Fuzzy Logic Based Broken Bar Fault Diagnosis and Behavior Study of Induction Machine

Chouidira, Ibrahim; Khodja, Djalal Eddine; Chakroune, Salim

doi:10.18280/jesa.530210

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Te fgure indicates that the length of misclassifcation periods is less than 0.7 s; thus, all misclassifcations can be ignored [19,20]. Welch's spectral density analysis in the experimental test to monitor EMA performance [22][23][24][25].…”

Section: Level Of Fault Severity Predictionmentioning

confidence: 99%

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

Abed

2023

Journal of Engineering

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Monitoring the condition of the aircraft actuators in various operating and environmental circumstances, this paper presents a method for measuring the surface roughness of aircraft actuators. The proposed method starts with the current and vibration signal as failure indicators and a dual-tree complex wavelet transformation (DTCWT) to generate the necessary features. Time-delay neural networks (TDNNs) have been developed for real-time performance monitoring to categorize problems and determine their severity. The simulation results show that the suggested method can accurately identify various faults.

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Section: Level Of Fault Severity Predictionmentioning

confidence: 99%

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

Abed

2023

Journal of Engineering

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“…A study of asynchronous motor malfunctions has shown that they are classified according to their nature. We distinguish: Bearing faults at 41% [5,6], stator faults at 37% [7,8], rotor faults at 10% [9,10], and other faults at 12% [11,12].…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis in Induction Machines Based on Electromagnetic Torque Spectral Frequencies Analysis

Touil,

Babaa,

Kratz

et al. 2024

JESA

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The main aim of this article is to achieve predictive maintenance by proposing reliable residuals specific to outer ring bearing faults. Our work falls within the general context of maintenance, with particular emphasis on vibration analysis techniques. For an in-depth study of the fault, we use spectral analysis of electromagnetic torque, which gives very satisfactory results compared with work based on line current or neutral voltage fault signatures due to the similarity in the mechanical nature of the fault and the signal to be studied. In fact, an analytical calculation of the electromagnetic torque has been developed to obtain the specific frequencies of the fault under consideration. To highlight our results, the simulation of the analytical calculation of this fault was implemented in MATLAB/SIMULINK using the Fast Fourier Transform method to extract fault signatures. The induction motor's performance was analyzed under various operating conditions, including both healthy and faulty states. Finally, an experimental study will support our analytical developments.

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“…•Data-driven approaches use either a static [16], probabilistic or artificial intelligence model such as artificial neural networks [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Real Time Assessment of Novel Predictive Maintenance System based on Artificial Intelligence for Rotating Machines

Mahdi¹,

Ali²,

Youssra³

et al. 2022

JESA

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Predictive maintenance (M4.0) allows more targeted and efficient use of resources, reduces unplanned downtime, and increases production and equipment performance compared to classical existing maintenance (M3.0). This paper deals with the development of a new ecosystem that adopts the new technologies of Industry 4.0 to drive real-time monitoring and diagnosis of engine defects. The proposed architecture is based on implementing a process of identifying critical components and extracting related data (speed and acceleration) based on IoT technology. A neural model (ANN) is implemented for monitoring, detecting and diagnosing engine faults with high accuracy compared to existing techniques. The effectiveness and reliability are validated through real-time test bench studies.

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Fuzzy Logic Based Broken Bar Fault Diagnosis and Behavior Study of Induction Machine

Cited by 7 publications

References 13 publications

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

Bearing Fault Diagnosis in Induction Machines Based on Electromagnetic Torque Spectral Frequencies Analysis

Real Time Assessment of Novel Predictive Maintenance System based on Artificial Intelligence for Rotating Machines

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