Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 28, No. 4 (April 2015) 573-582   

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  INTELLIGENT HEALTH EVALUATION METHOD OF SLEWING BEARING ADOPTING MULTIPLE TYPES OF SIGNALS FROM MONITORING SYSTEM
 
H. Wang, R. Hong, J. Chen and M. Tang
 
( Received: August 11, 2014 – Accepted: January 29, 2015 )
 
 

Abstract    Slewing bearing, which is widely applied in tank, excavator and wind turbine, is a critical component of rotational machine. Standard procedure for bearing life calculation and condition assessment was established in general rolling bearings, nevertheless, relatively less literatures, in regard to the health condition assessment of slewing bearing, were published in past. Real time health condition assessment for slewing bearing is used for the purpose of avoiding catastrophic failures by detectable and preventative measurement. In this paper, a new strategy was present for health evaluation of slewing bearing based on multiple characteristic parameters, and ANN (Artificial Neural Network ) and ANFIS(Adaptive Neuro-Fuzzy Inference System ) models were demonstrated to predicted the health condition of slewing bearings. The prediction capabilities offered by ANN and ANFIS were shown by using data obtained from full life test of slewing bearings in NJUT test System. Various statistical performance indexes were utilized to compare the performance of two predicted models. The results suggest that ANFIS-based prediction model outperforms ANN models.

 

Keywords    Slewing bearing, artificial neural network, ELMAN, BP, adaptive neuron-fuzzy inference system, fuzzy clustering, health condition evaluation

 

چکیده    بلبرینگ اسلیوینگ (slewing) که به طور گسترده در تانک، ماشینهای خاکبرداری و توربین بادی به کار میرود، یکی از اجزای مهم ماشین آلات چرخشی است. روش استاندارد برای محاسبه طول زندگی و شرایط ارزیابی سلامت برای یاطاقانهای عمومی تدوین شده است. با این وجود، تحقیقات نسبتا کمتری برای ارزیابی وضعیت سلامت ازبلبرینگ اسلیوینگ شده است در گذشته منتشر شده است. ارزیابی وضعیت سلامت زمان واقعی بلبرینگ اسلیوینگ برای اجتناب از شکست فاجعه بار توسط اندازه گیری قابل تشخیص و پیشگیری استفاده میشود. در این مقاله، یک استراتژی جدید برای ارزیابی سلامت بلبرینگاسلیوینگ بر اساس پارامترهای چندگانه مشخصهها ارائه، ومدلهایANN (شبکه عصبی مصنوعی) و ANFIS (سیستم استنتاج تطبیقی ​​عصبی فازی) برای پیش بینی وضعیت سلامت از یاطاقان اسلیوینگ نمایش داده شده است .قابلیت پیش بینی ارائه شده توسط ANN و ANFIS با اطلاعات به دست آمده از آزمون طول کامل عمر از یاطاقان اسلیوینگ در سیستم آزمون NJUT نشان داده شده است. شاخص عملکردهای مختلف آماری برای مقایسه عملکرد دو مدل پیش بینی استفاده شده است. نتایج نشان می دهد که مدل پیش بینی بر اساس ANFIS-بهتر از مدل ANNاست .

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