Predicting Future States With $n$ -Dimensional Markov Chains for Fault Diagnosis

This paper introduces a novel method of predicting future concentrations of elements in lubrication oil, for the aim of identifying possible anomalies in continued operation aboard a large marine vessel. The research carried out is supported by a discussion of previous work in the field of fault detection in tribological mechanisms, although with a focus upon two stroke marine diesel engines. The approach taken implements an
n
-dimensional Markov chain model with a singular weighted connection between layers. The approach leverages the computational simplicity of the Markov chain and combines this with a weighted decision calculated from the correlational coefficients between variables, with the notable assumption that interconnectivity between elements is not constant. The approach is compared to an established method, which is the Kalman filter, with promising results for future work and extension of the method to include expert knowledge in the decision making process.