Predictive Analytics for Mechanical Engineering: A Beginners Guide (SpringerBriefs in Applied Sciences and Technology)

✍ Scribed by Parikshit N. Mahalle, Pravin P. Hujare, Gitanjali Rahul Shinde

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 109
• Category: Library

✦ Synopsis

This book focus on key component required for building predictive maintenance model. The current trend of Maintenance 4.0 leans towards the preventive mechanism enabled by predictive approach and condition-based smart maintenance. The intelligent decision support, earlier detection of spare part failure, fatigue detection is the main slices of intelligent and predictive maintenance system (PMS) leading towards Maintenance 4.0 This book presents prominent use cases of mechanical engineering using PMS along with the benefits. Basic understanding of data preparation is required for development of any AI application; in view of this, the types of the data and data preparation processes, and tools are also presented in this book.

✦ Table of Contents

Foreword
Preface
Contents
About the Authors
1 Introduction to Predictive Analytics
1.1 Data Analytics
1.2 Type of Data Analytics
1.2.1 Descriptive Analytics
1.2.2 Diagnostic Analytics
1.2.3 Predictive Analytics
1.2.4 Prescriptive Analytics
1.3 Techniques of Data Analytics
1.4 Predictive Analytics and Data Science
1.4.1 Predictive Analytics Process
1.5 Applications of Predictive Analytics
1.6 Summary
References
2 Data Acquisition and Preparation
2.1 Exploratory Data Analysis
2.2 Types of Dataset
2.2.1 Categorization Based on Data Formats
2.2.2 Categorization Based on Data Source
2.2.3 Categorization Based on Perspective of Data Generation
2.3 Data Preprocessing
2.3.1 Data Cleaning
2.3.2 Data Transformation
2.3.3 Data Reduction
2.4 Tools
2.5 Summary
References
3 Intelligent Approaches
3.1 Overview
3.2 Conventional Learning Techniques
3.3 Deep Learning
3.4 Deep Neural Networks
3.5 Applications
3.6 Popular Techniques
3.7 Summary
References
4 Predictive Maintenance
4.1 Overview
4.2 Predictive Maintenance and Machine Learning
4.3 Predictive Maintenance Model
4.4 Implementation of Predictive Maintenance
4.5 Summary
References
5 Predictive Maintenance for Mechanical Design System
5.1 Overview
5.2 Design Issues
5.2.1 Perception of Diagnostics and Prognostics
5.2.2 ML Algorithms for Fault Detection
5.2.3 Machine Learning-Based Bearing and Gear Health Indicators
5.2.4 AI for Condition-Based Monitoring and Fault Detection Diagnosis
5.3 Case Study
5.3.1 Predictive Analysis for the Useful Life of Bearing
5.3.2 Predictive Analysis for Gear Tooth Failure
5.4 Summary
References
6 Predictive Maintenance for Manufacturing
6.1 Overview
6.2 Design Issues
6.3 Case Studies
6.3.1 Predictive Analysis for Sound Absorption of Acoustic Material
6.4 Summary
References
7 Conclusions
7.1 Summary
7.2 Research Opening
7.3 Future Outlook