-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in Disease Diagnosis, Drug Delivery and Treatment
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Leading-Edge Breakthroughs in Artificial Intelligence
- Machine Learning in Biomedical Science and Healthcare Informatics
- Marine Nanoscience and Nanotechnology
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Rotating Machinery Fundamentals and Advances
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Computing and Optimization
Browse Subject Areas
For Authors
Submit a ProposalCondition-Based Maintenance and Residual Life Prediction
 | Edited by Chandan Deep Singh, Davinder Singh. Kanwal Jit Singh, and Harleen Kaur Copyright: 2025 | Expected Pub Date:2025// ISBN: 9781119933120 | Hardcover | 288 pages Price: $225 USD  |
One Line Description
Condition-Based Maintenance and Residual Life Prediction is essential for those looking to effectively implement condition-based maintenance strategies and enhance fault detection through a comprehensive understanding of vibration data analysis and residual life prediction, addressing key challenges in the field.
Audience
Undergraduate, graduate, and post-graduate researchers and academicians and industrialists interested in using maintenance to improve the longevity of equipment
Undergraduate, graduate, and post-graduate researchers and academicians and industrialists interested in using maintenance to improve the longevity of equipment
Description
Issues related to condition-based maintenance include its high initial cost, new techniques that can be difficult to implement due to resistance, older equipment that can be difficult to retrofit with sensors and monitoring equipment, and difficult-to-access equipment during production that is difficult to spot-measure. Keeping the above issues in mind, a general handbook for condition-based maintenance and residual life prediction is required to carry out in fault detection.
Condition-Based Maintenance and Residual Life Prediction aims to develop, analyze, and model condition-based maintenance and residual life prediction through vibration data. The analysis of vibration responses will aid in developing a fault detection system. The sources of vibration may be due to the presence of different types of defects, such as cracks in the shaft, a bent shaft, or misalignment of shafts. This will give designers a diagnostic tool for predicting the trends of vibration conditions, leading to early fault detection. The devised tool will be capable of quantifying the amplitude of vibration based on the severity of defects. With the features available in the devised diagnostic tool, the proposed model can be used for design, predictive maintenance, and condition-based maintenance.
Back to Top
Issues related to condition-based maintenance include its high initial cost, new techniques that can be difficult to implement due to resistance, older equipment that can be difficult to retrofit with sensors and monitoring equipment, and difficult-to-access equipment during production that is difficult to spot-measure. Keeping the above issues in mind, a general handbook for condition-based maintenance and residual life prediction is required to carry out in fault detection.
Condition-Based Maintenance and Residual Life Prediction aims to develop, analyze, and model condition-based maintenance and residual life prediction through vibration data. The analysis of vibration responses will aid in developing a fault detection system. The sources of vibration may be due to the presence of different types of defects, such as cracks in the shaft, a bent shaft, or misalignment of shafts. This will give designers a diagnostic tool for predicting the trends of vibration conditions, leading to early fault detection. The devised tool will be capable of quantifying the amplitude of vibration based on the severity of defects. With the features available in the devised diagnostic tool, the proposed model can be used for design, predictive maintenance, and condition-based maintenance.
Back to Top
Author / Editor Details
Chandan Deep Singh, PhD is an assistant professor in the Department of Mechanical Engineering, Punjabi University, India. He has published over 58 books, 6 chapters, and 100 papers in various peer-reviewed international journals and conferences. Additionally, he serves as an editor and mentor and is currently working on four industry-sponsored projects.
Chandan Deep Singh, PhD is an assistant professor in the Department of Mechanical Engineering, Punjabi University, India. He has published over 58 books, 6 chapters, and 100 papers in various peer-reviewed international journals and conferences. Additionally, he serves as an editor and mentor and is currently working on four industry-sponsored projects.
Davinder Singh, PhD is an assistant professor in the Department of Mechanical Engineering, India. He has published over 25 papers in various international journals and conferences and serves as a mentor for graduate and post-graduate students. His main research areas include production and industrial engineering, manufacturing technology, and innovation management.
Kanwaljit Singh, PhD is an associate professor in the Department of Mechanical Engineering, Guru Kashi University, India. He has published over 50 papers in various international journals and conferences and serves as a mentor to graduate and post-graduate students. His main research areas include production and industrial engineering, and ultrasonic machining.
Harleen Kaur, PhD is an industry professional with over 11 years of experience currently working with Dr. Singh on an industry-sponsored project. She has published more than ten research papers in various international journals and conferences, three book chapters, and 20 books with international publishers.
Back to Top
Table of Contents
1. Maintenance
Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh
1.1 Introduction and Meaning
1.2 Need for Maintenance
1.3 Importance of Maintenance
1.4 Objectives of Maintenance
1.5 The Role of the Maintenance Department
1.6 Responsibilities of a Maintenance Engineer
1.7 Principles of Maintenance
1.8 Maintenance Planning
1.9 Management Organization and Structures
1.10 Types of Maintenance (Figure 1.2)
1.10.1 Breakdown (Reactive) Maintenance
1.10.2 Preventive Maintenance
1.10.3 Predictive Maintenance
1.10.4 Corrective Maintenance
1.10.5 Condition-Based Maintenance
1.11 Economics of Maintenance
1.12 Maintenance Scheduling
1.13 Conclusion
References
2. Condition-Based Maintenance
Rajdeep Singh and Chandan Deep Singh
Introduction
Applications of Condition-Based Maintenance
Advantages and Disadvantages of Condition-Based Maintenance
Various PdM Techniques
References
3. Condition Monitoring
Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh
3.1 Introduction and Meaning
3.2 Advantages of Condition Monitoring
3.3 Condition Monitoring Applications
3.4 Four Pillars of Condition Monitoring
3.5 Setting Up a Condition Monitoring (CM) Activity
3.6 Condition Monitoring Types
3.7 Condition Monitoring Techniques
3.8 Condition Monitoring and Predictive Maintenance: Cost-Benefit Tradeoffs
3.9 Conclusion
References
4. Advanced Maintenance Techniques
Davinder Singh and Talwinder Singh
4.1 Introduction
4.1.1 Challenge of Maintenance Function
4.2 Traditional Maintenance Techniques
4.2.1 Preventive Maintenance (PM)
4.2.2 Condition-Based Maintenance
4.2.3 Total Productive Maintenance (TPM)
4.2.4 Computerized Maintenance Management Systems (CMMS)
4.2.5 Reliability-Centered Maintenance (RCM)
4.2.6 Predictive Maintenance
4.2.7 Risk-Based Maintenance (RBM)
4.2.8 Breakdown Maintenance (BM)
4.3 Advanced Maintenance Techniques
4.3.1 Intelligent Maintenance System (IMS)
4.3.2 Smart Maintenance
4.4 Conclusions
References
5. Unveiling the Future: Residual Life Prediction for Enhanced Asset Management
Maninder Singh, Mukhtiar Singh, Jasvinder Singh, Mandeep Singh and Harjit Singh
5.1 Introduction
5.1.1 Overview of the Key Challenges and Limitations in Accurate Estimation
5.1.2 Objectives of the Chapter
5.2 Residual Life Prediction Techniques
5.2.1 Prognostic Models
5.2.2 Statistical Approaches for Residual Life Prediction
5.2.3 Machine Learning Techniques for Residual Life Prediction
5.3 Applications of Residual Life Prediction
5.4 Conclusion
6. Analysis of Vibration
Rajdeep Singh and Chandan Deep Singh
Introduction
What is Vibration Analysis?
Vibration Analysis Methodology
Categories of Vibration Measurement
Vibration Analysis: Measurement Parameters
Vibration Analysis: Tools and Technology
Benefits of Continuous Vibration Monitoring
References
7. Modeling for Vibration
Rajdeep Singh and Chandan Deep Singh
7.1 Introduction
7.2 Modeling Techniques for Vibration Analysis
7.2.1 ANSYS Simulation
7.2.2 ABACUS Simulation
7.2.3 HyperMesh OptiStruct Solver Simulation
7.2.4 COMSOL Simulation
7.2.5 Mathematical Modeling
7.2.5.1 MATLAB Simulation
7.2.5.2 Miscellaneous Techniques
7.3 Conclusions
References
8. Impact of Condition-Based Maintenance (CBM) and Residual Life Prediction (RLP) on Environmental Issues
Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak
8.1 Introduction
8.2 Goals of Condition-Based Maintenance
8.3 Maintenance Strategies
8.4 Determination of CBM Failure Point
8.4.1 Vibration Monitoring
8.4.2 Process-Parameter Monitoring
8.4.3 Thermography
8.4.4 Tribology
8.4.5 Visual Examination
8.5 Decision-Making in Condition-Based Maintenance
8.6 Decision Models for CBM
8.7 Proportional Hazards Modeling
8.8 Maintenance Planning and Scheduling
8.9 Maintenance Concepts and Strategies
8.9.1 Reliability-Centered Maintenance (RCM)
8.9.2 Equipment Failure Behavior
8.9.3 Condition-Based Maintenance (CBM)
8.9.4 Condition-Based Maintenance Plus (CBMp)
8.10 Condition-Based Maintenance (CBM) Technology Enablers
8.10.1 Diagnostics
8.10.2 Prognostics
8.10.3 Usage-Based Modeling
8.10.4 Data Mining in CBM
8.10.5 Artificial Intelligence in CBM
8.10.6 Open System Architecture-CBM (OSA-CBM)
8.11 Survey of Recent Developments in CBM
8.12 Application Areas of CBM
8.12.1 Automobiles
8.12.2 IT Infrastructure
8.12.3 Process/Manufacturing Industry
8.13 Open Research Challenges
8.13.1 Real-Time Prognostics
8.13.2 Data Quality: Preparation and Selection
8.14 Residual Life Prediction
8.15 Impact of Environmental Policies on Maintenance
8.15.1 Impact of Maintenance Practices
8.15.2 How to Reduce Maintenance Environmental Footprint Employing Sustainability-Focused Reliability Strategies
8.16 Conclusion
References
9. Sustainability Issues in Condition-Based Maintenance and Residual Life Prediction
Simranjit Singh Sidhu and Gurpreet Singh Sidhu
9.1 Introduction
9.2 Definition and Principles of CBM
9.2.1 Benefits and Potential of CBM
9.2.2 Sustainability Challenges in CBM
9.2.2.1 Data Management and Integration
9.2.2.2 Technological Advancements and Compatibility
9.2.2.3 Human Factors and Organizational Culture
9.2.2.4 Economic Viability and Returnbon Investment
9.2.3 Strategies for Enhancing CBM Sustainability
9.3 Residual Life Prediction (RLP)
9.3.1 Objectives and Applications of RLP
9.3.2 Challenges to Sustainability in RLP
9.3.2.1 Data Availability and Quality
9.3.2.2 Model Development and Validation
9.3.2.3 Variable Operating Conditions
9.3.2.4 Uncertainty and Confidence Estimation
9.3.3 Approaches for Ensuring RLP Sustainability
9.3.3.1 Data Collection and Management
9.3.3.2 Model Development and Validation
9.3.3.3 Integration with Maintenance Systems
9.3.3.4 Continuous Improvement and Adaptation
9.4 Synergies Between CBM and RLP
9.4.1 Challenges and Opportunities of Integration
9.4.2 Best Practices for Integration
9.4.2.1 Establish Data Integration Framework
9.4.2.2 Align Maintenance Strategies
9.4.2.3 Develop Advanced Analytical Models
9.4.2.4 Enhance Data Quality and Availability
9.4.2.5 Foster Collaboration and Knowledge Sharing
9.4.2.6 Continuous Monitoring and Improvement
9.4.2.7 Change Management and Stakeholder Engagement
9.4.2.8 Scalability and Flexibility
9.5 Conclusion and Recommendations
9.5.1 Key Findings
9.5.2 Recommendations for Policy and Practice
9.5.3 Future Research Directions
References
Bibliography
10. Role of CBM and RLP in the Performance of the Manufacturing Industry
Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh
10.1 Introduction
10.2 What is Condition-Based Maintenance (CBM)?
10.2.1 Definition:– What Does Condition-Based Maintenance (CBM) Mean?
10.2.2 CBM Typically Involves the Following Steps
10.3 Types of Condition-Based Maintenance
10.4 When to Use Condition-Based Maintenance
10.5 Steps to Take Before Implementing Condition-Based Maintenance
10.6 Challenges of Condition-Based Maintenance
10.7 Benefits of Condition-Based Maintenance
10.8 Role of Condition-Based Maintenance (CBM) on the Performance of the Manufacturing Industry
10.9 Residual Life Prediction
10.10 Role of Residual Life Prediction on the Performance of the Manufacturing Industry
10.11 Conclusion
References
11. Impact of Competencies on Condition-Based Maintenance and Residual Life Prediction
Rajdeep Singh, Chandan Deep Singh and Talwinder Singh
11.1 Introduction
11.1.1 Concept of Condition-Based Maintenance
11.1.2 Decision-Making in CBM
11.1.2.1 CBM Decision Models
11.1.2.2 Proportional Hazards Modelling
11.2 Application Areas of CBM
11.2.1 Automobile Sector
11.2.2 IT Infrastructure
11.2.3 Process/Manufacturing Industry
11.3 Residual Life Prediction
11.3.1 Technical Approach
11.3.2 Future Needs and Critical Issues
11.4 Competency Framework
11.4.1 Competency Work Areas
11.4.2 Competency Effect on CMB and RLP
11.5 Conclusions
References
12. Sustainability Issues in CBM and RLP: Case Studies
Simranjit Singh Sidhu and Gurpreet Singh Sidhu
12.1 Medium Industry Case Study
12.2 Objectives of Implementing Maintenance Improvement Initiatives
12.3 Need for Maintenance
12.4 Phase–Wise Implementation of Maintenance Practices
12.4.1 Phase 1: Transition
12.4.2 Phase 2: Intermediate
12.4.3 Phase 3: Maturity
12.5 Small Industry Case Study
12.6 Research Methodology
12.7 Steps to Improve the Weaknesses Identified Through SWOT Analysis
12.8 Appropriate Measures Implemented for the Hydraulic Bending Machine
12.9 Results and Discussion
12.10 Conclusions
References
Index
Back to Top
1. Maintenance
Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh
1.1 Introduction and Meaning
1.2 Need for Maintenance
1.3 Importance of Maintenance
1.4 Objectives of Maintenance
1.5 The Role of the Maintenance Department
1.6 Responsibilities of a Maintenance Engineer
1.7 Principles of Maintenance
1.8 Maintenance Planning
1.9 Management Organization and Structures
1.10 Types of Maintenance (Figure 1.2)
1.10.1 Breakdown (Reactive) Maintenance
1.10.2 Preventive Maintenance
1.10.3 Predictive Maintenance
1.10.4 Corrective Maintenance
1.10.5 Condition-Based Maintenance
1.11 Economics of Maintenance
1.12 Maintenance Scheduling
1.13 Conclusion
References
2. Condition-Based Maintenance
Rajdeep Singh and Chandan Deep Singh
Introduction
Applications of Condition-Based Maintenance
Advantages and Disadvantages of Condition-Based Maintenance
Various PdM Techniques
References
3. Condition Monitoring
Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh
3.1 Introduction and Meaning
3.2 Advantages of Condition Monitoring
3.3 Condition Monitoring Applications
3.4 Four Pillars of Condition Monitoring
3.5 Setting Up a Condition Monitoring (CM) Activity
3.6 Condition Monitoring Types
3.7 Condition Monitoring Techniques
3.8 Condition Monitoring and Predictive Maintenance: Cost-Benefit Tradeoffs
3.9 Conclusion
References
4. Advanced Maintenance Techniques
Davinder Singh and Talwinder Singh
4.1 Introduction
4.1.1 Challenge of Maintenance Function
4.2 Traditional Maintenance Techniques
4.2.1 Preventive Maintenance (PM)
4.2.2 Condition-Based Maintenance
4.2.3 Total Productive Maintenance (TPM)
4.2.4 Computerized Maintenance Management Systems (CMMS)
4.2.5 Reliability-Centered Maintenance (RCM)
4.2.6 Predictive Maintenance
4.2.7 Risk-Based Maintenance (RBM)
4.2.8 Breakdown Maintenance (BM)
4.3 Advanced Maintenance Techniques
4.3.1 Intelligent Maintenance System (IMS)
4.3.2 Smart Maintenance
4.4 Conclusions
References
5. Unveiling the Future: Residual Life Prediction for Enhanced Asset Management
Maninder Singh, Mukhtiar Singh, Jasvinder Singh, Mandeep Singh and Harjit Singh
5.1 Introduction
5.1.1 Overview of the Key Challenges and Limitations in Accurate Estimation
5.1.2 Objectives of the Chapter
5.2 Residual Life Prediction Techniques
5.2.1 Prognostic Models
5.2.2 Statistical Approaches for Residual Life Prediction
5.2.3 Machine Learning Techniques for Residual Life Prediction
5.3 Applications of Residual Life Prediction
5.4 Conclusion
6. Analysis of Vibration
Rajdeep Singh and Chandan Deep Singh
Introduction
What is Vibration Analysis?
Vibration Analysis Methodology
Categories of Vibration Measurement
Vibration Analysis: Measurement Parameters
Vibration Analysis: Tools and Technology
Benefits of Continuous Vibration Monitoring
References
7. Modeling for Vibration
Rajdeep Singh and Chandan Deep Singh
7.1 Introduction
7.2 Modeling Techniques for Vibration Analysis
7.2.1 ANSYS Simulation
7.2.2 ABACUS Simulation
7.2.3 HyperMesh OptiStruct Solver Simulation
7.2.4 COMSOL Simulation
7.2.5 Mathematical Modeling
7.2.5.1 MATLAB Simulation
7.2.5.2 Miscellaneous Techniques
7.3 Conclusions
References
8. Impact of Condition-Based Maintenance (CBM) and Residual Life Prediction (RLP) on Environmental Issues
Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak
8.1 Introduction
8.2 Goals of Condition-Based Maintenance
8.3 Maintenance Strategies
8.4 Determination of CBM Failure Point
8.4.1 Vibration Monitoring
8.4.2 Process-Parameter Monitoring
8.4.3 Thermography
8.4.4 Tribology
8.4.5 Visual Examination
8.5 Decision-Making in Condition-Based Maintenance
8.6 Decision Models for CBM
8.7 Proportional Hazards Modeling
8.8 Maintenance Planning and Scheduling
8.9 Maintenance Concepts and Strategies
8.9.1 Reliability-Centered Maintenance (RCM)
8.9.2 Equipment Failure Behavior
8.9.3 Condition-Based Maintenance (CBM)
8.9.4 Condition-Based Maintenance Plus (CBMp)
8.10 Condition-Based Maintenance (CBM) Technology Enablers
8.10.1 Diagnostics
8.10.2 Prognostics
8.10.3 Usage-Based Modeling
8.10.4 Data Mining in CBM
8.10.5 Artificial Intelligence in CBM
8.10.6 Open System Architecture-CBM (OSA-CBM)
8.11 Survey of Recent Developments in CBM
8.12 Application Areas of CBM
8.12.1 Automobiles
8.12.2 IT Infrastructure
8.12.3 Process/Manufacturing Industry
8.13 Open Research Challenges
8.13.1 Real-Time Prognostics
8.13.2 Data Quality: Preparation and Selection
8.14 Residual Life Prediction
8.15 Impact of Environmental Policies on Maintenance
8.15.1 Impact of Maintenance Practices
8.15.2 How to Reduce Maintenance Environmental Footprint Employing Sustainability-Focused Reliability Strategies
8.16 Conclusion
References
9. Sustainability Issues in Condition-Based Maintenance and Residual Life Prediction
Simranjit Singh Sidhu and Gurpreet Singh Sidhu
9.1 Introduction
9.2 Definition and Principles of CBM
9.2.1 Benefits and Potential of CBM
9.2.2 Sustainability Challenges in CBM
9.2.2.1 Data Management and Integration
9.2.2.2 Technological Advancements and Compatibility
9.2.2.3 Human Factors and Organizational Culture
9.2.2.4 Economic Viability and Returnbon Investment
9.2.3 Strategies for Enhancing CBM Sustainability
9.3 Residual Life Prediction (RLP)
9.3.1 Objectives and Applications of RLP
9.3.2 Challenges to Sustainability in RLP
9.3.2.1 Data Availability and Quality
9.3.2.2 Model Development and Validation
9.3.2.3 Variable Operating Conditions
9.3.2.4 Uncertainty and Confidence Estimation
9.3.3 Approaches for Ensuring RLP Sustainability
9.3.3.1 Data Collection and Management
9.3.3.2 Model Development and Validation
9.3.3.3 Integration with Maintenance Systems
9.3.3.4 Continuous Improvement and Adaptation
9.4 Synergies Between CBM and RLP
9.4.1 Challenges and Opportunities of Integration
9.4.2 Best Practices for Integration
9.4.2.1 Establish Data Integration Framework
9.4.2.2 Align Maintenance Strategies
9.4.2.3 Develop Advanced Analytical Models
9.4.2.4 Enhance Data Quality and Availability
9.4.2.5 Foster Collaboration and Knowledge Sharing
9.4.2.6 Continuous Monitoring and Improvement
9.4.2.7 Change Management and Stakeholder Engagement
9.4.2.8 Scalability and Flexibility
9.5 Conclusion and Recommendations
9.5.1 Key Findings
9.5.2 Recommendations for Policy and Practice
9.5.3 Future Research Directions
References
Bibliography
10. Role of CBM and RLP in the Performance of the Manufacturing Industry
Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh
10.1 Introduction
10.2 What is Condition-Based Maintenance (CBM)?
10.2.1 Definition:– What Does Condition-Based Maintenance (CBM) Mean?
10.2.2 CBM Typically Involves the Following Steps
10.3 Types of Condition-Based Maintenance
10.4 When to Use Condition-Based Maintenance
10.5 Steps to Take Before Implementing Condition-Based Maintenance
10.6 Challenges of Condition-Based Maintenance
10.7 Benefits of Condition-Based Maintenance
10.8 Role of Condition-Based Maintenance (CBM) on the Performance of the Manufacturing Industry
10.9 Residual Life Prediction
10.10 Role of Residual Life Prediction on the Performance of the Manufacturing Industry
10.11 Conclusion
References
11. Impact of Competencies on Condition-Based Maintenance and Residual Life Prediction
Rajdeep Singh, Chandan Deep Singh and Talwinder Singh
11.1 Introduction
11.1.1 Concept of Condition-Based Maintenance
11.1.2 Decision-Making in CBM
11.1.2.1 CBM Decision Models
11.1.2.2 Proportional Hazards Modelling
11.2 Application Areas of CBM
11.2.1 Automobile Sector
11.2.2 IT Infrastructure
11.2.3 Process/Manufacturing Industry
11.3 Residual Life Prediction
11.3.1 Technical Approach
11.3.2 Future Needs and Critical Issues
11.4 Competency Framework
11.4.1 Competency Work Areas
11.4.2 Competency Effect on CMB and RLP
11.5 Conclusions
References
12. Sustainability Issues in CBM and RLP: Case Studies
Simranjit Singh Sidhu and Gurpreet Singh Sidhu
12.1 Medium Industry Case Study
12.2 Objectives of Implementing Maintenance Improvement Initiatives
12.3 Need for Maintenance
12.4 Phase–Wise Implementation of Maintenance Practices
12.4.1 Phase 1: Transition
12.4.2 Phase 2: Intermediate
12.4.3 Phase 3: Maturity
12.5 Small Industry Case Study
12.6 Research Methodology
12.7 Steps to Improve the Weaknesses Identified Through SWOT Analysis
12.8 Appropriate Measures Implemented for the Hydraulic Bending Machine
12.9 Results and Discussion
12.10 Conclusions
References
Index
Back to Top