Combined intelligent methods based on wireless sensor networks for condition monitoring and fault diagnosis

• Published in: Journal of intelligent manufacturing Vol. 26; no. 4; pp. 717 - 729
• Main Authors: Aydin, Ilhan, Karaköse, Mehmet, Akin, Erhan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-08-2015; Springer Nature B.V
• Subjects: Artificial intelligence; Bayesian analysis; Business and Management; CAM; Classification; Communication; Computer aided manufacturing; Condition monitoring; Construction; Control; Factories; Fault diagnosis; Faults; Fuzzy logic; Machine learning; Machines; Manufacturing; Mechatronics; Methods; Motors; Neural networks; Processes; Production; Robotics; Sensors; Signal processing; Studies; Vibration; Wireless networks; United Kingdom > UK; Fuzzy logic; Fault diagnosis; Wireless sensor networks; Gaussian mixture models; Induction motors; Bayesian classification method
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-013-0829-8

This study presents new combined methods based on multiple wireless sensor system for real-time condition monitoring of electric machines. The established experimental setup measures multiple signals such as current and vibration on a common wireless node. The proposed methods are low-cost, intelligent, and non-intrusive. The proposed wireless network based framework is useful for analyzing and monitoring of signals from multiple induction motors. Motor current and vibration signals are simultaneously read from multiple motors through wireless nodes and the faults are estimated using two combined methods. Phase space analysis of vibration data and amplitudes of three phase current signals are used as features in combined intelligent classifiers. Stator related faults are diagnosed by analyzing the magnitudes of read current signals with fuzz logic. The vibration signal taken from the two-axis acceleration meter is normalized and phase space of this signal is constructed. The change in phase spaces are analyzed with machine learning techniques based on Gaussian Mixture Models and Bayesian classification to detect bearing faults. The phase space of vibration signals is constructed by using non-linear time series analysis and Gaussian mixtures are obtained for healthy and each faulty conditions. The constructed mixture models are classified according to their distribution on phase space by using Bayesian classification method. Four motor operating conditions- stator open phase fault, one and two bearing imbalance faults, and healthy condition are considered and related signals are obtained to evaluate the proposed system. The accuracy of the proposed system is confirmed by experimental data.