A Transformer-based Approach for aAnomaly Detection in Wire eElectrical Discharge
- سال انتشار: 1401
- محل انتشار: مجله مهندسی برق مجلسی، دوره: 16، شماره: 4
- کد COI اختصاصی: JR_MJEE-16-4_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 213
نویسندگان
Medical technical college, Al-Farahidi University, Baghdad, Iraq
Department of Petroleum Engineering, Al-Kitab University, Altun Kupri, Iraq
Al-Nisour University College, Baghdad, Iraq
Building and Construction Engineering Technology Department, AL-Mustaqbal University College, Hillah ۵۱۰۰۱, Iraq
Medical Device Engineering, Ashur University College, Baghdad, Iraq
Al-Hadi University College, Baghdad,۱۰۰۱۱, Iraq
چکیده
Although theoretical models of manufacturing processes are useful for understanding physical events, it can be challenging to apply them in real-world industrial settings. When huge data are accessible, artificial intelligence approaches in the context of Industry ۴.۰ can offer effective answers to real production challenges. Deep learning is increasingly being used in the realm of artificial intelligence to address a variety of issues relating to information and communication technology, but it is still limited or perhaps nonexistent in the industrial sector. In this study, wire electrical discharge machining—a sophisticated machining technique primarily used for computer hardware components—is applied to effectively forecast unforeseen occurrences. By identifying hidden patterns in process signals, anomalies, such as changes in the thickness of a machined item, may be efficiently anticipated before they occur. In this study, a model for anomaly detection in the sequence of thickness change in the machined component based on transformers is suggested. Our method is able to achieve ۹۴.۳۲ % and ۹۴.۱۶ % accuracy in Z ۱۳۵ and Z ۱۵ datasets, respectively. Also, it forecasts the abnormalities inside the sequence ۱.۱ seconds in advance, according to our tests on a dataset that has been introduced.کلیدواژه ها
transformers, wire electrical discharge, Anomaly detectionاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.