Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

Abstract

The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

Ensemble approaches for leveraging machine learning models in load estimation

“…The first and largest category of papers relates to systems that use sensor data to build machine learning models that then predict the loads on the aircraft over time. A few such as Oldersma [6], Qing [7], Mucha [8] and Gallimard [9], use physical monitoring sensors such as strain gauges and temperature sensors to determine the state of the loads and train the machine learning model. Others, such as Isom [10] and Qing [7], use external validation sensors like vibration, Piezoelectric sensors, or accelerometers to infer the state of the aircraft at a point in time.…”

New Operational Load Monitoring Approach Using Digital Image Correlation and Image Classification Networks

“…optical fibres) sensors are utilized [4]. The authors in their previous work [5,6] have proven that by implementing artificial intelligence (AI) techniques to OLM processes, the amount of required sensor data can be significantly reduced (e.g. number of strain gauges).…”

The influence of mounting place of FBG sensor in composite on the base of FEM simulation.

“…In recent years, the use of smart structures got more attention in aircraft systems and massive space structures. Active structural geometry and vibration control, as well as structural health monitoring, can be provided by sensors incorporated in structural components [5]. Sensors can be surface mounted or embedded in structural material.…”