Paper Info
Title
Enhanced Industrial Machinery Condition Monitoring Methodology Based on Novelty Detection and Multi-Modal Analysis.
Abstract
This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on principal component analysis and linear discriminant analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a feed-forward neural network and one-class support vector machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.
Year
Venue
Field
2016
IEEE Access
Data mining,Novelty detection,Computer science,Fault detection and isolation,A priori and a posteriori,Support vector machine,Feature extraction,Condition monitoring,Artificial intelligence,Linear discriminant analysis,Artificial neural network,Machine learning
DocType
Volume
Citations 
Journal
4
1
PageRank 
References 
Authors
0.35
17
6
Name
Order
Citations
PageRank
Jesus A. Carino182.52
Miguel Delgado Prieto2858.94
Daniel Zurita382.52
Marta Millán440.77
Juan Antonio Ortega Redondo520829.56
René de Jesús Romero-Troncoso610.35