Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

✍ Scribed by Gunjan Soni (editor), Om Prakash Yadav (editor), Gaurav Kumar Badhotiya (editor), Mangey Ram (editor)

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 261
• Edition: 1
• Category: Library

✦ Synopsis

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.

The book

• Discusses basic as well as advance research in the field of prognostics.

• Explores integration of data collection, fault detection, degradation modeling and reliability prediction in one volume.

• Covers prognostics and health management (PHM) of engineering systems.

• Discusses latest approaches in the field of prognostics based on machine learning.

The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
About the editors
List of contributors
Chapter 1: A bibliometric analysis of research on tool condition monitoring
1.1 Introduction
1.2 Data collection and research methodology
1.3 Bibliometric analysis
1.3.1 Research growth
1.3.2 Most productive authors
1.3.2.1 h-index of the first ten most productive authors
1.3.3 Authors’ dominance ranking
1.3.4 Top manuscripts per citations
1.3.5 Country-wise analysis
1.3.5.1 Country-wise collaboration
1.3.6 Most relevant sources
1.3.7 Keyword analysis
1.3.8 Co-citation analysis
1.4 Conclusion
Appendix 1
References
Chapter 2: Predicting restoration factor for different maintenance types
2.1 Introduction
2.2 Proposed model
2.2.1 Steps of RF prediction
2.2.1.1 To study the given system and tabulate the components of the system
2.2.1.2 To tabulate replaced components for different maintenance types
2.2.1.3 To apply AHP for calculating weightage of each component
2.2.1.4 To calculate RF for different PM and CM
2.2.1.5 To calculate failure distribution parameters
2.3 Case study
2.3.1 System description
2.3.2 Parameter estimation using MLE
2.3.3 Parameter estimation using proposed approach
2.3.3.1 Apply AHP to calculate weightage of each component
2.3.3.2 RF calculation for different types of PM and CM
2.3.3.3 Calculating failure distribution parameters
2.3.3.4 Validation
2.4 Conclusion
References
Chapter 3: Measurement and modeling of cutting tool temperature during dry turning operation of DSS
3.1 Introduction
3.2 Materials and methods
3.3 Results and discussion
3.4 Empirical modeling
3.5 Conclusions
References
Chapter 4: Leaf disease recognition: Comparative analysis of various convolutional neural network algorithms
4.1 Introduction
4.2 Literature review
4.3 Dataset
4.4 Methodology
4.4.1 Convolutional neural network (CNN)
4.4.1.1 VGG-19
4.4.1.2 VGG-16
4.4.1.3 Inception V3
4.4.1.4 DenseNet-121(proposed approach)
4.5 Results and discussion
4.6 Conclusion
References
Chapter 5: On the validity of parallel plate assumption for modelling leakage flow past hydraulic piston-cylinder configurations
5.1 Introduction
5.2 The leakage flow models
5.2.1 Parallel plate model
5.2.2 Annular flow model
5.3 Results and discussion
5.3.1 Comparison of leakage flow models
5.3.2 Validation against the orifice plate model
5.4 Concluding remarks
Acknowledgement
References
Chapter 6: Development of a hybrid MGWO-optimized support vector machine approach for tool wear estimation
6.1 Introduction
6.2 Materials and methods
6.2.1 Experimental dataset
6.2.1.1 Data acquisition and processing
6.2.1.2 Tool wear
6.2.2 Support vector regression
6.2.3 Grey wolf optimization
6.2.4 Modified grey wolf optimization
6.2.5 Particle swarm optimization
6.3 Results and discussion
6.4 Conclusion and future work
References
Chapter 7: The energy consumption optimization using machine learning technique in electrical arc furnaces (EAF)
7.1 Introduction
7.2 Literature review
7.3 Methodology
7.3.1 Data-driven model for power consumption in EAF
7.3.2 Understanding key elements affecting EAF’s electricity consumption
7.3.3 Influencing feature identification for modeling
7.3.4 Creating the heats’ dataset
7.4 Result and discussion
7.4.1 Managerial implications
7.5 Conclusion limitations and future scope
References
Chapter 8: PID-based ANN control of dynamic systems
8.1 Introduction
8.2 Mathematical modeling of inverted double pendulum
8.3 PID-based ANN control of inverted double pendulum system
8.4 Simulation and results comparison
8.5 Conclusion
References
Chapter 9: Fatigue damage prognosis of offshore piping
9.1 Introduction
9.2 Understanding piping fatigue
9.3 Fatigue damage prognosis
9.3.1 General
9.3.2 Remaining useful life prediction
9.4 Case study
9.4.1 Background
9.4.2 Computational fluid dynamics analysis
9.4.3 Deterministic RUL assessment
9.4.4 Probabilistic RUL assessment
9.5 Conclusion
References
Chapter 10: Minimization of joint angle jerk for industrial manipulator based on prognostic behaviour
10.1 Introduction
10.2 System description
10.3 Algorithms and objective functions
10.3.1 Objective function
10.3.2 Modified objective function
10.3.3 Particle swarm optimization
10.4 Results and discussion
10.4.1 Performance evaluation for objective function F 1
10.4.2 Performance evaluation for modified objective function
10.5 Conclusion
References
Chapter 11: Estimation of bearing remaining useful life using exponential degradation model and random forest algorithm
11.1 Introduction
11.2 The proposed RUL estimate approach
11.2.1 Features extraction
11.2.2 Root mean square
11.2.3 Feature fitting
11.2.4 Training of random forest
11.2.5 RUL prediction
11.3 Experimental result and discussion
11.3.1 Experimental data
11.3.2 RUL prediction using the proposed approach
11.3.2.1 Relative accuracy
11.3.2.2 Cumulative relative accuracy
11.4 Conclusion
References
Chapter 12: Machine learning-based predictive maintenance for diagnostics and prognostics of engineering systems
12.1 Introduction and overview
12.2 Diagnostics and prognostics based on predictive maintenance
12.2.1 Diagnostics
12.2.2 Prognostics
12.3 Machine learning for predictive maintenance
12.3.1 Machine learning-based predictive maintenance pipeline
12.3.2 Evaluating machine learning models
12.4 Machine learning-based predictive maintenance in engineering systems
12.4.1 Machining systems
12.4.1.1 Cutting tools
12.4.1.2 Bearings
12.4.2 Automotive systems
12.4.2.1 Brake system
12.4.2.2 Gear system
12.4.3 Production systems
12.4.4 Thermal systems
12.4.4.1 Boilers
12.4.4.2 Chillers
12.5 Summary
References
Index