چکیده مقاله
Condition monitoring CM of engine load is becoming increasingly important in modern maintenance and control systems As a problem, torque estimation needs intensive efforts and costly sensors or devices such as dynamometer In this research, a model was proposed based on soft computing technique to estimate ITM285 tractor engine torque using some low cost sensors Adaptive neuro fuzzy inference system ANFIS was used for engine torque estimation, based on the data obtained from some inexpensive sensors including engine speed, fuel mass flow and exhaust gas temperature Three methods namely grid partitioning GP , sub clustering SC and fuzzy c means FCM were used to construct the fuzzy inference system FIS The results showed that the FCM was the most suitable method for the purpose of engine load condition monitoring It is concluded that models based on soft computing especially ANFIS are able to estimate the engine torque using data obtained from some inexpensive and accessible sensors
کلیدواژهها
نویسندگان
Majid Rajabi Vandechali
PhD student, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
Mohammad Hossein Abbaspour-Fard
Professor, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad,Mashhad, Iran
Abbas Rohani
Assistant Professor, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
شیوه ارجاع
Rajabi Vandechali, Majid and Abbaspour-Fard, Mohammad Hossein and Rohani, Abbas,1396,Condition monitoring of engine load using a new model based on adaptive neuro fuzzy inference system (ANFIS),2rd International Conference on Soft Computing,Rudsar
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی محاسبات نرم1 آذر 1396 · رودسر