Simplified algorithm for abnormal situation prevention in load following applications including plugged line diagnostics in a dynamic process

• US 8,055,479 B2
• Filed: 10/10/2007
• Issued: 11/08/2011
• Est. Priority Date: 10/10/2007
• Status: Active Grant

• Current Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Original Assignee: Fisher-Rosemount Systems Incorporated (Emerson Electric Company)
• Inventors: Miller, John Philip
• Primary Examiner(s): Lau; Tung S
• Application Number:
  US11/973,960
• Publication Number:
  US 20090097537A1
• Time in Patent Office: 1,490 Days
• Field of Search: 702/189
• US Class Current: 702/189
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Abstract

Systems and methods are provided for detecting abnormal conditions and preventing abnormal situations from occurring in controlled processes. Statistical signatures of a monitored variable are modeled as a function of the statistical signatures of a load variable. The statistical signatures of the monitored variable may be modeled according to an extensible regression model or a simplified load following algorithm. The systems and methods may be advantageously applied to detect plugged impulse lines in a differential pressure flow measuring device.

Claims

1. A system for detecting a plugged impulse line in a process measuring device, the system comprising:

a sensor for detecting a pressure in a process line, the sensor providing a pressure signal indicative of the pressure; and

one or more processors implementing a first statistical process monitoring (SPM) block, a second SPM block, and a plugged line diagnostic block;

the first SPM block configured to calculate mean values from a plurality of pressure signal sample values collected during predefined sample windows, and the second SPM block configured to calculate standard deviation values from a plurality of pressure signal sample values collected during the predefined sample windows; and

the plugged line diagnostic block configured to mathematically model the standard deviation values as a function of corresponding calculated mean values, to predict standard deviation values based on the calculated mean values, compare predicted standard deviation values to corresponding calculated standard deviation values, and detect a plugged impulse line when a predicted standard deviation value differs from a corresponding calculated standard deviation value by more than a predefined amount.

2. The system of claim 1 further comprising a filter adapted to remove frequency components from the pressure signal to provide a filtered pressure signal, wherein the second SPM block is configured to calculate standard deviation values from a plurality of filtered pressure signal sample values.

3. The system of claim 1 wherein the diagnostic block comprises an extensible regression model.

4. The system of claim 1 wherein the plugged line diagnostic block comprises an array of data points, each data point defined by a pair of corresponding pressure signal mean and pressure signal standard deviation values, the array of data points defining a function that models the pressure signal standard deviation values as a function of the pressure signal mean values.

5. The system of claim 4 wherein the plugged line diagnostic block further comprises a learning function in which new data points are added to the array, and a monitoring function in which a predicted value of the pressure signal standard deviation is determined based on the pressure signal mean value of a new data point and the predicted value of the standard deviation of the pressure signal is compared to the actual standard deviation of the pressure signal value of the new data point.

6. The system of claim 5 wherein the learning function is executed when the pressure signal mean value of the new data point is less than or greater than the pressure signal mean value of all data points in the array.

7. The system of claim 5 wherein the array is limited to a fixed number of points and wherein the learning function further comprises removing a point from the array when a new point is added.

8. The system of claim 7 wherein the point removed from the array comprises a point which, when removed from the array, will result in a least amount of error between the function defined by the array including the point and a function defined by the array having the point removed.

9. The system of claim 8 wherein the error between the function defined by the array including the point, and the function defined by the array with the point removed from the array comprises the area of a triangle defined by the point and two other points in the array immediately adjacent to the point.

10. The system of claim 8 wherein calculating the error between the function defined by the array including the point, and the function defined by the array with the point removed from the array comprises calculating an integral square error between the two functions.

11. The system of claim 1 wherein the diagnostic block includes a configurable sensitivity level such that a plugged impulse line will be detected when the predicted standard deviation of the pressure signal value differs from the corresponding calculated standard deviation value of the pressure signal by an amount determined by a configured sensitivity level.

12. The system of claim 11, wherein the configurable sensitivity level includes a high sensitivity setting such that a plugged impulse line is detected when the predicted standard deviation value differs from the calculated standard deviation value by 60% or more.

13. The system of claim 11, wherein the configurable sensitivity level includes a medium sensitivity setting such that a plugged impulse line is detected when the predicted standard deviation value differs from the calculated standard deviation value by 70% or more.

14. The system of claim 11, wherein the configurable sensitivity level includes a low sensitivity setting such that a plugged impulse line is detected when the predicted standard deviation value differs from the calculated standard deviation value by 80% or more.

15. The system of claim 1 wherein at least one of the one or more processors is implemented in at least one of:

a process control field device;

a field device interface module;

a handheld communicator;

or a process controller.

16. The system of claim 1 wherein at least one of the first SPM block, the second SPM block, and the diagnostic block is implemented as a stand alone software application.

17. The system of claim 1 wherein at least one of the first SPM block, the second SPM block, and the diagnostic block is implemented as part of an abnormal situation prevention system.