Theme | Artificial Intelligence, Automation, Industry 4.0 |
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Project title | Acoustic detection of defective idlers in use (AkuTra) |
Project duration | 01.12.2024 – 30.11.2026 |
Belt conveyor systems are a common technology for transporting bulk materials worldwide. Among other things, they consist of a large number of idlers that carry the conveyor belt with the bulk material. Wear can damage the bearings of the idlers. This leads to increased friction and thus to the idlers heating up. In the worst case, this can lead to sparking and ignition of the system. Hot-running roller bearings are the source of ignition in 10 % of fires that occur at belt conveyor systems.
Unplanned system downtimes and devastating accidents can be avoided by monitoring the condition of the idlers to detect damage at an early stage, especially mechanical bearing damage. No practicable solution for monitoring the condition of idlers or large belt conveyor systems during operation has yet become established on the market.
The aim of the research is to achieve better and more economical monitoring of idlers during operation. Damaged and defective idlers generate vibrations and noises during operation that differ from the normal state. These acoustic signals are to be detected by suitable sensors. In order to record these signals, a measuring unit is to be developed that is attached to the conveyor belt and travels with the goods during transportation. The measuring technology is to record the signals from the idlers, an the evaluation unit is to process the signals and detect irregularities – possibly with the help of artificial intelligence (AI). The evaluation unit is to be trained using various idlers, both defective and non-defective ones. This can initially take place on test benches and later on belt conveyor systems in practice.
Jobs
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Artificial Intelligence, Automation, Process monitoring
Bachelor thesis, Project thesis