Condition Assessment Method Research Articles

Time-dependent robustness-based condition assessment of RC bridges subjected to corrosion

At present, the main methods of bridge condition assessment are reliability analysis and analytic hierarchy process method. Robustness, as a measure of the structural ability to maintain the performance after the occurrence of variations, can provide a different perspective based more on the structural property itself. Although robustness has been mainly used in structural design optimization against progressive collapse and extreme events, it can also be used in assessing the condition of bridges with material deterioration. In this paper, a time-varying robustness index model is proposed to quantify the condition of the reinforced concrete bridge girder under corrosion. In this model, the robustness index represents the average performance of the concrete girder in the remaining life. The method is exemplified through a case study of an existing reinforced concrete bridge girder. Combining with the current specification, the robustness index is compared with the reliability analysis results of the girder in the case study. The result shows that when the reliability index drops below the target reliability index, the robustness index descends to Grade 5 specified in the Code for Maintenance of Highway Bridge and Culvers (JTG H11-2004). Both results indicate that the safety of the girder cannot satisfy demand.

A method for evaluating and upgrading systems with parallel structures with forced redundancy

The objects of the study are parallel-structure machine systems with redundancy associated with safety assurance of continuous material flow. The problem concerns systems in which the supply of materials takes place continuously (24 hours a day), and the system of operated machines must ensure the receipt and movement of the material at a strictly defined time and in the desired quantity. It is a system where the presence of a failure poses a threat to human life and environmental degradation. This paper presents a method for system condition assessment and upgrading for maintaining proper operation under conditions of continuous operation. A database of information about the current parameters of the system components (measurements, monitoring) is necessary for condition assessment. The method also uses lean techniques (including TPM). System evaluation and selection criteria for a suitable structure in terms of further operation were proposed. Exemplification was performed for an underground mine drainage system. As a part of the identification, selected parameters of the system components were measured, and their characteristics (motors, pumps, pipelines) were developed. The results of the analysis and the values of the adopted criteria were compared to the indicators for new pump sets. A two-option system upgrade was proposed, in addition to machine operating schedules, maintenance periods, and overhaul cycles.

A health condition assessment method of wind turbine generator system based on multiple turbines cooperation

To improve the reliability and optimize the maintenance strategy of a large-scale wind farm, a health condition assessment method of wind turbine generator system (WTGS) based on multiple turbines cooperation is presented in this paper. First, a health condition assessment framework is established by analyzing the existing monitoring characteristics of SCADA system. Second, to quantify the health condition of a WTGS, the concept of synthetic degradation factor is introduced and calculated using variable weight of assessment indices. Third, in order to acquire the health level of a WTGS effectively, based on the idea of multiple turbines cooperation, the health condition assessment model is proposed by utilizing the moving average method and quantile algorithm. Finally, by using SCADA monitoring data of a wind farm with 33 × 1.5 MW WTGSs including temperature, pressure, vibration, and so on; the processes of condition assessment are performed, and the effectiveness is also compared to a traditional assessment method with the fixed threshold. The results show that the condition assessment that utilizes the proposed method can acquire the change of health conditions and has a better coherence with real health conditions.

Operational state assessment of cross-linked polyethylene power cable on the basis of an optimized cloud matter-element theory

Polyethylene power cables are likely to encounter high failure rates due to adverse operation conditions. In order to ensure a safe and reliable operation of the Polyethylene power cables, a state assessment approach should be conducted to evaluate the actual operation state of the Polyethylene power cables and determine the maintenance schedule. Several techniques which are used to preempt cable failure and schedule efficient maintenance in the past work have been reviewed, each of which is found with limitation. Taking a 10 kV – Polyethylene power cable as an example and considering the uncertainty of cloud model as well as the qualitative characteristics of matter-element theory, a cable operation state assessment method based on improved cloud matter-element theory with optimal entropy was proposed. Firstly, aiming at the randomness and fuzziness of operation condition level boundary, cloud matter-element theory was proposed to construct the index system of state evaluation. Secondly, based on a comprehensive consideration on the characteristics of clarity and fuzziness of grade division, the optimized cloud entropy calculation method suitable for fuzzy indices is proposed, and the traditional extension cloud theory is improved. Finally, according to the cloud matter-element model correlation degree assessment matrix and comprehensive weight, the operation condition grade of Polyethylene power cable is comprehensively evaluated. The method could effectively deal with the uncertainties in the state assessment according to the changes of the power cable operation condition, and the assessment results were more objective and reasonable. The proposed method in this paper conquers the drawbacks of the past work and can effectively deal with the uncertainty factors in transformer state assessment. The evaluation results are closer to the real state of the equipment and have wider application scenarios.

Corrections to “Study on Energy Storage Magnet State Assessment Method Considering Temperature Rise” [Mar 21 Art. no. 4600411

Corrects the complete affiliation of Shuqiang Guo, Li Ren, Ying Xu, Jing Shi, Guilun Chen, Zheng Li, Yuejin Tang, and Jingdong Li from what was originally given in the above-named article (ibid., vol. 31, no. 2, Mar. 2021, Art. no. 4600411).

Structural health assessment techniques for in-service timber poles

Timber utility poles are widely used in power distribution and telecommunication networks. Wood is subjected to deterioration with time, and routine condition assessment works are carried out for in-service timber poles to identify potential defects and their extent for renewal planning. These condition assessment methods need to be reliable and accurate to represent the actual status of poles. Incorrect interpretations regarding the structural integrity of poles may lead to premature pole replacements due to misdiagnosed defects or pole failures due to overlooked defects. The aim of this paper is to provide a state-of-the-art review of various condition assessment techniques for in-service timber poles. The paper reviews the conventional pole inspection methods, semi-destructive techniques and recent developments in non-destructive testing (NDT) techniques. The performance of these techniques is compared and their relative advantages and shortcomings are discussed. Further, the timber deterioration modelling techniques are outlined along with the probabilistic approaches for the condition assessment of timber poles. Finally, potential research gaps that need to be addressed in the future to overcome the issues related to the condition assessment of timber poles are discussed.

개선된 Fuzzy-RPN 기법을 활용한 전력설비 유지보수 우선순위 결정에 관한 연구

Due to the rapid development of industry and the introduction of renewable energy, power system has became more complex and had various problems. The operator of power utilities have made efforts in enhancing the efficiency based on the characteristics of these facilities. This paper suggests an optimal maintenance strategy for electric power system based on a reliability assessment. The reliability assessment is performed by risk priority number(RPN) developed by severity(S), occurrence(O), detection(D). The traditional RPN method for ranking and assessing the risk of potential failure mode have a crucial problem to be considered on the analysis results. The proposed RPN method using Fuzzy inference system is a method to resolve the ambiguity of the results with the traditional condition assessment method. The New Fuzzy-RPN method is tested using actual recorded data of power system.

Comprehensive evaluation of power flow and adjustment method to restore solvability based on GCRNN and DDQN

In view of the problem of manpower and time consumption caused by unsolvable power flow in current AC / DC hybrid power grid with new energy, a power flow state assessment and solvability recovery adjustment method based on graph convolutional recurrent neural network (GCRNN) and deep double Q-learning network (DDQN) is proposed. First, active power balance degree, reactive power balance degree and channel transmission margin are defined and weighted together to evaluate the solvability of power flow. Then, a power flow state evaluation model based on GCRNN is proposed by combining graph convolution with recurrent neural network, where the input is tangent vector of power flow equations, and the output is comprehensive evaluation index of solvability. Next, the state space, action space and multiple rewards of deep reinforcement learning are formed. By introducing the search direction, learning information extraction and search space constraints, an automatic adjustment method to restore power flow solvability is designed. Finally, the simulation results show that the proposed method performs well both in New England 39-bus standard system and actual power grid of China. Compared with the results obtained by other methods, the superiority of the proposed method is verified.

IR Spectroscopy as a Method for Online Electrolyte State Assessment in RFBs

AbstractThe transition from fossil to renewable energy sources requires adequate storage technologies due to the intermittency of the supplied energy. With respect to this, organic redox‐flow batteries (ORFBs) represent a promising concept for the storage of electricity on a large scale at economically justifiable costs. However, these storage technologies can only be operated reliably if parameters representing the actual condition of the storage medium (i.e., the electrolyte) can be accurately assessed. These so‐called electrolyte state variables are represented by two key figures of merit: state of charge (SOC), a measure of the amount of charge that the electrolyte currently holds; and state of health (SOH), representing the amount of charge that the electrolyte is able to store given its current condition. The herein presented IR‐based approach is able to simultaneously provide reliable, fast, accurate, and precise estimates for both SOC and SOH parameters at any point in time and independent of the current battery status. The method is able to provide a time resolution in the range of minutes, is independent of the electrolyte temperature and can be applied to nearly all organic‐based redox‐active materials and solvents, while potentially being applicable to inorganic RFBs, such as vanadium‐based systems, as well.

Using measured rotation on a beam to detect changes in its structural condition

ABSTRACT A recent survey of Europe’s highway infrastructure has concluded that almost half of Europe’s bridges are nearing the end of their design live. Work in the wider Structural Health Monitoring sector is aiming to develop reliable and cost-effective methods for verifying condition, remaining service life and safety of ageing structures. Most bridge condition assessment methods are based on deflection, acceleration or strain measurements. This paper looks at the possibility of using rotation measurements as a main parameter to identify damage. This study looks at numerical analyses of a moving point load on a one-dimensional bridge model to provide the theoretical basis of the proposed damage detection method. It is shown that when local damage occurs, even when it is remote from a sensor location, it results in an increase in the magnitude of rotation measurements. This study looks at how best to exploit this fact for damage detection. A number of damage scenarios, sensor locations, and load arrangements are investigated in this study and their influence on the ability of the algorithm to detect damage are reported.

Video-based neonatal state assessment method for timing of procedures.

Procedures should be performed when an infant is most receptive to disruptions in order to reduce the stress on the infant. However, frequent direct observations place a heavy burden on medical staff. There is therefore a need for a method for quantitatively and automatically evaluating the neonatal state. Ten infants in our hospital were enrolled in this study. The states of the infants were assessed by medical staff using the Brazelton Neonatal Behavioral Assessment Scale and were recorded on video at the same time. The recorded states were reclassified as activity levels, a new state classification method that includes middle activity, which is the appropriate time for a procedure. Using image analysis, motions of the infant were quantified as two indices: activity and pause time. Activity and pause time were compared for each activity level. The cutoff values of the indices were calculated, and the sensitivity and specificity of the middle activity were calculated. There was a significant difference between all groups of activity level (P<0.01). The maximum sensitivity and specificity of middle activity were 71.7% and 51.2%, respectively. The neonatal state of infants can be quantitatively and automatically evaluated using video cameras, and the activity level can be used to determine an appropriate time for procedures in infants. This will reduce the burden on medical staff and lead to less stressful procedures for infants.

A new deep domain adaptation method with joint adversarial training for online detection of bearing early fault

For online early fault detection of rolling bearings in non-stop scenarios, one of the main concerns is the model bias caused by the distribution shift between offline and online working conditions. Under such concern, how to improve the feature sensitivity to early faults and the robustness of detection model has become a key challenge of improving the effectiveness of online detection. To solve this problem, a new online early fault detection method is proposed in this paper based on a strategy of deep transfer learning. First, a new robust state assessment method is presented. By introducing priori degradation information in the anomaly detection process of the isolated forest algorithm, this method can accurately assess the normal state and early fault state under noise interference. Second, a new deep domain adaptation algorithm is proposed. The algorithm uses the results of state assessment as output labels, and designs a deep domain adaptation neural network for joint adversarial training at feature level and model level simultaneously. Then a domain-invariant feature representation can be extracted from the data of different working conditions, and an online detection model can then be constructed. Comparative experiments are run on two bearing datasets IEEE PHM Challenge 2012 and XJTU-SY, and the results verifies the effectiveness of the proposed method in false alarm number and detection location.

Switchgear Condition Assessment and Lifecycle Management: Standards, Failure Statistics, Condition Assessment, Partial Discharge Analysis, Maintenance Approaches, and Future Trends

In this article, switchgear standards, failure statistics, and condition assessment methods with a special focus on medium and high voltage classes are critically reviewed.

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder

Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

Limited sensor-based bridge condition assessment using vehicle-induced nonstationary measurements

Retrofitting and maintenance of bridges using vehicle-induced measurements have attracted increasing attention in the field of vibration-based bridge health monitoring. In this paper, a novel condition assessment method is proposed that uses a fewer number of sensors for monitoring bridges when subjected to moving vehicles. As a vehicle travels over a bridge, the bridge responses are measured at different locations, and the traffic-induced vibrations are analyzed to identify modal parameters of the bridge using a limited number of sensors. This study investigates a viable solution to various practical challenges associated with bridge inspection using vehicle-induced measurements, such as sensor location, vehicle speeds and vehicle parameters, and severity of the damage. Unlike ambient vibration measurements, moving vehicle-induced bridge vibration is nonstationary in nature. In this paper, a novel hybrid method is proposed by integrating Time-Varying Filtering-based Empirical Mode Decomposition (TVF-EMD) with Synchro-extracting Transform (SET) to track the nonstationary modal parameters of the bridge. The proposed approach is validated using numerical and full-scale studies comprising of a steel bridge subjected to passing vehicles.

Research on gear fault diagnosis based on feature fusion optimization and improved two hidden layer extreme learning machine

Aiming at gear fault diagnosis in rotating machinery, a novel state assessment method based on feature fusion optimization and improved two-hidden-layer extreme learning machine (ITELM) was proposed in order to improve the fault diagnosis accuracy. Firstly, variational mode decomposition (VMD) and wavelet packet (WP) are employed to decompose the vibration signal, and statistical parameter features are extracted. Then, ReliefF algorithm is adopted to manage the extracted features and obtain the optimal feature subset. Finally, in order to avoid the network structure limitation of the existing two-hidden layer extreme learning machine with same hidden-layer node number, ITELM with different hidden-layer node number is proposed to evaluate the gear running states. Meanwhile, the empirical formula and fuzzy logic reasoning methods are developed in order to effectively determine the number of nodes for first and second hidden layers, respectively. The experimental results from different perspectives verified the effectiveness of the proposed method.

Analysis of the power transformer condition assessment in PLN Western Java Transmission unit

Transformers are critical equipment of the electrical power supply system and the most expensive asset in the electric power transmission network. Therefore, power transformer condition assessment method is needed to determine the actual condition of power transformer on operating. Indeed, they are several methods of condition assessment for power transformers have been implemented. The transformer data is divided into different levels and parts, so multi-parameter analysis is carried on both by direct condition data and by indirect computing history data. Some method use only one-step process, while others through many stages. This paper proposes a power transformer condition assessment with three stages. First step are Health Index and Susceptibility. Second step is Probability of Failure, that is combination between Health Index and Susceptibility. The last stage is Risk Based Priority as a result of combining Probability of Failure and Criticality Level. The outputs of condition assessment are analysis lifetime prediction, abnormality, recommendations for next action and determine maintenance or replacement priorities. This study has investigated 303 units of power transformers in Western Java Transmission unit. The result of study is useful for the utility company to improve their transformers asset quality.

ДИВЕРСИФІКАЦІЯ ЕКСПОРТУ В АЗЕРБАЙДЖАНІ: ОЦІНКА ПОТЕНЦІАЛУ СТАЛОГО РОЗВИТКУ СІЛЬСЬКОГО ГОСПОДАРСТВА

The purpose of the article is to analyze the development of the agriculture sector in Azerbaijan and access the potential for its sustainable development. In the research process, the following scientific methods have been used: empirical method (for the comprehensive assessment of the current state of the research object) along with abstract and logical methods. The assessment of diversification was conducted using Herfindahl-Hirschman Index (HHI). Research results. The results of scientific research on the development of the agriculture sector in Azerbaijan have been presented. The current state of the sector has been demonstrated and the potential for further development as a part of the diversification strategy has been applied. Elements of scientific novelty. An overview of the sector as one of the major contributors to the appliance of diversification policy has been made. The aforementioned results lead to the conclusion that due to the high potential, the sector could further be considered as one of the significant contributors to the non-oil sector. Practical significance. The dynamics and consistencies of the agriculture sector in terms of value-added have been presented. Conclusion. To provide effective and efficient support to the sector’s development, the government has to have an insight into the sector’s current economic situation. As a result, a need for in-depth research in terms of expansion of the information base of the sector emerges. This policy includes data collection and analysis, monitoring, and evaluation in order to ensure the indication of business opportunities. Systematic analysis and assessment have to be applied to become a useful tool for identifying the needs of the sector. It is worth mentioning that the food supply chain organization in Azerbaijan is comparatively week with poor collaboration among chain actors, including farmers, traders, and processors. Most sales of agricultural goods flow through the wholesale market network throughout the country. Taking into consideration the aforementioned, it becomes clear that the expansion of the agricultural export will broaden the links and network accelerating the process of globalization. Therefore, an analysis of the existing data on the agriculture sector in Azerbaijan illustrates that the country has great potential for the development of this sector which may contribute to the economy and integration into the global market. Keywords: Azerbaijan, diversification, agriculture, export, resources.

Reliability evaluation of multi-agent integrated energy systems with fully distributed communication

The reliability evaluation of integrEated energy systems becomes very important, because it is the basis of planning and operation. However, different stakeholders in integrated energy systems bring difficulty to evaluation process due to data privacy protect. In this paper, a reliability evaluation approach for multi-agent integrated energy systems via fully distributed communication is presented. Firstly, a reliability evaluation model of multi-agent integrated energy systems is established, where wind turbines, plug-in electric vehicles and gas storages are considered in the structure. Moreover, a re-dispatching model with voltage and gas pressure constraints is presented to minimize operation cost, when a contingent incident happens. Through second-order cone and Big M relaxation, the re-dispatching model is reformulated to a solvable one. Furthermore, a state assessment method with fully distributed communication is proposed to reduce information exchange, which is presented based on Monte Carlo simulation and alternating direction method of multipliers with Gaussian back substitution. Reliability indices of each agent and whole system can be calculated via a little non-privacy message transferring. Finally, the proposed model and approach are tested on a modified MA-IES. The relevant failure among different sub-systems is analyzed and the effectiveness of fully distributed communication is validated.

Fast Falling Weight Deflectometer Method for Condition Assessment of RC Bridges

In this paper, the use of Fast Falling Weight Deflectometer (Fast-FWD) is analyzed as a non-destructive and quick test procedure to evaluate the efficiency of short-span bridges. The Fast-FWD is an instrument that can produce a broadband dynamic force up to an impact value of 120 KN: The impact is constant and replicable, providing accurate action measures of bridge stiffness in a truly short period (30 ms). In this paper, a single-span reinforced concrete bridge is investigated, using the Fast-FWD. The considered bridge, approximately 12.0 m long and 15.5 m wide, was in critical condition. The bridge is in a suburban principal road near to the City of Cagliari in Sardinia (Italy), with an Annual Average Daily Traffic of 13,500 vehicles/day, and was suddenly closed, creating serious problems for urban mobility. In these conditions, the investigation through other standard techniques is time-consuming and labor intensive. For this reason, it is important to introduce methods that can be rapid, accurate and cost-efficient. In this paper, bridge stiffness values obtained during the in situ experimental campaign were compared with finite element models values. The Fast-FWD has the potential to provide engineering information that can help us to better understand bridge condition, in a rapid and cost-effective procedure.