A review on artificial intelligence in high-speed rail

High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided

Maintenance optimization in industry 4.0

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience

Predictive maintenance using digital twins: A systematic literature review

Context: Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review. Objective: This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research. Method: A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed. Results: This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry. Conclusion: This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models. 2022Scopus2-s2.0-8513459995

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

State-of-the-art in integrated vehicle health management

Integrated vehicle health management (IVHM) is a collection of data relevant to the present and future performance of a vehicle system and its transformation into information can be used to support operational decisions. This design and operation concept embraces an integration of sensors, communication technologies, and artificial intelligence to provide vehicle-wide abilities to diagnose problems and recommend solutions. This article aims to report the state-of-the-art of IVHM research by presenting a systematic review of the literature. The literature from different sources is collated and analysed, and the major emerging themes are presented. On this basis, the article describes the IVHM concept and its evolution, discusses configurations and existing applications along with main drivers, potential benefits and barriers to adoption, summarizes design guidelines and available methods, and identifies future research challenges

Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities