Highlights
Need
Minimize equipment downtime through advanced analytics
Solution
ML algorithm for predictive equipment maintenance
Results
63.8%
reduction of equipment downtime
36%
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.
Want to create an AI/ML solution? We’re ready to help!HQSoftware has a team of skilled professionals ready to tackle the project. Ask me!
Anna Halias
Business Development Manager
Customer
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.
Solution
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.
The algorithm also allowed for reducing manual processes by automating some operations.
Process
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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Sergei Vardomatski
Founder