Intelligent Data Analysis for Performance Evaluation and Fault Diagnosis in Complex Systems

Abstract

The paper proposes an efficient computational strategy for remote performance analysis and diagnosis of construction machines and other complex systems. A special information compression (IC) method is used to send the information obtained from various sensors to the maintenance center in a compact and economical way. The IC method uses the neural-gas unsupervised learning algorithm to locate a predefined number of neurons in the densest data areas of the parameter space. These neurons serve as a kind of information granules of the current machine operation that are later sent in a wireless way to the maintenance center for further information recovery (IR) and performance analysis. Here a special weighted moving window average (MWA) method is used, as well as an original fuzzy inference-based analysis for comparison of different operations and discovery of possible deteriorations. A knowledge-based fault diagnosis method is also proposed and analyzed in the paper. The whole IC/IR computational strategy is illustrated on real experimental data from a hydraulic excavator which demonstrate its merits and applicability.