Sensor Fault Detection and Isolation System

The purpose of this research is to develop a Fault Detection and Isolation (FDI) system which is capable to diagnosis multiple sensor faults in nonlinear cases. In order to lead this study closer to real world applications in oil industries, the system parameters of the applied system are assumed to be unknown. In the first step of the proposed method, phase space reconstruction techniques are used to reconstruct the phase space of the applied system. This step is aimed to infer the system property by the collected sensor measurements. The second step is to use the reconstructed phase space to predict future sensor measurements, and residual signals are generated by comparing the actually measured measurements to the predicted measurements. Since, in practice, residual signals will not perfectly equal to zero in the fault-free situation, Multiple Hypothesis Shiryayev Sequential Probability Test (MHSSPT) is introduced to further process those residual signals, and the diagnostic results are presented in probability. In addition, the proposed method is extended to a non-stationary case by using the conservation/dissipation property in phase space.

The proposed method is examined by both of simulated data and real process data to support that it is capable of detecting and isolating multiple sensor faults in nonlinear cases. In the section of simulation results, a three tank model is introduced for generating simulated data. The three tank model is modeled according to a nonlinear laboratory setup DTS200. On the other hand, in the section of experimental results, the real process data collected from a sugar factory actuator system are used to examine the proposed method.

According to our results obtained from simulations and experiments, the proposed method is capable to indicate both of healthy and faulty situations. These results further confirm that the proposed method is able to deal with not only simulated data but also real process data.