نوع مقاله : مقاله پژوهشی
نویسندگان
دانشکده مهندسی مکانیک، دانشگاه تبریز، تبریز، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Bearings are widely used parts in the industry and play a crucial role in the performance of industrial systems and machinery. Therefore, their failure can cause significant damage and even halt production. Due to their importance and widespread use, various methods have been developed for troubleshooting and estimating their remaining useful life. This study introduces a novel approach for predicting the remaining lifespan of industrial bearings. The technique involves initially decomposing the vibration signals from the bearing into intrinsic mode functions (IMFs) using the empirical mode decomposition (EMD) method. Among the calculated IMFs, the one most effective at indicating the degradation rate of the ball bearing is chosen for training the neural network. The selection of the IMF is carried out using a proposed energy-based method. The variance statistical parameter for the selected IMF is calculated after selecting the appropriate Intrinsic Mode Function. Next, the Weibull function is fitted to the resulting data, and the obtained graphs are used to train the neural network. Subsequently, the results are smoothed using the exponential smoothing function. The final performance of the proposed algorithm is evaluated using experimental data. The results indicate that the neural network can effectively track the degradation process of the bearing and assess its remaining lifespan.
کلیدواژهها [English]
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