Control Loop Performance Monitoring - GE
Control Loop PerformanceMonitoring 2020 General Electric Company
ContentsChapter 1: Control Loop Performance Monitoring OverviewWhat is Process Control?2What is a Control Loop?2What is a PID Control Loop?2What is Control Loop Performance Monitoring?3The Asset Model in CLPM4CLPM Components5User Workflow in CLPM6CLPM Terminology7Chapter 2: Get Started with CLPM8Getting Started with CLPM9Prerequisites for CLPM9Define the Asset Model10Deploy Data Collection10Deploy CLPM Analytics15Access Loop and Fleet Dashboards45Chapter 3: The Fleet Report46The Fleet Report47Access the Fleet Report47Modify the Date Range for a Fleet Report47Average Control Loop Performance Chart48The Diagnostic Alerts Filtering Smart Filter48The Control Loop Performance Table49Chapter 4: The Loop Reportii152About the Loop Report53Access the Loop Report53Modify the Date for a Loop Report54Control Loop Performance Monitoring
The Process Variable Performance Chart54The Manipulated Variable Distribution Chart55The Control Overview Chart57The Error Distribution Chart59The Control Mode Summary Table60The Controller Performance Table60The PV Performance Table63The Error Statistics Table63The Controller Configuration Table64Chapter 5: The Loop Analysis Template65About the Loop Analysis Template66Access the Loop Analysis Template66Modify the Date for a Loop Analysis66Chapter 6: Manage Loops68Add a Loop69Modify a Loop69Delete a Loop70Chapter 7: Reference71CLPM Terminology72Control Loop Asset Definition72KPI Reference for CLPMChapter 8: Release Notes107126Control Loop Performance Monitoring Release Notes Q2 2019127Control Loop Performance Monitoring Release Notes Q1 2019127iii
Copyright GE Digital 2020 General Electric Company.GE, the GE Monogram, and Predix are either registered trademarks or trademarks of General ElectricCompany. All other trademarks are the property of their respective owners.This document may contain Confidential/Proprietary information of General Electric Company and/or itssuppliers or vendors. Distribution or reproduction is prohibited without permission.THIS DOCUMENT AND ITS CONTENTS ARE PROVIDED "AS IS," WITH NO REPRESENTATION ORWARRANTIES OF ANY KIND, WHETHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TOWARRANTIES OF DESIGN, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. ALL OTHERLIABILITY ARISING FROM RELIANCE UPON ANY INFORMATION CONTAINED HEREIN IS EXPRESSLYDISCLAIMED.Access to and use of the software described in this document is conditioned on acceptance of the EndUser License Agreement and compliance with its terms.iv 2020 General Electric Company
Chapter1Control Loop Performance Monitoring OverviewTopics: What is Process Control?What is a Control Loop?What is a PID Control Loop?What is Control LoopPerformance Monitoring?The Asset Model in CLPMCLPM ComponentsUser Workflow in CLPMCLPM Terminology 2020 General Electric Company1
What is Process Control?Process control is an engineering discipline for maintaining the output of a specific process within adesired range.A control system is a device or set of devices used to manage the behavior of other devices or systems inan industrial process facility.What is a Control Loop?A control loop is the fundamental building block of industrial control systems. It consists of all the physicalcomponents and control functions necessary to automatically adjust the value of a measured ProcessVariable (PV) to equal the value of a desired Setpoint (SP).A control loop includes the process sensor, the controller function, and the Final Control Element (FCE), allof which are required for automatic control.The difference between the SP value and the current value of the PV is called the Error, and this is used bythe control algorithm to calculate adjustments to the Manipulated Variable (MV). The MV is sent to theFCE (also referred to as the actuator) to bring the PV back to the SP value, using a closed loop feedbackmechanism. This mechanism allows control systems to adapt to varying process operating circumstancesand disturbances. Disturbances can sometimes be measured and compensated for by means of aDisturbance Variable (DV).What is a PID Control Loop?A PID control loop is a generic control loop feedback mechanism that uses a well-known PID(F) algorithm.It is very common in industrial control systems.The PIDF (Proportional–Integral–Derivative-Filter) control algorithm involves four separate tuningconstant parameters: Proportional value (P) 2The variable control required in proportion to the current needs of the system. 2020 General Electric Company
Integral value (I) The integral control that applies ever-increasing control actions, until the error is reduced to zero.Derivative value (D) The derivative value of the algorithm is determined by how quickly the error changes over time.Filter value (F) Adding a filter (F) to the PID controller helps to remove noise signals from the system and enablesbetter control under some conditions.Many different implementations (variations) of the PID(F) algorithm are used in industry today.What is PID Control Loop Tuning?When a disturbance to the process occurs, the task of the controller is to reduce the error signal to zeroand restore the PV back to the SP value, as effectively, efficiently, and quickly as possible. Choosing theright PIDF values for the parameters in the PIDF controller algorithm allows the controller to achieve thisgoal in the most effective way possible. This is referred to as loop tuning: selecting the best PIDF values toget the most accurate and effective control loop performance behavior. If the loop is tuned properly, theresponse time to disturbances is reduced. If the loop is tuned too aggressively, cycling and overshoot ofthe setpoint value may occur.What is Control Loop Performance Monitoring?Control Loop Performance Monitoring (CLPM) provides a solution to monitor the performance of anenterprise fleet of industrial process control loops, to detect and identify underperforming control loops,and to help to diagnose control loop problems.Industrial process plants typically have hundreds or thousands of control loops configured inside PLC(Programmable Logic Controller), DCS (Distributed Control System), and SCADA (Supervisory Control andData Acquisition) systems to automate and control plant operations.Control Loops are located at the interface between equipment and process. They also operate industrialequipment to form an industrial process.The stability, reliability, and optimal performance of an industrial process is dependent on the stabilityand performance of the fleet of control loops used to operate and control that process. Industrial process 2020 General Electric Company3
plants are also capital-intensive assets. Suboptimal control loops can directly impact equipmentreliability, equipment lifetime, and operational performance.It is therefore essential to manage the performance of control loops as valuable assets in an industrialenterprise facility that should be continuously monitored and optimized.Evaluating Control Loop Performance Control loops are performing well when the monitored process variable (PV) varies within the definedupper and lower control limits from the desired SP value.Control loops are performing poorly where the monitored PV functions outside of the desired processcontrol limits.The Asset Model in CLPMThe asset model is central to CLPM in a number of ways: Loops are defined as assets, according to a predefined template/definition for a control loop.Deployment of data collections in Edge Analytics is driven by the asset model.CLPM configuration is driven from the asset.Loop thresholds and control limits are defined on the asset.Edge data is extracted from the asset.Actions on a control mode are defined on the asset.Consult the Control Loop Asset Definition for details.4 2020 General Electric Company
CLPM ComponentsCLPM is made up of the following components working together.Figure 1: CLPM componentsThese components enable the following functionality.1.2.3.4.5.6.Loops are defined as assets, according to a predefined template for a control loop.Selected tags are collected into TimeSeries for each loop.Loop analytics are deployed.Alerts produced by these analytics are triggered under conditions defined in analytic configuration.Data produced by these analytics is viewed via the Fleet Dashboard and Loop Dashboard.Issues identified with loops (cases) are resolved. 2020 General Electric Company5
User Workflow in CLPMFollowing the CLPM workflow / process described below helps users monitor and optimize theperformance of an enterprise fleet of control loops. Underperforming control loops can be identified,prioritized, and optimized.Figure 2: User workflow / process1. The Fleet DashboardUse the Fleet Dashboard to rank the performance of all control loops contained in any level of anenterprise asset model (Enterprise, Segment, Site, etc.) over a specified time period. This helps inidentifying underperforming control loops and prioritizing them for optimization. Consult the FleetDashboard section of this documentation for details.OR1. AlertsAlerts notify you about potential problems with control loops. Consult the section of this documentationon deploying CLPM analytics for details on analytics that trigger alerts and how to configure them.2. The Loop DashboardUse the Loop Dashboard to visualize the performance of any individual control loop in detail over aspecified time period. This helps in assessing control loop performance and diagnosing potential causes ofsuboptimal performance. Consult the Loop Dashboard section of this documentation for details.3. Loop KPI AnalysisAnalyze the historical performance of loop KPIs over any historical time period to gain further insight.Loop KPIs can be assessed in relation to other loop KPIs or compared with the same loop KPIs calculatedusing other data and other tags available in Predix TimeSeries. Consult the KPI Reference for CLPM formore on loop KPIs.6 2020 General Electric Company
4. Case ManagementAction, collaborate on, and resolve any issues identified with control loops. Consult the ApplicationAnalytics documentation for details.CLPM TerminologyThe following terms are frequently used in control loop performance monitoring (CLPM).TermDescriptionCLPMControl Loop Performance MonitoringPV (Process Variable)This is the measured process variable, reflecting the actual stateof the process that is being controlled.SP (Setpoint)This is the desired value (target value) to which the PV iscontrolled.ErrorThis is the difference between the PV and SP. It is calculated asError PV-SP. Note that this is not an absolute value.ControllerThe subsystem containing the control algorithm thatmanipulates the MV to control the PV to the SP.MV (Manipulated Variable)The variable manipulated (adjusted) by the controller to controlthe PV to the SP.DV (Disturbance Variable)A measured disturbance variable that can be used by thecontroller to compensate for the impact of the disturbance onthe PV.P (Proportional Value)Normally the Proportional Gain value of the PID controller.I (Integral Value)Normally the Integral Gain value of the PID controller.D (Derivative Value)Normally the Derivative Gain value of the PID controller.F (Filter Value)Normally the Filter Time Constant value of the PID controller.Final Control Element (FCE)/ ActuatorThe device that is physically affected by a change in the MV soas to have an impact on the process. 2020 General Electric Company7
Chapter2Get Started with CLPMTopics: 8Getting Started with CLPMPrerequisites for CLPMDefine the Asset ModelDeploy Data CollectionDeploy CLPM AnalyticsAccess Loop and FleetDashboards 2020 General Electric Company
Getting Started with CLPMThis section provides instructions for getting started with CLPM.At the high level, this involves the following steps, to be performed by the CLPM solution administrator.1. Set up the prerequisites for CLPM.2. Define the asset model.3. Deploy data collection. This includes the following:a. Set up control mode logic.4. Deploy CLPM analytics.5. Access loop and fleet dashboards.Prerequisites for CLPMTo get started with CLPM, first ensure that you have met these prerequisites.Tenancy RequirementsEnsure that your OPM application instance has the following apps and services set up: Application AnalyticsThe Asset Model ServiceThe Time Series ServiceThe Dashboard ServicePredix Event HubPredix InsightsKPI ManagementAnalysis ServiceAlert ServiceAnalytics RequirementsEnsure that you have access to the CLPM analytics in the Analytics Catalog.Browser RequirementsOnly the Chrome 43 or higher browser is supported for accessing the Loop and Fleet Dashboards. 2020 General Electric Company9
Define the Asset ModelConstruct the asset ingestion file required by Application Analytics and perform asset ingestion.The asset ingestion files (there can be several files or they can be combined into one) are JSON files thatspecify customer assets and their hierarchical structure or asset model. In the context of CLPM, theassets in this file are control loops that together define an industrial control system.The basic structure of the files is as outlined in the APM Assets documentation for asset ingestion.In addition to the required attributes that are standard for all such files, files describing control loopassets require custom attributes defined in the Control Loop Asset Definition for CLPM that you need tospecify.Note: It is important to ensure that your asset ingestion file conforms to the control loop asset definitionfor CLPM. If your file does not conform to this definition, CLPM will be unable to function correctly.To define the asset model, carefully follow the instructions given with the Control Loop Asset Definitionfor CLPM.Deploy Data CollectionCollect data into TimeSeries for all the analytic input tags as defined in your asset model. Therecommended rate for data collection is every 5 seconds or slower.Once you have set up data collection, you are ready to set up control mode logic.Set Up Control Mode LogicSet up control mode logic for CLPM by creating and deploying your Control Mode Analytic. Use the Pythonexample provided here to create a Predix Insights-based analytic.What Is the Control Mode Analytic?CLPM analytics require an input tag that describes the current control mode for each loop. The controlmode tag must contain values of type double and must be one of the following:Tag ValueDescription1A tag value of 1 indicates a control loop that is currentlyoperating manually. The controller is not controlling the process.2A tag value of 2 indicates a control loop where the process iscurrently being controlled by the controller.3A tag value of 3 indicates a controller that is currently set toCASCADE.4A tag value of 4 indicates a process that is currently shut down.Note: Most KPIs will not be calculated for this control mode.CLPM provides an example Predix Insights analytic to get you started towards developing your owncontrol mode analytic. The resulting analytic will vary depending on your available data, but each controlmode analytic must produce a result that matches one of the tag values in the table above.10 2020 General Electric Company
Example Control Mode AnalyticThe Python script that follows can be used as the basis for you Control Mode Analytic.'''Created on Aug 30, 2018@author:'''from pyspark.sql import SparkSessionfrom pyspark.sql import *from pyspark.sql import functionsfrom pyspark.sql.types import *from pyspark.sql.dataframe import *from pyspark.sql.functions import *import sysimport timefrom datetime import datetimeclass ControlModeScript():# ################ DO NOT EDIT ########################MANUAL "1.0"AUTO "2.0"CASCADE "3.0"SHUTDOWN "4.0"QUALITY GOOD "3"QUALITY BAD "0"# ################ END ######################### ################ MODIFY THIS FUNCTION ######################### When modifying this function, TQVs for the custom tags (Tag VV,Tag XX, Tag YY, Tag ZZ) can be obtained as follows:#* timestamp is common amongst all tags#* quality XX is quality of Tag XX, quality YY is quality ofTag YY and so on#* value XX is value of Tag XX, value YY is value of Tag YYand so on#def control mode query(self):# The example SQL query below does the following:# select timestamp as the timestamp of the tag# always select quality as good# if quality is bad#select value as shutdown# else if value of tag XX is 1#select value as MANUAL# else if value of tag YY is 1#select value as AUTO# else if value of tag ZZ is 1#select value as CASCADE# else#select value as SHUTDOWNstring "timestamp as timestamp, CASE WHEN quality XX ! 3 ORquality YY ! 3 OR quality ZZ ! 3 OR quality VV ! 3 THEN " self.SHUTDOWNstring " WHEN value XX 1 THEN " self.MANUAL " WHENvalue YY 1 THEN " self.AUTO " WHEN value ZZ 1 THEN " self.CASCADE " WHEN value VV 1 THEN " self.SHUTDOWN " ELSE " self.SHUTDOWN " ENDas value," self.QUALITY GOOD " as quality"return string 2020 General Electric Company11
# ################ END ######################### ################ DO NOT EDIT ########################def run job(self, spark session, runtime config, job json,context dict, logger):try:spark spark sessionlogger.info("Starting analytic.")configContext context dict["configDS"]tsContext context dict["timeseriesReadDS"]configDF configContext.sql("select * from " context dict["configDS"].table sDF tsContext.sql("select * from " context dict["timeseriesReadDS"].table F")Tag XXDDF tsContext.sql("SELECT c.AssetSourceKey asasset, t.timestamp as timestamp, t.value as value XX, t.quality asquality XX FROM timeseriesReadDF " " t JOIN configDF c on t.tag c.MappingValueWHERE c.MappingKey 'Tag XX' DISTRIBUTE BY asset")Tag XXDDF.createOrReplaceTempView("tag XXDDF")Tag YYDDF tsContext.sql("SELECT c.AssetSourceKey asasset, t.timestamp as timestamp, t.value as value YY, t.quality asquality YY FROM timeseriesReadDF" " t JOIN configDF con t.tag c.MappingValue WHERE c.MappingKey 'Tag YY' DISTRIBUTE BYasset")Tag YYDDF.createOrReplaceTempView("tag YYDDF")Tag ZZDDF tsContext.sql("SELECT c.AssetSourceKey asasset, t.timestamp as timestamp, t.value as value ZZ, t.quality asquality ZZ FROM timeseriesReadDF" " t join configDF con t.tag c.MappingValue WHERE c.MappingKey 'Tag ZZ' DISTRIBUTE BYasset")Tag ZZDDF.createOrReplaceTempView("tag ZZDDF")Tag VVDDF tsContext.sql("SELECT c.AssetSourceKey asasset, t.timestamp as timestamp, t.value as value VV, t.quality asquality VV FROM timeseriesReadDF" " t JOIN configDF con t.tag c.MappingValue WHERE c.MappingKey 'Tag VV' DISTRIBUTE BYasset")Tag VVDDF.createOrReplaceTempView("tag VVDDF")Tag XXYYDF tsContext.sql("SELECTCOALESCE(tag XXDDF.asset, tag YYDDF.asset) asasset,COALESCE(tag XXDDF.timestamp,tag YYDDF.timestamp) as timestamp,tag XXDDF.value XX, tag XXDDF.quality XX,tag YYDDF.quality YY,tag YYDDF.value YY FROM tag XXDDF FULL OUTER JOINtag YYDDF ON tag XXDDF.asset tag YYDDF.asset and tag XXDDF.timestamp tag YYDDF.timestamp DISTRIBUTE BY asset")Tag ZZVVDF tsContext.sql("SELECTCOALESCE(tag ZZDDF.asset, tag VVDDF.asset) asasset,COALESCE(tag ZZDDF.timestamp,tag VVDDF.timestamp) astimestamp,tag ZZDDF.quality ZZ, tag ZZDDF.value ZZ,tag VVDDF.quality VV,tag VVDDF.value VV FROM tag ZZDDF FULL OUTER JOIN12 2020 General Electric Company
tag VVDDF ON tag ZZDDF.asset tag VVDDF.asset and tag ZZDDF.timestamp tag VVDDF.timestamp DISTRIBUTE BY asset")Tag XXYYDF.createOrReplaceTempView("tag XXYYDF")Tag ZZVVDF.createOrReplaceTempView("tag ZZVVDF")timeseriesDF tsContext.sql("SELECTCOALESCE(tag XXYYDF.asset,tag ZZVVDF.asset) as asset,COALESCE(tag XXYYDF.timestamp,tag ZZVVDF.timestamp) as timestamp,tag XXYYDF.quality XX, tag XXYYDF.value XX,tag XXYYDF.quality YY,tag XXYYDF.value YY,tag ZZVVDF.quality ZZ,tag ZZVVDF.value ZZ,tag ZZVVDF.quality VV,tag ZZVVDF.value VV FROM tag XXYYDF FULL OUTERJOIN tag ZZVVDF ON tag XXYYDF.asset tag ZZVVDF.asset andtag XXYYDF.timestamp tag View("collectedDS")queryString "SELECT configDF.MappingValue as tag, "queryString self.control mode query()queryString " FROM collectedDS JOIN configDF oncollectedDS.asset configDF.assetSourceKey WHERE configDF.mappingType 'OutputMappings'"resultDF TempView("resultDF")timeseriesWriteDF tsContext.sql("SELECT tag,timestamp, CAST((value) as double) as value, quality FROM resultDF ")logger.info("Returning result.")result {"timeseriesWriteDS" : timeseriesWriteDF}return resultexcept Exception as e:print("ERROR RETURNED")logger.info("Error: " str(e))exc tb sys.exc info()[2]logger.info("Line number: " str(exc tb.tb lineno))# ################ END ########################Control Mode ConstantsThe script includes the following constants to represent the various control modes: MANUALAUTOCASCADESHUTDOWNAvailable TagsThe following configurable tags can be queried in the script. These tags will be mapped to tags in PredixTimeSeries. Tag VVTag XXTag YYTag ZZFor these tags, the timestamp, quality, and value (T,Q,V) are represented in a table with the followingcolumns: 2020 General Electric Company13
Timestampvalue VVquality VVvalue XXquality XXvalue YYquality YYvalue ZZquality ZZNote: The timestamp is common among all these tags.Example QueryThe script includes the following example query:def control mode query(self):string "timestamp as timestamp, CASE WHEN quality XX ! 3 ORquality YY ! 3 OR quality ZZ ! 3 OR quality VV ! 3 THEN " self.SHUTDOWNstring " WHEN value XX 1 THEN " self.MANUAL " WHENvalue YY 1 THEN " self.AUTO " WHEN value ZZ 1 THEN " self.CASCADE " WHEN value VV 1 THEN " self.SHUTDOWN " ELSE " self.SHUTDOWN " ENDas value," self.QUALITY GOOD " as quality"return stringThe preceding query does the following:1. Sets the control mode timestamp to the timestamp used by these tags.2. Sets the control mode quality to good.3. Sets the control mode value as follows:a.b.c.d.e.If the quality of any of the tags is bad, sets the control mode value to SHUTDOWN.Else if the value of tag XX is 1, sets the control mode value to MANUAL.Else if the value of tag YY is 1, sets the control mode value to AUTO.Else if the value of tag ZZ is 1, sets the control mode value to CASCADE.Else, sets the control mode value to SHUTDOWN.You should create your own query, based on your own business needs.Modify the Control Mode AnalyticModify the control mode query() function in the Python script, building up a SQL string to queryone or more of the available tags and (based on the T,Q,V of these tags) to return a T,Q,V for the controlmode.Note: You should modify ONLY the control mode query() function in this template. Do not modifyanything else in the template.Note: You MUST use Tag VV in your query. Using the other available tags in your query is optional.Create Your Analytic TemplateOnce you have modified the Python script to include the control mode logic you require, do the following.Procedure1. For the Control Mode Template Analytic, create a Spark analytic called OPM-CLPM-Control Mode,following the guidance given in the Spark and Application Analytics documentation.2. Publish the analytic to the Analytics Catalog.3. Set up the input and output definitions as follows:14 2020 General Electric Company
Table 1: INPUT DEFINITION: TAGSNameTypeRequiredTag VVDoubleYesTag XXDoubleNoTag YYDoubleNoTag ZZDoubleNoNote: For each of the tags used in your Control Mode Analytic, ensure that you specify thecorresponding tag as Required in the input definition.Table 2: OUTPUT DEFINITION: TAGSNameTypeRequiredControl ModeDoubleYesNext StepsNow that you have set up the control mode logic, you are ready to deploy the CLPM analytics.Deploy CLPM AnalyticsDeploy each of the CLPM analytics as described in this section.Overview of CLPM AnalyticsThe following specialized analytics are provided with CLPM, and these are used for data collection andtransformation.Table 3: CLPM AnalyticsAnalytic NameUseLocationOPM-CLPM-Control ModeOperates on scheduled data and containsthe control mode logic.Will be in your Analytics Catalog if youhave correctly performed the step to SetUp Control Mode Logic.OPM-CLPM-PV StatisticsScheduled and produces the followingKPIs based on the incoming data:Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator. PV Error Lower SP Threshold Upper SP ThresholdConsult the KPI Reference for details onthe KPIs produced by this analytic.OPM-CLPM-PerformanceScheduled and produces a set ofperformance KPIs based on the incomingdata.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.Consult the KPI Reference for details onthe KPIs produced by this analytic. 2020 General Electric Company15
Analytic NameUseLocationOPM-CLPM-Performance ExtScheduled and produces a set ofperformance KPIs based on the incomingdata.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.Consult the KPI Reference for details onthe KPIs produced by this analytic.OPM-CLPM-Config ChangeScheduled and produces the Total PIDFChanges KPI based on the incoming data.Consult the KPI Reference for details onthis KPI.OPM-CLPM-Alert PerfScheduled and produces an alert for pooroverall performance.The Overall Performance KPI is comparedto a threshold configured on the analytic.For the last 3 hourly calculated samples, ifthe Overall Performance KPI is greaterthan the threshold, an alert is triggered.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.Consult the KPI Reference for details onthe Overall Performance KPI.OPM-CLPM-Alert LimitsScheduled and produces an alert forlimits being exceeded.The Percentage Limits Exceeded KPI iscompared to a threshold configured onthe analytic. For the last 3 hourlycalculated samples, if the PercentageControl On KPI is 100% and thePercentage Limits Exceeded KPI is greaterthan the threshold, an alert is triggered.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.Consult the KPI Reference for details onthe Percentage Limits Exceeded KPI andPercentage Control On KPI.16 2020 General Electric Company
Analytic NameUseLocationOPM-CLPM-Alert ManualScheduled and produces an alert forcontrol mode changing to Manual Mode.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.Control mode changes are examined overa data window of one hour. If the lastcontrol mode change detected is achange from Auto, Cascade, or ShutdownMode to Manual Mode, an alert istriggered.For example, the following control modesrecorded over the window have thefollowing corresponding results. (Auto A, Cascade C, Shutdown S, Manual M)OPM-CLPM-Alert PV Quality SMASAM : A to M is last change.Alert! AAAMMM : A to M is last change.Alert! MMCMCM : C to M is last change.Alert! AAAAMS : M to S is last change. Noalert. MMMMMS : M to S is last change. Noalert. SMMMMM : S to M is last change.Alert!Scheduled and produces an alert for poorPV sensor data, indicating that a possiblePV sensor health problem has beendetected.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.This alert is triggered when the controlmode is not Shutdown and either of thefollowing is true: Percentage of PV sensor data of goodquality is lower than a thresholdconfigured on the analytic. The PV Variance KPI is zero (that is,the PV is flatlining).Consult the KPI Reference for details onthe PV Variance KPI.Recommendation: If running this analytictriggers an alert, check the PV sensordata collection and sensor health. 2020 General Electric Company17
Analytic NameUseLocationOPM-CLPM-Alert TuningScheduled and produces an alertindicating possible loop tuning or designproblems.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.This alert is triggered when all of thefollowing are true: The Reversal Count KPI is greaterthan a threshold configured on theanalytic. The Reversal Amplitude KPI is greaterthan a threshold configured on theanalytic. The Percentage MV Saturation KPI isless than a threshold configured onthe analytic. The Percentage Control On KPI is100%. The Percentage Limits Exceeded KPIis greater than a threshold configuredon the analytic.Consult the KPI Reference for details oneach of the KPIs involved in triggering thisalert.Recommendation: If running this analytictriggers an alert, review the loop tuningand design.OPM-CLPM-Alert MV QualityScheduled and produces an alert for poorMV sensor data, indicating that a possibleactuator health problem has beendetected.Located in your Analytics Catalog. If youdo not find it there, contact your tenantadministrator.This alert is triggered when the controlleris on and any of the following is true: Percentage of MV sensor data ofgood quality is lower than a thresholdconfigured on the analytic. The Movement Index KPI is zero (thatis, the MV is flatlining). The Percentage MV Saturation KPI isgreater than a threshold configuredon the analytic.Consult the KPI Reference for details oneach of the KPIs involved in triggering thisalert.Recommendation: If running this analytictriggers an alert, check the MV sensordata collection and actuator health.18 2020 General Electric Company
Deploy analyticsBefore You BeginMake sure that you have set up control mode logic.About This TaskThis task describes how to deploy a single CLPM analytic.Note: Using the step
2. The Loop Dashboard Use the Loop Dashboard to visualize the performance of any individual control loop in detail over a specified time period. This helps in assessing control loop performance and diagnosing potential causes of suboptimal performance. Consult the Loop Dashboard section of this documentation for details. 3. Loop KPI Analysis
power system: the automatic load frequency control loop (ALFC) and the automatic voltage control loop (AVR). There is little interaction from the ALFC loop to the AVR loop but the AVR loop does affect the ALFC loop to a certain extent. The AVR loop tends to be much faster than the ALFC loop with time constants of much less than one second.
the opinions of those with direct ties to the West Loop. I live in the West Loop I work in the West Loop I own property in the West Loop I own a business in the West Loop I am generally interested in the West Loop* Other (please specify) * 0 20 40 60 80 100 23.6% 59.7% 5.6% 4.9% 85.4% 32.6% 18 West Loop Design Guidelines Community Feedback 1 .
The Do-While and For loop structures are pretest loops. In a posttest loop, the condition is evaluated at the end of the repeating section of code. This is also called an exit-controlled loop. The Do/Loop Until construct is a posttest loop. Do While Loop The while loop allows you to di
while loop Format: while condition: loop body loop body 18 one or more instructions to be repeated condition loop body false true After the loop condition becomes false during the loop body, the loop body still runs to completion (before its check before the next turn) and exit the loop and go on with the next step.
In a pretest loop, the loop control expression is tested first. If it is true then the loop actions are executed; if it is false the loop is terminated. In a post-test loop, the loop actions are executed first. Then the loop control expression is tested. If it is true, a new iteration is started; otherwis
White Center CSO – counter loop and portable info loop kit White Salmon CSO – portable info loop kit Yakima 1 & 2 DVR – counter loop and portable info loop kit Yakima CSO – counter loop and portable info loop kit Updat
K. Webb ESE 499 15 Open-Loop Frequency Response & Stability Open-loop Bode plot can be used to assess stability But, we need to know if system is closed-loop stable for low gain or high gain Here, we'll assume open-loop-stable systems Closed-loop stable for low gain Open-loop Bode plot can tell us: Is a system closed-loop stable? If so, how stable?
with representatives from the Anatomy sector. These relate to the consent provisions of the Human Tissue Act 2004 (HT Act), governance and quality systems, traceability and premises. 3. The Standards reinforce the HT Act’s intention that: a) consent is paramount in relation to activities involving the removal, storage and use of human tissue; b) bodies of the deceased and organs and tissue .
