Patent Document

RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application No. 61/646,095, filed on May 11, 2012, and is a continuation-in-part of U.S. application Ser. No. 12/786,052, filed on May 24, 2010, which claims the benefit of U.S. Provisional Application No. 61/217,370, filed on May 29, 2009. The entire teachings of the above applications are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     Multivariable Predictive Control (MPC) is the most widely used advanced process control technology in process industries, with more than 5,000 worldwide applications currently in service. MPC, which is sometimes also referred to as multivariate control (MVC), employs a model predictive controller that relies on dynamic models of an underlying process, e.g., linear models obtained by system identification. 
     A common and challenging problem is that MPC control performance degrades with time due to inevitable changes in the underlying subject process, such as equipment modifications, changes in operating strategy, feed rate and quality changes, de-bottlenecking, instrumentation degradation, etc. Such degradation of control performance results in loss of benefits. Among all possible causes of control performance degradation, the process model&#39;s predictive quality is the primary factor in most cases. To sustain good control performance, the model&#39;s predictive quality needs to be monitored, and the model needs be periodically audited and updated. 
     To address the technically challenging problem of model quality auditing and online model identification and adaptation, the Assignee developed an innovative approach for model quality estimation and model adaptation (see U.S. Patent Application Publication No. US 2011/0130850 A1, the parent related application), and a new method for non-invasive closed loop step testing (see U.S. Provisional Application No. 61/596,459, filed on Feb. 8, 2012) that expanded the automated closed loop step testing techniques in Assignee&#39;s U.S. Pat. Nos. 7,209,793 and 6,819,964. Each of the above techniques help in monitoring model quality and generating informative plant test data in a more efficient way. Once the process data becomes available through either open/closed-loop plant tests or historical plant operation records, a necessary and also important step is data screening and selection for model quality estimation and model identification. There are two important reasons for performing data screening and selection. First, the process data received from open/closed-loop plant tests are costly due to not only the engineer&#39;s work during the designed plant testing, but also the intervention (interruption) to plant production. Therefore the usage of those plant test data should be maximized. Second, collected process time series data may contain segments of samples over periods such as unit/equipment shut-downs, measurement equipment errors, variable values at High/Low limits, control output saturated or frozen, etc. If these data are included in the calculation of model quality estimation or model identification, the results can be contaminated and become unreliable. 
     In industrial practice of the prior art, a control engineer spends hours to days viewing all the process variables in time series plots and visually finding those data samples unsuitable for model identification purpose. To exclude the found “bad” data samples, the control engineer manually marks such data segments in software as “bad data slices” through a pertinent user interface. For confirmation and double-check on whether there are any “bad data” missed manual slicing, the engineer tries a number of model identification runs until the identification algorithm goes through all the data without failures and the resulting models look normal and make sense from the engineer&#39;s view. A typical APC project may contain 50-200 process time series variables and the data collection window may cover a period of weeks to months. Conducting the data screening and selection task by hand may take an experienced engineer days to weeks of intensive work. 
     In addition, there are two other shortcomings by using the conventional approach to data screening and selection for model quality estimation and identification. One is that any of the marked “bad data slices” in a time series will cause a data loss of a large piece of good data (e.g., one time to steady state (TTSS) for a FIR model and 40-60 samples for a subspace model) following the “bad data slice”, as a side effect due to the required re-initialization. The other drawback is that a conventional approach is not suitable for frequent runs in an online application (such as that described in U.S. Patent Application Publication No. US 2011/0130850 A1) where a pre-scheduled automated data screening and selection operation is needed to serve plant testing monitoring. Once a process variable hits its High/Low limit, becomes saturated, or loses measurements, the automated data screening and selection module should alert the operator and report the situation timely, so that the engineer may take actions to make corrections/adjustments and avoid a time and data loss on the plant testing. 
     There have been some general data preprocessing methods reported in process model identification text books (e.g., Lennart Ljung, “System Identification—Theory for The Users” Second Edition, Prentice Hall, 1999), but there is no systematical method for automated data screening and selection. A recently reported method of data selection from historic data for model identification (Daniel Peretzki, Alf J. Isaksson, Andre Carvalho Bittencourt, Krister Forsman, “Data Mining of Historic Data for Process Identification”, AIChE Annual Meeting, 2011) is focused on finding useful intervals by use of a Laguerre approximation model and limited to only single-input and single-output (SISO) PID (Proportional-Integral-Derivative Controller) loops. In industrial APC practice, based on Applicants&#39; knowledge, automated data screening and selection has been APC (Process Control) engineers&#39; “dream”, where neither systematic solution nor commercial tools are available yet. 
     SUMMARY OF THE INVENTION 
     The innovation presented by Applicants herein address the above issues in the art. Applicants provide a new apparatus and method for automated data selection in model quality estimation and identification in multivariable process control. 
     Embodiments of the present invention provide a new apparatus and method to address the foregoing problems by (1) detecting and excluding datasets that are unsuitable for model quality estimation and model identification, and (2) repairing and patching certain datasets to maximize usage of data in an MPC application. 
     Embodiments provide expansion of the model quality estimation and model adaptation method in multivariable process control described by Assignee in: 
     Related parent patent application, U.S. Publication No. US 2011/0130850 A1, published on Jun. 2, 2011, 
     U.S. application Ser. No. 13/760,949 Filed Feb. 6, 2013 (claiming the benefit of U.S. Provisional Application No. 61/596,459, filed on Feb. 8, 2012), and 
     U.S. Pat. No. 7,209,793, issued on Apr. 24, 2007, each incorporated herein by reference in their entirety. Embodiments can be configured and executed to screen and select data for model quality estimation and model identification while minimizing negative impact of the undesirable data and maximizing the overall usage of available process data. 
     Embodiments provide several new automated data screening and selection techniques to select data suitable for model assessment and model re-identification. 
     Embodiments provide an innovative approach to testing and validating bad data segments found, which can help to minimize data loss. 
     Embodiments provide a novel technique to repair and patch certain bad data samples via an internal MISO (Multi-Input, Single-Output) model predictive method, which helps replace some bad data samples with model-based predictive values. In turn, this technique maximizes the overall data usage. 
     An embodiment is an apparatus comprising a four-tier automated data screening and selection system that can be configured and execute with a hierarchy and work flow as shown in  FIG. 2 . 
     An example embodiment of the present invention is method of screening and selecting data automatically for model identification and model adaptation in a multivariable predictive controller (MPC). Given an online controller having an existing model, the method includes loading process data from a subject process and storing the process data in a database accessible by said model, using a rule-based data selector for detecting and excluding data segments of stored process data that are unsuitable for model quality estimation and for model identification, validating the excluded data segments to minimize the data loss from bad data segments being excluded, repairing certain data segments of the stored process data to maximize usage of data in a MPC application; and updating the existing model using resulting process data as stored in the database. 
     An embodiment includes three different methods/modules of data screening and selecting and one data repairing method/module to serve as the automated data selection apparatus. These methods/modules are summarized as follows:
         1. Data Quality Based Basic Data Screen and Selection Method: Data segments are screened and invalid data are detected and marked as “bad” for exclusion based on online data quality status, data measurement availability, individual variable or controller on/off switching, and process events etc.   2. PID Control Loop Association Based Method: Variables are grouped according to their associated PID control loops. For each PID loop, process variables (PV) are compared against their corresponding set points (SP). All PV data showing significant deviations from their SP, such as auto-off-mode, measurement frozen, spikes due to unknown disturbances and PID in manual mode etc., are marked as “bad data slices” and these data segments are sliced out (or otherwise filtered) from the datasets before running model quality estimation and model identification.   3. Model Predictive Method: For those dependent variables that have no PID associations, an internal model is built to generate dependent predictions by using the available independent variables&#39; measurements. Then (1) model predictions are evaluated against their measurements, and (2) spikes and significant deviations in moving trends are assessed. The evaluated and assessed data may be marked as “bad data slices” and thus excluded from the dataset for model quality estimation and identification.   4. Bad Data Repairing Method: In model identification, once a “bad data” segment (slice) is excluded from the dataset, an initialization of the identification algorithm causes a data loss of about a length of 40-60 data samples in the good data segment following the marked “bad data” slice. To avoid losing too many good data points, an interpolation method of the present invention is applied to all short data segments (slices) by replacing rather than deleting their measurements with interpolated values.       

     In an embodiment, using rule-based data selector for detecting and excluding data segments of process data that are unsuitable for model quality estimation and for model identification includes the steps of: 
     a. Collecting process data variables and storing collected process data variables in the database at a given sampling frequency as time series variables. 
     b. Loading data status, special values, and value limits of variables of the subject process with their corresponding time series from the database. 
     c. Screening a given time series variable as a dependent process variable or an independent process variable and apply basic data screening filters to detect and mark data segments of the time series as Good Data or Bad Data according to given data quality measurement parameters.
 
d. Grouping time series variables according to their associated proportional-integral-derivative (PID) loops.
 
e. Comparing process variables (PV) against their corresponding set points (SP) in each PID loop and applying data screening filters.
 
f. Generating predictions for dependent variables without a PID association using available independent variable measurements.
 
g. Evaluating the generated predictions for dependent variables without a PID association against corresponding available dependent variable measurements and applying data screening methods.
 
h. Identifying and generating Bad Data slices in the given time series variable using the data segments marked as Bad Data and a data slice generator to exclude the Bad Data segments from the time series.
 
     In an embodiment, validating excluded data segments to minimize the data loss from bad data segments being excluded includes the steps of: 
     i. Testing Bad Data segments of the time series not removed by a basic data filter by comparing the model qualities (MQ) when the Bad Dada segments are included and excluded in the model identification. 
     j. Confirming Bad Data segments as Bad Data based on the MQ comparison, otherwise unmarking candidate Bad Data segments. 
     In an embodiment, repairing certain time series to maximize usage of data in a MPC application includes the steps of: 
     k. For the given time series variable, selecting all confirmed Bad Data segments with length less than a half time to steady-state (TTSS) for interpolation. 
     l. Patching segments in the time series where data portions have been removed by the data slice generator with interpolated data segments. 
     m. Minimizing the negative impacts of replacing Bad Data segments with interpolated data segments by patching Bad Data slices with their interpolated slices with a smooth connection between the end points of a interpolated data segments. 
     In some embodiments, the process data variables of step (a) includes manipulated variables (MVs), measurements of control variables (CVs) of the subject process, calculated values of independent variables, calculated values of dependent variables, all data vectors of the subject process, and all PID controller loops. 
     In some embodiments, the basic data screening methods of step (c) include a PID controller output (OP) saturation and High/Low limit detector, a frozen signal/measurement detector if the given time series is a dependent variable, and a PID manual mode detector if the given time series is an independent variable. 
     In an embodiment, comparing process variables (PV) against their corresponding set points (SP) in each PID look and applying data screening filters is performing a PID loop association search and finding available SP, PV, and OP among all process variables, determining whether a PV and SP pair or a PV, SP and control output (OP) triple are associated with the time series in a PID loop, and, if PV and SP pair or PV, SP and OP triple is associated with the time series in the same PID loop, using a rule-based data selector to apply data screening filters to detect data sequences showing deviations from their SP and marking the corresponding segments of data as Bad Data. 
     In an embodiment, the data screening filters include a PV spike detector if the given time series is a dependent variable, a PID manual mode detector, and a SP in closed-loop mode detector if the given time series is an independent variable. An embodiment of evaluating the generated predictions for dependent variables without a PID association against their measurements and applying data screening filters (step (g)) is, if no PV and SP pair or PV, SP and OP triple is associated with the current time series variable in the same PID loop and the time series variable is a dependent process variable, using data screening filters to detect data segments showing spikes and deviations in moving trend and marking the corresponding segments of data as Bad Data according to given data screening filters. The data screening filters can include a PV spike detector and a trend mismatch/unknown disturbances detector. 
     In some embodiments, the trend mismatch/unknown disturbances detector includes generating a model prediction of the time series variable via a multiple-input single-output (MISO) model, calculating a prediction error for unknown disturbances, detecting if a control variable (CV) time series does not parallel the predicted MISO model values, detecting if a large sustained shift in the average value of the CV has occurred outside of the normal movement and determining if it is associated with the model prediction, and selecting for exclusion the data segments of the time series variable showing difference in trends beyond the prediction error and making selected data segments as Bad Data. 
     In another embodiment, testing Bad Data slices includes setting Bad Data segments as Candidate Bad Data segments and generating Good Data segments by excluding Candidate Bad Data segments for each time series variable, for a dependent variable, configuring a multiple-input single-output (MISO) model identification and loading the independent variables as inputs and the dependent variable as output in the MISO model, applying the generated Good Data segments to the configured MISO model and calculating a baseline model quality index (MQ) by performing a model quality assessment on the MISO model, and running a MISO case identification on a Candidate Bad Data segment of the data segments not removed by a basic data filter and calculating a candidate MQ by performing a model quality assessment on the MISO model corresponding to the Candidate Bad Data segment. 
     In yet another embodiment, confirming Candidate Bad Data segments as Bad Data includes, given a tunable parameter as a threshold, marking Candidate Bad Data segment as a Good Data segment if difference between baseline MQ and candidate MQ is within the tunable threshold and removing corresponding data slices from a list of Bad Data slices. 
     In another embodiment, patching segments in the time series where data portions have been removed by the data slice generator with interpolated data segments includes, if the time series variable is an independent variable, checking the Good Data segments adjacent to the selected Bad Data segments to determine if the Good Data sample values before a Start Point and after a End Point of a Bad Data segment are within a given threshold, and, if true, creating an interpolation slice by linear interpolation and replacing the selected Bad Data segments with the interpolation slice. If the selected time series variable is a dependent variable, configuring a MISO model case with the dependent variable and associated independent variables, and running a MISO identification case using the resultant MISO model to generate a model prediction on the dependent variable, and creating an interpolated slice for Bad Data segments if model predictive values are available for the Bad Data segment. If the time series variable is a dependent variable, checking the Good Data segments adjacent to the selected Bad Data segments to determine if the calculated average values over a given length before the Start Point and after the End Point of a Bad Data segment are within the given threshold, and if true, creating an interpolation slice by linear interpolation and replacing the selected Bad Data segments with the interpolation slice, the interpolation slice smoothly connected to the Good Data segments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention. 
         FIG. 1  is a MPC system diagram that an embodiment of the present invention is applied to. 
         FIG. 2  is a workflow diagram of the automated data screening and data selection apparatus of  FIG. 1 . 
         FIG. 3  is a flow diagram of a data slice generator of embodiments. 
         FIG. 4  is a schematic illustration of a data graph that embodiments automatically read plant test/operational data status and generate good and bad data segments. 
         FIG. 5  is a schematic illustration of a data graph from which embodiments perform data repair to gaps in data, due to removal of bad data, by interpolation across remaining good data. 
         FIG. 6  is a schematic illustration of a data graph having measurement values at their high and low limits that embodiments detect and segment as inappropriate for model identification. 
         FIG. 7  is a schematic illustration of a data graph from which embodiments detect instrument saturation. 
         FIG. 8  is a schematic illustration of a data graph from which embodiments detect frozen signal/measurement. 
         FIGS. 9A and 9B  are schematic illustrations of data graphs from which embodiments detect significant spikes, disturbances or changes in operating point in process variables of the PID loops. 
         FIG. 10  is a schematic illustration of a data graph from which embodiments detect when a PID loop is in cascade for a sufficiently long period of time to conclude that this data will degrade the accuracy of model identification results. 
         FIG. 11  is a schematic illustration of a data graph from which embodiments detect when a PID loop output is saturated such that a process variable is no longer able to follow the corresponding set point. 
         FIG. 12  is a schematic illustration of a data graph from which embodiments detect when a PID loop output has been switched to manual mode and the corresponding set point is now tracking the process variable. 
         FIG. 13  is a schematic illustration of a data graph from which embodiments detect whether the PID process variable is not broadly following the set point trend and is moving in an opposite direction. 
         FIG. 14  is a schematic illustration of a data graph from which embodiments detect whether controlled variables do not show parallel movement to the predictions. 
         FIG. 15  is a schematic illustration of a data graph from which embodiments detect that a large sustained shift in the average value of a controlled variable (CV) has occurred outside of the normal movement, which is not explained by the CV prediction indicating a missing independent variable. 
         FIG. 16  is a schematic illustration of a data graph from which embodiments detect bad CV data and replace these bad data segments with predicted data without causing discontinuities in the repaired CV sequence. 
         FIG. 17  is a schematic illustration of a data graph from which embodiments determine the subset of data where PID modes were correctly maintained and no saturation occurred. 
         FIGS. 18A-C  are schematic illustrations of a graphical user interface allowing users to add, delete and modify bad data slices in embodiments. 
         FIG. 19  is a block diagram of a computer network in which embodiments of the invention are implemented to serve one or more process units. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     A description of example embodiments of the invention follows. 
     The teachings of all patents, published applications, and references cited herein are incorporated by reference in their entirety. 
     Embodiments provide expansion of model quality estimation and model adaptation in multivariable process control techniques described in related parent application (U.S. Patent Application Publication No. US 2011/0130850 A1 by Assignee). Embodiments can be configured and executed to screen and select required data for model quality assessment and/or model re-identification in the systems of the parent disclosure, while minimizing the intensive work on data selection and preparation commonly done by a process control engineer manually. Accordingly, the present invention provides an improvement over the prior state of the art. 
     Embodiments provide a new apparatus and method for screening and selecting plant test/operational data automatically for online or offline model identification and online model adaptation in multivariable predictive controller (MPC) applications. Different modes of operation are provided in embodiments, namely: Basic Data Screen mode, PID Loop Data Selection mode, Model Predictive Data Selection mode, Data Slices Validation mode, and Data Repairing mode. Below, an overview of system work flow and system elements are provided, followed by a discussion of operational modes, process modules/methods and mathematical details. 
     As used herein, the term “data screening and selection” may generally be used synonymously with data filtering, segmentation and the like. 
       FIG. 1  is a block diagram of a MPC control system embodying the present invention. An MPC controller  110  controls a subject dynamical process  120  (such as that of a processing plant) based on optimal targets  130 . The controller  110  includes an automated tester to perturb the process  120  in an embodiment. While  FIG. 1  shows the controller and the tester together as  110 , it should be understood by one of ordinary skill in the art that in another embodiment, a separate controller and tester may be provided. The controller  110  sends control/test signals  101  that set the values of manipulated variables (MVs) of the subject process  120 . The MVs are independent variables in the sense that they can be set as desired to perturb the process and to control the controlled variables (CVs) within their constraints set and operation limits. Measurements of control variables (CVs)  150  resulting after perturbation are fed back to the controller  110 . Control variables are so named because they are controlled through the manipulated variables; thus, the CVs are dependent variables that depend on the MVs. A real-time database  102  is used to store historical data regarding the MVs and the corresponding CVs. Although multiple optimal targets  130 , MVs, and CVs  150  are shown in  FIG. 1 , it should be understood that there may be a single optimal target, MV, and/or CV. 
     An automated data selection and online model identification module  140  selects only the valid and informative data series in segments, and identifies a whole or a subset of the multi-input multi-output (MIMO) model of controller  110  based on selected inputs and outputs data from the process  120  and the database  102 . The module  140  may be part of performance diagnostics which diagnose performance of the controller model as described in parent related U.S. patent application Ser. No. 12/786,052 (by Assignee), herein incorporated by reference in its entirety. In turn, module  140  updates (adapts) the model to improve performance. 
       FIG. 2  is a flow diagram depicting the major work flow in an embodiment of the invention. The automated data screen and selection apparatus  200  starts its operation  210  with loading all controller  110  relevant data sequences or time series. That is, apparatus  200  collects data related to MVs  101  and measurements of CVs  150  and stores the collected data into the database  102  at a specified sampling frequency, usually the same as used by the MPC controller  110 . The data may also include calculated values of independent variables and dependent variables collected from process  120  and stored in database  102 . 
     Apparatus  200  executes its task in a sequential way. For each time series (or the sampled values of a variable over time), Basic Data Selection method/module  220  is first applied based on the variable associated data sample quality. In this module  220 , all recorded data status, special values and High/Low value limits of variables are loaded into the apparatus  200  together with the time series values. An independent/dependent variable attribute check  230  on this time series of data is performed and corresponding data screening criteria are applied. For instance, if the subject data corresponds to dependent process variables, then method  232  detects data segments with frozen measurements and controller output (OP) saturation (values that have reached the High/Low limits and stay there for a long time). Method  232  marks the detected data segments as “bad data” slices. If the subject data corresponds to independent process variables, then method  231  detects data samples with High/Low saturation or with set points (SP) in “tracking” process variable (PV) mode. This typically occurs when a PID loop is in manual (MAN) mode. Method  231  then selects the detected data segment as “bad data” slices for exclusion. 
     After Basic Data Screening and Selection module/methods  220  described above, the remaining good data segments of the time series is passed to  240  for a PID loop association search among all loaded available time series of data from  210 . Decision junction  240  determines whether a PV and SP pair or a PV, SP and OP triple are associated with the current time series in a same PID loop. If so, then a PID loop based data screen and selection method/module  250  starts to execute. If the PV and SP are available, a time series of PID loop control error {e(t)} is created and used to detect PV spikes as described later. 
     In module  250 , additional data screening is performed if the selected time series is from a Dep. (dependent process variable) and is also an OP (output) in a PID loop. Once a constant OP value over a time window is detected by screening OP changes, module  250  concludes that the PID loop is in manual mode. In such a case, the unchanged dependent variable may lead to wrong conclusions in model quality estimation or model identification. Therefore, module  250  marks the corresponding segment of data samples as “bad data” slices. 
     When neither pair nor triple PID associated variables is found in  240 , and the selected time series is tested as a Dep. (dependent process variable) in  245 , a model predictive data screen and selection module/method  260  is used to further assist detecting unknown disturbances. 
     An internal multi-input, single-output (MISO) model identification is performed in module  260 , where the inputs are those defined independent variables in the controller model and the single output is the currently selected variable (time series). A model prediction of the selected time series is generated via the internally identified MISO model. Corresponding prediction error {pe(t)=CV(t)−CVPred(t)} is calculated for unknown disturbances data screening and detection in the work flow. 
     A module/method that detects when the CV does not show parallel movement to those predicted values is used in  260 , and module  260  selects for exclusion those data segments showing significant differences in trends. Module  260  marks the selected data segments as “unknown disturbances.” 
     A bad data slices validation module/method  270  is called after all bad data segments have been created from  210  through  260 . Due to the required re-initialization after each “bad data” or excluded data segment (slice) on data assembly for model identification, many good data points can become useless. When the number of “bad” data slices increase, the “good” data samples used for initialization become very “expensive”. To reduce this kind of high “cost” initialization, the present invention implements two strategies. One is to re-validate all “candidate” bad data slices by using the Assignee&#39;s techniques previously described in the parent related U.S. application Ser. No. 12/786,052 and herein incorporated by reference. In module/method  270 , each of the candidate “bad” data slices is tested by comparing the model qualities when the tested “bad” data segment is included in the model identification to that of when the tested “bad” data segment is excluded in the model identification. If the model quality decreases significantly by a measure of MQ (model quality) index, then the tested candidate “bad” data segment is confirmed as “bad”. Otherwise, module  270  unmarks the “bad” data segment and removes that data segment from the “bad data slice” list. 
     Another strategy to minimize the negative impacts created by “bad data slices” is to generate interpolated values patching the short “bad data slices” (i.e., segments where data portions have been removed). A module/method  280  generates MV interpolation values and CV predictions and patches the selected “bad” data slices. Module  280  is formed of two elements, one is a MISO identification engine and the other is an algorithm that patches the short “bad” data slices through a “smooth” connection between the two end-points of two neighborhood good data points. Each of these elements is further detailed below. 
     The foregoing process  220 - 280  is repeated on a next variable  290  as illustrated in  FIG. 2 . 
     Components of the Apparatus  200   
     Multiple components for carrying out the above methods of automated data screening and selection are provided in the embodiments. These include: 
     a rule-based data selector, 
     a data-slice generator, 
     a PV spike detector, 
     an OP saturation and High/Low limit detector, 
     a frozen signal/measurement detector, 
     a PID manual mode detector, 
     a SP in closed-loop mode detector, 
     a trend mismatch/unknown disturbances detector, 
     a slice validation module using MQ, and 
     a slice reparation/patch module. 
     Each of these components is configured as follows. It is understood that other components and configurations are in the purview of the skilled artisan given this description. 
     A Common Rule-Based Data Selector: 
     To automate the process of data screening and selection in various cases and under different rules/criteria, a common rule-based data marker/selector is provided. For a given time series, a list of data-sample-status-flags are created internally, which memorizes each data sample&#39;s status-mark. The status flags can be set for any rule-based criterion. For example, the following rules will “mark” a list of samples&#39; status flags: 
     
       
         
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                   
                 Comments/ 
               
               
                 Status 
                 Criterion 
                 Flag value 
                 Ref. FIGS. 
               
               
                   
               
             
             
               
                 Good 
                 STA = 0 
                  0 
                 Default value 
               
               
                 Bad 
                 STA = −1 
                 −1 
                 FIG. 4 
               
               
                 Off 
                 STA = −2 
                 −1 
                 FIG. 4 
               
               
                 Invalid value 
                 Value = −9999 
                 −1 
                 FIG. 4 
               
               
                 Missing value 
                 Value = −10001 
                 −1 
                 FIG. 4 
               
               
                 Over H/L limits 
                 Value &gt; H limit or 
                 −1 
                 FIG. 6 
               
               
                   
                 Value &lt; L limit 
               
               
                 OP Saturation 
                 Value = Constant at a 
                 −1 
                 FIG. 7 and 
               
               
                   
                 H/L operational limits 
                   
                 FIG. 11 
               
               
                 Signal Frozen 
                 Value = Constant for a 
                 −1 
                 FIG. 8 
               
               
                   
                 long period, e.g. 
               
               
                   
                 (T &gt; 1/4TTSS) 
               
               
                 PV Spikes 
                 |e(t)| &gt; k * STD 
                 −1, −2, −3 
                 FIGS. 9A 
               
               
                   
                   
                   
                 and 9B 
               
               
                 SP tracks PV 
                 SP(t) − PV(t) &lt; ε 
                 −1 
                 FIG. 12 
               
               
                 (PID in 
               
               
                 MAN mode) 
               
               
                 No SP Step moves 
                 dSP(t) ! = 0 
                 −1 
                 FIG. 10 
               
               
                 PV_trend ! = 
                 dSP(t − k) − 
                 −1 
                 FIG. 13, 
               
               
                 SP_trend 
                 dPV(t − k) &gt; γ 
                   
                 Unknown 
               
               
                   
                   
                   
                 disturbances 
               
               
                 PV_trend ! = 
                 dPVpred(t − k) − 
                 −1 
                 FIG. 14, 15, 
               
               
                 PVpred_trend 
                 dPV(t − k) &gt; γ 
                   
                 Unknown 
               
               
                   
                   
                   
                 disturbances 
               
               
                 Patched with 
                   
                 +1 
                 FIGS. 5 
               
               
                 Interpolated Values 
                   
                   
                 and 16. 
               
               
                   
               
             
          
         
       
     
     A Common Data Slice Generator: 
     Based on the Flag values, continuous “bad data” samples with the same status are converted into (marked as) categorized “bad data” slices. The common data slice generator  300  serves to create all bad data slices under different criteria.  FIG. 3  is illustrative of one embodiment of a data slice generator  300 . 
     Data slice generator  300  begins by loading a time series of data x(t) such as that illustrated in  FIG. 3  and having a corresponding set of Flag values (i.e., from Table 1) as set by the Rule-based Data Selector. At step  301 , data slice generator  300  sets time t=1 and initial values for parameters count, start and length. If a Status Flag value is negative (i.e., less than zero), then decision junction  303  proceeds with steps  305  and  306  which increment through the data points that form the “bad data” slice. If the Status Flag value is non-negative (i.e., zero or positive), then decision junction  303  proceeds with steps  306  and  307  which create/define a new “bad data” slice. 
     A PV Spike Detector: 
     If the given time series is a Dep (dependent variable) and it is also a PV (process variable) in a PID loop, then PV spike patterns may be detected. When the corresponding SP (set point) of this time series of data is found available in  240  ( FIG. 2 ), a new time series of control error {e(t)} is calculated by subtracting PV values from SP (i.e., e(t)=SP(t)−PV(t)), and is used for detection of PV spikes due to either instrument measurement error (e.g., short-term offline/reset/calibration) or unknown large disturbances into the process  120 . Thus PV Spike Detector looks for and detects data patterns shown in  FIGS. 9A-9B  and  13 . 
     A statistical calculation on the new time series {e(t)} results in mean Ē and standard deviation STD E : 
                 E   _     =       1   M     ⁢       ∑     S   =   1     M     ⁢           ⁢     (       1     N   S       ⁢       ∑     t   =   1       N   S       ⁢           ⁢     e   ⁡     (   t   )           )           ,     
     ⁢       STD   E     =       (       1     N   -   1       ⁢     (       ∑     t   =   1       N   S       ⁢           ⁢       (       e   ⁡     (   t   )       -     E   _       )     2       )       )       1   /   2               
where the S represents a “good” data segment (also called data slice),
 
N S  and N are the total number of samples of each “good” data segment and of all “good” data segments.
 
All significant spikes on PV are detected based on the following criterion:
 
PV( t )=spikes, if | e ( t )|≧ k ·STD E    k ε(1,6)  k   0 =3.0;
 
where k 0  is a default value, representing a 3-sigma rule in statistics.
 
     Any data segment detected by the above approach is marked as a “bad data slice” because such data will deteriorate the results of model quality estimation or model re-identification if not excluded.  FIG. 9A  shows an example of a PV  902  spike detected in data region  903  with associated variable  901 .  FIG. 9B  shows a similar PV  905  spike detected in data region  906  with associated variable  904 . 
     An OP Saturation and High/Low Limit Detector: 
     If the given time series is a Dep (dependent variable), then any data segment at its High/Low limits will no longer be able to represent the causal independent-dependent relations correctly. Therefore, a criterion to check is whether a data value is reaching and staying at a limit value. This criterion is applied to generate corresponding Flag values for model quality estimation or model identification purposes as listed in Table 1.  FIGS. 6 ,  7 , and  11  illustrate such a situation with High/Low limit values and OP saturations respectively.  FIG. 6  shows a data series  601  with detected data regions  602  where the data series  601  is detected as reaching its high value.  FIG. 7  shows time series of a process variable  602  with associated setpoint  603  and output  601 , where the output is detected as saturated in data region  604 .  FIG. 11  shows embodiments detecting when a PID loop output is saturated  1105  when a process variable  1102  is no longer able to follow the corresponding set point  1103  (as opposed to data region  1104 ), such that output  1101  becomes saturated. 
     A Frozen Signal/Measurement Detector: 
     If the given time series is a Dep (dependent variable), then any data segment with a frozen value will not be able to represent the CV response correctly and should be marked as “bad data” for model identification purposes.  FIG. 8  shows an example of a PV signal  801  (measurement) that is frozen for more than one TTSS (Time to steady state) in data regions  802 . In one embodiment, TTSS=60 minutes. The Frozen Signal/Measurement detector detects for such data/signal patterns. 
     A PID Manual Mode Detector: 
     If the given time series is an Ind (independent variable) and it is also a SP (set point) in its PID loop, when the PID loop is in MAN (manual) mode, the SP will track the PV (process variable) while the actual process independent variable (typically a valve opening position or flow rate) is changed by operator through an OP. In such a case, any readings from the SP value cannot represent the true relationship between INDs and DEPs.  FIG. 12  illustrates an example of a PID loop in manual mode data range  1204  that was detected automatically by the PID Manual Mode Detector where PV  1202  overlaps setpoint  1203  while output  1201  is constant. As a comparison,  FIG. 17  shows a graph from which embodiments determined the subset of data  1704  where PID modes were correctly maintained and no OP (PID output)  1701  saturation  1705  occurred. In the detection region  1704 , SP  1703  is tracking PV  1702 . 
     A SP in Closed-Loop Mode Detector: 
     If the given time series is an independent variable and it is also a SP (set point) in its PID loop, when the PID loop is in Cascade mode, the SP will follow an external variable such as an OP from the outer PID loop. In such cases, if there is no clean step-moves for a long time, the data segment of the SP will become unsuitable for model identification.  FIG. 10  indicates a situation when the SP  1002  was detected in Closed-Loop mode in data region  1003  following variable  1001 . 
     A Trend Mismatch/Unknown Disturbances Detector: 
     In addition to all cases and data screen and selection strategies discussed above, there are still cases where a DEP or CV time series may show significant sudden jumps/drops or slowing, but constant ramp ups/downs. In such cases, the DEP or CV is responding to unknown disturbances in the process  120  (e.g., missed FF (feed forward), unknown process disturbances, etc.) and the data segment becomes non-causal and will create negative impacts on model identification if the data is used.  FIG. 13  shows detected data ranges  1203  where a PV  1301  is not broadly following SP  1302 .  FIG. 14  shows examples where the CV measurements  1401  contain several jumps where the model predictions  1402  have no such matched changes and therefore are detected by the detector as a “bad data” slice  1403 . Similarly,  FIG. 15  shows CV measurements  1501  contain several drops where the model predictions  1502  have no such matched changes and therefore are detected by the detector as a “bad data” slice  1503 . 
     A Slice Validation Module  270  Using MQ: 
     To minimize the data loss due to too many bad data sample segments being selected, an innovative approach/module  270  is provided to test and validate all candidate bad data segments before finalizing the bad data slices. The provided approach 270 is formed of the following steps: 
     Step 1: 
     Set all detected bad data slices as “candidate bad data slices”, generate good data slices by excluding candidate bad data slices for each time series variable; 
     Step 2: 
     For each DEP (dependent variable), configure a MISO model identification case that takes all INDs as inputs and single DEP as output; 
     Step 3: 
     Apply all good data slices created in Step 1 to the configured MISO case in Step 2 for a subspace model identification run; 
     Step 4: 
     Use the dataset created in Step 1 do a Model Quality Assessment on the resulting MISO model from Step 3 and using the techniques described in the parent related application. Record the MQ index value, a KPI (key performance indicator), as a baseline value; 
     Step 5: 
     For each bad data slice among “candidate bad data slices”, reset that data segment to a “good” data slice if it is not a data slice found in the basic data screening (e.g., value=−9999, −10001); 
     Step 6: 
     Use the dataset defined in Step 5, run a MISO case identification similar to Step 3 except for that one “candidate bad data slice” that was ignored (removed and treated as good data in Step 5); 
     Step 7: 
     Repeat Step 4, evaluate the identified MISO model in Step 6 by calculating the MQ index value with the same dataset used in Step 4, and record the KPI of the new model; 
     Step 8: 
     Compare the KPI value obtained in Step 7 with its baseline value calculated in Step 4; if the new KPI value is sufficiently close to its baseline value, e.g., |KPI(new)−KPI(baseline)|&lt;ε (where ε is a tunable threshold), then the negative impact of the selected/tested “candidate” bad data slice is not significant, and therefore the selected bad data slice is de-activated by being removed from the bad data slice list for model identification; 
     Step 9: 
     Move to next “candidate bad data slice” and repeat the validation process from Step 5 through Step 8 above until all candidate bad data slices are assessed and validated. 
     A Slice Reparation/Patch Approach/Module  280 : 
     To minimize the data loss due to bad data sample segments, another innovative approach/module  280  is provided to repair and patch short candidate bad data segments before finalizing the bad data slices. One embodiment of the approach is described as follows. 
     Step 1: 
     Select a time series variable {x(t)}, all candidate bad data slices validated in Slice Validation Module  270  are screened, only those data slices with length less than a half time to steady-state (i.e., all bad data slices satisfying (Length&lt;½ TTSS)), are selected for patch/fix; 
     Step 2: 
     If the selected time series is an IND (independent variable), loop through all selected candidate bad data slices in Stepl and do the following data patch:
         (1) Assume the start index=Start, and the number of samples in slice=Length, then the slice ending sample index End=(Start+Length−1);   (2) Check the good sample values next to the two ends of the bad data slice to see if they satisfying |x(t=Start−1)−x(t=End+1)|&lt;ε, where ε is a tunable threshold;   (3) If the condition in (2) is satisfied, create an “interpolation slice” with the following values generated by linear interpolation:
 
{ {circumflex over (x)} ( t )= x   0   +k[x ( t )− x   0 ( t )]} where  x   0   =x ( t =Start−1), t =Start, . . . ,End;
       

     
       
         
           
             k 
             = 
             
               
                 
                   x 
                   ⁡ 
                   
                     ( 
                     
                       t 
                       = 
                       
                         End 
                         + 
                         1 
                       
                     
                     ) 
                   
                 
                 - 
                 
                   x 
                   ⁡ 
                   
                     ( 
                     
                       t 
                       = 
                       
                         Start 
                         - 
                         1 
                       
                     
                     ) 
                   
                 
               
               
                 ( 
                 
                   End 
                   - 
                   Start 
                   + 
                   2 
                 
                 ) 
               
             
           
         
       
         
         
           
             (4) Replace the values of {x(t)} with interpolated values generated in (3) for the selected bad data slice. 
           
         
       
    
     Step 3: 
     If the selected time series is a DEP (dependent variable), configure a MISO case with this DEP and all INDs, apply all candidate bad data slices and run a model identification; use the identified MISO model and its input dataset generating model predictions on this DEP; 
     Step 4: 
     Loop through all selected candidate bad data slices in Step 1 (of Module  280 ) for the DEP, if the model predictive values generated in Step 3 (of Module  280 ) are available (predictive values may not always be available, e.g., due to model re-initialization after a bad data slice) for a candidate bad data slice, then an “interpolated slice” is created with the following equation:
 
{ {circumflex over (x)} ( t )= x   pred ( t )} where  t =(Start−TTSS), . . . ,(End+TTSS);
 
     It is worthy to note that each of above interpolated slices consists of (2×TTSS) more samples than the corresponding bad data slice for a good connection (see below for details). 
     Step 5: 
     A special algorithm (described below) calculates two connection points for a flexible data slice patch which ensures the smoothness of the patch and minimizes the data loss; 
     Step 6: 
     Move to next candidate data slice and repeat the process  280  Steps 3 through 5 as above. 
     An Algorithm for Slice Interpolation Connection: 
     To minimize the negative impacts of replaced bad data slices by interpolated data samples, a special algorithm is provided for a smooth connection between the two end points of a short bad data slice.  FIG. 4  shows a time series of a process variable  401  and an associated segment of bad data  402 .  FIG. 5  shows the result of patching the same data segment of the process variable  501  with interpolated data segments in data range  502 .  FIG. 16  shows a PV time series  1601  with detected bad CV data  1603  and replaced bad data segments with predicted data  1602  without causing discontinuities in the repaired CV sequence. The algorithm is comprised of the following steps: 
     Step 1: 
     Read the user specified start point (UI_Start) and end point (UI_End) for a data slice patch from UI (default values UI_Start=Start, UI_End=End, are calculated for the data slice shown in  FIGS. 5 and 16 .) 
     Step 2: 
     UI_Start and UI_End points by examining the following:
         If UI_Start ⊂ (Start−TTSS, End), UI_Start is Valid;   If UI_End ⊂ (Start,End+TTSS), UI_End is Valid.   If both UI_Start and UI_End are valid, then move to Step 3;       

     Step 3: 
     Calculate the Two Connection Points: 
     For smooth connection, two average values around the connecting points UI_Start and UI_End are used instead of two single measurement points. In the case of the underlying time series being noisy, the calculation below is particularly important and helpful to patching the bad data slice: 
     
       
         
           
             
               
                 x 
                 _ 
               
               Start 
             
             = 
             
               
                 ∑ 
                 
                   t 
                   = 
                   
                     t 
                     0 
                   
                 
                 
                   
                     t 
                     0 
                   
                   + 
                   n 
                 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 x 
                 ⁡ 
                 
                   ( 
                   t 
                   ) 
                 
               
             
           
         
       
       
         
           
             
               
                 where 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 
                   t 
                   0 
                 
               
               = 
               
                 
                   UI_Start 
                   - 
                   
                     n 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     and 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     n 
                   
                 
                 = 
                 10 
               
             
             ; 
           
         
       
       
         
           
             
               
                 x 
                 _ 
               
               End 
             
             = 
             
               
                 ∑ 
                 
                   t 
                   = 
                   
                     t 
                     f 
                   
                 
                 
                   
                     t 
                     f 
                   
                   + 
                   n 
                 
               
               ⁢ 
               
                   
               
               ⁢ 
               
                 x 
                 ⁡ 
                 
                   ( 
                   t 
                   ) 
                 
               
             
           
         
       
       
         
           
             
               where 
               ⁢ 
               
                   
               
               ⁢ 
               
                 t 
                 f 
               
             
             = 
             
               UI_End 
               - 
               n 
             
           
         
       
     
     Step 4: 
     Calculate Interpolated Values: 
     To best patch a selected bad data slice, a set of model predictive values rather than a straight line (often used in the prior art), are filled into the data slice to replace the original undesirable values. Considering the possible bias between the model predictive values and the original measurements, and a possible data/prediction drafting during the time period over the slice, the following calculation with position adjustments is provided:
 
Δ offset ( t )=Δ offset ( t   0 )×(Δ offset ( t   f )−Δ offset ( t   0 ))×( t−t   0 )/( t   f   −t   0 );
 
 {circumflex over (x)} ( t )=Δ offset ( t )+ x   PRED ( t );
 
with Δ offset ( t   0 )=   x     start   −x   PRED ( t   0 ),
 
and Δ offset ( t   f )=   x     End   −x   PRED ( t   f )
 
     A Graphical User Interface (GUI) and Algorithms to Allow Users Adding, Deleting and Modifying Slices 
     Automated data selection apparatus and methods can help engineers prepare process data effectively, but it is still necessary and important to allow users to view, add, delete or edit the auto-generated bad data slices. A GUI (graphical user interface) or Web page and supporting algorithm (software) are provided in some embodiments to provide such functions.  FIGS. 18A ,  18 B, and  18 C illustrate the GUI. In  FIG. 18A , the GUI  1802  allows a user to mark a data range  1081  by mouse clicks and add as a bad data slice to either a vector or a dataset (all vectors).  FIG. 18B  shows a GUI element  1804  (e.g., pop up menu) that enables the user to delete an existing bad data slice  1803  or edit it by opening a new dialog shown in  FIG. 18C .  FIG. 18C  illustrates an example dialog box enabling editing of a data slice. Here the user is allowed to edit the bad data slice by re-typing Begin  1805  and End  1806  indexes (alternatively, Date and Time). Then the user confirms the changes by clicking on the OK button  1807 , or, alternatively, cancels the changes by clicking on the Cancel button  1808 . Other selection and confirmation configurations are suitable. 
       FIG. 19  illustrates a computer network and client-server environment in which embodiments of the present invention may be implemented. Client computer(s)/devices  710  and server computer(s)  720  provide processing, storage, and input/output devices executing application programs and the like. Typically, the present invention is installed as an apparatus in server computer  720 . The server computer  720  and client computer(s)/devices  710  can be linked through communications network  750  to other computing devices, including other client devices/processes  710 - 1  and Distributed Control System (DCS)  730 . Communications network  750  can be part of a remote access network, a global network (e.g., the Internet), a worldwide collection of computers, Local area or Wide area networks, and gateways that currently use respective protocols (TCP/IP, Bluetooth, etc.) to communicate with one another. Other electronic device/computer network architectures are suitable. 
     An example of implementation of the present invention may be as shown in  FIG. 19  where the DCS system  730  controls one or more process units  740 . An apparatus and method (e.g., process/modules  110 ,  140 ,  102 ,  200  and  300 ) described above is installed to run in the server computer  720 . A user may access the server application from one or more client computers  710  that allow a remote connection through security and firewall in the network  750 . A user may view, manage, change operations of one or more process units  740  through the DCS system  730 . The described application may be configured by user from one or more client computers ( 710 ) and run in the server computer  720 . 
     The present invention may be implemented in a variety of computer architectures. The computer network system  700  of  FIG. 19  is for purposes of illustration and not limitation of the present invention. 
     Server computer  720  includes memory, a CPU (central processing unit) and the like. Memory provides volatile storage for computer software instructions and data used to implement an embodiment of the present invention (e.g., the controller  110 , data screening and model identification  140 , model adaptation, and supports processes  200 ,  300 , GUI of  FIG. 18 , etc. as described above). Disk storage provides non-volatile storage for computer software instructions and data used to implement an embodiment of the present invention. The central processor unit provides for the execution of computer instructions. In one embodiment, the processor routines and data are a computer program product, including a computer readable medium (e.g., a removable storage medium such as one or more DVD-ROM&#39;s, CD-ROM&#39;s, diskettes, tapes, etc.) that provides at least a portion of the software instructions for the invention system. The computer program product can be installed by any suitable software installation procedure, as is well known in the art. In another embodiment, at least a portion of the invention software instructions may also be downloaded over a cable, communication, and/or wireless connection or operable through a network portal. 
     While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.

Technology Category: g