Identification-based condition monitoring using neural network approach

The paper focuses on enhancement of condition monitoring techniques in application to hydro- and electromechanical servomechanisms, which are widely used both in industrial robots and aircraft equipment. Its particular contribution lies in the area of neural network application for identification data analysis, which allows early diagnosis of process faults, while the plant is still operating in a controllable region. The proposed technique has been implemented in a software tool that allows to automate the decision-making process and to visualize the analysis results