Saeid Jorkesh

McMaster University

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Javad Poshtan

Iran University of Science and Technology

Azadeh Gholaminejad

McMaster University

Majid Poshtan

California Polytechnic State University

Amin Ramezani

Baylor College of Medicine

Saeid Habibi

McMaster University

Ryan Ahmed

McMaster University

Mina Ekresh

Tarbiat Modares University

Reza Hosseininejad

McMaster University

Siyuan Xu

McMaster University

Sakineh Pashaee

Tarbiat Modares University

Cooperative Institutions

McMaster University

Tarbiat Modares University

Iran University of Science and Technology

California Polytechnic State University

Cal Poly Corporation

American Society For Engineering Education

Tulane University

Southern California University for Professional Studies

University of Southern California

California Energy Commission

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Fault Diagnosing Of An Induction Motor Based On Signal Fusion Using One-Dimensional Convolutional Neural Network

Sakineh Pashaee Amin Ramezani Mina Ekresh Saeid Jorkesh

The detection and classification of induction motor faults using a one-dimensional convolutional neural network is discussed in this paper. A one-dimensional deep neural network is learned utilizing three-phase current and voltage signals from an induction motor system. The results of experiments show that the one-dimensional deep convolutional neural network method effectively diagnoses the induction motor conditions (Bearing fault, Rotor bar broken, short circuit stator winding 8% and 12.5 %).

SIGNAL (programming language)

10.1109/icspis54653.2021.9729338

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Citations (4)

Induction motors fault diagnosis using a stacked sparse auto-encoder deep neural network

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering (2022)

Saeid Jorkesh Azadeh Gholaminejad Javad Poshtan

In this article, deep neural network and stacked sparse auto-encoder deep neural network performances in fault diagnosis are compared. Methods are employed experimentally for the detection and isolation of an induction motor’s condition (healthy, bearing outer race fault, stator winding short circuit, and rotor broken bar) in the presence of unbalanced power supply and pump dry running disturbances. Pre-processing and de-noising is performed on three-phase electrical current signals using fast Fourier transform and independence component analysis algorithm, respectively. Experimental results show that sparse auto-encoder deep neural network method has outperformed and diagnosed the aforementioned faults in the presence of disturbances with a highly reliable accuracy rate of 90.65%.

10.1177/09596518221125960

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A comparative case study between shallow and deep neural networks in induction motor's fault diagnosis

IET Science Measurement & Technology (2023)

Azadeh Gholaminejad Saeid Jorkesh Javad Poshtan

Abstract Here, performance of auto‐encoder deep neural networks in detection and isolation of induction motor states (healthy, bearing outer race fault, stator winding short circuit and broken rotor bar) in the presence of unbalanced power supply and electro‐pump dry running disturbances is investigated. Easily available three‐phase electrical current signals are denoised using independent component analysis, and then the frequency‐domain signal is used to train a neural network. A comparison is made between shallow and deep neural networks and also between the conventional structure of deep methods and the encoder–decoder structure in terms of training and test accuracy and robustness. In fact, the depth is increased and the effectiveness is investigated. At the end, it is shown that an encoder–decoder structure leads to the best result in terms of accuracy and robustness. The algorithms are examined experimentally, and the results show that the auto‐encoder deep neural network can detect the aforementioned faults with a high reliability in the presence of disturbances.

Robustness

10.1049/smt2.12143

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Fault Diagnosis and Isolation of an Electro-Pump using Neural Data Fusion

2022 IEEE Energy Conversion Congress and Exposition (ECCE) (2019)

Saeid Jorkesh Javad Poshtan Majid Poshtan

In this paper, the authors present successful experimental testing by Data Fusion method to detect and isolate five different physical faults on a 3-phase 380-V, WYE connected, two-pole 5.5 hp, 50 Hz, squirrel-cage induction motor connected to a water pump under its rated load. The tested faults included a bearing outer race, broken rotor bar, stator winding short circuit, unbalanced power source, and dry running faults of the connected water pump. The faults were applied separately in this paper; however, common mode faults testing results will be published in our future article. Temporal features are extracted from three-phase current and voltage signals using independent components analysis method or ICA; then features are fused adopting a neural network with the reference of the motor's healthy condition. Remarkable reliability analysis of the testing results is presented in this paper. Results indicate that neural data fusion method is a reliable non-intrusive diagnostic motor testing under normal loading.

Squirrel-cage rotor

Bar (unit)

10.1109/ecce.2019.8912970

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Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach

IEEE Access (2024)

Saeid Jorkesh Ryan Ahmed Saeid Habibi Reza Hosseininejad Siyuan Xu

State of charge

State of health

10.1109/access.2024.3488596

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Citations (0)

Fault diagnosis of an induction motor using data fusion based on neural networks

IET Science Measurement & Technology (2021)

Saeid Jorkesh Javad Poshtan

Abstract In this paper, neural network‐based data fusion is used to detect fault and isolate stator winding short circuit, outer bearing race, and broken rotor bar defects in an induction motor. In addition, the robustness of the proposed method against the disturbance introduced by the coupled pump's unbalanced power source and dry running is investigated. First, three‐phase current and voltage signals are separated by means of independent component analysis (ICA), then extracted features are combined by adopting neural networks, and finally, the system's health condition is evaluated. Experimental results indicate that data fusion based on neural networks can evaluate with high reliability the system's health condition and provide better robustness in the presence of disturbances.

Sensor Fusion

10.1049/smt2.12068

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Citations (17)