A lightweight neural network with strong robustness for bearing fault diagnosis

Yao, Dechen; Liu, Hengchang; Yang, Jianwei; Li, Xi

doi:10.1016/j.measurement.2020.107756

Cited by 90 publications

(37 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results indicate that the latter model had a higher accuracy rate. In [ 31 ], a stacked inverted residual CNN (SIRCNN), which is a lightweight model, is proposed to diagnose rolling bearing faults. The time domain vibration signal is transformed into a 2D image after normalization.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Wang

Kao

Perng

2021

Sensors

View full text Add to dashboard Cite

The early diagnosis of a motor is important. Many researchers have used deep learning to diagnose motor applications. This paper proposes a one-dimensional convolutional neural network for the diagnosis of permanent magnet synchronous motors. The one-dimensional convolutional neural network model is weakly supervised and consists of multiple convolutional feature-extraction modules. Through the analysis of the torque and current signals of the motors, the motors can be diagnosed under a wide range of speeds, variable loads, and eccentricity effects. The advantage of the proposed method is that the feature-extraction modules can extract multiscale features from complex conditions. The number of training parameters was reduced so as to solve the overfitting problem. Furthermore, the class feature map was proposed to automatically determine the frequency component that contributes to the classification using the weak learning method. The experimental results reveal that the proposed model can effectively diagnose three different motor states—healthy state, demagnetization fault state, and bearing fault state. In addition, the model can detect eccentric effects. By combining the current and torque features, the classification accuracy of the proposed model is up to 98.85%, which is higher than that of classical machine-learning methods such as the k-nearest neighbor and support vector machine.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Wang

Kao

Perng

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…They also proposed a bearing fault diagnosis method based on a fully-connected winner-take-all autoencoder, which limits the maximum activation rate of each neuron of the sample, and uses soft voting to classify the collection, The model shows has a certain noise robustness [ 37 ]. Yao et al proposed an intelligent bearing fault diagnosis method based on Stacked Inverted Residual Convolution Neural Network (SIRCNN), which through the application of depthwise separable convolution and inverted residual structure to ensure the lightweight of the model and accuracy in noisy environments [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Shi

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

show abstract

“…Recently, with the rapid development and maturity of the Industrial Internet of Things technology, the concept of lightweight has arisen from the need for models with lower storage and computational costs in actual applications [25]. Liu et al [26] proposed a lightweight MT-1DCNN for exploring the possibility of using auxiliary tasks to improve the performance of the fault diagnosis task.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the proposed model's complexity, the number and size of convolution kernels are reasonably reduced. Yao et al [25] proposed a SIRCNN for bearing fault diagnosis. The depthwise separable convolution and inverted residual structure were adopted to ensure the accuracy of the model in noisy environments while achieving lightweight.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis

et al. 2020

View full text Add to dashboard Cite

The vibration signals collected from rolling bearings in industrial systems are highly complex and contain intense environmental noise, which challenges the performance of traditional fault diagnosis methods. Moreover, the applicability of the model in engineering practice, especially in the Industrial Internet of Things context, puts forward higher requirements for its storage and computational costs. Considering these challenges, this article proposes an enhanced lightweight multiscale convolutional neural network (CNN) for rolling bearing fault diagnosis. Our contributions mainly fall into three aspects. Firstly, the proposed model is modular and easy to expand, which combines the idea of multiscale learning with attention mechanism and residual learning, enabling the network to extract more abundant and discriminative fault features directly from the raw vibration signal. Consequently, the proposed model can perform better. Secondly, the interpretability of the multiscale learning mechanism is explored by visualizing the extraction process of multiscale features. Finally, for the first time, we introduce the depthwise separable convolution into multiscale CNN to reduce the storage and computational costs of the model, which realizes the lightweight of the model and improves its applicability in the Industrial Internet of Things context. The experimental results on the rolling bearing dataset demonstrate that, compared with the state-of-the-art multiscale CNN models, the proposed model has better discriminative fault feature extraction ability and antinoise ability, and is more suitable for practical industrial systems.

show abstract

A lightweight neural network with strong robustness for bearing fault diagnosis

Cited by 90 publications

References 22 publications

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Enhanced Lightweight Multiscale Convolutional Neural Network for Rolling Bearing Fault Diagnosis

Contact Info

Product

Resources

About