train_weibull_model {CNAIM} | R Documentation |
Training function for Weibull model
Description
This function uses transformer fault statistics data to train a Weibull model: Based on the environmental
factors determining a transformer's expected lifetime, the set of all data points is first partitioned into five parts.
Then a multilinear estimate for the expected lifetime of a transformer is trained for each part separately, and the
corresponding Weibull shape and scale parameters for the five parts are estimated. The function returns the shape and scale
parameters needed for the function predict_weibull_model
().
Usage
train_weibull_model(transformer_faults_data)
Arguments
transformer_faults_data |
Data frame. Contains past data on transformer faults, together with environmental factors. Must contain the following fields: utilisation_pct: Numeric or "Default", placement: "Indoor", "Outdoor" or "Default", altitude_m: Numeric or "Default", distance_from_coast_km: Numeric or "Default", corrosion_category_index: Numeric or "Default", partial_discharge: "Low", "Medium", "High (Not Confirmed)", "High (Confirmed)" or "Default", oil_acidity: Numeric or "Default", temperature_reading: "Normal", "Moderately High", "Very High" or "Default", observed_condition: "No deterioration", "Superficial/minor deterioration", "Slight Deterioration", "Some deterioration", "Substantial deterioration" or "Default" age: Numeric |
Value
Data frame. All shape and scale parameters needed for the function predict_weibull_model
().
Source
https://www.cnaim.io/docs/fault-analysis/
Examples
train_weibull_model(transformer_faults_data = transformer_11kv_faults)