Maximizing the availability of assets to earn revenue directly impacts financial return on assets and shareholder value. CMMS is a new approach to integrate applications which can be used to achieve this outcome. This type of system, when applied in a railway environment, can allow its operator to automate and better coordinate Operations and Management (O&M) activities such as maintenance work orders, inventory management and asset management. The railways in Australia have matured enough to understand that instead of continuing the historical cycle of break-fix work orders and static maintenance schedules, there are huge potential cost savings in predictive maintenance. Modern analytics software solutions can identify subtle patterns or abnormal events to accurately predict future performance of the asset. The vendor hype surrounding predictive maintenance packages has many examples of unsuccessful implementations, where the product has not met the expectations of the rail operator. This is due to a number of factors but mainly because railways have not analysed the needs of the business and requirements of the maintenance strategy that they want to pursue prior to procuring a package. This paper will look at the problems of building requirements to suit a particular predictive maintenance package versus analysing the needs of the business to allow the market to develop a product that meets the business needs.

1. Computerised Maintenance
Management Systems for Railways
using Big Data

2. Application of IoT and Big Data integration with Computerised
Maintenance Management Systems:
 Provides a Single Point of Truth (SPOT)
 Streamline rail maintenance planning and workflow process management
through automation
 Unlocking the next level of operational effectiveness in asset management
Key Take-Away Points

3. Computerised Maintenance Management System is a:
 Work Order Management System.
 Maintenance Notification System.
 Repository of Asset Lifecycle History
What is a Computerised Maintenance
Management System (CMMS)?

4. Train Control unable to call points –
Breakdown Notification in CMMS.
Manufacturer recommended Point Machine
Service is due – Planned Maintenance
Notification in CMMS
Track Inspector observes name plate
missing from Point Machine – Ad-Hoc
Notification created in CMMS
An Example of CMMS Functionality in the
case of the humble Point Machine

5. Scenario: Failed LED Signal Lamp
Action: Breakdown Notification is logged in
CMMS
Result: Work crew is dispatched to replace the
signal lamp
Issue: Unplanned outage, high cost to mobilise
crew at short notice. Production affected
Break-Fix Approach to Maintenance

6. Scenario: Periodic maintenance advised by
manufacturer for point machines
Action: Planned work order logged in
advance in CMMS
Result: Work crew is dispatched to perform
maintenance
Issue: Labour intensive, maintenance
despite no failure, risk of causing damage
Preventative Maintenance Approach

7. Scenario: Rail Grinding
Action: Work order created in
CMMS
Result: Work crew is dispatched and
problem is fixed
Issue: Labour intensive, often not
effective and doesn’t provide
significant benefits
Ad-hoc Approach: Point Machine

8.  Traditional methods can be labour intensive
 Ignored planned maintenance increases the risk of catastrophic asset
failures which could result in critical operational and safety consequences
 Return of investment for activities are not realised
Maintenance is performed despite absence of failure
The Need for Change

9.  Creates Work Orders with no traceability.
 Decision making process sits outside the System
 Lack of data analytics
Limitations of a standalone CMMS for
Maintenance

10. IoT and Big Data in Rail

11.  Predictive Maintenance
Predicting maintenance interventions using the data rather than reacting. Still
requires data to support the decision making process.
Smarter Maintenance Strategies
Leveraging from Big Data Analytics
 Risk Based Maintenance
Maintenance decision making based on asset criticality and risk. Still
requires data to understand frequency of failures.
 Reliability Centered Maintenance
Maintenance based on analysis of Failure modes and effects. Still
requires data to understand the failure modes.

12. Failure of Big Data Applications
Lack of full Integration Leads to Data Silos
Fragment Systems & Data Storage
When information resides and gets reviewed in
different places it is possible to miss critical pieces
of the puzzle.
Ivory Tower Solutions
Looking only at products in the market without
understanding the requirements leads to
solutions that cannot be applied in a practical
way.

13. Big Data Analytic
driving decisions in
the CMMS
What is not happening presently?
Cross- Functional Communications
Measuring Point functionality of the CMMS

14. Rail Operator: Austrian Railway Company (OBB-Infrastruktur)
Issue: Asset intensive infrastructure & rising operation cost €2.75 billion
(Approx. AUD $4.38 billion)
Solution: Integrated Infrastructure Management (IIM) System
Result: Integrated all expert systems in one single application, thus avoiding
isolated applications and enabling easy access to information
Fully Integrated Infrastructure
Management System –A Case Study

15. Asset Classes – Austrian Railway
13,677 switches including 10,361 heated.
24,786 signals
1,069 stations
6,344 bridges
246 tunnels
3,278 level crossings
4,496 buildings
Fully Integrated Infrastructure
Management System – A Case Study

16. System Architecture of a Fully
Integrated System

17. Cockpit view of the entire network

18. Estimating Maintenance Costs based
on work history

19. Real time Reporting for Safety,
Quality and Costs

20.  Support in shut planning, because of better data integration in the CMMS
 Workforce automation based on predicted maintenance cycles vs planned
preventive maintenance cycles
 Central repository of condition monitoring data and maintenance history in
the same database.
Benefits of a Fully Integrated CMMS

21.  An integrated Computerised Maintenance Management System is the
Single Point Of Truth (SPOT) for a business that is asset intensive.
Big Data and IoT need to feedback into Reliability Centered Maintenance
(RCM) process to manage planned maintenance activities in the CMMS.
 Using Big Data and IoT to unlock hidden values in Asset Management.
Summary – Key Points

22. Thank You
Questions ?