Health assessment for piston pump based on Laplacian eigenmaps-random forests method

Abstract

A piston pump is one of the key components in a hydraulic system. Therefore, a failure may severely hurt the reliability of the hydraulic system and cause great loss. Currently, how to effectively evaluate the health condition of the piston pump remains an open problem. In this paper, a novel health assessment method based on an integration of Laplacian eigenmaps and random forests is proposed, which takes full advantage of both the fusion ability of correlated features enabled by the Laplacian eigenmaps and the optimized feature-selection ability provided by the random forest. The proposed method (LE-RF) is applied to piston pumps for validation. The results indicate that the Laplacian eigenmaps-random forest (LE-RF) technique can provide an effective tool for piston pump health condition assessment. Compare with other manifold methods, e.g., LLE and ISOMAP and classification method of KNN, the LE-RF method can achieve better and more accurate assessment results.