A Fault Diagnosis Method to Hydraulic Tube Tester Production Process

The hydraulic tube tester process can be characterized as a data-rich process as it can generate a large number of process measurements on many variables in short time. A data mining based multiway independent component analysis and multiway fisher discriminant analysis (MICA-FDA) method is developed for fault diagnosis for the hydraulic tube tester process, where MICA models are derived to find the underlying components from normal process data, and then MFD A models are built for fault diagnosis from the various known fault data. The proposed method is applied to a hydraulic tube tester production process. Results of simulation clearly demonstrate the effectiveness and feasibility of the proposed method.