Data-Driven Fault Diagnosis: A Machine Learning Approach for Industrial Components delves into the application of machine learning techniques for achieving robust and efficient fault diagnosis in industrial components.
The book covers a range of key topics, including data acquisition and preprocessing, feature engineering, model selection and training, and real-time implementation of diagnostic systems. It examines popular machine learning algorithms such as support vector machines, convolutional neural networks, and extreme learning machines, highlighting their strengths and limitations in different industrial contexts. Practical case studies and real-world examples from various sectors illustrate the real-world impact of these techniques.
The aim of this book is to empower engineers, data scientists, and researchers with the knowledge and tools necessary to implement data-driven fault diagnosis systems in their respective industrial domains.