposed with multi-wavelet. It means that the initial data need to be converted to r-dimensional data, and then is input to a tower algorithm. If preprocessing is done, multi-wavelet properties will be destroyed. Due to balanced multi-wavelet has unique properties, the preprocessing can be omitted. In this paper, a balanced multi-wavelet called CL4BAL is designed through balancing original CL4 multi-wavelet and is applied in the vibration signal processing. Comparing the frequency band index after decomposition and reconstruction of CL4BAL and CL4 multi-wavelet, it can be proved that CL4BAL is much better than that of CL4 multi-wavelet in bearing fault diagnosis.

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