Line Maintenance Research Articles

Water Delivery Performance of Canal Irrigation: A Case Study of Chalakudy River Diversion Scheme

The role of irrigation is important in mitigating the challenges involved in food production under the changing climate scenarios. Adequacy and equity in water distribution are the primary concern regarding the performance of any irrigation scheme. Conveyance efficiency and two adequacy indicators, namely, Relative Water Supply and Adequacy were used to evaluate the performance of Chalakudy River Diversion Scheme (CRDS) located in central Kerala, India, in the present study, Water withdrawal data collected from Irrigation Department, and data collected from field measurements of canal discharge and seepage loss were used in the study. Irrigation requirement of the canal command area was worked out using the FAO’s CROPWAT 8.0 software. The results revealed that the average annual irrigation demand of the CRDS command area is 46.90 Mm3. Measured flow rate through the canal system showed that the flow decreases towards the tail end. Loss of water through seepage from the canal was high in the irrigation scheme due to the highly damaged condition of canal lining and poor maintenance. The CRDS canal system’s overall conveyance efficiency was found to be 51%. Based on Relative Water Supply and Adequacy Indicator, functioning of the irrigation scheme falls in the category of ‘fair’. Apart from proper repair and maintenance of canal system, conjunctive use of groundwater and canal water for irrigation is suggested towards the tail reaches to alleviate irrigation water inadequacy problems in the CRDS command area.

Dynamic Line Maintenance by Hybrid Programmable Matter

Dynamic Line Maintenance by Hybrid Programmable Matter

Rural Electrification: Prospects, Challenges and Role of Community for Bulk Buying Method in Rural Municipality of Lalitpur District

Rural electrification is the process of bringing electrical power to rural and remote areas. Bulk buying method in rural electrification is when a licensed organization (cooperative in this case) buys electricity from NEA in bulk in cheaper price and distributes it to rural areas in normal prices. The profit generated will be used in the maintenance and expansion of the electric grid lines. The profit is also used in different awareness programs and uses of electricity. The Rural electrification via bulk buying method is a new type of rural electrification process. Despite this being used in some parts of Nepal from 2061 BS, it is not a wide spread process and people don’t know about it. The objective of this study is to find out about the benefit of this method over other electrification methods like solar, small hydropower plants. The different difficulties and hinder and faced by the rural population as well as the organizations trying to implement this process. The role of the community in the implementation and sustainability of this process and how it will impact their life.
 This methodology focuses on answering questions relating to “what” than the “why” of rural electrification. Descriptive as well as analytical methods are being used. Observation of the place and who the bulk buying method is being used and survey of the people was done for Descriptive method. Analysis method refers to the process of analyzing survey results from universe of the study.
 Rural areas are very isolated and the communities there have very different lifestyles than those of us living in urban areas where electricity is given. Rural electrification with the use improves individual’s quality of life, enables community services such as health and education (consumption use) and enables business entities to carry out professional activities (productive use) for rural populations. All of these benefits are possible without creating unintended negative consequences for local ecosystems and the global climate. Some people don’t believe that this process is good for these communities, but the advantages clearly show that electrification will benefit them and improve their quality of life.

Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

AbstractAutomatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.

Remote Monitoring and Maintenance for Equipment and Production Lines on Industrial Internet: A Literature Review

Monitoring and maintaining equipment and production lines ensure stable production by detecting and resolving abnormalities immediately. In the Industrial Internet, operational technology and advanced information technology are fused to improve the digitalization and intelligence of monitoring and maintenance. This paper provides a comprehensive survey of monitoring and maintenance of equipment and production lines on the Industrial Internet. Firstly, a brief review of its architecture is given, and a reference architecture is summarized accordingly, clarifying the key enabling technologies involved. These key technologies are data collection technologies, edge computing, advanced communication technologies, fog computing, big data, artificial intelligence, and digital twins. For each of the key technologies, we provide a detailed literature review of their state-of-the-art advances. Finally, we discuss the challenges that it currently faces and give some suggestions for future research directions.

Clinical characteristics and survival analysis of Chinese ovarian cancer patients with RAD51D germline mutations

ObjectivesWe aimed to describe the behavior among Chinese ovarian cancer patients with RAD51D germline mutations at our institution.MethodsNext-generation sequencing (NGS) was conducted for the entire coding regions and exon/intron boundaries of the RAD51D genes in 781 Chinese ovarian cancer patients treated at our institution from January 1, 2015 to August 1, 2021. Clinicopathological characteristics, treatment modalities, and outcomes were assessed for ovarian cancer patients with RAD51D germline mutations.ResultsRAD51D germline pathogenic mutations were detected in 1.7% (13/781) of patients in this cohort. RAD51D c. 270_271dup (p. Lys91fs) mutation was the most common mutation which was found in 7 patients (7/13, 53.1%). Patients median age at diagnosis was 58 years (range: 45–69 years). 46.2% (6/13) of them were diagnosed after 60 years. Only 1 patient (1/13, 7.7%) had a family history of ovarian or breast cancer. And 1 patient (1/13, 7.7%) had a personal history of breast cancer. The FIGO 2014 distribution by stage was: stage II in 1 patient (7.7%), stage III in 9 patients (69.2%) and stage IV in 3 patient (23.1%). 92.3% (12/13) patients had high-grade serous carcinoma. 2 patients (2/13, 15.4%) had a primary peritoneal cancer. The majority of patients in the entire cohort were reported to be platinum sensitive (92.3%, 12/13) with a platinum-free interval (PFI) of > 6 months.For patients who received PARPis for 2nd line maintenance treatment (n = 5), 2 patients discontinued PARPis treatment after 33.5 and 8.1 months of duration. Other 3 patients are still on therapy with a duration of 2.4, 13.8 and 30.1 months at the date of data cutoff. 1 patient received PARPi as salvage treatment with a duration of only 1.2 months.Nine patients (9/13, 69.2%) relapsed during follow up and all of them relapsed within 2 years after diagnosis, among which 88.9% (8/9) were classified as platinum-sensitive recurrence (PSR), and only 1 patient was classified as platinum-resistant recurrence (PRR). Median PFS for the entire cohort was 17.3 months. Median PFS for the PSR subgroup was 15.9 months. 2 patients died during follow-up. The OS of these 2 patients was 17.2 and 39.6 months. The 5-year OS rate was 67.5%.ConclusionsRAD51D germline mutations are more frequent in Chinese ovarian cancer patients than other population. Few patients have a family history of ovarian or breast cancer, and personal history of breast cancer. Most patients are diagnosed after 50 years. The sensitivity to PARP inhibitors of patients with RAD51D germline mutations need a further analysis.

Application and Comparison of Non-Contact Vibration Monitoring Methods for Concrete Railway Sleepers

This paper describes the non-contact vibration monitoring of prestressed reinforced concrete railway sleepers. The monitoring was carried out using physical measuring equipment consisting of a seismograph and geophones, a robotic total station (RTS) and scanning laser Doppler vibrometry (SLDV) equipment. Measurements of the dynamic response of sleepers to the dynamic loading induced by the running of train sets provides data that give an insight into the actual state of the sleeper structure at the moment when it is most stressed. The main objective of the study was to identify the most appropriate form of monitoring to monitor events related to the occurrence of cracks in the rail sill, which can also be caused by the impact of vibration. As is well-known, monitoring the dynamic response of railway sleepers is very important for the sustainable management and maintenance of railway lines. The vibration analysis was carried out in the field on the Ormož–Ptuj section of the railway line, in Velika Nedelja and in the laboratory of the UM FGPA, where the vibration was simulated using a hydraulic kit with a static and pulsed force of 100 kN. Several cycles of measurement were carried out and the results were compared with the results of the field monitoring. The vibration parameters measured for the concrete sleepers are necessary to develop a realistic dynamic model of the railway line, which will be able to predict its response to impact loads and the possible occurrence of damage, as has recently been observed for concrete sleepers in several European Union countries.

Application of artificial intelligence in early fault detection of transmission line-a case study in India

<span lang="EN-US">Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.</span>

Icing Detection Method of Transmission Line Based on Machine Vision

The conventional icing detection method is mainly based on the manual detection of icing status. The texture correlation characteristics of icing are variables, which lead to large errors in the icing edge detection and affect the subsequent maintenance of transmission lines. Therefore, the transmission line icing detection method based on machine vision is designed in this paper. The method comprises the following steps of extracting the icing texture feature of the transmission line, namely extracting the correlation feature of an icing image, identifying an icing area by using machine vision technology, carrying out edge detection on the transmission line and finally obtaining the icing thickness of the transmission line to realize the accurate detection on the icing of the power transmission line. The comparative experimental results show that the detection error of this method is small, the detection accuracy is high, and it can be applied to real life.

TALASUR trial: a single arm phase II trial assessing efficacy and safety of TALazoparib and Avelumab as maintenance therapy in platinum-Sensitive metastatic or locally advanced URothelial carcinoma

BackgroundUrothelial carcinoma (UC) is the ninth most commonly diagnosed cancer worldwide, with a 3.8/1 male to female ratio. Platinum-based chemotherapy is the first line standard of care for fit patients with advanced UC. However, despite a response rate (RR) for approximately half of patients receiving standard chemotherapy, durable responses are rare (median progression-free progression (PFS) around 8 months). Recently, immune checkpoint inhibitors (ICI) have emerged as new therapeutic options. Among them, Avelumab, an anti-PD-L1 antibody, was assessed in maintenance treatment, demonstrating an overall survival improvement in the JAVELIN Bladder-100 phase III trial. These findings led to its approval as first line maintenance therapy for patients with locally advanced or metastatic UC who have not progressed on prior platinum-containing chemotherapy. However, disease progression as best response was noticed for 37% of patients under Avelumab as maintenance treatment.UC has targetable genomic alterations, including DNA damage repair (DDR) alterations. DDR deficiency is known to major sensitivity to both platinum-based chemotherapy and PD-1/PD-L1 blockade and the combination of ICI and PARP inhibitors showed promising results. It therefore warrants to assess the interest of combining ICI plus PARP inhibitors as maintenance treatment in UC patients.MethodsThe TALASUR trial is a single-arm multicenter phase 2 study aiming to assess the antitumor activity of the combination of Avelumab with Talazoparib among patients with locally advanced/metastatic UC in maintenance therapy after platinum-based chemotherapy.The primary objective is to determine the efficacy of the combination, assessed through PFS. Secondary objectives are as follows: safety profile of the association, objective response, duration of tumoral response, disease control rate, time to subsequent therapy, quality of life. A blood and tumor collections will be also constituted.Patient will receive the combination therapy of daily oral Talazoparib (1 mg/day) and intra-venous Avelumab 800 mg on days 1 and 15, in a 28-day cycle. Fifty patients will be enrolled.DiscussionTalazoparib with Avelumab combination may have additive activity when administrated jointly. We hypothesize that combination will increase the antitumor activity in UC first line maintenance setting with an acceptable safety profile.Trial registrationNCT04678362, registered December 21, 2020. Protocol version: Version 1.3 dated from 2020 09 11.

Maintenance of gynoecious lines through induction of hermaphrodite flowers and in vitro culture in bitter gourd (Momordica charantia. L)

Maintenance of gynoecious lines through induction of hermaphrodite flowers and in vitro culture in bitter gourd (Momordica charantia. L)

A precise inversion method of cable joint core temperature with non-embedded detection based on UMAP and ISSA-BPNN

Obtaining the core temperature of the cable joint is vital to ensure the safe operation of the modern power system with integration. To improve the speed and accuracy of core temperature inversion, this study proposed a non-embedded cable joint temperature inversion method named uniform manifold approximation and projection (UMAP) and the improved sparrow search algorithm (ISSA) optimized the back propagation neural network (BPNN). Firstly, UMAP is used to reduce the feature dimension of sample data input and enhance the data visualization effect. After dimension reduction, the model input features are consistent with the international ampacity calculation standard, and the calculation speed and accuracy of the model are improved. To improve the optimization ability of SSA, the Tent chaotic operator is introduced, and then the ISSA is used to optimize BPNN to address the issue of unstable output and easy falling into a local minimum. At last, the optimization ability and temperature inversion effect of the improved model were compared with other competing algorithms based on the 10 kV cable joint temperature-rise test and CEC2017 benchmark function. The experimental results show that the proposed method shortens the calculation time of the model, and the mean absolute error of temperature inversion is about 0.1°C. The overall performance is the most outstanding, the training data set is unbiased, and the interpretability of the model improves, which can provide a reliable reference for line operation and maintenance personnel.

Differential triboelectric nanogenerator for transmission line vibration suppression and energy harvesting in the grid

The large vibration of the transmission line caused by wind and ice has brought about significant damage to the grid. The transmission line maintenance has a huge demand on energy supply. Therefore, a differential triboelectric nanogenerator (Di-TENG) for suppressing line vibration and energy harvesting is proposed, designed, analyzed and tested. The Di-TENG mainly consists of two fixed pulleys, two flexible steel cables, two pull springs, one generator unit and two guide rails. A model of the line covered with ice is established and theoretical analysis of the transmission line vibration suppression is implemented. The energy harvesting performance of the Di-TENG is also investigated. An energy harvesting performance testing platform and a transmission line oscillation system were built in the laboratory environment. A practical experiment was conducted on a real transmission line system. The experimental results show that the Di-TENG can decrease the vibration of an ice-covered transmission line by up to 28%. The Di-TENG can generate an open-circuit voltage of 180 V, a short-circuit current of 6.6 μA and a transfer charge of 73 nC, and a peak power of 0.73 mW. This study demonstrates the potential application of the Di-TENG for vibration suppression and energy harvesting in the grid. The Di-TENG provides new ideas for self-powered systems based on transmission lines.

Influence of Electromagnet-Rail Coupling on Vertical Dynamics of EMS Maglev Trains

Active control is essential for EMS maglev trains to achieve stable suspension. Currently, the main line's suspension performs well, but in areas with low track stiffness, such as the garage, turnouts, and other lines, unexpected coupling vibration is more likely to occur. Control parameters, vehicle parameters, and rail parameters are all closely related to this phenomenon. In this study, the vehicle-rail coupling dynamic equation with secondary suspension system is first established, and used to disclose the effects of different parameters on the electromagnet-rail coupling vibration of the EMS maglev train. Next, the authors adopted the proportional-derivative (PD) controller, and proposed the concept of maglev train control frequency. Next, a general simulation model was established based on the MATLAB/Simulink, and numerical simulation was carried out to reveal how the secondary suspension frequency, the control frequency and the rail frequency affect the electromagnet-rail coupling vibration. The research results provide a reference for the design of maglev trains, controllers, and tracks, laying a theoretical basis for the maintenance of maglev commercial lines.

Reducing Central-Line-Associated Bloodstream Infections (CLABSI): An Improvement Project in a Specialized Tertiary Hospital.

Central-line-associated bloodstream infections (CLABSI) are preventable hospital-acquired infections that harm and prolong a patient's hospital stay and increase unnecessary hospital expenditure. In collaboration with infection control and hospital epidemiology, the quality management department initiated a performance improvement project to address 144 CLABSI events in 2017. The performance improvement project team used brainstorming to create change ideas presented in a driver diagram. The team then applied plan-do-study-act (PDSA) cycles to implement and monitor the improvements. Finally, the team applied a bundle of interventions that included the following: (a) policies and procedures for central line insertion, maintenance and removal processes standardization, (b) physicians training for line insertion by simulation, (c) an awareness campaign that included recognizing healthcare workers and enhancing patient engagement, (d) performing root cause analysis for the CLABSI events, and (e) automation of central line insertion, and (f) maintenance bundles documentation in the hospital information system. The CLABSI rate per 1000 patient days dropped from 1.5 per 1000 device days to 1.03 per 1000 device days. In addition, CLABSI events reduction was 24% by 2018. Then further reduction of 15% occurred by the end of 2019. Thus, the overall decrease in CLABSI events was 35% from the baseline number of events (i.e., 144 events) in 2017. Another achievement of the project is that 10 units reported zero CLABSI events in 2018 and 2019, of which four units had a high central line utilization rate, more than 40%. The implemented interventions effectively reduced the CLABSI events in the hospital. The project team will continue implementing more interventions with monitoring, aiming to achieve zero CLABSIs over time in all the hospital units.

Research on the quality evaluation method of rail transit vehicles maintenance based on the life cycle

In order to adapt to the current life cycle management mode of rail transit, this paper proposes a quality evaluation model based on vehicle life cycle maintenance. This model has three parts, which are objective evaluation, subjective evaluation and operational impact. The statistical process control method (SPC) is used in the objective evaluation. The subjective evaluation uses fuzzy algorithms to calculate the factors and weights that affect the maintenance quality, and then weights the actual number of failures to obtain the line deductions. Operation impact evaluation takes the main line failure as the analysis object, and combines the time, location and impact of the failure to obtain the line’s operational quality score. Then calculate the score of these three parts, the final line maintenance quality score. This evaluation system can more truly reflect the maintenance quality, and it has been applied in some pilot lines.

Preparatory Railway Track Geometry Estimation Based on GNSS and IMU Systems

The article discusses an important issue of railway line construction and maintenance, which fundamentally is the verification of geometric parameters of the railway track. For this purpose, mobile measurements have been performed using a measuring platform with two properly arranged GNSS receivers, which made it possible to determine the base vector of the platform. The measuring functionality of the system was extended by IMU. In this article, the effect of measuring conditions on the accuracy of the results collected from GNSS receivers is analyzed. In particular, the advisability of digital filtering of the recorded coordinates to eliminate disturbances is indicated. The article also presents the possible use of GNSS devices and the IMU unit for determining the direction angle and the longitudinal and lateral inclination angles of the railway track. This makes it possible to verify the track geometry in the horizontal plane by determining the positions of straight sections, circular arcs, and transition curves. It is indicated that the results of measurements are repeatable despite the dynamic interaction between the railway track and the measuring platform. The results confirm the usefulness of the applied GNSS and IMU signal processing method for monitoring the geometrical parameters of the railway track in operating conditions.

Design and optimization of carbon fiber eight-wire lifter with exterior cladding structure

The UHV eight-wire lifter is an important tool in frequent use for the construction and maintenance of UHV transmission lines, which is made of titanium alloy and weighs about 40 kg. Due to high-weight, the work efficiency is greatly reduced. In this paper, the design of a lightweight UHV eight-wire lifter is proposed by using carbon fiber cladding structure. The effects of three factors on the maximum load-bearing load of the carbon fiber exterior cladding structural member are excavated through the failed experiment of the carbon fiber exterior cladding tensile and shear test pieces and the finite element progressive damage numerical calculation. The orthogonal experiment is employed to obtain the optimal cladding structure of the eight-wire lifter with a carbon fiber exterior coating cladding. After that, carbon fiber eight-wire lifter is developed and subjected to mechanical tests. The test results show that the performance of carbon fiber eight-wire lifter meets the mechanical performance requirements of eight-wire lifter. Compared with the titanium alloy eight-wire lifter, the maximum load-bearing load of the carbon fiber eight-wire lifter is increased by 20.5%, and the weight is decreased by 37.5%.

Phenotypic variation in biomass and related traits among four generations advanced lines of Cleome (Gynandropsis gynandra L. (Briq.)).

Gynandropsis gynandra (spider plant) is an African traditional leafy vegetable rich in minerals, vitamins and health-promoting compounds with potential for health promotion, micronutrients supplementation and income generation for stakeholders, including pharmaceutical companies. However, information on biomass productivity is limited and consequently constrains breeders’ ability to select high-yielding genotypes and end-users to make decisions on suitable cultivation and production systems. This study aimed to assess the phenotypic variability in biomass and related traits in a collection of G. gynandra advanced lines to select elite genotypes for improved cultivar development. Seventy-one advanced lines selected from accessions originating from Asia, West Africa, East Africa and Southern Africa were evaluated over two years with two replicates in a greenhouse using a 9 x 8 alpha lattice design. Significant statistical differences were observed among lines and genotype origins for all fourteen biomass and related traits. The results revealed three clusters, with each cluster dominated by lines derived from accessions from Asia (Cluster 1), West Africa (Cluster 2), and East/Southern Africa (Cluster 3). The West African and East/Southern African groups were comparable in biomass productivity and superior to the Asian group. Specifically, the West African group had a low number of long primary branches, high dry matter content and flowered early. The East/Southern African group was characterized by broad leaves, late flowering, a high number of short primary branches and medium dry matter content and was a candidate for cultivar release. The maintenance of lines’ membership to their group of origin strengthens the hypothesis of geographical signature in cleome diversity and genetic driver of the observed variation. High genetic variance, broad-sense heritability and genetic gains showed the potential to improve biomass yield and related traits. Significant and positive correlations among biomass per plant, plant height, stem diameter and leaf size showed the potential of simultaneous and direct selection for farmers’ desired traits. The present results provide insights into the diversity of spider plant genotypes for biomass productivity and represent key resources for further improvement in the species.

Design of an Underground Transmission Line Condition Fault Monitoring System for Power Grids Based on Data Analysis Algorithms

With the continuous expansion of the scale, capacity, and coverage of the modern transmission network, the role of the power system in the national economy is increasingly prominent, and the disconnection of the power system will have a huge impact on society and people’s lives. Due to the long transport distance and wide coverage of transmission lines, natural conditions and human factors have caused great difficulties in line operation and maintenance. How to effectively improve the operation and maintenance of transmission lines to ensure the stability and safety of the power grid has become a common problem for the power industry and scientific researchers to discuss. In recent years, the information society has stepped into the era of big data, and big data has developed rapidly, becoming a hot area favored by academia and industry, and is widely used. Through big data analysis, potential operation rules can be discovered from a large amount of grid information, providing maintenance personnel with corresponding maintenance decision support. Using big data technology for transmission line fault analysis can effectively reduce accident processing time and avoid accident expansion. Therefore, this paper combines the underground transmission line fault of power grid with the fuzzy KNN algorithm model to apply the underground transmission line state intelligent monitoring system and conducts the study of real-time data collection and fault diagnosis analysis of the underground transmission line fault of power grid, and this paper conducts the transmission line fault analysis experiment, which fully confirms the feasibility and effectiveness of the algorithm model proposed in the paper and concludes that the data analysis model proposed in this paper. The proposed data analysis model has good innovation and practical feasibility.