Abstract
Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.
Highlights
Structural health monitoring (SHM) is a discipline that makes use of sensors permanently attached to a structure together with different software analysis developments in order to detect damage and assess the proper performance of structures
Compared with the works previously reviewed, the methodology described on the current work presents a new point of view, since this uses an artificial immune system (AIS) and some damage indices to define feature vectors which represents the structure under different conditions by allowing the fact that the damage detection process can be understood as a pattern recognition approach
The damage detection is based on the affinity values between the elements in the memory cell set of the healthy state (MCSH), acting as antibodies, and the data coming from the structure to test (TD, test data), acting as antigens
Summary
Structural health monitoring (SHM) is a discipline that makes use of sensors permanently attached to a structure together with different software analysis developments in order to detect damage and assess the proper performance of structures. In 2008, da Silva et al [3] developed a strategy to perform structural health monitoring This strategy included three different phases as follows: (i) the use of principal component analysis to reduce the dimensionality of the time series data; (ii) the design of an autoregressive-moving-average (ARMA) model using data from the healthy structure under several environmental and operational conditions; and (iii) the identification of the state of the structure through a fuzzy clustering approach. Compared with the works previously reviewed, the methodology described on the current work presents a new point of view, since this uses an artificial immune system (AIS) and some damage indices to define feature vectors which represents the structure under different conditions by allowing the fact that the damage detection process can be understood as a pattern recognition approach.
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