Itron Analytics asset management

Itron Analytics' Transformer Load Management application contains a Predicted Failures chart that displays the estimated end of life date for your utility's transformers. Without Itron Analytics, most utilities’ best transformer end of life date estimates are based solely on comparing a transformer's installation date to its manufacturer’s expected life span rating. However, we know that the load placed on a transformer plays a factor in its life span. Transformers that are under consistent heavy loading conditions are not expected to function for the duration of the manufacturer’s expected life span rating, while transformers with lighter loading conditions may outlast the manufacturer’s expected life span. Itron Analytics leverages service dates, manufacturer rating, measured load data, and even weather information to generate a far more accurate simulation of the transformer's effective age. This feature helps you avoid the cost, risk, and inconvenience of a transformer failing while it is in the field and assists with equipment replacement cost forecasting.

Tip: Itron Analytics requires at least as one full calendar year (01Jan-31Dec) of data for a transformer before any modification to the transformer life span estimation can be made. When less than a full year of data is present, a one-to-one ratio of usage to lost life is assumed. For example, one month of use equals one less month of life. When multiple years of data are present, the number of years used for loss of life calculations are configurable. For more information, see Editing Transformer Load Management settings.

  • Install year.  The year the transformer was installed.

  • Manufacturer's life span rating.  The manufacturer-supplied expected life of the transformer.

  • Estimated end of life year.  The year the transformer is predicted to stop functioning.

  • Measured loss of life data.  The transformer loss of life data Itron Analytics has either collected or calculated. For DTM meters, Itron Analytics collects the loss of life data from the DTM meter. For non-DTM meters, Itron Analytics calculates the loss of life data based on the transformer’s connected service points’ aggregated usage data.

  • Data analysis window.  A number of years of measured loss of life data for calculating historic and future aging rates. The default value is 1. This also acts as a threshold- if Itron Analytics does not have enough data to meet the threshold, no algorithm is applied and time since installation is directly compared to the manufacturer's life span rating to generate an estimated end of life year.

  • Historic years.  The number of years between the install year and the start of the measured loss of life data.

  • Historic aging rate.  A rate applied to the historic years that is based on the oldest data analysis window of measured loss of life data.

  • Historic loss of life.  The number of years deducted from a transformer’s life expectancy, based on the historic aging rate applied to the historic years.

  • Future aging rate.  A rate applied to the future years that is based on the newest data analysis window of measured loss of life data.

  • Future years.  The number of years calculated as a difference between the culmination of the manufacturer's life span rating minus the historic loss of life and measured loss of life data.

  • Future loss of life.  The number of years remaining until the estimated end of life year.

<estimated end of life year> = ( (<manufacturer's life span rating> - ( (<historic years> * <historic aging rate>) + <measured loss of life data>) ) / <future aging rate>) + Today's Date

For example, given the following values:

  • Install year = 2005

  • Manufacturer's life span rating = 30 years

  • Data analysis window = 3 years

  • Measured loss of life data = 7 years of data starting in 2012. Average rate of 1.49. Total measured loss of life is 10.43.

    • 2012, 1.8

    • 2013, 1.9

    • 2014, 1.6

      • Average of three oldest values provides historic aging rate of 1.77.

    • 2015, 1.4

    • 2016, 1.5

    • 2017, 1.2

    • 2018, 1.0

      • Average of three newest values provides future aging rate of 1.23.

We can calculate the following:

  • 2023 = ( (30 - ( (7 * 1.77) + 10.43) ) / 1.23) + 2018