Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation with Deep Neural Networks

Composite materials can be modified according to the requirements of applications and hence their applications are increasing significantly with time. Due to the complex nature of the aging of composites, it is equally challenging to establish structural health monitoring techniques. One of the most applied non-destructive techniques for this class of materials is using Lamb waves to quantify the damage. Another important advancement in damage detection is the application of deep neural networks. The data-driven methods have proven to be most efficient for damage detection in composites. For both of these advanced methods, the burning question always has been the requirement of data and quality of data. In this paper, these measurements were used to create a framework based on a deep neural network for efficient fault diagnostics. The research question developed for this paper was: can data fusion techniques used along with data augmentation improve the damage diagnostics using the convolutional neural network? The specific aims developed to answer this research question were (1) highlighting the importance of data fusion methods, (2) underlining the importance of data augmentation techniques, (3) generalization abilities of the proposed framework, and (4) sensitivity of the size of the dataset. The results obtained through the analysis concluded that the artificial intelligence techniques along with the Lamb wave measurements can efficiently improve the fault diagnostics of complex materials such as composites.

Best practices about rail defects: a review of the book “Defects of rails. Stresses and Damages”, Vol. 1 by K.-O. Edel, G. Budnitskiy, T. Schnitzer

The review analyzes a monograph published by Springer Vieweg publishing house, which presents scientific approaches to the problems of defect formation in railway rails. The advantages of the book under review include the analysis of statistical data on rail failures, description of test methods and damage diagnostics. The book discusses in detail the widely occurring types of contact-fatigue defects formed during operation in the rail head: internal longitudinal shelling, multiple parallel head checks, surface squats und studs in the middle of the rolling surface with a greater or lesser degree deformations

Fusion-based damage diagnostics for stiffened composite panels

Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based methodology between data from two SHM techniques in order to surpass the capabilities of a single SHM technique. The aim is to show that by considering data fusion, a synergy can be obtained, resulting in a comprehensive damage assessment, not possible using a single SHM technique. For this purpose, three single-stiffener carbon–epoxy panels were subjected to fatigue compression after impact tests. Two SHM techniques monitored damage growth under the applied fatigue loads: acoustic emission and distributed fiber optic strain sensing. Four acoustic emission sensors were placed on each panel, thereby allowing for damage detection, localization, type identification (delamination), and severity assessment. The optical fibers were adhered to the stiffener feet’ surface, and its strain measurements were used for damage detection, disbond localization, damage type identification (stiffness degradation and disbond growth), and severity assessment. Different fusion techniques are presented in order to integrate the acoustic emission and strain data. For damage detection and severity assessment, a hybrid health indicator is obtained by feature-level fusion while a complementary and cooperative fusion of the diagnostic results is developed for damage localization and type identification. We show that damage growth can be monitored up until final failure, thereby performing a simultaneous damage assessment on all four SHM levels. In this manner, we demonstrate that by proposing a fusion-based approach toward SHM of composite structures, the intrinsic capacity of each SHM technique can be utilized, leading to synergistic effects for damage diagnostics.

Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study

Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.

COVID-19 lung damage: Diagnostics and treatment at the primary health care

The lecture is devoted to lung damage in new coronavirus infection. The description of the pathogenetic mechanisms of lung damage is given. The characteristics of the morphological changes in the lungs with severe disease and their characteristics are given. Provides information on instrumental and laboratory diagnosis of lung lesions in COVID-19. Particular attention is paid to highlighting controversial and complex issues of managing patients with lung damage in COVID-19 in outpatient practice. In particular, controversial issues of terminology for defining lung damage, the complexity of differentiating viral lung damage and other causes, the complexity of diagnosing bacterial superinfection and its treatment. Aspects of patient management important for primary care physician are highlighted: current treatment regimens for COVID-19 patients with lung damage, the timing of the control X-ray examination. It provides information on the most common mistakes encountered in the management of patients with COVID-19 (over-prescribing radiation methods, over-prescribing antibacterial medications).

APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

Kidney damage in essential arterial hypertension: pathogenetic issues for early diagnostics

We review main issues of early diagnostics of kidney damage in patients with essential hypertension. The remodeling of renal vessels and the underlying mechanisms are discussed. The evidence-based data are reviewed to substantiate the use of laboratory methods for the kidney damage diagnostics (calculation of glomerular fltration rate and microalbuminuria). We discuss the role of Doppler methods in the assessment of intrarenal hemodynamics (the resistance index and blood flow acceleration time) for timely detection of changes in renal vessels and the choice of management strategy.

Analysis of Surface Defects in Composites Using Digital Image Correlation and Acoustic Emission

This work employs the acoustic emission (AE) method for material state monitoring. AE presents a non‑destructive evaluation technique, which could be used for detection of microstructural changes in composite material. Work describes the process of acquisition of AE in tensile loading of carbon composite materials. In course of tensile stress, the composite was monitored with optical method, applying principles of digital image correction (DIC). Optical stereovision method enables calculation of field shift and field of proportional deformation at composite surface. The objective is analysis of damage in carbon composite materials and employ the methodology of AE signal processing for facilitation of early damage diagnostics and prediction of structural failure. For this purpose, the experimental setup was designed to obtain results from 50 nominally identical composite samples in tensile loading test. Force load applied on samples was synchronically recorded along with AE and image data. Experimental data were subsequently analysed in a way enabling the description of typical phenomena in course of every measurement. Results show that observation of AE sources could be employed in facilitation of early damage diagnostics and establishment of failure prognosis. It is about internal changes in composite material.