Geometry Inspection Research Articles

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Laser Scanner-Based Hyperboloid Cooling Tower Geometry Inspection: Thickness and Deformation Mapping.

Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure's verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure's theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure's axis to the northeast and geometric imperfections of the hyperboloid shell from -0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use.

Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities

ABSTRACT Ballast layer defects are the primary cause for rapid track geometry degradation. Detecting these defects in real-time during track inspections is urgently needed to ensure safe train operations. To achieve this, an indicator, the track degradation rate (TDR) was proposed. This rate is calculated using track geometry inspection data to locate and predict railway-line sections with ballast layer defects. The TDR is determined by the monthly standard deviation of the rail longitudinal level, which is one aspect of track geometry. The Ballast Layer Health Classification (BLHC) is designed by assessing the two successive TDRs before and after track geometry maintenance actions. The BLHC is used to categorize the conditions of the ballast layer, including normal periodic deterioration, abrupt deterioration, effective maintenance, rising deterioration, and severe deterioration. Both the TDR and BLHC were validated through field assessments of ballast layer conditions, where the two indicators were found to be effective in revealing defects. The results indicate that the TDR is sensitive to ballast layer defects, while the BLHC can quickly identify the location of these defects. Consequently, the BLHC can provide real-time guidance for ballast layer maintenance.

Nut Geometry Inspection Using Improved Hough Line and Circle Methods.

Nuts are the cornerstone of human industrial construction, especially A-grade nuts that can only be used in power plants, precision instruments, aircraft, and rockets. However, the traditional nuts inspection method is to manually operate the measuring instrument for conducting an inspection, so the quality of the A-grade nut cannot be guaranteed. In this work, a machine vision-based inspection system was proposed, which performs a real-time geometric inspection of the nuts before and after tapping on the production line. In order to automatically screen out A-Grade nuts on the production line, there are 7 inspections within this proposed nut inspection system. The measurements of parallel, opposite side length, straightness, radius, roundness, concentricity, and eccentricity were proposed. To shorten the overall detection time regarding nut production, the program needed to be accurate and uncomplicated. By modifying the Hough line and Hough circle, the algorithm became faster and more suitable for nut detection. The optimized Hough line and Hough circle can be used for all measures in the testing process.

Similarity based position calibration for multisource track inspection data

Track geometry inspection data have long endured positional errors and shifts, hindering the quality evaluation, deterioration analysis and maintenance work of railway track. Besides, vehicle response inspection data are also typically misaligned or shifted from each other, but it tends to be ignored by people. In light of this situation, a position calibration method on the basis of a simple similarity metric, i.e. correlation coefficient, is proposed to tackle the positional errors and shifts among multisource track inspection data concurrently. For the convenience of practical implementation, a scheme containing three parts is designed to fulfil the purpose and a two-step procedure is adopted by all the three parts. According to the performance test on a dataset containing five inspection runs of a high-speed railway in China, the positional error between inspection data and actual locations can be greatly diminished, and the data inspected at different times and from different sources are well aligned with each other, with a time consumption of 0.66 s on average for a 1 km segment.

Main Factors on Effect of Precise Measurement and Precise Tamping Based on BP Neural Network

With the continuous development of precise measurement and precise tamping (PMPT) technology on Chinese railway conventional speed lines, the efficiency of machinery tamping operation and the quality of the track have been effectively improved. A variety of PMPT modes have been tried in the field operation, however there are some differences in the operation effect. The quality of the tamping operation is affected by multiple factors. In order to identify the key factors affecting the operation quality and to further improve the tamping operation effect, this paper establishes both the database of PMPT operation modes and the selection index system for evaluating the operation effect. Based on mega multi-source heterogeneous data and track geometry inspection data, this paper adopts the Back Propagation Neural Network (BPNN) prognosis model to quantify and sort the main factors affecting the effect of PMPT. The research results show that the initial quality of the track before tamping, whether the stabilizing operation or the tamping modes have great influence weights. It can scientifically guide the field operation to control the key factors and put forward some practical suggestions for promoting the field application of PMPT and the optimization of operation modes on the conventional speed lines.

Identifying Parametric Models Used to Estimate Track Irregularities of a High-Speed Railway

This study aims to identify parametric models to estimate track irregularities in high-speed railways with simple acceleration measurements. The primary contribution of current research is the development of effective parametric models with smaller parameters. These parameters are derived from the measured data via a specialized track geometry inspection system. An adaptive Kalman filter algorithm, using the displacement estimated from the acceleration signals as the input and measured track irregularities as the output, is applied to obtain the model’s unknown parameters. These models are applied to acceleration measured from high-speed rail vehicles in operation, and track irregularities are estimated in spatial and wavelength domains. The estimated irregularities are compared to the track geometry inspection system’s results.

Sequential State and Unknown Parameter Estimation Strategy and its Application to a Sensor Fusion Problem

Soft-sensing and state-space-based methods play crucial roles in fault-tolerant sensor fusion schemes. Kalman filtering is one of the most employed sensor fusion methods. In practice, filtering (or state estimation) is sensitive to initial conditions, variations in model parameters, and faults in sensors and actuators. Cumulatively, these uncertainties that affect the state estimator can be modeled as unknown parameters. In this article, we present the development of a discrete-time minimum variance unbiased estimator (MVUE) that is robust to a class of unknown parameters. The estimation gain of MVUE is designed to facilitate unbiased state estimates in the presence of unknown parameters. We then use the innovations that are generated by the proposed state estimator (MVUE) to estimate the unknown parameters, thus proposing a sequential state and unknown parameter estimation strategy. The development of the decoupled state and unknown parameter estimation strategy is motivated by the fault-tolerant sensor fusion exercise for railway track geometry inspection. This exercise aims to formulate the state-space realization of the track geometry inspection problem to enable sequential state and unknown parameter estimation. The track geometry parameters, initial conditions, and sensor faults are considered unknown parameters for the application. The simulation analysis shows the importance of developing the trinity of the state-space realization for track geometry inspection, the state estimation framework, and the decoupled unknown parameter estimator.

Experimental evaluation of multi-jointed soft detection arm of geometry inspection gauges for detecting internal convex defects in natural gas pipelines

Regular deformation inspection of in-service natural gas pipelines can reduce accidents caused by pipeline failure. In this study, a novel contact-testing geometry inspection gauge is proposed, and its multi-jointed soft detection arm is the focus of our study. 3D printing technology and mold casting were used to fabricate a newly designed detection arm. A finite element method was used to analyze the process of the detection arm passing through the convex defect and optimize its joint number. Then, a series of comparison experiments were conducted with various convex defects to evaluate the superiority of the measuring performance of the multi-jointed soft detection arm. The experimental results show that compared to the no-jointed detection arm, the multi-jointed detection arm has a more accurate experimental value, especially for defects with closer heights (8 and 10 mm) and is relatively more stable in repeated experiments. This study provides options for the design of a flexible caliper pipeline inspection gauge for deformation inspection in pipelines.

Robotised Geometric Inspection of Thin-Walled Aerospace Casings.

This paper deals with the development of dimensional control technology for the production of ADT (accessory drive train) gearbox housing. The project included the development of a robotic geometry inspection station for thin-walled aerospace casings. Laser profilometers from two brands, Cognex and Keyence, were used to measure. A proprietary software solution for arc measurements is presented. The obtained solutions were compared and verified in accordance with the requirements of the manufacturer Pratt and Whitney Rzeszów S.A. The results of the work indicated that correct solutions were obtained with a very large reduction in control time. In addition, the measurement is fully automated and transferable to the company’s electronic systems.

Evaluating Rail Surface Roughness from Axle-Box Acceleration Measurements: Computational Metrology Approach

This study develops a new methodology based on computational metrology techniques to measure rail surface roughness from vertical axle-box accelerations. A VIA rail passenger rail car, operating in eastern Ontario, Canada, was instrumented with accelerometers on the car body, trucks, and axle-boxes. The rail surface was measured by a heavy track geometry inspection car. A Gaussian filter was applied to the measured rail surface data and the rail surface roughness was quantified by the root mean square (RMS), as recommended by ISO 11562 for evaluating engineering surfaces. Values of rail surface RMS roughness calculated from axle-box accelerometer data are verified with the measured surface RMS roughness. The overlap ratio and length of the moving window over which the rail surface RMS roughness is calculated are studied with respect to roughness wavelengths, statistical considerations, and maintenance planning purposes. The effect of rail car operating speed as well as the difference between axle-box accelerations measured at two axles of the instrumented rail car on the accuracy of estimating the rail surface RMS roughness are assessed. Filtering techniques and application limitations for calculating roughness are also discussed. The results of this study suggest axle-box acceleration data provide a useful assessment of rail surface roughness for the typical wavelengths between 3 and 25 m and a complementary technique to light and heavy track geometry inspections.

A Machine Vision System Based on Driving Recorder for Automatic Inspection of Rail Curvature

Because of long distance of railway lines, it is difficult to find an appropriate method to inspect the rail track condition efficiently and accurately. In this article, a machine vision system based on driving recorder and image signal processing is proposed to evaluate the rail curvature automatically. The proposed machine vision system consists of four modules including the video acquisition module, the image extraction module, the image processing module, and the track condition assessment module. Three classic edge detection methods are adopted and compared for rail edge detection. In line with the videos of driving recorder, coordinate systems for train and rail are defined in the Lagrangian space, and the track curvature is estimated using the proposed chord offset method and double measurement method. For evaluating the track condition, an index describing the concordance between the train and track is defined. In the case study, a set of videos from the driving recorders of trains during their in-service operations are analyzed by the proposed technique, and the obtained results are verified by comparison with those obtained by a track geometry inspection vehicle. It is shown that the proposed technique can evaluate the track curvature accurately. Moreover, the influence of the position of deployed driving recorder, the focal length and anti-shake of camera on the accuracy of evaluation results is discussed. It is testified that the proposed technique provides a simple and reliable way to inspect the track curvature.

Assessment and prediction of high speed railway bridge long-term deformation based on track geometry inspection big data

This paper proposes a low-cost, data-driven approach to assess and predict bridge deformation using track inspection big data, which is primarily used for assessing track conditions. Firstly, a Bridge Deformation Assessment model with a sophisticated signal processing process is introduced to manipulate track geometry inspection data for extracting bridge-related components. Secondly, a Bridge Dynamical Deformation index (BDD index) is defined to quantify bridge deformation based on track geometry inspection data. Thirdly, the Temperature-Time-Deformation model (TTD model) is established to describe bridge deformation with respect to ambient temperature and length of service time of the bridge. Three types of TTD equations are proposed, including exponential-, hyperbolic- and linear-TTD equations. Fourthly, a track geometry inspection dataset over 2.6 years involving 563 bridge spans is applied as a case study. It is found that the BDD index changes with ambient temperature by 0.02 mm/°C on average, and increases with time by 0.2 mm/year during the 2.6-year period. Furthermore, a prediction on the amount of increase of the BDD index over the following 3 years is given with a 95% confidence level. It is expected that BDD index will increase by 0.5 mm in 2 years and 0.7 mm in 3 years according to the TTD model. Finally, the model uncertainty is discussed from data aspect and model aspect. The methods in this paper are of reference value for research topics on bridge condition evolution, rail geometry degradation and prediction-based infrastructure maintenance.

Simulation assisted process chain design for the manufacturing of bulk hybrid shafts with tailored properties

To manufacture semi-finished hybrid workpieces with tailored properties, a finite element simulation assisted process chain design was investigated. This includes the process steps of cross wedge rolling, hot geometry inspection, induction hardening, and fatigue testing. The process chain allows the utilisation of material combinations such as high-strength steels with low-cost and easy to process steels. Here, plasma transferred arc welding is applied to supply the process chain with hybrid specimen featuring different steel grades. An overview of the numerical approaches to consider the various physical phenomena in each of the process steps is presented. The properties of the component behaviour were investigated via the finite element method (FEM) and theoretical approaches. At first, the manufacturing of a hybrid workpiece featuring a near net shape geometry with improved mechanical properties due to recrystallising the weld was computed, using the example of a cross wedge rolling process. The rolling process was designed by means of FEM to determine suitable process parameters and to reduce experimental testing. An optical multi-scale geometry inspection of the hot workpiece is meant to be carried out after each manufacturing step to detect potential undesired forming or cooling-induced deformations. Due to the heat transfer from the hot component to the ambient medium, an optical measurement is affected by the developing inhomogeneous refractive index field in air. To gain a basic understanding of the refractive index field and induced light deflection effects, computations were conducted using heat transfer and ray tracing simulations. According to the proposed process route, a subsequent local heat treatment of the hybrid component is required to adapt the mechanical properties by a spray cooling assisted induction hardening. The heat treatment step was computed via a 2D FEM calculation. After finishing by machining, the hybrid material shafts are examined in fatigue tests under load conditions. To predict the component’s lifetime under rolling contact fatigue, a damage accumulation model was combined with an FE simulation. The resulting residual stress state after quenching and the geometry after the finishing process were used as input data for the fatigue life calculations.

Effective time interval for railway track geometry inspection

A proper decision-making scheme for track geometry maintenance requires a knowledge of the real condition of track geometry. Therefore, the track must be inspected by measurement cars at different time intervals. The frequency of track geometry inspection plays a crucial role in decision-making and has always been a big concern for infrastructure managers. The inspection interval should be chosen properly, it means that the small period can decrease the capacity of line and affect the operation of network and the big period can result in low quality of track and in some cases derailments and possible loss of human lives. The aim of this paper is to determine the effective inspection interval such that the total maintenance cost is minimized. In the proposed cost model, the costs of inspection, preventive maintenance, corrective maintenance and the penalty for exceeding the corrective maintenance level are considered. A case study is performed on a real dataset collected from a railway line in Iran. The standard deviation of longitudinal level is considered to measure track geometry degradation. A widely applied linear model is used to model track geometry degradation over time. Monte Carlo technique is used to simulate the track geometry behavior under various track geometry inspection intervals. In addition, a set of sensitivity analyses are carried out to assess the effect of various inspection intervals on different terms of maintenance cost. The results indicate that not only can substantial costs be saved by setting effective inspection intervals, but also the time during which the track suffers from bad conditions is dramatically reduced. The result of this study has shown the appropriate inspection interval for the studied case can result in 13.6 percent decrease in maintenance cost in comparison with the current maintenance policy. Besides, it would lead to more reliable railway track by preventing the system exceed the corrective threshold.

Development and metrological validation of a new automated scanner system for freeform measurements on wind turbine blades in the production

Geometrical defects on the surface of wind turbine blades can severely degrade the blade during operation and lead to reduced lift, which in turn reduces the power output of the turbine. This paper presents an automated surface geometry inspection system, which is designed based on manufacturing requirements. Estimating the measurement uncertainty and establishing traceability is difficult for huge, freeform objects. An approach based on the Modular Freeform Gauge (MFG) method is presented and used to estimate the measurement uncertainty of the system. An expanded measurement uncertainty of 665 μm (k=2) was established for the system and verified by measurements on a 55 meter long blade.

Optimize the joining technology of CFRP and aluminium sheets using hybrid and high strength adhesive

In order to reduce the weight of a vehicle the designers have to use different materials pairing. During the light building the vehicle needs to complete the mechanical and structural requirements, these quality properties are especially important in the competition racings. To fulfil the needs while minimize the weight hybrid material usage and bonding are used. One of the critical joining is the Carbon Fiber Reinforced Plastic (CFRP) and aluminium pairing. In this paper we investigated the adhesive joining technologies of these hybrid pairings using hybrid and high strength adhesive. Our goal is to optimize the hybrid bonding while different surface treatment methods are used on the aluminium substrate. The mechanical properties, the surface roughness and the breaking method were investigates using tensile test and macro geometry inspection. In the results the connection between the surface topography and the mechanical properties were analysed.

Development of a 3D scan posture-correction procedure to facilitate the direct-digital splinting approach

ABSTRACTThe direct-digital upper-limb orthopaedic splinting process employs portable 3D scanning systems to capture anatomical surface geometry. However, the resultant quality of a complete upper-limb scan geometry may be detrimentally affected by involuntary movements of an unsupported limb, and the inability to maintain the correct posture due to patient disability. The aim of this research study is to develop an upper-limb 3D scanning posture-correction procedure that addresses these limitations. The methodology covers four stages: (a) 3D scanning; (b) rigging; (c) posture correction; and (d) geometry inspection. Manual and semi-automated rigging procedures were developed to define segments and joints of the wrist on the upper-limb scans. A semi-automated definition was developed to precisely and repeatedly articulate the defined hand segment. To assess feasibility and accuracy of the developed posture-correction procedure, the hand segment on scans in neutral hand posture were articulated with the developed posture-correction procedure as closely as possible to the corresponding hand positions on the scans in extended hand postures. The reported virtual posture-correction procedure provides a basis for research into anatomy articulation and the optimisation of complex splints and orthoses.

Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.

A threshold-regression model for track geometry degradation

Track-related failures are a major factor contributing to train derailments in the United States. Therefore, determining the failure time is critical for safety purposes. Traditionally, failure time in track geometry has been modeled using defect data. However, unless it is an accident due to extreme events, track geometry fails as a result of an underlying degradation process. The first hitting time is referred to the probability distribution of the time at which the degradation path first reaches a safety threshold. This paper presents the formulation and implementation of the first hitting time in railway track geometry degradation using track geometry inspection data. The underlying degradation path is modeled as a Wiener process with drift, and the first hitting time follows an inverse Gaussian distribution. The results provide a more robust representation of the failure time in track geometry using degradation data.