Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

• Published in: IEEE transactions on industrial electronics (1982) Vol. 60; no. 8; pp. 3398 - 3407
• Main Authors: Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega, J. A., Henao, H.
• Format: Journal Article Publication
• Language: English
• Published: IEEE 01-08-2013; Institute of Electrical and Electronics Engineers (IEEE)
• Subjects: Ball bearings; Ball bearings classification algorithms condition monitoring fault diagnosis feature extraction induction motors neural networks vibrations; Biological neural networks; classification algorithms; condition monitoring; Control; Electric fault location; Electric motors, Induction; Electricitat; Energia elèctrica; Energies; Enginyeria mecànica; fault diagnosis; Feature extraction; induction motors; Motors; Motors elèctrics; Motors elèctrics d'inducció; Màquines i mecanismes; neural networks; Neural networks (Computer science); Processos de fabricació mecànica; Rodaments de boles; Shape; Support vector machine classification; Transmissió; Vectors; Vibració; Vibration; Vibrations; Xarxes neuronals (Informàtica); Àrees temàtiques de la UPC
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2012.2219838

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.