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Artificial Intelligence for Maintenance 4.0: A.I Manual for Maintenance Engineers
The motivation for this book second edition is to give my homely contribution to the application of the Artificial Intelligence concept to maintenance engineering with examples of application of different A.I methods such as Unsupervised Machine Learning, Supervised Machine Learning Prediction, Supervised Machine Learning Classification, Deep learning, Reinforcement Learning and Natural language process by using MATLAB and R to maintenance cases.
The first chapter introduces the concept of Artificial intelligence and its application on the maintenance domain.
The second chapter introduces the maintenance concepts by explaining the different types of maintenance as well as equipment criticality classification and state of art maintenance management systems.
The third Chapter introduces the Prognostic Health management concept and how to predict the Remaining Useful Life and State of Health based on sensor data or data from the non-destructive test.
The fourth Chapter introduces the concept of Different types of Machine learning methods such as Unsupervised Machine Learning and Supervised Machine Learning.
The Fifth Chapter introduces the concept of Artificial Intelligence Unsupervised Machine Learning (UML) that aims to group data based on its features by demonstrating examples of maintenance planning and equipment clustering. By doing so, different UML methods such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), K-Means, Gaussian Mixture, Hierarchical Cluster, and Neural Network Self Organized Map are presented.
The sixth chapter presents the Artificial intelligence Supervised Machine Learning Classification that aims to classify the equipment based on different classes of risk or criticality as well as to define high levels of equipment degradation for alarm set up. Therefore, different methods with several examples applied to maintenance such as K-Nearest Neighbour (K-NN), Decision Tree, Naïve Bayes, Discriminant Analysis, Support Vector Machine (SVM) and Neural Network Classification are presented.
The seventh chapter presents the Artificial Intelligence Supervised Machine Learning Regression methods that aims to predict a dependent variable such as RUL or SoH based on the past dependent and independent variable values. Therefore, different methods with several examples of RUL regression prediction take place such as Linear Regression, Support Vector Machine, Decision Tree, Ridge and Lasso methods, Stepwise and Neural network.
The eighth chapter presents the concept of Ensemble methods that aims to reduce the classification or regression prediction error by creating new subset samples from the main database. The different Ensemble methods such as Random tree
The ninth chapter aims to present the concept of Deep Neural Network and Convolutional Neural Network by giving an example of image classification applied to RUL prediction and Infrared thermography image classification.
The tenth chapter aim to clarify the Reinforcement Learning concepts and demonstrate the application of such method to maintenance cases where the maintenance specialist is represented by a virtual agent, who take decision based on the environment states and is influenced by the optimum policy which drives the agent action to the highest reward, that is so called exploitation.
The eleventh chapter clarify and exemplify the concepts of Natural language process and exemplify how to assess the sentiment of text that can be used as a tool to assess the Maintenance team sentiment based on their messages or even other team satisfaction with maintenance work.
The chapter twelve aims to present the concept of Asset Management Intelligence 4.0 that aims to encompass the concepts defined in the ISO55001 and the PHM and Artificial Intelligence concepts presented in the previous chapter.
The first chapter introduces the concept of Artificial intelligence and its application on the maintenance domain.
The second chapter introduces the maintenance concepts by explaining the different types of maintenance as well as equipment criticality classification and state of art maintenance management systems.
The third Chapter introduces the Prognostic Health management concept and how to predict the Remaining Useful Life and State of Health based on sensor data or data from the non-destructive test.
The fourth Chapter introduces the concept of Different types of Machine learning methods such as Unsupervised Machine Learning and Supervised Machine Learning.
The Fifth Chapter introduces the concept of Artificial Intelligence Unsupervised Machine Learning (UML) that aims to group data based on its features by demonstrating examples of maintenance planning and equipment clustering. By doing so, different UML methods such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), K-Means, Gaussian Mixture, Hierarchical Cluster, and Neural Network Self Organized Map are presented.
The sixth chapter presents the Artificial intelligence Supervised Machine Learning Classification that aims to classify the equipment based on different classes of risk or criticality as well as to define high levels of equipment degradation for alarm set up. Therefore, different methods with several examples applied to maintenance such as K-Nearest Neighbour (K-NN), Decision Tree, Naïve Bayes, Discriminant Analysis, Support Vector Machine (SVM) and Neural Network Classification are presented.
The seventh chapter presents the Artificial Intelligence Supervised Machine Learning Regression methods that aims to predict a dependent variable such as RUL or SoH based on the past dependent and independent variable values. Therefore, different methods with several examples of RUL regression prediction take place such as Linear Regression, Support Vector Machine, Decision Tree, Ridge and Lasso methods, Stepwise and Neural network.
The eighth chapter presents the concept of Ensemble methods that aims to reduce the classification or regression prediction error by creating new subset samples from the main database. The different Ensemble methods such as Random tree
The ninth chapter aims to present the concept of Deep Neural Network and Convolutional Neural Network by giving an example of image classification applied to RUL prediction and Infrared thermography image classification.
The tenth chapter aim to clarify the Reinforcement Learning concepts and demonstrate the application of such method to maintenance cases where the maintenance specialist is represented by a virtual agent, who take decision based on the environment states and is influenced by the optimum policy which drives the agent action to the highest reward, that is so called exploitation.
The eleventh chapter clarify and exemplify the concepts of Natural language process and exemplify how to assess the sentiment of text that can be used as a tool to assess the Maintenance team sentiment based on their messages or even other team satisfaction with maintenance work.
The chapter twelve aims to present the concept of Asset Management Intelligence 4.0 that aims to encompass the concepts defined in the ISO55001 and the PHM and Artificial Intelligence concepts presented in the previous chapter.
745 pages, Kindle Edition
Published August 30, 2024
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