Fuzzy-Based Statistical Feature Extraction for Detecting Broken Rotor Bars in Line-Fed and Inverter-Fed Induction Motors

Abstract

This paper presents the use of a fuzzy-based statistical feature extraction from the air gap disturbances for diagnosing broken rotor bars in large induction motors fed by line or an inverter. The method is based on the analysis of the magnetic flux density variation in a Hall Effect Sensor, installed between two stator slots of the motor. The proposed method combines a fuzzy inference system and a support vector machine technique for time-domain assessment of the magnetic flux density, in order to detect a single fault or multiple broken bars in the rotor. In this approach, it is possible to detect not only the existence of failures, but also its severity. Moreover, it is not necessary to estimate the slip of the motor, usually required by other methods and the damaged rotor detection was also evaluated for oscillating load conditions. Thus, the present approach can overcome some drawbacks of the traditional MCSA method, particularly in operational cases where false positive and false negative indications are more frequently. The efficiency of this approach has been proven using some computational simulation results and experimental tests to detect fully broken rotor bars in a 7.5 kW squirrel cage induction machine fed by line and an inverter.

A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis

“…The three phase's stator and rotor voltages are then written 19,20 : …”

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

“…Then, the BRB fault is diagnosed by measuring the times between zero crossings (TSZCs) of the tooth magnetic flux in T domain. In [109], a fuzzy logic technique is applied to quantify each half cycle of the airgap flux signals, and eight temporal features are extracted from the fuzzified half cycles. The extracted features are fed into a support vector machine (SVM) classifier.…”

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

“…The main challenge of flux-based induction machines faults monitoring, which makes these flux-based methods complicated, is related to the implementation of flux sensors [16,17]. Recently, artificial intelligence methods along with proper pre-processing step were introduced to overcome some drawbacks of pervious methods to avoid positive and negative false alarm [18,19]. A summary of the comparison of the methods presented in recent years in this field is given in the Table I.…”