Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network

Deep learning method is gradually applied in the field of mechanical equipment fault diagnosis because it can learn complex and useful features automatically from the vibration signals. Among the many intelligent diagnostic models, convolutional neural network has been gradually applied to intelligent fault diagnosis of bearings due to its advantages of local connection and weight sharing. However, there are still some drawbacks. (1) The training process of convolutional neural network is slow and unstable. It has more training parameters. (2) It cannot perform well under different working conditions, such as noisy environment and different workloads. In this paper, a novel model named adaptive and fast convolutional neural network with wide receptive field is presented to overcome the aforementioned deficiencies. The prime innovations include the following. First, a deep convolutional neural network architecture is constructed using the scaled exponential linear unit activation function and global average pooling. The model has fewer training parameters and can converge rapidly and stably. Second, the model has a wide receptive field with two medium and three small length convolutional kernels. It also has high diagnostic accuracy and robustness when the environment is noisy and workloads are changed compared with other models. Furthermore, to demonstrate how the wide receptive field convolutional neural network model works, the reasons for high model performance are analyzed and the learned features are also visualized. Finally, the wide receptive field convolutional neural network model is verified by the vibration dataset collected in the background of high noise, and the results indicate that it has high diagnostic performance.

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ASM1D-GAN: An Intelligent Fault Diagnosis Method Based on Assembled 1D Convolutional Neural Network and Generative Adversarial Networks

Journal of Signal Processing Systems ◽

10.1007/s11265-019-01463-8 ◽

2019 ◽

Vol 91 (10) ◽

pp. 1237-1247 ◽

Cited By ~ 6

Author(s):

Shengyao Gao ◽

Xueren Wang ◽

Xuhong Miao ◽

Changwei Su ◽

Yibin Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Generative Adversarial Networks ◽

Intelligent Fault Diagnosis ◽

Adversarial Networks ◽

Diagnosis Method

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Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Sensors ◽

10.3390/s21072411 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2411

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk ◽

Mihael Gudlin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Bayesian Optimization ◽

Intelligent Fault Diagnosis ◽

Accelerometer Signal ◽

Rotary Machinery

Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

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A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset

Sensors ◽

10.3390/s21206754 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6754

Author(s):

Hongtao Tang ◽

Shengbo Gao ◽

Lei Wang ◽

Xixing Li ◽

Bing Li ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rolling Bearing ◽

Environmental Noise ◽

Experimental Result ◽

Grayscale Image ◽

Intelligent Fault Diagnosis ◽

Rolling Bearings ◽

Generative Adversarial Network

Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fault diagnosis because of the severe imbalance and distribution differences in fault data due to weak early fault features and interference from environmental noise. An intelligent fault diagnosis strategy for rolling bearings based on grayscale image transformation, a generative adversative network, and a convolutional neural network was proposed to solve this problem. First, the original vibration signal is converted into a grayscale image. Then more training samples are generated using GANs to solve severe imbalance and distribution differences in fault data. Finally, the rolling bearing condition detection and fault identification are carried out by using SECNN. The availability of the method is substantiated by experiments on datasets with different data imbalance ratios. In addition, the superiority of this diagnosis strategy is verified by comparing it with other mainstream intelligent diagnosis techniques. The experimental result demonstrates that this strategy can reach more than 99.6% recognition accuracy even under substantial environmental noise interference or changing working conditions and has good stability in the presence of a severe imbalance in fault data.

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Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network

Computers in Industry ◽

10.1016/j.compind.2018.12.001 ◽

2019 ◽

Vol 108 ◽

pp. 53-61 ◽

Cited By ~ 44

Author(s):

Chunzhi Wu ◽

Pengcheng Jiang ◽

Chuang Ding ◽

Fuzhou Feng ◽

Tang Chen

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Rotating Machinery ◽

Intelligent Fault Diagnosis ◽

One Dimensional

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A Novel Fault Diagnosis Algorithm for Rolling Bearings Based on One-Dimensional Convolutional Neural Network and INPSO-SVM

Applied Sciences ◽

10.3390/app10124303 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4303

Author(s):

Yang Shao ◽

Xianfeng Yuan ◽

Chengjin Zhang ◽

Yong Song ◽

Qingyang Xu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Experimental Results ◽

Learning Ability ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Rolling Bearings ◽

One Dimensional ◽

Hybrid Framework

Deep learning based intelligent fault diagnosis methods have become a research hotspot in the fields of fault diagnosis and the health management of rolling bearings in recent years. To effectively identify incipient faults in rotating machinery, this paper proposes a novel hybrid intelligent fault diagnosis framework based on a convolutional neural network and support vector machine (SVM). First, an improved one-dimensional convolutional neural network (1DCNN) was adopted to extract fault features, and the state information and intrinsic properties of the raw vibration signals were mined. Second, the extracted features were used to train the SVM, which was applied to classify the fault category. The proposed hybrid framework combined the excellent classification performance of the SVM for small samples and the strong feature-learning ability of CNN network. In order to tune the parameters of the SVM, an improved novel particle swarm optimization algorithm (INPSO) which combined the Tent map and Lévy flight strategy was proposed. Numerical experimental results indicated that the proposed PSO variant had a better performance in searching accuracy and convergence speed. At last, multiple groups of rolling bearing fault diagnosis experiments were carried out and experimental results showed that, with the proposed 1DCNN-INPSO-SVM model, the hybrid framework was capable of diagnosing with high precision for rolling bearings and superior to some traditional fault diagnosis methods.

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