Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble
It is well known that the vibration signals are unstable when there is some failure in machinery. So in this paper, the cone-shaped kernel distributions (CKD) of vibration acceleration signals acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images. Meanwhile, non-negative matrix factorization (NMF) was used to decompose multivariate data, and neural network ensemble (NNE), which is of better generalization capability for classification than a single neural network, was used to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from timefrequency distributions. It is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method.
bpnn back propagation neural network ckd cone-shaped kernel distribution cwam combination weights adaptive modulation fa factor analysis ica independent component analysis il independent learning ncl negative correlation learning nmf none-negative matrix factorization nn neural network nne neural network ensemble pca principal component analysis svd singular value decomposition tdc top dead center tfd timefrequency distribution.