Practical Test Data Research Articles

D-Distance Technique to Determine Failure Probability of Power Circuit Breaker

In this paper, a new D-distance factor is proposed to determine the failure probability and to prioritize maintenance actions of power circuit breakers in high-voltage substations. The D-distance factor is calculated by using the condition index and renovation index of a high-voltage circuit breaker (HVCB). To facilitate effective decision-making on maintenance with a simple method and less computational effort, the proposed model incorporates the weighting–scoring method (WSM) and analytical hierarchy process (AHP) with the various diagnostic methods for condition index assessments as well as the operation requirements of HVCBs for renovation index assessments. Many significant parameters from circuit breaker testing, such as insulation resistance, contact resistance, contact timing, SF6 gas measurements, gas leakage rate, visual inspection, etc., have been considered for condition index determination. In addition to these, other significant parameters, such as age of the circuit breaker, age of the interrupter and mechanism, number of fault current interruptions, actual load current to rated current ratio, actual short circuit current to rated interrupting current ratio, maintenance ability, spare parts availability, maintenance expertise level, etc., are also considered for renovation index determination. To validate the proposed model, the practical test data of twenty 115 kV HVCBs in various substations of a distribution utility in Thailand were utilized and tested. By analyzing the actual condition and operation requirement of the circuit breaker, the output, as the condition index and renovation index using the proposed method, is discussed with HVCB experts in the utility to adjust the scores and weights of all criteria to obtain the most accurate and reliable model. The results show that the D-distance technique measured from the risk matrix, which is defined as the failure probability, can be used to rank the maintenance schedule from urgent to normal maintenance tasks. In addition, various failure probabilities in the risk matrix of the circuit breaker can be used to determine the appropriate maintenance strategies for the power circuit breaker in each group. Finally, the proposed method could help the utility managers and maintenance engineers manage the maintenance planning effectively and easily for thousands of HVCBs in the grid, and it can be further applied with other high-voltage equipment in both transmission and distribution systems to facilitate the maintenance activities according to available costs and human resources.

Development of E-Module Based on Problem Based Learning by Using Adobe Flash Professional CS6 on the Material of Human Respiratory and Excretory Systems for Students of Class VIII SMPN 25 Padang

Good results from education must be accompanied by effective learning. Effective learning one of which is influenced by the use of teaching materials. Preliminary investigation data at SMPN 25 Padang show that there is no teaching material in the form of an e-module that can visualize the subject matter as a whole. Based on the results of the questionnaire, it is known that 67.16% of students have difficulty understanding the concepts of the respiratory system and the excretory system. This causes the low learning outcomes for students, so teaching materials are needed that can assist in visualizing the subject matter as a whole. This study aims to produce an e-module based on problem based learning using adobe flash professional CS6 on the material of the human respiratory and excretory systems for students of class VIII that is valid, practical, and effective. This type of research is the Plomp model development research which consists of three phase, namely the initial preliminary research phase, the development or prototyping phase, and the assessment phase. The research subjects are students of class VIII SMPN 25 Padang Academic Year 2021-2022. The research data were obtained from validity, practicality, and effectiveness tests. The validity test data was obtained through a validation sheet by lecturers as validators. Practical test data was obtained from the results of the questionnaire response analysis of teachers and students. Effectiveness test data were obtained from multiple choice question sheets for assessing cognitive competence, observation sheets for assessing affective competence and psychomotor competence, and essay question sheets for assessing students creative thinking skills. The results of validation by validators on the e-module based on problem-based learning show an average value of 86.35% (very valid). The results of the practicality assessment by science teachers showed an average of 90.87% (very practical) and the results of the practicality assessment by students obtained an average value of 89.25% (very practical). The results of the effectiveness test from the cognitive, affective, and psychomotor aspects of students showed that the problem based learning based e-module was very effective and the value of creative thinking skills obtained an average of 74.24 (creative). Thus, it can be concluded that the problem based learning based e-module developed is very valid, very practical, very effective, and can train creative thinking skills.

An Optimization of Master S-N Curve Fitting Method Based on Improved Neighborhood Rough Set

In this paper, a novel master S-N curve fitting method based on improved neighborhood rough set is proposed. The neighborhood relationship is used to granulate the fatigue information system of aluminum alloy welded joints, and the neighborhood rough set model is constructed from the information perspective. Based on conditional entropy, a neighborhood rough set attribute reduction algorithm (NRSBCE) is proposed. By means of this algorithm, comprehensive mathematical analysis model for fatigue life influencing factors is established and optimization of the S-N curve fitting method is introduced. In this work, the fatigue decision system of the aluminum welded joints is constructed firstly. The key influencing factors of the welded joints are obtained by means of NRSBCE algorithm subsequently. After that, the concept of fatigue characteristic domain is introduced according to the reduction result of the NRSBCE algorithm. Then, the master S-N curve fitting is carried out according to the divided fatigue characteristic domain. Correspondingly, a set of S-N curve clusters are fitted. Finally, fatigue analysis system for welded joints based on the fatigue characteristic domain is designed and developed. In the system, the equations of master S-N curve based on the mesh-insensitive structural stress method and the master S-N curve cluster based on the fatigue characteristic domain could be both obtained by uploading the practical fatigue test data of the aluminum alloy welded joints. This work helps to further reduce the dispersion degree of the fatigue sample data of the aluminum alloy welded joints thus provide reference for fatigue design.

Bullet fast matching based on single point laser detection

Bullet trace detection’s main task is determining the type of bullet and the identity of the gun that fired it. In order to solve conventional bullet trace matching method problems, a bullet fast matching method based on single point laser detection is proposed. First, adaptive control of the bullet center position and cylinder axis were performed. Then, a laser displacement sensor was implemented to perform a 360° detection in order to trace rifling on the bullet surface in the circumferential direction; grayscale morphological filtering was implemented in order to de-noise detection data, and the Pearson correlation coefficient was implemented in order to perform the trace similarity matching calculation, thereby achieving a fast bullet trace matching. Moreover, the algorithm’s effectiveness was verified via practical testing data.

Prediction of Multidimensional Spatial Variation Data via Bayesian Tensor Completion

This paper presents a multidimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor computation. A tensor is a high-dimensional generalization of a matrix or a vector. By exploiting the hidden low-rank property of a high-dimensional data array, the large amount of unknown variation testing data may be predicted from a few random measurement samples. The tensor rank, which decides the complexity of a tensor representation, is decided by an available variational Bayesian approach. Our approach is validated by a practical chip testing data set, and it can be easily generalized to characterize the process variations of multiple wafers. Our approach is more efficient than the previous virtual probe techniques in terms of memory and computational cost when handling high-dimensional chip testing data.

Analysis on monitoring and controlling techniques about blasting vibration effect of open channel in Taishan nuclear power station

The blasting for bedrock excavation on land for open channel project has a great influence on lock gate in Taishan Nuclear Power Station, therefore, based on blasting vibration monitoring data, the attenuation law of blasting vibration signal has been studied through regression analysis of practical test data by Sadaovsk empirical formula and corresponding time-frequency characteristics was analyzed by Empirical Mode Decomposition based on Hilbert-Huang transform. As for those monitoring data, the results of blast vibration velocity for vertical direction are generally larger than horizontal radial and horizontal tangential direction in the near field of blasting source and the peak particle velocity of vertical direction is usually lower than horizontal radial and horizontal tangential direction in the far field of blasting source; at the same time, their main vibration frequency mostly vary from 10 Hz to 80 Hz which is much higher than natural frequency of lock gate and is beneficial to structural safety and stability of surrounding rock mass for reducing the probability of resonance. To ensure the safety of lock gate, it is of great significance to control maximum explosive weight per delay in advance for different distance from monitoring point to the explosion source according to Safety Regulations for Blasting (GB6722-2014), which shows the excellent effect on blasting damage control of the lock gate and surrounding rock mass. The results from the analysis can be for reference to similar blasting design and blasting construction.

Experimental Investigation on Thermal Management of Electric Vehicle Battery Module with Paraffin/Expanded Graphite Composite Phase Change Material

The temperature has to be controlled adequately to maintain the electric vehicles (EVs) within a safety range. Using paraffin as the heat dissipation source to control the temperature rise is developed. And the expanded graphite (EG) is applied to improve the thermal conductivity. In this study, the paraffin and EG composite phase change material (PCM) was prepared and characterized. And then, the composite PCM have been applied in the 42110 LiFePO4 battery module (48 V/10 Ah) for experimental research. Different discharge rate and pulse experiments were carried out at various working conditions, including room temperature (25°C), high temperature (35°C), and low temperature (−20°C). Furthermore, in order to obtain the practical loading test data, a battery pack with the similar specifications by 2S∗2P with PCM-based modules were installed in the EVs for various practical road experiments including the flat ground, 5°, 10°, and 20° slope. Testing results indicated that the PCM cooling system can control the peak temperature under 42°C and balance the maximum temperature difference within 5°C. Even in extreme high-discharge pulse current process, peak temperature can be controlled within 50°C. The aforementioned results exhibit that PCM cooling in battery thermal management has promising advantages over traditional air cooling.

Remaining Useful Life Prediction for a Machine With Multiple Dependent Features Based on Bayesian Dynamic Linear Model and Copulas

Degradation modeling and remaining useful life (RUL) prediction for products with multiple degradation features are hot topics in the prognostic and health management. The key to this problem is to describe the dependence among multiple degradation features effectively. In this paper, a multivariate degradation modeling approach based on the Bayesian dynamic linear model (BDLM) is proposed to calculate the RULs of degradation features, and the Copula function is employed to capture the dependence among RUL distributions. A combined BDLM is used to establish the multivariate degradation model, which includes two typical BDLMs, namely, the linear growth model and seasonal factors model. After the model parameters get calibrated by the maximum likelihood estimation, the model can predict the degradation process of features. Once the failure thresholds are given, the probability density function and cumulative distribution function (CDF) of RUL for each degradation feature can be obtained. Since these RUL distributions are not independent of each other, the Copula function is adopted herein to couple the CDFs. Finally, some practical testing data of a microwave component, which has two degradation features, are utilized to validate our proposed method. This paper provides a new idea for the multivariate degradation modeling and RUL prediction.

ASSESSMENT OF THE QUALITY OF DIGITAL TERRAIN MODEL PRODUCED FROM UNMANNED AERIAL SYSTEM IMAGERY

Abstract. Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

ASSESSMENT OF THE QUALITY OF DIGITAL TERRAIN MODEL PRODUCED FROM UNMANNED AERIAL SYSTEM IMAGERY

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

Induction Motor Relaying Scheme for External Faults Detection and Classification Using Subtractive Clustering Based Sugeno Fuzzy Inference System

Conventional thermal relays generally suffer from inaccurate induction motor external faults identification. Recent developments in diagnosis systems have led to the scope of radically different diagnosis strategies based on artificial intelligence techniques. This study use fuzzy logic as a software tool and relay logic for external faults identification of an induction motor. In this study, a subtractive clustering based Sugeno-type fuzzy inference system is utilized to classify five external faults and the normal condition of a low-voltage three-phase induction motor using simulation and practical data sets. Five external faults- overload, over-voltage, under-voltage, single-phasing, and voltage unbalance-along with normal conditions are simulated on an induction motor over a wide range of operating voltage and load torque; sampled three-phase RMS voltages and currents are used as input feature vectors to train the fuzzy inference system. Average total classification accuracy and average root means square error obtained with ten-times random sub-sampling cross-validation of test data sets are used to find the best accurate and generalized fuzzy inference system configuration among different cluster radius fuzzy inference systems for both simulation and real-time data sets. This method accurately detects external faults with 94.37% average total classification accuracy for practical test data sets.

A novel satellite-equipped receiver for autonomous monitoring of GNSS navigation signal quality

Global navigation satellite system (GNSS) comes with potential unavoidable application risks such as the sudden distortion or failure of navigation signals because its satellites are generally operated until failure. In order to solve the problems associated with these risks, receiver autonomous integrity monitoring (RAIM) and ground-based signal quality monitoring stations are widely used. Although these technologies can protect the user from the risks, they are expensive and have limited region coverage. Autonomous monitoring of satellite signal quality is an effective method to eliminate these shortcomings of the RAIM and ground-based signal quality monitoring stations; thus, a new navigation signal quality monitoring receiver which can be equipped on the satellite platform of GNSS is proposed in this paper. Because this satellite-equipped receiver is tightly coupled with navigation payload, the system architecture and its preliminary design procedure are first introduced. In theory, code-tracking loop is able to provide accurate time delay estimation of received signals. However, because of the nonlinear characteristics of the navigation payload, the traditional code-tracking loop introduces errors. To eliminate these errors, the dummy massive parallel correlators (DMPC) technique is proposed. This technique can reconstruct the cross correlation function of a navigation signal with a high code phase resolution. Combining the DMPC and direct radio frequency (RF) sampling technology, the satellite-equipped receiver can calibrate the differential code bias (DCB) accurately. In the meantime, the abnormities and failures of navigation signal can also be monitored. Finally, the accuracy of DCB calibration and the performance of fault monitoring have been verified by practical test data and numerical simulation data, respectively. The results show that the accuracy of DCB calibration is less than 0.1 ns and the novel satellite-equipped receiver can monitor the signal quality effectively.

Structural Parameter Identification of Articulated Arm Coordinate Measuring Machines

Precise structural parameter identification of a robotic articulated arm coordinate measuring machine (AACMM) is essential for improving its measuring accuracy, particularly in robotic applications. This paper presents a constructive parameter identification approach for robotic AACMMs. We first develop a mathematical kinematic model of the AACMM based on the Denavit-Hartenberg (DH) approach established for robotic systems. This model is further calibrated and verified via the practical test data. Based on the difference between the calculated coordinates of the AACMM probe via the kinematic model and the given reference coordinates, a parameter identification approach is proposed to estimate the structural parameters in terms of the test data set. The Jacobian matrix is further analyzed to determine the solvability of the identification model. It shows that there are two coupling parameters, which can be removed in the regressor. Finally, a parameter identification algorithm taking the least-square solution of the identification model as the structural parameters by using the obtained poses data is suggested. Practical experiments based on a robotic AACMM test rig are carried out, and the results reveal the effectiveness and robustness of the proposed identification approach.

A hybrid intelligent approach for modeling nonlinear complex data: A case study, prediction, Southwest Iran

One of the significant issues in the area of engineering is the modeling complex data with the target of prediction, classification, clustering, etc. Nevertheless, by the rapid growth of the technology, the high complexity and nonlinearity of the existing data is increased such that they put most of the traditional forecast models such as linear and nonlinear models prone to bias. In order to solve this issue, this paper suggest a newly introduced forecasting model called support vector regression (SVR) model to reach more accurate modeling and thus prediction of the complex data. In order to adjust the setting parameters of this model, the clonal selection algorithm (CSA) as a powerful optimization tool is employed. In addition, a sufficient modification is proposed to improve the search ability of this algorithm effectively. The practical test data (from 230 kV substation of Southwest Iran) are employed to demonstrate the satisfying performance of the proposed hybrid model.

Short term load forecasting in power systems using a hybrid approach based on SVR technique

Optimal operation management of distribution systems requires suitable tools for accurate forecasting. By the appearance of new technologies such as electric vehicles, renewable sources, etc, the variation of the electrical loads is intensified. In order to solve this issue, a new powerful forecast method based on support vector regression (SVR) is proposed to model the behavior of load consumption. According to the high complexity and severity of the problem, a new optimization approach based on firefly algorithm (FA) is proposed to adjust the optimal parameters of the SVR. The new version of FA called θ-FA is used to search the solutions in the polar space instead of the traditional Cartesian space. The objective function is to minimize the average forecast error considering the SVR constraints. The feasibility and high performance of the proposed method are examined on practical test data.

Parameter Extraction and Characteristics Study for Manganese-Type Lithium-Ion Battery

In this paper, we propose the battery transient response model and parameter extraction method for studying the dynamic behaviors of Manganese-type Lithium-Ion battery. The background knowledge of the battery structure and its operating principle are also concluded. Several aspects of operating conditions, such as charging and discharging operations, environments of terminal currents and temperatures, are considered through the experiments for understanding the battery behaviors. The characteristics of internal effective series resistance, battery capacity, and capacity deterioration to the operational and environmental conditions are also evaluated and analyzed physically. The transient response model and the extracted parameters are validated with a Spice simulation for the practical testing data. The terminal voltage response shows an acceptable conformity for the experiment and simulation to different periods and amplitudes of pulse currents.

Comprehensive assessment algorithm for calculating CEP of positioning accuracy

This study aims to evaluate the inertial navigation systems’ performance. To this end, we comprehensively investigate 12 methods for calculating the circular error probability (CEP). Using data processing techniques, we classify these methods into three categories: parameterization of one-dimensional variables, computation of bi-normal random variables, and numerical integration. From these methods, we develop algorithms and programs, which are subsequently applied in evaluating the positioning accuracy of navigation systems. Using the algorithms and programs, we perform simulations to evaluate applicability and evaluation accuracy under the same initial conditions. Practical test data are employed to validate the results. Using the comparison of the methods as bases, we put forward a comprehensive practical method regarding sample number and evaluation accuracy. Our results serve as a theoretical reference for the development of different approaches to assessing inertial positioning accuracy in accordance with applicable standards.

Stress and Deformation Characteristics of Rock-Fill Dam with Asphalt Concrete Core Wall Founded on Deep Overburden

Abstract: The stress and deformation of rock-fill dam with asphalt concrete core wall founded on deep overburden is calculated and analyzed by Duncan E-ν model and double-yield-surface model through three-dimensional finite element method. The stress and deformation of dams in water storage period is compared by the two models, the results show that the deformation distribution of dam core via two different models are coincide one another. The horizontal displacement and vertical displacement of rock-fill dam with asphalt concrete core wall by double-yield-surface model is smaller than which by Duncan E-ν model in the period of water storage. Furthermore, the horizontal displacement and vertical displacement by double-yield-surface model, which are close to the practical test data through the deformation via two models are in good agreement. The analysis of core-wall stress via double-yield-surface model is more reasonable than the Duncan E-ν model. The analysis result of resisting hydraulic fracturing of core dams by DuncanE-ν model is coincide which of core dams by double-yield-surface model.

Comprehensive CEP Evaluation Method for Calculating Positioning Precision of Navigation Systems

This study aims to evaluate the navigation performance of navigation systems. To this end, we comprehensively investigate 12 methods for calculating circular error probability (CEP). Using data processing techniques, we classify these methods into three categories: parameterization of one-dimensional variables, computation of bi-normal random variables, and numerical integration. From these methods, we develop algorithms and programs, which are subsequently applied in evaluating the positioning accuracy of navigation systems. Using the algorithms and programs, we perform simulations to evaluate applicability and evaluation accuracy under the same initial conditions. Practical test data are employed to validate the results. Using the comparison of the methods as bases, we put forward a comprehensive practical method regarding sample number and evaluation accuracy.

HF‐measure: A new measurement for evaluating clusters in protein–protein interaction networks

Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes, detecting functional modules and predicting protein functions. In the past few years, many clustering methods have been proposed. However, it is still a challenging task to evaluate how well the protein clusters are identified. Even for two of the most popular measurements, F-measure and p-value, bias exists when evaluating the identified clusters. In this paper, we propose two new types of measurements to evaluate clusters more finely and distinctly. One is hF-measure(Tf) , a topology-free measurement and another is hF-measure(Tb) , a topology-based measurement. Unlike F-measure, the new measurements of hF-measure(Tf) and hF-measure(Tb) can discriminate between different types of errors. Both artificial test data and practical test data were used to evaluate the effectiveness of hF-measure(Tf) and hF-measure(Tb) . For the artificial test data, artificial errors were generated by replacing some cluster members with functionally similar or non-similar members. The practical test data was produced by seven clustering algorithms Markov Clustering, Molecular Complex Detection, HC-PIN, SPICI, CPM, Core-Attachment and RRW. The experimental results on artificial and practical test data both show that hF-measure(Tf) and hF-measure(Tb) evaluate clusters more accurately compared to F-measure. Especially, hF-measure(Tb) can capture the topology changes in clusters, which can also be used to the analysis of dynamic network.