Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

He, Jinghan; Wu, Ping; Tong, Yizhi; Zhang, Xu-Jie; Lei, Meizhen; Gao, Jiajian

doi:10.3390/s21217319

Cited by 24 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rotating machinery using bearings is one of the most important components of a wide range of mechanical setups from small motors to turbines, compressors and heavy ground and air vehicles [1,2]. Different faults arise during the mechanical and industrial process, generating vibration and Acoustic Emission (AE) signals [3,4]. These signals have different characteristics due to the nature of faults, the complexity of the underlying industrial setup and the correlation between different mechanical components [5,6].…”

Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

show abstract

Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The MDCNN is compared with other methods such as LeNet5-GN, dropout fully convolutional neural network (DFCNN) [35], multi-scale convolutional neural network (MSCNN) [36] and 1D-CNN [37]. The parameters of the comparative model are shown in table 4.…”

Section: Fault Diagnosis Under Different Working Loadsmentioning

confidence: 99%

Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network

Lei¹,

Xue²,

Jiao³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Safe and reliable operation of mechanical equipment depends on timely and accurate fault diagnosis. When the actual working conditions are complex and variable and the available sample data set is small, recognition accuracy of the rolling bearing fault diagnosis model is low. To solve this problem, a novel method based on Markov transition field (MTF) and multi-dimension convolutional neural network (MDCNN) is proposed in this paper. Firstly, the original vibration signals are converted into two-dimensional images containing temporal correlation by MTF. Then, a neural network model is constructed by using multi-dimension attention (MDA) and E-Relu activation function to fully extract fault feature information. Finally, the MTF images are input into the model and the data is normalized using the group normalization method. The MDCNN model is validated on two different data sets, and the results show that compared with other intelligent fault diagnosis methods, the MDCNN has higher fault diagnosis accuracy and stronger robustness under both variable working conditions and small sample data sets conditions.

show abstract

“…By constructing a multi-scale receptive field fusion module, they realized the fusion of multi-scale features, thereby improving the diagnostic accuracy of the model. He et al [19] proposed an improved multi-scale convolutional neural network (IMSCNN) bearing FD method, which achieved excellent diagnostic performance through the multi-scale feature fusion ability of IMSCNN. Liu et al [20] bolstered the robustness and generalizability of their bearing FD model by formulating a multi-scale convolutional neural network model.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [27] proposed a domain-adaptive method for mechanical FD predicated on deep learning, incorporating adversarial learning to capitalize on data procured from sensors at diverse locations for FD tasks. He et al [19] integrated AdaBN into convolutional neural networks to amplify domain adaptability. Yao et al [22] employed a cyclically consistent GAN model, designing a GAN to generate new samples for unknown conditions based on known conditions, and then using these new samples to train the network model.…”

Section: Introductionmentioning

confidence: 99%

A bearing fault detection and remaining useful life prediction method based on a multi-branch residual feature fusion mechanism and optimized weight allocation

Yao,

Liang,

Tan

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Within the context of rapidly progressing industrial sectors, rolling bearings have become a fundamental component across an array of mechanical systems. Their fault detection and Remaining Useful Life (RUL) estimations are vital for ensuring industrial production safety. Yet, the understated characteristics of early-stage, minor faults in bearing degradation often escape detection. Additionally, numerous existing networks overlook the critical information embedded in multi-scale features, consequently diminishing the accuracy of predictions and classifications. The present study proposes MM-InfoGAN, an innovative approach for intelligent fault detection and RUL prediction to address these issues. MM-InfoGAN augments the network's ability to extract bearing fault characteristics and RUL data, employing a multi-branch residual feature fusion network structure coupled with an attention mechanism. Moreover, it refines the weight allocation strategy for geometric loss and introduces a novel loss function. This function optimizes weight distribution during the Generative Adversarial Network's (GAN's) training phase, expediting the attainment of network equilibrium. The efficacy of the comprehensive MM-InfoGAN model and its integrated modules was substantiated through comparative and ablation experiments conducted on the XJTU-SY dataset and IMS Bearing dataset.

show abstract

Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Cited by 24 publications

References 30 publications

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Rolling bearing fault diagnosis by Markov transition field and multi-dimension convolutional neural network

A bearing fault detection and remaining useful life prediction method based on a multi-branch residual feature fusion mechanism and optimized weight allocation

Contact Info

Product

Resources

About