Source: http://www.google.co.uk/patents/US6397114
Timestamp: 2017-12-18 03:32:02
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Matched Legal Cases: ['art 2', 'art 3', 'art 3', 'art 2', 'art 2', 'art 4', 'art 4', 'application No. 09', 'application No. 09']

Patent US6397114 - Device in a process system for detecting events - Google Patents
A process device couples to a process control loop. The process device receives process signals. A memory in the process device contains a nominal parameter value and a rule. Computing circuitry calculates a statistical parameter of the process signal and operates on the statistical parameter and the...http://www.google.co.uk/patents/US6397114?utm_source=gb-gplus-sharePatent US6397114 - Device in a process system for detecting events
Publication number US6397114 B1
Application number US 09/303,869
Filing date 3 May 1999
Also published as CN1185841A, CN1188759C, DE69705471D1, DE69705471T2, EP0829038A1, EP0829038B1, US6017143, US6119047, US6532392, WO1997036215A1
Publication number 09303869, 303869, US 6397114 B1, US 6397114B1, US-B1-6397114, US6397114 B1, US6397114B1
Patent Citations (206), Non-Patent Citations (126), Referenced by (215), Classifications (18), Legal Events (3)
Device in a process system for detecting events
US 6397114 B1
A process device couples to a process control loop. The process device receives process signals. A memory in the process device contains a nominal parameter value and a rule. Computing circuitry calculates a statistical parameter of the process signal and operates on the statistical parameter and the stored nominal value based upon the stored rule and responsively provides an event output based upon the operation. Output circuitry provides an output in response to the event output
1. A process device coupled to a process control loop configured to control a process fluid, comprising:
a process variable input to receive a process variable of the process control loop, the process variable related to the process;
a process event input to receive a process event generated by a process device on the process control loop related to an occurrence of an event in the process, the process event selected from the group of process events consisting of drift, bias, noise, stuck and spike;
a control signal input to receive a control signal and to control the process fluid; and
an inference engine coupled to the process variable input the control signal input and the process event input, the inference engine having a faulty element output which is a function of the process variable, the control signal and the process event, the faulty element output identifies a faulty component in the process which caused the process event.
2. The process device of claim 1 wherein the inference engine includes stored process data.
3. The process device of claim 2 wherein the process data comprises process history information.
4. The process device of claim 2 wherein the process data comprises logged process variables.
5. The process device of claim 1 wherein the process event is detected with a rule.
6. The process device of claim 1 wherein the process event is detected with a rule, a nominal parameter value and a statistical parameter.
7. The process device of claim 6 wherein the nominal parameter value comprises a trained value and a sensitivity parameter.
8. The process device of claim 6 wherein the statistical parameter is selected from the group consisting of standard deviation, mean, sample variance, root-mean-square (RMS), range, and rate of change.
9. The process device of claim 1 wherein the process control loop is selected from the group consisting of two wire process control loops, three wire process control loops and four wire process control loops.
10. The process device of claim 9 wherein the device is completely powered with power received from the process control loop.
11. The process device of claim 1 wherein the inference engine includes computation circuitry operating in accordance with a rule.
12. The process device of claim 11 wherein the faulty element output is a function of a comparison of the control signal and the process variable.
13. The process device of claim 1 wherein the inference engine performs transmitter diagnostics in response to a process event.
14. The process device of claim 1 wherein the inference engine instructs a device on the process control loop to perform on-board diagnostics.
15. The process device of claim 1 wherein the faulty element output comprises a warning.
16. The process device of claim 1 wherein the faulty element output provides information to an operator.
17. The process device of claim 1 wherein the inference engine includes fuzzy logic.
18. The process device of claim 1 wherein the inference engine includes a neural network.
19. The process device of claim 1 wherein the inference engine examines the process event and identifies a specific event in the process control system.
20. The process device of claim 1 wherein the inference engine initiates a shutdown procedure to shut down the process in the process event.
21. The process device of claim 1 wherein the process device comprises a transmitter.
22. The process device of claim 1 wherein the process device comprises a controller.
23. The process device of claim 1 wherein the process device comprises a communicator.
24. The process device of claim 1 wherein the process device comprises a control room equipment.
25. The process device of claim 1 wherein the process device comprises a PC.
26. The process device of claim 1 wherein the inference engine examines a plurality of process variables in response to a process event.
27. The process device of claim 1 wherein the inference engine identifies a known device condition in response to a process event.
28. The process device of claim 1 wherein the inference engine identifies a faulty component in response to a process event.
29. The process device of claim 1 wherein the inference engine comprises an expert system.
30. The process device of claim 1 wherein the inference engine further provides a condition output which identifies a known process condition.
31. A method of diagnosing a process controlled by a process control loop configured to control a process fluid, comprising:
obtaining a process variable of the process control loop related to the operation of process;
obtaining a process event related to an event generated by a process device on the process control loop in the process, the process selected from the group of process events consisting of drift, bias, noise, stuck and spike;
obtaining a control signal used to control the process fluid; and
computing a faulty element output related to a faulty element in the process control system in response to the process variable, the process event, and the control signal, the faulty element being the cause of the process event.
32. The method of claim 31 wherein providing a faulty element output includes informing an operator.
33. The method of claim 31 wherein providing a faulty element output comprises comparing the process variable, process event and control signal to process data.
34. The method of claim 31 wherein the process data comprises process history information.
35. The method of claim 31 wherein the process data comprises logged process variables.
36. The method of claim 31 wherein the process event is detected with a rule.
37. The method of claim 36 including detecting the process event by applying a rule using a nominal parameter value and a statistical parameter.
38. The method of claim 37 wherein the nominal parameter value comprises a trained value and a sensitivity parameter.
39. The method of claim 31 wherein providing a faulty element output includes instructing a process device to perform on-board diagnostics.
40. The method of claim 31 including evaluating results of the diagnostics through on-board diagnostics residents in a process device.
41. The method of claim 31 wherein providing a faulty element output includes identifying a component.
42. The method of claim 31 wherein providing a faulty element output includes identifying a process condition.
43. The method of claim 31 wherein providing a faulty element output includes applying fuzzy logic to the process variable.
44. The method of claim 31 wherein providing a faulty element output includes applying the process variable to a neural network.
45. The method of claim 31 wherein providing a faulty element output includes applying a rule to the process variable.
46. A process device comprising a transmitter implementing the method of claim 31.
47. A process device comprising a controller implementing the method of claim 31.
48. A process device comprising a PC implementing the method of claim 31.
49. A process device comprising control room equipment implementing the method of claim 31.
50. The method of claim 31 including identifying a known process condition in response to the variable, process event and control signal.
51. A computer-readable medium having stored thereon instructions executable by a microprocessor system in a process device to identify a fault in a process control loop configured to control a process fluid, the instructions comprising:
obtaining a process variable of the process control loop related to operation of the process;
obtaining a process event generated by a process device on the process control looprelated to an event in the process, the process selected from the group of process events consisting of drift, bias, noise, stuck and spike;
computing a faulty element output related to a faulty element in the process control system in response to the process variable, the process event, and the control signal element in the process in response to the process variable, the process event, and the control signal, the faulty element being the cause of the process event.
52. A process device coupled to a process control loop used to control a process fluid, comprising:
a process variable input means for receiving a process variable of the process related to operation of the process;
a process event input means for receiving a process event of the process controlled by the process control loop generated by a process device on the process control loop, the process selected from the group of process events consisting of drift, bias, noise, stuck and spike;
a control signal input means for requiring a control signal used to control the process fluid; and
inference engine means for providing a faulty element output as a function of the process variable control signal and the process event, the faulty element output identifying a faulty component in the process control system, the faulty element being the cause of the process event.
This is a Divisional application of U.S. Ser. No. 08/623,569, filed Mar. 28, 1996, now U.S. Pat. No. 6,017,143 entitled “DEVICE IN A PROCESS SYSTEM FOR DETECTING EVENTS”.
FIG. 1 is a diagram showing an example of a process control system 2 which includes process piping 4 which carries a process fluid and two wire process control loop 6 carrying loop current I. A transmitter 8, controller 10, which couples to a final control element in the loop such as an actuator, valve, a pump, motor or solenoid, communicator 12, and control room 14 are all part of process control loop 6. It is understood that loop 6 is shown in one configuration and any appropriate process control loop may be used such as a 4-20 mA loop, 2, 3 or 4 wire loop, multi-drop loop and a loop operating in accordance with the HART®, Fieldbus or other digital or analog communication protocol. In operation, transmitter 8 senses a process variable such as flow using sensor 16 and transmits the sensed process variable over loop 6. The process variable may be received by controller/valve actuator 10, communicator 12 and/or control room equipment 14. Controller 10 is shown coupled to valve 18 and is capable of controlling the process by adjusting valve 18 thereby changing the flow in pipe 4. Controller 10 receives a control input over loop 6 from, for example, control room 14, transmitter 8 or communicator 12 and responsively adjusts valve 18. In another embodiment, controller 10 internally generates the control signal based upon process signals received over loop 6. Communicator 12 may be the portable communicator shown in FIG. 1 or may be a permanently mounted process unit which monitors the process and performs computations. Process devices include, for example, transmitter 8 (such as a 3095 transmitter available from Rosemount Inc.), controller 10, communicator 12 and control room 14 shown in FIG. 1 Another type of process device is a PC, programmable logic unit (PLC) or other computer coupled to the loop using appropriate I/O circuitry to allow monitoring, managing, and/or transmitting on the loop.
Microprocessor 46 acts in accordance with instructions stored in memory 48. Memory 48 also contains trained values 78, rules 80 and sensitivity parameters 82 in accordance with the present invention. The combination of the sensitivity parameters 82 and the trained values 78 provide a nominal value 79. FIG. 3 is a block diagram 83 showing a logical implementation of device 40. Logical block 84 receives process signals and calculates statistical parameters for the process signals. These statistical parameters include standard deviation, mean, sample variance, root-mean-square (RMS), range (ΔR) and rate of change (ROC) of the process signal, for example. These are given by the following equations: mean = x _ = 1 N  ∑ i = 1 N  X i Eq .  1 RMS = 1 N  ∑ i = 1 N  X i 2 Eq .  2 σ =  standard   deviation = variance =  S 2 = 1 n - 1  ∑ i = 1 N  ( x i - x _ ) 2 Eq .  3 ROC = r i = x i - x i - 1 T Eq .  4
ΔR=x MAX −x MIN Eq. 5
if σ>βσ′
Yet another combination of a rule, statistical value, tuning parameters and trained values detect a stuck condition in a process signal. A “stuck” process signal is one which a condition of the process signal does not vary with time. Stuck sensitivity is controlled by adjusting the sensitivity parameter 82 gamma (γ). A value for gamma (γ) is expressed as a percentage of the trained standard deviation (σ′) and represents how small a change in standard deviation from the trained value triggers detection of a stuck event. For example, if a user wishes to detect a stuck condition when the process signal noise level is half of the trained value, should be set equal to 50 percent (0.5). Further, range of the signal (ΔR) can be used to eliminate errors that arise with small signals. One example rule is:
(σ+ΔR)≦γ(σ′+ΔR′) then a stuck event is detected.
if ROC>δ·Δr MAX then a spike event is detected
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U.S. Classification 700/51, 702/59, 702/183, 714/735, 702/33, 700/32
International Classification G05B23/02, G05B13/02, G05B11/36, G05B21/02
Cooperative Classification G05B23/0278, G05B23/0221, G05B21/02, G05B13/0275
European Classification G05B23/02S4D, G05B23/02S6J2, G05B21/02, G05B13/02C2
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