Abstract
In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.
Highlights
Nowadays, modern industrial systems are becoming more and more complex and sophisticated
The fault detection and diagnosis approach of induction motor (IM) in closed loop drives is given in the following steps: Firstly, on-line fault detection using the model-based technique in order to highlight the appearance of incipient faults based on residual stator current
This paper presents a fast detection and recognition approach of the mixed and simple fault, the broken rotor bars and the static air-gap eccentricity faults for IM in the closed-loop drive
Summary
Modern industrial systems are becoming more and more complex and sophisticated. Based on the aforementioned state of the art, the Multi-ANFIS (MANFIS) technique proposed in this work to overcome the problem of accuracy and single ANFIS output, which is used to identify and distinguish the mixed and the simple faults of the broken rotor bars and static air-gap eccentricity even at low slip in IM closed-loop drives. This technique used the features extracted from the HFFT technique under different loads and fault severities as an input data set for the training algorithm.
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