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Predictive Maintenance

Case Studies

About manufacturer of advanced building automation systems  Company

KPI's client is a manufacturer of advanced building automation systems (BAS) to centrally control and manage HVAC, refrigeration, and lighting systems for businesses.  Over 45,000 sites worldwide use their technology, with more than 10,000 actively managed 


  • Apache_Spark_KPI Partners (1)
  • MLflow
  • xgboost
  • Hadoop_KPI Partners (1)
  • Seldon KPI Partners-1


The objective of the case study was to develop accurate predictive models that could identify potential commercial HVAC equipment failures. The goal was to detect pre-failure conditions within a specific time frame to allow for proactive maintenance services to be deployed. In addition, the project involved analyzing extensive and diverse data sources, including legacy systems, third-party data, time-series data, and location data.


The Before State

  • Develop accurate predictive models to identify commercial HVAC equipment that is trending toward failure
  • Models must detect pre-failure conditions within a timeframe that allows proactive maintenance services to be deployed
  • Large volumes of disparate data sources (legacy systems, third-party, time-series, location data)


What KPI Delivered

  • A series of XGBoost classifiers that could predict individual unit pre-failure conditions with over 90% accuracy

  • Harmonized data sets that included terabytes of sub-second time series IoT streams, third-party API weather forecasts, and building/mechanical features

  • Integrated MLOps prediction API into existing proprietary dashboards


The After State

  • Remote building engineers were now able to be alerted to likely HVAC failures up to 5 days in advance

  •  XX% Reduction in unnecessary truck rolls (service tech dispatched)

  • Highly scalable and repeatable processes as additional sites and units come online


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