---
title: Advanced analytics for aging generator fleets
date: 2023-11-14T14:34:00-05:00
author: jessica napert
canonical_url: "https://www.bbaconsultants.com/en-ca/projects/advanced-analytics-for-aging-generator-fleets"
section: Projects
---
As part of TransAlta’s long-term strategy to improve their capability to track the condition of their generation plant over time, BBA performed a comprehensive review of their demographics, inspection, maintenance and telemetric data. We identified key indicators that strongly correlate to end-of-life criteria and failure modes and combined them into an asset health index (AHI), which serves as a benchmark for measuring overall asset health. The AHI was then used to assess the condition of four steam turbine generator units as a proof-of-concept.

 

- Date May 20, 2021
- Region Central Canada
- Client  TransAlta
 
 

 

 

   1. ## Our experts' solutions 
    
    Steam turbine generators are among the most complex assets to manage in terms of component degradation and failure modes. To overcome this challenge, each asset was segmented into its rotor, stator and supporting equipment. Degradation factors for each component were aggregated to formulate the AHI for each component. An overall AHI for each unit was also calculated by combining the rotor, stator and supporting AHIs.
    
    The framework uses data from both online (monitoring) and offline (testing) sources to provide a comprehensive portrait of asset health. This solution gave the client valuable insights into not only the generator’s overall condition, but the condition of each component. These insights can be used for reliability-centered maintenance and risk-based planning.
 
        

  

  

 

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 1. ## Our experts' solutions 
    
    Steam turbine generators are among the most complex assets to manage in terms of component degradation and failure modes. To overcome this challenge, each asset was segmented into its rotor, stator and supporting equipment. Degradation factors for each component were aggregated to formulate the AHI for each component. An overall AHI for each unit was also calculated by combining the rotor, stator and supporting AHIs.
    
    The framework uses data from both online (monitoring) and offline (testing) sources to provide a comprehensive portrait of asset health. This solution gave the client valuable insights into not only the generator’s overall condition, but the condition of each component. These insights can be used for reliability-centered maintenance and risk-based planning.