A practical approach for the indirect prediction of structural fatigue from measured flight parameters

Abstract

The perceived benefits of permanent usage monitoring equipment in helicopters include savings in the cost of helicopter maintenance and a favourable impact on safety and fleet management. Unlike indirect fatigue monitoring, direct load monitoring relies on a large number of sensors which requires high operational and maintenance costs. In this paper, a learning network has been developed, and during training sessions allowed to learn how to predict fatigue damage indirectly from flight parameters. Each training example consists of instantaneous fatigue damage induced during a small time step, and flight parameters measured during the time step. The instantaneous damage values are evaluated through a new approach called the progressive damage model. By considering the laws of aerodynamics and dynamics, the network combines the flight parameters in such a way that the resulting features can be mapped into fatigue. About 10.5 flying hours of data were used to train the network. After training, the network was blind tested using flight parameters from 5.8 flying hours. The network was found to predict the fatigue damage of two rotating components indirectly from the flight parameters with accuracy better than the accuracy of a strain gauge system with 5 per cent measurement error.

Development of a parametric-based indirect aircraft structural usage monitoring system using artificial neural networks

“…All these potential sources of error should be understood and taken account of in the design and in the formulation of design accuracy requirements; there is no mileage in setting unachievable accuracy requirements. Azzam (8,9) refers to this process as establishing 'truth margins'.…”

“…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.…”

“…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.…”

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

“…As suggested by the journal articles by Vichare et al (2004), Azzam (1997), and Baybutt et al (2009), it is intuitive to consider usage conditions, both past and future, when estimating the RUL of a system. Type-II methods attempt to do this by characterizing the lifetime of an average component operating in the specific environment.…”

A life consumption monitoring methodology for electronic systems

“…As a result, many nonlinear damage theories have been proposed to account for the nonlinearity in damage accumulation, and several researchers have reviewed, evaluated, and compared the various cumulative damage accumulation theories available [14]. In general, Miner's rule is recommended for its simplicity, versatility, and reasonable accuracy [15].…”