A feature extraction and visualization method for fault detection of marine diesel engines

A feature extraction and visualization method is developed for fault detection of marine diesel engines. It firstly constructs a time-frequency reference signal to supervise the vibration-source separation of kernel independent component analysis. Then perform the manifold learning algorithm (t-SNE) to visualize the fault features. Lastly, an extreme learning machine is applied to the fault pattern recognition.