State Evaluation Method Research Articles

Robustness-Based Condition Evaluation Framework for Through Tied-Arch Bridge

For through tied-arch bridges, the corrosion and degradation of the cable’s load-bearing capacity are the main external causes of cable breakage and subsequent structural failure; nevertheless, the progressive collapse after cable breakage is primarily attributed to the weak structural robustness, which makes a huge potential risk of bridge operation and maintenance (O&M). Whereas, as the basis of O&M, current condition evaluation method has not yet taken into consideration the influence of different consequences of cable breakage, resulting in unscientific conclusions for O&M decisions frequently. In this study, a robustness-based condition evaluation framework for the overall structure of the through tied-arch bridge is presented, consisting of three stages: (1) structural robustness assessment associated with tie-bar and suspender failure respectively; (2) classification of evaluation process by the results of robustness assessment, within which the overall structure is either evaluated using the code method or evaluated directly as unqualified (Condition I and II), or the structural member weightings are adjusted according to the robustness weightings (Condition III); and (3) condition evaluation of entire bridge. The evaluation processes of three conditions are further presented in accordance with the tied-arch structure and suspended deck system. To establish the robustness weightings in Condition III, the safety redundancy indexes of through tied-arch structures as well as the suspended deck system are calculated separately, combined with robustness assessment result. Applying the proposed framework to evaluating the condition of a through tied-arch bridge with different structures, the analysis and comparison indicates that, due to the robustness of the through tied-arch structure and suspended deck system, the variation in the potential risk of accidents induced by cable failure is shown intuitively through the evaluation results, which better meets the needs of guiding bridge O&M decisions consequently.

Cable current-carrying status analysis and early fault warning method based on temperature information

Long-term operation of cables and equipment under abnormal currents can cause insulation aging and reduce the service life of the equipment. Identifying the current-carrying status accurately and early detection of potential failures of power cables is particularly significant for ensuring the operation of power equipment safely and economically. This paper proposed a method for detecting the current-carrying status of power cables and an early fault warning based on temperature information. Based on the theoretical basis of the relationship between temperature and current. According to IEC 61000-3-7 and IEC 60038 standards, the current-carrying state evaluation method is analyzed and given, and the early fault warning model is established. Finally, the paper takes the cable connector and the lightning arrester in the power ring network cabinet as an example to verify the method’s feasibility. It shows that the characteristic information of the relative temperature variation curves under different current carrying states significantly differs. Includes the slope of hour 9, the slope of hour 2, and the maximum. Then the total duration under abnormal current operation is calculated, and the early warning of faults can realize.

Dynamic State Evaluation Method of Power Transformer Based on Mahalanobis–Taguchi System and Health Index

Health status assessment is the key link of transformer-condition-based maintenance. The health status assessment method of power transformers mostly adopts the method based on the health index, which has the problems of multiple parameters of each component and strong subjectivity in the selection of weight value, which is easily causes misjudgment. However, the existing online monitoring system for dissolved gas in transformer oil (DGA) can judge the normal or abnormal state of the transformer according to the gas concentration in a monitoring cycle. Still, there are problems, such as fuzzy evaluation results and inaccurate judgment. This paper proposes a dynamic state evaluation method for power transformers based on the Mahalanobis–Taguchi system. First, the oil chromatography online monitoring time series is used to screen key features using the Mahalanobis–Taguchi system to reduce the problem of excessive parameters of each component. Then, a Mahalanobis distance (MD) calculation is introduced to avoid subjectivity in weight selection. The health index (HI) model of a single transformer is built using the MD calculated from all DGA data of a single transformer. Box–Cox transformation and 3 σ criteria determine the alert value and threshold value of all transformer His. Finally, taking two transformers as examples, we verify that the proposed method can reflect the dynamic changes of transformer operation status and give early warning on time, avoiding the subjectivity of parameter and weight selection in the health index, which easily causes misjudgment and other problems and can provide a decision-making basis for transformer condition-based maintenance strategies.

Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries

The accurate estimation of the state of charge, the state of health and the prediction of remaining useful life of lithium–ion batteries is an important component of battery management. It is of great significance to prolong battery life and ensure the reliability of the battery system. Many researchers have completed a large amount of work on battery state evaluation and RUL prediction methods and proposed a variety of methods. This paper first introduces the definition of the SOC, the SOH and the existing estimation methods. Then, the definition of RUL is introduced, and the main methods are classified and compared. Finally, the challenges of lithium–ion battery state estimation and RUL prediction are summarized, and the direction for future development is proposed.

Evaluation of Operating State for Smart Electricity Meters Based on Transformer–Encoder–BiLSTM

The reliable operating state of smart electricity meters is significant in industrial applications. Faulty meters or meters in a poor measurement state will seriously impact both customers and stakeholders. However, the maintenance personnel now still use the manually periodic sampling inspection from the batch of smart electricity meters to evaluate the state of the entire batch, which has limitations of blindness, poor real time, inadequate, and insufficient examination. With the population of smart meters, it is feasible to evaluate the health condition of these devices with big data and artificial intelligence technology. One significant contribution of this article is first proposing an operating state evaluation method of smart electricity meters based on the transformer–encoder and bidirectional long-term and short-term memory. The evaluation indicators and preprocess of meters’ data are carefully selected. A deep neural network is constructed and trained, the experimental verification is carried out, and the performance of the proposed method is compared with that of other traditional methods. The results show that the average classification accuracy of the proposed neural network model is 99.5%. Besides, compared with conventional machine learning and deep learning models, the proposed model is suitable for the operation state evaluation of smart electricity meters. From the experimental result, the potential benefit of the proposed method is that it could improve the accuracy and robustness of state evaluation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Structural Behavior Prediction Model for Asphalt Pavements: A Deep Neural Network Approach

Abstract Structural behavior of pavements is assessed using various destructive and nondestructive tests, albeit they are found to be cost-intensive. There is a need to develop cost-effective structural condition evaluation methods that are scientifically sound so appropriate maintenance interventions can be performed at the right time. The objective of this research study was to develop a Deep Neural Network (DNN)–based approach to predict pavement structural condition using functional, traffic, and climatic characteristics. A DNN was developed to calculate the deflection bowl parameters along with peak surface deflections from roughness, traffic, pavement age, pavement temperature, and climatic conditions. Over 26,000 data points covering various geographic locations were used to establish a global model (R2 = 82 % for the test data) to evaluate the structural integrity of asphalt pavement layers. It is envisioned that this study would assist roadway agencies in assessing the overall condition of asphalt pavements synergizing functional and structural characteristics.

A Condition Evaluation Simplified Method for Traction Converter Power Module Based on Operating Interval Segmentation

In the actual operation of urban rail vehicles, it is essential to evaluate the condition of the traction converter IGBT modules. Considering the fixed line and the similarity of operation conditions between adjacent stations, this paper proposes an efficient and accurate simplified simulation method to evaluate IGBT conditions based on operating interval segmentation (OIS). Firstly, this paper proposes the framework for a condition evaluation method by segmenting operating intervals based on the similarity of average power loss between neighboring stations. The framework makes it possible to reduce the number of simulations to shorten the simulation time while ensuring the state trend estimation accuracy. Secondly, this paper proposes a basic interval segmentation model that uses the operating conditions as inputs to implement the segmentation of the line and is able to simplify the operation conditions of entire line. Finally, the simulation and analysis of the temperature and stress fields of IGBT modules based on segmented intervals completes the IGBT module condition evaluation and realizes the combination of lifetime calculation with actual operating conditions and internal stresses. The validity of the method is verified by comparing the interval segmentation simulation with actual test results. The results show that the method can effectively characterize the temperature and stress trends of traction converter IGBT modules in the whole line, which could support the fatigue mechanism and lifetime assessment reliability study of IGBT modules.

Platform-Based Manufacturing Service Collaboration: A Supply-Demand Aware Adaptive Scheduling Mechanism

With the development of new-generated IT technologies and the launch of a series of industrial Internet of things platforms, service-oriented manufacturing is an inevitable trend of manufacturing industry. Therefore, the platform-based manufacturing service collaboration (MSC) becomes a recognized answer to the complex and personalized manufacturing demands. However, the changes in both supply and demand of the platform in its operation process are usually unpredictable. To cope with the scheduling problem on the platform-based MSC with the dynamic uncertainties of both supply and demand, an adaptive scheduling mechanism is explored in this article. In which, the real-time system state evaluation method considering supply and demand are designed, and a supply-demand aware rescheduling trigger judgement is proposed. Experimental results show the effectiveness and adaptiveness of the proposed mechanism, which also provides a reference for other MSC scheduling problems towards different dynamic situations.

An evaluation method of health condition for wind turbine based on asymmetric proximity

The accurate condition assessment of wind turbines greatly influences the refined asset management and maintenance scheduling of wind farms. To address the challenges of existing assessment methods in selecting the reliability value and determining wind turbine status levels of being in transition, this study proposes a wind turbine condition evaluation method based on asymmetric proximity. Firstly, the state evaluation index system consisting of the wind turbine performance and output state indices is constructed, and the weighting factors are calculated comprehensively by integrating the subjective and objective weights. Then, the membership function of the index layer is established based on the set pair analysis, and the membership of the target layer is deduced by the weighted average operator. Finally, the proximity degrees between status levels and target membership degrees are calculated, and the wind turbine state is determined based on the proximity principle. Case studies demonstrate that the accuracy rate of the proposed method is up to 97%, which is 6% and 8% higher than the maximum membership principle and the reliability criterion, respectively.

A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch

Large-scale grids have gradually become the dominant trend in power systems, which has increased the importance of solving the challenges associated with large-scale economic dispatch (LED). An increase in the number of decision variables enlarges the search-space scale in LED. In addition to increasing the difficulty of solving algorithms, huge amounts of computing resources are consumed. To overcome this problem, we proposed a surrogate-assisted adaptive bat algorithm (GARCBA). On the one hand, to reduce the execution time of LED problems, we proposed a generalized regression neural network surrogate model based on a self-adaptive “minimizing the predictor” sampling strategy, which replaces the original fuel cost functions with a shorter computing time. On the other hand, we also proposed an improved hybrid bat algorithm (RCBA) named GARCBA to execute LED optimization problems. Specifically, we developed an evolutionary state evaluation (ESE) method to increase the performance of the original RCBA. Moreover, we introduced the ESE to analyze the population distribution, fitness, and effective radius of the random black hole in the original RCBA. We achieved a substantial improvement in computational time, accuracy, and convergence when using the GARCBA to solve LED problems, and we demonstrated this method’s effectiveness with three sets of simulations.

A New Deep Tensor Autoencoder Network for Unsupervised Health Indicator Construction and Degradation State Evaluation of Metro Wheel

For metro vehicles, wheel performance degradation is inevitable due to the continuous wear of tread and rim. It is of significant value to apply machine learning techniques to evaluate the wheel’s degradation state with vibration signals, as it can lessen expert dependence and optimize repair strategy. In the real metro operation process, however, the wheels’ vibration signal is easily disturbed by irregular noise from many factors, such as load, road condition, and temperature, etc., and lacks essential tendency characteristics for the intelligent state evaluation. To solve this problem, this paper proposes a new unsupervised degradation state evaluation method for metro wheels against irregular noise disturbance. First, a new deep tensor autoencoder (TAE) network is proposed based on the merit of tensor representation in exploring the intrinsic information from raw signals. By extracting the core tensor from hidden features via Tucker decomposition, a new decoding process is rebuilt and a tensorized tendency regularizer is designed with the core tensor. Moreover, a new training algorithm with alternating minimization scheme is constructed to seek the optimal tensor representation and unsupervised feature extraction. Second, a health indicator (HI) of the degradation process is built with the extracted features. A HI-based state assessment algorithm is further proposed to adaptively determine the interval of state change in order to substitute the first/second warning in the current maintenance strategy. A set of comparative experiments are conducted using the wheel data from Beijing Subway. The results demonstrate that TAE can extract features with good monotonicity and tendency from the raw signals under irregular noise disturbance. More importantly, the obtained warning positions can precisely match the wheel diameter’s change in the actual repair record, which offers an easy-to-deploy and intelligent solution for the health management of metro wheels.

A New Method for Rolling Bearing Condition Evaluation Method Under Varying Speeds

A New Method for Rolling Bearing Condition Evaluation Method Under Varying Speeds

Application of High-voltage Circuit Breaker Status Evaluation Method Based on Primary and Secondary Integrated Switch Detection Platform

This paper proposes a high-voltage circuit breakers status evaluation method based on the primary and secondary integrated switch detection platform to cope with the poor effect of existing evaluation methods. A high-voltage circuit breaker status evaluation index system was determined in combination with the actual situation of high-voltage circuit breakers. The subjective weight of the status of the high-voltage circuit breaker was determined with the analytic hierarchy process, the objective weight of the status of the high-voltage circuit breaker was calculated by the entropy method, and the analog acquisition accuracy of the high-voltage circuit breaker was improved with the amplification circuit design of the genetic algorithm. After the weight was determined, the overall weighted evaluation was performed, and the status of the high-voltage circuit breaker was evaluated through primary and secondary integration. Through comparative experiments, it can be seen that the results of the weight proportion test on the high-voltage circuit breaker status evaluation based on the primary and secondary integration are greater than that of the traditional high-voltage circuit breaker status evaluation method. Therefore, the designed evaluation method based on the primary and secondary integration can be better applied to the distribution network and protect the circuit.

Research on state assessment method of subway 35kv switchgear based on multi-feature quantities

The health status of 35kv switchgear directly affects the safe operation of subway. In view of the current problems of single feature quantity, inadequate detection elements and inaccurate overall state evaluation of switch gear, a state evaluation method for metro 35kv switch gear based on multi-feature quantity was proposed. The characteristic parameters such as transient ground voltage, ultrasonic value, cable joint temperature and bus temperature were determined by triangular fuzzy number method with constant weight. The variable weight method was introduced to ensure the balance of state evaluation, and the state evaluation of switch cabinet was carried out with matter-element extenics. The actual detection data of 35kv switchgear 8DA10 in a Shanghai metro substation is taken as an example to verify the effectiveness of the algorithm.

State estimation of a lithium-ion battery based on multi-feature indicators of ultrasonic guided waves

Ultrasonic non-destructive testing technology has been applied to battery state estimation applications to ensure the safety of the energy storage system. However, the accuracy and robustness of battery state estimation should be improved. In this paper, the state estimation of a lithium-ion battery based on multi-feature indicators of ultrasonic guided waves is studied. Piezoelectric ceramic ultrasonic probes with a fixed angle are used as the transducers. Eleven feature indicators of ultrasonic signals are analyzed. The appropriate feature indicators for battery state estimation are determined based on sensitivity analysis and correlation analysis. Considering the frequency response characteristics of the probe and the battery, the multi-frequency response characteristics of the battery are analyzed. Finally, seven feature indicators with multi-frequency excitation are selected. Subsequently, an adaptive machine learning model is designed to estimate the battery state. Based on the experimental results, the root mean square error (RMSE) of the battery state of charge (SOC) estimation result is less than 2.36 %. The applicability of the proposed method is verified by battery fully charged and non-fully charged experiments. Meanwhile, the method can quickly diagnose the side reaction process under abuse conditions such as overcharge and overdischarge, which provides a new method for non-destructive battery state evaluation.

Data Mining in the Vibration Signal of the Trip Mechanism in Circuit Breakers Based on VMD-PSR

To address the difficulty in characterizing early mechanical faults in the trip mechanism of circuit breakers, a data mining method based on variational mode decomposition (VMD) and phase space reconstruction (PSR) method was proposed. First, the vibration signal in the trip stage was separated from the whole according to the current features. Then, it was decomposed using the VMD algorithm to obtain the intrinsic mode functions (IMFs) and these sub signals were mapped to high-dimensional phase space based on the PSR algorithm. Then, the features of the attractor trail shape and the recurrence plot matrixes were extracted. In order to judge the fault in the trip mechanism, a fault simulation test was carried out and the characteristic under different faults was analyzed. Based on these samples, a fault identification model is established by support vector machine (SVM) and the effectiveness is verified by other test samples. The accuracy of the SVM model is 98%, which is higher than that of the BPNN and KNN clustering models. This research supplements the existing method for condition evaluation of the trip mechanism and can provide a reference for circuit breaker fault diagnosis.

Health condition evaluation method for motorized spindle on the basis of optimised VMD and GMM-HMM

Health condition evaluation method for motorized spindle on the basis of optimised VMD and GMM-HMM

A Comprehensive Operation Status Evaluation Method for Mining XLPE Cables.

At present, the online insulation monitoring and fault diagnosis of mining cables are extensively discussed, while their operation status assessment has not been deeply studied. Considering that mining cables are closely related to the safe and stable operation of coal mine power supply systems, a comprehensive evaluation method including the Analytic Hierarchy Process (AHP), the membership cloud theory, and the D-S evidence theory is proposed in this paper in order to accurately assess the operation status of the mining XLPE cable. Firstly, the membership cloud is introduced to solve the index membership degree and the weights are calculated by an improved weight vector calculation method. Secondly, the conversion from the base layer indicator membership degree to the target layer trust degree is realized based on the D-S evidence theory. Then, the cable operation status is judged via the trust degree maximum and the distribution of conflict coefficients is further analyzed to warn the indicators with a bad status in the base layer. Finally, the feasibility of the proposed evaluation method is verified by a sufficient and detailed case analysis.

An aero-engine state evaluation method based on weighted Hellinger distance

In the process of prediction and health management (PHM) about remaining useful life (RUL) of aero-engine, the state evaluation of aero-engine by constructing health index (HI) plays a very important role. At present, in the research of aero-engine state evaluation, there are still many problems: the analysis of the impact between operating conditions and sensor measurement data is insufficient, the standard for sensor data sequence selection is not established, and the HI construction is not reasonable. Aiming at these problems, firstly, a data reconstruction method is used to standardize the sensor data sequences, so as to eliminate the coupling effect between operating conditions and sensor measurement data. Secondly, different from the screening of sensor data sequences by observing the trend manually, a monotonicity standard is proposed. Then, a HI calculation method based on weighted Hellinger distance is proposed, which can construct HI more reasonably and better characterize the health condition of aero-engine. Finally, based on the standard data set of aero-engine degradation process provided by NASA in 2008, a typical HI construction method based on linear regression mapping and a similarity-based RUL prediction method are selected for comparison to verify performance of this method. The results show that the proposed method has better evaluation performance, and the HI constructed by this method is also better in RUL prediction.

Effect of Cementious Materials on Corrsion in Carbonated Concrete

Reinforced Concrete(RC) is an world wide used material than any other man made material in construction. Today civil engineers are facing a major issue with corrosion in RC. The usage of cementious material in concrete prevents the economics loss and damage of structure. Under condition of carbonated concrete, the quantification of corrosion is determined by using condition evaluation methods like galvanostatic method. By using cementious substances like Silica Fume(SF), Ground Granulated Blast Furnace Slag (GGBS), the galvanostatic method will catalyze long lasting and sustainable concrete for the assessment of carbonation induced corrosion. This paper mainly reports on the study of Corrosion Assessment in carbonated and non carbonated specimens. This investigation includes four series (one carbonated and three non-carbonated) of cubes and beams in standard sizes. The reinforcement provided with 12mm diameter as main reinforcement and 8mm diameter as vertical stirrups. The proportions includes M30 Grade of concrete with GGBS (50%), Silica Fume(3%, 5%,10%) as a cement replacements along with superplasticizers. The methodology was adopted by conducting various test like flexural strength, compressive strength, corrosion rate, degree of corrosion, carbonation depth. The results found were effective, as flexural strength and compressive strength were increased due to the addition of cementious materials, where as carbonation depth and corrosion rate decreased.