Automatic segmentation and prognostic method of a turbofan engine using manifold learning and spectral clustering algorithms:

摘要

In most of the previous fault diagnostic literatures, the fault modes and states are pre-determined (i.e. the model structure (topology) is a priori known). However, in practical situation, the monitoring data, especially for the entire life-cycle data, nothing is known about the nature and the origin of the degradation (i.e. the model structure is unknown). Moreover, there is no consensus, how to determine the optimal model structure. In this condition, the different model structures may lead to different fault diagnosis/prognosis results. To address the optimal structure–selection problem, this article presents an automatic segmentation method based on Laplacian eigenmaps manifold learning and adaptive spectral clustering algorithms. Given an entire lifetime data of turbofan engine, we attempt to automatically segment the data into a sequence of contiguous regions corresponding to the degradation states. Furthermore, intrinsic dimensionality estimation, nonlinear dimension reduction, and the optimal num...