Automatic control loop monitoring and diagnostics
First Claim
1. A method of diagnosing a malfunction of a process control system which includes at least one closed loop control loop comprising measuring a histogram of tracking error of said control loop, determining distortion of said tracking error relative to a Gaussian distribution, and indicating a malfunction in the process in the event a deviation from said Gaussian distribution of said tracking error exceeds predetermined limits, wherein said distortion (K) is measured by subtracting from a height of a tracking error histogram bar of said histogram centered on zero, a number of samples multiplied by an area between a pair of limits defining a normal density about a mean of said histogram, and then indicating a malfunction in the process in the event a value of K is different from 0 by a predetermined amount.
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Accused Products
Abstract
A method of diagnosing a malfunction of a process control system which includes at least one closed loop control loop comprising measuring a histogram of tracking error of the control loop, determining distortion of the tracking error relative to a Gaussian distribution, and indicating a malfunction in the process in the event a deviation from the Gaussian distribution of the tracking error exceeds predetermined limits.
131 Citations
21 Claims
- 1. A method of diagnosing a malfunction of a process control system which includes at least one closed loop control loop comprising measuring a histogram of tracking error of said control loop, determining distortion of said tracking error relative to a Gaussian distribution, and indicating a malfunction in the process in the event a deviation from said Gaussian distribution of said tracking error exceeds predetermined limits, wherein said distortion (K) is measured by subtracting from a height of a tracking error histogram bar of said histogram centered on zero, a number of samples multiplied by an area between a pair of limits defining a normal density about a mean of said histogram, and then indicating a malfunction in the process in the event a value of K is different from 0 by a predetermined amount.
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3. A method of automatic assessment of control loop performance of an industrial machine comprising:
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(a) collecting operating data comprising time series of controlled variable measurements and control loop set points simultaneously from predetermined control loops, for a period of at least approximately 100 times a longest time constant of said predetermined control loops, (b) subtracting measured variable data from set point data to obtain tracking errors, (c) determining an amount by which observed variance of a tracking error exceeds a minimum value, after non-linear elements have been removed from a loop, exploiting prior estimates of process time constant and dead-time to provide a raw index, (d) testing for any interactions between control loops which may be inflating in an abnormal manner an estimate of said raw index, (e) determining a modified raw index for a particular loop in the event said inflated estimates are detected, and (f) distinguishing between control loops that are malfunctioning, those that are not malfunctioning, and those that are possibly malfunctioning and are perturbed by interacting malfunctioning control loops, based on said raw index and said modified raw index. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 17, 18, 19, 20, 21)
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11. A method of automatic assessment of control loop performance of and industrial machine comprising:
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(a) identifying a current control loop in a group of control loops, (b) obtaining operating data and prior dynamic information for said control loop, (c) calculating a raw performance index for said control loop, (d) indicating said current control loop as potentially malfunctioning in the event said raw performance index is greater than a predetermined threshold, (e) in the event said control loop is indicated as potentially malfunctioning, computing a fast Fourier transform of a tracking error, and filter products of said transform to remove spurious peaks, (f) identifying primary and secondary spectral peaks contributing more than a threshold variance in a predetermined bandwidth for said control loop, (g) selecting another control loop in said group of control loops and repeat steps (a)-(g) until a last control loop in said group has been processed, (h) divide potentially malfunctioning loops with approximately coincident spectral peaks into possibly interacting classes, (i) determine a modified performance index for all control loops belonging to a class, and (j) apply a histogram test to spectral peaks of all control loops in a class to determine a category of malfunction. - View Dependent Claims (12, 13)
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14. A method of determining a category of malfunction of a process comprising:
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(a) tracking error variations of narrow spectral bandwidth in each of plural control loops of said process, (b) comparing spectral peaks of said error variations to detect coincidences of peaks which are indicative of interaction between said plural control loops, and (c) quantifying effects of said error variations which have said coincidences of peaks, and as a result determining malfunctioning of a control loop. - View Dependent Claims (15, 16)
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Specification