In this paper, a new approach to sensor validation in real time is described that is based on (1) representation of the sensor signal by wavelets, (2) decomposition of the signal for different frequency ranges, (3) formation of a matrix of lagged details from a window of the original sensor data at different frequencies, (4) application of PCA decomposition to the matrix of details, and (5) diagnosis of the existence of faults via tests on the PCA statistics such as T 2 and Q. The proposed strategy is able to isolate the effect of noise and process changes from the effects of physical changes in the sensor itself. For comparison we applied all of the tests and diagnoses to the original signal itself as well as to the wavelet details. To demonstrate the circumstances under which the above strategy might be used, a simulated noisy signal from a CSTR and a temperature signal from an operating pilot distillation column were analyzed. Faults were introduced into the thermocouple, and the diagnosis carried out. The results of the diagnosis indicated that the proposed strategy had low type I (false alarm) and type II (failure to detect faults) errors.

Sensor validation is a topic of widespread importance. A new approach to sensor validation in real time is described that is based on (1) representation of the sensor signal by wavelets, (2) decomposition of the signal into different frequency ranges, (3) calculation of useful features of the signal at different frequencies, and (4) diagnosis of faulty operation via nonparametric statistical tests. The proposed strategy is able to isolate the effect of noise and process changes from the effects of physical changes in the sensor itself. To clarify the circumstances under which the above strategy could be used, a noisy signal from a simulated thermocouple in a dynamic continuous nonlinear unsteady state stirred tank reactor (CSTR) was analyzed. Faults were introduced into the thermocouple, and the diagnosis was carried out. The results of the diagnosis indicated that the proposed strategy had low type I (false alarm) and type II (failure to detect faults) errors and was distinctly better than a standard test for changes in a nonstationary signal of unknown characteristics.

No abstract

A simple method for the autotuning of proportional−integral−derivative (PID) controllers which keeps the PID controllers in a closed loop is proposed. A limit cycle is generated through setpoint relay to extract relevant information about the process dynamics. Ziegler−Nichols tuning formulas based on the reaction curve method and the ultimate gain and ultimate period method are used to calculate the PID parameters. Extensive simulations have shown that the proposed method is accurate and needs not to switch off the existing controller.