Solution For Inspection Research Articles

An autonomous drone-based system for inspection of electrical substations

In the years to come, large power grid operators will operate and maintain an ever-increasing asset base. New innovative solutions are needed to increase the quality and efficiency of asset management to avoid corresponding growth in resources and cost. To this end, autonomous unmanned aerial vehicles (UAVs) provide a range of possibilities. Here, we present a novel prototype solution for autonomous and remotely operated inspection missions with resident drones on electrical substations, comprising: (1) an autonomous drone with sense and avoid and robustness to harsh weather capability; (2) a drone hangar for remote operations; and (3) drone operations and data acquisition management software. Further, we discuss the possibilities and challenges that such a system offers and give an overview of requirements that are key to realizing the potential of drones for improved asset management. These requirements are based on years of operational experience with electrical substations combined with the lessons learned during the development and testing of our drone system. We also experimentally investigate safety distances between the drone and high-voltage infrastructure. We demonstrate the usefulness of our autonomous inspection solution through extensive field testing at one of Statnett’s fully operational electrical substations.

Comparative in-vitro Study of the Trachospray, a New Device for Topical Anaesthesia of the Upper Airway.

BackgroundObtaining complete topical anaesthesia of the airway remains a clinical challenge. Particle size is one of the most important variables for the dose deposited and the distribution of aerosols in the airways. The mass median aerodynamic diameter of the particles should be in the range of 5–20 µm. We developed the “Trachospray” as a soft mist spray device for local anaesthetics. This in-vitro comparative test was designed to compare the performance of the new Trachospray device with two existing medical devices. The performance was determined by comparing the spray deposition patterns in the mouth, throat, trachea and lungs.MethodsThe human airway was simulated with an artificial idealized mouth and throat model, connected to a Next Generation Impactor. Four measurements were taken for each device (Trachospray, jet nebulizer and a spray pump) with 5.85% NaCl. A fifth measurement was carried out with 0.5% fluorescein solution for a visual inspection of the deposition patterns. The mass median aerodynamic diameter and geometric standard deviation of the droplets were measured.ResultsThe Trachospray produced an even coverage in the mouth, hypopharynx and vocal cords, with only a small lung fraction. The jet nebulizer produced a much thinner layer coverage of the tongue and surface around the vocal chords with a high lung deposition. The spray pump produced big droplets which deposited mainly at the hypopharynx.ConclusionThe Trachospray device deposits local anaesthetics in the targeted areas for topical anaesthesia of the airway and has promising characteristics for providing effective airway anaesthesia.

UAV in Construction Site Monitoring and Concrete Strength Estimation

Infrastructure is one of the main pillars and parameter for India’s overall development. Monitoring and evaluation of the construction activities and concrete elements at early ages is necessary to increase structural stability and durability. There is a need for sophisticated technologies for real-time monitoring of construction activities for visually updating the progress in construction, which facilitates for further planning of the project. This can be achieved only by advanced tools, unmanned vehicles and computer vision-based techniques. Unmanned Aerial Vehicle (UAV) coupled with Remote Sensing System helps in accurate, time and cost-effective monitoring process in all phases of construction activities. The main objective of this paper is to design a semi-automated and practical Remote Sensing Non-Destructive Method (RSNDT) for monitoring the early age concrete elements. A UAV equipped with visible and Thermal Infrared Remote Sensing system is proposed as an effective solution for concrete work inspection in early ages of construction. The visible band images are used for updating and checking the dimensional accuracy of freshly constructed structural elements. The NIR band is used to detect concrete strength-related aspects. Thermography principles are applied on laboratory-cured fresh concrete specimens for mapping the temperature emission through hydration process, that is directly proportional to the strength attainment of concrete. The laboratory test results are used to correlate with UAV thermal images of site-cured concrete elements and estimate the strength. This method facilitates in rapid and real-time monitoring of site to report the condition of structural elements at early ages of construction.

An integrated ROV solution for underwater net-cage inspection in fish farms using computer vision

This paper reports the integration of a remotely operated vehicle (ROV) solution for monitoring water quality in fish farms. The robotic system includes a RGB camera for real-time video capturing and a set of integrated sensors to measure hydro-climatic data. Computer vision algorithms were implemented with the aim of inspecting net-cages in fish farms. A comprehensive software solution was developed to allow a seamless use of the vision algorithms proposed in this work. Our system was designed to process underwater imagery captured by the ROV in order to determine net patterns associated with net failure. The system was tested in a dam under real conditions. ROC data were computed to demonstrate the accuracy of the proposed system during underwater fish cage inspection. On average, we obtained an accuracy of 0.91 regarding net pattern reconstruction tasks, while an accuracy of 0.79 for net damage detection under different underwater scenarios.

Development of Innovative Solutions for Intelligent Inspection of Products of Additive Manufacturing for Navy Supply

Using of 3D printed spare parts for Navy supply supported by mentioned inspection meets the requirement for enhancing the logistic performance by provide to military end-users possibilities to produce spare parts using additive manufacturing solutions, particularly in the context of overseas operations, that can be easily presented with several examples. But with application of additive manufacturing in naval operations to product different objects in a variety of materials in an open water environment, we need to start with development of innovative solutions for intelligent inspection of products of selective laser melting from metallic powders is a topic of great importance in nowadays if we talk about real strategies in logistic support including overseas naval outposts support equipment and the reverse engineering of obsolescent parts such as many of those that could be found on the perspective sea platforms with particular focus on maritime littoral and high sea areas—demonstrate advantages of additive manufacturing.

Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model

Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser-based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human-dominated defect classification and evaluation work. This study presents a 3D model-based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi-class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model-based automatic track surface defect inspection.

Case Study-Spiking Neural Network Hardware System for Structural Health Monitoring.

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.

Guest Editorial: Non‐Destructive Testing

Guest Editorial: Non‐Destructive Testing

Learning‐based image scale estimation using surface textures for quantitative visual inspection of regions‐of‐interest

AbstractA major shortfall of vision‐based inspection solutions is the lack of scale information, required to resolve inspection regions to a physical scale. To address this challenge, a learning‐based scale estimation technique is proposed. The underlying assumption is that the surface texture of structures, captured in images, contains enough information to estimate scale for each corresponding image (e.g., pixel/mm). This permits the training of a regression model to establish the relationship between surface textures, captured in images, and scales. A convolutional neural network is trained to extract scale‐related features from textures captured in images. Then, the trained model can be exploited to estimate scales for all images that are captured from a structure's surfaces with similar textures. The capability of the proposed technique is demonstrated using data collected from surface textures of three different structures. An average scale estimation error, from images of each structure, is less than 15%.

Selective generation of ultrasonic guided waves for damage detection in rectangular bars

Ultrasonic guided waves are used in non-destructive testing and structural health monitoring solutions for long-range inspection, in applications ranging from Civil Engineering to Aerospace. In order to ease the inspection process, it is generally preferable to generate a carefully selected single mode. Although single mode Lamb wave generation is not difficult to achieve in infinite plate-like structures, with carefully polarized or sized piezoceramic elements, for example, such selective generation is much more difficult in a rectangular bar. In this article, we consider the propagation along a thin plate of finite rectangular cross section, which corresponds to a rectangular bar. The finite lateral width leads to a greater density of modes compared to an infinite plate. The authors have previously addressed this matter and developed a methodology for the selective generation of modes in the harmonic regime. This article extends this methodology to selective mode generation for finite time excitation, such as bursts. Results are presented for single mode generation of A0,0 and A0,1 in an aluminum bar instrumented with eight piezoelectric transducers. The waveguide modal basis is calculated with the two-dimensional semi-analytical finite element method, and measurements are conducted using a three-dimensional laser-Doppler vibrometer. To illustrate the potential of the method for structural health monitoring purposes, the detection of a defect simulated by a pair of magnets placed at various positions over the bar width is demonstrated.

From Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection

The consequences of food adulteration can be far reaching. In the past, inexpensive adulterants were used to inflate different products, leading to severe health issues. Contamination of food has many causes and can be physical (plant stems in tea), chemical (melamine in infant formula), or biological (bacterial contamination). Employing suitable sensor systems along the production process is a requirement for food safety. In this article, different approaches to food inspection are illustrated, and exemplary scenarios outline the potential of different sensor systems along the spectrum.

Application of optical technologies in the US poultry slaughter facilities for the detection of poultry carcase condemnation

ABSTRACT 1. This article reviews the studies on optical technologies for automated poultry carcase inspection, discusses challenges and potential solutions in their real-time applications in poultry slaughter facilities. 2. Over the past few decades, extensive research has been underway to develop an optical technology-based machine vision system for automated inspection of poultry carcases and viscera. Such an automated technology will not only aid in carcase inspection to maximise food safety, but it will also support the U.S. New Poultry Inspection System’s aim to foster innovation in poultry processing as well as increase line speed. 3. Many earlier studies based on visible and near-infrared spectroscopy showed promise, but could not be implemented successfully in an on-line poultry processing plant. Currently, multi- and hyper-spectral imaging-based machine vision systems have shown promising outcomes. 5. The critical hurdles for real-time application of automated imaging technology in poultry carcase inspection include high-speed processing lines, slaughter facilities environment and variation in broiler rearing practices. Therefore, further improvement in imaging and machine vision technologies based on physiochemical properties on poultry carcases, the establishment of more technology friendly inspection station, and an integrated data management for different rearing practices are essential to overcome those hurdles.

Automatic hotspots detection based on UAV infrared images for large‐scale PV plant

Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour-intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large-scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep-learning-based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep-learning-based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.

Numerical Methods for Optimization of the Horizontal Directional Drilling (HDD) Well Path Trajectory

Advances in the field of material engineering, computerization, automation, and equipment miniaturization enable modernization of the existing technologies and development of new solutions for construction, inspection, and renovation of underground pipelines. Underground pipe installations are used in the energy sector, gas industry, telecommunications, water and sewage transport, heating, chemical industry, and environmental engineering. In order to build new pipeline networks, dig and no-dig techniques are used. Horizontal Directional Drilling (HDD) is one of the most popular trenchless technologies. The effectiveness of HDD technology application is mostly determined by its properly designed trajectory. Drilling failures and complications, which often accompany the application of the HDD technology, result from poor design of the well path in relation to the existing geological and drilling conditions. The article presented two concepts of Horizontal Directional Drilling well path trajectory design: Classic sectional, which is a combination of straight and curvilinear sections, and a single-section chain curve trajectory (catenary). Taking into account the advantages and disadvantages of the catenary trajectory relative to the sectional trajectory, the author’s solution was presented, which was the implementation of the sectional trajectory with a maximum shape similarity to the catenary trajectory. The new approach allowed us to take advantage of a chain curve trajectory and was easier to implement using the available technology. The least squares method, based on deviations from a catenary trajectory, was set as the matching criterion. The process of searching for a trajectory, being a combination of straight and curvilinear sections as similar as possible to a catenary-type trajectory, was carried out using two methodologies: State space search and a genetic algorithm. The article shows the pros and cons of both optimization methodologies. Taking into account the technical and technological limitations of HDD drilling devices, a new approach was proposed, combining the methodology of state space search with the genetic algorithm. A calculation example showed the application of the proposed methodology in an engineering design process.

A Novel Infrared Thermography Sensing Approach for Rapid, Quantitative Assessment of Damage in Aircraft Composites.

The current necessity of the scientific and industrial community, for reduction of aircraft maintenance cost and duration, prioritizes the need for development of innovative nondestructive techniques enabling fast and reliable defect detection on aircraft fuselage and wing skin parts. Herein, a new low-cost thermographic strategy, termed Pulsed Phase-Informed Lock-in Thermography, operating on the synergy of two independent, active infrared thermography techniques, is reported for the fast and quantitative assessment of superficial and subsurface damage in aircraft-grade composite materials. The two-step approach relies on the fast, initial qualitative assessment, by Pulsed Phase Thermography, of defect location and the identification of the optimal material-intrinsic frequency, over which lock-in thermography is subsequently applied for the quantification of the damage’s dilatational characteristics. A state-of-the-art ultra-compact infrared thermography module envisioned to form part of a fully-automated autonomous nondestructive testing inspection solution for aircraft was conceived, developed, and tested on aircraft-grade composite specimens with impact damages induced at variable energy levels and on a full-scale aircraft fuselage skin composite panel. The latter task was performed in semi-automated mode with the infrared thermography module mounted on the prototype autonomous vortex robot platform. The timescale requirement for a full assessment of damage(s) within the sensor’s field of view is of the order of 60 s which, in combination with the high precision of the methodology, unfolds unprecedented potential towards the reduction in duration and costs of tactical aircraft maintenance, optimization of efficiency and minimization of accidents.

A Deep Learning-Based Ultrasonic Pattern Recognition Method for Inspecting Girth Weld Cracking of Gas Pipeline

Electromagnetic Acoustic Transducer (EMAT) has become one of the fastest-growing solutions for pipeline weld inspection over the past decade due to its non-contact advantage. One primary problem of EMAT is that it has relatively lower energy transition efficiency compared to widely used piezoelectric transducers, coupled with the effect of lift-off and the non-uniformity issue of welding material, the Signal-to-Noise Ratio (SNR) can be significantly restricted. This brings great difficulty in interpreting the EMAT signal measured from pipeline girth welds. To overcome this challenge, this paper presents a deep learning-based ultrasonic pattern recognition method to identify the pipeline girth weld cracking automatically. The proposed method utilizes a deep Convolution Neural Network (CNN) integrated with a pre-trained Support Vector Machine (SVM) classifier to extract the high-level features from the time-frequency representation of A-scan signals measured by bulk-wave EMAT and classify these signals into defective or non-defective groups. To validate the proposed method, a set of experiments is carried out to classify A-scan signals measured from the girth welds of an ex-service type 813-X70 gas pipeline. A comparative investigation is also undertaken to demonstrate the superiority of the proposed method against the conventional ultrasonic pattern recognition methods for evaluation.

A cost effective solution for pavement crack inspection using cameras and deep neural networks

Automatic crack detection on pavement surfaces is an important research field in the scope of developing an intelligent transportation infrastructure system. In this paper, a cost effective solution for road crack inspection by mounting the commercial grade sport camera, GoPro, on the rear of the moving vehicle is introduced. Also, a novel method called ConnCrack combining conditional Wasserstein generative adversarial network and connectivity maps is proposed for road crack detection. In this method, a 121-layer densely connected neural network with deconvolution layers for multi-level feature fusion is used as generator, and a 5-layer fully convolutional network is used as discriminator. To overcome the scattered output issue related to deconvolution layers, connectivity maps are introduced to represent the crack information within the proposed ConnCrack. The proposed method is tested on a publicly available dataset as well our collected data. The results show that the proposed method achieves state-of-the-art performance compared with other existing methods in terms of precision, recall and F1 score.

A Data-Flow Oriented Deep Ensemble Learning Method for Real-Time Surface Defect Inspection

Real-time surface defect inspection plays an important role in the quality control of automated production. For the surface defect inspection, flowing data are a common data form in the automated pipeline production. Although flowing data provide rich information for surface defect inspection, there are still a lot of dynamic distribution challenges caused by the flowing data, such as the domain shift phenomenon and imbalanced training data. However, many existing industrial inspection solutions still use static strategies. To determine the dynamic distribution influence on the data flow domain, this article proposes a new deep ensemble learning method with domain fluctuation adaptation. Specifically, a new distribution discrepancy identifier based on estimation of the data set distribution and data characteristic is proposed. It utilizes advantages of both the deep convolutional neural network (CNN) and the shallow feature-based learning method to achieve higher robustness and fine-grained detection in streaming data scenes. In order to validate the proposed method, an inspection bench test system, as a part of a real industrial surface mount technology production line, is designed and fabricated. The proposed inference model is successfully applied to an embedded terminal with a hybrid and heterogeneous computing architecture. At last, the method is validated on the data collected from the manufacturer. The result suggests that the proposed method possesses a competitive mean average precision (mAP) rate with good adaptation and robustness in industrial streaming data scenes.

Smart Camera for Quality Inspection and Grading of Food Products

Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. This paper focuses on the development of an embedded solution with learning capability to alleviate these challenges. Three simple application cases are included to demonstrate the operation of this unique solution. Two datasets of more challenging cases were created to analyze and demonstrate the performance of our visual inspection algorithm. One dataset includes infrared images of Medjool dates of four levels of skin delamination for surface quality grading. The other one consists of grayscale images of oysters with varying shape for shape quality evaluation. Our algorithm achieved a grading accuracy of 95.0% on the date dataset and 98.6% on the oyster dataset, both easily surpassed manual grading, which constantly faces the challenges of human fatigue or other distractions. Details of the design and functions of our smart camera and our simple visual inspection algorithm are discussed in this paper.

Two-stream convolutional neural network for non-destructive subsurface defect detection via similarity comparison of lock-in thermography signals

Active infrared thermography is a safe, fast, and low-cost solution for subsurface defects inspection, providing quality control in many industrial production tasks. In this paper, we explore deep learning-based approaches to analyze lock-in thermography image sequences for non-destructive testing and evaluation (NDT&E) of subsurface defects. Different from most existing Convolutional Neural Network (CNN) models that directly classify individual regions/pixels as defective and non-defective ones, we present a novel two-stream CNN architecture to extract/compare features in a pair of 1D thermal signal sequences for accurate classification/differentiation of defective and non-defective regions. In this manner, we can significantly increase the size of the training data by pairing two individually captured 1D thermal signals, thereby greatly easing the requirement for collecting a large number of thermal sequences of specimens with defects to train deep CNN models. Moreover, we experimentally investigate a number of network alternatives, identifying the optimal fusion scheme/stage for differentiating the thermal behaviors of defective and non-defective regions. Experimental results demonstrate that our proposed method, directly learning how to construct feature representations from a large number of real-captured thermal signal pairs, outperforms the well-established lock-in thermography data processing techniques on specimens made of different materials and at various excitation frequencies.