Predictive Maintenance – case study

Here is a real example of how our R&D and project execution deliver measurable results in predictive maintenance.

1. Challenges:

A  manufacturer of bending machines — key components in production lines worldwide — faced a critical challenge. Any unplanned downtime of these machines resulted in significant financial losses. The company sought a solution that would enable early detection of signals indicating an increased risk of machine failure, allowing them to take preventive measures before costly breakdowns occurred.

2. Solution:

Based on data collected from multiple machines, StatSoft developed a machine learning model capable of identifying early warning signs of potential malfunctions.

The solution leveraged an anomaly score — a synthetic indicator derived from unsupervised learning models — which provided a more comprehensive reflection of the machine’s overall condition and enabled proactive issue detection.

StatSoft also built a system that automatically: reads data and parameters from the production environment, performs data cleaning and preprocessing and finally generates forecasts and anomaly alerts.

3. Insights & Result:

Machine operators gained a powerful tool for optimizing maintenance schedules and replacing worn components before failures occurred.

As a result, the manufacturer achieved a significant competitive advantage by extending the machines’ operational uptime and improving the reliability of their equipment in customer production lines