Neural Network-based Classification for Engine Load

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.