Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission

Abstract

Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.