Detection of Broken Rotor Bars in Induction Motor Using Derivative Free Kalman Filters

Int. Trans. Electr. Energ. Syst.

2013

Summary In this article, a novel fault diagnosis scheme, based on uncertainty bounds violation, is being presented for the case of broken bars in squirrel‐cage three‐phase induction motors. The fault diagnosis is performed in two steps. First, the parameters of the healthy induction motor are identified using a set membership identification approach, where corresponding uncertainty bounds are also being provided. Second, the proposed uncertainty bounds violation conditions for the fault diagnosis are evaluated online, on the converged identified model, during a sliding time window. Multiple simulation results are presented that demonstrate the efficacy of the proposed scheme toward fault detection and diagnosis among different numbers of broken bars. Copyright © 2013 John Wiley & Sons, Ltd.

Section: Simulation Resultsmentioning

confidence: 99%

Δ r_{ra, b, c} = r_{r} \frac{3 n_{italicbb}}{N_{b} - 3 n_{italicbb}}

Section: Broken Bar Model Of Three‐phase Induction Motormentioning

confidence: 99%

Broken bars fault diagnosis based on uncertainty bounds violation for three-phase induction motors

Int. Trans. Electr. Energ. Syst.

2013

“…The resistance changes are being derived based on the assumption that the broken bars are contiguous, neither the end ring resistance nor the magnetizing current is taken into account. For the case of n bb ∈ Z + broken rotor bars, the increment Δr ra; rb;rc in each phase is being obtained as (Chen & Zivanovic, 2009;Zivanovic, 2009;Kumar et al, 2011):…”

Section: Broken Bar Model Of Three Phase Induction Motormentioning

confidence: 99%

Faults Classification Scheme for Three Phase Induction Motor

International Journal of System Dynamics Applications

2014

In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed classification algorithm is applied to detect of two kinds of induction machine faults, which area) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. And thus a clear classification of the healthy and the faulty cases can be robustly performed, by having the optimal hyper plane. This method can separate the feature current classes in a low dimensional subspace. Robust linear discrimination has been one of the most widely used fault detection methods in real-life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time applies a straight-forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results in different fault types.

“…The resistance changes are being derived based on the assumption that the broken bars are contiguous, neither the end ring resistance nor the magnetizing current is taken into account. For the case of n bb broken rotor bars, the increment Δr ra,b,c in each phase is being obtained as [22], [23]:…”

Section: B Broken Bar Model Of Three Phase Induction Motormentioning

confidence: 99%

A Fault diagnosis scheme for three phase induction motors based on uncertainty bounds

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

2012

Abstract-The aim of this article is to present a fault diagnosis scheme for the case of squirrel-cage Three Phase Induction Motors based on uncertainty bounds violation conditions. The suggested scheme has the capability to diagnose two types of faults: a) broken rotor bar and b) short circuit in stator winding. The fault diagnosis is being performed through a two steps procedure. In the first step the parameters of the healthy induction motor are being identified by utilizing a Set Membership Identification approach, where corresponding uncertainty bounds are also being provided. In the second step, specific proposed bound violation conditions for the fault detection and fault diagnosis are being on-line evaluated during a sliding time window. Multiple simulation results are being presented that prove the efficacy of the proposed scheme towards fault detection and fault diagnosis.