Machine Monitoring

Classify errors and monitor performance

In industrial machinery, sensor data often presents complex patterns obscured by noise, making monitoring and analysis a significant challenge. To effectively facilitate models that classify errors and monitor performance, it is crucial to analyse sensor data accurately. A robust analysis that can identify valuable patterns leads to earlier detection of machine failures, providing opportunities for root cause analysis and design improvements.

The Anomaly Surfer is a model we use to enable new solutions. This model leverages advanced technologies to address the stochastic behaviour of sensors, enabling reliable analyses. It is compatible with all types of data and seamlessly integrates signal processing techniques with deep learning methodologies to distinguish anomalies. By combining both time and frequency domains, our model effectively removes the "blindfold," rendering previously hidden anomalies visible.

Adaptive nature

One of the key features of Anomaly Surfer is its adaptive nature; it automatically adjusts to the unique characteristics of incoming data. This adaptability not only enhances the precision of anomaly detection but also ensures that the model remains effective across various industrial contexts. As a result, businesses can implement proactive maintenance strategies, reducing downtime and optimizing operational efficiency. The insights gained from our model empower engineers and decision-makers to refine their processes, thereby enhancing overall machine performance and reliability.