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The feasibility of sensor fault detection using noise analysis is evaluated. The noise powers at various frequency bands present in the sensor output are calculated using power spectrum density estimation and compared with historically established noise pattern to identify any abnormalities. The method is applicable to systems for which the noise is stationary under normal operating conditions. Principal component analysis (PCA) is used to reduce the space of secondary variables derived from the power spectrum. T 2 statistics is used to detect deviations from the norm. We take advantage of the low-pass filtering characteristics exhibited by most process plants and closed-loop control systems, which allows the noise power at higher frequency bands to be used in the fault detection. The algorithm does not require a process model because it focuses on characterization of each individual sensor and the measurement it generates. Experimental studies with two kinds of garden variety sensors (off the shelf temperature and pressure sensors) are used to validate the feasibility of the proposed approach.

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This paper deals with the identification of stable linear systems from input−output response data with low signal/noise ratios. Specifically we focus on nonparametric (finite impulse or step response, FIR or FSR) models widely used in model predictive control. A polynomial kernel representation is proposed to reduce the number of parameters needed to represent the model. This leads to parsimonious yet robust models. Linear least-squares estimation can be used with these polynomial models. The time delay and response time of the process can be explicitly included as parameters in the model; however, the nonlinear optimization method must then be used. Various properties of this model including the variance of parameter estimates are given in the paper. Both colored and white noise are considered. Simulation and experimental results are used to illustrate the advantages of this approach especially at low signal/noise ratios. The polynomials act as an adaptive filter to remove the noise.