Data-Driven Fault Diagnosis A Machine Learning Approach for Industrial Components
Data-Driven Fault Diagnosis: A Machine Learning Approach for Industrial Components by Govind Vashishtha, published by Taylor & Francis Group on September 23, 2025, offers an insightful exploration into the use of machine learning techniques for effective fault diagnosis in industrial components. This 192-page book presents a comprehensive overview of essential topics such as data acquisition, preprocessing, feature engineering, and the implementation of diagnostic systems in real-time.
Readers will find a detailed examination of various machine learning algorithms, including Support Vector Machines, Convolutional Neural Networks, and Extreme Learning Machines, along with their respective strengths and limitations in industrial applications. The book also features practical case studies and real-world examples from sectors like manufacturing, energy, and transportation, aimed at equipping engineers, data scientists, and researchers with the necessary knowledge and tools for implementing data-driven fault diagnosis systems in their fields.
Official synopsis Publisher
Data-Driven Fault Diagnosis 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 like Support Vector Machines, Convolutional Neural Network, and Extreme Learning Machine, highlighting their strengths and limitations in different industrial contexts. Practical case studies and real-world examples from various sectors like manufacturing, energy, and transportation 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.
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