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1. How to predict the life and repair cycles of the critical assets? (Conveyance Pipes,  Penstock pipes)

Benefits
Proper asset management
Reduce unplanned outage

Stakeholder
O&M
TSD

Analytics problem
1. How to develop model to predict the life of critical asset and repair cycles.

Data Needed:
1. Conveyance Pipe thickness (from new to present)
2. Deterioration rate
3. Life of the pipes
4. Location of the pipes
5. Design requirement of the pipes
6. Condition assessment of the pipe
Algorithms
1. Prediction
2. Clustering

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Predicting The Repair Cycle of xx Hydro Power Plant Turbine

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Predicting The Repair Cycle of xx Hydro Power Plant Turbine

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Problem Statement
Plant xxx has experienced unplanned downtime due to turbine scouring repairs, resulting to revenue loss.

Usability Requirements
Defined schedules for repairs.

Key Stakeholders
-O&M
-Suppliers and Contractors
-Customers
-Finance Team

Benefits
-Minimized Unplanned Downtime
-Optimized Resources through Planning

ANALYTICS PROBLEM FRAMING

Analytics Problem Statement
1. Develop a model to predict the failure of turbine runner.

2. Prescribe schedule for repair.

KRI
1. Zero unplanned downtime due to turbine runner repair.
2. Decrease inventory cost (JIT)

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1. Predictive Boiler leak determination
2. Asset Life Cycle Management of Major Assets (Turbine, CWP, IDF, FDF)
3. Obsolency Resolution (when will be the right time to upgrade)

Stakeholders:
1. TEC
2. TLI
3. PEC

Benefits:
1. High Plant Availability and Reliability
2. Proper Resource management (capex)

Analytics Framing
1. Boiler leakage contributes 70% of the cause of Downtime on CFB and PC plants, aim is to develop a tool or program to predict the right timing to perform thermal protection improvement and prepare the necessary resources needed.
2. Zero (0) Forced Outage due to Boiler leak

Data Needed:
1. Coal Quality
2. IDF and FDF load (amp)
3. Capacity Factor
4. Emission levels
5. Boiler Pressure
6. Tube Mapping baseline
7. Water Quality

Application:
1. Determining the selection set of engineers/operators who can be grouped together to improve start up time.
2. Reinforce Tribology interpretation

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BP Statement
High Revenue loss due to  Sibulan turbine runner failure

Key Stakeholder
TSD , Sibulan Grid O&M

Benefits
-Reduce downtime and revenue loss
-Improve Maintenance planning
-Leverage DS to improve predictive maintenance strategy

Analytic Problem statement
Improve maintenance capability to predict failure of turbine Runner

Stakeholder
DS team
OT
O&M Team

Metrics
1. zero  downtime
2. Reduce  spare parts inventory cost
3. Improve turbine efficiency

Data needed
Flow, power, vibration, energy production
Turbine efficiency, OEM specs

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How do we access historical test results(Values) on-line without compromising the integrity of the data?

>Data to be integrated into an APP where trending can be viewed
Expected Result: able to determine health condition based on the trending and determine the MTBF-Mean Time Before Failure
Metric:
1. Easy access by all technical staff
2. able to view and predict before equipment failure
3. integrity of the data is preserved

test results for transformers like insulation, winding impedance, etc with corresponding limits

linear regression--prediction

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