Source: http://www.google.com.tw/patents/US6397114?hl=zh-TW
Timestamp: 2013-05-22 11:55:13
Document Index: 311425768

Matched Legal Cases: ['art 2', 'art 2', 'art 3', 'art 3', 'art 2', 'art 2', 'art 4', 'art 4', 'application No. 09', 'application No. 09']

�M�Q US6397114 - Device in a process system for detecting events - Google �M�Q�j�M �Ϥ� �a�� Play YouTube �s�D Gmail ���ݵw�� ��h »�i���M�Q�j�M | �������� | �n�J�i���M�Q�j�M�M�QA 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.com.tw/patents/US6397114?utm_source=gb-gplus-share�M�Q US6397114 - Device in a process system for detecting events���}��US6397114 B1�X���������v�ӽЮѽs��09/303,869�o�G���2002�~5��28���ӽФ��1999�~5��3�� �u���v���1996�~3��28����L���}�M�Q��CN1185841ACN1188759CEP0829038A1EP0829038B1US6017143US6119047US6532392WO1997036215A1�o��HEvren EryurekJogesh Warrior��M�Q�v�HRosemount Inc. ���M�Q������700/51702/59702/183714/735702/33700/32��ڱM�Q������G05B23/02G05B13/02G05B11/36G05B21/02 �X�@����G05B23/0221G05B21/02G05B13/0275G05B23/0278 �ڬw������G05B 23/02S4DG05B 23/02S6J2G05B 21/02G05B 13/02C2�ѦҤ��m�M�Q�ޥ� (103)�D�M�Q�ޥ� (126)�Q�H�U�M�Q�ޥ� (58)�~���s�����M�Q�ӼЧ� ���M�Q�ӼЧ��M�Q����T�� �ڬw�M�Q��Device in a process system for detecting eventsUS 6397114 B1�K�n 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
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��.
Any of the process devices 8, 10, 12 or 14 shown in FIG. 1 may include event monitoring circuitry in accordance with the present invention. FIG. 2 is a block diagram of a process device 40 forming part of loop 6. Device 40 is shown generically and may comprise any process device such as transmitter 8, controller 10, communicator 12 or control room equipment 14. Control room equipment 14 may comprise, for example, a DCS system implemented with a PLC and controller 10 may also comprise a ��smart�� motor and pump. Process device 40 includes I/O circuitry 42 coupled to loop 6 at terminals 44. I/O circuitry has preselected input and output impedance known in the art to facilitate appropriate communication from and to device 40. Device 40 includes microprocessor 46, coupled to I/O circuitry 42, memory 48 coupled to microprocessor 46 and clock 50 coupled to microprocessor 46. Microprocessor 46 receives a process signal input 52. Block input is intended to signify input of any process signal, and as explained above, the process signal input may be a process variable, or a control signal and may be received from loop 6 using I/O circuitry 42 or may be generated internally within field device 40. Field device 40 is shown with a sensor input channel 54 and a control channel 56. Typically, a transmitter such as transmitter 8 will exclusively include sensor input channel 54 while a controller such as controller 10 will exclusively include a control channel 56. Other devices on loop 6 such as communicator 12 and control room equipment 14 may not include channels 54 and 56. It is understood that device 40 may contain a plurality of channels to monitor a plurality of process variables and/or control a plurality of control elements as appropriate.
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 (�GR) and rate of change (ROC) of the process signal, for example. These are given by the following equations: mean = x _ = 1 N �H ∑ i = 1 N �H X i Eq . �H 1 RMS = 1 N �H ∑ i = 1 N �H X i 2 Eq . �H 2 �m = �H standard �H �H deviation = variance = �H S 2 = 1 n - 1 �H ∑ i = 1 N �H ( x i - x _ ) 2 Eq . �H 3 ROC = r i = x i - x i - 1 T Eq . �H 4 �GR=x MAX −x MIN Eq. 5
Drift occurs when a process signal changes over time from its true (i.e. nominal) value. One embodiment of the invention includes a rule which operates on a statistical parameter mean (�g), the trained parameter mean (�g��) and a tuning parameter alpha (�\) to detect drift.
Drift sensitivity is controlled by a single sensitivity parameter, alpha (�\). Alpha (�\) represents a percentage above or below the normal mean signal level that is tolerable before a drift or event is detected. The following rule performed by rule calculation block 86 detects a drift event:
if �g<�g�� (1−�\) then a negative drift event
if �g>�g�� (1+�\) then a positive drift event, where �g is the current mean of the process signal from 84, �g�� is the trained mean from 78 and �\ is the sensitivity parameter from 82 which defines the acceptable variations from the mean. Additionally, the mean is monitored over time. A drift event is only detected if, over a series of consecutive sampling period, the mean is moving away from the trained value. The trained mean (�g��) may be learned by training device 40 during normal operation of the process.
Bias is the result of a temporary drift ��stabilizing�� at a certain level above or below the expected signal level. Once the drift stops, the resulting signal has a bias, sometimes called an offset from the true/nominal value. A bias is detected using the same rule used for drift. Additionally, the mean is monitored over time. If the mean is not continuing to move away from the trained mean (�g��), then it is determined that the event is bias, not drift.
A different combination of a rule, tuning parameters and trained values detect noise in the process signal. Noise detection sensitivity is adjusted by adjusting the sensitivity parameter beta (�]). Beta (�]) is the amount the current standard deviation (�m) can be above the trained standard deviation (�m��) before detection of a noise event. For example, if the user desires to detect a noise event when the process signal is twice as noisy as the trained value, �] should be sent to 2.0. Range (�GR) is also used by the rule to differentiate noise from normal signal variations. An example rule for noise detection is:
if �m>�]�m�� and
if �GR>�GR�� then noise detected. Where �m and �m�� are the current and trained standard deviation �GR and �GR�� are the current and trained range, respectively, and �] is the noise sensitivity parameter.
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 (�^). A value for gamma (�^) is expressed as a percentage of the trained standard deviation (�m��) 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 (�GR) can be used to eliminate errors that arise with small signals. One example rule is:
(�m+�GR)�أ^(�m��+�GR��) then a stuck event is detected. Spike
A different combination of a rule, a statistical value, trained value and sensitivity parameter is used to detect a spike event. A spike event occurs when the signal momentarily goes to an extreme value. Sensitivity to spikes in the process signal is controlled by adjusting a sensitivity parameter from �_ stored in 82. �_ is the acceptable trained maximum rate of change (�GPmax) between two consecutive data points. For example, if the user wishes to detect any spikes that have a rate of change (ROC) from block 84 that is 30% greater than �Grmax from block 78 relative to the trained value, �_ from 82 should be set to 1.30. An example rule is:
if ROC>�_�P�Gr MAX then a spike event is detected Other rules include a cyclic rule to detect cyclical oscillations in the process signal and an erratic rule to detect erratic behavior in the process signal. It should be understood that other rules may be implemented to observe other events in the process signal and may use different formulas or computational techniques to detect and event. A rule may operate on more than one statistical parameter or on more than one process signal. For example, if a process variable such as flow rate exceeds a predetermined limit while another process variable such as process temperature spikes, a rule could determine that the process is overheating and an emergency shut down condition could exist. Furthermore, another type of rule is implemented in fuzzy logic in which the statistical parameter is operated on by a sensitivity parameter which is a membership function applied to the trained values.
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