Neural Network-based Classification for Engine Load

被引：0
|
作者：
Shahid, Syed Maaz [1 ]
Jo, BaekDu [1 ]
Ko, Sunghoon [2 ]
Kwon, Sungoh [1 ]
机构：
[1] Univ Ulsan, Sch Elect Engn, Ulsan, South Korea
[2] THyundai Heavy Ind, Ulsan, South Korea
来源：
2019 1ST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (ICAIIC 2019) | 2019年
基金：
新加坡国家研究基金会;
关键词：
Engine; Cylinder Banks; Load Classification; Artificial Intelligence;
D O I：
10.1109/icaiic.2019.8669078
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose an engine load classification algorithm using torque data in the crank-angle domain. Engine cylinder operation is different at different engine loads. Engine load information helps to predict the chances or understanding the behavior of a malfunction in engine operation. Hence, we developed an engine load classifier based on signal processing and using an artificial neural network. To that end, we use a magnetic pickup sensor to extract a four-stroke V-type diesel engine's operational information. The pickup sensor's signals are converted to the crank-angle domain (CAD) signal and CAD signals are used in conjunction with the proposed classifier to classify the engine load. For verification, we considered two engine loads (100% and 75%) for a V-type 12-cylinder diesel engine. The proposed algorithm classifies these engine loads with 100% efficiency.
引用
收藏
页码：568 / 571
页数：4
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