An overview of some industrial use 

cases for technical language 

processing (TLP)

12 April 2021

Sarah Lukens, Ph.D.

GE Digital
470

days

314

days

AIC Model XYZ RELIABLE Model 123

Comparison of MTBF (days) 

Use case #1: Enables consistent reliability metrics 

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Description Before: 

Breakdown 

Indicator

After: 

Is a Failure 

event?

Seal is leaking badly FALSE True

Block valve is broken 

open and inoperable

FALSE True

00120-Pump 1 work 

request

FALSE False

Check impeller size FALSE False

Before: Inability to calculate Mean Time 

Between Failure (MTBF)

After: Benchmarking comparison of MTBF is 

possible

Company 1 Company 2 

Lukens, S., Markham, M., Naik, M., & Laplante, M. (2019). Data-driven approach to estimate 

maintenance life cycle cost of assets. Model-Based Enterprise Summit (MBE 2019)
Example: Chemical company is missing $1.5 million annual savings and 

does even not know it

Accurate reliability metrics enables identifying opportunities

3

Before TLP After TLP

Comparison of MTBF of pumps before and after TLP 

utilized on data

• If performed as well as peers, 

would save $850/pump for 

~1800 pumps

• Avoiding about 600 failures

• Resulting in $1.5 million annual 

opportunity

Unrealistically “good” 

MTBF values

https://www.ge.com/digital/blog/asset-answers
Root cause analysis revealed no proper procedures in place for motor maintenance. 

Once remedied, failures ceased with estimating annual savings of $54,000 for the one 

pump alone

Use case #2: Finding hidden bad actors

4

https://www.ge.com/digital/blog/technical-language-processing-how-put-industrial-data-work
Failure mode characterization can be used for reliability-data based 

survival models such as Weibull analysis 

Use case #3: Enables reliability distribution fitting

5

Dog-leg shape – mixed 

failure mode information 

inappropriate for 

statistical modeling

Weibull distribution for 

valve leakage failures 

on compressors

Before After
Repair or replace? Simulation model:

Reliability distribution fitting can be used for decision making

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$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

$900,000

2018 2020 2022 2024 2026 2028

Cumulative cost

Year

Cumulative anuual maintenance costs during 

product lifecycle with purchase price 

AICM1 RELIABLE M2

Model 1 is more reliability but costs 

more than Model 2

2 years payback period 

- may justify the higher 

price tag

https://www.ge.com/digital/blog/digital-twin-approach-designing-cost-effective-maintenance-strategies
Objective: Identify recommended fix 

Value: Reduce repeat maintenance trips, 

reduce downtime, and reduce 

maintenance costs

Interface: Tool usable by the 

maintenance technician or engineer in 

the field

Data: any historical data which captures 

asset failure, maintenance events, and 

repair actions

Use case #4: Framework for prescriptive maintenance

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Enables APM workflow which can recommend fix based on 

historical data

8

Step 1: Classify & structure failure 

events/defects from repair data Step 2: For each defect, identify 

past actions. For each action, 

determine effectiveness.

Example: Replace seal cartridge 

has been the most effective fix 

historically for seal degradation

Example: Degraded seal 

Deployed through Predix APM
Use case #4: Inspection analytics

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~1000 out of 

2300 

inspections 

have potential 

degradation

Assets with localized corrosion and UT 

programs with corrosion rate lower than 5 

mL/yr are candidates for RBI analysis

TLP

Nair, V. & Lukens, S. (March/April 2018), Inspectioneering Journal 
Inspection analytics enables making intelligent decisions during a 

turnaround

10 Nair, V. & Lukens, S. (March/April 2018), Inspectioneering Journal 
TLP enables industrial 

companies to make 

decisions using their 

historical data.

The value of TLP comes with 

integrating the data into 

business processes in ways 

that create value.

Thank you!

Putting industrial data to work with TLP

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Contact me: sarah.lukens@ge.com