Semi-automated Estimation of 

Reliability Measures from 

Maintenance Work Order Records

Tyler Bikaun, PhD Candidate (UWA)

TLP Community of Interest Workshop, 2021 Model-Based 

Enterprise Summit, NIST, April 12 - 16, 2021
Slow and Tedious Not Reproducible Lacks Standardisation

Not Scalable Tacit Knowledge

Motivation

Acquiring reliability measures for assets is crucial for maintenance 

strategy validation and optimisation

Industry Challenges

Our Challenge
Context and Datasets

We use technical language processing to emulate a prototypical reliability engineer 

and assess the impacts decisions made when processing records have on resulting 

reliability measures

Work Order Descriptions

Plant A B

H2G-33 damaged seal 48 DAY THROAT BUSH ROTATION and internal inspection to assess 

remaining life

Asset Type Pump Pump SCE Pump Seized K2G36A Change out motor

MWOs 14,508 89,259

H11 Drive belts snapped Please change out Electrical motor on #1 sump as it has been 

submerged in liquor

Mean 

Tokens 5.5 8.0

H11 High vibration, realign 

unit D2G62B

TIS Vacuum and clear scale from #1 Horizontal sump pit and discharge 

line . Pit is currently blocked and causing flooding.
Pipeline and Scenario Modelling

Gazetteer with single EOL1 S1 term

S3

S2

Expanded EOL gazetteer and 

structured fields

Expanded EOL gazetteer

Scenarios

1 EOL refers to end-of-life
Pipeline Results – Overall

1All experiments performed with $2000 or 8 hour threshold, and each asset required at least 5 pieces of evidence for MTBF estimation.

Plant A Plant B

Assets 903 3079

Samples 3112 35170

Failures 1874 6746

Suspensions 14 2850

Eligible Assets1 93 669

Compute Time 22s 91s

F1 Score 79.3% 54.3%

Evaluation

Human evaluation of classifications on 20 

assets sampled i.i.d from each data set

Performance

Minimal SME resources required and only 

minutes to compute
Pipeline Results –Scenario Modelling

Scenario Asset2 MWOs MTBF 

(days)

Asset2 MWOs MTBF

(days)

S1

A1

58T/15F/0S 184.8

B1

134T/0F/23S -

S2 58T/29F/0S 97.1 134T/0F/23S -

S3 20T/9F/0S 226.2 115T/9F/16S 185.6

S1

A2

90T/17F/0S 181.9

B2

- -

S2 90T/30T/0S 101.9 10T/7F/0S 318.8

S3 40T/14F/0S 209.9 15T/6F/2S 334.1

S1

A3

31T/11F/0S 244.3

B3

- -

S2 31T.15F/0S 191.7 137T/1F/16S -

S3 10T/7F/0S 408.5 114T/5F/11S 301.4

1All experiments performed with $2000 or 8 hour threshold, and each asset required at least 5 pieces of evidence for MTBF estimation.

2A1 – centrifugal pump, A2 – piston pump, A3 – peristaltic pump, B1 – Warman 8/6 FAH, B2 – Worthington 10LR15A, B3 – Warman 10/6 FM
Discussion

Fast Scalable

Reproducible1 Standardised

Less than 10 min end-to-end time for pipeline 

construction, validation and application

Process 1,000s of assets with 10,000s of records 

in minutes without significant SME resources

Transparent pipeline construction and 

application with configurable parameterisation

Consistent adherence to industry standard 

statistical life time estimation

Code will be available at: https://code-ittc.csiro.au/tyler.bikaun/mtbf_from_mwo
Caveats, Limitations and Future Work

• Need structured fortuitous data fields and expert rules to reason about EOL events

• Currently only applicable to individual assets not sub-systems/systems

Caveats and Limitations

• Increase applicability to systems/sub-systems

• Improved reasoning over unstructured data fields

• Further reduce SME resource requirements

Future Work
Thank You

This research is funded by the ARC Training Centre for Transforming Maintenance through Data Science and the Mineral Research Institute of Western Australia
For More Information:

ARC Training Centre for Transforming Maintenance 

through Data Science

Email | tyler.bikaun@research.uwa.edu.au

Web | http:www.maintenance.org.au

All Icons in this presentation were source from Noun Project: Expand (IconMark), Speed (sandiindra, Adrien Coquet), Recycle 

(emka angelina), Expert (Becris), Standard (Justin Blake, US), AI (Waleed Elagamy, EG)