TY - GEN
T1 - A Fast Fault Diagnosis Method for The Unlabeled Signal Based on Improved PSO-DBSCAN Algorithm
AU - Wei, Shijie
AU - Mu, Huina
AU - Zhang, Pengbo
AU - Yi, Xiaojian
AU - Cui, Yuhang
N1 - Publisher Copyright:
© ESREL 2021. Published by Research Publishing, Singapore.
PY - 2021
Y1 - 2021
N2 - The fault diagnosis of different components with supervised learning method usual requires a large number of training samples. In practical engineering applications, the diagnosis efficiency is low and the failure rate is high due to the small amount of training samples. In order to solve these problems, a step-by-step fast fault diagnosis method based on improved Particle Swarm Optimization (PSO)- Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and Least Squares Support Vector Machine (LSSVM) is proposed. Firstly, the original signal is pre-processed by normalization and wavelet threshold de-noising. Then, the dimensionality reduction by Principal Component Analysis (PCA) is used as the input of the improved PSO-DBSCAN algorithm to cluster the data, and the train samples are formed after the data categories. Secondly, the train samples are used as the input of LSSVM to train the fault classifier. Finally, by using the trained classifier to classify other data, the working state of the component can be obtained. In this paper, by simulating a certain type of engine oil monitoring data, the accuracy of the classification result is 96.67%, which verifies the feasibility and effectiveness of the method, and realizes the fast fault diagnosis of unlabelled signals.
AB - The fault diagnosis of different components with supervised learning method usual requires a large number of training samples. In practical engineering applications, the diagnosis efficiency is low and the failure rate is high due to the small amount of training samples. In order to solve these problems, a step-by-step fast fault diagnosis method based on improved Particle Swarm Optimization (PSO)- Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and Least Squares Support Vector Machine (LSSVM) is proposed. Firstly, the original signal is pre-processed by normalization and wavelet threshold de-noising. Then, the dimensionality reduction by Principal Component Analysis (PCA) is used as the input of the improved PSO-DBSCAN algorithm to cluster the data, and the train samples are formed after the data categories. Secondly, the train samples are used as the input of LSSVM to train the fault classifier. Finally, by using the trained classifier to classify other data, the working state of the component can be obtained. In this paper, by simulating a certain type of engine oil monitoring data, the accuracy of the classification result is 96.67%, which verifies the feasibility and effectiveness of the method, and realizes the fast fault diagnosis of unlabelled signals.
KW - Clustering Algorithm
KW - Fault Diagnosis
KW - Improved PSO-DBSCAN Algorithm
KW - Least Square Support Vector Machines
KW - Unlabeled Signal
KW - Wavelet Threshold De-noising
UR - http://www.scopus.com/inward/record.url?scp=85135470370&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-2016-8_139-cd
DO - 10.3850/978-981-18-2016-8_139-cd
M3 - Conference contribution
AN - SCOPUS:85135470370
SN - 9789811820168
T3 - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
SP - 72
EP - 78
BT - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
A2 - Castanier, Bruno
A2 - Cepin, Marko
A2 - Bigaud, David
A2 - Berenguer, Christophe
PB - Research Publishing, Singapore
T2 - 31st European Safety and Reliability Conference, ESREL 2021
Y2 - 19 September 2021 through 23 September 2021
ER -