Intro
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