Information Extraction Using Spectral Analysis of the Chattering of the Smooth Variable Structure Filter

Abstract

Smooth variable Structure Filter (SVSF) is a model-based robust nonlinear filtering technique, based on the variable structure concept formulated in a predictor-corrector form. It is used for estimating the states of a system and is robust against noise and modeling uncertainties. It ensures stability in the face of model mismatch resulting from a poor model or fault, at the expense of corrective actions, which cause chattering. The chattering contains mismatch footprints that can be exploited to identify system faults and determine their severity. In this paper, information extraction from chattering is investigated to identify model mismatch based on the spectral contents of the chattering signal. To verify the effectiveness of the developed framework for chattering analysis, two case studies are considered. First, the power spectrum of the chattering signal has been employed to identify mismatch and the potential of recovering the temporal information of the model mismatch from the spectrogram is studied, using Short Time Fourier Transform (STFT) for an underdamped second-order system. Then, the proposed strategy is applied to detect and measure the severity of leakage and friction faults as well as the bulk modulus mismatch in an electro-hydraulic actuator.

Keywords

• Fault detection
• information extraction
• smooth variable structure filter
• spectral analysis
• STFT