R. Li
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8 records found
1
Aircraft maintenance has been further developed with predictive maintenance instead of solely condition-based maintenance. Prognostics and health management (PHM) with advanced technologies can utilize real-time and historical health state information to provide actionable information, enabling predictive maintenance decision-making. In this case, the methodology of how to design the PHM systems is an issue to be faced. The state of the art has provided several conceptual design methodologies and associated methods to support the conceptual requirements development of PHM systems. However, there is no rigorous process available for requirements definition. Existing options for requirements derivation are lacking details, which restricting PHM system design and development. This constitutes a major drawback and hurdle towards the successful design of PHM systems in practice. This paper consequently proposes a methodology for the systematic derivation of system requirements towards PHM system development. Besides, this methodology defines detailed processes for requirements definition, and positions mean through which various categories of requirements can be derived through appropriate analyses in detail. Sequences of interoperability requirements categories and associated flow-down perspectives are identified. To evaluate the applicability, this paper undertakes the case study of requirements definition for a generic PHM system, which provides a comprehensive application of the methodology. Designers can perform requirements definition under this methodology as guidance towards the design of a successful PHM system, providing solutions for predicting remaining useful life (RUL) to support aircraft predictive maintenance.
Aviation systems are characterized by the synergic interaction between their components from different technological domains. These interactions enable the system to achieve more functionalities than the sum of the functionalities of its components considered independently. Recently, Model-Based Systems Engineering (MBSE) is an interdisciplinary approach for handling complexity during product development. However, the generic methodology guides the engineers toward a specific model-based system design is lacking. Besides, it still calls for efforts on investing in a specific case study. To overcomes the gaps, this paper proposes a generic model-based system engineering methodology to conduct the mission and system-specific design for aviation systems development.
Stakeholder-oriented systematic design methodology for prognostic and health management system
Stakeholder expectation definition
Prognostic and health management (PHM) describes a set of capabilities that enable to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL), leading to the effective and efficient maintenance and operation of assets such as aircraft. Prior research has considered the methodological factors of PHM system design, but typically, only one or a few aspects are addressed. For example, several studies address system engineering (SE) principles for application towards PHM design methodology, and a concept of requirements from a theoretical standpoint, while other papers present requirement specification and flow-down approaches for PHM systems. However, the state of the art lacks a systematic methodology that formulates all aspects of designing and comprehensively engineering a PHM system. Meanwhile, the process and specific implementation of capturing stakeholders’ expectations and requirements are usually lacking details. To overcome these drawbacks, this paper proposes a stakeholder-oriented design methodology for developing a PHM system from a systems engineering perspective, contributing to a consistent and reusable representation of the design. Further, it emphasizes the process and deployment of stakeholder expectations definition in detail, involving the steps of identifying stakeholders, capture their expectations/requirements, and stakeholder and requirement analysis. Two case studies illustrate the applicability of the proposed methodology. The proposed stakeholder-oriented design methodology enables the integration of the bespoke main tasks to design a PHM system, in which sufficient stakeholder involvement and consideration of their interests can lead to more precise and better design information. Moreover, the methodology comprehensively covers the aspects of traceability, consistency, and reusability to capture and define stakeholders and their expectations for a successful design.
Prognostic and Health Management (PHM) systems support aircraft maintenance through the provision of diagnostic and prognostic capabilities, leveraging the increased availability of sensor data on modern aircraft. Diagnostics provide the functionalities of failure detection and isolation, whereas prognostics can predict the remaining useful life (RUL) of the system. In literature, PHM technologies have been studied from different perspectives, covering various aims such as improving aircraft system reliability, availability, safety and reducing the maintenance cost. From a design perspective, several conceptual formulations of design methodologies are available, enabling a set of PHM system architectures based on different frameworks and the derivation of system requirements. However, a systematic methodology towards a consistent definition of PHM architectures has not been well established. The characteristics of architectures have not been dealt with in depth. To address these gaps, this paper presents a systematic methodology for PHM architecture definition to ensure a more complete and consistent design during the development phase of the product lifecycle. Moreover, a generic PHM architecture in accordance with this systematic methodology is proposed in this article. A case study is conducted to verify and validate the architecture, ensuring it meets the requirements for a correct and complete representation of PHM characteristics.
A comparative study of Data-driven Prognostic Approaches
Stochastic and Statistical Models