Automatic Diagnosis of Electromechanical Faults in Induction Motors Based on the Transient Analysis of the Stray Flux via MUSIC Methods

In the induction motor predictive maintenance area, there is a continuous search for new techniques and methods that can provide additional information for a more reliable determination of the motor condition. In this context, the analysis of the stray flux has drawn the interest of many researchers. The simplicity, low cost and potential of this technique makes it attractive for complementing the diagnosis provided by other well-established methods. More specifically, the study of this quantity under the starting has been recently proposed as a valuable tool for the diagnosis of certain electromechanical faults. Despite this fact, the research in this approach is still incipient and the employed signal processing tools must be still optimized for a better visualization of the fault components. Moreover, the development of advanced algorithms that enable the automatic identification of the resulting transient patterns is another crucial target within this area. This article presents an advanced algorithm based on the combined application of MUSIC and neural networks that enables the automatic identification of the time-frequency patterns created by the stray flux fault components under starting as well as the subsequent determination of the fault severity level. Two faults are considered in the work: rotor problems and misalignments. Also, different positions of the external coil sensor are studied. The results prove the potential of the intelligent algorithm for the reliable diagnosis of electromechanical faults.