Clustered Correlation Health Scan Anomaly Detection Algorithm Applied for Fault Diagnosis in the Cylinders of a Marine Dual-Fuel Engine

A novel anomaly detection algorithm is presented to analyze a group of signals that must be correlated under normal conditions. The method is called Clustered Correlation Health Scan (CCH-Scan). It detects abnormal signals, the durations corresponding to abnormalities, and the degree of abnormality. This algorithm is applied to a case study on fault diagnosis in the cylinders of a 12-cylinder marine dual-fuel engine. In particular, 12 Exhaust Valve Closing Dead Time (ECDT) signals are analyzed to detect abnormalities. Although these signals are critical and any abnormality in them requires urgent intervention, this is the first time they have been discussed in the literature. The details of the algorithm are elaborated, its parameters are studied, and the effects of these parameters on the results are measured and analyzed using a quality score. In addition, a metric to measure the degree of abnormality of the signal is introduced. The method detects abnormal signals, the durations of abnormalities, and the degrees of abnormalities. The results align with ground-truth data from an available technical industrial maintenance report. The approach demonstrates promising potential for application in various other contexts.