A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions

Ma, Huijie; Li, Shunming; An, Zenghui

doi:10.3390/app9081603

Cited by 16 publications

(10 citation statements)

References 30 publications

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“…This diagnostic method firstly performs a fast Fourier transform on acoustic emission signals and generates the spectral energy maps, and then extracts the fault feature and classifies the spectral energy maps using CNN network. The study [36] added the nuisance attribute projection (NAP) to the loss function of the CNN, and then trained the CNN with the fault data at several speeds. NAP is usually used in speed recognition; it was used to map the original fault signal to the feature domain to eliminate the influences of load, rotation speed and the noise.…”

Section: A Brief Review Of the Fault Diagnosis Based On Deep Learning Methods Under The Variable Speed Conditionmentioning

confidence: 99%

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

Peng

et al. 2021

Sensors

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When performing fault diagnosis tasks on bearings, the change of any bearing’s rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%—on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.

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Section: A Brief Review Of the Fault Diagnosis Based On Deep Learning Methods Under The Variable Speed Conditionmentioning

confidence: 99%

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

Peng

et al. 2021

Sensors

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“…Zhuang et al proposed a method utilizing stacked residual dilated convolutions with high denoising capability; however, it has high complexity in terms of the number of parameters and training time [44]. Ma et al proposed a method based on the idea of unsupervised learning with the loss function to solve the problem of diagnosis under variable rotational speeds [45]. Haedong et al [46] proposed a CNN-based method using orbit plot images as input data to classify the fault modes [46].…”

Section: Related Workmentioning

confidence: 99%

2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

Pham

Kim

2021

Machines

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Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level of degradation, are increasingly necessary for factories. Owing to the advent of deep neural networks, especially convolutional neural networks (CNNs), intelligent FD methods have achieved significantly higher performance in terms of accuracy. However, in addition to accuracy, the efficiency issue still needs to be weathered in complicated diagnosis scenarios to adapt to real industrial environments. Here, we introduce a method based on multi-output classification, which utilizes the correlated features extracted for bearing compound fault type classification and crack-size classification to serve both aims. Additionally, the synergy of a time–frequency signal processing method and the proposed two-dimensional CNN helped the method perform well under the condition of variable rotational speeds. Monitoring signals of acoustic emission also had advantages for incipient FD. The experimental results indicated that utilizing correlated features in multi-output classification improved both the accuracy and efficiency of multi-task diagnosis compared to conventional CNN-based multiclass classification.

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“…Jiang et al [27][28][29] proposed a method by combining NAP and hidden Markov model (HMM) to evaluate the performance degradation of rolling bearings, and then they combined NAP with Student's t-hidden Markov model (Student's t-HMM) to obtain more accurate performance degradation assessments of rolling bearings. 30 Ma et al 31 imported NAP to the structure of convolutional neural network (CNN) to simplify the diagnosis process of rolling bearings under various speed conditions. Gandhi et al 32 introduced NAP to eliminate the interference of system dependent features and improve the robustness of fault diagnosis system of synchronous generators.…”

Section: Introductionmentioning

confidence: 99%

Robust fault diagnosis of rolling bearings via entropy-weighted nuisance attribute projection and neural network under various operating conditions

Yang

Yuan

et al. 2022

Structural Health Monitoring

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Rolling bearings are crucial components in the fields of mechanical, civil, and aerospace engineering. They sometimes work under various operating conditions, which makes it harder to distinguish faults from normal signals. Nuisance attribute projection (NAP) is a technique that has been widely used in audio and image recognition to eliminate interference information in the extracted feature space. In constructing the weighted matrix of NAP, the setting of the weighted value represents the degree of interference between the feature vectors. The interference is either taken into consideration in whole, or not considered at all, which will inevitably lead to information loss. In our work, an entropy-weighted NAP (EWNAP) is proposed to deal with such “bipolar problem” in constructing the weighted matrix. The eigenvalues of covariance matrix of collected signals contain dynamical information, and the fuzzy entropy is adopted to evaluate the dispersion degree of these eigenvalues. After normalization, these entropy values are used to express the weight relationship in the weighted matrix of EWNAP. The features processed by EWNAP can be used as samples and combined with neural network to achieve fault diagnosis of rolling bearings. Furthermore, a fault diagnosis approach with insufficient data is demonstrated to validate the effectiveness of the proposed scheme. In the case studies, Case Western Reserve University bearing database and data collected from the bearing fault simulation bench are used. These case studies show that the proposed EWNAP alleviates the interference caused by various operating conditions, and the comparative analysis confirms that the proposed method works better than the conventional methods.

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A Fault Diagnosis Approach for Rolling Bearing Based on Convolutional Neural Network and Nuisance Attribute Projection under Various Speed Conditions

Cited by 16 publications

References 30 publications

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

Robust fault diagnosis of rolling bearings via entropy-weighted nuisance attribute projection and neural network under various operating conditions

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