Publications Scientifiques

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Author: search.filters.author.Benazzouz, DjamelSubject: search.filters.subject.Defect

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    Gear fault detection, identification and classification using MLP neural network
    (Springer, 2023) Afia, Adel; Ouelmokhtar, Hand; Gougam, Fawzi; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, Djamel
    Gear fault detection, identification and classification are highly complicated tasks, as the faults which affect gearboxes tend to share similar frequency signatures. Therefore, load and speed changes in a rotating machinery inevitably provide inaccurate results. However, identifying the fault remains critical, as each individual gear fault influences overall mechanism operation in different manners. Therefore, defect identification and classification appear as the hardest challenge for a geared systems. An automatic method to detect, identify and classify different gear failures is presented in this paper. The intelligent approach consists of a combination of MODWPT, entropy and MLPNN. MODWPT was developed to decompose the signals with a uniform frequency bandwidth. Entropy is employed to build the feature matrix in the feature extraction phase. Then, MLP offers a very efficient classification tool for features classification stage. Based on data sets taken from a gearbox bench test with a good and five varied gear states under various loads and speeds, experimental results presented the efficiency of our technique
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    New gear fault diagnosis method based on MODWPT and neural network for feature extraction and classification
    (ASTM International, 2019) Afia, Adel; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem; Fedala, Semchedine
    Gear fault diagnosis using vibration signals has become the subject of intensive studies to detect any sudden failure. However, these signals exhibit nonlinear and nonstationary behaviors when the rotating machine operates under multiple working conditions. Furthermore, fault features extraction and classification of multiple gear states are always unsatisfactory and considered as a huge task. This is the main reason that motivates us to develop a new intelligent gear fault diagnosis method in order to automatically identify and classify several kinds of gear defects under different work conditions. So in this article, we propose a combination between the maximal overlap discrete wavelet packet transform (MODWPT), entropy indicator, and a multilayer perceptron (MLP) neural network as a new automatic fault diagnosis approach. MODWPT decomposes the data signal into several components using a uniform frequency bandwidth. Each decomposed component is selected to extract feature vector using entropy indicator. Finally, MLP provides a powerful automatic tool for identifying and classifying the aforementioned extracted features. Experimental vibration signals of healthy gear; gear with general surface wear; gear with chipped tooth in length; gear with chipped tooth in width; gear with missing tooth; and gear with tooth root crack are recorded under fifteen different work conditions to test the effectiveness of the suggested technique. Experimental results affirm that our proposed approach can successfully detect, identify, and classify the gear fault pattern in all cases