Investigations on interpretable nuclear-grade hydraulic damper monitoring and diagnosis from multi-source high-dimensional profile data

Abstract

Condition monitoring and fault diagnosis is important to ensure smooth machine operation, in which the use of multi-source high-dimensional profile data composed of multiple physical signals collected by multiple sensors for machine health monitoring and fault diagnosis has not been fully explored. In this paper, interpretable monitoring and diagnostic methods are proposed to analyze multi-source high-dimensional profile data and identify equipment operational status. Firstly, polynomials of segmenting profiles as well as a Lasso model are established to obtain sparse features. Then, for offline data with historical fault labels, an optimal linear hyperplane classifier is constructed to interpretably study the impact of different features on a decision. For online data with only normal samples, constructing an optimal hypersphere enables the measurement of operational status. Large-scale nuclear-grade hydraulic damper profile data are used to demonstrate how the proposed interpretable models work and show their physical interpretation for monitoring and fault diagnosis.