Health Index Framework for Condition Monitoring and Health Prediction

Abstract

In the field of Maintenance, Repair and Overhaul (MRO), stakeholders such as operators or service providers have to keep track of the health status of fleets of complex systems. The ability to estimate the future health status of these systems and their components becomes more pivotal when seeking to efficiently operate and maintain these systems. Today, these stakeholders have access to a lot of different data sources regarding fleet, operation schedule, ambient condition, system and component information. Many different prognostic methods from different disciplines are available and will further improve henceforward. In many cases these data sources and methods function as isolated methods in their own field. This fragmentation makes a holistic prognosis very challenging in many cases. Therefore, stakeholders need information integrating methods and tools to gain an exhaustive insight into the health status development of the complex assets they are operating or maintaining, in order to make well-founded decisions regarding operation or maintenance planning. In this paper, a Python-based health index framework is presented. It enables users to integrate operation schedules of different detail levels with enriching data sources such as ambient condition data. Furthermore, it provides methods to design complex asset systems which are linked via their construction, function or degradation mechanisms/ health indices via transfer relations. It allows to monitor the asset’s condition based on operation data and to simulate different operation scenarios regarding the health index development.

Platform health management for aircraft maintenance – a review

“…83 In this way, from Figure 3, the health of components will be propagated to the subsystem level, the system level, and then to the platform. Health results reflect the health state of components and are typically referred to as health index as it was used in Kamtsiuris 12 for developing a health index framework, Khan et al 79 for health index behaviour based on vehicle level reasoning, and Yin et al 103 for constructing a health index for aircraft ECS. The potential to apply AI in a platform solution can be seen in how it has, for example, through the application of Physics-Informed Neural Networks (PINNs); which belong to a group of neural networks that, in both their design and training, take into account physical principles and limitations, been applied in areas like modelling cumulative damage (fatigue crack) in airplanes 104 and machine degradation assessment.…”

“…The methods in these approaches can be applied to Figure 3 to aggregate the health results from the component, subsystem, and system levels to the platform. Kamtsiuris et al 12 tackle health estimation of physical assets by employing an inheritance mechanism. The authors describe a system as a set of subsystems, components, or parts that are connected structurally and functionally in a hierarchy.…”

“…81 This presents various questions to be answered in order to provide operational decisions, as shown in Figure 3. In this vein, it is important to lay down the dependencies that exist among the components, subsystems, and systems as it was applied in damage propagation modelling for aircraft engine by Abhinav, 82 and Roemer, 83 and Roemer 12 in developing a hierarchical reasoning structure for aerospace IVHM, where distinctions between independent relationships, serial dependencies, and parallel dependencies were made uses a parent–child approach to establish this relationship for a health index framework for condition monitoring. From a platform view, answering the questions at the base of the pyramid in Figure 3 help to answer the one at the apex, when the dependencies that exist among them are established.

Figure 3.Health management pyramid.

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“…10 Implementing IVHM is usually supplemented with AI due to AI's ability to create methodologies that utilize the decision-making capabilities of AI techniques, such as deep learning (DL) and machine learning (ML), to develop fault diagnostics and prognostics systems. 11 To provide decision support to both maintainers and operators, at the platform level of health management, the health of components can be ascertained and propagated through its corresponding subsystems and systems, 12 while providing health information at every level.…”