Abstract -This paper presents a machine learning technique for fault diagnostics in induction motor drives. A normal model and an extensive range of faulted models for the inverter-motor combination were developed and implemented using a generic commercial simulation tool to generate voltages and current signals at a broad range of operating points selected by a machine learning algorithm. A structured neural network system has been designed, developed and trained to detect and isolate the most common types of faults: single switch open circuit faults, post-short circuits, short circuits, and the unknown faults. Extensive simulation experiments were conducted to test the system with added noise, and the results show that the structured neural network system which was trained by using the proposed machine learning approach gives high accuracy in detecting whether a faulty condition has occurred, thus isolating and pin-pointing to the type of faulty conditions occurring in power electronics inverter based electrical drives. Finally, the authors show that the proposed structured neural network system has the capability of real-time detection of any of the faulty conditions mentioned above within 20 milliseconds or less. There exists a good amount of work in the literature [6] [7] on fault diagnostics of internal combustion (IC) engine vehicles. However, for electric or hybrid vehicles, fault diagnostic techniques have not been well investigated yet, since EV/HEV is still in the relatively early stage in the automotive industry compared to IC engine vehicles. A closely related work by Ribeiro, Jacobina and Silva [8] , was based on the direct comparison of voltages measured at a few key points of the system with application to an electric drive in which the effect of closed loop control is not considered. Additional references exist in the area of diagnostics in the motor and power inverters [9] [10] [11] [12] [13] [14] . There are a number of intelligent systems approaches which have been investigated in signal fault diagnosis. Rule-based expert systems and decision trees are two traditional diagnostic techniques, but they have serious limitations. A rule-based system often has difficulties in dealing with novel faults and acquiring complete knowledge to build a reliable rule-base. A decision tree can be very large for a complex system, and it is
A neural network system was also designed to detect and isolate usual types of failures, such as short circuits, post short circuits, single-switch opencircuit faults, and unknown faults REF .
Intelligent diagnosis of open and short circuit faults in electric drive inverters for real-time applications
Iet Power Electronics
Engineering