Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox

The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and m...

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Vydáno v:	Entropy (Basel, Switzerland) Ročník 21; číslo 9; s. 861
Hlavní autoři:	Zhou, Shenghan, Xu, Xingxing, Xiao, Yiyong, Chang, Wenbing, Qian, Silin, Pan, Xing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 03.09.2019 MDPI
Témata:	Algorithms approximate entropy variance Artificial intelligence Comparative studies Crack propagation Damage damage indicators extraction Diesel generators Dynamic programming Dynamic stability dynamic time warping Dynamical systems Euclidean geometry Fault diagnosis Gearboxes Life prediction Low pass filters Methods Nonlinear dynamics Parameters Performance evaluation Prediction models Principal components analysis Probability distribution remaining useful life (RUL) Similarity similarity fusion Similarity measures Support vector machines Useful life Vibration vibration monitoring
ISSN:	1099-4300, 1099-4300
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Shrnutí:	The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and multi-sample. Firstly, based on the time domain and frequency domain extraction of vibration signals, the performance damage indicator system of a gearbox is established to select the optimal damage indicators for RUL prediction. Low-pass filtering based on approximate entropy variance (Aev) is introduced in this process because of its stability. Secondly, this paper constructs Dynamic Time Warping Distance (DTWD) as a similarity measurement function, which belongs to the nonlinear dynamic programming algorithm. It performed better than the traditional Euclidean distance. Thirdly, based on DTWD, similarity fusion of multi-parameter and multi-sample methods is proposed here to achieve RUL prediction. Next, the performance evaluation indicator Q is adopted to evaluate the RUL prediction accuracy of different methods. Finally, the proposed method is verified by experiments, and the Multivariable Support Vector Machine (MSVM) and Principal Component Analysis (PCA) are introduced for comparative studies. The results show that the Mean Absolute Percentage Error (MAPE) of the similarity fusion of multi-parameter and multi-sample methods proposed here is below 14%, which is lower than MSVM’s and PCA’s. Additionally, the RUL prediction based on the DTWD function in multi-sample similarity fusion exhibits the best accuracy.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1099-4300 1099-4300
DOI:	10.3390/e21090861