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Real-time Analytics of Industrial Machine Performance: 63.8% Reduction in Equipment Downtime

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Industry: Manufacturing, AI/ML



Minimize equipment downtime through advanced analytics


ML algorithm for predictive equipment maintenance



reduction of equipment downtime


increase in automation

Partnering with HQSoftware, the customer received an advanced Machine Learning algorithm that provides real-time insights on equipment performance and predicts potential issues, enabling proactive elimination.

This advancement helped our customer decrease equipment downtime by up to 63.8%, which was the main goal before the project started.

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Anna Halias
Business Development Manager


The customer is an American company that provides machine analytics solutions for equipment maintenance across various domains, from the automotive industry to healthcare equipment manufacturers.


The main task of our team was to enhance the client’s existing system with ML capabilities, enabling predictive equipment maintenance. With the new algorithm, the system provides:

  • analytics industrial machines performance;
  • precise measurement of overall equipment effectiveness (OEE);
  • analysis of machine availability – run time to planned production time ratio;
  • insights on machine production issues;
  • visualization of the financial impact of underperforming machines.


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The algorithm also allowed for reducing manual processes by automating some operations.


Leveraging our experience in AI and Machine Learning, HQSoftware’s developers reviewed two suitable approaches in Machine Learning for predictive maintenance:

  • Classification approach for predicting a possibility of equipment failure in the next steps.
  • Regression approach for predicting how much time is left before the next equipment failure.

The team applied both approaches to provide comprehensive information on equipment status.

To enable the ML algorithm to predict equipment failures, the team decided to collect time series data. After the data on the equipment is collected, the Data Enrichment phase begins. Sometimes, sensor data is incomplete or lacks important interrelationships, so the main goal is to transform data to initiate the Processing Zone stage. Once processed, the data is utilized by the ML algorithm for analysis.


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