A Framework for Rapid Prototyping of PHM Analytics for Complex Systems using a Supervised Data-Driven Approach
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Abstract
Prognostic and Health Management (PHM) solutions are becoming increasingly popular in industries that rely on large
systems such as aircraft, spacecraft, and power plants. PHM analytic solutions are designed to monitor the health of each
subsystem and component and apply predictive analytic to improve system reliability and safety, reduce the cost and decrease time spent on unscheduled maintenance. However, identifying correlations between different components and
associated monitors in these large systems can be challenging. To address this issue and achieve maximum utilization of available monitoring signals, a methodology is required that can identify correlations between degraded or failed components and the features engineered from the monitors and sensors. This paper introduces a framework that enables rapid prototyping of analytics, allowing users to seamlessly move from designing and discovering features to developing models for a specific event or component of interest. The framework has three main components: feature exploration, data
preparation, and model development. Feature exploration focuses on feature engineering using raw monitor data from all available monitors. Data preparation purges the data, and down-selects relevant features based on correlation defined in the feature exploration part. The data preparation step also creates a training dataset. Model development enables
quick testing and comparison of multiple supervised Machine Learning (ML) models. To demonstrate the framework, this paper presents an example of a remaining useful life model for an aircraft component. While the examples and simulations are aircraft-focused, the principles behind the framework can be applied to other large systems.
How to Cite
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