Sensor failure detection system

Advanced concepts for detecting, isolating, and accommodating sensor failures were studied to determine their applicability to the gas turbine control problem. Five concepts were formulated based upon such techniques as Kalman filters and a screening process led to the selection of one advanced concept for further evaluation. The selected advanced concept uses a Kalman filter to generate residuals, a weighted sum square residuals technique to detect soft failures, likelihood ratio testing of a bank of Kalman filters for isolation, and reconfiguring of the normal mode Kalman filter by eliminating the failed input to accommodate the failure. The advanced concept was compared to a baseline parameter synthesis technique. The advanced concept was shown to be a viable concept for detecting, isolating, and accommodating sensor failures for the gas turbine applications

Monitoring Processes with Changing Variances

Statistical process control (SPC) has evolved beyond its classical applications in manufacturing to monitoring economic and social phenomena. This extension requires consideration of autocorrelated and possibly non-stationary time series. Less attention has been paid to the possibility that the variance of the process may also change over time. In this paper we use the innovations state space modeling framework to develop conditionally heteroscedastic models. We provide examples to show that the incorrect use of homoscedastic models may lead to erroneous decisions about the nature of the process. The framework is extended to include counts data, when we also introduce a new type of chart, the P-value chart, to accommodate the changes in distributional form from one period to the next.control charts, count data, GARCH, heteroscedasticity, innovations, state space, statistical process control

Monitoring Processes with Changing Variances

Statistical process control (SPC) has evolved beyond its classical applications in manufacturing to monitoring economic and social phenomena. This extension requires consideration of autocorrelated and possibly non-stationary time series. Less attention has been paid to the possibility that the variance of the process may also change over time. In this paper we use the innovations state space modeling framework to develop conditionally heteroscedastic models. We provide examples to show that the incorrect use of homoscedastic models may lead to erroneous decisions about the nature of the process. The framework is extended to include counts data, when we also introduce a new type of chart, the P-value chart, to accommodate the changes in distributional form from one period to the next.Control charts, count data, GARCH, heteroscedasticity, innovations, state space, statistical process control

A Study for Detection of Drift in Sensor Measurements

This study aims to develop methods for detection of drift in sensor measurements. The study consists of three major components; 1) residual generation, 2) statistical change detection, and 3) model building. To identify the statistical properties of the residuals and to utilize them for detection of the drift, a new method for estimation of the drift rate is proposed. The method formulates an augmented system matrix model and processes the model using a Kalman filter. An analytical method for estimation of the drift rate is also derived. A Hamiltonian approach is used for evaluation of the steady state covariance of the residuals. The steady state covariance and the estimated drift rate enable the existence of the drift in the measurements to be determined in a statistical way using the change detection algorithms. The statistical change detection algorithms process the residuals to determine the drift statistically. In the study, performance of the major algorithms, including the Exponentially Weighted Moving average (EWMA), Cumulative Sum (CUSUM) control chart, and Generalized Likelihood Ratio Test (GRLT), are investigated. A new method for detection of the change, named the Standardized Sum of the Innovation Test (SSIT), is also proposed. The statistical properties of the decision function of the SSIT are derived to set the decision threshold statistically. A method for estimation of the mean delay of the SSIT is also derived. The mean delay of the SSIT is shown in a demonstration and is the shortest of the change detection algorithms. For demonstration purposes, mathematical models of a pressurizer in a CANada Deuterium Uranium (CANDU) nuclear power plant are developed. The mathematical models in the form of nonlinear differential equations are verified by comparing the simulation results with those of the industry standard code known as CATHENA (Canadian Algorithm for Thermal Hydraulic Network Analysis). The developed algorithms have been successfully applied to the pressurizer model for detection and estimation of pressure sensor drifts. The results convincingly demonstrate the effectiveness of the proposed algorithms in the detection of the drift

A Bayesian Scheme to Detect Changes in the Mean of a Short Run Process

1 online resource (PDF, 24 pages

Estimating Risk-adjusted Process Performance with a Bias/Variance Trade-off

Decision makers responsible for managing the performance of a process commonly base their decisions on an estimate of present performance, a comparison of estimates across multiple streams, and the trend in performance estimates over time. Their decisions are well-informed when the risk-adjusted estimates of the performance measure (or parameter) are accurate and precise. The work is motivated by three applications to estimate a parameter at the present time from a stream of data where the parameter drifts slowly in an unpredictable way over time. It is common practice to estimate its value using either present time data only or using present and historical data. When sample sizes by time period are small, an estimate based on present time data is imprecise and can lead to uninformative or misleading conclusions. We can choose to estimate the parameter using an aggregate of historical and present time data but this choice trades more bias for less variability when the parameter is drifting over time. We propose to regulate the bias/variance trade-off using estimating equations that down-weight past data. We derive approximations for the variance of the estimator and the distribution of a hypothesis test statistic involving the estimator through known asymptotic properties of the estimating functions. We study the proposed approach relative to current practices with real or realistic data from each application. We offer simulations and analytic examples to generalize the comparisons and validate the approximations. We explore considerations related to implementing the proposed approach. We suggest future work to extend the applicability of this work

Estimating Risk-adjusted Process Performance with a Bias/Variance Trade-off

Decision makers responsible for managing the performance of a process commonly base their decisions on an estimate of present performance, a comparison of estimates across multiple streams, and the trend in performance estimates over time. Their decisions are well-informed when the risk-adjusted estimates of the performance measure (or parameter) are accurate and precise. The work is motivated by three applications to estimate a parameter at the present time from a stream of data where the parameter drifts slowly in an unpredictable way over time. It is common practice to estimate its value using either present time data only or using present and historical data. When sample sizes by time period are small, an estimate based on present time data is imprecise and can lead to uninformative or misleading conclusions. We can choose to estimate the parameter using an aggregate of historical and present time data but this choice trades more bias for less variability when the parameter is drifting over time. We propose to regulate the bias/variance trade-off using estimating equations that down-weight past data. We derive approximations for the variance of the estimator and the distribution of a hypothesis test statistic involving the estimator through known asymptotic properties of the estimating functions. We study the proposed approach relative to current practices with real or realistic data from each application. We offer simulations and analytic examples to generalize the comparisons and validate the approximations. We explore considerations related to implementing the proposed approach. We suggest future work to extend the applicability of this work

Double Exponentially Weighted Moving Average Control Chart for the Individual Based on a Linear Prediction

Industrial process quality control frequently uses the Exponentially Weighted Moving Average control chart (EWMA CC) and the double EWMA CC (DEWMA CC) to detect small shifts in a process when the sample size =1. The EWMA CC was initially developed and evaluated in 1959. In 2005, the EWMA technique was extended to the DEWMA. Continued research into DEWMA has developed and assessed several alternatives, including multivariate control charts. These studies focus on detecting small shifts in process. In practice, however, we occasionally wish to detect small trends instead of shifts in the process. The effectiveness of these methods to determine small trends in a process has not been thoroughly researched in the current literature. This research proposes a new control chart, based on the fundamental theorem of exponential smoothing prediction, first presented by Brown and Meyer in 1961. The new chart is called “The Double Exponentially Weighted Moving Average Based on a Linear Prediction” (DEWMABLP) control chart. This study presents a simulation to contrast the efficiency of DEWMABLP, EWMA, DEWMA, and classical Shewhart control charts when small trends are introduced. A conclusion is the DEWMABLP control chart can be used to monitoring small shifts. Also, results suggest that the new control chart is more efficient than the other control charts not only for small drifts, but also for small shifts