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Sandeep Chandran PROBLEMS WITH UNIFORMANCE ASSET SENTINEL€¦ · PREDICT PROCESS AND EQUIPMENT...
Transcript of Sandeep Chandran PROBLEMS WITH UNIFORMANCE ASSET SENTINEL€¦ · PREDICT PROCESS AND EQUIPMENT...
PREDICT PROCESS AND EQUIPMENT PROBLEMS WITH UNIFORMANCE ASSET SENTINEL
Sandeep Chandran28 September 2017
Honeywell Proprietary - © 2017 by Honeywell International Inc. All rights reserved. Creating a Digital Representation of Physical Process - Connect / Analyze / Visualize / Act
Digital Transformation Example… Where is my bike rack?1
Where its been?
When will it get there?
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What is ‘Prediction’?
To Tell in Advance of Foretell
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Honeywell Proprietary - © 2017 by Honeywell International Inc. All rights reserved. Goal of Prediction is to ACT
Why Prediction?•To drive proactive response:
- … to prepare for, intervene in, or control an expected occurrence or situation, especially a negative or difficult one; anticipatory
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T1 T2 T3
Predictions
Honeywell Proprietary - © 2017 by Honeywell International Inc. All rights reserved. Don’t try this at home in a spreadsheet
Elements of Predictive / Proactive Solutions• Continuous monitoring “Digitization” / Digital Twin • Run-time analytics
- Data pre-processing / cleansing- Basic calculations- First Principles Models- Data driven / parameter estimation
• Fault Detection- Simple limit violations- Complex logic
• Fault Management (workflow)- Detect- Decide- Act
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Honeywell Proprietary - © 2017 by Honeywell International Inc. All rights reserved. What is your process and equipment analytics platform?
How These Decisions are Made Today
Find / Import Data ReportsTag Name Tag Desc
XUTT901.PV HE91 Shell In Temp
XUTT902.PV HE91 Shell Out Temp
Analyze Data(Excel & Other Tools)
Time ConsumingT1 T2 T3
Blind Spots
• Missed Opportunities• Difficult to Sustain
• Varied Approaches & Results• Difficult to Share Information
86.1988
91.4
Manually Search & Collect
Isolated Tools& Systems
Manual Periodic Calculations
Reactive & Informal Work
Process
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ConnectConnect and store relevant real-time process and event databoth on-site & in the cloud
VisualizeInteractive visualization of trends, charts, and graphics across variety of data sources for process & manufacturing intelligence
AnalyzeCalculate, analyze, and detect risks and opportunities with asset-centric advanced analytics
ActNotifications and workflow for accelerated decision-making to maximize business performance
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Uniformance® Suite Creating the Digital Twin
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Asset Sentinel Executive
Insight
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Uniformance Suite – Along with PHD… the following
Event Management
Investigations
Performance Curves
Event Management
Notifications
Business Dashboards
Process Graphics & Trends
Heat Exchanger
Gas / SteamTurbine
Compressor
Pump
Furnace
AssetLibrary
Calculations(Actual & Predicted)
Head=3960*HPFlow
User Defined Calculations
Pre-Processing / Cleansing
UniSim® - Estimate/ Predicted
Data Driven
Even Detection(Compare Actual to
Predicted)
Fault Models
Fault Prioritization(Fault Severity * Asset
Criticality)
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Example Equipment ModelsPumpRequired instrumentation:• Pump Flow• Pump Suction & Discharge Pressure• Driver PowerAvailable Outputs:• Power, Load• Actual Head, Efficiency• Expected Head, Efficiency• Head & Efficiency Deviation Faults• Compressor Load High / Low Limit• Performance Warnings High &
Critical• Surge Warning• Bearing Vibration & Temp Faults
Heat ExchangerRequired instrumentation:• Inlet and Outlet Temp for shell and tube streams• Inlet and Outlet Pressures for shell and tube streams• Shell & Tube Stream FlowAvailable Outputs:• Heat Exchanger Duty• Actual Heat Transfer Coefficient• Expected Heat Transfer Coefficient• Fouling Factor• Heat Transfer EfficiencyFaults• Heat Transfer Efficiency Warning / Critical• Coolant Temperature High• Fouling Warning / High
• Fouling Factor• Tube Pressure Difference Warning/High• Shell Pressure Difference Warning / High
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Process Model - CDUProcess Operations:• Column head temperature & pressure• Feed temperature• Condenser duty• Bottom stripping steam• Reflux ratio• Sidestream tray temperature• Pumparounds duty• Tower flooding proactive detection with
impact on fractionation yields and fractionation capacity
• Monitoring of unit yields and fractionation by correlating operating condition such as total pump around heat recovery, condenser capacity, reflux ratio, steam/hydrocarbon ratio.
Quality:• LPG C5+ content• Naptha RVP• Naptha PT95%• Jet / Kero Flash point• Jet Freezing point• Gasoil PT95%Corrosion / Reliability• monitoring thickness and acid crude
properties
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Event Management – Orientation10
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Event Management - Detect1111
Column Alert
Alert Details
Alert Logic Alert triggers
Fault Tree
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Event Detection - Decide1212
SymptomsFault
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Event Management - Decide1313
Alert Logic Alert triggers
Fault Tree
Help Tab
Recommendations
Links to supporting documentation
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Performance Monitoring – Column Attribute Overview14
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Process Monitoring – Performance Attributes15
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Performance Monitoring – Integrated Trending16
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Event Management – Act (Close-out)1717
Alert Logic
Fault Tree
Reason Code, Cause, Action
Impact• Cost – negative• Savings - positive
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Machine Learning & Big Data - Marketing vs Reality
There is no magic weight loss pill – it is HARD WORK
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Machine Learning
Gartner’s 2016 Hype Cycle
Data Data
Prediction
Big- Data IIoT
Machine LearningCloud
Cloud
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Asset Sentinel (Complex Event Processing)
Rule Definition and Creation Process
Runtime Analytics & Off-line Analytics
Process DataReal-time & Historical
(Small Data)
Normal & Abnormal• First Principles• Statistical• State estimation
Model
Event Detection
Deviation Detection• Heuristic• Trained
Visual Analytics(Process Data)
• Pattern search• Value Search• Combinations• Cleanse / Filter
Process / Equipment Engineer
Data Scientist
Time Required / Skillsets Required
Statistical Analytics(Process Data)
• Multivariate statistical (PCA, PLS, Kernel Regression…)
• Black-box (Neural Nets…)
Big Data Analytics
• Data vol. & variety (unstructured / text)
• Feature Selection / Extraction
• ML (Random Forest, SVM, Naïve Bayes…)
AnalyticsAlgorithms
Manual Rule Creation
• Calculations• If-then-else rules
logic
T_Diff = L_EGT – R_EGTIf T_Diff > T_Diff_Hi_Limit
T_Diff_Hi_Alarm = TRUE
Event Management• Notification• Investigation• Close-out
Analytics Data Infrastructure & ManagementStorage - (Cloud Historian)
Connectivity (Time-series, Event, Transactional)
Data Prep
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Proactive Detection Approaches – Start at the top and work down
*see slides for more details Hybrid Approaches Needed
Approach Technique Examples Complexity Risk
1. Physical & HeuristicBasic perf mon for broad set of assets & detection deviation from predicted vs actual
• UOP – Connected Perf. Services• Middle East Gas co.– 1200 assets
Low Low
2. Univariate Prediction Predicting single variable time to reach a value Regression e.g. (H_TimeFit) or Soft Sensor
• Heat Exchanger Fouling prediction• Transmitter Inferential Model*
3. Adaptive Filtering/ Thresholding
Data cleansing and moving window of historical window & comparecurrent short term to historical
• Extensively used on offshore O&G compressor vibration
4. Basic PatternRecognition
Detect behavior of single or group of sensors according to know heuristic relationships
• O&G – Spike with amplitude of Xpsi occurring Y times over Z minutes
5. Data Driven -Multivariate Early Event Detection
Statistical pattern detection and recognition including OLS, PLS, PCA, Neural Nets, etc.
• Choke Valve Leak Detection*• Multiple projects*• Furnace Flooding POC*
6. Big DataBig Data using variety of data sources including maintenance and reliability data
• Haul Truck Engine Prediction Example*
• Honeywell Aero APU Example*High High
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Exception Based Surveillance (EBS) Shell Deep Water Assets in Gulf of Mexico & Brazil• Fully operational
for 4-5 yrs• Doubled in
capacity in last 2-3 yrs
• Integrated with Remote Ops
Offshore Oil & Gas Surveillance (Analytics Platform)
Background
Problem / Need
Example of steps 1-4 on previous slide without using data driven ML models
Solution
Results / Lessons
Workprocess for event detection, triage (investigation) and close-outDeveloped over 220+ 'algorithms‘ using 1st principles models, adaptive thresholding, univariate prediction & pattern recognition
Production surveillance & diagnostic system to provide predictive notifications to reduce 'deferment‘Pure data-driven models not actionable and too many false positives.
Report $30M/year reduction in deferment & costsApplication of hueristic / engineering rules reduces # of false positives and increases actionability.Evaluated 'data driven' models with limited results.POC on 'visual' analytics to accelerate 'algorithm development
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Sample of Bridge Surveillance Groups - SummarySurveillance Category Surveillance Group Surveillance Types
Surface Engineering
Chemical Performance Surveillance 8
Compressor Optimization Surveillance 4
Compressor Reliability Surveillance 16
Instrumentation System Surveillance 3
Process System Surveillance 16
Pump and Generator Surveillance 15
Water Injection Surveillance 13
Water Separation Surveillance 8
Subsea Engineering
Chemical Performance Surveillance 8
Choke Performance Surveillance 7
Christmas Tree Surveillance 1
Electric Submersible Pump Surveillance 26
Flowline Surveillance 5
HPU Surveillance 6
Multiphase Flow Meter Surveillance 37
EPU Surveillance 10
POD Electrical and Hydraulic Surveillance 7
Valve Surveillance 2
Subsurface Engineering
Annulus Pressure Surveillance 5
Choke Performance Surveillance 7
Gas Lift Surveillance 6
Operating Guidelines Surveillance 4
Rate and Phase Surveillance 1
Sand Management Surveillance 1
Shut In Well Surveillance 5
• 30M data elements processed per day• 230K daily executed anomaly detections
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Visual Analytics – Accelerating Surveillance Model Development 23
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Step#1: Period of Interest +30min and -30min of lab sample – Propane data
Step#2: Calculate Analyzer Average Value in the period of interest and mark timestamp as middle i.e. 7:00 AM
3 Step#3: Calculate the deviation between average analyzer value and Raw Lab data
4 Step#4: Identify period where the deviation are higher than 30%
5 Step#5: Tune threshold to desired level of sensitivity.
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Historian
Seeq (Visual Analtyics)Asset Sentinel
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Asset Sentinel / Seeq Integration
Trends & Graphics
Event Investigations
Heat Exchanger
Gas / SteamTurbine
Compressor
Pump
Furnace
AssetLibrary
Calculations
Head=3960*HPFlow
User Defined Calculations
Pre-Processing / Cleansing
Event Mgt.
Fault Models
Asset Model Visual Rule Discovery
Asset Model
Conditions,Series,
Patterns
Cleansed / Enriched Data
OPC-HDA & DAS
OPC-HDA & DAS
OPC-DAPHD Other
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• Offshore Oil & Gas with large population of aging electrical submersible pumps and choke valves
ESP Standing Valve Leak Detection
Background
Problem / Need
Example of Data Driven Model for more complex problem
Solution
Results / Lessons
DPCA markedly better than PCA in this use case confirmed leaking conditions DPCA model detected 3leak conditions detected in training data set
Dynamic PCA trained with leak and no leak data. Dynamic PCA determines lag in data and shifts matrix accordingly.T2 (representing variance from mean) & SPE (indicating residual variance) indices on moving time window used to detect leakage when pump stops
Challenge to accurately detect choke valve seal leakage when ESP stops with minimum # of false positives. When is flow & riser pressure change considered to be a leak?
DPCA PCATrue Positive 31 22
Fales Positive 1 9
False Negative 1 2
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Combination of T2 and SPE indicate Fault26
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Mobile Equipment Monitor
• Corporate mobile equipment monitoring program (500+ assets) with suite of rule-based predictors
Mining Haul Truck Engine Failure Prediction
Algorithm helps prioritize overhaul planning via probability of failure
Background
Problem / Need
Desire to augment prediction capability on engine failure
Solution
Merged alarm data (OEM and rule based alerts), oil analysis & engine failure data.Created correlation model (oil dilution, soot, engine de-rate, after cooler, oil filter…)
Results / Lessons
Combination of approaches (oil analysis, heuristic rules, & machine learning) increased fleet availability by 6% and reduced /hr operating cost by 13%
Example of Big Data Analytics – more complex problem
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• 2000+ Gas Turbine global fleet monitoring program (1st principles model w fuzzy logic)
Aerospace Auxiliary Power Unit (gas turbine) Failure Prediction
Background
Problem / Need
Need to improve prediction for 2 failure modes• Auto-shutdown (service
interruption / unplanned maint)• Severe wear (high overhaul costs)
Solution
Merged operational data and maint (text data mining)Classifier for features/failures correlation:• Random Forest – contributors• SVM – separation of variables• Naïve Bayes - probabilities
Results / Lessons
17% improvement in predictability of sever wear43% improvement in predicting auto-shutdown
Example of Big Data Analytics – more complex problem
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Techniques Applied on Bridge Compressor
Sample Algorithms Used on Projects• Principal Component Analysis (PCA) - Unsupervised
- Reveals the inner linear structure of the data and explains the variance by transforming the data into a new, lower-dimensional subspace
- Q statistic measures the lack of model fit for each sample based on the distance a data point falls from the PC model
• Support Vector Machine (SVM) – Supervised/Semi- Learns a decision function for novelty detection to classify new data as similar or
different to the training set
• AutoEncoder (deep learning) - Unsupervised- DeepLearning methodology also known as Replicator Neural Network - Aims at reducing feature space in order to distill the essential aspects of the data- Autoencoding is the non-linear alternative to PCA
Honeywell Proprietary - © 2017 by Honeywell International Inc. All rights reserved. Start with objective in mind and fail fast
Data Drive Modeling – Lessons & Recommendations• Start with clear objective of what problem you are trying to solve
- Trained fault detector High “failure to feature” ratio to reliably predict particular failure model Lower false positive rate Only detects what you have experienced in the past Tend to be more ‘actionable’ to diagnose the problem
- Generic Anomaly Detector Trained on normal data – detects all anomalous behavior High false positive rate Difficult to attribute specific action associated with model anomalies 50% - 80% of problems found are sensor problems
• Set realistic expectations –- Model should match operating situation at scale- Machine learning and statistical methods are not flawless- Define work process and event management infrastructure to deliver results (i.e. Bridge)
• Signal to perform the task is not always in data (no volume will help)• Sufficient volume of quality data required to perform the task • Selection of correct machine learning technique critical to success
- Packaged product vs. programming environment like R
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Honeywell Connected Plant Solutions31
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IIoT by Honeywell Ecosystem
Ecosystem Critical to Add Domain Knowledge to Solve Challenging Problems
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Honeywell IIoT Open & Secure Framework
DCS Process Data
Data Scientists
Knowledge Vendors
Honeywell App Store
• EPCs• OEMs• SIs• Process Licensors
External App Developers
Equip. Vendorsex. Flow Serve, MHI, etc.
Ancillary System Data ex. SAP, ERP, LIMS, etc.
• Smart and Secure Collaboration
• Advanced Analytics
• Self Serve Analytics
• Data Management and Onsite Control
• Smart & Connected Assets & Devices
Enterprise Historian
Collaboration & Visualization
Advanced Analytics
KPI & Calculation engine
Ecosystem Domain Expertise
Analytics Framework
Uniformance Suite (on-prem)
Uniformance Suite
Cloud Historian
Context Model
Time Series Data
Big DataStorage
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Digital Transformation Benefits33
Apply High Skill Resources to High Skill Tasks
Single Version of the Truth Enabling Process Intelligence
Reduce Missed Opportunities &Accelerate Response
Formalize Work Processes& Standard Work
Detect
DecideAct Inform
& Improve
0%
50%
100%
Current FutureData Gathering Analyzing Solving
T1 T2 T3
Alerts!
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Value – Reduce Unplanned Capacity Loss / Lost Profit Opps
Rare Frequent
High
Cos
t Im
pact
Low
Frequency
Process Deviations
Unit Shutdown
Degraded Efficiency
Non-Critical Eq. Failure
Critical Eq. Failure
Reduced Capacity Loss
Original Capacity Loss
Mitigation Strategies
Leading Cause
Contact Honeywell for a Demo or An Asset Sentinel Test Drive
Check it out!35
• Visit Uniformance Asset Sentinel Website• Visit UOP Connected Performance Services for examples of Asset Sentinel at work• Brochure, Case Studies, and More• See it at Honeywell Users Group Americas and EMEA• Watch product demo videos on the Uniformance YouTube playlist at
www.hwll.co/UniformanceVideos• Watch the Lundin Edvard Grieg Video Case Study • Visit Uniformance.com for quick access to more information or to request a product
demonstration