Paper Info
Title
Kernel spectral clustering for predicting maintenance of industrial machines
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.
Year
DOI
Venue
2013
10.1109/CIDM.2013.6597215
Computational Intelligence and Data Mining
Keywords
Field
DocType
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
Data mining,Anomaly detection,Computer science,Fault detection and isolation,Model selection,Unsupervised learning,Artificial intelligence,Cluster analysis,Downtime,Machine learning,Maintenance actions,Maintenance engineering
Conference
Citations 
PageRank 
References 
5
0.71
6
Authors
4
Name
Order
Citations
PageRank
Rocco Langone116413.41
Carlos Alzate224115.53
Bart De Ketelaere3134.45
Johan A. K. Suykens463553.51