Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks

To improve feature learning ability and accurately diagnose the faults of rolling bearings under a strong background noise environment, we present a new shrinkage function named leaky thresholding to replace the soft thresholding in the deep residual shrinkage networks (DRSNs). In this work, we discover that such improved deep residual shrinkage networks (IDRSNs) can be realized by using a group searching method to optimize the slope value of leaky thresholding, and leaky thresholding in the IDRSNs can more effectively eliminate the noise of signal features. We highlight that our techniques can significantly improve the performance on various fundamental tasks. Experimental results show that IDRSNs achieve better fault diagnosis results on noised vibration signals compared with DRSNs. Moreover, we also provide a normalized processing to further improve the fault diagnosing accuracy of rolling bearing under a strong background noise environment.

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A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing

Shock and Vibration ◽

10.1155/2020/8891905 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Jinyu Tong ◽

Jin Luo ◽

Haiyang Pan ◽

Jinde Zheng ◽

Qing Zhang

Keyword(s):

Fault Diagnosis ◽

Working Conditions ◽

Search Algorithm ◽

Feature Learning ◽

Cuckoo Search ◽

Rolling Bearing ◽

Cuckoo Search Algorithm ◽

Learning Ability ◽

Penalty Term ◽

Experimental Data Analysis

To enhance the performance of deep auto-encoder (AE) under complex working conditions, a novel deep auto-encoder network method for rolling bearing fault diagnosis is proposed in this paper. First, multiscale analysis is adopted to extract the multiscale features from the raw vibration signals of rolling bearing. Second, the sparse penalty term and contractive penalty term are used simultaneously to regularize the loss function of auto-encoder to enhance the feature learning ability of networks. Finally, the cuckoo search algorithm (CS) is used to find the optimal hyperparameters automatically. The proposed method is applied to the experimental data analysis. The results indicate that the proposed method could more effectively distinguish fault categories and severities of rolling bearings under different working conditions than other methods.

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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 and Prediction Based on VMD-CWT and MobileNet

10.5121/csit.2021.110305 ◽

2021 ◽

Author(s):

Jing Zhu ◽

Aidong Deng ◽

Shuo Xue ◽

Xue Ding ◽

Shun Zhang

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Model Complexity ◽

Data Sets ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Network Training ◽

Signal Features ◽

Order Of Magnitude ◽

Diagnosis Method

When deep learning is used for rolling bearing fault diagnosis, there are problems of high model complexity, time-consuming, and large memory. In order to solve this problem. This paper presents an intelligent diagnosis method of rolling bearings based on VMD-CWT feature extraction and MobileNet, VMD is used to extract the signal features, and then wavelet transform is used to extract the timefrequency features. After the image is enhanced, the MobileNet network is trained. In order to accelerate the convergence speed, this paper adds transfer learning in the network training process, and migrates the weights of the first several layers pretrained to the corresponding network. Experimental results based on bearing fault data sets show that after adopting VMD-CWT, the accuracy of mobilenet increased from 68.7% to 94%, and its network parameters were reduced by an order of magnitude compared with CNN.

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Multi-feature learning-based extreme learning machine for rolling bearing fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211048585 ◽

2021 ◽

pp. 1748006X2110485

Author(s):

Longkui Zheng ◽

Yang Xiang ◽

Chenxing Sheng

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Feature Learning ◽

Rolling Bearing ◽

Working Environment ◽

Learning Methods ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Learning Machine ◽

Hidden Layer

Rolling bearing has been becoming an important part of human life and work. The working environment of rolling bearing is very complex and variable, which makes it difficult for fault diagnosis and monitor of rolling bearing from raw vibration data. Then, in this paper, a novel multi-feature learning-based extreme learning machine is proposed for rolling bearing fault diagnosis (FL-ELM). Extreme learning machine (ELM) is a fast and generalized algorithm proposed for training single-hidden-layer feed-forward networks (SLFNs), which has fast computing speed and small testing error. The novel architecture has two hidden layers and an experience pool sandwiched between two hidden layers. The first hidden layer consists of multi-feature learning methods. The experience pool is used to sort and choose new data, with old data being filtered out. Firstly, the first hidden layer is adopted for feature extraction. Secondly, the experience pool is used to rearrange and select data, which is extracted by first hidden layer. Thirdly, ELM is employed to further learn and classify. The proposed method (FL-ELM) is applied to the rolling bearing fault diagnosis. The results confirm that the proposed method is more effective than traditional methods and standard deep learning methods.

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Fault Diagnosis of Rolling Bearing Based on MED and Morphological Filtering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.3220 ◽

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.

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