QUALITY CONTROL for Production UP-TIME and PRODUCT QUALITY
Can you hear repeating failure in your production line or process facility? What about the production equipment you use? If you can tell in an instant when something is off in the sound signature, where the sound is a key indicator of faults that might not meet the standards of the finished product, Squarehead can digitalize and automate that human sense and experience – even in extremely noisy environments.
We have developed and delivered numerous machine learning models for various types of fault detection. Combining these models with our acoustic sensors that listens beyond the noise and zooms into the components we are most interested in, you can listen for specific fault indicators. This enables an automatic quality control procedure without exposing staff to possible hazardous environments. Additionally, this can remove the individual bias of quality control staff. Together, these capabilities ensure as much up-time as possible, a more continuous production, less unscheduled down-time, and fewer ruined finished products and batches.
Striving for perfection? Elevate your quality control procedures by adding sound measurement sensors and machine learning to your routines.
How it works
Audio deviations are a common sign of equipment, product, and process failure, yet these deviations are hard to detect in highly noise-filled areas typically found in production facilities. Our arrays can listen beyond the noise to a specific part of a machinery. We can automatically record examples of a specific fault signature as well as normal operating sounds, label the data correctly, and use the labeled data to train a machine learning model. The trained model will then automatically trigger on the specific fault signature, while ignoring normal operating sounds, and alarm the PLC or control system. In this way, we combine sound measurement sensors and machine learning to automate the quality control procedure.
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Sound is often a key indicator of faults in equipment, processes, or finished products—especially when other sensors or visual inspections may miss the issue. If trained operators can “hear” when something is wrong, this capability can now be digitized and automated using Squarehead’s technology, even in extremely noisy industrial environments.
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Our solution combines advanced microphone arrays with machine learning models. The arrays focus on specific machinery components, filtering out background noise. Fault sounds are recorded, labeled, and used to train models that can detect specific acoustic signatures of failure, triggering alarms when those sounds reoccur—without being confused by normal operating noise.
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By automating sound-based fault detection, companies can ensure more consistent quality control without relying solely on human judgment. This reduces individual bias, increases staff safety by limiting exposure to hazardous areas, minimizes downtime, and helps avoid waste due to undetected faults in products or processes.
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Yes. Squarehead’s approach involves recording real-world examples of both normal and faulty operating conditions on your specific machines. These labeled audio samples are then used to train custom machine learning models that recognize and respond to faults that are unique to your environment.
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Once trained, the machine learning model automatically monitors sound in real time. When a fault signature is detected, it sends a signal over an industrial protocol to your existing PLC or control system to trigger an alert or action. This creates a seamless and automated quality control loop.
