Error Tracing Research Articles

Performance Evaluation and Error Tracing of Rotary Rayleigh Doppler Wind LiDAR

In the study of atmospheric wind fields from the upper troposphere to the stratosphere (10 km to 50 km), direct detection wind LiDAR is considered a promising method that offers high-precision atmospheric wind field data. In 2020, Xie et al. of the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, developed an innovative rotating Rayleigh Doppler wind LiDAR (RRDWL). The system aims to achieve single-LiDAR detection of atmospheric wind fields by rotating the entire device cabin. In 2022, the feasibility of the system was successfully validated in laboratory conditions, and field deployment was completed. Due to the structural differences between this system and traditional direct-detection wind LiDAR, performance tests were conducted to evaluate its continuous detection capability in outdoor environments. Subsequently, based on the test results and error analysis, further analysis was carried out to identify the main factors affecting the system’s detection performance. Finally, the error analysis and traceability of the detection results were conducted, and corresponding measures were discussed to provide a theoretical foundation for optimizing the performance of RRDWL.

Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network

Abstract NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

Parameter Calibration of Wind Farm With Error Tracing Technique and Correlated Parameter Identification

With the increasing penetrations of wind power in power systems, periodical calibration of the large-scale Wind Farm (WF) model is crucial to maintaining high-quality model performance for stability investigations. However, the existing calibration methods mainly focus on correcting synchronous generators, not the more dynamical WF model. Challenges in tracing errors of inaccurate models and identifying the correlated parameters would further restrain the calibrating performance. To address such limitations, a novel parameter calibration approach for the WF model is developed explicitly in this article. With the development of a trajectory sensitivity based dominant parameters selection strategy, the parameters which significantly impact the model responses can be precisely located. Then, for discriminating the ill-conditioned parameters (deviating from their actual values), an improved error tracing method incorporating Hybrid Dynamic Simulation (HDS) and Hilbert-Huang Transform (HHT) is proposed. Lastly, to enhance the model performance, the ill-conditioned parameters are iteratively tuned by a well-designed multistage identification approach, with the consideration of parameter correlations. Additionally, theoretical proof of the error tracing technique is provided to guarantee the proposed method's validity mathematically. With realistic WF and a modified IEEE 39-bus system, the proposed method demonstrated its excellent performance in tracing and tuning inaccurate parameters.

A machining error tracing method based on MEA-BP neural network for quality improvement of gear hubs

ABSTRACT The machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.

Determining scalar illuminance from cubic illuminance data. Part 1: Error tracing

The cubic illumination metre has been taken as the most thorough and practical tool for the field measurement of vector illuminance so far. However, uncertainty in the measurement of scalar illuminance has influenced its reliability. This study determined how the errors of the existing approaches were generated using illustrations and simulations based on the illuminance solid concept. Following the error analysis of the existing approaches, potential alternative solutions for improving its accuracy were proposed. This study can help pave the way for developing more accurate vector illuminance-based metrics in natural lighting environments using a cubic metre.

A new approach for developing EPB-TBM disc cutter wear prediction equations in granite stratum using backpropagation neural network

Accurate prediction on Tunnel Boring Machine (TBM) cutter wear can lower the risk of cutter replacement and provide a reference for project management. A workflow is proposed to develop a new empirical disc cutter wear prediction model with Backpropagation Neural Network (BPNN) by incorporating parameters from TBM operation, geological conditions, and cutter layout. The procedure is examined by a case study to develop an empirical equation quantifying cutter wear by the reduction in cutter radius instead of the service life. A dataset with 12 different parameters is established from a 595-ring tunnel section of Guangzhou Metro Line 18 project constructed in granite and migmatitic granite strata. Cumulative Error Rate (CER), defined as the ratio of the error between the predicted and actual value of cutter wear to the actual value, is used for model performance evaluation and error tracing. The top 3 rated BPNN models get the best average CER with 9.10% Bias and 11.34% Variance. Sensitivity analysis are performed on these outstanding models providing references for the development of prediction equations. The log–log equation outperforms quadratic and level-level equations and reaches an average CER with 18.77% Bias and 8.77% Variance, which is a great improvement compared with other published empirical equations. Additionally, error tracing and equation explanation are performed to provide optimization options for future studies.

Position Measurement System Based on Image Trajectory Tracking Control of Directional Conveyor

This paper proposes a control trajectory tracking of direction angle conveyor using image possessing. Conveyor directional is one of the most important topics in classification products. Generally, the other systems cannot be aware of real-time detection and high precision for angle tracking of object works classification. Currently, image detecting and processing systems are widely used and becoming to applying for control field in the future trend. This work installs camera webcam for visual recognition system based on using MATLAB to determine angle position and visual processing, which can recognize the area and calculate the current position of object work, incomplete angle position, and complete angle position. In the paper focus detecting directional angle to trajectory tracking of adaptive control is also responsible for enhancing angle precision between object product and position setpoint target, closed-loop control and tracking error method are designed and implemented on the programmable logic controller (PLC) for improving the efficacy of the conveyor system. The experimental close loop control presents the capability to maintain the setpoint angle position. and the adaptive error tracing with superior response of error controller. The adaptive error tracking gains using a proportional controller. The angle position control from this proposed technique can operate on the different target setpoint and improve efficiently control the directional conveyor.

Flexible method for accurate calibration of large-scale vision metrology system based on virtual 3-D targets and laser tracker

For the purpose of obtaining high-precision in stereo vision calibration, a large-size precise calibration target, which can cover more than half of the field of view is vital. However, large-scale calibration targets are very difficult to fabricate. Based on the idea of error tracing, a high-precision calibration method for vision system with large field of view by constructing a virtual 3-D calibration target with a laser tracker was proposed in this article. A virtual 3-D calibration target that covers the whole measurement space can be established flexibly and the measurement precision of the vision system can be traceable to the laser tracker. First, virtual 3-D targets by calculating rigid body transformation with unit quaternion method were constructed. Then, the high-order distortion camera model was taken into consideration. Besides, the calibration parameters were solved with Levenberg–Marquardt optimization algorithm. In the experiment, a binocular stereo vision system with the field of view of 4 × 3 × 2 m3 was built for verifying the validity and precision of the proposed calibration method. It is measured that the accuracy with the proposed method can be greatly improved comparing with traditional plane calibration method. The method can be widely used in industrial applications, such as in the field of calibrating large-scale vision-based coordinate metrology, and six-degrees of freedom pose tracking system for dimensional measurement of workpiece, as well as robotics geometrical accuracy detection and compensation.

The hardness of resilience for nested aggregation query

Resilience problem is defined on a database d, given a boolean query q where q(d) is initially true, and an integer k, it is to find the tuple set d′ of smallest size such that the query result q(d∖d′) becomes false. As a potential explanation of a specified query, resilience plays a fundamental and important role in query explanation, database debugging and error tracing. Complexity results of resilience decision on relational algebraic queries have been studied previously. In this paper, we investigate the resilience decision problem on aggregation queries. New results on the hardness are provided. We show that, this problem is polynomially intractable on nested COUNT and SUM query both under data complexity and parametric complexity, and even it is NP-hard to approximate it within a constant ratio under the active domain constraint.

Research on error tracing method of five-axis CNC machine tool linkage error

Since their ability to machine complex curved surface, five-axis CNC machine tools have been widely used in the machining field. To guarantee the accuracy of the complex surface machining, multi-axis linkage performance detection of five-axis machine tools is necessary. RTCP (rotation tool center point) is one of the basic essential functions for five-axis machine tools, which could effectively reduce the nonlinear errors caused by rotation axes when five axes move synchronously. On the basis of RTCP function, a way to detect multi-axes linkage performance of five-axis machine tools is introduced in this paper. Some simulations that exhibit the relationship between the tool center error trajectories and the factors influencing the linkage performance of the machine tool are established based on the linkage error model. Analyzing the rule of tool center error trajectories, a linkage error tracing method is proposed based on feature extraction and trajectory similarity. The experiments based on RTCP are carried out, and the tool center point error variation is shown to be consistent with the simulation result. Furthermore, the error of the error tracing method is less than 5%, which means the error tracing method could find the mismatch parameters effectively. This attempt provides a theoretical support for linkage performance detection and error tracing of five-axis CNC machine tools.

Theoretical error compensation when measuring an S-shaped test piece

S-shaped test piece aims to assess the performance of five-axis numerical control (NC) machine tools. When the draft international standard (DIS) was introduced at the 79th ISO/TC39SC2 meeting, it was agreed that this test piece would be included. The S-shaped test piece, however, has undeveloped surfaces, which contribute to theoretical error. Because the test piece is used to assess the performance of machine tools and to conduct error tracing, theoretical error should not be included in the detection results obtained by the coordinate measuring machine (CMM). Therefore, the S-shaped test piece, excluding the influences of theoretical error, is crucial to research. This paper calculates the theoretical error of the S-shaped test piece when processed with the single-point offset (SPO) position method and proposes pre-compensation (PRC) and post-compensation (POC) methods to eliminate the influences of theoretical error. We conducted a theoretical analysis to compare three methods, the two compensation methods and the one uncompensated method, and verified the results through actual experiments. Research from principle and practice demonstrates that both the PRC and POC methods compensated for theoretical error up to 0.01 mm and that PRC is more accurate when considering the difference of approximately ±0.0015 mm.

A geometric error tracing method based on the Monte Carlo theory of the five-axis gantry machining center

This article proposes a tracing method to identify key geometric errors for a computer numerical control machine tool by cutting an S-shaped test piece. Adjacent part relationships and machine tool errors transform relationships are described by topology of the machining center. Global sensitivity analysis method based on quasi-Monte Carlo was used to analyze machining errors. Using this method, key geometric errors with significant influence on machining errors were obtained. Compensation of the key errors was used to experimentally improve machining errors for the S-shaped test piece. This method fundamentally determines the inherent connection and influence between geometric errors and machining errors. Key geometric errors that have great influence on machining errors can be determined quickly with this method. Thus, the proposed tracing method could provide effective guidance for the design and use of machine tools.

Method of Machining Error Tracing and Processing Perfor¬mance Evaluation for Five-axis CNC Machine Tool Based on the Comprehensive Evaluation System

Method of Machining Error Tracing and Processing Perfor¬mance Evaluation for Five-axis CNC Machine Tool Based on the Comprehensive Evaluation System

Error Tracing of the Complex CNC Machine Tools Based on “S” Shaped Workpiece

Error Tracing of the Complex CNC Machine Tools Based on “S” Shaped Workpiece

A Novel Rotor Profile Error Tracing and Compensation Strategy for High Precision Machining of Screw Rotor Based on Trial Cutting of Limited Samples

The machining precision plays an important role in the operation reliability and service life of screw rotors. However, the actual machined rotor profile of screw rotors is different from its theoretical profile due to the errors of the machine tool. This paper proposes a novel method of error tracing and compensation to reduce the machined errors of rotor profile on the basis of the limited sample trials, and the method is based on a matrix of error compensation. The errors of rotor profile are obtained based on limited sample trials machining of screw rotors,and fitted into piecewise smooth data. A matrix of error compensation is established to compute the errors of rotor profile to generate a compensated rotor profile. The compensated rotor profile is then used to regenerate forming tool and a new rotor product is processed on the same machine tool. And the errors of new rotor profile are smaller and can be reduced within (−0.01 mm, 0.01 mm) after compensations. Finally, the actual machining examples illustrate that the method of error compensation can not only satisfy the machining requirement of high-precision rotors, but also has the characteristics of good stability and applicability.

Research on the Machining Process Error Tracing Method of the Improved Support Vector Machine Based on the Particle Swarm Optimization

Aiming at the problem of the error tracing of the quality fluctuation in machining process, the error tracing method about forced deformation is proposed based on the error coupling model. The improved support vector machine (SVM) error tracing method based on the particle swarm optimization (PSO) is built to the error decomposition and tracing of the forced deformation with the unclear causal relation. The results show that the proposed method can not only trace the quality characteristics of parts, but also trace the error sources which lead to the quality fluctuation in machining system.

Research on Method of Process System with Error Tracing Based on PSO

In machining process, the machining systems are usually described by the stable theoretical model. However, there are still various process errors existed. To recognize, compensate and eliminate the errors, a method of process system error tracing based on PSO is proposed to explore the errors existing in the machining process. The determined theoretical model is defined and actual measurement data samples are used to find the optimal solution, get the deviation value of each error sources. Finally, the process system error tracing of the Arc Cam is taken as an example to prove the stability and validity of proposed method.

Research on Geometric Error Tracing of NC Machine Tools

Geometric accuracy is an important performance criterion for NC machine tools, and the 9-line method based on laser measuring technology is widely used for error tracing. Three kinds of application problems of the traditional 9-line method are analyzed, and a new geometric error identification model is established by modifying the traditional method. The new model can improve the stability of identification results effectively, and make it possible that the manufacturing and assembly errors of the joints can be traced. Moreover, since the traditional 9-line method has principle errors in roll angle's identification, an experiment-based method is proposed to eliminate the principle errors. The effectiveness of these approaches are verified by both computational and experimental results.

Research on uncertainty in measurement assisted alignment in aircraft assembly

Operations in assembling and joining large size aircraft components are changed to novel digital and flexible ways by digital measurement assisted alignment. Positions and orientations (P&O) of aligned components are critical characters which assure geometrical positions and relationships of those components. Therefore, evaluating the P&O of a component is considered necessary and critical for ensuring accuracy in aircraft assembly. Uncertainty of position and orientation (U-P&O), as a part of the evaluating result of P&O, needs to be given for ensuring the integrity and credibility of the result; furthermore, U-P&O is necessary for error tracing and quality evaluating of measurement assisted aircraft assembly. However, current research mainly focuses on the process integration of measurement with assembly, and usually ignores the uncertainty of measured result and its influence on quality evaluation. This paper focuses on the expression, analysis, and application of U-P&O in measurement assisted alignment. The geometrical and algebraical connotations of U-P&O are presented. Then, an analytical algorithm for evaluating the multi-dimensional U-P&O is given, and the effect factors and characteristics of U-P&O are discussed. Finally, U-P&O is used to evaluate alignment in aircraft assembly for quality evaluating and improving. Cases are introduced with the methodology.

Error Compensation Method of Harmonic Analysis for Time Grating Sensor

Due to the complexity of measurement system, it is hard correct errors by using traditional error separation and error tracing technology. To reduce the production cost and improve the measurement accuracy, a novel error compensation method based on harmonic analysis is presented in this paper. In this way, high precision time grating sensors can be manufactured with low precision mechanical machining method. The experiment results prove that errors can be reduce within ±2″.