Introduction

BRB Detection Method Using Global Optimization Algorithm

Joint Algorithm Based on Particle Swarm Optimization and Trust Region

Detection Procedure of TR-MBPSO-Based Method and Simulation Analysis

Experimental Verification

Conclusions

References

Abstract

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar (BRB) fault diagnosis of induction motors. Discrete Fourier transform (DFT) is the most popular technique in this feld, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence efect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm (called TR-MBPSO) based on a modifed bare-bones particle swarm optimization (BPSO) and trust region (TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modifed BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10−۴ , but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.

Introduction

Induction motors are widely used in the industry, owing to many advantages such as simple construction, reliability and high efciency. Although such motors are considerably reliable and robust, they still sufer from internal machine faults caused by corrosive and dusty environments. One of the most common faults is a broken rotor bar (BRB), which accounts for approximately 10% of total induction motor faults [1]. Terefore, early BRB detection in induction motors is surely signifcant. When a broken bar occurs in the rotor, the geometry and magnetic fux of the motor are unbalanced. New sideband frequency components at (1±۲s)f1 Hz will appear in the stator current, where s is the slip and f1 is the power supply frequency [2]. Tis implies that the BRB fault can be detected efciently by using the frequencies and amplitudes of (1±۲s)f1 components. Tus, motor current signature analysis (MCSA), which is non-invasive, is the most widely used technique for BRB detection.