scholarly journals Fault Injection Model of Induction Motor for Stator Interturn Fault Diagnosis Research Based on HILS

2021 ◽  
Vol 12 (4) ◽  
pp. 170
Author(s):  
Xuhao Zhang ◽  
Kun Han ◽  
Hu Cao ◽  
Ziying Wang ◽  
Ke Huo
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Fault Injection ◽  
Short Circuit ◽  
Rail Transportation ◽  
Hardware In The Loop ◽  
Healthy State ◽  
Proposed Model ◽  
Injection Model ◽  
First Time

Recently, in order to ensure the reliability and safety of trains, online condition monitoring and fault diagnosis of traction induction motors have become active issues in the area of rail transportation. The fault diagnosis algorithm can be developed and debugged in a real-time environment based on hardware-in-the-loop simulation (HILS). However, the dynamic space model of induction motors with stator interturn short-circuit faults faces the problem that the faulty state and the healthy state are not compatible, which is inconvenient for the HILS. In this paper, a fault injection model is proposed for the first time, which can realize the online switching between the healthy state and the faulty state of the motor. The feasibility and effectiveness of the proposed model are verified by simulation experiments the based on MATLAB/Simulink and dSPACE HILS platforms.

Verification of Fault Control Measures Based on Fault Injection for MCU Control System

2014 ◽  
Vol 484-485 ◽  
pp. 325-331
Author(s):  
Dao Sen Niu ◽  
Xiao Dong Liu ◽  
Shou Qun Sun ◽  
Yang Liu
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Fault Injection ◽  
Short Circuit ◽  
Experimental Results ◽  
Control Measures ◽  
Level Variation ◽  
Software Fault ◽  
Hardware Faults ◽  
Software Measures

To verify the validity of fault control measures, a verification platform with software fault injection and hardware fault injection is developed to conduct fault diagnosis measures for MCU control system. For the faults occurring in the internal units of a controller, program debugger is employed to simulate software or hardware faults by varying the data; for the faults occurring in peripheral circuits, a circuit of fault-settings is employed to simulate hardware faults, i.e., open-/short-circuit and electrical level variation. This verification platform is applied to evaluate software measures to control the faults/errors in accordance with IEC60335/IEC60730/UL1998/CSA22.2.08, and a case of induction cooker is presented shows how it works. Experimental results show that the verification platform runs stably and accurately, and has a big value in practice.

Application of Probabilistic Neural Networks in Fault Diagnosis of Three-Phase Induction Motors

2013 ◽  
Vol 433-435 ◽  
pp. 705-708 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Probabilistic Neural Network ◽  
Short Circuit ◽  
Model And Simulation ◽  
Fast Training ◽  
Three Phase ◽  
Short Circuits ◽  
Stator Fault

In fault diagnosis of three-phase induction motors, traditional methods usually fail because of the complex system of three-phase induction motors. Short circuit is a very common stator fault in all the faults of three-phase induction motors. Probabilistic neural network is a kind of artificial neural network which is widely used due to its fast training and simple structure. In this paper, the diagnosis method based on probabilistic neural network is proposed to deal with stator short circuits. First, the principle and structure of probabilistic neural network is studied in this paper. Second, the method of fault setting and fault feature extraction of three-phase induction motors is proposed on the basis of the fault diagnosis of stator short circuits. Then the establishment of the diagnosis model based on probabilistic neural network is illustrated with details. At last, training and simulation tests are done for the model. And simulation results show that this method is very practical with its high accuracy and fast speed.

Fault Diagnosis of Induction Motors Based on RBF Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 85-88 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Rbf Neural Network ◽  
Short Circuit ◽  
Three Phase ◽  
Diagnosis Accuracy ◽  
Simulation Results

In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

scholarly journals An accurate inter-turn short circuit faults model dedicated to induction motors

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fatima Babaa ◽  
Ouafae Bennis
Keyword(s):  
Induction Motors ◽  
Model Simulation ◽  
Short Circuit ◽  
Induction Machine ◽  
Industrial Systems ◽  
Proposed Model ◽  
Three Phase ◽  
Fault Prognosis ◽  
Short Circuits ◽  
Short Circuit Fault

Safety, disponibility and continuity of industrial systems are major issue in maintenance. In the last decades, these points are the important axes in the field of research. In fact, in many industrial processes research has picked up a fervent place and a particular importance in the area of fault diagnosis of electrical machines, in fact, a fault prognosis has become almost indispensable. The need of a mathematical model of three-phase induction machine, suitable for the simulation of machines behaviour under fault conditions, has received considerable attention. The paper presents a new practical and more precise model for induction motors after introducing inter turn short circuits faults. The proposed model is based on coupled magnetic circuit theory, capable to take into account any electrical asymmetry conditions. To verify the exactitude and the effectiveness of the model, simulation results for induction machine under interturn short circuit fault are presented. In spite of its simplicity, the proposed model is able to provide useful indications for diagnostic purposes. Experimental study is presented at the end of the paper to show that the proposed model predicts the induction machine behavior with a good accuracy.

scholarly journals Common Switch Fault Diagnosis for Two-Stage DC-DC Converters Used in Energy Harvesting Applications

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 293 ◽  
Author(s):  
Ehsan Jamshidpour ◽  
Philippe Poure ◽  
Shahrokh Saadate
Keyword(s):  
Fault Diagnosis ◽  
Energy Harvesting ◽  
Fault Detection ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Open Circuit ◽  
Hardware In The Loop ◽  
Two Stage ◽  
Synchronous Control ◽  
Detection Algorithms

This paper proposes a new Unified Switch Fault Diagnosis (UFD) approach for two-stage non-isolated DC-DC converters used in energy harvesting applications. The proposed UFD is compared with a switch fault diagnosis consisting of two separate fault detection algorithms, working in parallel for each converter. The proposed UFD is simpler than the two parallel fault diagnosis methods in realization. Moreover, it can detect both types of switch failures, open circuit and short circuit switch faults. It can also be used for any two-stage non-isolated DC-DC converters based on two single switch converters, no matter the converter circuits in each stage. Some selected simulation and Hardware-in-the-Loop (HIL) experimentation results confirm the validity and efficiency of the proposed UFD. Also, the proposed UFD is applied successfully for fault-tolerant operation of a buck/buck–boost two-stage converter with synchronous control and a redundant switch.

A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation

2021 ◽  
Vol 93 ◽  
pp. 107234
Author(s):  
Arturo Mejia-Barron ◽  
Guillermo Tapia-Tinoco ◽  
Jose R. Razo-Hernandez ◽  
Martin Valtierra-Rodriguez ◽  
David Granados-Lieberman
Keyword(s):  
Neural Network ◽  
Induction Motors ◽  
Short Circuit ◽  
Hardware In The Loop
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