How Artificial Intelligence Is Revolutionizing CBM and Failure Prediction in Retail

Artificial Intelligence is changing the game in predictive maintenance. In refrigeration and air conditioning systems—especially in supermarkets—the use of Artificial Intelligence in CBM (Condition-Based Maintenance) is helping to predict failures, reduce waste, and optimize equipment performance. In this article, you'll discover how technologies like Machine Learning and intelligent agents are transforming daily operations.

How Artificial Intelligence Improves Failure Prediction in CBM

Generative Artificial Intelligence and Machine Learning are revolutionizing Condition-Based Maintenance. While AI simulates the human ability to think and communicate, Machine Learning allows systems to learn and improve automatically through experience.

This combination makes operations smarter, more efficient, and more sustainable. Here’s how:

Real-time data analysis

Machine Learning algorithms continuously analyze sensor data, detecting patterns and anomalies. For example, they can identify a temperature rise in a cold chamber and correlate it with a door left open or a compressor shutdown.

Continuous learning

Using both historical and real-time data, Machine Learning-based systems become increasingly accurate. This enables maintenance strategies to adapt to various equipment and operational contexts.

Identification of complex relationships

AI links variables like temperature, energy consumption, and compressor performance, generating actionable insights that support decision-making—almost as if they had been analyzed by a human expert.

Practical Examples of AI in Commercial Refrigeration

AI in action:

Open door detection:

Continuous monitoring alerts when cold room doors are left open too long, helping the operations team respond quickly.

Low performance identification:

Equipment operating with reduced efficiency is automatically detected, with maintenance suggestions.

Failure prediction in compressors:

AI forecasts failures before they impact operations, allowing for planned maintenance.

Refrigerant leak detection:

Unusual pressure and temperature patterns trigger safety alerts, preventing risk and waste.

Energy consumption analysis:

Algorithms identify abnormal electricity usage, optimizing system efficiency.

Other applications:

Diagnosis of electrical overloads and heat exchanger failures based on historical and operational data.

AI Agents: The New Trend in Smart Maintenance

One of the most promising trends in CBM is the use of AI agents. Inteligência Artificial. These agents act as virtual assistants:

Interpret real-time data

Provide personalized recommendations

Execute automated actions to prevent failures

These agents improve response time and make maintenance more strategic. We’ll soon publish an exclusive article about this innovation.

Benefits of Condition-Based Maintenance with Artificial Intelligence

✅ Resource optimization: Accurate predictions prevent costly emergency maintenance that disrupts operations.

✅ Energy efficiency: Automated adjustments based on data analysis ensure equipment runs at peak performance.

✅ Proactive planning: Predictive maintenance is scheduled strategically, without operational impact.

✅ • Sustainability: More efficient systems consume less energy, helping reduce carbon emissions.

Conclusion: AI Is Essential for Retail Efficiency

The combination of Machine Learning and Generative Artificial Intelligence is redefining how supermarkets manage their refrigeration and air conditioning systems. With failure prediction, cost reduction, and improved energy efficiency, these technologies are essential for anyone seeking innovation and real results.

Ready to bring AI into your supermarket?

Talk to our specialists and take your maintenance to the next level with NEO Estech technology. NEO Estech.