Data processing: measuring – calibrating – or testing – Measurement system – Performance or efficiency evaluation
Reexamination Certificate
1998-10-27
2002-03-12
Wachsman, Hal (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Performance or efficiency evaluation
C714S746000
Reexamination Certificate
active
06356857
ABSTRACT:
FIELD OF THE INVENTION
The present invention pertains, in general, to process controllers and process monitoring systems that make use of sensors for measuring process variables. In particular it relates to a system that detects and identifies one or more sensor faults, classifies the types of sensor fault, and replaces erroneous sensor values with estimates of the correct process variable values.
BACKGROUND OF THE INVENTION
Sensor validation is an important step for many model based applications in the process industries. Typical model based applications include model predictive control applications (MPC), and inferential sensing applications in which costly or infrequent measurements, available from laboratory samples or hardware analyzers, are replaced by regularly available inferred values from the model.
In a typical MPC application, steady state optimization is performed to find the optimal target values for the controlled and manipulated variables. If the sensors are faulty, the optimized target values are not valid. Therefore, an effective sensor validation approach that detects and identifies faulty sensors on-line is required. Once a faulty sensor is identified, it is desirable to estimate the fault magnitude and replace it with the best reconstruction in order to maintain the control system on-line even though a sensor has failed.
A typical inferential sensor application is in the area of predictive emissions monitoring systems (PEMS). Federal and/or state regulations may require air-polluting plants to monitor their emissions such as nitrogen oxides (NO
x
), oxygen (O
2
), and carbon monoxide (CO). Hardware continuous emissions monitoring systems (CEMS) have both a high initial cost and a high maintenance cost. CEMS can be replaced by PEMS provided the PEMS is shown to be sufficiently accurate and reliable. One of the quality assurance requirements for PEMS is that each sensor that is used in the PEMS model be monitored for failure, and have a strategy for dealing with sensor failure so as to minimize down-time.
The term sensor validation refers, for this patent application, to multivariate model based sensor validation. This is an approach which makes use of redundancy in the plant measurements. Typically sensor measurements exhibit a correlation structure which can be established by a training procedure using collected or historian data. This correlation structure can be monitored online; when the correlation structure is broken a possible sensor fault has occured. However the breaking of this correlation structure could also be due to process upset, process transition, or some other reason unrelated to sensor fault. The main objective is to determine if this really is a sensor fault and, if so, to identify the offending sensor. The various phases of sensor validation can be summarized as:
Detection
This phase detects a change in the correlation structure; it may or may not be a sensor fault.
Identification
This phase determines if this is a sensor fault and identifies the particular sensor.
Estimation
This phase estimates the size of the fault which allows reconstruction of the true value and replacement of the faulty value.
Classification
This phase classifies the type of sensor fault—complete failure, bias, drift, or precision loss.
Depending on the particular approach, these phases may overlap. There have been several patents granted that address the topic of multivariate model based sensor validation. The key ones are:
Qin et al. U.S. Pat. No. 5,680,409 “Method and Apparatus for detecting and identifying faulty sensors in a process”.
Keeler et al. U.S. Pat. No. 5,548,528 “Virtual Continuous Emission Monitoring System”.
Hopkins et al. U.S. Pat. No. 5,442,562 “Method of contolling a manufacturing process using multivariate analysis”.
Qin et al. address sensor validation within the context of process control. The preferred embodiment is based on PCA (Principal Components Analysis) and performs identification through an optimal reconstruction procedure: each sensor value is reconstructed on the assumption it is at fault, then identification and classification is done by tracking indices derived from the reconstruction error.
Keeler et al. address sensor validation explicitly within the context of PEMS. The disclosed system focuses on the inferential sensor technology and the use of neural networks for PEMS. The sensor validation technology uses a sub-optimal reconstruction procedure for identification, does not address classification, and makes use of an “encoder” neural network which is a non-linear version of PCA. Encoder networks are also described in Mark Kramer “Nonlinear principal component analysis using autoassociative neural networks”,
AIChE Journal,
37 (2), pp. 233-243 (1991).
Hopkins et al. address sensor validation within the context of process monitoring (multivariate statistical process control), and make use of PCA or PLS (Partial Least Squares). Identification is by means of contribution analysis. Detection is achieved by monitoring principal component “scores” or score statistics and comparing with standard confidence intervals. Identfication is by examining the contributions of each original measurement to the offending score. The method does not attempt to classify fault types.
SUMMARY OF THE INVENTION
The present invention provides a new apparatus and method for the detection, identification, estimation, reconstruction, and classification of faulty sensors. The approach makes use of a normal process model that can be built from first principles or from data using statistical methods such as partial least squares (PLS) or principal component analysis. In the preferred embodiment, the process model is based on a PCA model in which the number of principal components is chosen to optimize the reconstruction of faulty sensor values as described in Qin and Dunia “Determining the number of principal components for best reconstruction”,
Proc. of the
5-
th IFAC Symposium on Dynamics and Control of Process Systems,
359-364, Corfu, Greece, Jun. 8-10, 1998.
The detection phase uses a detection index based on the model equation error. An exponentially weighted moving average (EWMA) filter is applied to the detection index to reduce false alarms due to temporary transients. The filtered detection index (FDI) is compared to a statistically derived threshold in order to detect possible faults. Detection of a possible fault condition triggers the identification phase of the invention.
The key component of this invention is the identification phase. To determine whether a detection alarm is due to one or more faulty sensors, and to identify the offending sensor(s), a series of detectors are constructed which are insensitive to one subset of faults but most sensitive to the others. These detectors are based on structured residuals (SRs) constructed by means of a novel approach referred to a structured residual approach with maximized sensitivity (SRAMS). Structured residuals are generally described in Gertler and Singer, “A new structural framework for parity equation based failure detection and isolation”,
Automatica
26:381-388, 1990. An exponentially weighted moving average (EWMA) filter is applied to the SRs to reduce false alarms due to temporary transients. The SRs are also squared and normalized so as to equitably compare different SRs. Identification is achieved by comparing these normalized squared filtered structured residuals (NSFSRs) to statistically inferred confidence limits. In addition to NSFSRs, indices based on the accumulation of the normalized structured residuals (NSRs) from the time of detection are monitored and compared for use in the identification of faulty sensors. Two such indices are the generalized likelihood ratio (GLR) index, and the normalized cumulative variance (NCUMVAR) index. The NCUMVAR index is primarily useful for identifying sensors with precision degradation.
The fault magnitude is then optimally estimated based on the model, faulty data, and the assumption that the faulty sensors have been correctly identified.
Guiver John P.
Qin S. Joe
Aspen Technology Inc.
Hamilton Brook Smith & Reynolds P.C.
Pretlow Demetrius
Wachsman Hal
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