Intelligent Prognostics for Engineering Systems with Machine Learning Techniques by Soni, Gunjan; Yadav, Om Prakash; Badhotiya, Gaurav Kumar;

Rövid leírás:

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

Hosszú leírás:

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.

Tartalomjegyzék:

Chapter 1: A Bibliometric Analysis of Research on Tool Condition Monitoring
Jeetesh Sharma, M.L. Mittal, Gunjan Soni

1.1 Introduction
1.2 Data Collection and Research Methodology
1.3 Bibliometric Analysis
1.4 Conclusion

Chapter 2: Predicting Restoration Factor for Different Maintenance Types
Neeraj Kumar Goyal, Tapash Kumar Das, Namrata Mohanty

2.1 Introduction
2.2 Proposed Model
2.3 Case Study
2.4 Conclusion

Chapter 3:  Measurement and Modeling of Cutting Tool Temperature during Dry Turning Operation of DSS
P. Kumar, O.P.Yadav

3.1. Introduction
3.2. Materials and methods
3.3. Results and discussion
3.4. Empirical Modeling
3.5. Conclusions

Chapter 4: Leaf disease recognition: Comparative Analysis of Various Convolutional Neural Network Algorithms
Vikas Kumar Roy, Ganpati Kumar Roy, Vasu Thakur, Nikhil Baliyan, Nupur Goyal

4.1 Introduction
4.2 Literature Review
4.3 Dataset
4.4 Methodology
4.5 Results and discussion
4.6 Conclusion

Chapter 5: On the Validity of Parallel Plate Assumption for Modelling Leakage Flow past Hydraulic Piston-Cylinder Configurations
Rishabh Gupta, Jatin Prakash, Ankur Miglani, Pavan Kumar Kankar

5.1 Introduction
5.2 The Leakage Flow Models
5.3 Results and discussion
5.4 Concluding remarks

Chapter 6: Development of a hybrid MGWO-optimized Support vector machine approach for tool wear estimation
N. Rajpurohit, Jeetesh Sharma, M. L. Mittal

6.1 Introduction
6.2 Materials and methods
6.3 Results and discussion
6.4 Conclusion and future work

Chapter 7: The Energy Consumption Optimization Using Machine Learning Technique in Electrical Arc Furnaces (EAF)
Rishabh Dwivedi, Ashutosh Mishra, Devesh Kumar, Amitkumar Patil

7.1 Introduction:
7.2 Literature Review
7.3 Methodology
7.4 Result and Discussion
7.4.1Managerial Implications
7.5 Conclusion Limitations and Future scope

Chapter 8: PID based ANN control of Dynamic Systems
A. Kharola

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 & Results Comparison
8.5 Conclusion

Chapter 9: Fatigue Damage Prognosis of Offshore Piping
A. Keprate, N. Bagalkot

9.1 Introduction
9.2 Understanding Piping Fatigue
9.3 Fatigue Damage Prognosis
9.4 Case Study
9.5 Conclusion

Chapter 10: Minimization of Joint Angle Jerk for Industrial Manipulator based on Prognostic Behaviour
Vaishnavi J, Bharat Singh, Ankit Vijayvargiya, Rajesh Kumar

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 (PSO)
10.4 Results and Discussion
10.5 Conclusion

Chapter 11: Estimation of bearing remaining useful life using exponential degradation model and random forest algorithm
Pawan, Jeetesh Sharma, M. L. Mittal

11.1 Introduction
11.2 The proposed RUL estimate approach
11.3 Experimental result and Discussion
11.4 Conclusion

Chapter 12: Machine Learning-based Predictive Maintenance for Diagnostics and Prognostics of Engineering Systems
Ramnath Prabhu Bam, Rajesh S. Prabhu Gaonkar, Clint Pazhayidam George
12.1 Introduction and Overview
12.2 Diagnostics and Prognostics based on Predictive Maintenance
12.3 Machine Learning for Predictive Maintenance
12.4 Machine learning-based Predictive Maintenance in Engineering Systems
12.5 Summary