Impact characterization on thin structures using machine learning approaches

Machine learning algorithms are trained and compared to identify and to characterise the impact on typical aerospace panels of different geometry. Experimental activities are conducted to build a proper impacts’ dataset. Polynomial regression algorithm and artificial neural network are applied and optimised to panels without stringer to test their capability to identify the impacts. Subsequently, the algorithms are applied to panels reinforced with stringers that represent a significant increase of complexity in terms of dynamic features of the system to test: the focus is not only on the impact position's detection but also on the event's severity. After the identification of the best algorithm, the corresponding machine learning model is deployed on an ARM processor mini-computer, implementing an impact detection system, able to be installed on board an aerial vehicle, making it a smart aircraft equipped with an artificial intelligence decision-making system

Impact characterization on RC airplane model in operation using machine learning

Structural Health Monitoring represents a growing field of great interest for aerospace engineering. This manuscript proposes an on-working SHM method for impact detection on RC airplane by ultrasounds, that is based on Machine Learning algorithms (polynomial regression and neural networks) and is useful to establish critical and dangerous operational conditions. The proposed method can be used to detect impact events both in metallic or composite structures, it is specifically designed to be used on typical fuselage and wing panels and is based on the propagation of Lamb waves in the structure on which PZT sensors are bonded for receiving signals. Algorithms are implemented in order to evaluate the impact location by post-processing the acquired signals. Several test cases are numerically studied before being tested in laboratory and reproduced on-working conditions. A good agreement between the numerical, laboratory and in-flight results is achieved

Model-Driven approach to Cyber Risk Analysis in Industry 4.0

The increasing adoption, in critical infrastructures and industrial automation, of physical control systems based on interconnected networks has led to a growing and previously unforeseen threat to information security for supervisory control and data acquisition (SCADA) and control systems distributed (DCS). It is essential that engineers and managers understand these problems and know the consequences of remote hacking. In the contest of Industrial Process are very commonly used risk assessment methods such as HHM, IIM, and RFRM that have been successfully applied to SCADA systems with many interdependencies and have highlighted the need for quantifiable metrics and the probability risk analysis (PRA) which includes methods such as FTA, ETA and FEMA and HAZOP. The goal of these methods is, in general, to determine the impact of a problem on the process plant and the risk reduction associated with a particular countermeasure. This document provides a methodology named CRiSP - Cyber Risk Analysis in Industrial Process System Environment. CRiSP tries to define a structured approach needed to analyze the consequence of an undesired remote manipulation. CRiSP allow to analyze the risk related to the manipulation of a single element of the plant and to analyze the consequence restricted to a portion of the plant. CRiSP helps to have a broad overview of cybersecurity and risk and to adopt the necessary countermeasure

Machine Learning regression models diagnosis for structural health monitoring

This work aims at determining the location of low speed impact events on thin aluminium panels, specifically designed to be used on typical aircraft fuselage and wing panels, by processing the acoustic emission signals. The detection principle is based on the propagation of the first antisymmetric lamb wave (A0 mode) in the panel on which four PZT sensors are bonded to receive the signals. The impact location is assessed with the use of a supervised machine learning algorithm that is based on linear regression, appropriately designated to post-process the acquired signals. Some experimental cases are reported in order to investigate the optimal kind and amount of training data to improve the performance of the algorithm and therefore the accuracy of the impact location estimation