Mijin Choi

Incipient crack detection in a composite wind turbine rotor blade

This article presents a performance optimization approach to incipient crack detection in a wind turbine rotor blade that underwent fatigue loading to failure. The objective of this article is to determine an optimal demarcation date, which is required to properly normalize active-sensing data collected and processed using disparate methods for the purpose of damage detection performance comparison. We propose that maximizing average damage detection performance with respect to a demarcation date would provide both an estimate of the true incipient damage onset date and the proper normalization enabling comparison of detection performance among the otherwise disparate data sets. This work focuses on the use of ultrasonic guided waves to detect incipient damage prior to the surfacing of a visible, catastrophic crack. The blade was instrumented with piezoelectric transducers, which were used in a pitch-catch mode over a range of excitation frequencies. With respect to specific excitation frequencies and transmission paths, higher excitation frequencies provided consistent detection results for paths along the rotor blade’s carbon fiber spar cap, but performance fell off with increasing excitation frequency for paths not along the spar cap. Lower excitation frequencies provided consistent detection performance among all sensor paths.

Novelty detection applied to vibration data from a CX-100 wind turbine blade under fatigue loading

The remarkable evolution of new generation wind turbines has led to a dramatic increase of wind turbine blade size. In turn, a reliable structural health monitoring (SHM) system will be a key factor for the successful implementation of such systems. Detection of damage at an early stage is a crucial issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of Frequency Response Functions (FRFs) extracted by using an input/output acquisition technique under a fatigue loading of a 9m CX-100 blade at the National Renewable Energy Laboratory (NREL) and National Wind Technology Center (NWTC) performed in the Los Alamos National Laboratory. The blade was harmonically excited at its first natural frequency using a Universal Resonant Excitation (UREX) system. For analysis, the Auto-Associative Neural Network (AANN) is a non-parametric method where a set of damage sensitive features gathered from the measured structure are used to train a network that acts as a novelty detector. This traditionally has a highly complex bottleneck structure with five layers in the AANN. In the current paper, a new attempt is also exploited based on an AANN with one hidden layer in order to reduce the theoretical and computational difficulties. Damage detection of composite bodies of blades is a grand challenge due to varying aerodynamic and gravitational loads and environmental conditions. A study of the noise tolerant capability of the AANN which is associated to its generalisation capacity is addressed. It will be shown that vibration response data combined with AANNs is a robust and powerful tool, offering novelty detection even when operational and environmental variations are present. The AANN is a method which has not yet been widely used in the structural health monitoring of composite blades.

Development of a Numerical Model for an Expanding Tube with Linear Explosive Using AUTODYN

Pyrotechnic devices have been employed in satellite launch vehicle missions, generally for the separation of structural subsystems such as stage and satellite separation. Expanding tubes are linear explosives enclosed by an oval steel tube and have been widely used for pyrotechnic joint separation systems. A numerical model is proposed for the prediction of the proper load of an expanding tube using a nonlinear dynamic analysis code, AUTODYN 2D and 3D. To compute a proper core load, numerical models of the open-ended steel tube and mild detonating tube encasing a high explosive were developed and compared with experimental results. 2D and 3D computational results showed good correlation with ballistic test results. The model will provide more flexibility in expanding tube design, leading to economic benefits in the overall expanding tube development procedure.

Machine Learning Applications for a Wind Turbine Blade under Continuous Fatigue Loading

Structural health monitoring (SHM) systems will be one of the leading factors in the successful establishment of wind turbines in the energy arena. Detection of damage at an early stage is a vital issue as blade failure would be a catastrophic result for the entire wind turbine. In this study the SHM analysis will be based on experimental measurements of vibration analysis, extracted of a 9m CX-100 blade under fatigue loading. For analysis, machine learning techniques utilised for failure detection of wind turbine blades will be applied, like non-linear Neural Networks, including Auto-Associative Neural Network (AANN) and Radial Basis Function (RBF) networks models.

Development of a laser-powered wireless strain gauge device using a continuous-wave laser and photovoltaic cell

Wireless sensors have emerged as a reliable method for structural health monitoring. Wireless sensors should ideally have their own power supply, which is a conventional battery in most cases. However, sensors are often retrieved, and batteries must be replaced because of their finite lifespan. In many applications, wireless sensors must be operated in locations that are difficult to access, and these systems often have a desired operational lifespan that exceeds that of conventional batteries. Given this limitation, research devoted to alternative methods, such as energy harvesting or wireless power transmission of batteries, has rapidly increased. In this article, we investigated potential solutions to this challenge by collecting energy from a laser beam to power a wireless sensor. The proposed laser power transmission system features the capabilities of transmitting power to and rapid switching direction to sensor nodes using a laser mirror positioner. The delivered light is captured by a photovoltaic cell and collected in a storage medium to supply the required power to a wireless strain gauge device. Validation of the proposed technology was performed by static and dynamic strain measurements, and the obtained signals from the wireless strain gauge device were compared with those of the wired data acquisition.

Multi-source energy harvesting for wireless SHM systems

In wireless SHM systems, energy harvesting technology is essential for a reliable long-term energy supply for wireless sensors. Conventional wireless SHM systems using single source energy harvesting (vibration, solar, and etc.) have limitations because it could not be operated adequately without enough ambient energy present. To overcome this obstacle, multi-source energy harvesting which utilizes several ambient energy sources simultaneously is necessary to accumulate enough electrical energy to power wireless embedded sensor nodes. This study proposes a multi-source energy harvesting technique using a MISO (Multiple Input, Single Output) circuit board developed and studied by the authors. For multi-source energy harvesting, piezoelectric bimorph and electro-magnetic energy harvesters are excited at the first natural frequency of each harvester, 126.7 and 12.5 Hz, respectively. Then, generated voltage from each energy harvester is combined using the MISO circuit and then used to charge a 0.1 F capacitor. Combined energy harvesting results presented better performance than that of a single energy source, demonstrating that this multi-source system could be a promising energy harvesting solution for wireless sensing systems.

Development of a laser-powered wireless ultrasonic device for aircraft structural health monitoring

In recent years, wireless sensor networks have emerged as a reliable method for structural health monitoring. The powering methods for these wireless sensors have become an important factor. In several applications, wireless sensors must be operated in locations that are difficult or even impossible to access, and these systems often have a desired operational life span that exceeds conventional batteries lifetime. Replacing the batteries is labor intensive and time consuming. It is very difficult or even impossible to replace batteries of embedded sensors in concrete or composite structures. Therefore, novel powering methods such as energy harvesting or wireless power transmission are necessary to guarantee long life spans for wireless sensors. This article presents a laser-powered wireless ultrasonic device, which is a wireless active sensor with wireless laser power transmission that provides long-lasting structural health monitoring. The laser beam is captured by a GaInP photovoltaic cell. The cell has a high spectral responsivity for the 532-nm laser beam. A supercapacitor is used to store and supply power to the device. Furthermore, to solve the line-of-sight issue, a smart component called the fiber optic bolt is also developed using a large-core hard polymer-clad fiber. The wireless ultrasonic device includes the actuator and the sensor interface to evaluate the structural damages. To demonstrate the feasibility of the device, we carried out the basic Lamb wave pitch-catch test to detect the structural damage (such as cracks and artificial corrosion) in an aircraft lug (which is an example of an inaccessible aircraft structure). Our investigations show that the results of the proposed wireless sensing system are in accordance with those of the wired system. This indicates the feasibility for implementing the proposed system for wireless structural health monitoring.