Sensor Data Analysis and Predictive Maintenance Systems

Where Did the Need Arise?

Machines and equipment used on production lines wear out over time. In traditional maintenance approaches, these units are either repaired after a failure (reactive maintenance) or replaced at fixed intervals (periodic maintenance).

However, both methods have serious disadvantages:

• Post-failure maintenance brings unplanned downtime and high costs
• In periodic maintenance, still-sound parts are replaced unnecessarily

Companies want to predict when maintenance should be performed before a failure occurs.

What Do We Offer?

At TiriBit Software, we develop predictive maintenance systems that analyze sensor data from machines, learn over time and estimate failures in advance.

The infrastructure we build:

• Continuously collects sensor data such as temperature, vibration, current, sound and speed
• Analyzes this data with Python-based algorithms
• Evaluates equipment health through statistical thresholds, anomaly detection and machine learning
• Alerts maintenance teams by detecting trends that indicate failure
• Provides real-time monitoring, historical comparison and maintenance planning with user-friendly interfaces

What Problems Does It Solve?

• Unplanned stops disrupting production
• Late detection of part failures
• Waste due to early replacement of intact parts
• Production deteriorating before failure info reaches operators

Where Is It Used?

• CNC machines and cutting tool equipment
• Electric motors and pump systems
• High-speed production lines
• Compressor, ventilation and conveyor systems
• Power generation facilities (e.g. generator, turbine, inverter)

Gains

• Unplanned downtime rate drops by up to 40%
• Need for unnecessary maintenance disappears
• Allows intervention before a failure
• Component lifetime is utilized most efficiently
• Maintenance teams act on data rather than guesswork