Fault Severity Sensing for Intelligent Remote Diagnosis in Electrical Induction Machines

Abstract

Electrical induction machines are widely used in the modern wind power production. As their repair cost is important and since their down-time leads to significant income loss, increasing their reliability and optimizing their proactive maintenance process are critical tasks. Many diagnosis systems have been proposed to resolve this issue. However, these systems are failing to recognize accurately the type and the severity level of detected faults in real time. In this chapter, a remote automated control approach applied for electrical induction machines has been suggested as an appropriate solution. It combines developed Fast-ESPRIT method, fault classification algorithm, and fuzzy inference system interconnected with vibration sensors, which are located on various wind turbine components. Furthermore, a new fault severity indicator has been formulated and evaluated to avoid false alarms. Study findings with computer simulation in Matlab prove the satisfactory robustness and performance of the proposed technique in fault classification and diagnosis.