Mechanical Fault Detection in Induction Motor Drives through Stator Current Monitoring - Theory and Application Examples

Available from: http://sciyo.com/articles/show/title/mechanical-fault-detection-in-induction-motor-drives-through-stator-current-monitoring-theory-and-apIn a wide variety of industrial applications, an increasing demand exists to improve the reliability and availability of induction motor drives. Common failures occurring in such drives can be classified into electrical and mechanical faults (rotor eccentricity, bearing faults, shaft misalignment, load unbalance, gearbox fault or general failure in the load part of the drive). Mechanical faults are most commonly detected through vibration or noise monitoring, but stator current monitoring is an interesting alternative. Indeed, current sensors are cost-effective, easy to implement, and most drives already contain such sensors for protection and control purposes. However, the effects of mechanical faults on the stator currents are more indirect compared to vibration or noise analysis. This work focuses on various aspects of mechanical fault detection through stator current monitoring, starting from a general theoretical analysis to signal processing methods for fault detection and several application examples

Method of analysing non-stationary electrical signals

International audienceConsidering the non-stationary operating conditions of wind turbines, electrical signals measured at the stator of their generators will also present variations around their fundamental frequency. This paper presents a method able to efficiently analyse electric quantities measured at the generator stator. The obtained outputs consist in electrical fea-tures that fully describe the electrical information contained in the mea-sured three-phase quantities. These features can be directly used or fur-ther analysed to obtain efficient fault indicators. The proposed method relies on using the instantaneous symmetrical components to describe the quantities and complex-valued filtering to select the content around the fundamental frequency. The obtained sample per sample algorithm can be implemented on-line, and is able to process stationary or non-stationary quantities in order to extract the useful information around the fundamental frequency. The performance of the proposed method, as well as its capability to detect mechanical faults, is illustrated using experimental data