Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, India. He has published over 58 books, six 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. Kanwal Jit 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.

• 1 Maintenance 1Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh1.1 Introduction and Meaning 11.2 Need for Maintenance 21.3 Importance of Maintenance 31.4 Objectives of Maintenance 31.5 The Role of the Maintenance Department 41.6 Responsibilities of a Maintenance Engineer 51.7 Principles of Maintenance 61.8 Maintenance Planning 81.9 Management Organization and Structures 101.10 Types of Maintenance (Figure 1.2) 121.10.1 Breakdown (Reactive) Maintenance 121.10.2 Preventive Maintenance 131.10.3 Predictive Maintenance 141.10.4 Corrective Maintenance 161.10.5 Condition-Based Maintenance 171.11 Economics of Maintenance 181.12 Maintenance Scheduling 191.13 Conclusion 20References 212 Condition-Based Maintenance 23Rajdeep Singh and Chandan Deep SinghIntroduction 23Applications of Condition-Based Maintenance 31Advantages and Disadvantages of Condition-Based Maintenance 39Various PdM Techniques 39References 443 Condition Monitoring 47Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh3.1 Introduction and Meaning 473.2 Advantages of Condition Monitoring 513.3 Condition Monitoring Applications 533.4 Four Pillars of Condition Monitoring 533.5 Setting Up a Condition Monitoring (CM) Activity 553.6 Condition Monitoring Types 563.7 Condition Monitoring Techniques 583.8 Condition Monitoring and Predictive Maintenance: Cost-Benefit Tradeoffs 613.9 Conclusion 62References 634 Advanced Maintenance Techniques 65Davinder Singh and Talwinder Singh4.1 Introduction 654.1.1 Challenge of Maintenance Function 664.2 Traditional Maintenance Techniques 674.2.1 Preventive Maintenance (PM) 674.2.2 Condition-Based Maintenance 694.2.3 Total Productive Maintenance (TPM) 704.2.4 Computerized Maintenance Management Systems (cmms) 724.2.5 Reliability-Centered Maintenance (RCM) 744.2.6 Predictive Maintenance 754.2.7 Risk-Based Maintenance (RBM) 764.2.8 Breakdown Maintenance (BM) 774.3 Advanced Maintenance Techniques 784.3.1 Intelligent Maintenance System (IMS) 784.3.2 Smart Maintenance 804.4 Conclusions 83References 835 Unveiling the Future: Residual Life Prediction for Enhanced Asset Management 87Maninder Singh, Mukhtiar Singh, Jasvinder Singh, Mandeep Singh and Harjit Singh5.1 Introduction 885.1.1 Overview of the Key Challenges and Limitations in Accurate Estimation 895.1.2 Objectives of the Chapter 915.2 Residual Life Prediction Techniques 925.2.1 Prognostic Models 925.2.2 Statistical Approaches for Residual Life Prediction 935.2.3 Machine Learning Techniques for Residual Life Prediction 955.3 Applications of Residual Life Prediction 965.4 Conclusion 97References 986 Analysis of Vibration 101Rajdeep Singh and Chandan Deep SinghIntroduction 101What is Vibration Analysis? 101Vibration Analysis Methodology 102Categories of Vibration Measurement 106Vibration Analysis: Measurement Parameters 108Vibration Analysis: Tools and Technology 110Benefits of Continuous Vibration Monitoring 110References 1117 Modeling for Vibration 115Rajdeep Singh and Chandan Deep Singh7.1 Introduction 1157.2 Modeling Techniques for Vibration Analysis 1167.2.1 ANSYS Simulation 1177.2.2 ABAQUS Simulation 1197.2.3 HyperMesh OptiStruct Solver Simulation 1197.2.4 COMSOL Simulation 1217.2.5 Mathematical Modeling 1237.2.5.1 MATLAB Simulation 1237.2.5.2 Miscellaneous Techniques 1247.3 Conclusions 126References 1278 Impact of Condition-Based Maintenance (CBM) and Residual Life Prediction (RLP) on Environmental Issues 131Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak8.1 Introduction 1328.2 Goals of Condition-Based Maintenance 1348.3 Maintenance Strategies 1358.4 Determination of CBM Failure Point 1378.4.1 Vibration Monitoring 1378.4.2 Process-Parameter Monitoring 1388.4.3 Thermography 1388.4.4 Tribology 1398.4.5 Visual Examination 1408.5 Decision-Making in Condition-Based Maintenance 1408.6 Decision Models for CBM 1418.7 Proportional Hazards Modeling 1438.8 Maintenance Planning and Scheduling 1448.9 Maintenance Concepts and Strategies 1468.9.1 Reliability-Centered Maintenance (RCM) 1468.9.2 Equipment Failure Behavior 1488.9.3 Condition-Based Maintenance (CBM) 1488.9.4 Condition-Based Maintenance Plus (CBMp) 1498.10 Condition-Based Maintenance (CBM) Technology Enablers 1508.10.1 Diagnostics 1508.10.2 Prognostics 1508.10.3 Usage-Based Modeling 1518.10.4 Data Mining in CBM 1528.10.5 Artificial Intelligence in CBM 1538.10.6 Open System Architecture-CBM (OSA-CBM) 1538.11 Survey of Recent Developments in CBM 1548.12 Application Areas of CBM 1578.12.1 Automobiles 1578.12.2 IT Infrastructure 1598.12.3 Process/Manufacturing Industry 1608.13 Open Research Challenges 1608.13.1 Real-Time Prognostics 1618.13.2 Data Quality: Preparation and Selection 1618.14 Residual Life Prediction 1628.15 Impact of Environmental Policies on Maintenance 1628.15.1 Impact of Maintenance Practices 1638.15.2 How to Reduce Maintenance Environmental Footprint Employing Sustainability-Focused Reliability Strategies 1648.16 Conclusion 164References 1659 Sustainability Issues in Condition-Based Maintenance and Residual Life Prediction 171Simranjit Singh Sidhu and Gurpreet Singh Sidhu9.1 Introduction 1729.2 Definition and Principles of CBM 1749.2.1 Benefits and Potential of CBM 1759.2.2 Sustainability Challenges in CBM 1779.2.2.1 Data Management and Integration 1779.2.2.2 Technological Advancements and Compatibility 1779.2.2.3 Human Factors and Organizational Culture 1779.2.2.4 Economic Viability and Return on Investment 1789.2.3 Strategies for Enhancing CBM Sustainability 1789.3 Residual Life Prediction (RLP) 1799.3.1 Objectives and Applications of RLP 1819.3.2 Challenges to Sustainability in RLP 1829.3.2.1 Data Availability and Quality 1829.3.2.2 Model Development and Validation 1829.3.2.3 Variable Operating Conditions 1829.3.2.4 Uncertainty and Confidence Estimation 1829.3.3 Approaches for Ensuring RLP Sustainability 1839.3.3.1 Data Collection and Management 1839.3.3.2 Model Development and Validation 1839.3.3.3 Integration with Maintenance Systems 1839.3.3.4 Continuous Improvement and Adaptation 1839.4 Synergies Between CBM and RLP 1839.4.1 Challenges and Opportunities of Integration 1849.4.2 Best Practices for Integration 1859.4.2.1 Establish Data Integration Framework 1859.4.2.2 Align Maintenance Strategies 1859.4.2.3 Develop Advanced Analytical Models 1859.4.2.4 Enhance Data Quality and Availability 1859.4.2.5 Foster Collaboration and Knowledge Sharing 1869.4.2.6 Continuous Monitoring and Improvement 1869.4.2.7 Change Management and Stakeholder Engagement 1869.4.2.8 Scalability and Flexibility 1869.5 Conclusion and Recommendations 1879.5.1 Key Findings 1879.5.2 Recommendations for Policy and Practice 1879.5.3 Future Research Directions 188References 189Bibliography 19010 Role of CBM and RLP in the Performance of the Manufacturing Industry 191Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh10.1 Introduction 19210.2 What is Condition-Based Maintenance (CBM)? 19410.2.1 Definition:– What Does Condition-Based Maintenance (CBM) Mean? 19410.2.2 CBM Typically Involves the Following Steps 19410.3 Types of Condition-Based Maintenance 19510.4 When to Use Condition-Based Maintenance 19710.5 Steps to Take Before Implementing Condition-Based Maintenance 19810.6 Challenges of Condition-Based Maintenance 19910.7 Benefits of Condition-Based Maintenance 20010.8 Role of Condition-Based Maintenance (CBM) on the Performance of the Manufacturing Industry 20110.9 Residual Life Prediction 20210.10 Role of Residual Life Prediction on the Performance of the Manufacturing Industry 20310.11 Conclusion 205References 20511 Impact of Competencies on Condition-Based Maintenance and Residual Life Prediction 207Rajdeep Singh, Chandan Deep Singh and Talwinder Singh11.1 Introduction 20711.1.1 Concept of Condition-Based Maintenance 20811.1.2 Decision-Making in CBM 21011.1.2.1 CBM Decision Models 21111.1.2.2 Proportional Hazards Modelling 21211.2 Application Areas of CBM 21211.2.1 Automobile Sector 21311.2.2 IT Infrastructure 21311.2.3 Process/Manufacturing Industry 21311.3 Residual Life Prediction 21411.3.1 Technical Approach 21611.3.2 Future Needs and Critical Issues 21811.4 Competency Framework 22011.4.1 Competency Work Areas 22111.4.2 Competency Effect on CMB and RLP 22311.5 Conclusions 228References 22912 Sustainability Issues in CBM and RLP: Case Studies 233Simranjit Singh Sidhu and Gurpreet Singh Sidhu12.1 Medium Industry Case Study 23412.2 Objectives of Implementing Maintenance Improvement Initiatives 23512.3 Need for Maintenance 23612.4 Phase–Wise Implementation of Maintenance Practices 23712.4.1 Phase 1: Transition 23712.4.2 Phase 2: Intermediate 24312.4.3 Phase 3: Maturity 24812.5 Small Industry Case Study 25212.6 Research Methodology 25312.7 Steps to Improve the Weaknesses Identified Through SWOT Analysis 25512.8 Appropriate Measures Implemented for the Hydraulic Bending Machine 25912.9 Results and Discussion 26212.10 Conclusions 267References 268Index 273

Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, India. He has published over 58 books, six 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. Kanwal Jit 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.

• 1 Maintenance 1Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh1.1 Introduction and Meaning 11.2 Need for Maintenance 21.3 Importance of Maintenance 31.4 Objectives of Maintenance 31.5 The Role of the Maintenance Department 41.6 Responsibilities of a Maintenance Engineer 51.7 Principles of Maintenance 61.8 Maintenance Planning 81.9 Management Organization and Structures 101.10 Types of Maintenance (Figure 1.2) 121.10.1 Breakdown (Reactive) Maintenance 121.10.2 Preventive Maintenance 131.10.3 Predictive Maintenance 141.10.4 Corrective Maintenance 161.10.5 Condition-Based Maintenance 171.11 Economics of Maintenance 181.12 Maintenance Scheduling 191.13 Conclusion 20References 212 Condition-Based Maintenance 23Rajdeep Singh and Chandan Deep SinghIntroduction 23Applications of Condition-Based Maintenance 31Advantages and Disadvantages of Condition-Based Maintenance 39Various PdM Techniques 39References 443 Condition Monitoring 47Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh3.1 Introduction and Meaning 473.2 Advantages of Condition Monitoring 513.3 Condition Monitoring Applications 533.4 Four Pillars of Condition Monitoring 533.5 Setting Up a Condition Monitoring (CM) Activity 553.6 Condition Monitoring Types 563.7 Condition Monitoring Techniques 583.8 Condition Monitoring and Predictive Maintenance: Cost-Benefit Tradeoffs 613.9 Conclusion 62References 634 Advanced Maintenance Techniques 65Davinder Singh and Talwinder Singh4.1 Introduction 654.1.1 Challenge of Maintenance Function 664.2 Traditional Maintenance Techniques 674.2.1 Preventive Maintenance (PM) 674.2.2 Condition-Based Maintenance 694.2.3 Total Productive Maintenance (TPM) 704.2.4 Computerized Maintenance Management Systems (cmms) 724.2.5 Reliability-Centered Maintenance (RCM) 744.2.6 Predictive Maintenance 754.2.7 Risk-Based Maintenance (RBM) 764.2.8 Breakdown Maintenance (BM) 774.3 Advanced Maintenance Techniques 784.3.1 Intelligent Maintenance System (IMS) 784.3.2 Smart Maintenance 804.4 Conclusions 83References 835 Unveiling the Future: Residual Life Prediction for Enhanced Asset Management 87Maninder Singh, Mukhtiar Singh, Jasvinder Singh, Mandeep Singh and Harjit Singh5.1 Introduction 885.1.1 Overview of the Key Challenges and Limitations in Accurate Estimation 895.1.2 Objectives of the Chapter 915.2 Residual Life Prediction Techniques 925.2.1 Prognostic Models 925.2.2 Statistical Approaches for Residual Life Prediction 935.2.3 Machine Learning Techniques for Residual Life Prediction 955.3 Applications of Residual Life Prediction 965.4 Conclusion 97References 986 Analysis of Vibration 101Rajdeep Singh and Chandan Deep SinghIntroduction 101What is Vibration Analysis? 101Vibration Analysis Methodology 102Categories of Vibration Measurement 106Vibration Analysis: Measurement Parameters 108Vibration Analysis: Tools and Technology 110Benefits of Continuous Vibration Monitoring 110References 1117 Modeling for Vibration 115Rajdeep Singh and Chandan Deep Singh7.1 Introduction 1157.2 Modeling Techniques for Vibration Analysis 1167.2.1 ANSYS Simulation 1177.2.2 ABAQUS Simulation 1197.2.3 HyperMesh OptiStruct Solver Simulation 1197.2.4 COMSOL Simulation 1217.2.5 Mathematical Modeling 1237.2.5.1 MATLAB Simulation 1237.2.5.2 Miscellaneous Techniques 1247.3 Conclusions 126References 1278 Impact of Condition-Based Maintenance (CBM) and Residual Life Prediction (RLP) on Environmental Issues 131Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak8.1 Introduction 1328.2 Goals of Condition-Based Maintenance 1348.3 Maintenance Strategies 1358.4 Determination of CBM Failure Point 1378.4.1 Vibration Monitoring 1378.4.2 Process-Parameter Monitoring 1388.4.3 Thermography 1388.4.4 Tribology 1398.4.5 Visual Examination 1408.5 Decision-Making in Condition-Based Maintenance 1408.6 Decision Models for CBM 1418.7 Proportional Hazards Modeling 1438.8 Maintenance Planning and Scheduling 1448.9 Maintenance Concepts and Strategies 1468.9.1 Reliability-Centered Maintenance (RCM) 1468.9.2 Equipment Failure Behavior 1488.9.3 Condition-Based Maintenance (CBM) 1488.9.4 Condition-Based Maintenance Plus (CBMp) 1498.10 Condition-Based Maintenance (CBM) Technology Enablers 1508.10.1 Diagnostics 1508.10.2 Prognostics 1508.10.3 Usage-Based Modeling 1518.10.4 Data Mining in CBM 1528.10.5 Artificial Intelligence in CBM 1538.10.6 Open System Architecture-CBM (OSA-CBM) 1538.11 Survey of Recent Developments in CBM 1548.12 Application Areas of CBM 1578.12.1 Automobiles 1578.12.2 IT Infrastructure 1598.12.3 Process/Manufacturing Industry 1608.13 Open Research Challenges 1608.13.1 Real-Time Prognostics 1618.13.2 Data Quality: Preparation and Selection 1618.14 Residual Life Prediction 1628.15 Impact of Environmental Policies on Maintenance 1628.15.1 Impact of Maintenance Practices 1638.15.2 How to Reduce Maintenance Environmental Footprint Employing Sustainability-Focused Reliability Strategies 1648.16 Conclusion 164References 1659 Sustainability Issues in Condition-Based Maintenance and Residual Life Prediction 171Simranjit Singh Sidhu and Gurpreet Singh Sidhu9.1 Introduction 1729.2 Definition and Principles of CBM 1749.2.1 Benefits and Potential of CBM 1759.2.2 Sustainability Challenges in CBM 1779.2.2.1 Data Management and Integration 1779.2.2.2 Technological Advancements and Compatibility 1779.2.2.3 Human Factors and Organizational Culture 1779.2.2.4 Economic Viability and Return on Investment 1789.2.3 Strategies for Enhancing CBM Sustainability 1789.3 Residual Life Prediction (RLP) 1799.3.1 Objectives and Applications of RLP 1819.3.2 Challenges to Sustainability in RLP 1829.3.2.1 Data Availability and Quality 1829.3.2.2 Model Development and Validation 1829.3.2.3 Variable Operating Conditions 1829.3.2.4 Uncertainty and Confidence Estimation 1829.3.3 Approaches for Ensuring RLP Sustainability 1839.3.3.1 Data Collection and Management 1839.3.3.2 Model Development and Validation 1839.3.3.3 Integration with Maintenance Systems 1839.3.3.4 Continuous Improvement and Adaptation 1839.4 Synergies Between CBM and RLP 1839.4.1 Challenges and Opportunities of Integration 1849.4.2 Best Practices for Integration 1859.4.2.1 Establish Data Integration Framework 1859.4.2.2 Align Maintenance Strategies 1859.4.2.3 Develop Advanced Analytical Models 1859.4.2.4 Enhance Data Quality and Availability 1859.4.2.5 Foster Collaboration and Knowledge Sharing 1869.4.2.6 Continuous Monitoring and Improvement 1869.4.2.7 Change Management and Stakeholder Engagement 1869.4.2.8 Scalability and Flexibility 1869.5 Conclusion and Recommendations 1879.5.1 Key Findings 1879.5.2 Recommendations for Policy and Practice 1879.5.3 Future Research Directions 188References 189Bibliography 19010 Role of CBM and RLP in the Performance of the Manufacturing Industry 191Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh10.1 Introduction 19210.2 What is Condition-Based Maintenance (CBM)? 19410.2.1 Definition:– What Does Condition-Based Maintenance (CBM) Mean? 19410.2.2 CBM Typically Involves the Following Steps 19410.3 Types of Condition-Based Maintenance 19510.4 When to Use Condition-Based Maintenance 19710.5 Steps to Take Before Implementing Condition-Based Maintenance 19810.6 Challenges of Condition-Based Maintenance 19910.7 Benefits of Condition-Based Maintenance 20010.8 Role of Condition-Based Maintenance (CBM) on the Performance of the Manufacturing Industry 20110.9 Residual Life Prediction 20210.10 Role of Residual Life Prediction on the Performance of the Manufacturing Industry 20310.11 Conclusion 205References 20511 Impact of Competencies on Condition-Based Maintenance and Residual Life Prediction 207Rajdeep Singh, Chandan Deep Singh and Talwinder Singh11.1 Introduction 20711.1.1 Concept of Condition-Based Maintenance 20811.1.2 Decision-Making in CBM 21011.1.2.1 CBM Decision Models 21111.1.2.2 Proportional Hazards Modelling 21211.2 Application Areas of CBM 21211.2.1 Automobile Sector 21311.2.2 IT Infrastructure 21311.2.3 Process/Manufacturing Industry 21311.3 Residual Life Prediction 21411.3.1 Technical Approach 21611.3.2 Future Needs and Critical Issues 21811.4 Competency Framework 22011.4.1 Competency Work Areas 22111.4.2 Competency Effect on CMB and RLP 22311.5 Conclusions 228References 22912 Sustainability Issues in CBM and RLP: Case Studies 233Simranjit Singh Sidhu and Gurpreet Singh Sidhu12.1 Medium Industry Case Study 23412.2 Objectives of Implementing Maintenance Improvement Initiatives 23512.3 Need for Maintenance 23612.4 Phase–Wise Implementation of Maintenance Practices 23712.4.1 Phase 1: Transition 23712.4.2 Phase 2: Intermediate 24312.4.3 Phase 3: Maturity 24812.5 Small Industry Case Study 25212.6 Research Methodology 25312.7 Steps to Improve the Weaknesses Identified Through SWOT Analysis 25512.8 Appropriate Measures Implemented for the Hydraulic Bending Machine 25912.9 Results and Discussion 26212.10 Conclusions 267References 268Index 273

Chandan Deep Singh, PhD, is an assistant professor in the Department of Mechanical Engineering, Punjabi University, India. He has published over 58 books, six 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. Kanwal Jit 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.

• 1 Maintenance 1Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh1.1 Introduction and Meaning 11.2 Need for Maintenance 21.3 Importance of Maintenance 31.4 Objectives of Maintenance 31.5 The Role of the Maintenance Department 41.6 Responsibilities of a Maintenance Engineer 51.7 Principles of Maintenance 61.8 Maintenance Planning 81.9 Management Organization and Structures 101.10 Types of Maintenance (Figure 1.2) 121.10.1 Breakdown (Reactive) Maintenance 121.10.2 Preventive Maintenance 131.10.3 Predictive Maintenance 141.10.4 Corrective Maintenance 161.10.5 Condition-Based Maintenance 171.11 Economics of Maintenance 181.12 Maintenance Scheduling 191.13 Conclusion 20References 212 Condition-Based Maintenance 23Rajdeep Singh and Chandan Deep SinghIntroduction 23Applications of Condition-Based Maintenance 31Advantages and Disadvantages of Condition-Based Maintenance 39Various PdM Techniques 39References 443 Condition Monitoring 47Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh3.1 Introduction and Meaning 473.2 Advantages of Condition Monitoring 513.3 Condition Monitoring Applications 533.4 Four Pillars of Condition Monitoring 533.5 Setting Up a Condition Monitoring (CM) Activity 553.6 Condition Monitoring Types 563.7 Condition Monitoring Techniques 583.8 Condition Monitoring and Predictive Maintenance: Cost-Benefit Tradeoffs 613.9 Conclusion 62References 634 Advanced Maintenance Techniques 65Davinder Singh and Talwinder Singh4.1 Introduction 654.1.1 Challenge of Maintenance Function 664.2 Traditional Maintenance Techniques 674.2.1 Preventive Maintenance (PM) 674.2.2 Condition-Based Maintenance 694.2.3 Total Productive Maintenance (TPM) 704.2.4 Computerized Maintenance Management Systems (cmms) 724.2.5 Reliability-Centered Maintenance (RCM) 744.2.6 Predictive Maintenance 754.2.7 Risk-Based Maintenance (RBM) 764.2.8 Breakdown Maintenance (BM) 774.3 Advanced Maintenance Techniques 784.3.1 Intelligent Maintenance System (IMS) 784.3.2 Smart Maintenance 804.4 Conclusions 83References 835 Unveiling the Future: Residual Life Prediction for Enhanced Asset Management 87Maninder Singh, Mukhtiar Singh, Jasvinder Singh, Mandeep Singh and Harjit Singh5.1 Introduction 885.1.1 Overview of the Key Challenges and Limitations in Accurate Estimation 895.1.2 Objectives of the Chapter 915.2 Residual Life Prediction Techniques 925.2.1 Prognostic Models 925.2.2 Statistical Approaches for Residual Life Prediction 935.2.3 Machine Learning Techniques for Residual Life Prediction 955.3 Applications of Residual Life Prediction 965.4 Conclusion 97References 986 Analysis of Vibration 101Rajdeep Singh and Chandan Deep SinghIntroduction 101What is Vibration Analysis? 101Vibration Analysis Methodology 102Categories of Vibration Measurement 106Vibration Analysis: Measurement Parameters 108Vibration Analysis: Tools and Technology 110Benefits of Continuous Vibration Monitoring 110References 1117 Modeling for Vibration 115Rajdeep Singh and Chandan Deep Singh7.1 Introduction 1157.2 Modeling Techniques for Vibration Analysis 1167.2.1 ANSYS Simulation 1177.2.2 ABAQUS Simulation 1197.2.3 HyperMesh OptiStruct Solver Simulation 1197.2.4 COMSOL Simulation 1217.2.5 Mathematical Modeling 1237.2.5.1 MATLAB Simulation 1237.2.5.2 Miscellaneous Techniques 1247.3 Conclusions 126References 1278 Impact of Condition-Based Maintenance (CBM) and Residual Life Prediction (RLP) on Environmental Issues 131Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak8.1 Introduction 1328.2 Goals of Condition-Based Maintenance 1348.3 Maintenance Strategies 1358.4 Determination of CBM Failure Point 1378.4.1 Vibration Monitoring 1378.4.2 Process-Parameter Monitoring 1388.4.3 Thermography 1388.4.4 Tribology 1398.4.5 Visual Examination 1408.5 Decision-Making in Condition-Based Maintenance 1408.6 Decision Models for CBM 1418.7 Proportional Hazards Modeling 1438.8 Maintenance Planning and Scheduling 1448.9 Maintenance Concepts and Strategies 1468.9.1 Reliability-Centered Maintenance (RCM) 1468.9.2 Equipment Failure Behavior 1488.9.3 Condition-Based Maintenance (CBM) 1488.9.4 Condition-Based Maintenance Plus (CBMp) 1498.10 Condition-Based Maintenance (CBM) Technology Enablers 1508.10.1 Diagnostics 1508.10.2 Prognostics 1508.10.3 Usage-Based Modeling 1518.10.4 Data Mining in CBM 1528.10.5 Artificial Intelligence in CBM 1538.10.6 Open System Architecture-CBM (OSA-CBM) 1538.11 Survey of Recent Developments in CBM 1548.12 Application Areas of CBM 1578.12.1 Automobiles 1578.12.2 IT Infrastructure 1598.12.3 Process/Manufacturing Industry 1608.13 Open Research Challenges 1608.13.1 Real-Time Prognostics 1618.13.2 Data Quality: Preparation and Selection 1618.14 Residual Life Prediction 1628.15 Impact of Environmental Policies on Maintenance 1628.15.1 Impact of Maintenance Practices 1638.15.2 How to Reduce Maintenance Environmental Footprint Employing Sustainability-Focused Reliability Strategies 1648.16 Conclusion 164References 1659 Sustainability Issues in Condition-Based Maintenance and Residual Life Prediction 171Simranjit Singh Sidhu and Gurpreet Singh Sidhu9.1 Introduction 1729.2 Definition and Principles of CBM 1749.2.1 Benefits and Potential of CBM 1759.2.2 Sustainability Challenges in CBM 1779.2.2.1 Data Management and Integration 1779.2.2.2 Technological Advancements and Compatibility 1779.2.2.3 Human Factors and Organizational Culture 1779.2.2.4 Economic Viability and Return on Investment 1789.2.3 Strategies for Enhancing CBM Sustainability 1789.3 Residual Life Prediction (RLP) 1799.3.1 Objectives and Applications of RLP 1819.3.2 Challenges to Sustainability in RLP 1829.3.2.1 Data Availability and Quality 1829.3.2.2 Model Development and Validation 1829.3.2.3 Variable Operating Conditions 1829.3.2.4 Uncertainty and Confidence Estimation 1829.3.3 Approaches for Ensuring RLP Sustainability 1839.3.3.1 Data Collection and Management 1839.3.3.2 Model Development and Validation 1839.3.3.3 Integration with Maintenance Systems 1839.3.3.4 Continuous Improvement and Adaptation 1839.4 Synergies Between CBM and RLP 1839.4.1 Challenges and Opportunities of Integration 1849.4.2 Best Practices for Integration 1859.4.2.1 Establish Data Integration Framework 1859.4.2.2 Align Maintenance Strategies 1859.4.2.3 Develop Advanced Analytical Models 1859.4.2.4 Enhance Data Quality and Availability 1859.4.2.5 Foster Collaboration and Knowledge Sharing 1869.4.2.6 Continuous Monitoring and Improvement 1869.4.2.7 Change Management and Stakeholder Engagement 1869.4.2.8 Scalability and Flexibility 1869.5 Conclusion and Recommendations 1879.5.1 Key Findings 1879.5.2 Recommendations for Policy and Practice 1879.5.3 Future Research Directions 188References 189Bibliography 19010 Role of CBM and RLP in the Performance of the Manufacturing Industry 191Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh10.1 Introduction 19210.2 What is Condition-Based Maintenance (CBM)? 19410.2.1 Definition:– What Does Condition-Based Maintenance (CBM) Mean? 19410.2.2 CBM Typically Involves the Following Steps 19410.3 Types of Condition-Based Maintenance 19510.4 When to Use Condition-Based Maintenance 19710.5 Steps to Take Before Implementing Condition-Based Maintenance 19810.6 Challenges of Condition-Based Maintenance 19910.7 Benefits of Condition-Based Maintenance 20010.8 Role of Condition-Based Maintenance (CBM) on the Performance of the Manufacturing Industry 20110.9 Residual Life Prediction 20210.10 Role of Residual Life Prediction on the Performance of the Manufacturing Industry 20310.11 Conclusion 205References 20511 Impact of Competencies on Condition-Based Maintenance and Residual Life Prediction 207Rajdeep Singh, Chandan Deep Singh and Talwinder Singh11.1 Introduction 20711.1.1 Concept of Condition-Based Maintenance 20811.1.2 Decision-Making in CBM 21011.1.2.1 CBM Decision Models 21111.1.2.2 Proportional Hazards Modelling 21211.2 Application Areas of CBM 21211.2.1 Automobile Sector 21311.2.2 IT Infrastructure 21311.2.3 Process/Manufacturing Industry 21311.3 Residual Life Prediction 21411.3.1 Technical Approach 21611.3.2 Future Needs and Critical Issues 21811.4 Competency Framework 22011.4.1 Competency Work Areas 22111.4.2 Competency Effect on CMB and RLP 22311.5 Conclusions 228References 22912 Sustainability Issues in CBM and RLP: Case Studies 233Simranjit Singh Sidhu and Gurpreet Singh Sidhu12.1 Medium Industry Case Study 23412.2 Objectives of Implementing Maintenance Improvement Initiatives 23512.3 Need for Maintenance 23612.4 Phase–Wise Implementation of Maintenance Practices 23712.4.1 Phase 1: Transition 23712.4.2 Phase 2: Intermediate 24312.4.3 Phase 3: Maturity 24812.5 Small Industry Case Study 25212.6 Research Methodology 25312.7 Steps to Improve the Weaknesses Identified Through SWOT Analysis 25512.8 Appropriate Measures Implemented for the Hydraulic Bending Machine 25912.9 Results and Discussion 26212.10 Conclusions 267References 268Index 273