{'fraction_non_alphanumeric': 0.013641755634638196, 'fraction_numerical': 0.0, 'mean_word_length': 5.694444444444445, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Investigation of an expert health monitoring system for aeronautical structures based on pattern recognition and acousto-ultrasonics

Aeronautical structures are subjected to damage during their service raising the necessity for periodic inspection and maintenance of their components so that structural integrity and safe operation can be guaranteed. Cost reduction related to minimizing the out-of-service time of the aircraft, together with the advantages offered by real-time and safe-life service monitoring, have led to a boom in the design of inexpensive and structurally integrated transducer networks comprising actuators, sensors, signal processing units and controllers. These kinds of automated systems are normally referred to as smart structures and offer a multitude of new solutions to engineering problems and multi-functional capabilities. It is thus expected that structural health monitoring (SHM) systems will become one of the leading technologies for assessing and assuring the structural integrity of future aircraft. This study is devoted to the development and experimental investigation of an SHM methodology for the detection of damage in real scale complex aeronautical structures. The work focuses on each aspect of the SHM system and highlights the potentialities of the health monitoring technique based on acousto-ultrasonics and data-driven modelling within the concepts of sensor data fusion, feature extraction and pattern recognition. The methodology is experimentally demonstrated on an aircraft skin panel and fuselage panel for which several damage scenarios are analysed. The detection performance in both structures is quantified and presented.