Inspection Model Research Articles

Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches.

Maturity degree and quality evaluation are important for strawberry harvest, trade, and consumption. Deep learning has been an efficient artificial intelligence tool for food and agro-products. Hyperspectral imaging coupled with deep learning was applied to determine the maturity degree and soluble solids content (SSC) of strawberries with four maturity degrees. Hyperspectral image of each strawberry was obtained and preprocessed, and the spectra were extracted from the images. One-dimension residual neural network (1D ResNet) and three-dimension (3D) ResNet were built using 1D spectra and 3D hyperspectral image as inputs for maturity degree evaluation. Good performances were obtained for maturity identification, with the classification accuracy over 84% for both 1D ResNet and 3D ResNet. The corresponding saliency maps showed that the pigments related wavelengths and image regions contributed more to the maturity identification. For SSC determination, 1D ResNet model was also built, with the determination of coefficient (R2) over 0.55 of the training, validation, and testing sets. The saliency maps of 1D ResNet for the SSC determination were also explored. The overall results showed that deep learning could be used to identify strawberry maturity degree and determine SSC. More efforts were needed to explore the use of 3D deep learning methods for the SSC determination. The close results of 1D ResNet and 3D ResNet for classification indicated that more samples might be used to improve the performances of 3D ResNet. The results in this study would help to develop 1D and 3D deep learning models for fruit quality inspection and other researches using hyperspectral imaging, providing efficient analysis approaches of fruit quality inspection using hyperspectral imaging.

BIM-based mixed-reality application for bridge inspection and maintenance

PurposeThe purpose of this study is to develop a building information modelling (BIM)-based mixed reality (MR) application to enhance and facilitate the process of managing bridge inspection and maintenance works remotely from office. It aims to address the ineffective decision-making process on maintenance tasks from the conventional method which relies on documents and 2D drawings on visual inspection. This study targets two key issues: creating a BIM-based model for bridge inspection and maintenance; and developing this model in a MR platform based on Microsoft Hololens.Design/methodology/approachLiterature review is conducted to determine the limitation of MR technology in the construction industry and identify the gaps of integration of BIM and MR for bridge inspection works. A new framework for a greater adoption of integrated BIM and Hololens is proposed. It consists of a bridge information model for inspection and a newly-developed Hololens application named “HoloBridge”. This application contains the functional modules that allow users to check and update the progress of inspection and maintenance. The application has been implemented for an existing bridge in South Korea as the case study.FindingsThe results from pilot implementation show that the inspection information management can be enhanced because the inspection database can be systematically captured, stored and managed through BIM-based models. The inspection information in MR environment has been improved in interpretation, visualization and visual interpretation of 3D models because of intuitively interactive in real-time simulation.Originality/valueThe proposed framework through “HoloBridge” application explores the potential of integrating BIM and MR technology by using Hololens. It provides new possibilities for remote inspection of bridge conditions.

Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision

Contractors should conduct strict quality inspection of the steel bars used in concrete structures and need to automate the process of quality inspection. The objective of this study is to develop an Artificial Intelligence Quality Inspection Model (AI-QIM) that can execute quality inspection on steel bars at the construction site. The proposed AI-QIM is built on the Mask Region-based Convolutional Neural Network (Mask R-CNN) technique, which can perform instance segmentation of steel bars. This object detection technique is integrated with a stereo vision camera to generate information on steel bar installation. A contractor can use the proposed AI-QIM to estimate the quantity, spacing, diameter, and length of steel bars during quality inspection. A sample case study indicated that the AI-QIM yielded a maximum relative error of 3% when measuring steel bar spacing and a maximum relative error of 8% when measuring steel bar lengths within a range of 1–2 m from a stereo camera.

Informatization of Intelligent Manufacturing to Green Innovation Reform of Tobacco Manufacturing Enterprises in the Era of Big Data

Objectives: Based on intelligent manufacturing technology, this paper studies the informatization method of green innovation reform of tobacco manufacturing enterprises in the era of big data. Methods: By combing the literature at home and abroad, this paper expounds the technologies of data mining and data visualization based on the idea of lean management. On this basis, the basis of data management in the silk workshop of X tobacco enterprise is investigated and analyzed in detail from the aspects of human resources, equipment and management environment. Taking the blade feeder in the silk workshop of X cigarette factory as the research object, this paper establishes the self inspection model of equipment health state by using the weighted sum method of overall linearity and local nonlinearity. Results: The system can self check the health status of equipment and visually display the results. This method realizes timely and effective inspection and early warning of equipment health status. At the same time, the visual Kanban of workshop site management can provide production and equipment information to relevant personnel in time. This meets its demand for data visualization and changes the current situation of workshop data table display. Conclusion: This method reengineers the information business process of tobacco enterprises and establishes a new performance evaluation system. This effectively improves the existing management system of the workshop.

Reliability and Inspection Modelling of Railway Signalling Systems

The railway is one of the most prominent models of transportation across the globe and it carries a large number of people, thus requiring high reliability, maintainability and safety. The reliability of railways mostly depends on an effective signalling system, making it one of the critical parts of railway operation. A signalling system is part of a large array of systems with interconnected components and subcomponents. Therefore, there is a need to make the signalling system more reliable and optimised with enhanced fault detection. Proper inspection and maintenance are required to make the signalling system reliable and safe. In this study, different inspection modelling techniques are applied to find the reliability of the signalling system. The signalling system has been divided into subsystems (signal unit, track unit, point-and-point machine) considering their importance and their effects on the failure rate of the entire signalling system. Inspection modelling of each subsystem has been conducted to provide the basis for the entire signalling system. A case study has been investigated to validate the model developed in one of the busiest tracks in eastern India. The obtained data thus are used to analyse the inspection pattern of signalling subsystems. Special attention to maintenance for inspection activities and logistics support has been taken into consideration, which is required to improve the reliability and maintainability of signalling subsystems and systems to make the railway signalling system sustainable in the long run.

Structural health monitoring and inspection of dams based on UAV photogrammetry with image 3D reconstruction

This study presents a three-dimensional (3D) reconstruction model based on unmanned aerial vehicle (UAV) images for dam emergency monitoring and inspection. The structure from motion method is adopted to generate a high-precision 3D dam model with scene geometry by matching the features in multiple overlapping images. Then, the generated model is used to calculate the 3D point coordinates and camera pose through the bundle adjustment system. Effect of ground control points on the model accuracy is analyzed by different position sets for adaptation to various situations. The proposed emergency monitoring model is verified on two rock-fill dams, inspection model is utilized on a concrete dam, and damage detection method is achieved on a small-scale dam model with loadable panels. Results show that the proposed 3D dam reconstruction model based on UAV images can obtain satisfactory accuracy and result in a significant improvement in the dam monitoring and inspection efficiency.

Economic production lot sizing under imperfect quality, on-line inspection, and inspection errors: Full vs. sampling inspection

A manufacturer’s choice between full inspection and sampling inspection can significantly impact economic production lot sizing decision for items with imperfect quality and inspection errors. This can further change depending on whether the manufacturer opts for on-line or off-line inspection. The present literature does not consider on-line inspection in the context of acceptance sampling for economic production lot sizing decision problem. In this paper, we develop an on-line sampling inspection model and argue that the choice between full inspection and sampling inspection usually depends on the threshold level of unit inspection cost. Our paper leads to several significant findings. Firstly, we establish that such an on-line sampling inspection scheme can lead to significant savings in inventory holding costs and lead to higher optimal expected profit. Secondly, we establish the intuitively anticipated result that for high unit inspection costs, on-line sampling leads to a higher optimal expected profit than a full inspection. Contrary to the common intuition, we also show that, even when inspection costs are low, sampling inspection could still be more profitable than full inspection in many situations. Finally, we establish that on-line sampling inspection provides a higher optimal expected profit than the usually preferred off-line sampling inspection. We also discuss the impact of relaxing some of the standard assumptions. In addition to our theoretical findings, through a detailed numerical analysis, we present the impact of changes in various parameters, particularly true defective fraction, proportion of inspection errors, sample size, rework effectiveness, and unit inspection cost on the choices between full versus sampling inspection.

Inspection—Corruption Game of Illegal Logging and Other Violations: Generalized Evolutionary Approach

Games of inspection and corruption are well developed in the game-theoretic literature. However, there are only a few publications that approach these problems from the evolutionary point of view. In previous papers of this author, a generalization of the replicator dynamics of the evolutionary game theory was suggested for inspection modeling, namely the pressure and resistance framework, where a large pool of small players plays against a distinguished major player and evolves according to certain myopic rules. In this paper, we develop this approach further in a setting of the two-level hierarchy, where a local inspector can be corrupted and is further controlled by the higher authority (thus combining the modeling of inspection and corruption in a unifying setting). Mathematical novelty arising in this investigation involves the analysis of the generalized replicator dynamics (or kinetic equation) with switching, which occurs on the “efficient frontier of corruption”. We try to avoid parameters that are difficult to observe or measure, leading to some clear practical consequences. We prove a result that can be called the “principle of quadratic fines”: We show that if the fine for violations (both for criminal businesses and corrupted inspectors) is proportional to the level of violations, the stable rest points of the dynamics support the maximal possible level of both corruption and violation. The situation changes if a convex fine is introduced. In particular, starting from the quadratic growth of the fine function, one can effectively control the level of violations. Concrete settings that we have in mind are illegal logging, the sales of products with substandard quality, and tax evasion.

Modeling and Imaging of Ultrasonic Array Inspection of Side Drilled Holes in Layered Anisotropic Media.

There has been an increase in the use of ultrasonic arrays for the detection of defects in composite structures used in the aerospace industry. The response of a defect embedded in such a medium is influenced by the inherent anisotropy of the bounding medium and the layering of the bounding medium and hence poses challenges for the interpretation of the full matrix capture (FMC) results. Modeling techniques can be used to understand and simulate the effect of the structure and the defect on the received signals. Existing modeling techniques, such as finite element methods (FEM), finite difference time domain (FDTD), and analytical solutions, are computationally inefficient or are singularly used for structures with complex geometries. In this paper, we develop a novel model based on the Gaussian-based recursive stiffness matrix approach to model the scattering from a side-drilled hole embedded in an anisotropic layered medium. The paper provides a novel method to calculate the transmission and reflection coefficients of plane waves traveling from a layered anisotropic medium into a semi-infinite anisotropic medium by combining the transfer matrix and stiffness matrix methods. The novelty of the paper is the developed model using Gaussian beams to simulate the scattering from a Side Drilled Hole (SDH) embedded in a multilayered composite laminate, which can be used in both immersion and contact setups. We describe a method to combine the scattering from defects with the model to simulate the response of a layered structure and to simulate the full matrix capture (FMC) signals that are received from an SDH embedded in a layered medium. The model-assisted correction total focusing method (MAC-TFM) imaging is used to image both the simulated and experimental results. The proposed method has been validated for both isotropic and anisotropic media by a qualitative and quantitative comparison with experimentally determined signals. The method proposed in this paper is modular, computationally inexpensive, and is in good agreement with experimentally determined signals, and it enables us to understand the effects of various parameters on the scattering of a defect embedded in a layered anisotropic medium.

A new MFL imaging and quantitative nondestructive evaluation method in wire rope defect detection

As a reliable and flexible loading tool, wire rope has been widely applied in various of industries and practical applications. Nondestructive testing and evaluation for wire rope defect inspection have become necessary methods in safety guaranteeing. However, traditional one-dimensional (1D) and two-dimensional (2D) magnetic flux leakage (MFL) signal processing methods for wire rope nondestructive evaluation (NDE) may lead to miss and false detection, especially under complex operation conditions with strong noise interference. Thus, a new MFL imaging and quantitative defect recognition method is proposed. Based on the reshaped sine function, wavelet transformation and grid entropy matrix reconstruction, five different MFL imaging algorithms are presented and compared. Besides, three MFL image processing methods through the Gabor filtering, morphological filtering, and small object feature extraction as well as the quantitative centroid distance calculation are also investigated. Finally, the quantitative evaluation model for wire rope defect inspection is built and tested in the case study and performance comparison, where six groups of wire rope testing signal recognition results not only verify the feasibility and validity of the proposed quantitative methods, but also demonstrate its great application promise in wire rope defect evaluation under sophisticated conditions. Additionally, advantages and disadvantages of the proposed method as well as the future wok are discussed.

Poate oare fi proiectat un model universal al organului de inspecție judiciară?

The purpose of the paper is to estimate the possibility to design a faultless model of judicial inspection body, which would contribute equally to guarantee judicial independence and judicial responsibility, as a mechanism of exercising good judicial self- governance. To achieve the purpose the organization and the functioning of the entity with specific attributions of judicial inspection have been examined and the international standards in this field have been interpreted. As a conclusion, we found that it is impossible to design a universal model of judicial inspection. This fact, however, does not exclude the possibility of institutionalization of a judicial inspection that is appropriate for a certain judicial system, for a certain legal system, with constant concern for the reformation of such an entity, in the general context of consolidating Justice.

An advanced Wigner–Ville time–frequency analysis of Lamb wave signals based upon an autoregressive model for efficient damage inspection

It is quite straightforward to generate and acquire ultrasonic guided waves in metallic or composite structures to investigate structural defects; however, the interpretation and evaluation of the reflected/transmitted signal to extract useful information is a challenging task. This is primarily due to the dispersion and multimodal behavior of Lamb waves, which are dependent on the frequency of the excitation wave and the thickness of the material under investigation. These multimodal and dispersive behaviours lead to a complex waveform structure, and therefore require an advanced signal processing technique to decipher useful information in the time and/or frequency domains. For this purpose, due to its desirable mathematical properties, the Wigner–Ville distribution (WVD) is considered to be a powerful tool for generating time–frequency (TF) spectra and estimating the temporal and spectral features of this type of complex signal. However, due to its quadratic nature, undesirable cross-terms and spurious energies are also generated, which limit the readability and the interpretation of the spectrum. To suppress this effect, this paper describes the use of an autoregressive model based upon Burg’s maximum entropy method to modify the kernel of the discrete WVD. This technique was applied to ultrasonic Lamb wave signals obtained using various numerical and experimental configurations to extract the useful discriminatory spectral and temporal information required for the mode identification, localization, and quantification of structural damage. When used for damage localization, the proposed method precisely estimated the distance between two closely spaced notches in a metallic plate from different simulated noisy signals with a maximum uncertainty of 5%, based upon an excellent TF energy distribution. Moreover, the energy concentration (EC) of the TF energy distribution, in combination with the variation of its instantaneous frequency (IF) curve, was also effective in identifying the overlapping modes of the Lamb wave signal. Finally, for damage quantification, three TF-based damage indices, namely EC, TF flux, and IF, were extracted from five sets of specimens using the proposed TF scheme and used to train the regression model. Model testing proved that the damage indices have the potential to predict crack sizes precisely and reliably.

Risk-Based Inspection Model for Hot Mix Asphalt Pavement Construction Projects

Hot mix asphalt (HMA) is a critical component in highway construction projects. There is a lack of studies that have investigated the causal relationship between inspection activities and quality of HMA pavement. This study addresses this knowledge gap by developing a risk-based analysis model. The model includes 14 HMA critical inspection activities. The fuzzy set theory (FS) was incorporated into the model to overcome the linguistic nature of the collected data. Bayesian belief networks (BBN) were used to investigate the causal relationship between the model variables. A case study was conducted to test and verify the model. The model is capable of calculating the probability of HMA risk levels, identifying the most likely potential causes of quality shortfall risk, and providing guidance to mitigate the risk via three risk scenarios. This study contributes to the construction engineering and management body of knowledge by proposing a risk-based inspection model to investigate the impact of risk on HMA pavement quality. The proposed model may help transportation agencies optimize inspection resources by updating probabilities based on actual inspection results.

A Novel Decomposition Model for Visual Rail Surface Inspection

Rail surface inspection plays a pivotal role in large-scale railway construction and development. However, accurately identifying possible defects involving a large variety of visual appearances and their dynamic illuminations remains challenging. In this paper, we fully explore and use the essential attributes of our defect structure data and the inherent temporal and spatial characteristics of the track to establish a general theoretical framework for practical applications. As such, our framework can overcome the bottleneck associated with machine vision inspection technology in complex rail environments. In particular, we consider a differential regular term for background rather than a traditional low-rank constraint to ensure that the model can tolerate dynamic background changes without losing sensitivity when detecting defects. To better capture the compactness and completeness of a defect, we introduce a tree-shaped hierarchical structure of sparse induction norms to encode the spatial structure of the defect area. The proposed model is evaluated with respect to two newly released Type-I/II rail surfaces discrete defects (RSDD) data sets and a practical rail line. Qualitative and quantitative evaluations show that the decomposition model can handle the dynamics of the track surface well and that the model can be used for structural detection of the defect area.

Erratum for “Integrating Building Information Model and Augmented Reality for Drone-Based Building Inspection” by Donghai Liu, Xietian Xia, Junjie Chen, and Shuai Li

Erratum for “Integrating Building Information Model and Augmented Reality for Drone-Based Building Inspection” by Donghai Liu, Xietian Xia, Junjie Chen, and Shuai Li

Automatic image detection of multi-type surface defects on wind turbine blades based on cascade deep learning network

A safe operation protocol of the wind blades is a critical factor to ensure the stability of a wind turbine. Sensors are most commonly applied for defect detection on wind turbine blades (WTBs). However, due to the high cost and the sensitivity to stochastic noise, computer vision-guided automatic detection remains a challenge for surface defect detection on WTBs in particularly, its accuracy in locating defects is yet to be optimized. In this paper, we developed a visual inspection model that can automatically and precisely classify and locate the surface defects, through the utilization of a deep learning framework based on the Cascade R-CNN. In order to obtain high mean average precision (mAP) according to the characteristics of the dataset, a model named Contextual Aligned-Deformable Cascade R-CNN (CAD Cascade R-CNN) using improved strategies of transfer learning, Deformable Convolution and Deformable RoI Align, as well as context information fusion is proposed and a dataset with surface defects categorized and labeled as crack, breakage and oil pollution is generated. Moreover to alleviate the problem of false detection under a complex background, an improved bisecting k-means is presented during the test process. The adaptability and generalization of the proposed CAD Cascade R-CNN model were validated by each type of defects in dataset and different IoU thresholds, whereas, each of the above improved strategies was verified by gradual ablation experiments. Finally experiments that compared with the baseline Cascade R-CNN, Faster R-CNN and YOLO-v3 demonstrate its superiority over these existing approaches with a maximum of 92.1% mAP.

A Fastener Inspection Method Based on Defective Sample Generation and Deep Convolutional Neural Network

For the safety of railways, well-trained workers are required to check the fastener constantly, which shows the disadvantage of large time cost, huge labor cost and might being dangerous to workers. To address this and achieve automatic detection, an inspection model based on deep convolutional neural network (DCNN) is adopted in this paper. However, the inspection model suffering from the unbalanced training samples of defective vs normal due to defective fasteners are far less than normal fasteners in real railways. To tackle this problem, a novel sample generation method is proposed to generate defective fastener samples using the normal fasteners to realize sample augmentation. The comprehensive experiments are conducted on the collected real fastener samples and generated samples. The experimental results show that our method has good performance for fastener inspection on unbalanced samples and outperforms other state-of-the-art methods.

Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions

Over the past centuries, millions of bridge infrastructures have been constructed globally. Many of those bridges are ageing and exhibit significant potential risks. Frequent risk-based inspection and maintenance management of highway bridges is particularly essential for public safety. At present, most bridges rely on manual inspection methods for management. The efficiency is extremely low, causing the risk of bridge deterioration and defects to increase day by day, reducing the load-bearing capacity of bridges, and restricting the normal and safe use of them. At present, the applications of digital twins in the construction industry have gained significant momentum and the industry has gradually entered the information age. In order to obtain and share relevant information, engineers and decision makers have adopted digital twins over the entire life cycle of a project, but their applications are still limited to data sharing and visualization. This study has further demonstrated the unprecedented applications of digital twins to sustainability and vulnerability assessments, which can enable the next generation risk-based inspection and maintenance framework. This study adopts the data obtained from a constructor of Zhongcheng Village Bridge in Zhejiang Province, China as a case study. The applications of digital twins to bridge model establishment, information collection and sharing, data processing, inspection and maintenance planning have been highlighted. Then, the integration of “digital twins (or Building Information Modelling, BIM) + bridge risk inspection model” has been established, which will become a more effective information platform for all stakeholders to mitigate risks and uncertainties of exposure to extreme weather conditions over the entire life cycle.

How to Advance Water Conservancy Inspection under the New Situation

As an important means of water conservancy project construction and management, water conservancy project inspection plays an important role in the development of water conservancy. As the List of Common Problems in Water Conservancy Project Construction Inspection (Trial) (hereinafter referred to as the List) issued by the Ministry of Water Resources in 2019 provides a more standard new model for water conservancy inspection, how to improve the quality and efficiency of water conservancy inspection with accurate and efficient use of the List and maximization of its effectiveness is worth considering.

Single-shot multibox detector- and building information modeling-based quality inspection model for construction projects

Identifying, locating, and evaluating concrete construction defects in the construction sector consumes time and is overlaid by subjectivity. This study proposes a single-shot multibox detector (SSD)-based and building information modeling (BIM)-based quality inspection framework to improve work efficiency and decrease unnecessary deviations resulting from human judgment. The SSD-based defect recognition model, which can identify surface defects with 86.56% average recall and 75.34% average precision, is trained using 1387 defect images of crack, honeycombing, water infiltration, rebar exposure, and edge or corner damage. Furthermore, a division algorithm is created to divide the BIM model surfaces into pieces, in which each piece is assigned with a unique location code number. After photographing a construction project according to the coding sequence, the defects in the images are identified and located on the BIM model. Then, the frequency of the quality defects, defect aggregation zones, and quality status in the BIM model are provided to the managers for fast-paced analysis. The case study results indicate that our inspection framework has the shortest inspection time and the highest kappa coefficients (0.816, 0.901, and 0.799) for crack, honeycombing, and reinforcement exposure prediction. The inspection accuracy of our model exceeds 98.5% for each defect. Our novel quality inspection framework can remarkably improve the efficiency and effectiveness of quality management and lead to a higher level of project management.