TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”
Unified Framework for PdM Algorithm Development: The pdm-tools Architecture
- 1 AGH University of Krakow, Poland
Abstract
This paper proposes a novel architecture for pdm-tools, a dedicated library designed to streamline the development process of predictive maintenance (PdM) algorithms within the Industry 4.0 paradigm. pdm-tools tackles the time-consuming nature of PdM algorithm development by encompassing five key components within a unified framework. pdm-tools tackles the time challenge by offering a unified workflow: generate synthetic vibration data (gearboxes, bearings, shafts) for rapid prototyping, eliminating initial real-world data collection. Data preparation and feature extraction ensure readiness through scaling, filtering, and extracting diverse condition indicators, empowering robust algorithms. Finally, user-defined model development and evaluation allow training, optimization, and selection of optimal PdM algorithms (machine learning/deep learning). By integrating these functionalities within a single architecture, pdm-tools empowers rapid development and evaluation of PdM algorithms, ultimately leading to a more efficient and effective implementation of predictive maintenance strategies within the Industry 4.0 landscape.
Keywords
References
- Hu, Yang, et al. "Prognostics and health management: A review from the perspectives of design, development and decision." Reliability Engineering & System Safety 217 (2022): 108063.M.
- Ran, Yongyi, et al. "A survey of predictive maintenance: Systems, purposes and approaches." arXiv preprint arXiv:1912.07383 (2019).
- Krupitzer, Christian, et al. "A survey on predictive maintenance for industry 4.0." arXiv preprint arXiv:2002.08224 (2020).
- Tiddens, Wieger, Jan Braaksma, and Tiedo Tinga. "Decision framework for predictive maintenance method selection." Applied Sciences 13.3 (2023): 2021.
- Namuduri, Srikanth, et al. "Deep learning methods for sensor based predictive maintenance and future perspectives for electrochemical sensors." Journal of The Electrochemical Society 167.3 (2020): 037552.
- Lalik, Krzysztof, et al. "Simulation Modeling for Mass Customization Furniture Production: Investigating Production Volume and Machine Load in scope of Industry 4.0 standard." Industry 4.0 8.4 (2023): 116-122.
- Lalik, Krzysztof, et al. "The steam pressure impacts reducing system for a biomass cogenerator based on monitoring of the frequency characteristics of the steam actuator." Vibrations in Physical Systems 30.2 (2019).
- Lalik, Krzysztof, et al. "Self-powered wireless sensor matrix for air pollution detection with a neural predictor." Energies 15.6 (2022): 1962
- Balazy, P., Paweł Gut, and Paweł Knap. "Neural classifying system for predictive maintenance of rotating devices." IOP Conference Series: Materials Science and Engineering. Vol. 1239. No. 1. IOP Publishing, 2022.
- Knap, Paweł, Krzysztof Lalik, and Patryk Bałazy. "Boosted Convolutional Neural Network Algorithm for the Classification of the Bearing Fault Form 1-D Raw Sensor Data." Sensors 23.9 (2023): 4295.
- Jablonski, Adam. Condition monitoring algorithms in Matlab. Springer Nature, 2021.
- Oppenheim, Alan V. Discrete-time signal processing. Pearson Education India, 1999.
- Aurélien Géron. „Hands-on Machine Learning with Scikit Learn, Keras, and TensorFlow, Concepts, Tools, and Techniques to Build Intelligent Systems (second edition)”. Sebastopol, CA: O’Reilly Media, Inc., 2019.