Recursive Singular Spectrum Analysis for Induction Machines Unbalanced Rotor Fault Diagnosis

Abstract

One of the major challenges of diagnosing rotor symmetry faults in induction machines (IMs) is severe modulation of fault and supply frequency components. In particular, existing techniques are not able to identify fault components in the case of low slips. In this article, this problem is tackled by proposing a novel approach. First, a new use of singular spectrum analysis (SSA), as a powerful spectrum analyzer, is introduced for fault detection. Our idea is to treat the stator current signature of the wound rotor IM as a time series. In this approach, the current signature is decomposed into several eigenvalue spectra (rather than frequency spectra) to find a subspace where the fault component is recognizable. Subsequently, the fault component is detected using some data-driven filters constructed with the knowledge about characteristics of supply and fault components. Then, an inexpensive peak localization procedure is applied to the power spectrum of the fault component to identify the exact frequency of the fault. The fault detection and localization methods are then combined in a recursive regime to further improve the diagnosis’ performance particularly at high rotor speeds and small rotor faults. The proposed approach is data-driven and is directly applied to the raw signal with no suppression or filtration of the frequency harmonics with a low computational complexity. The numerical results obtained with real data at several rotation speeds and fault severities unveil the effectiveness and real-time feature of the proposed approach.