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In inductively coupled power transfer (ICPT) systems, the mutual inductance and load are the key parameters to realize high performance control, which is difficult to measure directly in rail transit. Thus, a method realizing mutual inductance and load identification using only the output voltage and current of a high-frequency inverter is proposed in this paper. Dual-frequency modulation, where one is the resonant frequency and the other is the non-resonant frequency, is used in the high-frequency inverter. Then the amplitudes and phases of the output voltage and current of the high-frequency inverter at the resonant frequency and the non-resonant frequency can be obtained based on the fast Fourier transform (FFT) method. The circuit is decomposed according to different frequencies. Then a mathematical model of the ICPT system is established. Therefore, the mutual inductance and load can be identified. Finally, the effectiveness of the proposed method is verified based on an ICPT prototype. Experimental results show that the identification errors of mutual inductance and load are less than 5%. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing the challenge of low identification accuracy in traditional load identification methods based on the truncated singular value decomposition（TSVD）method，especially when the external load frequency approaches or reaches the natural frequency of the structure，the LSTM-CNN load identification model is proposed in this paper. This model combines the feature extraction capabilities of the convolutional neural network（CNN）with the long-term memory function of the long short-term memory network（LSTM）. The load identification method based on the LSTM-CNN model is then applied to research load time domain waveform identification on the GAR TEUR aircraft model. For model training and load identification，the response data and excitation data from the structure are corrected. The identification results are compared with the TSVD method，LSTM method，and DCNN method. Results show that the load identification method based on the LSTM-CNN model proves effective for sinusoidal load identification problems，especially under the natural frequency excitation of the structure. The method exhibits high identification accuracy and robust noise resistance capabilities.

AbstractAs a crucial load-bearing component, coupler’s safety is pivotal for sustainable railway industry development and is extensively scrutinized due to its complex loading environment. However, current research predominantly concentrates on the longitudinal dynamic behavior of couplers, neglecting factors such as nodding and shaking during traversals along curves and ramps. This study proposes an innovative method aimed at identifying multiple loads on couplers, which includes longitudinal tension and compression, lateral shaking, and vertical nodding forces. A theoretical load identification method based on strain sum and difference on coupler shank faces under various loading scenarios is established by finite element analysis. To facilitate accurate measurement, Wheatstone bridges are employed for three-dimensional force measurement, facilitating strain calculation and temperature compensation. The validity of the proposed approach is confirmed through comprehensive validation, encompassing finite element simulation, laboratory experimentation, and vehicle tests. Results demonstrate the robustness of the method, with maximum load deviations of 2.32% longitudinally, 22.52% horizontally, and 19.86% vertically observed during laboratory tests. Results indicate the proposed method’s accuracy and efficiency in heavy haul train coupler load assessment. [ABSTRACT FROM AUTHOR]

During the design and validation of structural engineering, the focus is on a population of similar structures, not just one. These structures face uncertainties from external environments and internal configurations, causing variability in responses under the same load. Identifying the real load from these dispersed responses is a significant challenge. This paper proposes an interval neural network (INN) method for identifying static concentrated loads, where the network parameters are internalized to create a new INN architecture. Additionally, the paper introduces an improved interval prediction quality loss function indicator named coverage and mean square criterion (CMSC), which balances the interval coverage rate and interval width of the identified load, ensuring that the median of the recognition interval is closer to the real load. The efficiency of the proposed method is assessed through three examples and validated through comparative research against other loss functions. Our research findings indicate that this approach enhances the interval accuracy, robustness, and generalization of load identification. This improvement is evident even when faced with challenges such as limited training data and significant noise interference. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring （NILM） not only makes the flow of electric energy transparent but also simplifies the installation process of smart meters， effectively reducing the cost of load monitoring. To enhance the accuracy of load recognition in NILM， a method for load recognition based on data augmentation and threshold-free recurrence plot （RP） is proposed. a denoising diffusion probability model （DDPM） is utilized to augment the load data of small samples to enhance the robustness of the load recognition method. Furthermore， a threshold-free RP， achieved by removing the Heaviside function of the recurrence graph， efficiently represents load characteristics. This is combined with a Transformer deep learning network to construct a load recognition framework. The proposed method is applied to three real-world datasets， and experimental results demonstrate its effectiveness in improving load recognition accuracy and enhancing classification performance.

Abstract Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Actual load identification is a most important task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged structural frames, and (3) structural health monitoring. Actual load values are used to determine the stress–strain state (SSS) of a structure and accomplish various engineering objectives. Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the article presents an integrated method combining basic provisions of structural mechanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make projections, and assembling structures to make final projections for steel frame structures subjected to elastic strain. Final projections serve to identify parameters of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient matrices applied to unit loads and (2) actual loads standardized using maximum load values. Cases of neural network training and parameters identification are provided for simple beams. The aim of this research is to enhance the reliability and durability of steel structures by predicting consequences of unfavorable load, including emergency impacts. The novelty of this study lies in the co-use of artificial intelligence elements and structural mechanics methods to predict load parameters using actual displacement curves of structures. This novel approach will enable engineering inspection teams to predict unfavorable load peaks, prevent emergency situations, and identify actual causes of emergencies triggered by excessive loading. [ABSTRACT FROM AUTHOR]

Non intrusive load monitoring belongs to the key technologies of intelligent power management systems, playing a crucial role in smart grids. To achieve accurate identification and prediction of electricity load, intelligent optimization algorithms are introduced into deep learning optimization for improvement. A load recognition model combining sparrow search algorithm and deep confidence network is designed, as well as a gated recurrent network prediction model on the grounds of particle swarm optimization. The relevant results showed that the sparrow search algorithm used in the study performed well on the solution performance evaluation metrics with a minimum value of 0.209 for the inverse generation distance and a maximum value of 0.814 for the hyper-volume. The accuracy and recall values of the optimized load identification model designed in the study were relatively high. When the accuracy was 0.9, the recall rate could reach 0.94. The recognition accuracy of the model on the basis of the test set could reach up to 0.924. The lowest classification error was only 0.05. The maximum F1 value of the harmonic evaluation index of the bidirectional gated recurrent network optimized by particle swarm optimization converged to 90.06%. The loss function had been optimized by particle swarm optimization, and both the convergence value and convergence speed had been markedly enhanced. The average absolute error and root mean square error of the prediction model were both below 0.3. Compared to the bidirectional gated recurrent model before optimization, the particle swarm optimization strategy had a significant improvement effect on prediction details. In addition, the research method had superior recognition response speed and adaptability in real application environments. This study helps to understand the load demand of the power system, optimize the operation of the power grid, and strengthen the reliability, efficiency, and sustainability of the power system. [ABSTRACT FROM AUTHOR]

To address the ill-conditioned problem and unknown noise interference in ship load identification, this study proposes the ill-conditioned Total Least Squares (TLS) load identification method using the anti-error principle of the IGG weight function (IGG-TLS). First, the weight factor is iteratively updated through the IGG weight function. The weight matrix is constructed adaptively by multiplying the weight factor with the initial unit weight matrix. Then, the regularization criterion for the ill-conditioned IGG-TLS model is established based on the Tikhonov regularization principle. And the Lagrange multiplier is employed to solve the IGG-TLS method. Finally, the load identification accuracies of the IGG-TLS method are investigated through the simulation analysis and experimental verification of the rectangular plate under different noise and SNR conditions. The results demonstrate that the IGG-TLS method can achieve high accuracy in load identification without the need to pre-construct the weight matrix, even when the SNR is unknown. Additionally, the IGG-TLS method can effectively correct the identification errors in the TLS method, demonstrating high accuracy and noise immunity under conditions of equal-weight and unequal-weight noise. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian inversion approach to identify earth pressures on in-service underground structures based on structural deformations. Ill-conditioning and non-uniqueness of solutions are major issues for load inversion problems. Traditional approaches are mostly based on an optimization framework where a smooth solution is uniquely determined using regularization techniques. However, these approaches require tuning of regularization factors that may be subjective and difficult to implement for pressure inversion on in-service underground structures. By contrast, the presented approach is based on a Bayesian framework. Instead of regularization techniques and corresponding tuning procedure, only physically plausible bounds are required for specifying constraints. The complete posterior distribution of feasible solutions is obtained based on Bayes' rules. By inferring the potential pressures with the complete posterior distribution, a natural regularization advantage can be shown. Specifically, this advantage is demonstrated in detail by a series of comparative tests: (1) the Bayesian posterior mean exhibits an inherent quality to smooth out ill-conditioned features of inversion solutions; (2) satisfactory inference of the pressures can be made even in the presence of non-uniqueness. These properties are valuable when observed data is noisy or limited. A recorded field example is also presented to show effectiveness of this approach in practical engineering. Finally, deficiencies and potential extensions are discussed. [ABSTRACT FROM AUTHOR]

In order to address the difficult problem of ball mill load identification during milling operation, the multi-scale fuzzy entropy algorithm is introduced into ball mill load identification and an innovative ball mill load identification method is proposed-the complete integrated empirical decomposition based on adaptive noise (CEEMDAN)-joint denoising with wavelet thresholding-multi-scale fuzzy entropy biased mean value (PMMFE) ball mill load identification method. Firstly, the vibration signals of ball mill bearings are denoised by the CEEMDAN-wavelet threshold joint denoising method and the analysis reveals that this method has obvious advantages over other denoising methods; secondly, the fuzzy entropy, multi-scale fuzzy entropy, and multi-scale fuzzy entropy deviation of denoised vibration signals are computed, the relationship between each entropy feature and the mill load is analysed in-depth and in an information-rich manner. Finally, the least squares support vector algorithm is used to identify the load of the feature vector. The analysis of the measured vibration signals reveals that the overall recognition rate of this method is 84.4%, which is significantly higher than that of other denoising methods and the combination of feature parameters, and the experiments show that the mill load recognition method based on CEEMDAN-wavelet thresholding-PMMFE is able to effectively identify the different loading states of ball mills. [ABSTRACT FROM AUTHOR]

In the context of virtual power plants (VPPs), the one-size-fits-all approach of traditional static desensitization methods proves inadequate due to the diverse and dynamic operational scenarios encountered. These methods fail to provide the necessary flexibility for varying data privacy requirements across different scenarios. To address this shortcoming, our research introduces a dynamic desensitization method specifically designed for VPPs. Leveraging machine learning for adaptive scene recognition, the method adjusts data privacy levels intelligently according to each unique scenario. A novel similarity utility function and a Gaussian processes-based differential privacy algorithm ensure tailored and efficient privacy protection. Experimental results highlight an 87.5% accuracy in scene recognition, validating our method's capability to adapt to diverse scenarios effectively. This study contributes to the field by providing a nuanced approach to data protection, effectively addressing the specific needs of complex VPP environments. [ABSTRACT FROM AUTHOR]

The fast, convenient, and accurate determination of railroad cars' load mass is critical to ensure safety and allow asset counting in railway infrastructure. In this paper, we propose a method for modeling the mechanical deformations that occur in the rail web under the influence of a static load transmitted through a railway wheel. According to the proposed method, a railroad car's weight can be determined from the rail deformation values. A solid model of a track section, including a railroad tie, rail, and wheel, is developed, and a multi-physics simulation technique that allows for the determination of the values of deformations and mechanical stresses in the strain gauge installation areas is presented. The influence of the loaded mass, the temperature of the rail, and the wheel position relative to the strain gauge location is considered. We also consider the possibility of using artificial neural networks to determine railroad cars' weight without specifying the coordinates of the wheel position. The effect of noise in the data on the accuracy of determining the railroad car weight is considered. [ABSTRACT FROM AUTHOR]

This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

Due to the randomness and uncertainty of household electricity use, efficient grid management faces challenges. Non-intrusive load monitoring (NILM) technology has become a pivotal solution to understanding the behavior of electricity consumers. However, traditional data acquisition terminals often struggle to balance cost and performance. To address this barrier, this study proposes a novel, low-cost, high-performance data acquisition terminal, which abandons the traditional dedicated chip solution and instead uses a microcontroller to complete all control and data processing tasks. At the same time, by using the Fast Fourier Transform (FFT), the current signal is converted into a frequency domain signal containing rich information such as amplitude and harmonics, providing great convenience for subsequent intelligent algorithm analysis and processing. This study transforms the non-intrusive load identification problem at the algorithm level into a change point detection problem. A proposed fusion algorithm comprises two layers: the first is based on decision tree algorithms XGBoost and LightGBM, used for feature extraction and preliminary classification; the second uses logistic regression algorithms for decoding and outputting results, achieving high-precision load identification. Experimental results show that the method proposed in this study can achieve more than 95% accuracy when dealing with complex scenarios of mixed use of high-power and low-power appliances. Compared with other algorithms, this method shows significant advantages in load identification accuracy.

Abstract As an important indicator of vehicle systems, tire load is a key factor in the structural design and safety assessment of vehicles. Direct measurement methods for tire loads are expensive and complicated, while conventional load identification methods are limited by low accuracy and poor robustness. This study aimed to propose a radial load identification method for rubber-tired vehicles based on a one-dimensional convolutional neural network (1D CNN) and bidirectional gated recurrent unit (BiGRU). Considering a priori information of the radial load data of tires and based on the observability of the vehicle vibration system, the proposed method selected feature sets and then retained the effective feature subsets through feature selection to construct samples with multiple time steps as input and with a single time step as output for network training. In doing so, the load prediction results were obtained, and the theoretical model was modified by integrating prediction accuracy, generalization performance, and robustness. Compared with traditional algorithms, the proposed method could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field.

During the design and validation of structural engineering, the focus is on a population of similar structures, not just one. These structures face uncertainties from external environments and internal configurations, causing variability in responses under the same load. Identifying the real load from these dispersed responses is a significant challenge. This paper proposes an interval neural network (INN) method for identifying static concentrated loads, where the network parameters are internalized to create a new INN architecture. Additionally, the paper introduces an improved interval prediction quality loss function indicator named coverage and mean square criterion (CMSC), which balances the interval coverage rate and interval width of the identified load, ensuring that the median of the recognition interval is closer to the real load. The efficiency of the proposed method is assessed through three examples and validated through comparative research against other loss functions. Our research findings indicate that this approach enhances the interval accuracy, robustness, and generalization of load identification. This improvement is evident even when faced with challenges such as limited training data and significant noise interference.

ObjectivesHigh fidelity finite element models and effective load identification technology are very important for ship structure health monitoring and evaluation. Therefore, a model updating and load identification method based on improved particle swarm optimization (PSO) is proposed.MethodsThe Rastrigin function is used to compare the improved PSO algorithm with the classical PSO algorithm. An I-beam structure is adopted with pressure applied at the middle of the beam. A limited number of strain sensors are pasted on the surface and divided into two sets: the measured set for model correction and load identification, and the monitoring set for verification. The elastic modulus of the block division of the I-beam is modified and verified, and the load identification based on the modified numerical high fidelity model is verified.Results In the test of the improved PSO algorithm by Rastrigin function, it shows better global optimal solution searching ability under different particle numbers. In the I-beam experiment, the elastic modulus of the two parts of the partition converges to the optimal solution after 23 iterations. By comparing the strain data of the test monitoring points with the data of the numerical calculation results after model correction, the relative error of the strain is within 2%, which verifies the correctness of the model updating method. In addition, the external load pressure of the structure is identified by the load identification method, and the error between the identified calculated value and the load applied in the test is within 2%. The maximum error between the strain value of the monitoring points calculated by the combination of the identified load and modified model and test data is 3.74%, which verifies the effectiveness of the load identification.ConclusionsThe proposed method has a good precision performance in the inversion of the global state of the structure, and can provide technical support for hull structure health monitoring, residual life prediction and predictive maintenance.

In this paper, a method to analyse load features utilising sample shifting technique (SST) for non-intrusive load identification (NILI) is presented and discussed. Fuzzy rules are used as the foundation for the identification logic. Voltage and current signals for electrical home appliances are acquired in order to develop their respective features. Two features like reactive power and total harmonic distortion for current (THD I), are created with the necessary computations of the samples using SST. A method based on fuzzy rules is created in order to identify different electrical equipment both for their individual as well as simultaneous running. Again, the performance of the proposed system is tested under the noisy environment while the accuracy of the system is found satisfactory. By utilising SST, the burden of computation is reduced in comparison to the other methods which are justified with the experimental results. • Only voltage and current information are used for load identification. • Sample Shifting Technique is used to extract load features. • Reactive power and THD for current are used as load features. • Fuzzy logic-based rules are applied for non-intrusive load identification. • The use of of SST assists in the extraction of load features and reduces the complexity of the hardware by eliminating the need for filters or ZCDs. • Individual load can be identified during practical multiple loads are working parallelly. • System shows robustness under noisy environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ability to effectively identify abnormal loads caused by hanging equipment local resonance problems is important for assessing the structural integrity of railway vehicles. The structural dynamics inverse problems analysis method is presented to minimise resonance effects and equipment-underframe interactions at different structural frequencies. A mathematical model of the structure forward and inverse dynamics is developed to simulate such interactions based on multibody system dynamics and finite elements theory. The Euler-Bernoulli beam model is also used to simulate the flexible carbody integrated with the inverse approach to compare the effects of reconstructed loads utilised by the optimised regularisation algorithm. The vehicle rolling vibration rig test is made to verify the identification effects. The results are found to be in good agreement with the predictions of the mathematical model using the measured acceleration and demonstrated the effectiveness and non-destructive test applicability. [ABSTRACT FROM AUTHOR]

• A continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. • Combination of modal truncation order and sensor layout optimization by modal strain energy. • The proposed algorithm has better accuracy and noise immunity than SSM and GKFM. • For continuous systems, the proposed algorithm has a solution time of 0.38s and is insensitive to the time step. • The proposed algorithm has good accuracy in experimental validation. Dynamic load identification based on structural responses is an important problem in the field of engineering and plays an important role in the condition assessment of mechanical structures. Current popular load identification methods such as the state-space method (SSM) and the Green's kernel function method (GKFM) are implemented on discrete systems with outstanding performance, but when dealing with complex continuous systems, there are some limitations such as low efficiency and inaccuracy. In this paper, a continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. Using modal coordinate transformation and modal truncation methods, the number of infinite vibration differential equations of a continuous system in physical space are converted to a finite number of vibration differential equations in modal space, where the modal truncation order and the optimal layout of the sensors are combined by modal strain energy. The Newmark-β method in modal space is derived and thus combined with SSM to obtain a load identification model Y =HF called SSM-Newmark-β method, which reduces the size of the transfer matrix H. The solution time of the proposed algorithm is 0.38 s for continuous systems, which is rather shorter than that for discrete systems. Furthermore, the simulations show that it has better accuracy and noise immunity than SSM and GKFM in the identification of sinusoidal load, impact load, and random load. The effect of time step is also discussed which reveals that the larger time step has less effect on the proposed algorithm. An experimental study is carried out on a cantilever beam system. The result verifies that the SSM-Newmark-β algorithm has better accuracy in load identification for the continuous system. This research provides a new sight for the real-time identification of dynamic loads for complex structures. [ABSTRACT FROM AUTHOR]

Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to real measurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducible way such that other researcher can use this setup to validate their methodologies. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Actual load identification is a most important task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged structural frames, and (3) structural health monitoring. Actual load values are used to determine the stress–strain state (SSS) of a structure and accomplish various engineering objectives. Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the article presents an integrated method combining basic provisions of structural mechanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make projections, and assembling structures to make final projections for steel frame structures subjected to elastic strain. Final projections serve to identify parameters of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient matrices applied to unit loads and (2) actual loads standardized using maximum load values. Cases of neural network training and parameters identification are provided for simple beams. The aim of this research is to enhance the reliability and durability of steel structures by predicting consequences of unfavorable load, including emergency impacts. The novelty of this study lies in the co-use of artificial intelligence elements and structural mechanics methods to predict load parameters using actual displacement curves of structures. This novel approach will enable engineering inspection teams to predict unfavorable load peaks, prevent emergency situations, and identify actual causes of emergencies triggered by excessive loading.

Abstract Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to real measurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducible way such that other researcher can use this setup to validate their methodologies.

Tikhonov regularization is frequently used to solve the ill-conditioned inverse problem in load identification, and the regularization parameter which plays a significant role in Tikhonov regularization is usually determined by L-curve method. However, two corners appear on the L-curve at some situations, which cause the L-curve method to fail to determine a proper regularization parameter. To improve the accuracy of load identification, a new form of regularization parameter which is based on the largest singular value of the impulse response function matrix and signal-to-noise ratio is developed, and a modified L-curve is plotted. When the norm of the solution and the norm of the residual are balanced, a proper regularization parameter is determined through the modified L-curve. Both simulation and experimental results show that the identified load by the modified L-curve is closer to the actual load than L-curve. It also reveals that the modified L-curve is reasonable and the new regularization parameter is correct, and the accuracy of load identification is improved effectively. [ABSTRACT FROM AUTHOR]

Accurately identifying the vehicle load on the bridge plays a vital role in structural-stress analysis and safety evaluation. Also, extracting the spatiotemporal information of the vehicle's is crucial for identifying the vehicle load. This study aimed to propose a vehicle spatiotemporal information-identification method based on machine-vision technology. First, digital video surveillance cameras were installed in the front and on the side of the monitoring section to capture real-time videos of vehicles passing through the monitoring section. The background-difference method was used to detect vehicles based on the frontal video. Subsequently, the transverse position was evaluated according to the distance between the vehicle's license plate and the lane line. Other vehicle parameters, including the vehicle's speed, the number of axles and the wheelbase, were identified based on the lateral video and the auxiliary lines with a known distance. Second, a laboratory model experiment and multiple field tests under different scenes were carried out to validate the efficiency and accuracy of the proposed method. The results indicated that the average identification errors of wheelbase for the model experiment and the field tests were all 1.12% and those of the vehicle's speed were 1.25% and 1.35, respectively. Also, the average deviations of the lateral position were 2.57 mm and 2.69 cm, respectively. The variances of the identified error of the three parameters for the field tests were 0.78%, 1.83 cm and 0.54%, respectively. This verified that the proposed method has high accuracy, reliability and good anti-noise performance. [ABSTRACT FROM AUTHOR]

The bending of steel truss structures is an important gauge for detecting, identifying, and evaluating potential issues with structural safety performance. The limitations and high cost of traditional monitoring methods make it challenging to carry out stable long-term monitoring. Therefore, this paper developed a displacement monitoring system for steel truss structures which fulfill the requirements of having low cost, high stability, and ease of operation. The system is based on the improved sub-pixel positioning technology, achieving precise positioning in unfavorable conditions such as long structure-camera distance, angle skew, and dim light. Then, this system was calibrated through field experiment and compared with other measurement systems. Finally, a load identification method was developed to identify discrepancies between the true load and the design load. This method uses optimization functions to identify the true load applied in the experiment, and the optimization parameter obtained by a genetic algorithm iteration is output as the optimal solution. The results suggest that the photogrammetric system performs well in practical engineering applications and can provide advantages including high precision, low cost, simple operation, etc. Results obtained by the load identification method agree well with measurements obtained from the actual structure, and can serve as a tool for evaluating the mechanical properties of similar structures. This method monitors potential risks of steel truss structures, and greatly improve the stability and safety of such structures. [ABSTRACT FROM AUTHOR]

A novel strain gauge force transducer was developed to minimize the number of strain gauges needed to determine the magnitudes of loads when the locations are known. This innovative methodology requires only one strain gauge for each force magnitude desired, reducing the complexity and cost associated with traditional approaches. The theory was verified with laboratory experiments. Seven uniaxial strain gauges were attached to the underside of a simply supported, slender, aluminum beam. One or more loads were applied either directly atop strain gauges or in known positions between strain gauges. Experiments were conducted on several different single and double-load configurations to evaluate the extent of the new methodology which yielded average errors under 5% for the cases where loads were direct atop strain gauges and 6.6% for the cases where the loads were between strain gauges. These findings indicate the potential of this novel strain gauge force transducer to revolutionize load measurement in scenarios where load locations are predetermined. [ABSTRACT FROM AUTHOR]

Monitoring electricity energy usage can help to reduce power consumption considerably. Among load monitoring techniques, non-intrusive load monitoring (NILM) provides a cost-efficient solution to identify individual load consumption details from the aggregate voltage and current measurements. Existing load monitoring techniques often require large datasets or use complex algorithms to obtain acceptable performance. In this paper, a NILM technique using six non-redundant current waveform features with rule-based set theory (CRuST) is proposed. The architecture consists of an event detection stage followed by preprocessing and framing of the current signal, feature extraction, and finally, the load identification stage. During the event detection stage, a change in connected loads is ascertained using current waveform features. Once an event is detected, the aggregate current is processed and framed to obtain the event-causing load current. From the obtained load current, the six features are extracted. Furthermore, the load identification stage determines the event-causing load, utilizing the features extracted and the appliance model. The results of the CRuST NILM are evaluated using performance metrics for different scenarios, and it is observed to provide more than 96% accuracy for all test cases. The CRuST NILM is also observed to have superior performance compared to the feed-forward back-propagation network model and a few other existing NILM techniques. [ABSTRACT FROM AUTHOR]

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The non-intrusive load identification method can judge the type and working situation of the electrical load without interfering with the user’s electricity consumption, which is an important part of smart grid technology. In this paper, a load identification method based on variational modal decomposition algorithm (VMD) is proposed, which can effectively identify different types of electrical loads. The effectiveness of VMD algorithm in the field of load identification is verified by conducting load recognition experiments. After optimizing the algorithm parameters, the Feature library membership threshold was tested and debugged, and finally a good load identification effect was obtained, which can be used as a means of identifying electricity load in urban management.

The insertion of non-invasive load sensing technology into the power supply entrance is conducive to promoting energy saving construction, power grid actual load prediction, the development of intelligent energy buildings, and the completion of intelligent energy network system construction. Based on this, a load discrimination method based on Artificial Bee Colony algorithm optimized Support Vector Machine (ABC-SVM) was proposed. First, the current signal of the main line is tested through events. After the transient event is detected, the negative charge transient current waveform of the target is separated, and its feature are extracted. Then, the features are input into the pre-trained ABC-SVM model for classification and recognition. In order to improve the performance of the classifier, particle swarm optimization algorithm was used to optimize the parameters of ABC-SVM classifier. Experimental results show that the recognition accuracy of this method is up to 97.69%, and the sample recognition speed is 1.53 μs, which has certain practicability.

Non-intrusive load monitoring is the main trend of green energy-saving electricity consumption at present, and load identification is a core part of non-invasive load monitoring. A support vector machine (SVM) is commonly used in load recognition, but there are still some problems in the parameter selection, resulting in a low recognition accuracy. Therefore, an improved equilibrium optimizer (IEO) is proposed to optimize the parameters of the SVM. Firstly, household appliance data are collected, and load features are extracted to build a self-test dataset; and secondly, Bernoulli chaotic mapping, adaptive factors and the Levy flight were introduced to improve the traditional equilibrium optimizer algorithm. The performance of the IEO algorithm is validated on test functions, and the SVM is optimized using the IEO algorithm to establish the IEO-SVM load identification model. Finally, the recognition effect of the IEO-SVM model is verified based on the self-test dataset and the public dataset. The results show that the IEO algorithm has good optimization accuracy and convergence speed on the test function. The IEO-SVM load recognition model achieves an accuracy of 99.428% on the self-test dataset and 100% accuracy on the public dataset, and the classification performance is significantly better than other classification algorithms, which can complete the load recognition task well. [ABSTRACT FROM AUTHOR]

To solve the problems of high power consumption and insufficient driving range of pure electric rear-drive commercial vehicle, a braking energy recovery method for the pure electric rear-drive commercial vehicle was proposed. The original H-type hydraulic pipeline layout of the brake system was kept, and the fixed ratio distribution of the front and rear axle hydraulic braking force was unchanged.Only one brake pedal force sensor was added, and the braking energy recovery strategy was proposed based on ECE R13 and I curves. AVL Cruise and Simulink were used to establish a joint simulation platform for vehicle, and a strategic model and a typical series scheme model with greatly increasing costs were built and compared. Under three typical conditions, the vehicle brake energy recycling simulation test was conducted. The results show that the proposed braking energy recovery control strategy with load identification can approach the upper limit of recovery within the safety limit at low cost, and the driving distance can be increased by more than 21.00% compared with the original design at full load. [ABSTRACT FROM AUTHOR]