An Empirical Study on Fault Diagnosis for Nonlinear Time Series using Linear Regression Method and FIR Network

抄録

A fault diagnosis scheme for nonlinear time series is employed for parts and tool breakage diagnosis problems. The fault is first detected from regression lines plotted for the raw time series. Finite Impulse Response (FIR) network is then used to estimate the unknown system for the normal condition data and to filter the abnormal condition data. To confirm and analyze the fault the regression lines are again plotted for the predicted normal and filtered abnormal conditions' data. The results obtained from the above application of fault diagnosis scheme are discussed.