Defect detection on thin-wall structure via dictionary learning

The importance of developing robust monitoring systems that can detect and locate progressive deterioration in structures or abrupt damage induced by extreme loading events is well recognized in the aerospace, mechanical, and civil engineering communities. In the case of civil structures such as buildings, bridges, off-shore platforms, or dams, the most commonly used approach for structural health monitoring ~SHM! relies on visual inspections that are labor intensive, often not timely, and may miss hidden damage. A good example of these limitations is found in the fractures induced by the 1994 Northridge earthquake in welded momentresisting beam-column connections in many steel buildings that were not discovered until several months after the earthquake because there was no distress apparent from visual inspections. An alternative to visual inspections and localized testing that is being aggressively investigated in various engineering disciplines is that of structural health monitoring based on vibration signals. A typical SHM system consists of a distributed set of motion sensors ~e.g., accelerometers, velocimeters, or fiber-optic strain gauges! connected through a data acquisition system to a central processing unit that may be in an operator room. For remote real-time monitoring of structures, the processed data must be on-line, possibly through a dedicated line or via an Internet link. The basic idea is well established, namely, the dynamic characteristics of a system ~usually natural frequencies and mode shapes! that can be identified from recorded motions are a function of the physical parameters ~mass and stiffness distributions!. Therefore, changes in physical properties resulting from damage may be inferred from changes in the identified dynamic characteristics using suitable algorithms. Then, in theory, damage may be detected, localized, and assessed through vibration monitoring. In practice, however, realization of the concept has proved to be a very challenging proposition because the inverse problem that must be solved, for typical operating conditions, is poorly conditioned, that is, results are sensitive to measurement noise and modeling errors. An important part of the current effort to affect positive progress in SHM technology in civil engineering structures is the development of well-defined benchmark problems that allow comparison of the performance of various techniques for realistic conditions. This special issue of the Journal of Engineering Mechanics contains eight papers that define and study a benchmark developed by the Task Group on Structural Health Monitoring that was formed in 1999 under the sponsorship of the Dynamics Committee of ASCE and the International Association of Structural Control ~IASC!. These papers expand on some partial and

This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques are carefully applied in the designed network architecture, which greatly facilitates the information flow, and increases the training efficiency and accuracy of feature extraction and propagation with fewer parameters in the network. Sub-pixel shuffling and dropout techniques are used in the designed network and applied to reduce the computational demand and improve training efficiency. The network is trained in a supervised manner, where the input and output are the measurements of the available channels at response available locations and desired channels at response unavailable locations. The proposed densely connected convolutional networks automatically extract the high-level features of the input data and construct the complicated nonlinear relationship between the responses of available and desired locations. Experimental studies are conducted using the measured acceleration responses from Guangzhou New Television Tower to investigate the effects of the locations of available responses, the numbers of available and unavailable channels, and measurement noise. The results demonstrate that the proposed approach can accurately reconstruct the responses in both time and frequency domains with strong noise immunity. The reconstructed response is further used for modal identification to demonstrate the usability and accuracy of the reconstructed responses. The applicability of the proposed approach for structural health monitoring is further proved by the highly consistent modal parameters identified from the reconstructed and true responses.

One critical challenge to achieving reliable wind turbine blade structural health monitoring (SHM) is mainly caused by composite laminates with an anisotropy nature and a hard-to-access property. The typical pitch-catch PZTs approach generally detects structural damage with both measured and baseline signals. However, the accuracy of imaging or tomography by delay-and-sum approaches based on these signals requires improvement in practice. Via the model of Lamb wave propagation and the establishment of a dictionary that corresponds to scatters, a robust sparse reconstruction approach for structural health monitoring comes into view for its promising performance. This paper proposes a neighbor dictionary that identifies the first crack location through sparse reconstruction and then presents a growth sparse pursuit algorithm that can precisely pursue the extension of the crack. An experiment with the goal of diagnosing a composite wind turbine blade with an artificial crack is performed, and it validates the proposed approach. The results give competitively accurate crack detection with the correct locations and extension length.

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

A wavelet-based approach is proposed for structural damage detection and health monitoring. Characteristics of representative vibration signals under the wavelet transformation are examined. The methodology is then applied to simulation data generated from a simple structural model subjected to a harmonic excitation. The model consists of multiple breakable springs, some of which may suffer irreversible damage when the response exceeds a threshold value or the number of cycles of motion is accumulated beyond their fatigue life. In cases of either abrupt or accumulative damages, occurrence of damage and the moment when it occurs can be clearly determined in the details of the wavelet decomposition of these data. Similar results are observed for the real acceleration data of the seismic response recorded on the roof of a building during the 1971 San Fernando earthquake. Effects of noise intensity and damage severity are investigated and presented by a detectability map. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.

Detectability Analysis of Structural Defects Using Lamb Waves: A Frequency-Based Approach for Structural Health Monitoring

The authors have been developing a new tiny (~12 mm cube and <4 g) Bluetooth wireless accelerometer unit based upon the technology that is prevalent in modern Smart Phones and Smart Watches. The primary intended application of this unit is for training/teaching purposes in vibration-based Experimental Modal Analysis (EMA) approaches for Structural System Identification (SSI) and Structural Health Monitoring (SHM). Digitised tri-axial acceleration data from a single unit can be streamed using purpose-specific software on a notebook or tablet at up to 400 Hz where the longest clear distance of this unit from the streaming device, (smart phone, tablet, laptop computer), is less than approx. 15 m. It is anticipated that tri-axial data synchronously captured from up to 8 accelerometer units at a sweep rate of 100 Hz per set, can be achieved on completion of the second stage of development of this system. This will certainly be suitable for use on model structures for teaching purposes, but also for industrial use (machinery; on-board vehicles; smaller structures) where the clear distance from the computer (or tablet) to these units is less than approx. 15 m. This paper focuses on the performance testing of a collection of commercially available accelerometers and compares these against the capabilities being aimed for in our new miniature accelerometer units.

The structures damage conditions assessment requires numerous precautions to ensure the safety of people during site visits and inspections. Among several methods providing useful information about the conservation status of the structures, dynamic monitoring techniques are suitable to retrieve the global behavior of the buildings. The anomalous features diagnosis of the structural dynamic response is an index of alterations of the material state and, in the worst cases, is related to the presence of damaged structural elements. This paper proposes the use of remote control systems for the structural evaluation of the damage state of buildings and describes the results achieved in an interesting application: the experimental dynamic analysis carried out on the inaccessible damaged bell tower of the Church of Santi Giacomo and Filippo in Mirandola (Italy). The study is based on observations performed using the IBIS-S ground-based radar interferometer to remotely measure the displacements of several elements of the building above 0.01 mm amplitude. This totally noninvasive and nondestructive approach has proved to be reliably implemented as a useful method to structural health monitoring procedures and especially for extensive and fast inspection analyses aiming at the first evaluation of the damage level and the soundness of slender buildings after earthquakes.

Floating covers used in waste water treatment plants are one of the many structures formed with membrane materials. These structures are usually large and can spread over an area measuring 470 m × 170 m. The aim of this paper is to describe recent work to develop an innovative and effective approach for structural health monitoring (SHM) of such large membrane-like infrastructure. This paper will propose a potentially cost-effective non-contact approach for full-field strain and stress mapping using an unmanned aerial vehicle (UAV) mounted with a digital camera and a global positioning system (GPS) tracker. The aim is to use the images acquired by the UAV to define the geometry of the floating cover using photogrammetry. In this manner, any changes in the geometry of the floating cover due to forces acting beneath resulting from its deployment and usage can be determined. The time-scale for these changes is in terms of weeks and months. The change in the geometry can be implemented as input conditions to a finite element model (FEM) for stress prediction. This will facilitate the determination of the state of distress of the floating cover. This paper investigates the possibility of using data recorded from a UAV to predict the strain level and assess the health of such structures. An investigation was first conducted on a laboratory sized membrane structure instrumented with strain gauges for comparison against strains, which were computed from 3D scans of the membrane geometry. Upon validating the technique in the laboratory, it was applied to a more realistic scenario: an outdoor test membrane structure and capable UAV were constructed to see if the shape of the membrane could be computed. The membrane displacements were then used to calculate the membrane stress and strain, state demonstrating a new way to perform structural health monitoring on membrane structures.

Acoustic emission (AE) is a phenomenon that occurs in materials that are exposed to stress. Defects in the material, such as cracks can grow and release energy in the form of a mechanical wave that propagates through the material. With the help of piezoelectric sensors, the mechanical wave is converted into an electrical signal. The signals are recorded by the measuring system and are available for further processing. Acoustic emission as a method that records and processes the incoming signals in real time is therefore very suitable for 24/7 long-term structural health monitoring. With new approaches in data processing, acoustic emission can be moved from the classic periodic test approach to an online monitoring process. The in-service monitoring brings measuring challenges with it, like application on hot surfaces and inside explosion hazard areas. New developments in hardware and software provide tailor-made solutions for a variety of monitoring tasks. The use of artificial intelligence algorithms helps to improve the analysis of measurement data and guarantees safe operation of pressure equipment and infrastructure around-the-clock. Some examples of continuous monitoring are shown on pressure vessels from the industry sector and on infrastructure buildings e.g., steel bridges. Also, a new lean system approach in structural health monitoring with acoustic emission is presented.

Vibration-based approach for structural health monitoring of ultra-high-performance concrete bridge

This study presents an approach for structural health monitoring (SHM) of remote and hazardous structures using unpiloted aerial vehicles (UAVs). The method focuses on overcoming the challenges associated with traditional sensor deployment techniques, which are often costly and risky due to the decaying nature of the targeted structures. Utilizing a multi-rotor UAV platform, a streaming camera is integrated into a recovery cone to aid in visual alignment during deployment and retrieval providing a safe and cost-effective means of sensor delivery. The paper covers the design of a video-broadcasting deployment system with integrated electropermanent magnets (EPMs), housed in a 3D-printed recovery cone, supplemented by redundancy measures to enhance safety and reliability. This proposed system significantly improves the user’s spatial awareness and aids in precise sensor package alignment, facilitated by multiple camera views providing a dual purpose of conducting visual inspection in addition to aiding in sensor delivery. The experimental analysis presented in this study validates the system’s effectiveness, demonstrating the utility of camera-aided sensor delivery for rapid SHM applications. Navigation challenges due to proximity to metal structures and the difficulties associated with signal strength and reflections are also reported. The contribution of this work is a methodology for aerial sensor deployment and retrieval using a lightweight 3D-printed recovery cone with integrated cameras for navigation and sensor alignment.

Inspection strategies with guided wave‐based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h‐NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.

Distributed Sensor Networks (DSN) has emerged as a sensing paradigms in the structural egineering field due to the application of Structural Health Monitoring (SHM) for large-scale structures which results in accurately diagnosing the health of structures and enhancing the re liability and robustness of monitoring systems. The multisensor network greatly enhances the feasibility of applying SHM and also provides awareness of structural damage. In this work, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. This approach assumes no knowledge of underlying models of the different data sources. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the low-dimensional data set corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. The damage in the blade is modeled by degrading the material property which in turn results in change of stiffness. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal to noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm can not only account for data from different sensors but also different types of sensor in the form of sensor fusion hereby making it attractive to exploit the distributed nature of sensor array. The distributed nature of sensor array is further exploited to determine the location of

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.