Fault diagnosis for diesel valve trains based on time–frequency images

In this paper, the Wigner–Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time–frequency images after the images were normalized. By this way, the fault diagnosis of valve train was transferred to the classification of time–frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time–frequency distributions before classification, the fault diagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods.