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

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