Model-Based Single-Fault Disambiguation Using Temporal Information and Genetic Algorithm: A Case Study on Hydraulic Drive System.

In this article, a special type of fault isolation problem is reported where temporal information is required for the estimation of parameters from susceptible fault sub-space. Any physical system always obeys certain mathematical constraints under the normal operating condition, and that is incorporated in a behavioral model, which is essentially flowing and force or moment balance. In a fault detection and identification (FDI) model, only concurrent states are known. In FDI, the constraint relationships need to be derived only in terms of known variables, i.e., the measurements and nominal parameters, which are termed analytical redundancy relations (ARRs). The numerical evaluation of ARRs is residuals, and those oscillate within a definite bound of error when computed using test data. In this work, a novel function is formulated using the ARRs at different time instants and that function is minimized to estimate the suspected parameter values belonging to the non-isolable sub-space. Single-fault hypothesis is considered, and a genetic algorithm (GA) is used for the optimization. This FDI analysis may be beneficial for power hydraulic-driven heavy machinery such as hydraulic excavators, dumpers, and front-end loaders, which are mostly used in mines and construction sites. [ABSTRACT FROM AUTHOR]