The use of sensors that measure various performance indicators of a machine or system makes it possible to accurately assess maintenance requirements. Advanced analysis methods such as machine learning and artificial intelligence are then used to determine maintenance requirements.
In contrast to traditional approaches, which provide for maintenance according to a fixed schedule or only react when a failure has already occurred, condition-based maintenance offers an efficient solution for avoiding unnecessary interventions and minimising downtimes. This proactive maintenance system helps to extend the service life of equipment while reducing operating costs.
Types of monitoring for condition-based maintenance
Condition-based maintenance includes various monitoring techniques that focus on the real-time condition of equipment to determine when maintenance action is required.
The most prominent early detection methods associated with condition-based maintenance include:
- Infrared thermography: This technology uses thermal imaging cameras to measure the thermal radiation of objects. Temperature irregularities and overheating, which indicate possible problems in the system, are visualised and can lead to the early detection of defects.
- Vibration monitoring: Vibration analysis uses special sensors to measure the vibrations of a machine. Changes in vibration patterns can indicate a variety of problems, such as imbalance, wear or bearing faults.
- Ultrasonic analysis: This technique uses high-frequency sound waves to identify leaks, cracks or other defects in materials and components that can lead to problems in the system.
- Pressure analysis: By measuring and analysing the pressure values within systems, performance parameters can be monitored and problems such as leaks or pressure drops can be detected at an early stage.
- Oil analysis: This method analyses samples of the machine oil to identify contaminants and abrasive particles that indicate wear or other problems within the machine.
- Electrical analysis: Electrical analysis assesses electrical characteristics such as voltage and current to identify electrical problems such as voltage fluctuations or faulty connections.
Note: These techniques offer significant advantages over traditional maintenance methods by allowing maintenance work to be planned and carried out precisely according to the actual needs of the equipment, resulting in a significant reduction in costs and an increase in efficiency.
Condition-based maintenance as a revolution in railway technology
Condition-based maintenance is playing an increasingly important role in the field of railway technology, especially when it comes to monitoring components such as pantographs and overhead line infrastructure, but also pantograph contact strips.
The lack of precise condition data often leads to unforeseen downtimes and high maintenance costs. Advanced monitoring solutions not only increase the availability of vehicles, but also improve efficiency.
These technologies collect important data on the condition of pantographs and overhead lines during operation. As a result, irregularities can be recognised and rectified at an early stage before they lead to costly breakdowns.
The Schunk Group is the leading provider of condition-based maintenance systems for railway operations. The Schunk OnTrack Monitoring System, which is attached directly to the pantograph, collects reliable and high-quality data that forms the basis for predictive measures. [Learn more!]
The measures for the early detection of maintenance requirements in railway technology include:
- Continuous monitoring of the contact pressure between the contact strip and contact wire
- Early detection of the wear condition of the contact strips
- Inspection and adjustment of the zigzag geometry of the overhead contact line to minimise wear and energy losses
- Efficient identification and localisation of hard spots or other irregularities on the overhead contact line
This data helps transport and infrastructure operators to create and implement optimal maintenance plans, thereby increasing the efficiency and reliability of the entire system. The ongoing collection and analysis of operating data also provides valuable insights for predictive maintenance strategies.
