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"Service life (Engineering)"
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Handbook of structural life assessment
This important, self-contained reference deals with structural life assessment (SLA) and structural health monitoring (SHM) in a combined form. SLA periodically evaluates the state and condition of a structural system and provides recommendations for possible maintenance actions or the end of structural service life. It is a diversified field and relies on the theories of fracture mechanics, fatigue damage process, and reliability theory. For common structures, their life assessment is not only governed by the theory of fracture mechanics and fatigue damage process, but by other factors such as corrosion, grounding, and sudden collision. On the other hand, SHM deals with the detection, prediction, and location of crack development online. Both SLA and SHM are combined in a unified and coherent treatment.
eBook
Research Agenda for Test Methods and Models to Simulate the Accelerated Aging of Infrastructure Materials
In the next several decades, a significant percentage of the country's transportation, communications, environmental, and power system infrastructures, as well as public buildings and facilities, will have to be renewed or replaced. Next-generation infrastructure will have to meet very high expectations in terms of durability, constructability, performance, and life-cycle cost. One way of meeting future expectations will be through improved, high-performance materials, but before new materials can be confidently deployed in the field, a thorough and comprehensive understanding must be developed of their long-term performance in a variety of applications and physical environments. The National Science Foundation (NSF) has launched an initiative to promote the development of innovative short-term laboratory or in-situ tests for making accurate, reliable predictions of the long-term performance of materials and requested that the National Research Council (NRC) conduct a workshop as a reconnaissance-level assessment of models and methods that are being used, or potentially could be used, to determine the long-term performance of infrastructure materials and components.
eBook
Dealing with Aging Process Facilities and Infrastructure
This book explores the many ways in which process facilities, equipment, and infrastructure might deteriorate upon continuous exposure to operating and climatic conditions. It covers the functional and physical failure modes for various categories of equipment and discusses the many warning signs of deterioration. This book also explains how to deal with equipment that may not be safe to operate. The book describes a risk-based strategy in which plant leaders and supervisors can make more informed decisions on aging situations and then communicate them to upper management effectively. Additionally, it discusses the dismantling and safe removal of facilities that are approaching their intended lifecycle or have passed it altogether.
eBook
An Optimized YOLOv11 Framework for the Efficient Multi-Category Defect Detection of Concrete Surface
Thoroughly and accurately identifying various defects on concrete surfaces is crucial to ensure structural safety and prolong service life. However, in actual engineering inspections, the varying shapes and complexities of concrete structural defects challenge the insufficient robustness and generalization of mainstream models, often leading to misdetections and under-detections, which ultimately jeopardize structural safety. To overcome the disadvantages above, an efficient concrete defect detection model called YOLOv11-EMC (efficient multi-category concrete defect detection) is proposed. Firstly, ordinary convolution is substituted with a modified deformable convolution to efficiently extract irregular defect features, and the model’s robustness and generalization are significantly enhanced. Then, the C3k2module is integrated with a revised dynamic convolution module, which reduces unnecessary computations while enhancing flexibility and feature representation. Experiments show that, compared with Yolov11, Yolov11-EMC has improved precision, recall, mAP50, and F1 by 8.3%, 2.1%, 4.3%, and 3% respectively. Results of drone field tests show that Yolov11-EMC successfully lowers false and under-detections while simultaneously increasing detection accuracy, providing a superior methodology to tasks that require identifying tangible flaws in practical engineering applications.
Journal Article
Service science
Features coverage of the service systems lifecycle, including service marketing, engineering, delivery, quality control, management, and sustainment Featuring an innovative and holistic approach, Service Science: The Foundations of Service Engineering and Management provides a new perspective of service research and practice. The book presents a practical approach to the service systems lifecycle framework, which aids in understanding and capturing market trends; analyzing the design and engineering of service products and delivery networks; executing service operations; and controlling and managing the service lifecycles for competitive advantage. Utilizing a combined theoretical and practical approach to discuss service science, Service Science: The Foundations of Service Engineering and Management also features: Case studies to illustrate how the presented theories and design principles are applied in practice to the definitions of fundamental service laws, including service interaction and socio-technical natures Computational thinking and system modeling such as abstraction, digitalization, holistic perspectives, and analytics Plentiful examples of service organizations such as automobile after-sale services, global project management networks, and express delivery services An interdisciplinary emphasis that includes integrated approaches from the fields of mathematics, engineering, industrial engineering, business, operations research, and management science A detailed analysis of the key concepts and body of knowledge for readers to master the foundations of service management Service Science: The Foundations of Service Engineering and Management is an ideal reference for practitioners in the contemporary service engineering and management field as well as researchers in applied mathematics, statistics, business/management science, operations research, industrial engineering, and economics. The book is also appropriate as a text for upper-undergraduate and graduate-level courses in industrial engineering, operations research, and management science as well as MBA students studying service management.
eBook
A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery
This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.
Journal Article
A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification
Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features. This limitation hinders their deployment on resource-constrained edge devices. To address this issue, we propose TBiGNet, a lightweight Transformer-based classification network model for RUL prediction. TBiGNet features an encoder–decoder architecture that outperforms traditional Transformer models by achieving over 15% higher accuracy while reducing computational load, memory access, and parameter size by more than 98%. In the encoder, we optimize the attention mechanism by integrating the individual linear mappings of queries, keys, and values into an efficient operation, reducing memory access overhead by 60%. Additionally, an adaptive feature pruning module is introduced to dynamically select critical features based on their importance, reducing redundancy and enhancing model accuracy by 6%. The decoder innovatively fuses two different types of features and leverages BiGRU to compensate for the limitations of the attention mechanism in capturing degradation features, resulting in a 7% accuracy improvement. Extensive experiments on the C-MAPSS dataset demonstrate that TBiGNet surpasses existing methods in terms of computational accuracy, model size, and memory access, showcasing significant technical advantages and application potential. Experiments on the C-MPASS dataset show that TBiGNet is superior to the existing methods in terms of calculation accuracy, model size and throughput, showing significant technical advantages and application potential.
Journal Article
An Augmented Reality-Assisted Prognostics and Health Management System Based on Deep Learning for IoT-Enabled Manufacturing
With increasingly advanced Internet of Things (IoT) technology, the composition of workshop equipment has become more and more complex. Based on this, the rate of system performance degradation and the probability of fault have both increased. Owing to this, not only has the difficulty of constructing the remaining useful life (RUL) model increased but also the improvement in speed of maintenance personnel cannot keep up with the speed of equipment replacement. Therefore, an augmented reality (AR)-assisted prognostics and health management system based on deep learning for IoT-enabled manufacturing is proposed in this paper. Firstly, the feature extraction model based on Convolutional Neural Network-Particle Swarm Optimization (PSO-CNN) is proposed with the purpose of excavating the internal associations in large amounts of production data. Based on this, the high-accuracy RUL prediction is accomplished by Gate Recurrent Unit (GRU)-attention, which can capture the long-term and short-term dependencies of time series and successfully solve the gradient disappearance problem of RNN. Moreover, more attention will be paid to important content with the help of the attention mechanism. Additionally, high-efficiency maintenance guidance and visible instructions can be accomplished by AR. On top of this, the remote expert can offer help when maintenance personnel encounters tough problems. Finally, a real case was implemented in a typical IoT-enabled workshop, which validated the effectiveness of the proposed approach.
Journal Article
Life cycle assessment in the built environment
Life cycle assessment enables the identification of a broad range of potential environmental impacts occurring across the entire life of a product, from its design through to its eventual disposal or reuse. The need for life cycle assessment to inform environmental design within the built environment is critical, due to the complex range of materials and processes required to construct and manage our buildings and infrastructure systems.
After outlining the framework for life cycle assessment, this book uses a range of case studies to demonstrate the innovative input-output-based hybrid approach for compiling a life cycle inventory. This approach enables a comprehensive analysis of a broad range of resource requirements and environmental outputs so that the potential environmental impacts of a building or infrastructure system can be ascertained. These case studies cover a range of elements that are part of the built environment, including a residential building, a commercial office building and a wind turbine, as well as individual building components such as a residential-scale photovoltaic system.
Comprehensively introducing and demonstrating the uses and benefits of life cycle assessment for built environment projects, this book will show you how to assess the environmental performance of your clients' projects, to compare design options across their entire life and to identify opportunities for improving environmental performance.
eBook