A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network

Zhao, Jing; Yang, Shaopu; Li, Qiang; Liu, Yongqiang; Gu, Xiaohui; Liu, Wenpeng

doi:10.1016/j.measurement.2021.109088

Cited by 109 publications

(43 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the rolling bearing fault diagnosis based on I-PixeHop, the onedimensional time-domain signals need to be converted into two-dimensional grayscale images. In general, the simple signal-to-image method (STIM) [26] is used to convert the one-dimensional time-domain signals into two-dimensional grayscale images, which is difficult to extract the continuous features in one-dimensional time-domain signals. Gram angle difference field (GADF) and Gram angle sum field (GAS-F) [27] can easily perform angular perspective on the onedimensional time-domain signals, thereby time correlations in different time intervals are identified.…”

Section: B Data Preprocessing Methodsmentioning

confidence: 99%

An Improved PixelHop Framework and its Application in Rolling Bearing Fault Diagnosis

Wan¹,

Zhou

Gong

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The PixelHop framework based on successive subspace learning (SSL) has been widely used in signal processing and computer vision, which can effectively improve the classification accuracy in high spatial resolution scenes through successive subspace growth. To solve the problems of insufficient feature extraction and dependence on prior knowledge in the PixelHop framework, an improved PixelHop (I-PixelHop) framework is proposed. On the basis of PixelHop framework, I-PixelHop has made the following improvements. 1) I-PixelHop fully extracts the continuous features in one-dimensional sequence data through the improved neighborhood expansion, which can provide a richer feature set. 2) The improved label-assisted regression (ILAG) unit uses the Bi-K-Means clustering algorithm to enable more correct clustering of similar samples, and it adopts the cross-entropy threshold method to alleviate the negative effects caused by the improper setting of the number of pseudo-classes. 3) The high-dimensional features are fully reused by adopting the pseudo dense connection structure to obtain a better feature set. Moreover, the proposed I-PixelHop framework is applied to the rolling bearing fault diagnosis. A series of experiments are carried out to verify the effectiveness of the proposed I-PixelHop. The experimental results show that the fault diagnosis accuracy of I-PixelHop can reach 98.91% and 98.74% on the two different rolling bearing fault datasets, and it also has satisfactory anti-noise ability, faster training speed, and smaller model size.

show abstract

Section: B Data Preprocessing Methodsmentioning

confidence: 99%

An Improved PixelHop Framework and its Application in Rolling Bearing Fault Diagnosis

Wan¹,

Zhou

Gong

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In the offline training stage, condition data is collected from the sensors installed on the machine. In the designed DTC, all condition data are sampled into 2-D samples by a signal to sample method [ 35 ]. Data augmentations are used to increase the complexity of the samples through horizontal flip, random resize, rotation or combinations of these methods, etc.…”

Section: The Framework Of Dtc-simclr Methodsmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples

Wei

Liu

Zou

et al. 2021

Sensors

View full text Add to dashboard Cite

Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled samples are required to support the algorithm of CNNs, and, in the case of a limited amount of labeled samples, this may lead to overfitting. In this article, a novel ResNet-based method is developed to achieve fault diagnoses for machines with very few samples. To be specific, data transformation combinations (DTCs) are designed based on mutual information. It is worth noting that the selected DTC, which can complete the training process of the 1-D ResNet quickly without increasing the amount of training data, can be randomly used for any batch training data. Meanwhile, a self-supervised learning method called 1-D SimCLR is adopted to obtain an effective feature encoder, which can be optimized with very few unlabeled samples. Then, a fault diagnosis model named DTC-SimCLR is constructed by combining the selected data transformation combination, the obtained feature encoder and a fully-connected layer-based classifier. In DTC-SimCLR, the parameters of the feature encoder are fixed, and the classifier is trained with very few labeled samples. Two machine fault datasets from a cutting tooth and a bearing are conducted to evaluate the performance of DTC-SimCLR. Testing results show that DTC-SimCLR has superior performance and diagnostic accuracy with very few samples.

show abstract

“…In [ 22 ], Zhu transformed the signal by short-time Fourier transform into a frequency domain map for fault diagnosis by CNN. In [ 23 ], Zhao transformed the one-dimensional vibration signal into a two-dimensional grayscale image and achieved diagnostic classification of faults by CNN. However, the vibration signal is a one-dimensional time series signal, and the data at each moment have a certain correlation.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Zhao

Sun

Lin

et al. 2021

Sensors

View full text Add to dashboard Cite

Rolling mill multi-row bearings are subjected to axial loads, which cause damage of rolling elements and cages, so the axial vibration signal contains rich fault character information. The vertical shock caused by the failure is weakened because multiple rows of bearings are subjected to radial forces together. Considering the special characters of rolling mill bearing vibration signals, a fault diagnosis method combining Adaptive Multivariate Variational Mode Decomposition (AMVMD) and Multi-channel One-dimensional Convolution Neural Network (MC1DCNN) is proposed to improve the diagnosis accuracy. Additionally, Deep Convolutional Generative Adversarial Network (DCGAN) is embedded in models to solve the problem of fault data scarcity. DCGAN is used to generate AMVMD reconstruction data to supplement the unbalanced dataset, and the MC1DCNN model is trained by the dataset to diagnose the real data. The proposed method is compared with a variety of diagnostic models, and the experimental results show that the method can effectively improve the diagnosis accuracy of rolling mill multi-row bearing under unbalanced dataset conditions. It is an important guide to the current problem of insufficient data and low diagnosis accuracy faced in the fault diagnosis of multi-row bearings such as rolling mills.

show abstract

A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network

Cited by 109 publications

References 30 publications

An Improved PixelHop Framework and its Application in Rolling Bearing Fault Diagnosis

An Improved PixelHop Framework and its Application in Rolling Bearing Fault Diagnosis

Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Contact Info

Product

Resources

About