Capstone Project: Predicting remaining Life of Turbine Blades Ziad Katrib April 5, 2016

Some context: Why is it important to predict remaining life on Turbine Blades

• The single largest cost in a combined cycle operation is the cost of fuel • This one is fairly understood and well predicted

• The second largest cost in a combined cycle operation is the cost of Major Maintenance

• Major Maintenance refers to the costs Turbine parts and labor required to maintain the equipment • Major Maintenance cost is monetized based on assumed parts life as suggested by the manufacturer • Those assumptions can be wildly inaccurate

• Major Maintenance cost is largely based on assumptions and manufacturing recommendations

and is less understood

• The 1st stage rotating blades, which is the largest cost item in Major Maintenance, will be evaluated

Anatomy of a gas turbine

Air

Nat Gas

Hot Exhaust

Compressor Turbine Combustion

Fig.1 Cutout of a gas turbine engine

Simplifying Capstone question

• Original question: Can we create a useful model that will predict actual remaining life of 1st stage turbine blades?

• For this particular type of blades the operating cycle consists of two runs only i.e. Run-Repair-Run- Retire from service:

To predict 1st stage blades remaining life one needs to only predict whether or not a set is

repairable after one operational cycle

• The question then becomes can one predict whether or not a set of blades is repairable?

Process: Data collection

• 1st stage blade repair status, manufacturer, engine type, dates in and out of engines, etc… were retrieved from the enterprise work management system ( a SQL database)

• All blade sets information was cross checked in other maintenance records to ensure data validity

• Blades sets with known Foreign Object Damage were removed from the dataset. The outcome of those blades is known with 100% certainty as Non Repairable

• Engine data for each blade set was retrieved from historian systems at 5 min samples. This data is in time series format and consists of: • MW • Exhaust Temperature • Vibration • Compressor pressure and temperature • Turbine Section temperature

• Engine data has been anonymized for intellectual property and commercial considerations: therefore sensor data is only referenced as sensor 1 , 2, 3 etc…

Process: Putting all data together

• Turbine operational data for each asset was pulled at 5 min intervals • 30 sets of data where then aggregated together, leading to 7.6 million rows

Fig. 2 snapshot of all data put together

Process: Data reduction and exploratory model

• The 7.6 Million rows were reduced to an aggregate ( average, or sum) of each measure by asset

• That meant only 30 rows of data were left

• Initial exploratory model: o Some Significant predictors were found o Misclassification rate for the exploratory model was at 25%

Process: De-aggregating significant variables

• Aggregation of 200K rows of data per asset removes important granularity • For the significant predictors found in the exploratory steps, new predictors where

created that summed up number of hours at multiple cutoff, e.g. number of hours at Temperatures above 1200 Deg F

• 24 new predictors needed to be investigated

• Multivariate correlations were applied to investigate collinearity and remove collinear variables. 9 independent predictors where then used for model building

Table 2 example of correlation matrix

Final model building: Step 1

• Due to the small population (only 30 assets), holdout set was limited to 10% ( 3 assets) • Logistic regression forward selection, was used on the training data set

3 out of 27 predicted incorrectly

Fig. 3 Stepwise forward selection output Fig. 4 Logistic regression output

Final model building: Diagnostics for initial model

• Interaction/quadratic terms and Marginal Model plots were investigated next.

Fig. 5 Quadratic and interactive terms investigation

Fig. 6 Marginal model plots show need for possibly more terms

Sensor 8 Sensor 1 Sensor 1 and 8

Model vs predicted show a mismatch

Final model building: adding the additional terms

Adding quadratic term and an interaction term improved BIC and misclassification rate. Marginal model plots for actual and predicted also now line up, pointing to a valid model

Testing the model on the holdout set

Asset Repairable Most Likely Repairable 10355946 1 1 10978512 1 1 10978589 0 1

• Further analysis of asset 10978589 shows that this asset had 3 instances of a rare event called Full Load Trip, it’s widely known that this type of event can cause additional damage to a Turbine internal components

• Full Load trip data can be difficult to compile from a historical perspective, therefore those events will not be included in the current model

Some interpretation

• Significant Factors from a parsimonious perspective:

1. Operational data 1

2. Number of hours running with Sensor data 1 and 8 above a certain threshold

3. Number of hours of sensor 8 above a certain threshold is essentially pointing to some engines that have higher torque and thrust rating

Model deployment

• Following internal business reviews: - Model should be deployed to production

- Some bidding hurdle rate might get affected with this new information

- Incorporate model for 2017 -2018 parts forecast

- Perform similar analysis on other internal components

Appendix

Running analysis in Enterprise miner

Running analysis in Enterprise miner

Ops_data2

Sensor 8

Sensor 1

Sensor 8

Sensor 1

Ops_data2