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 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 paper, this problem is tackled by proposing a novel approach. First, a new use of singular spectrum analysis (SSA), as a powerful spectrum analyser, is introduced for fault detection. Our idea is to treat the stator current signature of the wound rotor induction machine 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 recognisable. 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 localisation procedure is applied to the power spectrum of the fault component to identify the exact frequency of the fault. The fault detection and localisation 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.

A Fitted empirical demodulation for low frequency electrical signal evaluation

“…A very similar situation occurs for the others tested load conditions, 80% and 100%, as can be seen in the Figs. 13…”

“…The Motor current signal analysis (MCSA) is considered a non-invasive method and suitable for online monitoring. As a result, it is the most used technique for induction machine fault diagnosis [12][13][14][15][16][17][18][19][20]. Hence, to prove the effectiveness of the Fitted Empirical Demodulation, the authors applied the new tool in motor current signals for analysis the rotor bars conditions.…”

Rotor Asymmetries Faults Detection in Induction Machines Under the Impacts of Low-Frequency Load Torque Oscillation

“…To ensure the accuracy of the proposed method, an evaluation procedure using synthetic data is conducted. For this purpose, a signal with mathematical expression in (8), which has a dominant frequency fs=50 Hz with the variables s=0.02, fLTO=2.5Hz, Is=1, Ir=1, γ=0.05 and β=0.03 is considered (Fig. 3a).…”

“…In this regard, different methods for condition monitoring of electrical machines have been presented in recent years. Among them, motor current signature analysis (MCSA) due to simplicity and non-invasive nature has been attracted more attention [7,8]. Up to now, countless pre-processing techniques have been presented to detect and demodulate mechanical and electrical faults from main supply frequency in the stator current spectrum of IMs.…”

Manipulation of stator current signature for rotor asymmetries fault diagnosis of wound rotor induction machine

“…Induction machines have been widely used in different applications and are known as key components of industrial systems [1,2]. Since these machines are faced with different mechanical and electrical faults, condition monitoring of such systems is necessary [3,4]. A major part of electrical and mechanical faults in induction machines is related to the rotor asymmetries originated from broken rotor bar (BRB) in squirrel cage induction machine or faulty rotor phase in wound rotor induction machine (WRIM) [5,6].…”