Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults

• Published in: IEEE transactions on industrial informatics Vol. 16; no. 9; pp. 5985 - 5995
• Main Authors: Saucedo-Dorantes, Juan Jose, Delgado-Prieto, Miguel, Osornio-Rios, Roque Alfredo, Romero-Troncoso, Rene de Jesus
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Condition monitoring; Data integration; Discriminant analysis; Fault detection; feature extraction; incremental learning; Industrial applications; Inspection; Machine learning; novelty detection; Self organizing maps
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.2973731

The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this article proposes a novel multifault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection, and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a self-organizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios from two different cases of study, and the results compared with a classical approach.