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.
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.
1 Maintenance 1 Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh 1.1 Introduction and Meaning 1 1.2 Need for Maintenance 2 1.3 Importance of Maintenance 3 1.4 Objectives of Maintenance 3 1.5 The Role of the Maintenance Department 4 1.6 Responsibilities of a Maintenance Engineer 5 1.7 Principles of Maintenance 6 1.8 Maintenance Planning 8 1.9 Management Organization and Structures 10 1.10 Types of Maintenance (Figure 1.2) 12 1.11 Economics of Maintenance 18 1.12 Maintenance Scheduling 19 1.13 Conclusion 20 2 Condition-Based Maintenance 23 Rajdeep Singh and Chandan Deep Singh 2.1 Introduction 23 2.2 Applications of Condition-Based Maintenance 31 2.3 Advantages and Disadvantages of Condition-Based Maintenance 39 2.4 Various PdM Techniques 39 3 Condition Monitoring 47 Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh 3.1 Introduction and Meaning 47 3.2 Advantages of Condition Monitoring 51 3.3 Condition Monitoring Applications 53 3.4 Four Pillars of Condition Monitoring 53 3.5 Setting Up a Condition Monitoring (CM) Activity 55 3.6 Condition Monitoring Types 56 3.7 Condition Monitoring Techniques 58 3.8 Condition Monitoring and Predictive Maintenance: Cost-Benefit Tradeoffs 61 3.9 Conclusion 62 4 Advanced Maintenance Techniques 65 Davinder Singh and Talwinder Singh 4.1 Introduction 65 4.2 Traditional Maintenance Techniques 67 4.3 Advanced Maintenance Techniques 78 4.4 Conclusions 83 5 Unveiling the Future: Residual Life Prediction for Enhanced Asset Management 87 Maninder Singh, Mukhtiar Singh, Jasvinder Singh, Mandeep Singh and Harjit Singh 5.1 Introduction 88 5.2 Residual Life Prediction Techniques 92 5.3 Applications of Residual Life Prediction 96 5.4 Conclusion 97 6 Analysis of Vibration 101 Rajdeep Singh and Chandan Deep Singh 6.1 Introduction 101 6.2 What is Vibration Analysis? 101 6.3 Vibration Analysis Methodology 102 6.4 Categories of Vibration Measurement 106 6.5 Vibration Analysis: Measurement Parameters 108 6.6 Vibration Analysis: Tools and Technology 110 6.7 Benefits of Continuous Vibration Monitoring 110 7 Modeling for Vibration 115 Rajdeep Singh and Chandan Deep Singh 7.1 Introduction 115 7.2 Modeling Techniques for Vibration Analysis 116 7.3 Conclusions 126 8 Impact of Condition-Based Maintenance (CBM) and Residual Life Prediction (RLP) on Environmental Issues 131 Jasvinder Singh, Chandan Deep Singh and Dharmpal Deepak 8.1 Introduction 132 8.2 Goals of Condition-Based Maintenance 134 8.3 Maintenance Strategies 135 8.4 Determination of CBM Failure Point 137 8.5 Decision-Making in Condition-Based Maintenance 140 8.6 Decision Models for CBM 141 8.7 Proportional Hazards Modeling 143 8.8 Maintenance Planning and Scheduling 144 8.9 Maintenance Concepts and Strategies 146 8.10 Condition-Based Maintenance (CBM) Technology Enablers 150 8.11 Survey of Recent Developments in CBM 154 8.12 Application Areas of CBM 157 8.13 Open Research Challenges 160 8.14 Residual Life Prediction 162 8.15 Impact of Environmental Policies on Maintenance 162 8.16 Conclusion 164 9 Sustainability Issues in Condition-Based Maintenance and Residual Life Prediction 171 Simranjit Singh Sidhu and Gurpreet Singh Sidhu 9.1 Introduction 172 9.2 Definition and Principles of CBM 174 9.3 Residual Life Prediction (RLP) 179 9.4 Synergies Between CBM and RLP 183 9.5 Conclusion and Recommendations 187 10 Role of CBM and RLP in the Performance of the Manufacturing Industry 191 Harpreet Sharma, Chandan Deep Singh and Kanwal Jit Singh 10.1 Introduction 192 10.2 What is Condition-Based Maintenance (CBM)? 194 10.3 Types of Condition-Based Maintenance 195 10.4 When to Use Condition-Based Maintenance 197 10.5 Steps to Take Before Implementing Condition-Based Maintenance 198 10.6 Challenges of Condition-Based Maintenance 199 10.7 Benefits of Condition-Based Maintenance 200 10.8 Role of Condition-Based Maintenance (CBM) on the Performance of the Manufacturing Industry 201 10.9 Residual Life Prediction 202 10.10 Role of Residual Life Prediction on the Performance of the Manufacturing Industry 203 10.11 Conclusion 205 11 Impact of Competencies on Condition-Based Maintenance and Residual Life Prediction 207 Rajdeep Singh, Chandan Deep Singh and Talwinder Singh 11.1 Introduction 207 11.2 Application Areas of CBM 212 11.3 Residual Life Prediction 214 11.4 Competency Framework 220 11.5 Conclusions 228 12 Sustainability Issues in CBM and RLP: Case Studies 233 Simranjit Singh Sidhu and Gurpreet Singh Sidhu 12.1 Medium Industry Case Study 234 12.2 Objectives of Implementing Maintenance Improvement Initiatives 235 12.3 Need for Maintenance 236 12.4 Phase-Wise Implementation of Maintenance Practices 237 12.5 Small Industry Case Study 252 12.6 Research Methodology 253 12.7 Steps to Improve the Weaknesses Identified Through SWOT Analysis 255 12.8 Appropriate Measures Implemented for the Hydraulic Bending Machine 259 12.9 Results and Discussion 262 12.10 Conclusions 267 References 268 Index 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.
