Pro-active failure prediction & prevention in journal bearings
Deadline
Challenge
In current drivetrain applications, companies face the issue that they cannot prevent a failure, but rather can detect a failure & predict the remaining lifetime. This remaining lifetime is typically very short in e.g. journal bearings and can lead to unwanted machine failure and or high service interval costs. Therefore, we want to detect and prevent failures before they occur using novel sensors and active prevention measures in industrial applications using journal bearings.
Journal bearings, commonly used in industrial machinery under high loads, low speeds, or harsh conditions, pose challenges. Their failure is often catastrophic with no early signs, and conventional methods signal failures when it's too late, resulting in substantial economic losses.
The industry therefore needs:
- Predictive methods for early failure detection.
- Prevention methodologies to extend machine lifetime and reduce machine costs.
However, the industry faces several challenges:
- There are no predictive methods and sensors available for detecting and understanding the early failure mechanisms inside machines.
- No preventive methods can be taken to mitigate failures if they are not detected.
Addressing these needs and barriers is vital for proactive maintenance and cost-effective operations in industries relying on journal bearings.
Project goal
The goals of this project are:
- To investigate and understand the failure mechanisms of journal bearings;
- To establish how to sense these failures;
- To implement failure mitigation/prevention techniques.
Would you also like to participate in this project?
JOURN4LIFE_IRVA is an industrial research project. We are looking for companies that want to be part of the user group and work with us to valorise the project.
Interested? Fill in the form below and we will contact you as soon as possible.
