Faulty Variable Identification Technique for Data-Driven Fault Detection Within A Process Plant
AssigneeEmerson Process Management Power & Water Solutions, Inc.
InventorXu Cheng
Life of this patent
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Abstract
A real-time control system includes a faulty variable identification technique to implement a data-driven fault detection function that provides an operator with information that enables a higher level of situational awareness of the current and likely future operating conditions of the process plant. The faulty variable identification technique enables an operator to recognize when a process plant component is behaving abnormally to potentially take action, in a current time step, to alleviate the underlying cause of the problem, thus reducing the likelihood of or preventing a stall of the process control system or a failure of the process plant component.
Claims20
A system for identifying faulty variables in on-line data indicative of an on-line operation of a process, comprising:
- a process controller coupled to the process for controlling the process;
- and
- a fault detection system for identifying faulty variables in on-line data received from the process controller, wherein the fault detection system is configured to:
- detect an abnormal operating condition of the process by applying a multivariate state estimation technique (MSET) using a system matrix to the on-line data;
- build a respective new matrix for each unique pair of process variables represented by data included in the system matrix;
- train a respective miniature system matrix for each unique pair of process variables using the respective new matrix;
- define a respective fault index for each process variable included in the system matrix;
- determine estimated data for each unique pair of process variables by applying the MSET using the respective miniature system matrix to a respective portion of the on-line data,
- increment the respective fault index of both process variables included in a unique pair of process variables when an estimation error corresponding to the estimated data for one of the process variables included in the unique pair exceeds a corresponding estimation error threshold, and
- identify a faulty variable by determining a respective process variable with a largest fault index.
A method of identifying faulty variables in on-line data indicative of an on-line operation of a process, comprising:
- receiving, from a process controller, the on-line data;
- detecting an abnormal operating condition of the process by applying a multivariate state estimation technique (MSET) using a system matrix to the on-line data;
- building a respective new matrix for each unique pair of process variables represented by data included in the system matrix;
- training a respective miniature system matrix for each unique pair of process variables using the respective new matrix;
- defining a respective fault index for each process variable included in the system matrix;
- determining estimated data for each unique pair of process variables by applying the MSET using the respective miniature system matrix to a respective portion of the on-line data;
- incrementing the respective fault index of both process variables included in a unique pair of process variables when an estimation error corresponding to the estimated data for one of the process variables included in the unique pair exceeds a corresponding estimation error threshold;
- and
- identifying a faulty variable by determining a respective process variable with a largest fault index.
A method of locating faulty variables in on-line data indicative of an on-line operation of a process, comprising:
- receiving, from a process controller, the on-line data;
- detecting an abnormal operating condition of the process by applying a multivariate state estimation technique (MSET) using a current system matrix to the on-line data;
- (a) dividing the current system matrix into a first new matrix and a second new matrix;
- (b) training a first system matrix using the first new matrix;
- (c) training a second system matrix using the second new matrix;
- (d) determining a first estimated data set by applying the MSET using the first system matrix to a respective portion of the on-line data;
- (e) determining whether or not abnormal operating conditions exist within the respective portion of the on-line data by comparing the first estimated data set to the respective portion of the on-line data;
- (f) responsive to determining that abnormal operating conditions exist within the respective portion of the on-line data:
- determining whether or not a size of the first system matrix satisfies a size threshold, and
- responsive to determining that the size of the first system matrix does not satisfy the size threshold, designating the first system matrix as the current system matrix, and
- (g) responsive to determining that normal operating conditions exist within the respective portion of the on-line data:
- determining whether or not a size of the second system matrix satisfies the size threshold, and
- responsive to determining that the size of the second system matrix does not satisfy the size threshold, designating the second system matrix as the current system matrix;
- (h) iteratively performing steps (a)-(h) until the size of the first system matrix or the size of the second system matrix satisfies the size threshold.
Dependencies are inferred from the claim text.