Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine

• Published in: Applied soft computing 2019-08, Vol.81, p.105497, Article 105497
• Main Authors: Saucedo Dorantes, Juan Jose, Delgado Prieto, Miquel, Romero Troncoso, René, Osornio Rios, Roque A
• Format: Article
• Language: English
• Subjects: Anàlisi; Aplicacions de la informàtica; Aplicacions informàtiques a la física i l‘enginyeria; Application software; Condition monitoring; Diagnostic errors; Electric motors, Induction; Errors de diagnòstic; Fault diagnosis; Feature estimation; Feature reduction; Induction motor; Informàtica; Motors elèctrics d'inducció; Programari d'aplicació; Self-organizing feature maps; Sequential selection; Stator currents; Sèries temporals; Temperatura; Temperature; Temperatures; Time series analysis; Time-series analysi; Vibració; Vibration; Vibrations; Àrees temàtiques de la UPC
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105497

Strategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method. •A multiple-fault detection and identification scheme applied to electric machines.•Feature-level fusion approach by means of statistical-time features.•A distributed soft-feature reduction procedure based on three diagnosis levels.•A high-performative hierarchical SOM structure increasing diagnosis performance.•A reliability estimation scheme for diagnosis outcome assessment.