Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

2021 ◽  
Vol 13 (2) ◽  
pp. 58-79
Author(s):  
Imadeddine Harzelli ◽  
Abdelhamid Benakcha ◽  
Tarek Ameid ◽  
Arezki Menacer
Keyword(s):  
Induction Motor ◽  
Closed Loop ◽  
Sliding Mode ◽  
Fault Detection And Diagnosis ◽  
Data Set ◽  
Inference System ◽  
Stator Current ◽  
Model Based ◽  
Neuro Fuzzy ◽  
Detection And Diagnosis

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.

Modeling, Analysis, of Induction Motor's Stator Turns Fault Using Neuro-Fuzzy

2021 ◽  
pp. 498-520
Author(s):  
Hussein. A. Taha ◽  
M. E. Ammar ◽  
M. A. Moustafa Hassan
Keyword(s):  
Induction Motor ◽  
Diagnostic Tool ◽  
Short Circuit ◽  
Simulated Data ◽  
Fault Detection And Diagnosis ◽  
Modeling And Analysis ◽  
Motor Current ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Detection And Diagnosis

This chapter discusses modeling and analysis methods for fault detection and diagnosis of stator inter-turn short circuit in three-phase induction machines. dq frame was used to model the induction motor for both health and fault cases to facilitate recognition of motor current and simulate motor environment. Fault diagnosis system was designed with adaptive neuro-fuzzy inference system (ANFIS) to provide an efficient online diagnostic tool. ANFIS diagnostic tool was trained with simulated data that generated by induction motor healthy and faulty models. Approached tool is verified online with a motor under different loading conditions. It determines the fault severity values using the motor current signature analysis (MCSA). Developed tool performance is investigated with a case study of two HP three-phase induction motor using Matlab/Simulink® software.

scholarly journals Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique

2021 ◽  
Vol 9 (1) ◽  
pp. 45-59
Author(s):  
Annamalai Balamurugan ◽  
Thangavel Swaminathan Sivakumaran
Keyword(s):  
Standard Deviation ◽  
Fault Detection ◽  
Induction Motor ◽  
Fault Detection And Diagnosis ◽  
Gradient Boosting ◽  
Discrete Wavelet ◽  
Data Set ◽  
Statistical Measures ◽  
Detection And Diagnosis ◽  
Multi Phase

In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, S-Transform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision.

scholarly journals AN Adaptive Neuro Fuzzy Inference System Control For A Bearingless Induction Motor Based On A Disturbance Observer

2019 ◽  
Vol 9 (1) ◽  
pp. 906-912
Keyword(s):  
Induction Motor ◽  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Switching Function ◽  
System Control ◽  
Inference System ◽  
Regular Control ◽  
Neuro Fuzzy

There is some poor performance regarding controlling capacity of the bearing-less induction motor (BIM) when there are deviations in the parameters, outer disturbances and changes in the loads. So to solve this issue design of an adaptive exponential sliding-mode (AESM) controller and an observer for extended SM disturbance for finding system disturbance variables while operating are done. This adaptive exponential control is explained by combining order one norm and switching function law into regular control strategy. We can adjust the conjuction speed time adaptively as per variation of the SM switch surface and the system status. The controller used in this control strategy is Adaptive Neuro-Fuzzy Inference System (ANFIS). The observer used senses the speed and outer disturbances of the bearing-less induction motor. As feed forward contribution for system speed, the response of DSMO is utilized. The disturbance in the motor can be reduced by adjusting error in the speed by this feedback speed. From simulation output it can be seen that proposed system with ANFIS control strategy has good strength to control disturbances and to find the uncertain disturbances accurately. Hence the controlling capacity of the bearing-less induction motor (BIM) when there are deviations can be improved by using this proposed system.

Model-based fault detection and diagnosis for hydraulic braking systems

2003 ◽  
Vol 36 (5) ◽  
pp. 307-312 ◽  
Author(s):  
Harald Straky ◽  
Marco Muenchhof ◽  
Rolf Isermann
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Model Based ◽  
Detection And Diagnosis
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