کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4976760 1451836 2018 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Simulation-driven machine learning: Bearing fault classification
ترجمه فارسی عنوان
یادگیری ماشین شبیه سازی شده: طبقه بندی گسل باربری
کلمات کلیدی
فراگیری ماشین، نظارت بر وضعیت، غلتک، تشخیص گسل، نظارت بر سلامت پیشگویی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volume 99, 15 January 2018, Pages 403-419
نویسندگان
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