RUL estimation methods for intelligent predictive maintenance

Intelligent predictive maintenance and industry 5.0: AI, conversational agents and metaverses

Figure 1 - Key elements of Industry 4.0

Figure 2 - The transition from traditional predictive maintenance to predictive maintenance 4.0

Figure 3 - The Industry 5.0 paradigm shift

Figure 4 - The three pillars of Industry 5.0 defined by the European Community

Figure 5 - The 17 goals of the UN&#039;s Agenda 2030

Figure 6 - Industry 5.0 architecture

Figure 7 - The 5 industrial revolutions

Figure 8 - The principle of reinforcement learning

Figure 9 - Multi-layer and deep networks

Figure 10 - Example of a convolutional network for predictive maintenance (© FLUKE)

Figure 11 - Example of a recurrent neural network

Figure 12 - Relationships between machine learning, deep learning and deep neural networks

Figure 13 - Six-level hierarchical decomposition of an industrial process

Figure 14 - Graphical representation of RUL and its estimation

Figure 15 - Tree structure of RUL estimation levels

Figure 16 - Classification of RUL estimation methods

Figure 17 - RUL tree structure for a motor pump unit

Figure 18 - Links between CBM, PHM and Industry 4.0 and 5.0 tools

Figure 19 - Theoretical example of metaverse application in predictive maintenance