Intelligent prognostics for engineering systems with machine learning techniques. (2023)

• Record Type: Book
• Further Information: Note: Edited by Gunjan Soni, Om Prakash Yadav, Gaurav Kumar Badhotiya, Mangey Ram.
• Editors: Soni, Gunjan; Yadav, Om Prakash, 1964-; Badhotiya, Gaurav Kumar; Ram, Mangey
• Contents: 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
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2023
• Extent: 1 online resource (246 pages), illustrations (black and white, and colour)
• Subjects: 620.00285631; Engineering systems -- Data processing; Business forecasting -- Data processing; Machine learning
• Languages: English
• ISBNs: 9781000954104
• Notes: Note: Description based on CIP data; resource not viewed.
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