Using a genetic algorithm for detection and magnitude determination of turn faults in an induction motor

Raie, Abolghasem A.; Rashtchi, Vahid

doi:10.1007/s00202-002-0133-7

Cited by 33 publications

(21 citation statements)

References 8 publications

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“…This behavior is detrimental to a robust and reliable fault detection, since it can possibly lead to the estimation of incorrect faults, with a high likelihood of false positives or missed detections. More robust global search methods, such as Genetic Algorithms and Simulated Annealing offer a higher success rate in detecting the global function minimum, at the expense of a longer computational time [38,39].…”

Section: Proposed Fault Detection and Identification Algorithm And Prmentioning

confidence: 99%

“…For this reason, in recent years many technical applications in the field of mechatronics and electrical machines have been employing Genetic Algorithms for model-based diagnostic and prognostic tasks [39]. In the specific application of electrical machinery fault detection, an example can be found in [38], where GA-based fault detection is leveraged to identify stator turn-to-turn coil short circuit faults with a parameter identification on a model accounting for the short circuit fault.…”

Section: Proposed Fault Detection and Identification Algorithm And Prmentioning

confidence: 99%

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Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

et al. 2019

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Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.Aerospace 2019, 6, 94 2 of 15 occurrence of a failure before the scheduled substitution of the interested system. Monitoring the behavior of an actuator could enable to detect the early signs of different progressive faults, in order to timely correct them. If a tool would be available with a high grade of robustness to isolate those incipient faults, most failure modes of the flight control system could be identified before they start affecting the performance of the system in terms of dynamical response, stability, stall force, or positioning accuracy. The discipline aimed to this purpose is called Prognostics and Health Management (PHM) (as reported in [1]). The application of PHM strategies involves the analysis of functional parameters of the system in form of electrical signals: for this reason, the use of electrical actuators someway represents an advantage, because no conversion of the signals (and therefore no additional sensors) is needed. Due to the complexity and to the multi-disciplinary nature of the monitored systems, the FDI task on EMA systems is particularly challenging, since several failure modes interact and an acceptable accuracy is hardly ac...

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Section: Proposed Fault Detection and Identification Algorithm And Prmentioning

confidence: 99%

Section: Proposed Fault Detection and Identification Algorithm And Prmentioning

confidence: 99%

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

et al. 2019

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“…In Y-connected IMs that there is no access to the neutral point, the stator equations should be given in terms of line-to-line voltages. Rewriting the stator differential equations in terms of line-to-line voltages, two new independent equations obtain [14]. …”

Section: Winding Function Approachmentioning

confidence: 99%

Winding function approach to simulate induction motors under sleeve bearing fault

Ojaghi

Yazdandoost

2014

2014 IEEE International Conference on Industrial Technology (ICIT)

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This paper proposes dynamic modeling and simulation of squirrel cage induction motors under oil whirl fault within their sleeve bearings. Previous experimental results are used for defining an appropriate air gap function under the fault. This function is used in the winding function approach for the modeling and simulation purpose. Analytic approach and simulation results show presence of the same frequency components on the stator line current of the faulty motor. Some of these frequency components can be appropriate indices for diagnosing the fault.

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“…These inductances are obtained by applying winding function theory. If the inter‐turn fault is in phase “a” winding, the final dynamic equations will be very similar to those given in as follows:

[U_{sll}] = [R_{sll}] [I_{sll}] + \frac{d}{italicdt} [ψ_{sll}]

[0] = [R_{r}] [I_{r}] + \frac{d}{italicdt} [ψ_{r}]

T_{e} - T_{L} = J \frac{italicdω}{italicdt}

where [ U sll ] is the vector of the stator instantaneous voltages, [ I sll ] is the vector of the stator instantaneous currents, [ I r ] is the vector of instantaneous currents of the rotor meshes, [ ψ sll ] is the vector of the stator modified flux linkages, [ ψ r ] is the vector of flux linkages of the rotor meshes, [ R sll ] is the stator modified resistance matrix, and [ R r ] is the resistance matrix of the rotor meshes. Definition of non‐matrix symbols is given at the end of the paper.…”

Section: Modeling Squirrel‐cage Induction Motor With Inter‐turn Faultmentioning

confidence: 99%

“…These inductances are obtained by applying winding function theory. If the inter-turn fault is in phase "a" winding, the final dynamic equations will be very similar to those given in [78] as follows:…”

Section: Modeling Squirrel-cage Induction Motor With Inter-turn Faultmentioning

confidence: 99%

Diagnosis methods for stator winding faults in three-phase squirrel-cage induction motors

Ojaghi

Sabouri

Faiz

2013

Int. Trans. Electr. Energ. Syst.

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SUMMARYInduction motors condition monitoring is a very important process for immediate detection of the incipient faults. This prevents the spread of the faults up to risky degrees and avoids catastrophic stop of the motor and relevant production line. The stator winding fault is one of the most commonly occurring faults in alternating current machines. This paper is a comprehensive survey on the stator winding faults causes, diagnosis methods, indexes, and modeling and simulation methods. An efficient method is used to model and simulate the squirrel‐cage induction motor performance under inter‐turn fault. The results are used to obtain the performance of some common indexes of the fault and recognize the most proper index. Finally, some simulation results are compared with the corresponding experimental results and good agreements are achieved. Copyright © 2013 John Wiley & Sons, Ltd.

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Using a genetic algorithm for detection and magnitude determination of turn faults in an induction motor

Cited by 33 publications

References 8 publications

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Winding function approach to simulate induction motors under sleeve bearing fault

Diagnosis methods for stator winding faults in three-phase squirrel-cage induction motors

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