Browse Topic: Inspections
Civil aircraft, as typical complex product systems, exhibit characteristics such as a high concentration of high-tech technologies, strong interdisciplinarity, a high level of system integration, long development cycles, substantial project investments, and complex management. During the R&D process of civil aircraft projects, there are often high risks in performance, cost, and schedule. Delays in the schedule can lead to losses in project manpower and material resources, as well as project failure. A mature objective criteria system for maturity assessment provides a reference basis for determining whether the project has reached its optimal state at a specific stage, thereby reducing project management risks and increasing the probability of project success. This research will adopt a research approach combining theoretical studies with practical case analysis. First, it will conduct extensive and in-depth investigations into various maturity models and their applications across the entire product lifecycle within relevant fields. A requirement maturity model and requirement maturity KPI (Key Performance Indicator) indicators will be established to clarify the maturity status of requirements at different development stages, enabling judgment of whether the project is ready to proceed to the next development phase. Concurrently, by developing a KPI statistical system platform integrating application servers and data processing tools, a scientific and quantitative inspection mechanism will be implemented to visualize project development progress, status, and risk data. This will provide actionable insights for project decision-making and achieve effective project management and control.
In order to reduce traffic accidents caused by cars straying from lanes, a lane line recognition and deviation warning system based on machine vision is designed. It mainly includes image preprocessing, lane line detection, and the design of a deviation warning model. “In this study, an ROS-based intelligent vehicle-mounted camera is adopted for road image collection. To reduce the computational load of data processing while guaranteeing the algorithm’s accuracy and reliability, grayscale conversion and region of interest (ROI) extraction are implemented to finish the image preprocessing stage. Additionally, a fusion strategy of global and local thresholds is introduced to enhance both the operational speed and detection accuracy of the algorithm” use the Canny operator for the edge feature extraction; and complete the fitted lane lines with the improved Hough transform. Finally, based on the Kalman filter and camera viewpoint conversion coefficient algorithm, the lane line offset is detected in real time, and the deviation is judged in combination with the monitoring interface. Simulation experiments show that the system is able to effectively recognize the lane line and judge the deviation status under the condition of setting the offset threshold of 70 pixels, which significantly improves the accuracy and real-time performance of the lane deviation warning and provides effective technical support for reducing traffic accidents.
After four decades of research and 3.5 year prototype testing campaign, Penn State's pericyclic transmission technology demonstrator, dubbed the 'Pericycler', has achieved its operating speed of 5,000 RPM at 17 HP. The characterization of this system by experimental efficiency and vibration represents a major milestone in pericyclic gear technology. A post-test inspection procedure was performed to analyze component wear and validate hypotheses on mesh behavior. This work concludes with structural, tribological, and instrumentation modifications to the Pericycler for future testing.
Rolling-element bearings in rotorcraft dynamic systems are critical components susceptible to rolling contact fatigue (RCF), a dominant degradation mechanism manifesting through subsurface-initiated spalling, surface micropitting, and fatigue fractures. Robust inspection strategies compliant with EASA and FAA requirements are therefore essential. Traditional methods are often invasive, requiring disassembly, and are susceptible to human-factor errors. Smart Duplex introduces a design-for-monitoring architecture integrating in-situ videoscopic and coherence scanning interferometry (CSI) for high-resolution 3D surface mapping, including under partial grease coverage. This paper details a repeatability and reproducibility (R&R) framework ensuring metric consistency; a maintainability assessment projecting significant man-hour reductions and high availability; certification rationale emphasizing airworthiness improvements via enhanced detectability, workload reduction, and digitized inspection records; and an airworthiness mapping supporting threat assessments, Airworthiness Limitations Section (ALS) entries, and usage-based maintenance credits. By embedding sensing capability and digitizing inspection records, Smart Duplex minimizes downtime, mitigates human-factor errors, and facilitates predictive maintenance, optimizing cost, enhancing performance, and ultimately improving safety.
This presentation discusses the evolution of SMART Layer, based SHM system from a targeted inspection aid to a key enabler of IVHM and CBM strategies. Lessons learned from fielded rotorcraft applications are discussed, along with a practical path for integrating the SHM system components into both sustainment programs and future aircraft designs. The role of automation, data management, and health-state awareness in supporting aircraft readiness and lifecycle optimization is will also be discussed.
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
The light and light signaling devices installation test as per as per IS/ ISO 12509:2004 & IS/ISO 12509:2023 for Earth Moving Machinery / Construction Equipment Vehicles is a mandatory test to ensure the safety and comfort of both road users and operators. Considering the shape and size of construction equipment vehicles, accurate measurement of lighting installation requirements is crucial for ensuring safety and regulatory compliance. The international standard IS/ISO 12509:2004 & IS/ISO 12509:2023 outlines specific criteria for these installation requirements of lighting components, including the precise measurement of various dimensions to ensure optimal visibility and safety. Among these dimensional requirements, the dimension 'E' i.e., the “distance between the outer edges of the machine and the illuminating surface of the lighting device” plays a critical role in the performance of vehicle lighting systems. Traditional methods of measuring this dimension, such as using a measuring tape and long straight rod, in another method Using Rope, Plumb and Measuring tape have limitations in terms of precision and consistency due to machine size and shape. This paper presents a method development approach utilizing a 3-Dimensional planar laser for measuring dimension 'E' in Construction Equipment Vehicles (CEVs). Measurement through the planar laser method is found to offer significantly higher accuracy compared to conventional measuring techniques, particularly when applied to the complex shapes and sizes of CEV’s such as Motor Graders, Wheel Loaders and Backhoe Loaders. This approach not only enhances the measurement accuracy but also improves the efficiency of the testing process. The paper discusses the methodologies, results, comparison of 3 measuring methods and potential applications of Planar laser in the context of IS/ISO 12509:2004 & IS/ISO 12509:2023, offering a promising alternative method for future testing and certification of Construction Equipment Vehicle’s lighting systems.
In order to ensure the safety of urban rail transit, X-ray baggage inspection systems have been widely used. However, at the current, baggage inspection mainly relies on manual judgment, which has the problems of high labor cost, low efficiency and lack of objectivity. This paper aims to realize intelligent and automated baggage inspection and studies and designs a dangerous goods detection method based on spatial feature transformation network. This method adopts target detection technology based on Anchor mechanism, and transforms high-level semantic features spatially spatial transformation network layer. The feature pyramid fusion approach is employed to integrate the transformed high-level semantic features with the low-level detailed features, resulting in the generation of multi-scale features that are rich in semantic information. This innovative method enables accurate prediction of the categories and precise localization of various hazardous items within X-ray images, thereby significantly enhancing the detection accuracy.It proves the feasibility of using intelligence algorithms for automatic security inspection, and provides strong support for the implementation of automatic monitoring of urban rail transit baggage inspection.
Accurate defect quantification is crucial for ensuring the serviceability of aircraft engine parts. Traditional inspection methods, such as profile projectors and replicating compounds, suffer from inconsistencies, operator dependency, and ergonomic challenges. To address these limitations, the 4D InSpec® handheld 3D scanner was introduced as an advanced solution for defect measurement and analysis. This article evaluates the effectiveness of the 4D InSpec scanner through multiple statistical methods, including Gage Repeatability and Reproducibility (Gage R&R), Isoplot®, Youden plots, and Bland–Altman plots. A new concept of Probability of accurate Measurement (PoaM)© was introduced to capture the accuracy of the defect quantification based on their size. The results demonstrate a significant reduction in measurement variability, with Gage R&R improving from 39.9% (profile projector) to 8.5% (3D scanner), thus meeting the AS13100 Aerospace Quality Standard. Additionally, the 4D InSpec scanner improved detection accuracy, provided automated defect quantification, and eliminated the need for time-consuming replication processes. Beyond performance improvements, the adoption of the 4D InSpec scanner led to a 75% reduction in direct labor time, significant cost savings, and the elimination of ergonomic risks and human error associated with traditional inspection methods, and enhanced defect reporting and data collection. The article closes with implementation requirements and areas for future improvement.
This document provides user information on best practice methods and processes for the in-service inspection, evaluation, and cleaning of expanded beam (EB) fiber optic interconnect components (termini, alignment sleeves, and connectors), test equipment, and test leads based on the information provided in AIR6031 and ARP6283. This document provides the user with a decision-making tool to determine if the fiber optic components are acceptable for operation with EB fiber optic termini.
In today’s digital age, the use of “Internet-of-Things” devices (embedded with software and sensors) has become widespread. These devices include wireless equipment, autonomous machinery, wearable sensors, and security systems. Because of their intricate structures and properties there is a need to scrutinize them closely to assess their safety and utility and rule out any potential defects. But, at the same time, damage to the device during inspection must be avoided.
Warehouse logistics increasingly rely on automation in the form of autonomous mobile robots (AMRs), scanners, complex conveyors, and fleet management systems for seamless operation, but it’s the ubiquitous, century-old pallet that remains the critical support system. Make no mistake, if even one of those thousands of pallets is defective, it can create havoc in the warehouse.
Repartly, a startup based in Guetersloh, Germany, is using ABB’s collaborative robots to repair and refurbish electronic circuit boards in household appliances. Three GoFa cobots handle the sorting, visual inspection and precise soldering tasks enabling the company to enhance efficiency and maintain high quality standards.
As part of a larger project aimed at gaining a better understanding of factors that affect the quality of test results using anthropomorphic test devices (ATDs), the FAA tested the effects of dynamic loading of an ATD pelvis. The ATDs required in the aviation regulations were initially developed for the automotive crash environment, which does not include a vertical testing component. One of the two dynamic tests is a vertical impact, with the principal measurement being the compressive load in the lumbar spinal column, with a regulatory limit of 1500 lb. The lumbar load cell is mounted to the pelvis, and data collected could be affected by the performance of the ATD pelvis. The ability to define a vertical calibration test could be used to determine if the pelvis is acceptable for initial use or to monitor in-service degradation. Three ATD pelvises were compressed in a high-rate load frame. The peak load and loading rate of the pelvis compression were selected to simulate conditions achieved in transport category aircraft vertical seat testing. The primary test objective was to measure changes to the rubber and foam cover of the metallic pelvis during high cyclic loading. Each pelvis was subjected to over 100 cycles. Static dimensional measurements, based on a manufacturing tolerance evaluation, were collected during testing. The high-cycle testing did not deform the foam and rubber covers enough to exceed the total dimensional tolerance of the pelvises (± 0.120 in.). The appearance of visual damage was closely monitored throughout the testing. Similar visual damage was seen for each pelvis and occurred at low cycles — 15 to 30. Results suggest the appearance of damage minimally changed the dynamic response of the pelvis. Force-deflection data were also collected from each test series. These data showed minimal change during testing, with the deflection at 2000 lb. changing approximately 0.100 in. across the 105 cycles. This value is similar to the manufacturer’s tolerance for the height of the pelvis. Based on this, the number of vertical sled tests that would precipitate replacement may be over 100 cycles. Due to the harsh environment of dynamic sled testing, other factors, such as cuts in the foam and rubber due to belt loading, may trigger the removal of an ATD pelvis from service prior to the pelvis reaching a defined number of cycles. Future FAA research will evaluate how this change in pelvis force-deflection affects lumbar load.
Inspecting the interiors of tanks and ships for defects involves accessing confined and elevated spaces. This can be difficult and hazardous for a person. Ducted aerial vehicles that can hover close to the object of interest can achieve this in a safer and more efficient manner. Such a vehicle is desired to be compact, to have a high hover endurance and to be protected from impact. This paper describes a design concept comprising ducted coaxial counter-rotating rotors with a compact swashplate mechanism for cyclic pitch input to the lower rotor. An experimental setup was used to investigate the effect of the duct. A numerical Blade Element Momentum Theory model was developed and validated to inform rotor selection. A prototype was designed and built with a hover thrust of 9.17 N, outer diameter of 350 mm, and height 173 mm. The duct provided a thrust benefit of 32% for this configuration for a given power. The prototype achieved stable controlled flight in hover and in passing near walls and surrounding obstacles. Some oscillations in attitude were seen in flight. Future work includes improving the control algorithm of the vehicle.
On December 13, 2024, the U.S. Food and Drug Administration (FDA) notified the Medical Device Innovation Consortium (MDIC) of their final approval of the MDIC Report on the MedAccred Accreditation and Audit Program for Contract Sterilizers (Final Report). FDA inspections of firms, such as contract sterilizers, are pursuant to Title 21-Food and Drugs, Chapter 9 – Federal Food, Drug, and Devices, Part A-Drugs and Devices, Section 21 USC 360: Registration of producers of drugs or devices, Subsection (h) Inspections.1 The FDA notification is the culmination of a pilot study initiated by the Performance Review Institute (PRI) in 2023 in collaboration with MDIC and the FDA to evaluate PRI’s MedAccred Sterilization Audit and Accreditation Program of contract sterilizers. The agency confirmed that MedAccred is as an acceptable audit approach that may be leveraged for regulatory purposes as well as supplier oversight.
Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep learning model based on temporal information to solve the problem of predictive damage development, achieving early warning and dynamic evaluation effects. This study focuses on concrete crack development, and the CrackAE model is based on traditional semantic segmentation models and conditional autoencoder architecture. The model consists of an encoder and a decoder. The encoder accepts image data and outputs a feature map. The future map along with the conditional vector encoded based on physical temporal information, serves as the input to the decoder. The output of decoder is the development state of the crack at the specified prediction time. The model achieved an accuracy of 94.6% in real bending failure tests of concrete beams, indicating that the model meets high-precision prediction requirements. This validates the feasibility of deep learning in predicting damage development and provides new ideas for data collection and prediction in actual bridge maintenance.
Shipbuilders didn’t have the option of fiberglass when the nonprofit American Bureau of Shipping (ABS) was established 160 years ago to help safeguard life and property on the seas. Fortunately, technology to help better ensure the safety of ocean vessels has also come a long way in that time, in part because people have become a spacefaring species.
Fangzheng Liu, Nathan Perry, Tobias Roeddiger, Sean Auffinger, Joseph Paradiso, Ariel Ekblaw MIT Media Lab Cambridge, MA
Pipeline inspection is a crucial aspect of maintaining the integrity, safety, and reliability of the planet’s energy infrastructure. However, due to cost and scale challenges, infrastructure operators struggle to conduct accurate, large-scale inspections. A French startup, HyLight, offers a solution to precisely detect issues on the infrastructure, such as methane leaks on pipelines and defects on power lines at an industrial scale, without emitting greenhouse gases.
Designing non-destructive test (NDT) systems for aerospace clients can feel like engineering with blindfolds on. Even when the parts under test aren’t confidential, they can change rapidly as companies optimize their designs. This accelerated innovation helps launch more powerful, safer vehicles for use inside Earth’s orbit and beyond. But how do you create precision inspection systems without knowing what they’ll inspect in the field?
Pick-and-place machines are a type of automated equipment used to place objects into structured, organized locations. These machines are used for a variety of applications — from electronics assembly to packaging, bin picking, and even inspection — but many current pick-and-place solutions are limited. Current solutions lack “precise generalization,” or the ability to solve many tasks without compromising on accuracy.
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