This comprehensive review focuses on the integration of intelligent technologies, such as the Internet of Things (IoT), Artificial intelligence (AI), and Nondestructive Testing (NDT), in the Structural Health Monitoring (SHM) field of civil engineering. The article discusses intelligent technologies in SHM for residential, commercial, industrial, historical, and special buildings, such as nuclear power plants (NPPs). With the incorporation of intelligent technologies, there have been remarkable advancements in SHM, a crucial aspect of infrastructure safety, reliability, and durability. The combination of SHM and intelligent technologies provides a cost-effective and efficient building monitoring approach, significantly contributing to energy and resource conservation. This article explores using electronic instruments, such as sensors, microcontrollers, and embedded systems, to measure displacement, force, strain, and temperature, which are crucial for detecting structural damage. Implementing intelligent technologies in SHM reduces the reliance on manual and hazardous inspection practices, simplifying and reducing the cost of building monitoring. The article highlights the social, economic, and environmental benefits of adopting intelligent technologies in SHM by presenting key findings from existing research. This review aims to increase the reader’s understanding of the significance of these technologies in enhancing the efficiency of SHM in civil engineering by illuminating their advancements and applications. 

Development of Intelligent Technologies in SHM on the Innovative Diagnosis in Civil Engineering—A Comprehensive Review

Framework Based on Machine Learning Approach for Prediction of the Remaining Useful Life: A Case Study of an Aviation Engine

Abstract This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods. 

Challenges on prognostics and health management for wind turbine components

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area. 

Comprehensive Review of Machine Learning Techniques for Condition-Based Maintenance

The reliability of rotating machinery is critical for continuous industrial operations in the context of Industry 4.0. The essential assets of industries are motors, pumps, fans, generators, and compressors, these equipment’s have enormous importance, since their failure may lead to significant production losses and decreased competitiveness. The need for machine health monitoring is very essential. This paper proposes a hybrid Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) predictive maintenance model for anomaly detection. The model is optimized using a bio-inspired algorithm Henry Gas Solubility Optimization (HGSO) to attain the optimal hyper parameters and accuracy. The proposed model uses ISO 10816 vibration diagnostic standards to evaluate machine severity based on velocity and vibration data. The results of the proposed optimized model provide 94% accuracy. The model helps maintenance operators to analyze health severity status and avoid unplanned machine shutdowns

Anomaly Detection in Rotary Equipment using hybrid model with HGSO Optimization

In the last decade, the adoption of technological tools in manufacturing industry, such as the use of the Internet of Things (IoT) and Machine Learning (ML), has led to the advent of the industry 4.0 (I4.0). In this scenario, intelligent devices can generate large volumes of data about industrial machinery and equipment that can be used to make maintenance more efficient. Prognostics and Health Management (PHM) is an emerging maintenance strategy that uses systems’ Condition Monitoring through IoT sensors installed on machinery to diagnose their faults or estimate their Remaining Useful Life (RUL). This study aims to conduct a Systematic Literature Review (SLR) on the use of ML techniques in the field of PHM of industrial mechanical systems and equipment. 50 studies resulted eligible for the above-mentioned SLR. Diagnostics and prognostics approach and the ML algorithm types used in the 50 analyzed papers have been analyzed together with the Key Performance Indicators (KPIs) used for their validation. From the analyses, it was found that Shallow Learning and Deep Learning (DL) algorithms are the most applied ones, while KPIs are used differently according to the type of task classification or regression. Moreover, results highlighted that many authors still use artificial datasets to test their algorithms, instead of datasets based on real data retrieved by their components. For the last type of datasets, this paper also introduces a schematic framework to standardize the step-by-step diagnostics and prognostics process carried out by the authors.

Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review

Industry 4.0 (I4.0) is an emerging paradigm that is affecting several industrial fields. Many innovations have been introduced to optimize and improve the production efficiency by means of new technologies. Additive manufacturing (AM) is an enabling technology of I4.0 that is leading to innovations related to the production systems, but companies are looking to AM also for maintenance purposes. The aim of this study is to find the existing innovations that link AM and maintenance and to discuss on their benefits and feasibility. A systematic literature review (SLR) approach has been used to this purpose. The outcome of the SLR highlights that there are three main fields in which AM technologies can be very useful for maintenance purposes. 

A Survey on the Integration between Additive Manufacturing and Maintenance

This paper deals with the tricky issue of forecasting the number of daily orders received by a delivery company that operates through the internet. The research tries to address the problem through the Multilayer Perceptron Neural Network (MLP). The more important step of the methodology is the identification and characterization of the features to adopt as inputs for the MLP in the cited case. The number of visits (NVs) to the company website, months, days of the week and the public holidays are the four features used to predict the number of received orders (NOs). The Levenberg Marquardt back-propagation algorithm was used to train the model. The proposed methodology was applied by a delivery company, which operates in Italy, to forecast the daily demand. The results showed a good accuracy of the MLP in predicting the NOs, with a Root Mean Squared Error of the 20% from the actual NOs. 

Demand forecasting for delivery platforms by using neural network

Abstract This paper presents a novel reliability-based parametric methodology for quantifying the reliability of cost estimates for new composite aircraft components. In recent years, the aircraft production sector has increased its attention on optimizing their composite manufacturing operations. It has become clear that a key factor in the success of these operations is the consideration that not only technical factors, but also economic ones as well are relevant. Composite manufacturing variability is greatly influenced by many of these factors, and since manufacturing with composites is significantly more difficult than with more traditional materials, there are many sources of uncertainty that could influence the reliability of manufacturing cost estimates for new composite aircraft components. Therefore, it is worth considering these sources of uncertainty during the cost estimation process and to quantify the reliability of the cost estimates. To demonstrate the proposed methodology, a numerical example featuring a real-life composite aircraft component from a Boeing 787, with real-life data, is presented. Results show that the proposed methodology can quantify the uncertainty associated with cost estimates for new composite aircraft components in an effective manner, thereby supporting engineers in optimising the cost of their designs, helping them avoid errors in budget definition, and enabling them to allocate resources more efficiently. 

/pdf/a-cost-estimation-approach-for-aircraft-design-enhancement-1fmgpvlj.pdf

A cost estimation approach for aircraft design enhancement

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Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review

A Survey on the Integration between Additive Manufacturing and Maintenance

Demand forecasting for delivery platforms by using neural network

A cost estimation approach for aircraft design enhancement