Academic literature on the topic 'Smart inspection ROV'

After the Industry 4.0 discussion in Germany in 2011, much attention has been paid to smart factory in Korea. Since 2014, smart factories have been established and expanded in Korea. However, about 80% of them were established at a low level. In this paper, we analyze smart factory statuses in detail through an empirical research on 113 manufacturing companies that have established smart factories in Korea. We build a framework based on the resource-based view (RBV) and IT value creation process and analyze the results of five constructs—manufacturing strategy, organization, system, process, and performance—using basic statistical methodologies to derive the current statuses of manufacturing companies that have established smart factories. Our results show that implementing advanced technologies such as AI technology that can implement semi-finished and finished product quality inspection, manufacturing process optimization and product demand forecast is a challenge, particularly for SMEs. We also find that securing and managing facility data is a difficult problem. In addition, while output and material management ranked high, the utilization of integration systems, which is important when building a smart factory, was found to be extremely low. Lastly, the performance indicator results showed that yield management and defect rate were most important, while job creation through the introduction of smart factories was low. Based on the results of this study, the government may be able to determine effective smart factory policies and provide manufacturing companies with a guide on establishing a smart factory.

Abstract Smart meters allow electricity consumption readings at a high time resolution generating time series that can be investigated to extract valuable insights and detect frauds. Using a dataset with recordings from Chinese consumers, we propose an exploratory data analysis and processing to train several classifiers and assess the results. Good results are obtained with ensemble classifiers such as Random Forest (RF), eXtreme Gradient Boosting (XGB) and Multi-Layer Perceptron (MLP) with two layers and a relatively small number of neurons. Real-consumption dataset daily recorded in China consisting of over 42,000 consumers and over 1,000 days is processed with machine learning ML algorithms or classifiers to distinguish between normal and suspicious consumers. In this paper, we will compare a simple feature engineering method that consists in aggregating the data, calculating distances and density function with no feature engineering, proving that the first approach enhances the results and reduces the utility companies’ costs related to on-site inspections. The results are compared with AUC score and ROC curves as the input data is highly skewed.

The main parts of automobiles are the piston rod of the shock absorber and the steering rack of the steering gear, and their quality control is critical in the product process. In the process line, these products are normally inspected through visual inspection, sampling, and simple tensile tests; however, if there is a problem or abnormality, it is difficult to identify the type and location of the defect. Usually, these defects are likely to cause surface cracks during processing, which in turn accelerate the deterioration of the shock absorber and steering, causing serious problems in automobiles. As a result, the purpose of this study was to present, among non-destructive methods, a shock response test method and an analysis method that can efficiently and accurately determine the defects of the piston rod and steering rack. A test method and excitation frequency range that can measure major changes according to the location and degree of defects were proposed. A defect discrimination model was constructed using machine and deep learning through feature derivation in the time and frequency domains for the collected data. The analysis revealed that it was possible to effectively distinguish the characteristics according to the location as well as the presence or absence of defects in the frequency domain rather than the time domain. The results indicate that it will be possible to quickly and accurately check the presence or absence of defects in the shock absorber and steering in the automobile manufacturing process line in the future. It is expected that this will play an important role as a key factor in building a smart factory.

Pear decline (PD) is a serious disease of pear (Pyrus communis L.) caused by ‘Candidatus Phytoplasma pyri’, which belongs to the subgroup 16SrX-C of the apple proliferation (AP) group of phytoplasmas (3). Pear seedlings from the Agriculture and Agri-Food Canada (AAFC) pear breeding program, which have been selected for advanced test and grower trials, are routinely submitted to the Canadian Food Inspection Agency (CFIA) Sidney Laboratory (formerly, CFIA Centre for Plant Health, Saanichton, BC) for virus testing at the same time that propagation is initiated to produce trees for further evaluations. In early 2007, the CFIA reported that samples of two seedling selections submitted in 2005 tested positive for phytoplasmas by a nested PCR assay with phytoplasma universal primers P1/P7 (1), followed by phytoplasma universal primers fU5/rU3 (2) and real time PCR with universal phytoplasma primers developed by the CFIA-Sidney (personal communication). Phytoplasmas present in both selections were subsequently identified as ‘Ca. P. pyri' strains by nested PCR with the P1/P7 primers followed by PD/peach yellow leaf roll (PYLR)-specific primers fPD/rPDS (2,4). These were the first PD-positive results from many samples submitted over the years for testing. Following PD-positive diagnoses for the seedling trees, others propagated from these seedling trees were removed from the nursery. When tested by PD-specific nested PCR (P1/P7 then fPD/rPDS), one selection had 39 of 79 nursery trees (49%) that were PD positive, while the other selection had 27 of 96 trees (28%) testing as PD positive. PCR amplification of DNA isolated from leaves of six of the propagated trees, with primer pair fPD/rPDS, yielded an ~1,400-bp product that was sequenced. A consensus sequence of 1,313 bp (GenBank Accession No. GU565959) was subjected to a nucleotide BLAST search of the NCBI database and showed 100% nt identity with sequences of phytoplasmas PD1 (AJ542543) and PYLR (Y16394). Subsequently, the PD-positive results from leaf, dormant shoot, and root tissues from the original seedling trees were confirmed by PD-specific nested PCR. On the original seedling trees, visible symptoms typical of PD, especially premature leaf coloration, were observed in late summer 2008 and samples taken of green and red leaves were subjected to PD-specific PCR. Red leaves were PD-positive, while green leaves were mostly PD-negative. Pear leaves, dormant shoots, and roots collected from research and commercial orchards in southern Ontario in 2007 and 2008 were subjected to PD-specific nested PCR (P1/P7 then fPD/rPDS), AP-specific nested PCR (P1/P7 then fO1/rO1) (2), as well as the universal phytoplasma nested PCR (P1/P7 then fU5/rU3), resulting in the identification of PD-positive trees of several cultivars. The sequence of the 1,057-bp amplicon from accession PYR0190 (selection HW615), with AP-specific primers fO1/rO1, was deposited in GenBank (GU475131). Although there have been no previous reports of PD in Ontario, Canada, it would appear that PD has been present for some time based on the number and distribution (both geographic and cultivar) of positive samples. References: (1) S. Deng and C. Hiruki. J. Microbiol. Methods 14:53, 1991. (2) K.-H. Lorenz et al. Phytopathology 85:771, 1995. (3) E. Seemüller et al. J. Plant Pathol. 80:3, 1998. (4) C. D. Smart et al. Appl. Environ. Microbiol. 62:2988, 1996.

AbstractDesign of new unmanned underwater vehicles (UUVs) is a continuous process since decades, where finding an optimal design for a specific application is still a challenging subject. New inspection class category remotely operated vehicle (ROV) is developed to overcome some disadvantages of existing ROVs of the same category. It has been taken into consideration in the new design to be small, capable of maneuvering freely in 6 DOF, closed-hull body of minimum water resistive forces, low-cost components which work with high integration, one-duct tether (at low cost) and software program to be used with any PC instead of special control station. Detailed design of the internal network that combines sensors, thrusters’ controllers, and camera of the ROV is shown and smart communication architecture between the ROV and the control station is also introduced. Finally, in the computer layer, many aspects are discussed including communication protocol between the control station and the ROV, high-precision orientation angles calculation using inertial measurement unit (IMU), heading calculation of the ROV, and GUI of the control station.

Abstract In Dec. 2017, a four-loop 1300 MW Electricite de France (EdF) plant in France, Belleville Unit 2, experienced a complete wear through and separation of one of their thermal sleeves at a rodded control rod drive mechanism (CRDM) location. During low power physics testing and rod drop testing, the plant had difficulty stepping the control rod into the core. The rod was freed by exercising the drive rod but was then stopped prior to full insertion during the rod drop test. The failure to insert the rod was caused by the worn thermal sleeve flange remnant. In response to this operational experience, Westinghouse notified the U.S. Nuclear Regulatory Commission (NRC) of this defect pursuant to the requirements of title 10 of the Code of Federal Regulations (10 CFR) Part 21 and published a nuclear safety advisory letter (NSAL) that provides details on the thermal sleeve flange issue and inspection recommendations. In their notification, Westinghouse determined that there was no immediate safety concern, but a substantial safety concern may be possible in the unlikely event that there is interference with the movement of more than one control rod. This paper summarizes the NRC staff's safety analysis of this issue. The staff conducted detailed probabilistic and risk analyses and followed up with a smart sample inspection of the industry's thermal sleeve inspection programs to verify the analysis assumptions. The staff found the risk of core damage to be low, their assumptions appropriate, and the industry was following the details of the NSAL appropriately.

Summary Offshore fields present a growing need to guarantee safety and productivity while minimizing operational costs and increasing remote assistance. Brownfields are more exposed to risks due to the presence of aged assets requiring in depth inspections to assess potential life extensions. This challenge was tackled with a comprehensive approach to asset integrity management based on the enhanced use of digital solutions to enable new health care services on offshore assets, like CALM Buoys. In line with the recent Oil & Gas industry trends, new digital technologies have been recently developed and deployed on board our fleet of CALM (Catenary Anchor Leg Mooring) Buoys, such as the 3C Telemetry system, Inspection Tablets, the IDEA Web Portal and the Marine Drone. All these new digital solutions will be presented in the proposed paper concerning their technical capabilities and the overall integrity performance improvements achieved with their enhanced use on offshore assets. The 3C Telemetry system converts and upgrades CALM Buoys into smart, internet-friendly offloading terminals, connecting the system to Cloud services and ensuring secured data transmission, treatment, storage, and privacy, while delivering reliable accurate information to operators anywhere in the world. Inspection tablets are used to optimize health check campaigns on Buoys with a real-time and remote back office engineering support. These systems can also be connected to the IDEA (Imodco Digital Experience Access) Web Portal to allow online data visualization and analysis of the mooring systems performance. "The Marine Drone is an unmanned survey vehicle to perform diverless UWILD (Underwater Inspection in Lieu of Dry-docking). The system can perform in depth visual inspections with its ROV (Remotely Operated Vehicle) and high-resolution subsea layout mapping of CALM buoys’ structures with its 3D bathymetry system, all providing high quality digital data post processed by advanced analytical tools for integrity analysis and preventive maintenance planning" (Castro, R., et al. 2020). Data management has become the most valuable asset for companies seeking to have a better understanding and to continuously improve operations. This paper will demonstrate how Buoys and passive (process wise) equipment, like Turrets, can be operated in new ways: 1. Connected Asset (IoT): 3C Telemetry, Tablets, and the Marine Drone. 2. Platform to share/connect data to algorithms/users: IDEA System. 3. New operating business models enabled by health care approach.

The ability to be programmed for a wide range of tasks is what differentiates robots from dedicated automation. Consequently, robots can be faced with often-changing requirements and conditions. Conventional application development based on teach programming takes robots out of production and occupies personnel, limiting robots’ effectiveness in these environments. Off-line programming solves these problems, but robot inaccuracy must be compensated by a combination of calibration, compliance, and sensing. This complicates up-front systems engineering and application development, but results in systems that can operate in a wider range of requirements and conditions. Performance can be optimized if application tolerances and process uncertainties are known. If they often change, optimization must be done dynamically. Automating this optimization is a goal of smart manufacturing. With its trend of increasing connectivity between the components of robotic systems both within workcells and to the enterprise, exchanging this information has become more important. This includes tolerance information from design through process planning to production and inspection, and measurement uncertainty from sensors into operations. Standards such as ISO 10303 (STEP), the Quality Information Framework (QIF), the Robot Operating System (ROS), and MT-Connect support this exchange to varying degrees. Examples include the assignment of assembly tasks based on part tolerances and robot capabilities; the automated generation of robot paths with tolerances arising from sensed obstacles; and the optimization of part placement to minimize the effects of position uncertainty. This paper examines requirements for exchanging tolerance and uncertainty in robotics applications, identifies how these requirements are being met by existing standards, and suggests improvements to enable more automated information exchange.