Fault diagnosis of rolling bearing based on online transfer convolutional neural network

Xu, Quansheng; Bo, Zhu; Huo, Hanbing; Meng, Zong; Li, Jimeng; Fan, Fengjie; Cao, Lixiao

doi:10.1016/j.apacoust.2022.108703

Cited by 27 publications

(11 citation statements)

References 28 publications

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“…The three indexes for O-B-R faults are increased to 91.53%, 90.00% and 90.76%, respectively. The difference of each index in the test results of IF-EMD-AADPCI and SPCI samples is summarized in table 15. The total increase rate of each index is 838.05%.…”

Section: Experiments Results and Analysis For If-emd-aadpcimentioning

confidence: 98%

“…Fan et al [14] proposed a grid-like grayscale texture image to extract the features from the vibration signal. Xu et al [15] used multi-channel data fusion and grayscale images as the input to improve the diagnosis effect for rolling bearings. To sum up, for fault diagnosis of rolling bearings, both timefrequency transform images and grayscale images have been widely used, but the time-frequency transform is complicated, and the grayscale images are not intuitive enough to express the deep-level features, especially for complex cases such as composite faults of a rolling bearing and other elements such as rotor, gear or motor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

Fan¹,

Xue²,

Jiateng³

et al. 2023

Meas. Sci. Technol.

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Rolling bearing is a key element of rotating machine in safe and reliable operation, and its fault diagnosis is a research focus. When a single bearing fault fails to be addressed in time, it will cause the progressive composite faults between bearing and other elements. In this paper, the different composite fault cases of bearing and rotor are considered. First, an Information Fusion-Empirical Mode Decomposition-Angle Adaptive Distribution of Polar Coordinates Image(IF-EMD-AADPCI) method is proposed, which has an adaptive image expression ability of tested vibration signal, and then can provide the high-quality vibration image samples for the model training. Second, an intelligent diagnosis model combining Convolutional Neural Network(CNN) and Support Vector Machine(SVM) is proposed, which has an excellent generalization ability to recognize the different composite faults. Third, the different compound faults between rolling bearing and rotor are fabricated, tested and then diagnosed. The results show the test accuracy of the proposed method is higher than the conventional method and simple in the image transform, which proves that this work is effective for the composite fault diagnosis of rolling bearing and rotor.

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Section: Experiments Results and Analysis For If-emd-aadpcimentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

Fan¹,

Xue²,

Jiateng³

et al. 2023

Meas. Sci. Technol.

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show abstract

“…A rolling bearing fault diagnosis model based on off-CNN obtained source-domain features and model parameters in the whole connection layer and initialized on-CNN parameters (Quansheng Xu et al, 2022) [ 106 ]. The state detection and fault identification of rolling bearings were performed on gray scale images by a GAN using SECNN (Hongtao Tang et al, 2021) [ 107 ].…”

Section: Detection Methods Based On Two-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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“…CNN (Convolutional Neural Networks) is the most representative model of deep learning. Janssens et al 14 was the first paper to apply convolutional neural networks to bearing fault diagnosis, using the spatial structure in the data to effectively capture the covariance of the frequency decomposition of accelerometer signals; to balance the training speed and accuracy of the model, Guo et al 15 improved the traditional convolutional neural networks model by adding adaptive learning rate and momentum components to the weight update process; Xia et al 16 in the training process of convolutional neural networks both temporal and spatial information of the raw data from multiple sensors are considered; To deal with mechanical vibration signals with variable sequence length, Zhang et al 17 proposed a bearing fault diagnosis method based on residual learning algorithm, and the whole network uses a 1-dimensional convolutional layer to obtain local sequence features of the data information stream; For data that are difficult to obtain labels in practical situations, Meng et al 18 proposed a data enhancement technique, using deep convolutional neural network with residual learning algorithm as the main structure to obtain higher diagnostic accuracy with limited training data; Zhang et al 19 used a deep full convolutional neural network (DFCNN) containing four pairs of convolutional pooling layer pairs to convert vibration signals into images as input; Choudhary et al 8 proposed a fault diagnosis method for rotating machinery bearings combining CNN and thermal images, using various fault conditions explored the availability of thermal imaging in bearing fault diagnosis; Xu et al 20 proposed a rolling bearing fault diagnosis model based on online transfer convolutional neural network (OTCNN) with pre-trained network model and source domain features.…”

Section: Related Workmentioning

confidence: 99%

A graph neural network-based bearing fault detection method

Xiao

Yang

2023

Sci Rep

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Bearings are very important components in mechanical equipment, and detecting bearing failures helps ensure healthy operation of mechanical equipment and can prevent catastrophic accidents. Most of the well-established detection methods do not take into account the correlation between signals and are difficult to accurately identify those fault samples that have a low degree of failure. To address this problem, we propose a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples outputted by the GNN network fuse the feature information of their neighbors, which is beneficial to the downstream detection task; then the samples mapped by the GNN network are fed into base detector for fault detection; finally, the results determined by the integrated base detector algorithm are determined, and the top n samples with the highest outlier scores are the faulty samples. The experimental results with five state-of-the-art algorithms on publicly available datasets show that the GNNBFD algorithm improves the AUC by 6.4% compared to the next best algorithm, proving that the GNNBFD algorithm is effective and feasible.

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Fault diagnosis of rolling bearing based on online transfer convolutional neural network

Cited by 27 publications

References 28 publications

A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

A graph neural network-based bearing fault detection method

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