Risk-Based Prognostics and Health Management
It is often the case that risk assessment and prognostics are viewed as related but separate tasks. This chapter describes a risk-based approach to prognostics that seeks to provide a tighter coupling between risk assessment and fault prediction. We show how this can be achieved using the continuous-time Bayesian network as the underlying modeling framework. Furthermore, we provide an overview of the techniques that are available to derive these models from data and show how they might be used in practice to achieve tasks like decision support and performance-based logistics. This work is intended to provide an overview of the recent developments related to risk-based prognostics, and we hope that it will serve as a tutorial of sorts that will assist others in adopting these techniques.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Quantum Machine Learning for Health State Diagnosis and Prognostics
Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laborato…
BIG-bench Machine LearningManagementQuantum Machine LearningA deep adversarial approach based on multi-sensor fusion for remaining useful life prognostics
Multi-sensor systems are proliferating the asset management industry and by proxy, the structural health management community. Asset managers are beginning to require a prognostics and health management system to predict…
Asset ManagementManagementSensor FusionVariational InferenceUncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. …
Decision MakingManagementregressionUncertainty QuantificationBig Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and…
ManagementKnowledge-Aware Modeling with Frequency Adaptive Learning for Battery Health Prognostics
Battery health prognostics are critical for ensuring safety, efficiency, and sustainability in modern energy systems. However, it has been challenging to achieve accurate and robust prognostics due to complex battery deg…