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AutoCBM Automated Adaption of Condition-Based Maintenance methods for Manufacturing Systems Ensuring the technical availability of machines and equipment is the task of maintenance and an essential factor for the efficient operation of manufacturing systems. As digitization progresses, condition-based preventive maintenance strategies are becoming increasingly important. These strategies use data describing a machine's current condition to predict disturbances in order to prevent them by taking early measures. The problem here is that the model development is associated with a high level of expert and computing effort. In addition, the models cannot be reliably transferred from one use case to another. In the AutoCBM project, a software was developed that supports the development and evaluation of prognostic models. The core of the software is a machine learning algorithm that recommends the most appropriate model class. By automating the selection process, effort can be reduced, additional personnel can be empowered to create models, and faster scaling of condition-based maintenance strategies can be enabled. Contact persons: H. Engbers   (Project manager)S. Leohold Funded by: Land Bremen / EFRE Duration: 01.07.2020 - 30.06.2022 See project's publications List all projects |
