Optimize profitability, safety, and compliance with iso 14224 methods r4

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Optimize Profitability, Safety, and Compliance with ISO 14224 Methods
Tony Ciliberti PE
Principal Engineer | Reliability Dynamics
US Expert in ISO TC67/WG4
Reliability
Dynamics

São Paulo, Brazil, August 8, 2018
© Reliability Dynamics LLC 2018
Reliability Dynamics LLC
Company overview
Registered engineering company specializing in equipment reliability and
maintenance solutions for corporate software systems
Primary product is the Industry Standard Solution for Plant Maintenance
(ISPM®)
Current and recent customers
Brunei Methanol Company
Precision Drilling
Pembina Pipeline
QGOG (Maximo)
Maersk Drilling
Nexen Inc
Marathon Oil
Fortis Alberta

ISO 14224 Overview
Petroleum, petrochemical and natural gas industries — Collection and
exchange of reliability and maintenance data for equipment
Provides a comprehensive basis for the collection of equipment reliability and
maintenance (RM) data in a standard format
Managed by ISO TC67/WG4/PG1
 Revision 3 released in October 2016
 Now available in Portuguese!
Methods originate from the Offshore Reliability Database JIP (OREDA)
 OREDA Established 1981, data collection 1983-present
 OREDA widely considered to be the most successful initiative of its type
 OREDA participants helped to author the first version of ISO 14224 in 1999 and remain closely
involved in development of successive versions
---
ISO/TC 67: Materials, equipment and offshore structures for petroleum, petrochemical and natural gas industries
WG4: Reliability Engineering & Technology
PG1: Project Group 1 (ISO 14224)

Business Case for ISO-14224
Make better decisions for
your equipment assets
 Improve production throughput and
profitability
 Reduce operational risk
ISO 14224 provides:
 Proven methods for collecting high-
quality reliability and maintenance
data
 A toolset for data-driven decision-
making
 Corporate visibility of equipment
reliability and risk

Data Quality Framework for Big Data
Dimensions and Elements
We need “data collection principles and associated terms and
definitions that constitute a “reliability language” …for communicating
operational experience.”
Cai, L. & Zhu, Y., (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data
Science Journal. 14, p.2. DOI: http://doi.org/10.5334/dsj-2015-002

RM Data Quality Management
ISO 14224 Clause 7
Quality Assurance
 Proactive measures that increase probability
completed records are correct
 Standard methods for technical hierarchy
and failure data collection
 Structured data input with minimum data
requirements defined
 Data field relevancy by work type with
separate input forms and datasets
 System validations to ensure data
compliance
 User training on methods
Quality Control
 Review of completed records to ensure data
compliance (results-based sampling)
 Review of financial records to ensure
comprehensive reporting
 Results-based feedback/training to field
personnel

ISO 14224 Data Infrastructure
Corporate Reliability Metrics
Industrial IIoT
Platform and RM
Data Repositories
Corporate ERP System
· System of record for technical tags
· Work management system
· Initial data collection point for equipment failure data
ERP DATABASE
· Work execution
· Results recording
· Labor and material
bookings
Standard
reliability data
processesTechnical
hierarchy
Strategy development
Equipment
malfunction
reports
PM results
recording
Equipment
failure events
Orion eAPI
GE Predix Platform
· APM, OPM
SAP Leonardo Bridge
· AIN, ASPM, PDMS
IBM Watson
Bentley Assetwise
· OREDA
· CCPS PERD
Equipment
reliability
metrics
ISO 14224 Processes
High-
quality RM
data
Engineering and
Construction Phases
Plant information
database, e.g.
Hexagon SPO
· Technical tag list
· Equipment specifications,
attributes, and
interrelationships
Equipment
taxonomy definitions
DataM
apping
InterfaceCapital facilities
specification and
equipment selection
IoT/Big
Data
Analytics
Early fault detection
Risk assessment
Preventive
maintenance
and inspections
Manage risk, improve production availability, reduce cost, optimize new capital facilities

ISO 14224 Data Infrastructure
Risk Monitoring with Big Data Analytics
RiskAssessment
BigDataAnalyticsandIoT
Sensors
ERPData
Integration of Big Data Analytics and IIoT Sensors with ERP data
1a. Risk
assessment
methodologies
· LOPA
· HAZOP
· RBI
· RCM
· Other
2. Initial risk
ranking
1. Generate risk
scenarios
3. Identify
existing risk
control
measures
(“safeguards”)
and assumed
risk reduction
for each
5. Implement
additional or
improve existing
safeguards
4. Is risk
acceptable
(ALARP)?
Yes
7. Criteria for
evaluating actual
versus assumed
risk reduction
(“safeguard
effectiveness”)
8. Monitor
safeguard
effectiveness
No
10.
Equipment
failure data
12. Equipment
PM/inspection
results
9. IIoT
sensor
data
11. Spare
parts
availability
13. Personnel
qualifications
and training
6. Rank
residual riskCorporate
risk dashboard
14. Other
data
Automated processes

Corporate Risk and Reliability Metrics
A Bottom-Line Approach
IDENTIFY
Equipment failure events with the greatest consequences
Equipment causing those events (bad-actor equipment)
ANALYZE
High-consequence failure events
Failure patterns of bad-actor equipment causing them
Obtain details necessary to take corrective action
RESOLVE
Bad actor equipment reliability issues
Implement and prioritize corrective measures

IDENTIFY Elevated Risk
Corporate Metrics

IDENTIFY High-Consequence Failure Events
Corporate Metrics

ANALYZE Failure Details
Equipment-Level Analysis

ANALYZE Failure Details
Component-Level Analysis

RESOLVE Bad-Actor Equipment Issues
Identify and implement corrective measures, e.g.:
Modify equipment/process
Add/adjust preventive maintenance/inspections
Add/modify procedures
Personnel training
Prioritize corrective measures based on:
Actual consequences
Future consequence potential

Reliability
Dynamics
15
Master Data Infrastructure
Technical Hierarchy

Technical Hierarchy with ISO 14224
Introduction
Logical representation of all facilities
(taxonomy)
 One unique ID for each equipment object
throughout all systems and records and in
the field
 Hierarchical structure that is intuitive for
users to navigate
Infrastructure for collection of equipment
reliability and maintenance data
Consistent with standard methods
 ISO 14224
 ISO 15926-2, Section E3.3 (Lifecycle Data
Model)
 As applicable:
 SFI Grouping System
 NORSOK Z-DP-002 Coding System

Technical Hierarchy
Example of Oil and Gas Midstream Application
Asset register
Equipment
boundary envelope
Interrelated
equipment

Construction of Technical Structure
Example for K-102 Compressor

ISO 14224 Boundary Definition

ISO 14224 Equipment Subdivision

Construction of Technical Hierarchy
Equipment Properties for Compressors (subset)

Functional Location / Equipment Relationship
ISO 15926-2, Section E.3.3
Functional equipment location Tag P101 is an intangible object that defines process requirements for a
particular pumping service, e.g. pressure, temperature, flow, fluid type (Tag P101 in the example below)
Equipment items (serial numbers 1234 and 2345) define specific materialized objects that execute process
requirements
Field equipment change-outs are captured in SAP via corresponding equipment dismantle/installation
transactions (on 5/8/2001 S/N 1234 was dismantled and S/N 2345 installed)

ISO 14224 Technical Structure
Equipment
Subdivison
Use/Location
Data

K-102 Boundary Definition

Use/Location Data

Example: Offshore Drilling Contractor
Amato, T., Ciliberti, V. 2016. Case History of ISO 14224 Application in the QGOG Offshore Drilling Fleet, IADC Asset Integrity
Conference, 30 August, Houston, Texas USA

Technical Structure Specification
Offshore Drilling

Technical Structure
Asset Register and TDX Boundary Definition

Reliability
Dynamics
29
Malfunction and Condition
Reporting

Malfunction Reporting
Impart Data Quality on Inception
Step Details Responsibility
Work Initiation Problem Report (equipment-level
failure notations), system QA checks
Facility personnel
Approvals and
processing
Work approval, planning and
scheduling, create statistical records
Operations
Superintendent
Execution, repair
notes, and close-out
Repair Report (item-level failure
notations), system QA checks
Maintenance Lead
Technician
Failure data quality
control
QA/QC, consequence assessment,
and methods feedback
Reliability Engineer

Malfunction Problem Report (Work Initiation)
Equipment-Level Notations

Approvals and Processing
System Creation of Statistical Record

Malfunction Repair Report (Work Close-out)
Component-Level Notations

Equipment Malfunction Consequence Assessment
Enables Corporate View of HSE and Profitability
“If you can’t
measure it, you
can’t improve it!”
-Peter Drucker

Preventive Maintenance and Inspections
Program
 Administered as administrative or technical
tag level
 Results reported at technical tag level
PM Condition Report
 One condition report per technical tag
inspected
 Inspection verdict and condition details
 Generated from object list of PM inspection
order
Follow-on malfunction report
 Generated for any equipment
malfunction verdict
 Linked to PM Condition Report as
subordinate object

ISO 14224 Implementation Roadmap
Recognize RM data deficiencies
Enable CMMS/ERP with
ISO 14224 methods
Construct technical
hierarchy
Conduct training
and change
management
Define expected
results

Reliability
Dynamics
37
Thank you
Tony Ciliberti PE
Principal Engineer | Reliability Dynamics
tony.ciliberti@rd-eam.com

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