Publications Scientifiques

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Author: search.filters.author.Benazzouz, DjamelSubject: search.filters.subject.Faults diagnosis

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    Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques
    (SAGE, 2021) Mahami, Amine; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, Djamel
    In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.
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    A new transformer condition monitoring based on infrared thermography imaging and machine learning
    (Springer, 2023) Mahami, Amine; Bettahar, Toufik; Rahmoune, Chemseddine; Amrane, Foudil; Touati, Mohamed; Benazzouz, Djamel
    Electrical systems maintenance is becoming a crucial and an important part in the economic policies and that’s due their deep implication in the majority of the industrial installations. Electrical transmission and distribution relay mainly on transformers. Electrical transformers condition monitoring plays a major role in increasing their availability, enhancing their reliability and preventing further major failures and high cost maintenance. A new non-contact and non-intrusive method is adopted in this paper to monitor electrical transformers and diagnose their faults based on infrared thermography imaging techniques (IRT). When thermographs are obtained using an infrared camera for different states of the studied transformer, a dataset is then prepared for the following step. Features extraction was applied on the considered infrared images to be used later as input indicators for an automatic classification and identification of transformer’s healthy and several faulty states based machine learning methods (LS-SVM). This method was applied and compared with several IA techniques in order to select the most efficient one in term of accuracy and stability to be relied on in this purpose. The proposed technique, which is mainly based on IRT, features extraction and machine learning, has shown a remarkable efficiency in transformers condition monitoring and an accurate faults diagnosis, and can be generalized as a reliable and powerful tool in such problematics
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    Automated transformer fault diagnosis using infrared thermography imaging, GIST and machine learning technique
    (SAGE, 2022) Mahami, Amine; Rahmoune, Chemseddine; Benazzouz, Djamel
    Condition monitoring of electrical systems is vital in reducing maintenance costs and enhancing their reliability. By focusing on the monitoring of electrical transformers, which play a crucial role in electrical systems and are the main equipment for electrical transmission and distribution, drastic damages, undesirable loss of power and expensive curative maintenance could be avoided. In this paper, a novel noncontact and non-intrusive framework experimental method is used for the monitoring and the diagnosis of transformer faults based on an infrared thermography technique (IRT). The basic structure of this work begins with applying (IRT) to obtain a thermograph of the considered machine. Second, GIST features of the reference image and all images in the image database are extracted. At last, various faults patterns in the transformer are automatically identified using a machine learning method called Support Vector Machine (SVM). The proposed method effectiveness and capacity are evaluated based on the experimental infrared thermography (IRT) images and the diagnosis results by identifying nine sorts of electrical transformer states among which one is healthy and the remaining eight are of short circuit faults in common core winding type, and showing that it can be considered as a powerful diagnostic tool with high Classification Accuracy (CA) and stability compared to other previously used methods