Inspection Strategy Research Articles

A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter

Drive-by bridge inspection using acceleration responses of a passing vehicle has great potential for bridge structural health monitoring. It is, however, known that the road surface roughness is a big challenge for the practical application of this indirect approach. This paper presents a new two-step method for the bridge damage identification from only the dynamic responses of a passing vehicle without the road surface roughness information. A state-space equation of the vehicle model is derived based on the Newmark-[Formula: see text] method. In the first step, the road surface roughness is estimated from the dynamic responses of a passing vehicle using the dual Kalman filter (DKF). In the second step, the bridge damage is identified based on the interaction force sensitivity analysis with Tikhonov regularization. A vehicle–bridge interaction model with a wireless monitoring system has been built in the laboratory. Experimental investigation has been carried out for the interaction force and bridge surface roughness identification. Results show that the proposed method is effective and reliable to identify the interaction force and bridge surface roughness. Numerical simulations have also been conducted to study the effectiveness of the proposed method for bridge damage detection. The vehicle is modeled as a 4-degrees-of-freedom half-car and the bridge is modeled as a simply-supported beam. The local bridge damage is simulated as an elemental flexural stiffness reduction. Effects of measurement noise, surface roughness and vehicle speed on the identification are discussed.The results show that the proposed drive-by inspection strategy is efficient and accurate for a quick review on the bridge conditions.

Characterization and Optimization of Acoustic Field for Curved Array Probe

The focusing principle and acoustic field characteristics of curved array probe are studied. Delay laws are first computed in the same way as linear array probe, and its effects on the axial acoustic pressure distributions are quantitatively examined. It is shown that the maximum points of axial acoustic pressure occur at positions deviating from the predefined focal depths. To further analyze the focusing principle of curved array probe, simulations of acoustic field are conducted under different settings of focal depth and active aperture size. It reveals that the circular array profile and the inconsistent electronic delay laws make the pulsed ultrasonic waves unable to constructively interfere at target positions. Subsidiary beams arise and further interfere with the axial acoustic pressure distributions of the main beam. To control the transmitted acoustic field, and thus customize the inspection strategy, an optimized delay law calculation scheme is proposed. The good behavior is well validated both by theoretical calculation and experimental examination.

A Semi-quantitative Approach Development for Risk-based Inspection in a Petrochemical Plant

BACKGROUND: Recently, oil and gas industries have focused on risk-based methods to specifically compile inspection, maintenance, and management strategies worldwide.
 AIM: The provided method in this study is a management tool to recognize and then prioritize the equipment’s risks and, consequently, propose an inspection plan based on these risks. In fact, it is a risk analysis aspect of an inspection plan for industrial equipment.
 MATERIALS AND METHODS: The study is performed on de-ethanizer unit, considering variety of process equipment including distillation tower, exchanger, pressure vessel, drum, and related piping. To initiate the study, first, the risk-based inspection (RBI) team formed, then all required data were gathered and 22 equipment were identified based on the method.
 RESULTS: After RBI be performed and risks are estimated, 13.6% of equipment rated on high risk, 63.63% rated on medium risk, and 22.72% rated on low-risk classification. Based on specific risk criteria for equipment, we determined new parameters as inspection level and type of inspection and finally integrated them. As one of the obtained results, the overhaul maintenance of the unit was postponed to a later time and for equipment that should inspect before this year, reduction risk strategy and advance none distractive test defined, and their inspection time also postpone to 2015 from 2012 to 2013.
 CONCLUSION: This decision results in saving a shutdown cost, inspection cost, reduction of failure, and increasing of equipment reliability factor. The result of this study will be helpful to assist similar industries improving their risk management plans.

Strain Monitoring Strategy of Deformed Membrane Cover Using Unmanned Aerial Vehicle-Assisted 3D Photogrammetry

Large structures and high-value assets require inspection and integrity assessment methodologies that ensure maximum availability and operational capabilities. Large membranes are used as floating covers at the anaerobic wastewater lagoons of Melbourne Water’s Western Treatment Plant (WTP). A critical function of this high-value asset pertains to the harnessing of the biogas gas generated at these lagoons as well as protecting the environment from the release of odours and greenhouse gases. Therefore, a proactive inspection and efficient management strategy are required to ensure these expensive covers’ integrity and continued operation. Not only is identifying the state of stress on the floating cover crucial for its structural integrity assessment, but the development of rapid and non-contact inspections will significantly assist in determining the “real-life” performance of the cover for superior maintenance management. This study investigates a strain determination method for WTP floating cover which integrates unmanned aerial vehicle (UAV)-assisted photogrammetry with finite element analyses to determine the structural integrity of these covers. Collective aerial images were compiled to form 3D digital models of the deformed cover specimens, which were then employed in computational and statistical analyses to assess and predict the strain of the cover. The findings complement the future implementation of UAV-assisted aerial photogrammetry for structural health assessment of the large floating covers.

Online coverage and inspection planning for 3D modeling

In this study, we address an exploration problem when constructing complete 3D models in an unknown environment using a Micro-Aerial Vehicle. Most previous exploration methods were based on the Next-Best-View (NBV) approaches, which iteratively determine the most informative view, that exposes the greatest unknown area from the current partial model. However, these approaches sometimes miss minor unreconstructed regions like holes or sparse surfaces (while these can be important features). Furthermore, because the NBV methods iterate the next-best path from a current partial view, they sometimes produce unnecessarily long trajectories by revisiting known regions. To address these problems, we propose a novel exploration algorithm that integrates coverage and inspection strategies. The suggested algorithm first computes a global plan to cover unexplored regions to complete the target model sequentially. It then plans local inspection paths that comprehensively scans local frontiers. This approach reduces the total exploration time and improves the completeness of the reconstructed models. We evaluate the proposed algorithm in comparison with other state-of-the-art approaches through simulated and real-world experiments. The results show that our algorithm outperforms the other approaches and in particular improves the completeness of surface coverage.

A Three-Step Strategy for Recommendation in Mobile Police Video Secure Access

Mobile police video security access link is an important boundary channel to ensure the secure and trusted access of mobile video in the private video network. Therefore, this paper proposes a three-step strategy for access control, encrypted transmission, and wireless video security inspection. While carrying the ability to carry higher video streams, it is both safe and reliable, and has strong scalability in performance. This paper analyzes the access mechanism of SSL VPN in encryption, identity authentication and resource access control. Proposes a mobile police video security access link solution based on SSL VPN for network clients. Respectively presents a secure access solution for wired video and wireless video. And a load balancing access scheme based on NAT address translation is designed. Finally, a test scenario for the maximum number of concurrent users and server bandwidth performance is designed, implemented and analyzed. In actual application, the trial projects have been highly recognized by public security customers.

Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels. Through the design of a system that enables a robot to learn inspection strategies from a human operator, we present a hybrid semi-supervised system capable of learning interpretable and verifiable models from demonstrations. The system induces a controller program by learning from immersive demonstrations using sequential importance sampling. These visual servo controllers are parametrised by proportional gains and are visually verifiable through observation of the position of the robot in the environment. Clustering and effective particle size filtering allows the system to discover goals in the state space. These goals are used to label the original demonstration for end-to-end learning of behavioural models. The behavioural models are used for autonomous model predictive control and scrutinised for explanations. We implement causal sensitivity analysis to identify salient objects and generate counterfactual conditional explanations. These features enable decision making interpretation and post hoc discovery of the causes of a failure. The proposed system expands on previous approaches to program synthesis by incorporating repellers in the attribution prior of the sampling process. We successfully learn the hybrid system from an inspection scenario where an unmanned ground vehicle has to inspect, in a specific order, different areas of the environment. The system induces an interpretable computer program of the demonstration that can be synthesised to produce novel inspection behaviours. Importantly, the robot successfully runs the synthesised program on an unseen configuration of the environment while presenting explanations of its autonomous behaviour.

Bayesian decision analysis for the optimization of inspection and repair of spatially degrading concrete structures

Pre-posterior analyses can be used to determine the value of information (VoI) of inspection strategies. As such, decisions can be made on whether or not it is relevant to perform inspections and to what extent this is the case, before these are implemented. In this article, a pre-posterior analysis framework is developed for degrading (concrete) structures, accounting for time-dependent behaviour and spatial variability. To account for the time-dependent degradation, a deterioration model is implemented and failure probabilities and costs are evaluated at different points in time. In relation to the spatial nature of the degradation, the structure is discretized in zones and elements and spatial correlation is modelled by random fields and hyperparameters. Although the framework is generally applicable, it is applied to concrete structures subjected to corrosion. First, the framework is illustrated by a simple analytical example to demonstrate the calculation procedure and show how the distributions of hyperparameters and random fields in the corrosion model can be updated based on obtained inspection outcomes. More specifically, the information on non-inspected elements which can be extracted from measurements performed elsewhere in the structure is quantified. Finally, the framework is also applied to a case study in order to prove the feasibility to optimize the timing and location of inspections in relation to the corrosion state of a bridge girder, considering the VoI as a decision variable.

Optimal locations for non-destructive inspections to verify direct assessment of internally corroded pipelines

Abstract This paper introduces a method for risk-based inspection planning based on maximizing the value of information (VOI) to select optimal verification sites for validating the results of direct assessment (DA) of internally corroded pipelines. The proposed method improves existing industry practices for DA, which are not risk-based. The effect of different corrosion growth assumptions on the VOI and optimal inspection strategies is investigated. The results show that different corrosion growth assumptions lead to different optimal verification sites. They also demonstrate that the proposed model is effective at facilitating the selection of optimal verification sites and for improved decisions regarding maintenance actions for internally-corroded pipelines.

Research on Optimal Nonperiodic Inspection Strategy for Traction Power Supply Equipment of Urban Rail Transit Considering the Influence of Traction Impact Load

The inspection and maintenance work of traction power supply equipment (TPSE) is growing heavy with the construction and operation of a large number of urban rail transits (URTs). In order to improve the existing regular preventive maintenance mode and implement a “high-efficiency with low-cost” maintenance strategy, this article proposes an optimal nonperiodic inspection decision-making method for TPSE of URT considering the influence of impact load. First, the comprehensive degradation model of TPSE is established based on the linear Wiener process and the compound Poisson process. Second, the nonperiodic inspection model of TPSE in a single-maintenance period is established by introducing inspection planning functions for three types of degradation processes. With the lowest operating cost per unit time as the decision goal, the optimal maintenance plan is, thus, achieved. Finally, the proposed decision-making method is applied to the benchmark data taken from a URT administration in China related to a dry-type traction transformer in five years. The results show that the proposed method can help formulate efficient inspection strategies with the optimal cost. In addition, sensitivity analysis reflects the influence of different parameters on the optimization results, which helps to identify which parameter exerts the largest influence.

Uncertainty evaluation in the prediction of defects and costs for quality inspection planning in low-volume productions

Quality-inspection strategies play a pivotal role in providing consumers with high-quality and defect-free products. In order to withstand the competition, organizations have an increasing interest in designing quality controls that are effective in detecting defects and economically viable. Recent studies have proposed a preliminary method to evaluate inspection effectiveness and cost in low-volume assembly processes, characterized by the production of single units or small-sized lots, even spread in long periods. Based on this method, the present paper aims to define a procedure to evaluate the robustness of defect and cost predictions in quality inspections of low-volume productions. The research questions which are specifically addressed concern how the uncertainty of models for defectiveness prediction can be assessed, and how this uncertainty may affect the selection of the most effective and affordable inspection strategy. The proposed approach allows to accurately analyze and compare different inspection strategies in terms of effectiveness and cost. First, the uncertainties of the statistical variables of the model for defectiveness prediction are evaluated by applying the law of combination of variances. Then, by combining the contributions of several inspection design parameters, the uncertainty is propagated to two indicators which quantify the overall effectiveness and cost of inspection strategies. In order to test the proposed methodology, a practical application concerning the assembly of mechanical components in an industrial manufacturing context is presented and discussed.

Excitation of guided wave modes in arbitrary cross-section structures by applied surface tractions

To assure the integrity and safety of truss structures, much work has been done for inspection in bars using ultrasonic guided waves methods. Guided waves have been widely used for Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) because of their long propagation distance and reliable responses. Though good for assessment of guided wave in the regular section structures such as cylinders, pipes and rails, the excitation of guided wave modes in irregular bars lack further research due to the arbitrary cross-section. This paper presents the excitation and propagation of guided waves in arbitrary cross-section structures by coupling the normal mode expansion (NME) method with the semi-analytical finite element (SAFE) method. Then, a Tee-bar example of finite element (FE) simulations is performed to validate the proposed SAFE-NME method. Moreover, the validated method is used to develop a double mode inspection strategy for multi-damages identification, showing the potential for NDT & SHM applications.

TECNOMUSE: a novel, RPC-based, muon tomography scanner for the control of container terminals

Every year, 700 million twenty-foot (container) equivalent units pass through the container terminals of the harbours all over the world. Only a small percentage (34%) are scanned to inspect the presence of radioactive materials. The need for controls is hampered essentially by three factors: the amount of both time and personnel necessary to control each container and the use of scanning methods based on systems potentially harmful for the personnel itself. Muon tomography can become a strategy for fast and reliable inspection of containers without using ionizing radiation. This technology takes advantage of multiple Coulomb scattering of the muons (particle produced by cosmic rays) through media to understand the composition and the geometry of the scanned volume. The TECNOMUSE project has the purpose to realize a muon tomography scanner based on a novel geometry and, for the first time, using Resistive Plate Chambers detectors. In this work, the preliminary results from the TECNOMUSE scanner are evaluated via Monte Carlo simulations. Many different simulations have been made with the aim to assess the detection capabilities of the device, its spatial resolution and the time required to reconstruct and distinguish different materials.

Supply chain product quality control strategy in three types of distribution channels.

Based on a three-stage stackelberg dynamic game analysis, this paper constructs a product quality control strategy model for three types of distribution channels (direct channel, retail channel and mixed channel) in a three-echelon supply chain, which is composed of one manufacturer, one retailer and the final customer. This paper studies how to design a distribution channel strategy and provides a product quality control strategy. Furthermore, this paper analyzes three types of distribution channels strategy in the context of how they influence a manufacturer's product quality decision and quality prevention strategy, a retailer's product pricing decision and quality inspection strategy, and the final customer's product demand decision. We compare the manufacturer's product quality level, quality prevention effort level, wholesale price, direct sale price and the retailer's quality inspection effort level, retail price in three types of distribution channels and determine the manufacturer's and retailer's expected profits function and the final customer's consumer surplus. In addition, we introduce the distribution channels demand elasticity ratio to analyze the influence of determining the product quality control strategy. Most importantly, we conduct a numerical sample analysis that will prove the model's effectiveness and indicate a specific application in practice.

GTHI: A Heuristic Algorithm to Detect Malicious Users in Smart Grids

With many countries trying to establish their own smart grids, smart meters are massively deployed throughout the world. Although smart meters are manufactured with low tamper-resistant components, malicious users with just a moderate level of computer knowledge are able to launch cyber attacks. By manipulating electricity consumption readings to smaller values, malicious users can steal electricity from utility companies. To reduce the losses incurred by electricity theft, utility companies must provide preventative and detective methods to identify fraudulent behaviors. Our goal is to identify all malicious users in a neighborhood area network within the shortest detection time. To achieve this goal, we propose Group Testing based Heuristic Inspection (GTHI) algorithm, which can estimate the ratio of malicious users on-line, mainly by collecting the information that how many malicious users have been identified during the inspection process. Based upon the ratio of malicious users, the GTHI algorithm adaptively adjusts inspection strategies between an individual inspection strategy and a group testing strategy. This helps shorten the detection time. Furthermore, when applying the group testing strategy, the GTHI algorithm also determines the group size of users to be probed in line with the estimated malicious user ratio. Experiment results show that compared to existing methods, the GTHI algorithm has advantages of conducting fewer inspection steps or being more practical.

Planning offline inspection strategies in low-volume manufacturing processes

The design of appropriate and successful quality-inspection strategies plays an important role within manufacturing organizations. It is one of the leverage factors to ensure customers the expected quality level of products. In the case of low-volume or single-unit productions, such as those produced with emerging additive manufacturing (AM) technologies, the design of quality controls may be problematic due to the lack of historical data and the inadequacy of traditional statistical approaches. In literature some studies focused on the design and selection of in-process inspection strategies for low-volume productions. However, in some cases, such as AM productions, in-process inspections may not be adequate, easy to perform or cost-effective. To this end, the present work aims at identifying a general methodology for planning offline inspections for low-volume productions. The specific research question addressed concerns how to select the best compromise between effectiveness and affordability of alternative offline inspection strategies for such productions. The proposed method consists of formulating a probabilistic model for predicting defects and defining two performance indicators that outline the overall effectiveness and affordability of an offline inspection strategy. This approach is finally applied to a real low-volume AM production of parts manufactured by selective laser melting (SLM) technique.

Research of the Inspection Optimization Strategy for Distribution Network Based on Comprehensive Information Model

To improve the quality and efficiency of inspection tasks, this paper studies the path planning optimization strategy for distribution equipment inspection of distribution networks. The comprehensive information model of each type of distribution equipment to be inspected is established by integrating the real-time monitoring information, geographic information and historic information effectively. The inspection path optimization model with an admissible cost function is established and solved by using the heuristic search A* algorithm. Comparison of the results of several inspection path planning strategies are provided to verify the effectiveness of the proposed strategy.

High resolution X-ray computed tomography: A versatile non-destructive tool to characterize CFRP-based aircraft composite elements

The aim of the current study is to propose a versatile, non-destructive inspection strategy to evaluate the structure of two different aircraft carbon fibre reinforced polymer (CFRP) -based composite configurations, which are widely used for structural elements, respectively layered composite and sandwich structure. X-ray computed tomography (CT) has been used as a flexible method for assessment of porosity levels in CFRP components in both types of configuration, permitting to investigate the volume distribution of the individuated pores/voids, giving an interactive 3D exploration and quantitative analysis of porosity by the complete 3D volume rendering of the analysed composites. Moreover, specific data analysis could supply detailed information on size, shape and position of pores or defect or misalignment which could be a valuable indication for damage tolerant design methodology or local stress analysis. This presented non-destructive approach based on CT can be a good alternative to standard destructive methods based on the acid digestion or 2D microscopy for porosity evaluation in composite, especially for severe stressed element in aerospace structure. Of course, the high resolution X-ray CT cannot substitute the ultrasounds test for analysing full scale components, but it can be complementary to it to provide accurate quantitative results on small samples used as references.

A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface.

To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.

Forecasting Risk of Service Failures Between Successive Rail Inspections: A Data-Driven Approach

Service failure is an incident when a defect of size over threshold value is noticed and the track is taken out of service. They are a major concern for the railroads due to their impact on economic and safety aspects of the rail company. Despite the effective inspection strategies developed by the railroad, service failure may still occur in their network because of the growth of an undetected defect or the defect that is initiated after an inspection. Various factors influence the growth rate of a defect, causing service failure. Identifying and understanding these factors will assist in accurately predicting the risk of a service failure. This paper develops a data-driven approach based on accelerated failure time model using the data collected from one of the Class-I US railroads during the period from 2011 to 2016. Different distributions of the hazard function are evaluated, and the log-normal distribution is found to be the best fit. This study identifies the following factors: past rail defects, past geometry defects, days from the last grinding, product of rail age and degrees of curvature and infrastructural data such as the number of turnouts, grade of rail and rail segment length to have an influence on failure time of the rail. Temporal transferability model validation and comparison with other machine learning approaches are performed using the data collected in the year 2016. The results of this study are useful for railroads to develop effective strategies for rail inspections and preventive maintenance.