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Automated Train Inspection  – Optimize

Modern Camera Technology Significantly Reduces Rolling Stock Maintenance Intervals and Costs

A satisfied customer reports:

"For us, the automated train inspection represents a big step towards the digitalization, automation and optimization of our maintenance processes. That data allows for additional in-depth data mining and can be used on an ongoing basis. Due to the Analytics machine learning component, the system offers efficient, goal-driven and preventive quality control features, which enable us to strengthen the competitive position of our railway."


Failure probability based on the current rolling stock maintenance interval

Railway maintenance interval optimization

The Analytics software module calculates a statistical failure probability for every single component. These calculations can be used to optimize maintenance intervals based on component wear.

The accuracy of the predicted maintenance intervals increases based on the volume of analyzed data. Additional information, such as traveled distances (miles/kilometers) can be included to improve the accuracy even further.


Railway maintenance intervals can be optimized based on component quality assessments

Failure probability until the next maintenance interval

When known railway maintenance intervals are combined with existing analysis data, the Analytics software can calculate and display the failure probability for an entire train, for example, as color-coded diagrams, percentage values, trend plots, correlation tables and more. The accuracy can be increased by prioritizing or classifying particular components as operation-critical parts to prevent potential downtimes and related costs before they happen.

The Analytics software module can be used to evaluate the quality or deterioration of all monitored components. It enables maintenance crews to identify and systematically replace any wear- or failure-prone parts at the right time. This contributes to streamlining maintenance intervals, reducing time and spare part costs and leads to increased train availability.


Performance-enhancing external parameters

Adding Performance-enhancing Parameters

In addition to the data records created a variety of external parameters can be used to increase failure prognosis accuracy, including those that directly impact the components' lifespan.

Parameters such as weather conditions, traveled distance, velocity or component replacement and maintenance intervals for individual trains can be particularly useful.


Using the cloud for optimized data management

Uploading train data to the cloud increases both data safety and availability. Train data and analysis results can be accessed at any time from any location. The cloud eliminates data constraints, provides a central storage location and optimized data management for huge data volumes. Data from various systems at different locations can be analyzed and used simultaneously to explicitly highlight trends or dependencies (data mining).


Conclusion: Reliable Camera Systems Guarantee Operational and Process Stability in Rolling Stock Maintenance

All benefits at a glance:

Additional services:
  • Hyperspectral analysis enables the identification of gas and fluid leakages
  • Thermal imaging is used to detect component overheating (brakes, wheels, axleboxes)
  • Acoustic wheel inspection (axlebox bearings)
  • and much more

Our application experts are standing by to answer your calls or written requests. Please call +49 (0) 2630 91590-0 or use the contact form to submit your queries or send an email to support@psi-technics.com



Image Source: PSI Technics, Inodo, AdobeStock


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>Product Brochure Automated Train Inspection (PDF) >Analytics Product Flyer (PDF) >Industrial Image Processing / Camera Technology Product Flyer (PDF)

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