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How to Calculate Machine Availability

How To


Summary

This document explains how Machine Availability is calculated for this OEM in Maximo Renewables. It clarifies which data fields are used, how the Status tag determines availability, and how data‑quality filters can impact the final percentage.

Note:
Machine Availability is an operational metric, not a performance metric.
Because of this, low power output, performance deviations, or power‑curve differences have no effect on availability.

Objective

 

To define:

  • The exact formula used to calculate Machine Availability
  • The Status tag logic that classifies a turbine as Available or Unavailable
  • Why performance indicators (Active Power, Wind Speed, Power Curve) do not affect availability
  • How data‑quality filters may influence the final availability value

Environment

  • IBM Maximo Renewables
  • OEM turbines providing a Status tag
  • 10‑minute SCADA interval timestamp data (or implemented timestamp data)
  • Maximo Analytics data‑quality filtering layer

Steps

1. Data Source

For this OEM, Maximo uses the Status tag, which may include values such as:

Status ValueMeaningAvailability Classification
0ErrorUnavailable
1OKAvailable
2ServiceUnavailable
3PauseUnavailable
4Communication FailureUnavailable

 

Only Status = 1 is counted as "available."
All other Status values are considered "unavailable."


2. Availability Formula

Maximo uses the standard industry formula:

Machine Availability=(Number of timestamps where Status = 1Total timestamps)×100\text{Machine Availability} = \left(\frac{\text{Number of timestamps where Status = 1}}{\text{Total timestamps}}\right) \times 100

 

This is fully aligned with:

Availability=Total Time – DowntimeTotal Time×100\text{Availability} = \frac{\text{Total Time – Downtime}}{\text{Total Time}} \times 100
 

Where:

  • Downtime = timestamps where Status ≠ 1

3. Important: Performance Data Is Not Used

Machine Availability does NOT use:

  • Active Power
  • Wind Speed
  • Power Curve values
  • Efficiency or PR
  • Any AP threshold (15 kW, 19 kW, 40 kW, etc.)

These belong to turbine performance metrics, not operational availability.


4. Impact of Data‑Quality Filters

The Analytics layer applies filters that remove rows containing:

  • Missing critical tags
  • Repeated/duplicated rows
  • Invalid or corrupt values

Filtered rows:

  • Do not count as “available”
  • May reduce the availability numerator
  • Can significantly impact the final availability %
  • Are not visible in user export files

As a result, availability may decrease even if:

  • There are no breakdowns
  • No communication failures are displayed
  • AP/WS appear normal

5. Example Interpretation

If a turbine shows:

  • 144 total timestamps
  • 139 timestamps with Status = 1
  • 5 timestamps with Status = 3

Raw availability = 96.53%

However, if additional timestamps were filtered out due to missing critical tags, the number of valid "available" timestamps decreases, which can produce a final availability of 87.75%, even though only 5 Status‑3 intervals appear in the export file.

Additional Information

  • Machine Availability is an operational metric, not a performance metric.
  • Only the Status tag determines availability for this OEM.
  • Filtering explains gaps between exported Status data and the final availability percentage in the portal.

If additional clarification is needed, please feel free to contact the support team.

Document Location

Worldwide

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Document Information

Modified date:
02 April 2026

UID

ibm17262238