Research of Intelligent Fault Diagnosis Based on Machine Learning

Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

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The Intelligent Fault Diagnosis for Composite Systems Based on Machine Learning

2006 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2006.258337 ◽

2006 ◽

Cited By ~ 1

Author(s):

Li-hua Wu ◽

Yun-fei Jiang ◽

Wei Huang ◽

Ai-xiang Chen ◽

Xue-nong Zhang

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Intelligent Fault Diagnosis ◽

Composite Systems

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A Bearing Intelligent Fault Diagnosis Method Based on Cluster Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.152-154.1628 ◽

2012 ◽

Vol 152-154 ◽

pp. 1628-1633 ◽

Cited By ~ 3

Author(s):

Su Qun Cao ◽

Xiao Ming Zuo ◽

Ai Xiang Tao ◽

Jun Min Wang ◽

Xiang Zhi Chen

Keyword(s):

Machine Learning ◽

Cluster Analysis ◽

Fault Diagnosis ◽

Test Data ◽

Rolling Bearing ◽

Intelligent Fault Diagnosis ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Fault Type ◽

Diagnosis Method

In recent years, machine learning techniques have been widely used in intelligent fault diagnosis field. As a major unsupervised learning technology, cluster analysis plays an important role in fault intelligent diagnosis based on machine learning. In rolling bearing fault diagnosis, the traditional spectrum analysis method usually adopts the resonant demodulation technology, but when the inner circle, rolling body or multi-point faults produce composite modulation, it is difficulty to identify the fault type from demodulation spectral lines. According to this, a novel rolling bearing fault diagnosis method based on KFCM (Kernel-based Fuzzy C-Means) cluster analysis is proposed. Through clustering on test data and the known samples, the memberships of test data are obtained. From these, the rolling bearing fault type can be determined. Experimental results show that this method is effective.

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Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection

Procedia Manufacturing ◽

10.1016/j.promfg.2020.10.038 ◽

2020 ◽

Vol 51 ◽

pp. 266-273

Author(s):

Syed Muhammad Tayyab ◽

Eram Asghar ◽

Paolo Pennacchi ◽

Steven Chatterton

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Features Extraction ◽

Intelligent Fault Diagnosis ◽

Rotating Machine ◽

Machine Elements

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Construction of a deep sparse filtering network for rotating machinery fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211014852 ◽

2021 ◽

pp. 095440702110148

Author(s):

Chun Cheng ◽

Wei Zou ◽

Weiping Wang ◽

Michael Pecht

Keyword(s):

Fault Diagnosis ◽

Back Propagation ◽

Feature Learning ◽

Back Propagation Neural Network ◽

Rotating Machinery ◽

Fine Tuning ◽

Local Optimum ◽

Back Propagation Algorithm ◽

Intelligent Fault Diagnosis ◽

Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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