scholarly journals A method for bearing fault diagnosis of mine hoist using convolutional attention autoencoder

2021 ◽  
Author(s):  
Jie Zhang ◽  
Ke Yang ◽  
Yuanyuan Jiang ◽  
Ling Xia
Keyword(s):  
Fault Diagnosis ◽  
Input Signal ◽  
Experimental Validation ◽  
Failure Modes ◽  
Noisy Environment ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Diagnosis Method ◽  
Actual Operation ◽  
Mine Hoist

Abstract In view of the complex environment and frequent faults in the actual operation of mine hoist, a fault diagnosis method based on Convolution Attention Autoencoder (CAAE) is proposed through theoretical analysis and experimental verification to improve the diagnostic stability of mine hoist under strong noise. First, a CAAE is constructed, which uses a combination of a convolutional neural network (CNN) and a channel attention module (CAM) to compress and encode the input signal, and then the input signal is reconstructed by a decoder to train the CAAE to extract the original signal fault features. Then, a fault diagnosis classifier is constructed to classify different fault patterns. Finally, experimental validation is performed with the Case Western Reserve University bearing dataset. The results show that the method has a strong feature extraction capability and a high classification accuracy for bearing failure modes compared with existing methods. And the experiments on the application effect of the proposed method in noisy environment are conducted to verify that the method is highly effective and challenging.

scholarly journals A Novel Bearing Fault Diagnosis Method Based on GL-mRMR-SVM

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 784
Author(s):  
Xianghong Tang ◽  
Qiang He ◽  
Xin Gu ◽  
Chuanjiang Li ◽  
Huan Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Global Features ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Recognition ◽  
Fault Features ◽  
Svm Model ◽  
Diagnosis Method ◽  
The Time Domain

A convolutional neural network (CNN) has been used to successfully realize end-to-end bearing fault diagnosis due to its powerful feature extraction ability. However, the CNN is prone to focus on local information, ignoring the relationship between the whole and the part of the signal due to its unique structure. In addition, it extracts some fault features with poor robustness under noisy environment. A novel diagnosis model based on feature fusion and feature selection, GL-mRMR-SVM, is proposed to address this problem in this paper. First, the model combines the global features in the time-domain and frequency-domain of the raw data with the local features extracted by CNN to make full use of the signal information and overcome the weakness of traditional CNNs neglecting the overall signal. Then, the max-relevance min-redundancy (mRMR) algorithm is used to automatically extract the discriminative features from the fused features without any prior knowledge. Finally, the extracted discriminative features are input into the SVM for training and output the fault recognition results. The proposed GL-mRMR-SVM model was evaluated through experiments on bearing data of Case Western Reserve University (CWRU) and CUT-2 platform. The experimental results show that the proposed method is more effective than other intelligent diagnosis methods.

scholarly journals An Anti-Noise Fault Diagnosis Method of Bearing based on Multi-Scale 1DCNN

2020 ◽  
Author(s):  
Jie Cao ◽  
Zhidong He ◽  
Jinhua Wang ◽  
Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Activation Function ◽  
Diagnostic Model ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Fault Features ◽  
Convolution Structure ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Comparison Algorithms

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

scholarly journals A Bearing Fault Diagnosis Method Based on PAVME and MEDE

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1402
Author(s):  
Xiaoan Yan ◽  
Yadong Xu ◽  
Daoming She ◽  
Wan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Whale Optimization ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Mode Extraction ◽  
Parameter Adaptive

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.

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.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

Sign in / Sign up

Export Citation Format

Share Document