System and method for abnormal event detection in the operation of continuous industrial processes
First Claim
1. A system for abnormal event detection in an industrial process comprising a set of models including principal component analysis models to detect abnormal operations in said process wherein said process has been divided into equipment groups with minimal interaction between groups and process operating modes with process measurements as inputs to said models wherein separate principal component analysis models correspond to an equipment group and process operating modes and wherein each principal component analysis model is a linear combination of process measurements.
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Abstract
Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally.
These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation. The deviation between the expected pattern in the process operating data and the actual data pattern are interpreted by fuzzy Petri nets to determine the normality of the process operations. This is then used to help the operator localize and diagnose the root cause of the problem.
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Citations
48 Claims
- 1. A system for abnormal event detection in an industrial process comprising a set of models including principal component analysis models to detect abnormal operations in said process wherein said process has been divided into equipment groups with minimal interaction between groups and process operating modes with process measurements as inputs to said models wherein separate principal component analysis models correspond to an equipment group and process operating modes and wherein each principal component analysis model is a linear combination of process measurements.
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29. A method for developing an abnormal event detector for a industrial process comprising:
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a) dividing the process into equipment groups with minimal interaction between groups and/or operating modes having specific time periods where process behavior is significantly different, b) determining input variables and their operating range for said equipment and/or operating modes, c) determining historical data for said input variables. d) determining a historical data training set having no abnormal operation, e) creating a set of models including principal component analysis models included in the set, one for each of said equipment groups and/or operating modes using said historical data training set wherein each principal component is a linear combination of process measurements. - View Dependent Claims (30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
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- 43. A method for determining an abnormal event for an industrial process comprising comparing online measurements from said industrial process to measurements for normal operations using a set of models including principal component analysis models wherein said process has been divided into equipment groups having minimal interaction between groups and/or operating modes and/or engineering models wherein a principal component analysis model corresponds to each equipment group and each principal component is a linear combination of process measurements.
Specification