Intelligent approaches for anomaly detection in compressed air systems : a systematic review

Inefficiencies within compressed air systems (CASs) call for the integration of Industry 4.0 technologies for financially viable and sustainable operations. A systematic literature review of intelligent approaches within CASs was carried out, in which the research methodology was based on the PRISMA guidelines. The search was carried out on 1 November 2022 within two databases: Scopus and Web of Science. The research methodology resulted in 37 papers eligible for a qualitative and bibliometric analysis based on a set of research questions. These aimed to identify specific characteristics of the selected publications. Thus, the review performed a comprehensive analysis on mathematical approaches, multiple machine learning (ML) methods, the implementation of neural networks (NNs), the development of time-series techniques, comparative analysis, and hybrid techniques. This systematic literature review allowed the comparison of these approaches, while widening the perspective on how such methods can be implemented within CASs for a more intelligent approach. Any limitations or challenges faced were mitigated through an unbiased procedure of involving multiple databases, search terms, and researchers. Therefore, this systematic review resulted in discussions and implications for the definition of future implementations of intelligent approaches that could result in sustainable CASs.peer-reviewe

The use of mechanical redundancy for fault detection in non-stationary machinery

The classical approach to machinery fault detection is one where a machinery’s condition is constantly compared to an established baseline with deviations indicating the occurrence of a fault. With the absence of a well-established baseline, fault detection for variable duty machinery requires the use of complex machine learning and signal processing tools. These tools require extensive data collection and expert knowledge which limits their use for industrial applications. The thesis at hand investigates the problem of fault detection for a specific class of variable duty machinery; parallel machines with simultaneously loaded subsystems. As an industrial case study, the parallel drive stations of a novel material haulage system have been instrumented to confirm the mechanical response similarity between simultaneously loaded machines. Using a table-top fault simulator, a preliminary statistical algorithm was then developed for fault detection in bearings under non-stationary operation. Unlike other state of the art fault detection techniques used in monitoring variable duty machinery, the proposed algorithm avoided the need for complex machine learning tools and required no previous training. The limitations of the initial experimental setup necessitated the development of a new machinery fault simulator to expand the investigation to include transmission systems. The design, manufacturing and setup of the various subsystems within the new simulator are covered in this manuscript including the mechanical, hydraulic and control subsystems. To ensure that the new simulator has successfully met its design objectives, extensive data collection and analysis has been completed and is presented in this thesis. The results confirmed that the developed machine truly represents the operation of a simultaneously loaded machine and as such would serve as a research tool for investigating the application of classical fault detection techniques to parallel machines in non-stationary operation.Master's These

A conceptual design of a propulsion system for an autonomous underwater vehicle

The need for developing propulsion systems to support missions of increased endurance for autonomous underwater vehicles is investigated and a conceptual system is proposed, based on currently available technology and desired system characteristics. The investigation evaluates and ranks alternative energy sources and proposes the use of a closed Brayton cycle gas turbine power plant using a chemical energy heat source with a metallic fuel. A thruster system using electric propulsion motors and screw propellers is selected. Evaluation factors include reliability, depth independent operation, weight, endurance, quietness and efficiency. Reliability of the proposed system is analyzed and the design modified to meet proposed reliability requirements. A knowledge-based system is developed to manage the operation of the propulsion plant in an autonomous manner. A simulation system is developed using Common Lisp and the operation of the propulsion plant and its knowledge-based management system are evaluated using the simulator

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Operational modal analysis and prediction of remaining useful life for rotating machinery

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe significance of rotating machinery spans areas from household items to vital industry sectors, such as aerospace, automotive, railway, sea transport, resource extraction, and manufacturing. Hence, our technologised society depends on efficient and reliable operation of rotating machinery. To contribute to this aim, this thesis leverages measurable quantities during its operation for structural-mechanical evaluation employing Operational Modal Analysis (OMA) and the prediction of Remaining Useful Life (RUL). Modal parameters determined by OMA are central for the design, test, and validation of rotating machinery. This thesis introduces the first open parametric simulation dataset of rotating machinery during an acceleration run. As there is a lack of similar open datasets suitable for OMA, it lays a foundation for improved reproducibility and comparability of future research. Based on this, the Averaged Order-Based Modal Analysis (AOBMA) method is developed. The novel addition of scaling and weighted averaging of individual machine orders in AOBMA alleviates the analysis effort of the existing Order-Based Modal Analysis (OBMA) method by providing a unified set of modal parameters with higher accuracy. As such, AOBMA showed a lower mean absolute relative error of 0.03 for damping ratio estimations across compared modes while OBMA provided an error value of 0.32 depending on the processed order. At excitation with high harmonic contributions, AOBMA also resulted in the highest number of accurately identified modes among the compared methods. At a harmonic ratio of 0.8, for example, AOBMA identified an average of 11.9 modes per estimation, while OBMA and baseline OMA followed with 9.5 and 9 modes, respectively. Moreover, it is the first study, which systematically evaluates the impact of excitation conditions on the compared methods and finds an advantage of OBMA and AOBMA over traditional OMA regarding mode shape estimation accuracy. While OMA can be used to evaluate significant structural changes, Machine Learning (ML) methods have seen substantially greater success in condition monitoring, including RUL prediction. However, as these methods often require large amounts of time and cost- intensive training data, a novel data-efficient RUL prediction methodology is introduced, taking advantage of distinct healthy and faulty condition data. When the number of training sequences from an open dataset is reduced to 5%, an average prediction Root Mean Square Error (RMSE) of 24.9 operation cycles is achieved, outperforming the baseline method with an RMSE of 28.1. Motivated by environmental considerations, the impact of data reduction on the training duration of several method variants is quantified. When the full training set is utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method

A literature review of Artificial Intelligence applications in railway systems

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Publications of the Jet Propulsion Laboratory, 1977

This bibliography cites 900 externally distributed technical reports released during calendar year 1977, that resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. Report topics cover 81 subject areas related in some way to the various NASA programs. The publications are indexed by: (1) author, (2) subject, and (3) publication type and number. A descriptive entry appears under the name of each author of each publication; an abstract is included with the entry for the primary (first-listed) author