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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Veröffentlicht in:Entropy (Basel, Switzerland) Jg. 21; H. 9; S. 861
Hauptverfasser: Zhou, Shenghan, Xu, Xingxing, Xiao, Yiyong, Chang, Wenbing, Qian, Silin, Pan, Xing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 03.09.2019
MDPI
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e21090861