This paper proposes the use of a Hall effect sensor installed between two stator slots of a squirrel cage induction motor, spectral analysis of the air gap disturbances and machine learning approaches for diagnosing fully broken rotor bars in motors running at very low slip. The present work overcomes a typical drawback of broken bars diagnosis by using the traditional analysis of the motor current signature, usually when machines operate under low or no-load conditions. A Fast Fourier Transform (FFT) has been performed in Hall sensor data to identify some harmonic features and also some statistical data have been extracted to get time domain properties. These values were used as inputs for a Multilayer Perceptron Neural Network (MLP) classifier, a k-Nearest Neighbor (KNN) classifier and a support vector machine (SVM) classification technique. In addition, principal component analysis (PCA) has been applied to reduce data dimension among some original values, i.e., for both time and frequency features, and contribute to rotor failure detection. A Cross-Validation (CV) method has been used for classifier performance evaluation on each case. The efficiency of this approach was evaluated from a 7.5kW squirrel cage induction machine running at very low slip conditions and MLP classifier has achieved better results than the other intelligent methods.

This paper presents the use of an invasive method specially applied for broken rotor bars diagnosis in large induction machines, considering the frequency domain analysis of the magnetic flux density variation in a low-cost Hall effect sensor. The proposed method is based on the Fast Fourier transform, or spectral analysis of the resultant magnetic flux density, to contribute to a crack and bar breaks diagnosis on large squirrel cage motors. More particularly, this paper considers the use of a method capable of overcoming some drawbacks from traditional diagnostic techniques, such as motor current signature analysis during operation at very low slip. The results from the computational simulation and some experimental tests are presented and could be considered for rotor failure diagnostics in induction motors.

This paper proposes the rotor speed estimation of a squirrel cage induction motor fed by a sinusoidal power supply or an inverter based on the use of a Hall effect sensor installed between two stator slots of the machine. As well known in literature, inverter-fed induction motors usually contains a series of current harmonics and in case of rotor faults, such as broken bars, this operational condition becomes even more severe. The speed estimation has been extensively investigated for its use in control schemes, to compute output power or machine efficiency and particularly for fault diagnosis using motor current signature analysis. In this method, it is necessary an accurate speed estimation to prevent false positives and false negative indications. Therefore, the present research has been carried out by applying the Fast Fourier Transform and Hilbert Transform in the airgap signal measured by the Hall sensor, in order to estimate the rotational speed of the induction motor in a healthy condition and with broken rotor bars. This approach has been compared to the same signal processing techniques using the stator current phase, usually used for speed estimation in the state of the art solutions. The efficiency of this approach was evaluated from simulation and experimental tests, for motor running at distinct load torques, at very low slip conditions and considering a damaged rotor cage. The simulation and experimental results have shown a better accuracy with the measured or simulated speeds, using the airgap estimations when compared to those obtained from stator current processing.

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.

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