Hanfei Mei

On-line updating Gaussian mixture model for aircraft wing spar damage evaluation under time-varying boundary condition

Structural health monitoring technology for aerospace structures has gradually turned from fundamental research to practical implementations. However, real aerospace structures work under time-varying conditions that introduce uncertainties to signal features that are extracted from sensor signals, giving rise to difficulty in reliably evaluating the damage. This paper proposes an online updating Gaussian Mixture Model (GMM)-based damage evaluation method to improve damage evaluation reliability under time-varying conditions. In this method, Lambwave-signal variation indexes and principle component analysis (PCA) are adopted to obtain the signal features. A baseline GMM is constructed on the signal features acquired under timevarying conditions when the structure is in a healthy state. By adopting the online updating mechanism based on a moving feature sample set and inner probability structural reconstruction, the probability structures of the GMM can be updated over time with new monitoring signal features to track the damage progress online continuously under time-varying conditions. This method can be implemented without any physical model of damage or structure. A real aircraft wing spar, which is an important load-bearing structure of an aircraft, is adopted to validate the proposed method. The validation results show that the method is effective for edge crack growth monitoring of the wing spar bolts holes under the time-varying changes in the tightness degree of the bolts.

A Multi-Response-Based Wireless Impact Monitoring Network for Aircraft Composite Structures

Composite structures may be subjected to internal damages caused by impact events, which can seriously degrade the property of the structures. Hence, impact monitoring is of great importance to ensure the applications of composite, especially in aerospace engineering. This paper puts forward, for the first time, a new multi-response-based wireless sensor network (WSN) to realize the large-scale impact monitoring with the weight and complexity of the monitoring system reduced greatly. A novel multi-response-based global impact localization method that can unite multiple leaf nodes to solve the problems of localization confliction and mid-region localization is proposed for the WSN. Evaluations performed on large-scale complex composite structures have shown the advantages of the presented new methods.

Crack propagation monitoring in a full-scale aircraft fatigue test based on guided wave-Gaussian mixture model

For aerospace application of structural health monitoring (SHM) technology, the problem of reliable damage monitoring under time-varying conditions must be addressed and the SHM technology has to be fully validated on real aircraft structures under realistic load conditions on ground before it can reach the status of flight test. In this paper, the guided wave (GW) based SHM method is applied to a full-scale aircraft fatigue test which is one of the most similar test status to the flight test. To deal with the time-varying problem, a GW-Gaussian mixture model (GW-GMM) is proposed. The probability characteristic of GW features, which is introduced by time-varying conditions is modeled by GW-GMM. The weak cumulative variation trend of the crack propagation, which is mixed in time-varying influence can be tracked by the GW-GMM migration during on-line damage monitoring process. A best match based Kullback–Leibler divergence is proposed to measure the GW-GMM migration degree to reveal the crack propagation. The method is validated in the full-scale aircraft fatigue test. The validation results indicate that the reliable crack propagation monitoring of the left landing gear spar and the right wing panel under realistic load conditions are achieved.

An Improved Gaussian Mixture Model for Damage Propagation Monitoring of an Aircraft Wing Spar under Changing Structural Boundary Conditions

Structural Health Monitoring (SHM) technology is considered to be a key technology to reduce the maintenance cost and meanwhile ensure the operational safety of aircraft structures. It has gradually developed from theoretic and fundamental research to real-world engineering applications in recent decades. The problem of reliable damage monitoring under time-varying conditions is a main issue for the aerospace engineering applications of SHM technology. Among the existing SHM methods, Guided Wave (GW) and piezoelectric sensor-based SHM technique is a promising method due to its high damage sensitivity and long monitoring range. Nevertheless the reliability problem should be addressed. Several methods including environmental parameter compensation, baseline signal dependency reduction and data normalization, have been well studied but limitations remain. This paper proposes a damage propagation monitoring method based on an improved Gaussian Mixture Model (GMM). It can be used on-line without any structural mechanical model and a priori knowledge of damage and time-varying conditions. With this method, a baseline GMM is constructed first based on the GW features obtained under time-varying conditions when the structure under monitoring is in the healthy state. When a new GW feature is obtained during the on-line damage monitoring process, the GMM can be updated by an adaptive migration mechanism including dynamic learning and Gaussian components split-merge. The mixture probability distribution structure of the GMM and the number of Gaussian components can be optimized adaptively. Then an on-line GMM can be obtained. Finally, a best match based Kullback-Leibler (KL) divergence is studied to measure the migration degree between the baseline GMM and the on-line GMM to reveal the weak cumulative changes of the damage propagation mixed in the time-varying influence. A wing spar of an aircraft is used to validate the proposed method. The results indicate that the crack propagation under changing structural boundary conditions can be monitored reliably. The method is not limited by the properties of the structure, and thus it is feasible to be applied to composite structure.

Digital sequences and a time reversal-based impact region imaging and localization method

To reduce time and cost of damage inspection, on-line impact monitoring of aircraft composite structures is needed. A digital monitor based on an array of piezoelectric transducers (PZTs) is developed to record the impact region of impacts on-line. It is small in size, lightweight and has low power consumption, but there are two problems with the impact alarm region localization method of the digital monitor at the current stage. The first one is that the accuracy rate of the impact alarm region localization is low, especially on complex composite structures. The second problem is that the area of impact alarm region is large when a large scale structure is monitored and the number of PZTs is limited which increases the time and cost of damage inspections. To solve the two problems, an impact alarm region imaging and localization method based on digital sequences and time reversal is proposed. In this method, the frequency band of impact response signals is estimated based on the digital sequences first. Then, characteristic signals of impact response signals are constructed by sinusoidal modulation signals. Finally, the phase synthesis time reversal impact imaging method is adopted to obtain the impact region image. Depending on the image, an error ellipse is generated to give out the final impact alarm region. A validation experiment is implemented on a complex composite wing box of a real aircraft. The validation results show that the accuracy rate of impact alarm region localization is approximately 100%. The area of impact alarm region can be reduced and the number of PZTs needed to cover the same impact monitoring region is reduced by more than a half.

Damage evaluation by a guided wave-hidden Markov model based method

Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.

On a digital wireless impact-monitoring network for large-scale composite structures

Impact, which may occur during manufacture, service or maintenance, is one of the major concerns to be monitored throughout the lifetime of aircraft composite structures. Aiming at monitoring impacts online while minimizing the weight added to the aircraft to meet the strict limitations of aerospace engineering, this paper puts forward a new digital wireless network based on miniaturized wireless digital impact-monitoring nodes developed for large-scale composite structures. In addition to investigations on the design methods of the network architecture, time synchronization and implementation method, a conflict resolution method based on the feature parameters of digital sequences is first presented to address impact localization conflicts when several nodes are arranged close together. To verify the feasibility and stability of the wireless network, experiments are performed on a complex aircraft composite wing box and an unmanned aerial vehicle (UAV) composite wing. Experimental results show the successful design of the presented network.

Impact localization by a multi-radio sink–based wireless sensor network for large-scale structures

With the rapid development of wireless sensor network technology, more and more researchers have been interested in taking advantages of wireless sensor network to reduce weight and cost and to solve the installing problem of the wired structural health monitoring systems. A number of wireless-based structural health monitoring methods have been developed over the years to ensure the safety of large-scale structures. However, little research has been reported on wireless impact monitoring of large-scale composite structures due to the limitations of ordinary monitoring methods and wireless sensor networks. In this article, a wireless multi-radio sink which can access multiple communication channels is developed and adopted to build an impact monitoring wireless sensor network. Besides, a corresponding network architecture with an energy-weighted factor–based localization method adopted is presented to enable impact localization within the whole monitoring scope of the network. To verify the performance of the impact monitoring wireless sensor network, the experiments are performed on complex aircraft composite structures and the experimental results prove the effectiveness of the proposed wireless sensor network.

An Energy-weighted Factor Based Localization Method for On-line Wireless Impact Networking Monitoring

Due to the widely use of composite materials in aerospace vehicles, the poor impact resistance of such materials may cause serious degradation of aircraft structures. Aiming at monitoring impact online for large scale aircraft composite structures, a wireless impact monitoring network based on digital wireless impact monitors has been developed. However, the digital localization method adopted in the network only works in single monitor, resulting in the localization confliction among multiple monitors and the false impact localization result of mid-region. In order to meet the needs of wireless impact networking monitoring, this research puts forward an energy-weighted factor based localization method with the ability of solving localization confliction and locating mid-regions. To verify the performance of the method, a wireless impact monitoring network based on a number of monitors are built and conducted on a composite unmanned aerial vehicle structure. Experimental results show that the method performs well. doi: 10.12783/SHM2015/4