Kernel-based support vector machines for automated health status assessment in monitoring sensor data
| Autor: | Jose A. Pagan, Ricardo Sanz, Alberto Diez-Olivan, Basilio Sierra, Nguyen Lu Dang Khoa |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering media_common.quotation_subject 02 engineering and technology Machine learning computer.software_genre Industrial and Manufacturing Engineering Nonparametric density estimation 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Normality media_common business.industry Mechanical Engineering Bandwidth (signal processing) Condition monitoring Computer Science Applications Support vector machine Status assessment Hyperplane Control and Systems Engineering 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence Data mining business computer Software |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 95:327-340 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-017-1204-2 |
| Popis: | This paper presents a novel algorithm to assess the health status in monitoring sensor data using a kernel-based support vector machine (SVM) approach. Today, accurate fault prediction is a key issue raised by maintenance. In particular, automatically modelling the normal behaviour from condition monitoring data is probably one of the most challenging problems, specially when there is limited information of real faults. To overcome this difficulty, a data-driven learning framework based on nonparametric density estimation for outlier detection and ν-SVM for normality modelling, with optimal bandwidth selection, is proposed. A health score based on the log-normalisation of the distance to the separating hyperplane is also provided. Experimental results obtained when analysing the propagation of a critical fault over time in a marine diesel engine demonstrate the validity of the algorithm. The predictions of normality models learned were compared to those of the k-nearest neighbours (kNN) method. Low false positive rates on healthy data and improved prediction capacities are achieved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink