Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble

作者：Wang Qinghua; Zhang Youyun^*; Cai Lei; Zhu Yongsheng

来源：Mechanical Systems and Signal Processing, 2009, 23(5): 1683-1695.

DOI：10.1016/j.ymssp.2008.12.004

• Diesel engine
• Fault diagnosis
• Time-frequency image
• Neural network ensemble
• Non-negative matrix factorization

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 time-frequency distributions. it is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method.

• 出版日期2009-7
• 单位西安工业大学; 西安交通大学