An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection

• Published in: Revista IEEE América Latina 2020-06, Vol.18 (6), p.1093-1101
• Main Authors: Borges, Fernando, Pinto, Andrey, Ribeiro, Diogo, Barbosa, Tassio, Pereira, Daniel, Magalhaes, Ricardo, Barbosa, Bruno, Ferreira, Danton
• Format: Article
• Language: eng ; por
• Subjects: Accelerometers; Cantilever beams; Fault detection; Feature extraction; Higher order statistics; Induction motors; Mechanical Faults Detection; Monitoring; Prototyping; Statistics; Structural Health Monitoring; Support vector machines; Three axis; Vibration Signals
• ISSN: 1548-0992; 1548-0992
• DOI: 10.1109/TLA.2020.9099687

In this paper an unsupervised method to detect mechanical faults using support vector machines and higher-order statistics is proposed. The method extracts compact vector features – based on higher-order statistics – from vibration signals and uses the one-class support vector machine to build a closed region around the data from the health structure. The method was evaluated considering two cases: fault detection in a cantilever beam and in a three-phase induction motor. In both cases, the vibrations were collected by a 3 axis accelerometer sensor. The acquisition system was controlled by an open-source electronic prototyping ARDUINO® platform. After collecting the data, higher-order statistics-based features were extracted. These features were presented to the one-class support vector machine for fault detection. The proposed method was capable of identifying a closed region in a two-dimensional space so that events inside this region are signed as no faults and events outside this region are signed as faults. The method has two important characteristics: (i) it requires only healthy mechanical structures to be designed, and (ii) it operates in a low dimensional space (only two) constructed by the higher-order statistics features, which requires low computational cost in the operational phase.