Fault Diagnosis of Roller Bearings Based on  a Wavelet Neural Network and Manifold Learning

Wu, Lifeng; Yao, Beibei; Peng, Zhen; Guan, Yong

doi:10.3390/app7020158

Cited by 35 publications

(26 citation statements)

References 15 publications

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“…Presently, widely-used mechanical fault prediction methods employ artificial neural networks (ANNs) [4][5][6], support vector machines (SVMs) [7,8], deep learning [9][10][11], and other artificial intelligence (AI) technologies. For example, Ben et al [12] proposed the use of empirical mode decomposition and energy entropy for feature extraction, which was combined with an ANN for multifeature fusion to make bearing fault predictions.…”

Section: Introductionmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

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Section: Introductionmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

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“…Traditional fault diagnosis methods use efficient feature extraction and a machine learning algorithm, such as -nearest neighbors ( -NN), support vectors machines (SVMs), and artificial neural networks (ANNs), to perform fault diagnosis [16][17][18][19][20]. Feature extraction is a cumbersome process that requires expert knowledge and also adds to the complexity of the fault diagnosis scheme [21].…”

Section: Introductionmentioning

confidence: 99%

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder‐Based Deep Neural Network

Sohaib

Kim

2018

Shock and Vibration

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Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE-) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

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“…To enhance the accuracy of fault detection, statistics methods should be based on the frequency spectrum to reduce false and missing alarms [12]. Alternatively, machine learning methods, namely support vector machine (SVM) [13,14], decision tree (DT) [15], and various neural network architectures [16,17] combined with advanced signal processing can be used to find the complex relations on the feature space by using predefined time-frequency features, being based on fault characteristic frequencies. However, without the characteristic frequencies, the mentioned methods have great difficulty in classifying bearing faults [18].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

Hemmer

Khang

Robbersmyr

et al. 2018

Designs

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Detecting bearing faults is very important in preventing non-scheduled shutdowns, catastrophic failures, and production losses. Localized faults on bearings are normally detected based on characteristic frequencies associated with faults in time and frequency spectra. However, missing such characteristic frequency harmonics in a spectrum does not guarantee that a bearing is healthy, or noise might produce harmonics at characteristic frequencies in the healthy case. Further, some defects on roller bearings could not produce characteristic frequencies. To avoid misclassification, bearing defects can be detected via machine learning algorithms, namely convolutional neural network (CNN), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer learning are proposed. Transfer of knowledge is achieved by extracting features from a CNN pretrained on data from the imageNet database to classify faults in roller bearings. The effectiveness of the proposed method is investigated based on vibration and acoustic emission signal datasets from roller bearings with artificial damage. Finally, the accuracy and robustness of the fault classifiers are evaluated at different amounts of noise and training data.

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Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning

Cited by 35 publications

References 15 publications

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder‐Based Deep Neural Network

Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

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