Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks. Cheng, Y., Zhu, H., Wu, J., & Shao, X. IEEE Transactions on Industrial Informatics, 15(2):987–997, February, 2019. Conference Name: IEEE Transactions on Industrial Informaticsdoi abstract bibtex Machine health monitoring is of great importance in industrial informatics field. Recently, deep learning methods applied to machine health monitoring have been proven effective. However, the existing methods face enormous difficulties in extracting heterogeneous features indicating the variation until failure and revealing the inherent high-dimensional features of massive signals, which affect the accuracy and efficiency of machine health monitoring. In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in the time domain, frequency domain, and time-frequency domain are, respectively, extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, the AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. The results show that the performance of the proposed method is competitive with other existing methods.
@article{cheng_machine_2019,
title = {Machine {Health} {Monitoring} {Using} {Adaptive} {Kernel} {Spectral} {Clustering} and {Deep} {Long} {Short}-{Term} {Memory} {Recurrent} {Neural} {Networks}},
volume = {15},
issn = {1941-0050},
doi = {10.1109/TII.2018.2866549},
abstract = {Machine health monitoring is of great importance in industrial informatics field. Recently, deep learning methods applied to machine health monitoring have been proven effective. However, the existing methods face enormous difficulties in extracting heterogeneous features indicating the variation until failure and revealing the inherent high-dimensional features of massive signals, which affect the accuracy and efficiency of machine health monitoring. In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in the time domain, frequency domain, and time-frequency domain are, respectively, extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, the AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. The results show that the performance of the proposed method is competitive with other existing methods.},
number = {2},
journal = {IEEE Transactions on Industrial Informatics},
author = {Cheng, Yiwei and Zhu, Haiping and Wu, Jun and Shao, Xinyu},
month = feb,
year = {2019},
note = {Conference Name: IEEE Transactions on Industrial Informatics},
keywords = {Adaptive kernel spectral clustering (AKSC), Degradation, Feature extraction, Frequency-domain analysis, Machine learning, Monitoring, Time-domain analysis, anomaly detection, deep long short-term memory recurrent neural networks (LSTM-RNN), ecml, failure prognostics, machine health monitoring},
pages = {987--997},
}
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In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in the time domain, frequency domain, and time-frequency domain are, respectively, extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, the AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. 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