مدل سازی ترکیبی منطق فازی و شبکه عصبی مصنوعی برای پیش بینی خرابی ماشین آلات به منظور افزایش بهره وری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مهندسی صنایع، دانشگاه آزاد اسلامی،واحد علوم و تحقیقات ، تهران ، ایران

2 استادیار، عضو هیئت علمی دانشگاه آزاد اسلامی ، دانشکده مهندسی صنایع، واحد علوم و تحقیقات

3 موسسه ایرانداک

چکیده

در این پژوهش، یک رویکرد ترکیبی برپایه منطق فازی و شبکه عصبی مصنوعی برای پیش بینی خرابی ماشین آلات در راستای افزایش بهره‌وری ارائه می شود. مورد مطالعه این پژوهش یکی از کارخانجات صنعت خودروسازی با نام دیاکو ایده آریا بوده که در حوزه تولید قطعات خودرو فعالیت می کند. برای مدل سازی شبکه فازی-عصبی پرسپترون چند لایه(MLP)، نخست تعداد 100 خرابی و توقف در بازه زمانی 15 ماه جمع آوری شده و سپس در نرم افزار MATLAB وارد شده است. نتایج بدست آمده نشان می دهد پیاده سازی شبکه فازی-عصبی و پیش بینی زمان خرابی ماشین آلات سبب کاهش مدت زمان و هزینه تعمیرات شده است. بنابراین مدت زمان کاری و دسترس پذیری ماشین آلات افزایش یافته و در نهایت سبب افزایش میزان بهره وری به میزان 57 درصد می‌شود، همچنین، میزان دقت مدل فازی- عصبی توسعه داده شده 94 درصد برآورد شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Fuzzy logic and artificial neural network hybrid modeling to predict machine failure in order to increase productivity

نویسندگان [English]

  • Parviz Choopankari 1
  • amir azizi 2
  • mohammad javad ershadi 3
1 Industrial Engineering, Science and Research Unit, Islamic Azad University, Tehran, Iran
2 Assistant professor, faculty member of Islamic Azad University, Faculty of Industrial Engineering, Science and Research Unit
3 Irandoc Institute
چکیده [English]

In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.

کلیدواژه‌ها [English]

  • Productivity
  • prediction
  • neural network
  • fuzzylogic
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