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 d… Show more
“…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.…”
Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. A range of ensemble integration techniques were investigated in order to leverage multiple machine learning models to estimate main rotor yoke loads from flight state and control system parameters. The techniques included simple averaging, weighted averaging and forward selection. Performance of the models was evaluated using four metrics: root mean squared error, correlation coefficient and the interquartile ranges of these two metrics. When compared, every ensemble outperformed the best individual model. The ensembles using forward selection achieved the best performance. The resulting output is more robust, more highly correlated and achieves lower error values as compared to the top individual models. While individual model outputs can vary significantly, confidence in their results can be greatly increased through the use of a diverse set of models and ensemble techniques.
“…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.…”
Helicopter component load estimation can be achieved through a variety of machine learning techniques and algorithms. A range of ensemble integration techniques were investigated in order to leverage multiple machine learning models to estimate main rotor yoke loads from flight state and control system parameters. The techniques included simple averaging, weighted averaging and forward selection. Performance of the models was evaluated using four metrics: root mean squared error, correlation coefficient and the interquartile ranges of these two metrics. When compared, every ensemble outperformed the best individual model. The ensembles using forward selection achieved the best performance. The resulting output is more robust, more highly correlated and achieves lower error values as compared to the top individual models. While individual model outputs can vary significantly, confidence in their results can be greatly increased through the use of a diverse set of models and ensemble techniques.
“…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 following paper presents a novel approach that can be applied to Operational Load Monitoring and Structural Health Monitoring processes. The approach is based on artificial intelligence (AI) and digital image correlation (DIC) techniques. DIC is an optical method that allows measuring full-field structural displacements and strains. In the presented approach only a relatively small fragment of the material’s surface is monitored by DIC. The obtained partial image of strains or displacements is then processed by a carefully trained AI model, an image classification network, able to predict the state of whole structure (e.g. materials stresses, potential loss of material continuity). The assumption is that all possible load cases and states of the monitored structure can be identified and simulated, so the data obtained from simulations can then be used to train the image classification network. A numerical example is presented as proof of the presented concept. A modern lightweight aerostructure in the form of a hat-stiffened composite panel was used as monitored structure in the presented example and its Operational Load Monitoring was performed based on a relatively small fragment of normal strains map. The reference maps to train the network were simulated numerically. The prediction model estimates the Tsai-Wu failure criterion value for the whole composite material. The obtained accuracy of predictions proved the effectiveness and efficiency of the proposed approach.
“…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.…”
In composite materials, a real-time strain development can be measured by mounting a small-diameter fiber Bragg grating (FBG) sensors in structures. The mounting position of FBG sensors determines the accuracy in capturing and monitoring the mechanical behavior of composites. In this study, the influence of mounting place of FBG sensors on mechanical response of composite structures was investigated using Finite Element Method (FEM). The FEM model incorporates the composite material properties, such as fiber orientation and resin matrix. A parametric study is conducted by varying the mounting locations of FBG sensors within the composite laminate. In this study strain variation with respect to distance between FBG sensor and glass stiffener was investigated. The findings of this study provide valuable insights into the optimal mounting positions of FBG sensors within composite structures for reliable strain monitoring and damage detection. This knowledge can aid engineers and researchers in designing efficient and robust structural health monitoring systems, leading to enhanced safety, durability, and performance of composite materials in various applications, including aerospace, automotive, and civil engineering.
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