Enhanced Industrial Machinery Condition Monitoring Methodology Based on Novelty Detection and Multi-Modal Analysis

IEEE Access

Miguel Delgado-Prieto¹  Jesus A. Carino¹  Daniel Zurita¹  Juan Antonio Ortega Redondo¹  Rene Romero-Troncoso²  Marta Millan³ 

[1] Electronic Department, Technical University of Catalonia, Terrassa, Barcelona, Spain;HSP Digital Research Group, University of Guanajuato, Guanajuato, Mexico;MAPRO Sistemas de ensayo S.A. Company, Barcelona, Spain;

关键词: Condition monitoring;    fault detection;    machine learning;    novelty detection;   

DOI  :  10.1109/ACCESS.2016.2619382

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown