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

• US 8,712,731 B2
• Filed: 09/23/2011
• Issued: 04/29/2014
• 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 P.
• Primary Examiner(s): Lau, Tung S
• Application Number:
  US13/243,018
• Publication Number:
  US 20120066161A1
• Time in Patent Office: 949 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 method of detecting a plugged impulse line in a process measuring device, the method comprising:

detecting a pressure in a process flow line, and generating a pressure signal indicative of the pressure;

calculating mean values of sampled values of the pressure signal collected over predefined sample windows;

calculating standard deviation values of sampled values of the pressure signal collected over the predefined sample windows;

creating a model of standard deviation values of the pressure signal as a function of mean values of the pressure signal;

predicting standard deviation values of the pressure signal based on mean values of the pressure signal and the model;

comparing predicted standard deviation values of the pressure signal to calculated standard deviation values of the pressure signal; and

detecting a plugged impulse line when a predicted standard deviation value of the pressure signal differs from a corresponding calculated value of the standard deviation of the pressure signal by more than a predefined amount.

2. The method of claim 1 wherein modeling standard deviation values of the pressure signal as a function of mean values of the pressure signal comprises creating an extensible regression model based on standard deviation values of the pressure signal and mean values of the pressure signal gathered during a learning phase.

3. The method of claim 1 wherein creating a model of standard deviation values of the pressure signal as a function of the mean values of the pressure signal comprises implementing a learning function in which data points comprising an ordered pair of a mean value of the pressure signal and a standard deviation value of the pressure signal are added to an array of points that define the model of the standard deviation of the pressure signal as a function of the mean of the pressure signal.

4. The method of claim 3 wherein a new data point is added to the array when the mean value of the pressure signal of the new data point is either higher or lower than the mean value of the pressure signal of all points previously added to the array.

5. The method of claim 1 further comprising filtering the pressure signal to generate a filtered pressure signal, and wherein calculating standard deviation values of the pressure signal comprises calculating standard deviation values of the filtered pressure signal.