Reduce costs and improve efficiency by predicting maintenance requirements before they occur.

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Saama Technologies, Inc
Predictive Maintenance in Refineries
An Analytics Approach
August 20, 2015

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Saama Technologies, Inc
Business Challenges

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Saama Technologies, Inc
Maintenance – Industry Statistics
46%
Of Refinery Shutdowns
are for mechanical
failures
23%
Of Refinery
Shutdowns are for
maintenance
92%
Of Refinery
Shutdowns are
Unplanned
Source : Refinery power failures: causes, costs and solutions - Patrick J Christensen, William H Graf and Thomas W Yeung, Aug 2013

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Saama Technologies, Inc
The Challenge
One of the main challenges oil refineries
face is
…to maximize asset life span, in the
most economical way,
while not compromising on safety and
reliability
Classic Methods include
• Reactive Maintenance
• Preventive Maintenance
• Condition Based Maintenance
SOURCE: Scanderbeg SauerEnhanced Predictive Maintenance - Pierre Marchand, Oct 31, 2014

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Saama Technologies, Inc
Urgentand emergencyinterruptions to
operations due to equipment
breakdowns
Revenue Loss due to
Downtime
Inefficient Operations and
Supply Chain process
Inefficient Asset utilization
Resource expense for Root
cause analysis

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Excess Spare parts
Inventory
Unnecessary resource
Utilization
Opportunity Loss cost of
unused maintenance
records
High Costand lower efficiency of
Preventive(Unnecessary) maintenance
SOURCE: Parker Hannifin

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Saama Technologies, Inc
Predictive Analytics captures real time equipment data and
evaluates historical data to estimate equipment life cycle
for continuous
Equipment Health Monitoring

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Predictive Analytics System Advantages
An advanced analytics
foundation to optimize
operations planning
Ability to scour past
data, identify patterns &
model streaming data
Opportunity to analyze
real time monitoring data
Mine Recurring issues,
failure indicators &
resolutions
A Robust, scalable
solution which can
integrate with other
enterprise systems
i

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Application Opportunities
• What equipment to pull in for
maintenance & when
• What resources to source & allocate
for maintenance
• Birds eye view of real time equipment
health
• Measure wear and tear of equipment
in its lifetime
• Use Historical data to Identify Leading
failure indicators
• Root cause analysis of incident
Day to day maintenance
• What spare parts to keep
• Product inventory maintenance based
on upcoming maintenance
Inventory Management
Equipment Health Monitoring
Root Cause Analysis
Operations & Supply Chain
• Efficient supply chain management
using predicted maintenance time
• Efficient resource allocation

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Return on Investment
Using Predictive Maintenance as part of asset management program , a
typical 100,000-bpd refinery can have an estimated annual benefits of over
USD$3.5M per year.:
• Avoiding abnormal incidents…$500,000
• Reducing lost profit opportunities…$1,750,000
• Reducing maintenance budget…$800,000
• Improving staff productivity…$300,000
• Reducing liability insurance premiums…$200,000
Source : “Quantifying the ROI of an asset performance management program”. Hydrocarbon Processing. T Ayral and M Moran, Meridium, Inc. May 2007.

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Predictive Algorithms

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Saama Technologies, Inc
Predictive Algorithms for Maintenance
A predictive function based on Current
& Historical data used to derive the
measures
Predictive
Maintenance Design
Binary Logistic
Regression
Multinomial
Logistic Algorithm
Supervised
Learning Models
Explanatory variables
Usage duration
Temperature
Pressure
Flow Rate
Historical sensory
data
Forecasting Models
Health Score of
Equipment
Triggers Alarm
for maintenance
requirement
Usecase : Real wear measure of Equipment

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Saama Technologies, Inc
Usecase : Major failure Indicators
Find patterns in tracking variables correlating to failure
Historical
Maintenance Data
Decision trees
Regression based
models
Identify Root
Cause
Predict future
malfunctions
Usecase : Uptime Time before failure
Modelling historical data to calculate from streaming data
Lifespan Analysis
Model
Pearson
Correlation
Identify operating
Variables
associated with
Lifespan
Estimate
Equipment’s
remaining lifespan
Explanatory
Variables
Analytical
Model
Deduction or
Identification
Outcome
Historical &
Real - time
Maintenance Data
Predictive Algorithms for Maintenance

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Saama Technologies, Inc
Equipment Status Dashboard
Component FRC21
Unit ID 0010021
Location Holder 08
0
20
40
60
80
100
Equipment Age Real Wear Age
Percentage Age of Equipment
0
20
40
60
80
100
High Temp Pressure Vibration
Failure Indicators Component
Equipment
No. FRK03
Equipment
No FRK05
Equipment
No FRK06
Component 1
Component 2
Component 3
Component 4
Component 5
0
20
40
60
80
Equipment Wear Progress
Hours Under Use 6708
Unit ID 0010021
Installation Date 26-07-2015
No of Components 58
Hours till Failure 1677
Forecasted expiry 15-10-2015
Choose
Component
Component
Usage Statistics
Calculate Real
wear of equipment
Lifetime wear &
Warning Indicators
Single dashboard to report the overall health status for an entire manufacturing unit

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Conclusion
Enhance Predictive Maintenance by assimilating data
1. Real – time Sensor Data
2. Maintenance Data
3. Historical Data of Equipment
Identify characteristics affecting breakdown before it
happens. Enhance failure predictions
 Reduce unplanned shutdowns
 Predict when Maintenance is required
 Ensure Effective and efficient spending on proactive
maintenance
 Optimize operating conditions to maximize equipment
lifetime &
 Optimize Supply Chain Processes
Supply & Output
Inventory
Refinery Operations
Predictive Analytics

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Saama Technologies, Inc
References
• Enhanced Predictive Maintenance - Pierre Marchand, Oct 31, 2014
• Refinery power failures: causes, costs and solutions - Patrick J Christensen, William H Graf and
Thomas W Yeung, Aug 2013
• Proactively detect failure patterns to improve asset productivity and product quality - Predictive
Maintenance and Quality, IBM
• Quantifying the ROI of an asset performance management program”. Hydrocarbon Processing. T
Ayral and M Moran, Meridium, Inc. May 2007.