Learn Generalization Feature via Convolutional Neural Network: A Fault Diagnosis Scheme Toward Unseen Operating Conditions

Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating conditions. To obtain robust feature representation, the denoising autoencoder is used to denoise and reduce dimension of unlabeled rolling bearing signals. For those unlabeled target domain signals, a feature matching method based on multi-kernel maximum mean discrepancies between source domain and target domain is adopted to get enough labeled target domain samples. Then, these rolling bearing signals are converted to multi-dimensional graph samples and fed into a convolutional neural network model for fault diagnosis. To improve the generalization of convolutional neural network under variable operating conditions, we combine model-based transfer learning with feature-based transfer learning to initialize and optimize the convolutional neural network parameters. The effectiveness of the proposed method is validated through several comparative experiments of Case Western Reserve University data. The results demonstrate that the proposed method can learn features adaptively from noisy data and increase the accuracy rate by 2%–8% comparing with other models.

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Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

Energies ◽

10.3390/en13061475 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1475 ◽

Cited By ~ 4

Author(s):

Maciej Skowron ◽

Teresa Orlowska-Kowalska ◽

Marcin Wolkiewicz ◽

Czeslaw T. Kowalski

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Induction Motor ◽

Current Data ◽

Operating Conditions ◽

Stator Winding ◽

Stator Current ◽

Short Circuits ◽

The Impact

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

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Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i02.11953 ◽

2020 ◽

Vol 16 (02) ◽

pp. 47

Author(s):

Liqun Hou ◽

Zijing Li

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Vibration Signal ◽

Rolling Bearing ◽

Operating Conditions ◽

Fault Classification ◽

Rolling Ball ◽

Diagnosis Method

Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.

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Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Sensors ◽

10.3390/s20174930 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4930 ◽

Cited By ~ 1

Author(s):

Honglin Luo ◽

Lin Bo ◽

Chang Peng ◽

Dongming Hou

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Information Fusion ◽

High Speed ◽

High Reliability ◽

Operating Conditions ◽

High Speed Train ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions

IEEE Access ◽

10.1109/access.2019.2936625 ◽

2019 ◽

Vol 7 ◽

pp. 118954-118964 ◽

Cited By ~ 12

Author(s):

Huihui Qiao ◽

Taiyong Wang ◽

Peng Wang ◽

Lan Zhang ◽

Mingda Xu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rotating Machinery ◽

Operating Conditions ◽

Variable Operating Conditions ◽

Machinery Fault Diagnosis

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Rolling Bearing Fault Diagnosis Algorithm Based on Overlapping Group Sparse Model-Deep Complex Convolutional Neural Network

10.21203/rs.3.rs-888210/v1 ◽

2021 ◽

Author(s):

FENGPING AN ◽

Jianrong Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Operating Conditions ◽

Sparse Model ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Multi Scale

Abstract As the key component of a mechanical system, rolling bearings will cause paralysis of the entire mechanical system once they fail. In recent years, considering the high generalization ability and nonlinear modeling ability of deep learning, a rolling bearing fault diagnosis method based on deep learning has been formed, and good results have been achieved. However, because this kind of method is still in the initial development stage, its main problems are as follows. First, it is difficult to extract the composite fault signal feature of rolling bearing. Second, the existing deep learning rolling bearing fault diagnosis methods cannot well consider the problem of multi-scale information of rolling bearing signals. Therefore, this paper first proposes the overlapping group sparse model. It constructs weight coefficients by analyzing the salient features of the signal. It uses convex optimization techniques to solve the sparse optimization model, and applies the method to the feature extraction of rolling bearing composite faults. For the problem of multi-scale feature information extraction of rolling bearing composite fault signals, this paper proposes a new deep complex convolutional neural network model. This model fully considers the multi-scale information of rolling bearing signals. The complex information in this model not only contains rich representation ability, but also can extract more scale information. Finally, the classifier of this model is used to identify rolling bearing faults. Based on this, this paper proposes a new rolling bearing fault diagnosis algorithm based on overlapping group sparse model-deep complex convolutional neural network. The experimental results show that the method proposed in this paper can not only effectively identify rolling bearing faults under constant operating conditions, but also accurately identify rolling bearing fault signals under changing operating conditions. Additionally, the classification accuracy of the method proposed in this paper is greatly improved compared with traditional machine learning methods. It also has certain advantages over other deep learning methods.

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Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Applied Sciences ◽

10.3390/app9152950 ◽

2019 ◽

Vol 9 (15) ◽

pp. 2950 ◽

Cited By ~ 15

Author(s):

Jong-Hyun Lee ◽

Jae-Hyung Pack ◽

In-Soo Lee

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Induction Motor ◽

Induction Motors ◽

Vibration Signal ◽

Operating Conditions ◽

Industrial Equipment ◽

Diagnosis System ◽

Fault Diagnosis System

Induction motors are among the most important components of modern machinery and industrial equipment. Therefore, it is necessary to develop a fault diagnosis system that detects the operating conditions of and faults in induction motors early. This paper presents an induction motor fault diagnosis system based on a CNN (convolutional neural network) model. In the proposed method, vibration signal data are obtained from the induction motor experimental environment, and these values are input into the CNN. Then, the CNN performs fault diagnosis. In this study, fault diagnosis of an induction motor is performed in three states, namely, normal, rotor fault, and bearing fault. In addition, a GUI (graphical user interface) for the proposed fault diagnosis system is presented. The experimental results confirm that the proposed method is suitable for diagnosing rotor and bearing faults of induction motors.

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