Abstract
Reliability is pivotal significance for switched reluctance machine drives (SRD) applied to safety essential transportation and industrial fields. An inter-turn shorted-circuit fault (ISCF) could incite the machine to operate in unbalanced status, resulting in the noise increases. In the event such a fault remains untreated, the fault will further destroy the rest of the normal phases, even leading to a tragic incident for the entire drive application. To improve the reliability of SRD, an efficient on-line fault diagnosis method for ISCF should be proposed. This paper is focused on employing the strong track filter (STF) to achieve real-time phase resistance differences between before and after ISCF, which are used as features to diagnose the fault occurrence and the fault phase. Furthermore, a classification namely as linear discriminant analysis (LDA) is selected to estimate fault severity. Finally, simulation and experiments correspond to various running statuses are executed and their results can verify that the diagnosis method has accuracy and robustness.
Highlights
In the past decade, switched reluctance machines and their drives have obtained a great deal of regards and have been applied to transportation and industrial applications, including aerospace, power traction, hybrid vehicles [1]
The progressive diagnosis method plays an important role for heightening safe operation and reliability for Switched reluctance machines’ (SRM), which is the main issue of this paper
A new diagnosis method of stator inter-turn shorted-circuit fault (ISCF) was proposed for SRMs
Summary
In the past decade, switched reluctance machines and their drives have obtained a great deal of regards and have been applied to transportation and industrial applications, including aerospace, power traction, hybrid vehicles [1]. For permanent magnet synchronous motor, a combination of the values of the voltages and the stator currents obtain by wavelet transform was considered as the fault feature to diagnose the inter-turn faults in the literature [5]. In [6], the external vibration coupled with the stray magnetic field was analyzed to receive the vibration spectrum distinctions of healthy and faulty motor to implement non-invasive diagnosis for the rotor windings fault of the synchronous machine. The developing of diagnosis mode always lack enough samples since the fault samples are obtained difficultly in industrial processes This may cause the overfitting problem in case the model is developed by nonlinear methods [18].
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