A New Risk Management Methodology for Infrastructure Based on Real-Time Monitoring and Dynamic Interventions: An Example Application on an Air Handling Unit
Francesco Rota,Cinzia Talamo,Giancarlo Paganin,Claudio Martani +3 more
- Vol. 1, Iss: 2, pp 132-153
TLDR
A new dynamic risk management methodology is proposed to consistently model the service, estimate the risk, first statically, using fault tree analysis, and then dynamically, using sensing technologies for data gathering and data-driven models for dynamic probability estimate, and finally implement the required intervention measures to minimize the risk.Abstract:
For an effective risk management of complex buildings it is required to dynamically estimate the risk on the service and take proper responsive measures to contrast it. This implies being able to estimate the evolving probabilities of failures over time and the way their occurrence is trust in affecting the service. This is now possible thanks to the advent of new sensing technologies and data-driven models to estimate failure probabilities, as well as solid risk management methodologies to estimate their effect on the service. However, it needs to be considered that the implementation of a dynamic risk management in standard building operation has to consider the reconfiguration of some processes to include the use of enabling technologies. In this paper a new dynamic risk management methodology is proposed to consistently (i) model the service, estimate the risk, first (ii) statically, using fault tree analysis, and then (iii) dynamically, using sensing technologies for data gathering and data-driven models for dynamic probability estimate, and finally (iv) implement the required intervention measures to minimize the risk. Then an application of the methodology is presented, for the risk management of an air handling unit, using a convolutional neural network, and its outcomes discussed. Conclusions are also drawn on the implications of integrating such a methodology in the current whole building risk management process and several outlooks are proposed.read more
References
More filters
Journal ArticleDOI
Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Journal ArticleDOI
Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types
TL;DR: A framework for quasi real-time damage detection on video using the trained networks is developed and the robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures.
Journal ArticleDOI
Modal identification of simple structures with high-speed video using motion magnification
TL;DR: In this article, motion magnification has been developed for visualizing exaggerated versions of small displacements with an extension of the methodology to obtain the optical flow to measure displacements, which can be extended to modal identification in structures and the measurement of structural vibrations.
Journal ArticleDOI
Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network
Jaouher Ben Ali,Jaouher Ben Ali,Brigitte Chebel-Morello,Lotfi Saidi,Simon Malinowski,Farhat Fnaiech +5 more
TL;DR: In this article, a combination of simplified fuzzy adaptive Resonance theory map (SFAM) neural network and Weibull distribution (WD) is explored to predict the remaining useful life (RUL) of rolling element bearings.
Journal ArticleDOI
An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings
TL;DR: A self-evolving maintenance scheduler framework for maintenance management of wind turbines is introduced and an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed.
Related Papers (5)
Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations
Software effort and risk assessment using decision table trained by neural networks
S. Abbinaya,M. Senthil Kumar +1 more