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Geir Simensen Honeywell Hi-Spec Solutions Norway
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Strategic Opportunity
The history of seeking profitability from plant performance has been heavily weighted toward process operations. Plant and equipment availability Process operations Equipment and plant availability performance has been the wild card in business performance. Information Management
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Hi-Spec’s Unified Manufacturing Solutions
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To Help You Proactively Manage Your Assets At The Optimum Cost
Our Purpose To Help You Proactively Manage Your Assets At The Optimum Cost With Internet Speed & Reach!
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Industry Trends Asset Trends Expertise Trends Work Practice Trends
assets must run longer (>3 years between turnarounds) impact of asset failure can be very large Expertise Trends downsizing and aging workforce has caused “skill gap” at plant sites technology is evolving faster than it can be adopted Work Practice Trends Asset management is moving from reactive to proactive Formal abnormal situation management is improving operations effectiveness
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A Look At Plant Operations
A typical Production Profile for an Asset Intensive Facility for a calendar year. 95 days 79 days 62 days 47 days 23 days 30 days 16 days Days per Year 8 days 5 days < 60% Daily Production 95% 100% Production Target set by Enterprise
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Real Life Examples This plant had $24.2M in lost capacity due to asset availability & incidents! 24.2M This plant had 5.8% in lost capacity! 5.8% This plant lost $38.5M! And this plant lost $33.5M!
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Factors Affecting Plant Operations
Between 3-15% in Lost Capacity is attributed to asset un-availability and incidents Plant Operating Target Planning Constraints Plant Availability Operational Constraints Plant Incidents Production Management DCS/APC/ Optimization efforts NEW EMPHASIS!! Asset Management Days per Year Plant Capacity Limit < 60% Daily Production 95% 100% Manufacturing Execution Scheduling & ERP
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Factors Affecting Plant Operations
Get that 3-15% in Lost Capacity Back!! Planning Constraints Plant Availability Operational Constraints Plant Incidents Days per Year Plant Capacity Limit < 60% Daily Production 95% 100%
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How you manage the health of your assets
IMPROVE ABNORMAL SITUATION RESOLUTION ORIENT DECIDE ACT You become “aware” of a problem. You determine the cause and decide what to do about it. You take action to fix or respond to the problem. This is the how everybody makes decisions, whether they have formal procedures for each component, or whether they perform the elements internally. It is basically the work process that every human undergoes to go from an abnormal situation to a resolution…
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facilitates this work process
IMPROVE ABNORMAL SITUATION RESOLUTION ORIENT DECIDE ACT You become “aware” of a problem. You determine the cause and decide what to do about it. You take action to fix or respond to the problem. DIAGNOS -TICS DECISION SUPPORT MAINTENANCE RESPONSE @sset.MAX enables each element of the work process to ensure that actions are taken in a well informed, proactive mode. @sset.MAX facilitates this process by providing tools and philosophies behind each component. OPERATOR RESPONSE
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Diagnostics Scouts IntelliScout TurboScout Loop Scout Valve Scout
ORIENT DECIDE ACT CMMS IntelliScout TurboScout Loop Scout Valve Scout Transmitter Scout and more DIAGNOS -TICS DECISION SUPPORT OPERATOR TOOLS As the first step, we first must detect a potential or existing problem. This is an actual example of a heat exchanger that we have our application installed on. The green line represents where the HEX should have been operating based on our models, the blue line represents actual performance. An alert was issued at the red line, but due to the holidays, it was not a priority of operations. 3 weeks later, the HEX’s performance was finally noticed by the operators. Once the HEX was repaired, the blue lined jump back to normal. With this ability, you can plan repairs for the best time possible and save a lot of money.
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Diagnostics IntelliScout
ORIENT DECIDE ACT CMMS Detects asset specific problems before they occur Rotating & Reciprocating Machinery Heat Exchangers Pumps Instrumentation DIAGNOS -TICS DECISION SUPPORT OPERATOR TOOLS As the first step, we first must detect a potential or existing problem. This is an actual example of a heat exchanger that we have our application installed on. The green line represents where the HEX should have been operating based on our models, the blue line represents actual performance. An alert was issued at the red line, but due to the holidays, it was not a priority of operations. 3 weeks later, the HEX’s performance was finally noticed by the operators. Once the HEX was repaired, the blue lined jump back to normal. With this ability, you can plan repairs for the best time possible and save a lot of money.
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Principle of Intelliscout
Black box statistical model Set of correlated variables, relationships dependent on asset behaviour Example - Motor Driven Pump Discharge Pressure, Specific Gravity Intelliscout uses statistical methods to create a model of expected asset behaviour. It knows nothing about the physics of the particular asset in question. What is required is that a set of correlated variables that bound the asset in question is available, and that the relationship between these variables depends on asset condition. An example shown above is a motor driven pump. Motor current, inlet pressure, discharge pressure and flow are related, and their relationships define pump performance. In this example we can also write an analytical model to define these rleationships, but that may not always be possible. Motor Amps RPM (if variable) Flow, Suction Pressure, Temp
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Intelliscout Model predicts expected asset behaviour when in good condition Compare predicted behaviour with actual behaviour Use historian to read actual, write predicted Early warning of change in performance The model reads actual data for the set of variables chosen from your Process Control System historian - see not below. It then generates estimates of what the variable values should have been, based on the model and writes these back to the historian. A comparison is made between actual and estimated values, and if a deviation is detected, a symptom is raised in the Equipment Health Management Alert Manager software. For failure modes with a gradual rate of degradation, e.g. wear, fouling, Intelliscout will show a deviation from expected performance in plenty of time for corrective maintenance before a failure occurs, and with minimal impact on production. Even with more rapid failures, sufficient warning may be given to process operators to avoid the production impact of a trip, as will be shown in a case study later in this presentation. The types and direction of variation between the actual and predicted values can be mapped as individual symptoms and lead to a probable fault diagnosis in a maintenance decision support software system: Alert Manager. An example is a synchronous motor slurry pump with a valve on its output to control flow: if the valve is more open than expected, the discharge pressure is higher than expected and the inlet pressure is normal, scaling has probably accumulated at the valve restricting flow. In this example there is no flow meter. Note: A historian is a pre-requisite for Intelliscout, note that Intelliscout will work with non Honeywell a non Honeywell control system, all that is required is that the historian that sits on top of the DCS complies with OPC (OLE for Process Control) standard or there exists a Real time data Interface (RDI) between the historian in place and Honeyweel’s PHD - please contact the author if in doubt.
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Algorithms Steady State model Data clustering
Training space, linear, non linear Principal Component Analysis Technique to find the directions in which a cloud of data points is stretched most Radial Basis Functions Non linear functional approximation Intelliscout models are steady state models of asset behaviour - not dynamic models. This means that start up and shut down conditions may not be valid, and the data must be “smooth” or “well behaved” - discontinuities from step changes in process conditions may cause model errors. If there are distinct operating ranges, e.g. different crude stocks in an oil refinery, or summer / winter conditions, then this may be handled by having distinct models to handle the different operating conditions. This is analagous to representing a non linear curve as a set of linear segments. The choice of model can then be handled by a reference to another variable that represents operating states. Data is clustered to determine linear and non linear components and establish the training space (or the range of operations for which the model is valid) The model is based on Principal Component Analysis. Principal Component Analysis (PCA) is a technique to find the directions in which a cloud of data points is stretched most. These directions represent most of the information in the data. Knowing these directions allows us to store the data in a compressed form and later reconstruct the data with a minimal amount of distortion. PCA is used in statistics to extract the main relations in data of high dimensionality. A common way to find the Principal Components of a data set is by calculating the eigenvectors of the data correlation matrix. These vectors give the directions in which the data cloud is stretched most. The projections of the data on the eigenvectors are the Principal Components. The corresponding eigenvalues give an indication of the amount of information the respective Principal Components represent. Principal Components corresponding to large eigenvalues represent much information in the data set and thus tell us much about the relations between the data points. Reference: I.T. Jolliffe, Principal Component Analysis Springer Verlag, 1986, ISBN or ISBN
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Intelliscout Trained on data set representing normal behaviour
Analytical model may not be possible Too complex Under instrumented Intelliscout models should be trained with process data representing the normal range of operations. In this way, model behaviour is linked to process conditions. This results in fewer false alerts (or missed alerts) due to a change in the process - which is a common problem with traditional CBM techniques. A training range (start & stop time & date) and sample interval (1 minute upwards) is defined. The data for the set of variables selected in the model is then captured over the training range from the historian and sent via the Internet to an Intelliscout server which performs the number crunching. The model is returned back to the local Intelliscout system. Intelliscout offers the ability to model asset performance where an analytical model may not be possible due to insufficient instrumentation (e.g. no flow meter with a pump circuit), or the asset may be too complex to reasonably define its performance with a set of equations. If an analytical model is possible, we would recommend running the Intelliscout model in parallel with the analytical model - comparing them may give useful insights into asset performance. If there is redundant information in the variables included in the asset then it may be possible to perform sensor validation. Since the model is based on the correlations between the variables, if there is a problem with one variable only, and all the others are OK, then with redundant information this cannot be attributed to a problem with the asset, and the sensor at fault can be identified and an estimate provided for its value.
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Performance Monitoring & Optimisation
TurboScout Performance Monitoring & Optimisation
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Our Technological Solutions
Gas path analysis and component matching techniques to derive component fault indices Component matching techniques to minimise fuel consumption and hence exhaust emissions Larson - Miller creep methodology and component matching techniques to evaluate remaining life Principal of corresponding states for real gases
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On-line Performance Monitoring
Gas turbines fault indices Fault indices, alarms and complimentary trends Plant PV Gas turbines Compressors Scrubbers Coolers etc. Compressors fault indices Steady-state checks Scrubbers fault indices Coolers fault indices Database of fault indices
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Off-line Model Based Analysis
Optimisation (fuel consumption) Database of fault indices from On-line system Predictive maintenance Model based analysis Production based maintenance Optimisation (compressor wash)
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Loop Scout Loop Scout performs automated data collection on plant regulatory control loops Data is transmitted via the internet to an automated analysis engine for quick response Analysis report quickly focuses scarce engineering and maintenance resources on real problems Improved regulatory control performance provides a strong foundation for higher level control and optimisation As another detection tool, Loop Scout is one of our latest offerings that allows you to make sure you have a strong control foundation. With Loop Scout, basic regulatory control loops can be assessed and analyzed, before you install advanced process control. The success of multivariable control and real time optimization depend on a solid regulatory control infrastructure. Even More Details: This resulting study can perform a detailed analysis of a particular area of the refinery in preparation for implementation of advanced control. As we said before, the success of multivariable control and real time optimization depend on a solid regulatory control infrastructure. Using this study, manipulated variable control problems are identified and diagnosed, such that the control engineer can take appropriate action to improve the control. Post-analysis quantifies the improvement and closes the loop on learning. The plant is then ready for step testing and implementation of multivariable control.
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Transmitter Scout Continuous monitoring and tracking of transmitters
ORIENT DECIDE ACT Continuous monitoring and tracking of transmitters Provides diagnostic information based on soft failures reported from the transmitter Notifies when transmitter has been replaced or moved Maintenance DIAGNOS -TICS DECISION SUPPORT Operators
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Valve Scout ORIENT DECIDE ACT A Predictive Maintenance Solution for All Control Valves … Even Analog. Provides diagnostic information based on stroke travel and cycle thresholds Reduced costs through predictive valve maintenance Support of expanded asset management capability Improved productivity of maintenance resources Maintenance DIAGNOS -TICS DECISION SUPPORT Operators
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What Tools Do You Use for Maintenance?
Oil Analysis Infrared Thermography Data Historians Excel Spreadsheets Open Control Systems Intelliscout Loop Scout Turbo Scout MathCad Vibration Monitoring Process Simulation Calendar Traditional Condition Monitoring tools make little use of Process Control Data. However, this is one of the richest sources of information on how assets are performing. The process control system already monitors flows, temps, pressure, currents; most things that characterise asset performance. However this has remained in the realm of process engineers, while maintenance and reliability people have relied on their more traditional sources of information shown in the slide above. Process data can be made available for condition monitoring at little incremental cost using Intelliscout. Instrumentation already exists for process control purposes. Manual Calculations Motor Current Analysis Computerized Maintenance Management System Ultrasonic Noise Detection
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Integrated Decision Support
ORIENT DECIDE ACT Maintenance DIAGNOS -TICS DECISION SUPPORT Operators Alert Manager Receives Symptoms generated by the scouts + all other sources like vibration monitoring etc. Presents user with asset-specific information to act on, complete with recommendations and automated responses. Notifies the field maintenance staff and directs them to possible reasons, solutions, and supporting documentation.
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Integrated Decision Support
All assets All information for each asset Diagnostics Documentation Fault history Recommended actions Provide access to asset specific information Integration to existing systems Control System CMMS / Reliability tools
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Structured Troubleshooting
Fault files x Required evaluation Evaluation done, nothing found Symptom found to exist Provides information for current or potential faults Evaluation activities: Obtain current status of the symptoms key to isolating the fault. Repair activities: Activity to repair a confirmed fault. All symptoms defined for the asset. Status of failures: “?” potential, “!” confirmed and “X” eliminated. Related symptoms shown on expansion. Provide information for diagnosing current conditions. Fault History for This Asset: Status and history of faults. Faults for Assets of the Class Diagnosis: List of current and past faults for similar assets.
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x Required evaluation Evaluation done, nothing found
Symptom found to exist Alert Manager continuously monitors asset data, and when a symptom is recognised, it looks for the presence of absence of related systems to diagnose a fault. It handles the data overload problem and produces meaningful information for the maintenance practitioner: a diagnosed fault and corresponding Work Order Request to the Computer Maintenance Management System (CMMS). This view shows the contents of the Fault Folder. The possible faults for this pump are: cavitation, Coupling fault, Gland failure, Gland leak, Impeller installed backwards, Impeller rotating in the wrong direction, Inboard bearing fault, Internal wear, Motor DE Bearing fault, Motor NDE Bearing fault, Outboard bearing fault, Out board Gland Leak. Each fault is diagnosed when Alert manager recognises the presence and/or absence of a related set of symptoms. The fault -symptom trees for each asset are created using a simple drag and drop function. AND, OR and NOT functions are provided, so symptoms can be combined in any logical combination to produce a fault. In the example above, Cavitation is diagnosed when Condition Set 1 OR Condition Set 2 OR Condition Set 3 is true. Condition Set 1 is true when the pump has low flow and the Inlet Suction Pressure is low AND the Pump is off its curve AND Intelliscout has given a cavitation alert AND Intelliscout has given a Discharge Pressure Alert. Ideally, the input for the Alert Manager Fault -Symptom trees is the result of your RCM analysis or your FMECA. In this way, what you have learnt from this analysis is used in day to day diagnostic and decision support by all your maintenance & reliability staff - it does not merely sit on the shelf as a report, or reside in an offline database. In this way Alert manager operationalises the results of your RCM analysis. Similarly, Alert manager complements your CMMS. Your CMMS schedules maintenance tasks, respources, tracks inventory & costs. What your CMMS needs as an input is what to schedule when. Alert Manager provides this information on condition by requesting work orders when it has diagnosed a fault.
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This slide shows the Fault-Symptom tree for Internal Wear, which is diagnosed when The pump is off the curve AND Intelliscout motor current estimated values have deviated from the actual values AND when Intelliscout Discharge Pressure estimated values have deviated from the actual values. The above views show the contents of the Fault-Symptom trees. When an actual symptom occurs it is represented with a red tick against it. When a symptom is seen, Alert Manager looks for the presence or absence of all other symptoms related by Fault-Symptom trees. If the absence of a symptom is confirmed, it has a grey cross against it. If the presence or absence symptom cannot be confirmed then a magnifying glass is shown against it, meaning please check. This will usually be accompanied by an or pager message from Alert Manager requesting that the appropriate check be carried out. This can prevent work order flooding due to an accumulation of minor tasks. Alternatively, a work order request can be produced to check for presence of a symptom. Also shown in this slide is the folder containing the fault history for this asset. The start time shows when Alert Manager reported the problem. The end time is when the asset has been set to normal in Alert Manager. Often, records in a CMMS will show when WO request was raised, or when an activity was started or finished, but will bot related directly to times when production was affected. The on line diagnosis by Alert Manager means that the Alert Manager Fault history may be a better tool to assess production impacts of equipment failures. This is often a poorly measured but vital metric in assessing the progress of a maintenance improvement program, or the effectiveness of a maintenance department.
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Simple Configuration Diagnostic Builder Create Asset Types
Build Symptoms Build Faults Build Symptom & Fault Trees Configure the symptom & fault notifications , Pager User Alert Work Order How does all of this work? With the Asset Builder, you are able to leverage your vast amount of expertise as well as that of the asset manufacturer. The Builder lets you easily define symptoms, faults, build the relationships as well as determine the notifications. One a particular asset type has been configured, it can be used over and over again, for example motors, pumps, compressors, etc.
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Maintenance Management
ORIENT DECIDE ACT Available via our Partners Fully integrated with Decision Support Work orders triggered automatically CMMS DIAGNOS -TICS DECISION SUPPORT OPERATOR TOOLS
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Benefit - Higher Availability
EXAMPLE: Higher Availability Through: Early Awareness, Effective Troubleshooting, Efficient Resolution Fuel consumption increased Compressor moves toward surge due to lack of flow Compressor could not maintain flow Gas too hot, Volume larger Excess turbine creep life consumed Cooler Fouled Compressor starts to recycle gas Flash Gas Compressor, throughput $290K day
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Scenario… No symptoms detected
Display of controllers, valves and transmitters indicate normal
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Orient Valve Scout detects a variation in the travel profile of 77FV211 Alert Manager automatically executes a Loop Scout Detail on 77FC211 Recommended action : Run Loop Scout Expert.
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Decide Loop Scout Expert confirms valve stiction and discounts poor tuning
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Act Alert Manager automatically generates the Work Order for valve maintenance on 77FV211.
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Forventede muligheter med Alert Manager for Statoil Kvitebjørn (1 av 2)
Sikre/forbedre regularitet Tidlig feildeteksjon, “Alert” via / SMS Operatørstøtte for rask konsis feilhåndtering Avlasting av personell på plattformen Kontinuerlig overvåking av mange utstyrskategorier automatisert Standardisering av arbeidsrutiner og fokus på arbeidsprosesser Enkel tilgang til all informasjon (prosedyrer, manualer, leverandørinformasjon etc.) Intergrering av all tilstandsovervåking Forenklede / ferdigutfyllte arbeidsordre / notifikasjoner Delvis automatsk tolking av resultater Automatisk genererte funkjsonstester og rapporter
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Forventede muligheter med Alert Manager for Statoil Kvitebjørn (2 av 2)
Samle og systematisere erfaringsdata og feilhistorikk Forenkler feilsøking og er grunnlag for analyse av tilltak Forenkler deling av oppgaver mellom landbasert personell og plattform Landpersonell utfører tilstandsoppfølging og utsetdelse av arbeidsordre Automatisk varsling til landbasert personell fra AM Forenkler deling av oppgaver i et multidisiplint team Bevisstgjøring av teknisk tilstand Stimulere til målstyring Styring av vedlikeholdsprogram Reduksjon i forebyggende vedlikeholdsaktiviteter
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