DocumentCode :
3008020
Title :
Kernel spectral clustering for predicting maintenance of industrial machines
Author :
Langone, Rocco ; Alzate, Carlos ; De Ketelaere, Bart ; Suykens, Johan A. K.
Author_Institution :
Dept. of Electr. Eng. (ESAT), KU Leuven, Leuven, Belgium
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
39
Lastpage :
45
Abstract :
Early and accurate fault detection in modern industrial machines is crucial in order to minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. The process monitoring techniques that have been most effective in practice are based on the analysis of historical process data. In this paper we present a novel approach that uses Kernel Spectral Clustering (KSC) on the sensor data to distinguish between normal operating condition and abnormal situations. In other words, the main contribution is to show how KSC can be a valid tool also for outlier detection, a field where other techniques are more popular. KSC is a state-of-the-art unsupervised learning technique with out-of-sample ability and a systematic model selection scheme. Thanks to the abovementioned characteristics and the capability of discovering complex clustering boundaries, KSC is able to detect in advance the need of maintenance actions in the analyzed machine.
Keywords :
fault diagnosis; learning (artificial intelligence); maintenance engineering; mechanical engineering computing; pattern clustering; production equipment; safety; KSC; fault detection; industrial machines; kernel spectral clustering; maintenance prediction; manufacturing cost reduction; out-of-sample ability; plant operation safety; sensor data; state-of-the-art unsupervised learning technique; systematic model selection scheme; Accelerometers; Indexes; Kernel; Maintenance engineering; Monitoring; Principal component analysis; Tuning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Data Mining (CIDM), 2013 IEEE Symposium on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/CIDM.2013.6597215
Filename :
6597215
Link To Document :
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