Source: http://www.google.com/patents/US7957936?dq=U.S.+patent+number+7,325,728
Timestamp: 2014-09-17 03:58:14
Document Index: 602315934

Matched Legal Cases: ['art.\n22', 'art.\n23', 'art.\n25', 'art.\n27', 'Application No. 60', 'Application No. 60', 'Application No. 60', 'art 200', 'art 210', 'Application No. 05714115', 'Application No. 2006133970', 'Application No. 2006133970']

Patent US7957936 - Presentation system for abnormal situation prevention in a process plant - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsA system for visually presenting data receives signal processing data generated signal processing data collection blocks corresponding to devices associated with a process plant. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression...http://www.google.com/patents/US7957936?utm_source=gb-gplus-sharePatent US7957936 - Presentation system for abnormal situation prevention in a process plantAdvanced Patent SearchPublication numberUS7957936 B2Publication typeGrantApplication numberUS 12/029,166Publication dateJun 7, 2011Filing dateFeb 11, 2008Priority dateMar 1, 2001Also published asCA2557227A1, CN102520717A, EP1728132A2, US7389204, US20050197805, US20080168356, WO2005093531A2, WO2005093531A3Publication number029166, 12029166, US 7957936 B2, US 7957936B2, US-B2-7957936, US7957936 B2, US7957936B2InventorsEvren Eryurek, Kadir Kavaklioglu, John Philip MillerOriginal AssigneeFisher-Rosemount Systems, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (101), Non-Patent Citations (28), Referenced by (3), Classifications (8), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetPresentation system for abnormal situation prevention in a process plantUS 7957936 B2Abstract A system for visually presenting data receives signal processing data generated signal processing data collection blocks corresponding to devices associated with a process plant. The signal processing data collection blocks may generate data such as statistical data, frequency analysis data, auto regression data, wavelets data, etc. The system displays an image representative of the devices and representative of a context of the devices within the process plant. Additionally, data based on signal processing data corresponding to one or more devices is displayed. For example, the signal processing data for the device could be displayed. As another example, data may be generated based on the signal processing data and this generated data may be displayed.
Images(38) Claims(40)
1. A method for visually presenting data associated with a process plant, the method comprising:
receiving, via a communication network, signal processing data generated by a plurality of signal processing data collection blocks using process data or process variables as input, each signal processing data collection block disposed within a corresponding one of a plurality of process control devices associated with the process plant, the signal processing data comprising at least one of statistical data, frequency analysis data, regression data, wavelets data, data generated using a neural network, and fuzzy logic data;
displaying an image representative of at least one process control device of the plurality of process control devices and representative of a context of the at least one process control device within the process plant; and
displaying data based on signal processing data corresponding to the at least one process control device.
2. A method according to claim 1, wherein displaying the data based on signal processing data comprises displaying the data based on the signal processing data simultaneously with displaying the image representative of the at least one process control device of the plurality of process control devices and representative of the context of the at least one process control device within the process plant.
3. A method according to claim 1, wherein displaying the data based on signal processing data comprises displaying the data based on the signal processing data subsequently to displaying the image representative of the at least one process control device of the plurality of process control devices and representative of the context of the at least one process control device within the process plant.
4. A method according to claim 1, wherein displaying data based on signal processing data corresponding to the at least one process control device comprises displaying data based on signal processing data generated by a signal processing data collection block disposed within the at least one process control device, wherein the at least one process control device comprises a field device.
5. A method according to claim 1, wherein displaying data based on signal processing data corresponding to the at least one process control device comprises displaying data based on signal processing data generated by a signal processing data collection block disposed within a device different than the at least one process control device.
6. A method according to claim 5, wherein the device different than the at least one process control device comprises at least one of a field device, a process controller, a data historian, and a workstation.
7. A method according to claim 1, further comprising processing at least some of the signal processing data generated by the plurality of signal processing data collection blocks to generate the data based on the signal processing data corresponding to the at least one process control device.
8. A method according to claim 7, wherein processing the at least some of the signal processing data comprises processing signal processing data generated by a signal processing data collection block disposed within the at least one process control device.
9. A method according to claim 7, wherein processing the at least some of the signal processing data comprises processing signal processing data generated by a signal processing data collection block disposed within a device different than the at least one process control device.
10. A method according to claim 9, wherein the device different than the at least one process control device comprises at least one of a field device, a process controller, a data historian, and a workstation.
11. A method according to claim 7, wherein processing the at least some of the signal processing data comprises generating correlation data indicative of degrees of correlation among the at least some of the signal processing data.
12. A method according to claim 11, wherein displaying the data based on signal processing data comprises displaying the correlation data via a table.
13. A method according to claim 11, wherein displaying the data based on signal processing data comprises displaying the correlation data via a color-coded matrix.
14. A method according to claim 11, wherein displaying the data based on signal processing data comprises displaying the correlation data via a polar-coordinates plot.
15. A method according to claim 11, wherein displaying the data based on signal processing data comprises displaying the correlation data versus time.
16. A method according to claim 11, wherein processing the at least some of the signal processing data further comprises generating correlation change data indicative of degrees of variance of the correlation data from baselines.
17. A method according to claim 16, wherein displaying the data based on signal processing data comprises displaying the correlation change data via a table.
18. A method according to claim 16, wherein displaying the data based on signal processing data comprises displaying the correlation change data via a color-coded matrix.
19. A method according to claim 16, wherein displaying the data based on signal processing data comprises displaying the correlation change data via a polar-coordinates plot.
20. A method according to claim 16, wherein displaying the data based on signal processing data comprises displaying the correlation change data versus time.
21. A method according to claim 1, wherein displaying the data based on signal processing data comprises displaying the data based on signal processing data via at least one chart.
22. A method according to claim 21, wherein displaying the data based on signal processing data comprises displaying the data based on signal processing data via a 2-dimensional chart.
23. A method according to claim 21, wherein displaying the data based on signal processing data comprises displaying the data based on signal processing data via a plurality of 2-dimensional charts.
24. A method according to claim 21, wherein displaying the data based on signal processing data comprises displaying the data based on signal processing data via a 3-dimensional chart.
25. A method according to claim 1, wherein displaying the data based on signal processing data comprises displaying the data based on signal processing data via a table.
26. A method according to claim 1, wherein displaying the data based on signal processing data comprises displaying the data based on signal processing data via a scatter chart.
27. A method according to claim 1, wherein displaying the image representative of the at least one process control device and representative of the context comprises displaying an image representative of a hierarchy of at least a portion of the process plant.
28. A method according to claim 1, wherein displaying the image representative of the at least one process control device and representative of the context comprises displaying an image graphically illustrating at least a portion of the process plant.
29. A method according to claim 28, wherein displaying the image graphically illustrating the at least a portion of the process plant comprises indicating the at least one process control device in the image.
30. A method for visually presenting data associated with a process plant, the method comprising:
receiving, via a plurality of signal data processing signals transmitted over a communication network, signal processing data generated by a plurality of signal processing data collection blocks using process data or process variables as input, each signal processing data collection block disposed within a corresponding one of a plurality of process control devices associated with the process plant;
displaying an image representative of the plurality of process control devices and representative of a context of the plurality of process control devices within the process plant;
receiving an indication of at least one process control device of the plurality of process control devices via a user interface mechanism; and
displaying data based on signal processing data corresponding to the at least one process control device, wherein the displayed data represents at least one of:
a relationship between the signal processing data corresponding to the at least one process control device and signal processing data corresponding to at least one other process control device;
over a period of time the signal processing data corresponding to the at least one process control device; or
a correlation signal corresponding to a pair of signal processing data signals in the plurality of signal processing data signals.
31. A method according to claim 30, wherein displaying the data based on signal processing data comprises displaying the data based on the signal processing data simultaneously with displaying the image representative of the at least one process control device of the plurality of process control devices and representative of the context of the at least one process control device within the process plant.
32. A method according to claim 30, wherein displaying the data based on signal processing data comprises displaying the data based on the signal processing data subsequently to displaying the image representative of the at least one process control device of the plurality of process control devices and representative of the context of the at least one process control device within the process plant.
33. A method according to claim 30, wherein displaying data based on signal processing data corresponding to the at least one process control device comprises displaying data based on signal processing data generated by a signal processing data collection block disposed within the at least one process control device, wherein the at least one process control device comprises a field device.
34. A method according to claim 30, wherein displaying data based on signal processing data corresponding to the at least one process control device comprises displaying data based on signal processing data generated by a signal processing data collection block disposed within a device different than the at least one process control device.
35. A method according to claim 34, wherein the device different than the at least one process control device comprises at least one of a field device, a process controller, a data historian, and a workstation.
36. A method according to claim 30, further comprising processing at least some of the signal processing data generated by the plurality of signal processing data collection blocks to generate the data based on the signal processing data corresponding to the at least one process control device.
37. A method according to claim 30, wherein receiving the indication of the at least one process control device comprises receiving the indication of the at least one process control device via at least one of a selection of the at least one process control device in a graphical hierarchy of the plurality of process control devices and a selection of at least one item in a pull-down menu.
38. A method according to claim 30, wherein the signal processing data comprises at least one of statistical data, frequency analysis data, regression data, wavelets data, data generated using a neural network, and fuzzy logic data.
39. A method according to claim 30, wherein each of the signal processing data collection blocks comprises a statistical process monitoring (SPM) block performing one or more statistical calculations on process variables or process data generated by the corresponding one of the plurality of process control devices.
40. A method according to claim 1, wherein each of the signal processing data collection blocks comprises a statistical process monitoring (SPM) block performing one or more statistical calculations on process variables or process data generated by the corresponding one of the plurality of process control devices. Description
CROSS-REFERENCES TO RELATED APPLICATIONS This application is a continuation of, and claims the benefit of, U.S. patent application Ser. No. 10/972,155, filed on Oct. 22, 2004, and entitled �Presentation System For Abnormal Situation Prevention In A Process Plant.� U.S. patent application Ser. No. 10/972,155 claims the benefit of U.S. Provisional Patent Application No. 60/549,796, filed on Mar. 3, 2004, and entitled �Abnormal Situation Prevention In A Process Plant,� and is also a continuation-in-part of U.S. patent application Ser. No. 10/484,907, entitled �Integrated Alert Generation in a Process Plant,� filed on Jan. 26, 2004, which is a national stage application of PCT/US2003/06018, entitled �Integrated Alert Generation in a Process Plant,� filed on Feb. 28, 2003. U.S. patent application Ser. No. 10/484,907 is a continuation-in-part of, and claims priority to, U.S. patent application Ser. No. 10/104,586, entitled �Integrated Device Alerts in a Process Control System,� filed on Mar. 22, 2002, which is a continuation-in-part of, and claims priority to, U.S. patent application Ser. No. 09/896,967 entitled �Enhanced Hart Device Alerts in a Process Control System,� filed on Jun. 29, 2001, which in turn is a continuation-in-part of, and claims priority to, U.S. patent application Ser. No. 09/861,790, entitled �Enhanced Fieldbus Device Alerts in a Process Control System,� filed on May 21, 2001, which in turn claims the benefit of U.S. Provisional Patent Application No. 60/273,164, entitled �Asset Utilization Expert in a Process Control Plant,� filed on Mar. 1, 2001. U.S. patent application Ser. No. 10/484,907 is also a continuation-in-part of, and claims priority to, U.S. patent application Ser. No. 10/087,308 entitled, �Data Sharing in a Process Plant,� filed on Mar. 1, 2002, which claims the benefit of U.S. Provisional Application No. 60/273,164, entitled �Asset Utilization Expert in a Process Control Plant,� filed on Mar. 1, 2001.
U.S. patent application Ser. No. 10/972,224, filed on Oct. 22, 2004, and entitled �CONFIGURATION SYSTEM AND METHOD FOR ABNORMAL SITUATION PREVENTION IN A PROCESS PLANT;�
U.S. patent application Ser. No. 10/971,361 filed on Oct. 22, 2004, issued as U.S. Pat. No. 7,079,984 on Jul. 18, 2006, and entitled �ABNORMAL SITUATION PREVENTION IN A PROCESS PLANT.�
There is currently one technique that may be used to collect data that enables a user to predict the occurrence of certain abnormal situations within a process plant before these abnormal situations actually arise, with the purpose of taking steps to prevent the predicted abnormal situation before any significant loss within the process plant takes place. This procedure is disclosed in U.S. patent application Ser. No. 09/972,078, entitled �Root Cause Diagnostics� (based in part on-U.S. patent application Ser. No. 08/623,569, now U.S. Pat. No. 6,017,143). The entire disclosures of both of these applications are hereby incorporated by reference herein. Generally speaking, this technique places statistical data collection and processing blocks or statistical processing monitoring (SPM) blocks, in each of a number of devices, such as field devices, within a process plant. The statistical data collection and processing blocks collect, for example, process variable data and determine certain statistical measures associated with the collected data, such as a mean, a median, a standard deviation, etc. These statistical measures may then sent to a user and analyzed to recognize patterns suggesting the future occurrence of a known abnormal situation. Once a particular suspected future abnormal situation is detected, steps may be taken to correct the underlying problem, thereby avoiding the abnormal situation in the first place. However, the collection and analysis of this data may be time consuming and tedious for a typical maintenance operator, especially in process plants having a large number of field devices collecting this statistical data. Still further, while a maintenance person may be able to collect the statistical data, this person may not know how to best analyze or view the data or to determine what, if any, future abnormal situation may be suggested by the data.
Similarly, a power generation and distribution system 24 having power generating and distribution equipment 25 associated with the plant 10 is connected via, for example, a bus, to another computer 26 which runs and oversees the operation of the power generating and distribution equipment 25 within the plant 10. The computer 26 may execute known power control and diagnostics applications 27 such a as those provided by, for example, Liebert and ASCO or other companies to control and maintain the power generation and distribution equipment 25. Again, in many cases, outside consultants of service organizations may use service applications that temporarily acquire or measure data pertaining to the equipment 25 and use this data to perform analyses for the equipment 25 to detect problems, poor performance or other issues effecting the equipment 25. In these cases, the computers (such as the computer 26) running the analyses may not be connected to the rest of the system 10 via any communication line or may be connected only temporarily
As illustrated in FIG. 1, a computer system 30 implements at least a portion of an abnormal situation prevention system 35, and in particular, the computer system 30 stores and implements a configuration and data collection application 38, a viewing or interface application 40, which may include statistical collection and processing blocks, and a rules engine development and execution application 42 and, additionally, stores a statistical process monitoring database 43 that stores statistical data generated within certain devices within the process. Generally speaking, the configuration and data collection application 38 configures and communicates with each of a number of statistical data collection and analysis blocks (not shown in FIG. 1) located in the field devices 15, 16, the controllers 12B, 14B, the rotating equipment 20 or its supporting computer 22, the power generation equipment 25 or its supporting computer 26 and any other desired devices and equipment within the process plant 10, to thereby collect statistical data (or in some cases, process variable data) from each of these blocks with which to perform abnormal situation prevention. The configuration and data collection application 38 may be communicatively connected via a hardwired bus 45 to each of the computers or devices within the plant 10 or, alternatively, may be connected via; any other desired communication connection including, for example, wireless connections, dedicated connections which use OPC, intermittent connections, such as ones which rely on handheld devices to collect data, etc. Likewise, the application 38 may obtain data pertaining to the field devices and equipment within the process plant 10 via a LAN or a public connection, such as the Internet, a telephone connection, etc. (illustrated in FIG. 1 as an Internet connection 46) with such data being collected by, for example, a third party service provider. Further, the application 38 may be communicatively coupled to computers/devices in the plant 10 via a variety of techniques and/or protocols including, for example, Ethernet; Modbus, HTML, XML, proprietary techniques/protocols, etc. Thus, although particular examples using OPC to communicatively couple the application 38 to computers/devices in the plant 10 are described herein, one of ordinary skill in the art will recognize that a variety of other methods of coupling the application 38 to computers/devices in the plant 10 can be used as well. The application 38 may generally store the collected data in the database 43 Once the statistical data (or process variable data) is collected, the viewing application 40 may be used to process this data and/or to display the collected or processed statistical data (e.g., as stored in the database 43) in different manners to enable a user, such as a maintenance person, to better be able to determine the existence of or the predicted future existence of an abnormal situation and to take preemptive corrective actions. The rules engine development and execution application 42 may use one or more rules stored therein to analyze the collected data to determine the existence of, or to predict the future existence of an abnormal situation within the process plant 10. Additionally, the rules engine development and execution application 42 may enable an operator or other user to create additional rules to be implemented by a rules engine to detect or predict abnormal situations.
Generally speaking, the process controllers 60 store and execute one or more controller applications that implement control strategies using a number of different, independently executed, control modules or blocks. The control modules may each be made up of what are commonly referred to as function blocks, wherein each function block is a part or a subroutine of an overall control routine and operates in conjunction with other function blocks (via communications called links) to implement process control loops within the process plant 10. As is well known, function blocks, which may be objects in an object-oriented programming protocol, typically perform one of an input function, such as that associated with a transmitter, a sensor or other process parameter measurement device, a control function, such as that associated with a control routine that performs PID, fuzzy logic, etc. control, or an output function, which controls the operation of some device, such as a valve, to perform some physical function within the process plant 10. Of course, hybrid and other types of complex function blocks exist, such as model predictive controllers (MPCs), optimizers, etc. It is to be understood that while the Fieldbus protocol and the DeltaV� system protocol use control modules and function blocks designed and implemented in an object-oriented programming protocol, the control modules may be designed using any desired control programming scheme including, for example, sequential function blocks, ladder logic, etc., and are not limited to being designed using function blocks or any other particular programming technique
As illustrated in FIG. 2, the maintenance workstation 74 includes a processor 74A, a memory 74B and a display device 74C. The memory 74B stores the abnormal situation prevention applications 38, 40 and 42 discussed with respect to FIG. 1 in a manner that these applications can be implemented on the processor 74A to provide information to a user via the display 74C (or any other display device, such as a printer
FIG. 5 illustrates a portion of an example plant hierarchy 94, created by an OPC server, which depicts the devices and other elements of a process plant being scanned by the OPC server. The top level of the hierarchy 94 has nodes 96 and 98 called Modules and IO, wherein the Modules node 96 includes control strategy information, and the IO node 98 includes the hardware/device information. As illustrated in the example hierarchy of FIG. 5, the IO node 98 includes sub-nodes associated with Controllers (CTLR), Cards (C) and Ports (P) wherein, in this example, the Ports (P) are associated with Fieldbus segments actually present in the controller network. Further down in the hierarchy, Fieldbus devices are listed under their respective ports. In the example of FIG. 5, each Fieldbus device that contains an ADB includes a node named TRANSDUCER800 or TRANSDUCER1300 under the device. (In Rosemount 3051F devices, the ADB is called TRANSDUCER800, while in Rosemount 3051S devices the ADB is called TRANSDUCER1300). One such node 100 named TRANSDUCER800 is illustrated in the hierarchy of FIG. 5. The ADB node 100 includes the diagnostics information of interest. In this particular case, the application 38 is interested in the statistical process monitoring (SPM) parameters within the ADB node 100, which is expanded in the hierarchy of FIG. 5 to illustrate some of the elements associated with the ADB in a Rosemount 3051F device. Of course, the names �TRANSDUCER800� and �TRANSDUCER1300� are simply examples of names of known function blocks provided by one known manufacturer. Other ADB blocks or the SPM blocks may have other names, and/or the names may be different in a system other than one that utilizes OPC. In other implementations, different names may correspond to ADB blocks or the SPM blocks of other transducer blocks, function blocks, etc. later developed and/or provided by other manufacturers and/or as described in the Foundation Fieldbus specs, or could be blocks or other software elements in any other smart communication protocol (e.g., digital protocol) such as any element in the Profibus, HART, CAN, AS-Interface, HTML, XML, etc. protocols, to name but a few.
In the process of discovering or searching for the devices containing an ADB, the block 92 may store a list of devices detected as having an ADB, an SPM block or other type of data collection block, as indicated by the box 108 of FIG. 4. If desired, the devices listed in the box 108 may be displayed in a tree-view display according to their hierarchy. An example of such a hierarchical display 110 is illustrated in FIG. 6. As will be understood, the hierarchy 110 displayed in the view of FIG. 6 is a subset of the hierarchy that would be displayed under a control network display generated by a controller, as not all of the devices within the control display will typically include ADBs. In fact, the view 110 of FIG. 6 is actually a copy of a controller hierarchy including only the devices containing an ADB. As will be understood, the display of FIG. 6 illustrates that the devices PT-101 and PT-102 (connected to port P01 of card C01 of input/output device 101 of the controller named CTLR-002EC6) and devices PT-103, FT-201 and FT-202 (connected to port P02 of card C01 of input/output device 101 of the controller named CTLR-002EC6) each has an ADB therein.
To read any of the SPM parameters from a device, it is generally necessary to know the OPC item ID for that parameter. Typically, i.e., in the case of Fieldbus SPM blocks, the OPC item ID for an SPM parameter includes a Device ID followed by the item specifier. To locate the Device ID, the block 92 may, for each device node which has been determined to contain an ADB, find the sub-node SPM_ACTIVE. Next, the block 92 may obtain the OPC Item ID for the leaf �CV�. For example, the OPC Item ID might be �DEVICE:0011513051022201100534-030003969/800/SPM ACTIVE.CV�. The Device ID is then the OPC Item ID minus the suffix �SPM ACTIVE.CV�. Thus, in this example, the Device ID is �DEVICE:0011513051022201100534-03000396-9/800/�. Of course, this is but one manner of determining a Device ID in an OPC system, it being possible to use other techniques as well or instead.
Referring again to FIG. 4, a block 114 may next determine which of the devices stored in the box 108 have already been configured to perform statistical process monitoring. To perform this function, the block 114 may read the value SPM_ACTIVE.CV from the OPC server for each of the devices stored in the box 108. For example, for the transmitter PT-101 in the table above, the block 114 may read the OPC Item DEVICE:0011513051022201100534-030003969/800/SPM_ACTIVE.CV. This OPC item can take a value of 0 or 255. In the case of Fieldbus SPM blocks, if the value is 0, then SPM block is disabled for that device and if the value is 255, then the SPM block is enabled for that device. Upon checking if SPM is enabled for each device, the block 114 can divide all of the devices into two categories, namely, devices with SPM already configured, and devices with SPM not yet configured. These categories or lists of devices are illustrated by the boxes 116 and 118 in FIG. 4.
Generally speaking, the value of status is an 8-bit number ranging from 0 to 255. The status is a combination of 8 different bits that can be on or off. The bits are: Inactive (1), Learning (2), Verifying (4), No Detections (8), Mean Change (16), High Variation (32), Low Dynamics (64), and Not Licensed (128). All SPM Blocks that are licensed, but have not been configured, have a Status of Inactive % If the Status of an SPM Block is Inactive or Not Licensed, the block 120 may determine that this block will not be monitored because it is not generating any useful information. However, if the Status is any of the other possibilities, the block 120 may monitor the SPM block.
USL = ( 1 + Δ μ 100 ) � μ ( Equ . ⁢ 1 ) USL = ( 1 - Δ μ 100 ) � μ ( Equ . ⁢ 2 ) wherein Δμ is a user-specified mean limit, in percent. Of course, the viewing application 40 may calculate these values directly or may allow a user to input these values.
UCL = μ + 3 ⁢ ⁢ σ N ( Equ . ⁢ 3 ) LCL = μ - 3 ⁢ ⁢ σ N ( Eqn . ⁢ 4 ) where N=(Monitoring Cycle)*(60)*(Samples per second)
UCL = ( 1 + 3 2 ⁢ ( N - 1 ) ) � σ ( Equ . ⁢ 5 ) LCL = ( 1 - 3 2 ⁢ ( N - 1 ) ) � σ ( Equ . ⁢ 6 ) USL = ( 1 + Δ HV 100 ) � σ ( Equ . ⁢ 7 ) LSL = ( 1 + Δ LD 100 ) � σ ( Eqn . ⁢ 8 ) where ΔHV is a user-defined High Variation Limit, in percent, and ΔLD is a user-defined Low Dynamics Limit, with ΔLD<0.
f ⁡ ( x ) = 1 2 ⁢ ⁢ π ⁢ ⁢ σ ⁢ exp ⁡ ( - ( χ - μ ) 2 2 ⁢ ⁢ σ 2 ) ( Equ . ⁢ 9 ) The application 40 could calculate a capability index or measurement as:
C p = USL - LSL 6 ⁢ ⁢ σ ( Equ . ⁢ 10 ) and could calculate a correlation coefficient between two variables (which may include statistical variables), as:
R xy = ∑ i = 1 N ⁢ ⁢ ( x i - x _ ) ⁢ ( y i - y _ ) ⁢ ∑ i = 1 N ⁢ ⁢ ( x i - x _ ) 2 ⁢ ∑ i = 1 N ⁢ ⁢ ( y i - y _ ) 2 ( Equ . ⁢ 11 ) In another example, a correlation coefficient between two variables can be calculated as:
R xy = ∑ i = 1 N ⁢ ⁢ ( x i - x _ ) ⁢ ( y i - y _ ) ⁢ ∑ i = 1 N ⁢ ⁢ ( x i - x _ ) 2 ⁢ ∑ i = 1 N ⁢ ( y i - y _ ) 2 ( Equ . ⁢ 12 ) Of course, the viewing application 40 could perform other calculations for any variable or variables (including statistical variables as well as process variables) as desired or needed within the system to determine one or more abnormal situations within a process plant. Thus, for example, the application 40 or some routine therein may perform principle component analysis, regression analysis, neural network analysis, or any other single or multi-variable analysis on the collected data to perform abnormal situation detection and prevention.
Generally speaking, the graphs of FIGS. 13, 14, 16 and 17 are based upon plotting one or more SPM parameters versus time. However, the viewing application 40 may provide graphs that indicate or illustrate correlations between one or more SPM variables without regard to time. In one example, the viewing application 40 may produce a scatter chart that plots one SPM parameter against another. The viewing application 40, or a user may determine a correlation coefficient, which provides an indication of how well two SPM parameters (or some combination of two SPM parameters) are correlated together. FIG. 18 illustrates a scatter chart 200 which plots two SPM mean parameters with respect to one another. Here, it can generally be seen that the two means are proportionally correlated due to the basic straight line nature of the scatter points (i.e., as one mean increases, the other mean tends to increase). Points which fall well outside of the general scatter regions may indicate a potential problem within the plant. Of course, the viewing application 40 is not limited to providing two-dimensional scatter charts such as that of FIG. 18. In fact, the viewing application 40 may provide three or more dimensional scatter charts plotting three or more SPM parameters with respect to one another. FIG. 19, for example, illustrates a three-dimensional scatter chart 210 which plots the relationship of three SPM parameters with respect to one another and, in particular, the means of three process variables against each other.
1 ⁢ - ⁢ Norm ⁢ : ⁢  Δ ⁢ ⁢ C  1 = 1 N ⁢ ∑ i = 1 N ⁢ ⁢  Δ ⁢ ⁢ C i  ( Equ . ⁢ 13 ) 2 ⁢ - ⁢ Norm ⁢ : ⁢  Δ ⁢ ⁢ C  2 = ∑ i = 1 N ⁢ ⁢ Δ ⁢ ⁢ C i 2 N ( Equ . ⁢ 14 ) Infinity ⁢ - ⁢ Norm ⁢ : ⁢  Δ ⁢ ⁢ C  ∞ = max i = 1 N ⁢  Δ ⁢ ⁢ C i  ( Equ . ⁢ 15 ) where ΔCi is the ith correlation difference value, and N is the number of correlation difference values. The 1/N factor in equation 13 and the
m xy = ∑ i = 1 N ⁢ ⁢ ( x i - x _ ) ⁢ ( y i - y _ ) ⁢ ∑ i = 1 N ⁢ ⁢ ( x i - x _ ) 2 ( Equ . ⁢ 16 ) where xi is an ith sample of the X data set, yi is an ith sample of the Y data set, x is the mean of the samples in the X data set, y is the mean of the samples in the Y data set, and N is the number of samples in each of the data sets X and Y.
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