The Bearing Vibration Signal Collecting System Based on STM32F103C8T6

Vibration signal collecting is an important step in rolling bearing fault diagnosis process. The collected signal can exhibit effectiveness of the fault depend on the signal collecting system. Combine the attribute of the rolling bearing, a new signal collecting system base on the STM32F103C8T6 is designed in this paper. The new system is made of supply circuit, signal conditioning circuit, AD conversion circuit and communication module.

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A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽

10.1371/journal.pone.0246905 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0246905

Author(s):

Chunming Wu ◽

Zhou Zeng

Keyword(s):

Fault Diagnosis ◽

Noise Pollution ◽

Vibration Signal ◽

Rolling Bearing ◽

Rotating Machinery ◽

Generative Adversarial Network ◽

High Background ◽

Bearing Fault Diagnosis ◽

Adversarial Network ◽

Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

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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 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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Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

10.20855/ijav.2019.24.21325 ◽

2019 ◽

Vol 24 (2) ◽

pp. 303-311 ◽

Cited By ~ 5

Author(s):

Xiaoxia Zheng ◽

Guowang Zhou ◽

Dongdong Li ◽

Haohan Ren

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Mode Decomposition ◽

Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

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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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