Building Your Own CO2 Artificial Intelligence Troubleshooting Tool For Refrigeration with Nelson Sierra

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In this episode we have my good friend Nelson Sierra join us on CO2 Mondays. Nelson has developed a tool for Troubleshooting any refrigeration system with artificial intelligence (AI).  It involves utilizing advanced algorithms and data analytics to diagnose and resolve system issues. He also shared with us how he developed the AI tool and how it will help you as a technician troubleshoot better and faster. For end users, it will reduce downtime and help you be more proactive in your service and replacement of equipment. 

Here’s a general overview of how this device can be used for troubleshooting:

Real-time Data Collection: This device will use AI-enabled sensors and monitoring devices and collect real-time data from the compressor in the refrigeration system, such as temperature sensors, pressure transducers, and current and volt meters. This data is continuously fed into the AI system.

Anomaly Detection: AI algorithms analyze the collected data to establish normal operating patterns and establish baseline performance parameters for the refrigeration system. Deviations from these baselines are flagged as anomalies, indicating potential system issues or malfunctions.

Fault Diagnosis: When an anomaly is detected, AI algorithms analyze the data further to identify the root cause of the issue. By comparing the current data patterns with historical data and known fault profiles, the AI system can suggest probable causes for the anomaly.

Predictive Maintenance: the AI algorithms can also predict and anticipate future system faults based on historical data and pattern recognition. By identifying early warning signs, the AI system can schedule proactive maintenance or repairs to prevent equipment failure or system breakdowns.

Troubleshooting Recommendations: Once the AI system has diagnosed the issue, it can provide troubleshooting recommendations to address the problem. These recommendations can range from simple adjustments to complex repairs, guiding technicians on the necessary steps to rectify the problem efficiently.

As Nelson mentioned it’s important to note that AI-assisted troubleshooting is not meant to replace human expertise but rather to augment it. Technicians still play a crucial role in interpreting AI-generated recommendations, performing repairs, and ensuring the proper functioning of the refrigeration system.

By leveraging AI for troubleshooting, organizations can benefit from faster and more accurate fault detection, reduced downtime, proactive maintenance scheduling, and improved overall system performance.

Please give Nelson a follow-up and thank him for this contribution to the refrigeration industry:

LinkedIn –

Website –

Email –

Symbionte UN (prototype) – 


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