Attention is paid to overcoming these challenges, with future opportunities being enumerated. However, there are a number of limitations that hinder its widespread adoption and require further development. Based on the reviewed work, deep learning demonstrates plausible benefits for fault diagnosis and prognostics. Various architectures and related theories are discussed to clarify its potential. This article presents a systematic review of artificial intelligence based system health management with an emphasis on recent trends of deep learning within the field. It is an evolving research area with diverse application domains and hence its use for system health management applications must been researched if it can be used to increase overall system resilience or potential cost benefits for maintenance, repair, and overhaul activities. Deep learning has gained increasing attention due to its potential advantages with data classification and feature extraction problems. However, this integration inevitably faces several difficulties and challenges for the community indicating the need for novel approaches to address this vexing issue. Once trained, the logic for data processing can be embedded on on-board controllers whilst enabling real-time health assessment and analysis. By utilising condition data and on-site feedback, data models can be trained using machine learning and statistical concepts. ![]() This is because it can be used to detect anomalies, analyse failures and predict the future state based on up-to-date information. Given the advancements in modern technological capabilities, having an integrated health management and diagnostic strategy becomes an important part of a system's operational life-cycle. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio-temporal propagation of traffic congestion). Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. The most common definition of a fuzzy rule base R is the disjunctive interpretation initially proposed by Mamdani and found in most Fuzzy Controller applications (Mamdani and Assilian, 1975). This reasoning mechanism, with its interpolation properties, gives FL a robustness with respect to variations in the system's parameters, disturbances, etc., which is one of FL's main characteristics. These constraints are propagated by fuzzy inference operations, based on the generalized modus-ponens. The meaning of a linguistic variable may be interpreted as an elastic constraint on its value. These variables take fuzzy values that are characterized by a label (a sentence generated from the syntax) and a meaning (a membership function determined by a local semantic procedure). ![]() In particular, FL allows us to use linguistic variables to model dynamic systems. Fuzzy Logic Systems Fuzzy logic (FL) gives us a language, with syntax and local semantics, within which we can translate qualitative knowledge about the problem to be solved (Zadeh 1978 Ruspini et al., 1998). Additional information about AANN’s can be found in references (Kramer 1991 Kramer 1992 Mattern et al., 1998 Lerner et al., 1999 Berenji et al., 2004). Architecture of a 7-5-3-5-7 Auto Associative Neural Network In reference (Hu et al., 2007), we used AANN’s to estimate sensor measurement under normal conditions and then the residual between raw measurement and normal measurement were used to infer the conditions of the components and systems.
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