선박 엔진의 실린더 라이너의 손상 진단을 위한 신경회로망의 적용

Abstract

Doosan Engine(Received October 3, 2014; Revised November 10, 2014; Accepted November 12, 2014)Abstract − Marine diesel engines operate in environments in which damage easily occurs from corrosion.Recently, damage to cylinder liners has increased from corrosion wear caused by increased engine power. Thisdamage can cause serious problems in the economy. Thus, many researchers have treated and studied damagedcylinder liners. However, a method is necessary for real-time monitoring of damage to cylinder liners duringoperation of the engine, before serious damage can occur. This study carries out reciprocating friction and weartests on a cast iron specimen under various corrosion atmospheres and verifies the variations of friction coef-ficient and friction surface. Additionally, the friction coefficient and friction status are predicted by using a neuralnetwork that learns the vibration and frequency spectrum data from an acceleration sensor. According to our con-clusions, amplitude is distributed highly at high frequencies, and values of standard deviation and kurtosis arehigh when damage to the friction surface is serious. The accuracy rate of the friction coefficient predicted by theneural network is over 80% of the real measured value without NaCl, and application of the neural network isvery effective for diagnosing the friction condition and damage to the cylinder liner.Keywords −Marine diesel engine (선박엔진 ), Cylinder liner (실린더 라이너 ), Neural network (신경회로망 ), Damagediagnosis (손상진단), Vibration analysis (진동 분석)