Weakness analysis for components

We combine the fields of technical engineering with the management of complex technical systems & processes in order to identify and evaluate weak points.

Customers around the world rely on our software solutions
Customer reference Customer reference Logo of Innofreight DB Cargo

We pursue the goal of offering our customers holistic software solutions. For this reason, we strive for strategic partnerships with other companies from the railroad industry, research institutes and educational institutions to develop our solutions in further directions. Together with partners who have specific domain knowledge, we realize this vision. In this way, our customers benefit from user-oriented and best-practice solutions in different areas.

Potential weakness analysis

Detect weak points in the system at an early stage and react accordingly

Uptime Engineering GmbH has a distinctive expertise in the field of system engineering. Its core competence lies in modeling and consulting in connection with damage and failure behavior of technical systems.

The model-based software solution Uptime HarvestTM developed by Uptime Engineering for analyzing fleet data compares the current and past condition of systems and automatically detects deviations in system behavior. The core objective is to diagnose the cause of the weak points and derive risk-reducing recommendations for action to prevent unexpected failures.

The Boom Rail Solutions extention module Damage/Cause Analysis is based on the product Uptime HarvestTM and can be seamlessly integrated into the Boom Rail Solutions module landscape.

More about Uptime HarvestTM

Combined domain knowledge

Our competencies lie in the areas of engineering and management of complex technical systems & processes

Detect and eliminate weak points

Support for the optimization of service and maintenance activities.

The combination of Uptime Engineering’s Damage/Cause Analysis module and Boom Rail Solutions takes maintenance to the next level. As the two solutions work together, recommendations for condition-based and predictive maintenance are created. Based on these recommendations, users can continuously adapt the maintenance process to the current condition of the fleet.

Visualization of the operating status

Visualization of the operating status

Deviations in normal operation

Automatic detection of deviating behavior

Fast and easy root cause analysis

Faster and easier root cause identification

Derivation of risk-minimizing measures

Improvement of the maintenance process

Calculation of the useful life

Assessment of the remaining useful life

The system that learns

Learning system

Use Case

In the transport and traffic industry, the availability of the vehicle fleet is one of the most important parameters. The ability to react quickly to unforeseen events is the be-all and end-all here. The desired scenario of every company is to detect errors in the system at an early stage in order to be able to act in time and thus prevent unexpected failures.

The precondition for this is the use of data – both data from the train control system and additional sensor data. Instead of collecting as much data as possible, it is important for reliable predictions to collect the right data – i.e., data that provides information on failure risks. Of course, monitoring all components of a vehicle is not practical and only consumes unnecessary resources. 

 

For this reason, it is advisable to monitor safety-critical components on the one hand and components with a high probability of failure on the other. The optimum behavior in normal operation represents the comparative value to the actual state. If deviations are detected here, the recommendation system determines the possible causes and derives recommendations for action. This knowledge is not only of great importance to the operator, but also saves service personnel an enormous amount of time when searching for the cause. The solution offers an extremely high detection probability for deviating system characteristics under realistic load conditions. This is the key advantage of the solution.

Our experts in the field of weakness and root cause analysis

Head of Product Management
Robin Kühnast-Benedikt
Head of Product Management
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Product Owner und Consultant
Christian Zwetti
Head of Product & Consulting
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Product Manager
Esther Lichtenegger
Product Manager bei Uptime
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Senior Consultant
Michael Kropf
Senior Consultant­ bei Uptime
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If you have any questions or would like a demo presentation of our solutions, please contact us.

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