تعیین عمر مفید باقیمانده تجهیزات مبتنی بر تخمین مراحل زوال، با استفاده از روش ARMRS

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشگاه جامع امام حسین (ع)

2 دانشکده صنایع، دانشگاه علم و صنعت

3 دانشکده مکانیک، علم و صنعت

چکیده

 
پایش وضعیت، یکی از مهم­ترین روش­های مدیریت سلامت تجهیزات و نگهداری و تعمیرات (نگهداشت) مبتنی بر شرایط است. در چرخه «مدیریت سلامت و پیش‌بینی عیوب» که به‌نوعی شکل توسعه‌یافته‌تری برای نگهداشت مبتنی­بر شرایط است، ارزیابی وضعیت به‌عنوان    مهم­ترین جزء این چرخه به­شمار می‌آید. در این تحقیق، مدلی ارائه گردیده است که مبتنی­بر آن، می­توان با استفاده از ارزیابی وضعیت تجهیز، عمر مفید باقیمانده­را تخمین زد. در این مدل با استفاده از تعریف یک ویژگی جدید برای ارتعاش تجهیز، شبیه­سازی و پیش­بینی آن با استفاده از مدل رژیم سوئیچینگ مارکوف خود رگرسیون و ارائه­ رویکرد جدید جهت تلفیق اطلاعات حسگرهای پایش وضعیت مبتنی­بر خوشه­بندی فازی و تئوری دمپستر- شفر، وضعیت­ زوال تجهیز تعیین می‌گردد و عمر مفید باقیمانده­ آن تخمین زده می­شود. به‌منظور ارزیابی مدل، از داده­های مسابقه‌ی داده انجمن مدیریت سلامت و پیش‌بینی عیوب در سال 2012 که به‌منظور پیش­بینی عمر مفید باقیمانده­ یاتاقان، فراهم گردیده، استفاده و نتایج مطالعه با نتایج برنده آن، مقایسه شده است. نتایج به‌دست‌آمده از مقایسه، نشان‌دهنده‌ قابلیت رقابت مدل پیشنهادی با مدل برنده­ مسابقه داده است.

کلیدواژه‌ها

عنوان مقاله [English]

Determine the Remaining Useful Life in Rotating Equipment, based on Prognostics and the combination of Degradation Processes, Using the ARMRS & Theory of Evidence

نویسندگان [English]

  • saeed ramezani 1
  • alireza moini 2
  • mohammad riyahi 3
1 emam hosein
2 elm o sanAt
3 elm o sanAt
چکیده [English]

ABSTRACT
Condition Assessment is one of the most significant techniques of the equipment health management. PHM methodology cycle, is a developed form of Condition Based Maintenance (CBM). Condition Assessment is the most important step of this cycle. In this study, based on the model presented, using equipment Condition Assessment, the Remaining Useful Life (RUL) is estimated. Using the simulation and forecasting of a new feature for vibration of the equipment by Autoregressive Markov Regime Switching (ARMRS) method, equipment health condition is determined. Prior to forecasting the condition of the equipment, the equipment degradation state is determined by the fuzzy C-means clustering method. Based on the current state of equipment and pre-determined state of degradation, the Remaining Useful Life of the equipment is estimated. In order to evaluate the model, sensor data for PHM 2012 challenge have been used to forecast the bearing’s Remaining Useful Life And the results of the study have been compared with the winning results. One of the specifications of the proposed model is to determine the confidence intervals for Remaining Useful Life. Its innovations include the use of fuzzy clustering and evidence theory to integrate data and use Autoregressive Markov Regime Switching to prognosis.

کلیدواژه‌ها [English]

  • : Remaining Useful Life (RUL)
  • Prognostics & Health Management (PHM)
  • Autoregressive Markov Regime Switching (ARMRS)
  • Wavelet Decomposition
  • Theory of Evidence
  • Fuzzy clustering
  • Fuzzy C-Means
  • Kurtosis-Entropy
  • Feature
  • Degradation

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سابقه مقاله

  • تاریخ دریافت: 28 تیر 1397
  • تاریخ پذیرش: 17 آذر 1398
  • تاریخ انتشار: 01 دی 1398

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