Reliability engineering data analytics as base of operations for maintenance planning: A cutting tool case study

Author:

Bracke S.,Radetzky M.,Rosebrock C.

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference14 articles.

1. Amruthnath, N. and Gupta, T. (2018). A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), 355–361. doi: 10.1109/IEA.2018.8387124.

2. Reliability in Automotive and Mechanical Engineering;Bertsche,2008

3. Bracke, S. (2020). Preventive maintenance planning based on weibull distribution models: The impact of the random scatter behaviour of the threshold parameter. Proceedings of AMEST2020, 10th – 11th September 2020, Virtual Workshop, Cambridge, UK. International Federation of Atomatic Control (IFAC); 4th IFAC Workshop on Advanced Maintanance Engineering, Services and Technologies.

4. Bracke, S. and Neupert, F.G. (2020). Estimation of failure probabilities for maintenance activities regarding product fleets in use: Censored data handling – an automotive engineering case study. Proceedings of ESREL 2020 PSAM 15 (Eds.: Piero Baraldi, Francesco Di Maio and Enrico Zio), 1st – 6th November 2020, Venice, Italy. European Safety and Reliability Association (ESRA); International Association for Probabilistic Safety Assessment and Management. Research Publishing Services, Singapore.

5. Manufacturing processes chip removal - part 0: General; classification, subdivision, terms and definitions;DIN 8589-0,2003

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