Embedded control with predictive diagnostics algorithm of the induction machine drive system

Fault diagnostics of AC induction machines (ACIM) have been widely researched however, an integrated algorithm for motor control with automated fault detection, prevention and condition monitoring is still missing. Condition monitoring can reduce the downtime of the processes and increase the maximum interval between failures, thus minimizing the number and cost of unscheduled maintenances, which is highly beneficial. In addition, embedding a systems condition analysis algorithm in to the motor control algorithm results in to an integrated approach; this can reduce the cost of the system and enhance its integrity. Thus, a new algorithm is introduced for the control of ACIM and the power inverter with embedded systems fault prediction and diagnosis. The embedded control and diagnostics algorithm is implemented using the analog devices Blackfintrade ADSP-BF561 dual core digital signal processor (DSP). Core 1 is dedicated to implement the control algorithm while core 2 mainly implements the fault prediction and diagnostics algorithm. The diagnostics algorithm can shutdown the drive system on an imminent catastrophic fault. The algorithm employs various techniques to detect and predict different faults of the AC drive system. In this paper, the inverter fault prediction and diagnosis are covered. The DSP communicates the systems electrical and mechanical parameters to a Windows XPtrade PC through the USB connection, which is mainly used to monitors and displays the electrical parameters of the inverter and the motor it also allows update of the DSP controller for speed/torque adjustments