development of cost-effective indirect monitoring system because many of the relationships between parameters and strains are often non-linear and difficult to identify with more traditional regression techniques.Researchers in the aeronautical arena have published work using ANNs and similar mathematical techniques to identify loading actions, or to supplement sparse flight test data. Azzam et al (8,9) described the use of a mathematical network to predict loads. Azzam et al (9) and Wallace et al (10) described co-operative work programmes involving Smiths Aerospace, the UK Ministry of Defence and BAE Systems and reported, among other successes, excellent correlation between measured and predicted strains at a number of structural locations for the Tornado combat aircraft, over a large number of test flights. Azzam (8) also described the development of methods to predict damage in helicopter components and the prediction of high frequency events, such as buffet loading on the fin of a fixed-wing aircraft. Jacobs and Perez (11) used a hybrid cascade ANN to augment sparse buffet data by predicting the power spectral densities of surface pressures on a fighter aircraft vertical tail, from parametric data. Kim and Marciniak (12) reported the use of a back-propagation ANN to predict strains in the empennage of a Cessna 172P general aviation aircraft during discrete manoeuvres. Hill et al (13) described the use of a back-propagation ANN to synthesise strain and fatigue damage in several Lynx helicopter components. Furthermore, Manry and co-authors (14) applied ANNs to flight load synthesis, using Bell helicopter data. Levinski (15) used ANN technology to synthesise loads on the vertical tail of a F/A-18 aircraft, using wind tunnel pressure data. Wang (16) used a recurrent ANN to predict loads during discrete manoeuvres for a fighter aircraft. Reed and Cole (17) and Reed (18) reported the development of an ANN-based parametric fatigue monitor for the wing and tailplane of a military trainer aircraft. With the exception of the work reported by Azzam and his associates (8,9,10) and Reed and Cole (17,18) the majority of the methods used were developed to predict discrete loading actions or manoeuvres rather than to predict the usage of the aircraft throughout its operational envelope.Within the following sections, the architecture of the ANN core of the structural health and usage neural network (SHAUNN) is explained. Thereafter, a generic process for developing a SHAUNN system is outlined. Also, results from case-study demonstrator programmes are reported. Finally, conclusions are drawn and recommendations for further work are made. It is noteworthy that software development methods and practices are not discussed within this paper; a range of comprehensive standards and guidance, appropriate to the safety criticality of the software, already exists and is familiar to software developers.
From necessity, military aircraft often operate in a highly fatigue damaging environment and history has shown in lost lives and aircraft the consequences of failure to appreciate fully the usage environment. The need for robust and cost effective structural usage monitoring of military aircraft to ensure operations are conducted within acceptable levels of risk is paramount. Furthermore, increased economic pressures require ever-inventive methods to be employed to maximise the lives of military fleets; structural usage monitoring will be a key asset in this drive. A highly cost effective indirect structural health and usage neural network (SHAUNN) monitoring system is proposed. A SHAUNN uses regression relationships determined by artificial neural networks to predict stresses, strains, loads, or fatigue damage from flight parameters. Within this paper the development of a SHAUNN monitoring system is described. Flight parametric data, captured during Operational Loads Measurement of the Royal Air Force Dominie TMk1 aircraft have been used to predict stresses at the key structural location in the wing, using mapping relationships determined by artificial neural networks. A framework for the development of the SHAUNN monitoring system is discussed and the basic architecture of the multilayer perceptron artificial neural network is described. It is concluded that this technology could provide the basis for an accurate, cost-effective structural usage monitoring system and further work to investigate the prediction of ground – based stresses in the wing is recommended.
Artificial neural networks (ANNs) are able to learn from experience, generalize from examples, and identify underlying information from within noisy data. These characteristics, and the explosion in ANN techniques over the past 15 or 20 years, have led to an ever‐increasing role for ANNs within structural health monitoring (SHM) systems. Within this article, the development of ANNs and the basic principles behind their functionality, training, and deployment are described. Emphasis is placed upon the multilayer perceptron (MLP), as the workhorse of ANN methods; however, other techniques, including radial basis functions (RBF) and self‐organizing feature maps (SOFMs), are also detailed. Examples of the use of ANNs within SHM systems are presented and reference has been made to a wide range of texts throughout this article.
The RAF has been undertaking peacetime repairs on composite rotor blades, fibreglass radomes and panels on a variety of combat aircraft types for many years. However, the Service's main experience in the repair of aircraft structures manufactured from carbon fibre composite (CFC) material has been related to the Harrier II. In any future conflict involving air power, battle damaged aircraft, including the Harrier II, will have to be repaired rapidly to meet operational requirements. The repair techniques used may have to differ from those undertaken in peacetime but they must still aim to restore the full static strength of the aircraft within the time constraints imposed by the operational situation. Therefore, aircraft battle damage repair (ABDR) is a carefully planned alternative to conventional repair techniques. This paper describes the main CFC structures of the Harrier II and outlines the variety of RAF peace and wartime structural repair procedures applied to the aircraft. Examples are provided of various simple and complex repairs. Greater emphasis on adhesively bonded repairs is called for if Harrier II experience is to aid in the logistic support of Eurofighter 2000 (EF2000) in peace and war.
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