Intelligent Information Processing in the Problem of Propulsion System Condition Monitoring

Abstract

During the past years, in response to the gradually stringent regulations, the undertaken steps in the shipping industry spurred the technological improvement in ship and engine designs. Thus, new engines accept a wide variety of fuels, and electronic systems provide flexible control and online tuning. At the same time, simultaneous assessment of performance and condition monitoring is becoming increasingly important. In this respect, digitalization and the accompanying evolution of smart sensors and data acquisition systems give the possibility of applying complex analytics and machine learning algorithms to the compelling need of engine performance monitoring and failure identification. The present paper proposes a solution to the problem of condition monitoring using a method of statistical analysis of multidimensional information acquired from the sensors. The factor analysis method is used to derive a performance index showing engine state deviation from the normal condition. At the same time, principal factor loadings are used as features characterizing the contribution of every measured and analyzed parameter to the variance of the performance index. The benchmark of the developed method is illustrated using the simulation model of a diesel engine with incorporated models of failure states. The latter was developed and validated from the data measured on the test engine.