scholarly journals Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 36
Author(s):  
Xiaoan Yan ◽  
Yadong Xu ◽  
Daoming She ◽  
Wan Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Identification Accuracy ◽  
Learning Ability ◽  
Rolling Bearings ◽  
Softmax Classifier ◽  
Vibration Data ◽  
Fault Recognition ◽  
Fault Features ◽  
Latent Features

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

scholarly journals Design of Fault Diagnosis Algorithm for Electromechanical System Based on Artificial Intelligence

2021 ◽  
Vol 2050 (1) ◽  
pp. 012011
Author(s):  
Fuyou Zhao ◽  
Mingying Huo ◽  
Naiming Qi ◽  
Lianfeng Li ◽  
Weiwei Cui
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Sets ◽  
Learning Technology ◽  
Electrical System ◽  
Fault Recognition ◽  
Fault Features ◽  
Diagnosis Method

Abstract A relatively perfect system for the fault diagnosis of mechanical and electrical products has been formed through decades of development. Nevertheless, the traditional fault diagnosis methods fail to cope with the gradual huge mechanical and electrical system. As a result, the advantages of fault diagnosis mode driven by data are increasingly prominent. Meanwhile, the effect of fault diagnosis has exceeded the traditional fault diagnosis methods in many fields. Through the use of the deep learning technology based on artificial intelligence, it carries out mapping and fitting. By fully taking advantages of neural network, it can effectively obtain the accurate classification of fault data. A fault diagnosis method based on the fault data of mechanical and electrical system is designed in this thesis. When it comes to the basic process, it is to take data sets for different mechanical and electrical products. Through the use of feature engineering method, it extracts the fault features of data. Through the use of deep learning technology, it carries out the intelligent diagnosis. According to the experimental results, it indicates that the fault diagnosis method based on deep learning technology can distinguish a variety of fault modes in mechanical and electrical system in an effective way. What’s more, good classification results in fault recognition have been achieved by a variety of deep convolutional neural network structures, so the feasibility of the method is further verified.

scholarly journals Study on a Novel Fault Diagnosis Method Based on VMD and BLM

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 747 ◽  
Author(s):  
Jianjie Zheng ◽  
Yu Yuan ◽  
Li Zou ◽  
Wu Deng ◽  
Chen Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Identification Accuracy ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Vibration Data ◽  
Operation Speed ◽  
Mode Decomposition ◽  
Ac Motor ◽  
Diagnosis Method ◽  
Pseudo Inverse

The bearing system of an alternating current (AC) motor is a nonlinear dynamics system. The working state of rolling bearings directly determines whether the machine is in reliable operation. Therefore, it is very meaningful to study the fault diagnosis and prediction of rolling bearings. In this paper, a new fault diagnosis method based on variational mode decomposition (VMD), Hilbert transform (HT), and broad learning model (BLM), called VHBLFD is proposed for rolling bearings. In the VHBLFD method, the VMD is used to decompose the vibration signals to obtain intrinsic mode functions (IMFs). The HT is used to process the IMFs to obtain Hilbert envelope spectra, which are transformed into the mapped features and the enhancement nodes of BLM according to the complexity of the modeling tasks, and the nonlinear transformation mean according to the characteristics of input data. The BLM is used to classify faults of the rolling bearings of the AC motor. Next, the pseudo-inverse operation is used to obtain the fault diagnosis results. Finally, the VHBLFD is validated by actual vibration data. The experiment results show that the BLM can quickly and accurately be trained. The VHBLFD method can achieve higher identification accuracy for multi-states of rolling bearings and takes on fast operation speed and strong generalization ability.

scholarly journals Intelligent Deep Adversarial Network Fault Diagnosis Method Using Semisupervised Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Juan Xu ◽  
Yongfang Shi ◽  
Lei Shi ◽  
Zihui Ren ◽  
Yang Lu
Keyword(s):  
Fault Diagnosis ◽  
Original Data ◽  
Semisupervised Learning ◽  
Learning Ability ◽  
Time Frequency ◽  
Vibration Data ◽  
Time Domain Signal ◽  
Adversarial Network ◽  
Vibration Time ◽  
Network Fault

In recent years, deep learning has become a popular issue in the intelligent fault diagnosis of industrial equipment. Under practical working conditions, although the collected vibration data are of large capacity, most of the vibration data are not labeled. Collecting and labeling sufficient fault data for each condition are unrealistic. Therefore, constructing a reliable fault diagnosis model with a small amount of labeled vibration data is a significant problem. In this paper, the vibration time-domain signal of the fault bearing is transformed into a 2-dimensional image by wavelet transform to obtain the time-frequency domain information of the original data. A deep adversarial convolutional neural network based on semisupervised learning is proposed. A large amount of fake data generated by the generator and unlabeled true vibration data are used in the discriminator to learn the overall distribution of data by judging the authenticity of the input. Three regular terms for different loss functions are designed to constrain the parameters of the discriminator to improve the learning ability of the model. The proposed method is validated by two bearing fault diagnosis cases. The experiment results show that the proposed method has higher diagnostic accuracy than traditional deep models on multigroup small datasets of different capacities. The proposed method provides a new solution to the fault diagnosis problem with large vibration data but few labels.

scholarly journals A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4522
Author(s):  
Xihui Chen ◽  
Aimin Ji ◽  
Gang Cheng
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Feature Learning ◽  
Planetary Gear ◽  
Learning Ability ◽  
Energy Industry ◽  
Deep Feature ◽  
Gear Fault ◽  
Deep Feature Learning ◽  
Electromechanical Equipment

Planetary gear is the key component of the transmission system of electromechanical equipment for energy industry, and it is easy to damage, which affects the reliability and operation efficiency of electromechanical equipment of energy industry. Therefore, it is of great significance to extract the useful fault features and diagnose faults based on raw vibration signals. In this paper, a novel deep feature learning method based on the fused-stacked autoencoders (AEs) for planetary gear fault diagnosis was proposed. First, to improve the data learning ability and the robustness of feature extraction process of AE model, the sparse autoencoder (SAE) and the contractive autoencoder (CAE) were studied, respectively. Then, the quantum ant colony algorithm (QACA) was used to optimize the specific location and key parameters of SAEs and CAEs in deep learning architecture, and multiple SAEs and multiple CAEs were stacked alternately to form a novel deep learning architecture, which gave the deep learning architecture better data learning ability and robustness of feature extraction. The experimental results show that the proposed method can address the raw vibration signals of planetary gear. Compared with other deep learning architectures and shallow learning architecture, the proposed method has better diagnosis performance, and it is an effective method of deep feature learning and fault diagnosis.

scholarly journals Fault Diagnosis Method for Rolling Bearings Based on Composite Multiscale Fluctuation Dispersion Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 290 ◽  
Author(s):  
Xiong Gan ◽  
Hong Lu ◽  
Guangyou Yang
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Coarse Grained ◽  
K Nearest Neighbor ◽  
Rolling Bearings ◽  
Entropy Estimation ◽  
Fault Recognition ◽  
Knn Classifier ◽  
Diagnosis Method ◽  
The Stability

This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A simulation validates that CMFDE could improve the stability of entropy estimation. Meanwhile, a fault recognition method for rolling bearings based on CMFDE, the minimum redundancy maximum relevancy (mRMR) method, and the k nearest neighbor (kNN) classifier (CMFDE-mRMR-kNN) is developed. For the CMFDE-mRMR-kNN method, the CMFDE method is introduced to extract the fault characteristics of the rolling bearings. Then, the sensitive features are obtained by utilizing the mRMR method. Finally, the kNN classifier is used to recognize the different conditions of the rolling bearings. The effectiveness of the proposed CMFDE-mRMR-kNN method is verified by analyzing the standard experimental dataset. The experimental results show that the proposed fault diagnosis method can effectively classify the conditions of rolling bearings.

scholarly journals An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6576
Author(s):  
Shengnan Tang ◽  
Shouqi Yuan ◽  
Yong Zhu ◽  
Guangpeng Li
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Image Features ◽  
Learning Ability ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Intelligent Fault Diagnosis ◽  
Time Frequency ◽  
Hydraulic Axial Piston Pump ◽  
Axial Piston

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
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.

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

Symmetry ◽  
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.

scholarly journals Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

2019 ◽  
Vol 39 (4) ◽  
pp. 939-953 ◽  
Author(s):  
Dongying Han ◽  
Kai Liang ◽  
Peiming Shi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Deep Neural Network ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Fault Features

In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract feature from the spectral signal of the original signal. In addition, the original vibration signal is decomposed to a series of intrinsic mode function components by empirical mode decomposition, and the statistical features of each intrinsic mode function component are extracted by the deep neural network 2 in time domain and frequency domain. Second, the extraction features of the original signal spectrum and the extraction features of each intrinsic mode function component are evaluated, respectively. After features evaluation, the selected sensitive features are combined together to construct a joint feature. Finally, the joint feature is put into the deep neural network 3 to realize the automatic recognition of different fault states of rotating machinery. The experimental results show that the method proposed in this paper which integrated time-domain, frequency-domain statistical characteristics, empirical mode decomposition, feature selection, and deep learning methods can obtain the fault information in detail and can select sensitive features from a large number of fault features. The method can reduce the network size, improve the mechanical fault diagnosis classification accuracy, and has strong robustness.

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