Revolutionizing Maintenance with Predictive Sound Analysis
Predictive maintenance is the key strategy in the world of manufacturing. Detect machine malfunction early and repair it in a cost-efficient way. When the machine breaks down, the production stops. It causes delays in delivery and problems with meeting contract conditions. The maintenance work is unplanned so another challenge arises. Finding the technicians able to respond in a timely manner, skilled enough first to diagnose and then repair the machine.
It’s clear - the lack of action costs greatly and results in unpredicted downtime. It can cost even 150 000 CHF per minute. Suboptimal maintenance scheduling can cost as much as 1 200 000 CHF. In the times of Industry 4.0, we can use technology to prevent this.
Beyond Traditional Maintenance Solutions
There are several types of solutions for detecting and predicting machine breakdown. Mainstream solutions detect anomalies by monitoring temperature, vibration and voltage data. This approach leaves certain blind spots, missing information delivered via sound. That’s where the Datali solution enters.
Our approach? Predictive maintenance thanks to acoustic data. By leveraging machine learning and signal processing, our software detects anomalies in pumps, vans, fans and rails. Focusing on sound analysis, we detect failure signs that traditional sensor-based systems miss.
Just hear and see the difference between normal and abnormal machine sound:
Normal sound
Abnormal sound
In real life, only well-trained, expert-level engineers can hear the difference immediately. However, their time is precious. They just can’t always be present to observe the machines. Datali’s software automates this process, providing continuous and reliable detection of machine failures.
Intelligent Predictive Maintenance in Action
Our testing results are clear. The working model detects anomalies in real-time. In its first version, the model was able to catch 65% of anomalies with 20% of false alarms. With our expertise, we further improved these results. The example? Detecting 78% of anomalies at the cost of 20% false alarms.
Anomalies detected | 99% | 78% | 60% | 22% | 9% |
|---|---|---|---|---|---|
False alarms | 62% | 20% | 13% | 1% | <1% |
You can also adjust the threshold - the detection rate and false alarm rate to best suit your operational needs. With this flexibility, you can tailor the system to your requirements, ensuring the most efficient and cost-effective maintenance strategy.
As Industry 4.0 continues to reshape the manufacturing landscape, Datali’s sound-based predictive maintenance enables businesses to stay ahead of the game. By detecting the issues before they escalate, companies can dramatically reduce downtime, lower maintenance costs, and extend the life of their equipment.
The future of maintenance is here—and it’s listening.
Want to hear it? Contact us.
