On-site Maintenance Research Articles

On-Site Maintenance Services by Using Web 3.0 and React JS

Abstract: This paper explores the impact of Web 3.0 and semantic web technologies on business, highlighting the potential for new business models to emerge. Web 3.0 represents a paradigm shift from an information-centric approach to a knowledge-centric one, allowing people and machines to connect, evolve, share and use knowledge on an unprecedented scale. The integration of semantic technologies in Web 3.0 has the potential to drive significant improvements in efficiency, cost reduction, enhanced effectiveness, and introduce new functionalities that were previously unfeasible. Through an analysis of the role and impact of Web 3.0 in business, this paper identifies nine potential business models that have emerged as a result of semantic web technologies. Preventive Maintenance also may cause unneeded repair of parts that still have remaining useful life. Condition Based Maintenance (CBM) is a proactive maintenance approach that takes into account the real conditions of the parts using sensors and then offers.

Turning Telecommunication Fiber-Optic Cables into Distributed Acoustic Sensors for Vibration-Based Bridge Health Monitoring

We introduce a nondedicated bridge health monitoring (BHM) system that turns pre-existing telecommunication fiber-optic cables into distributed acoustic sensors to collect bridge dynamic strain responses. Due to extensively installed telecommunication fiber cables in the cities, our telecommunication cable-based system enables efficient and low-cost BHM without the requirement of on-site sensor installation and maintenance; however, it is challenging to extract bridge damage-sensitive information (e.g., natural frequencies and mode shapes) from this nondedicated strain data as it has large measurement noise and error propagation. To overcome the challenge, we develop a physics-guided system identification method that models strain mode shapes based on physics-guided parametric mode shape functions derived from bridge dynamics. We then estimate the displacement mode shape function by analytically double-integrating the modeled strain mode shape. Our method improves the accuracy of estimating bridge damage-sensitive features and reduces error propagation by constraining strain and displacement mode shapes with bridge dynamics. We evaluated our system on a concrete continuous three-span bridge in San Jose, California. Our system successfully identified the first three natural frequencies and reconstructed strain and displacement mode shapes in a meter-scale resolution.

Cases Integration System for Fault Diagnosis of CNC Machine Tools Based on Knowledge Graph

Due to the time-consuming and labor-consuming maintenance required by appealing to service engineers during the diagnosis of CNC machine tool faults, the CNC machine tool fault diagnosis system has become the economical practical choice of intelligent workshop now, which can help CNC machine tool operators quickly locate and eliminate faults. Knowledge graph technology plays a great role in the use of structured data, and has also achieved a certain degree of research progress and good applications in other professional fields. This article focuses on how to extract semi-structured and unstructured fault knowledge from the abundant CNC machine tool fault cases, fault repair manuals and on-site maintenance logs on the market. A complete system is used to integrate this fault diagnosis knowledge with different formats and content but interrelated, so as to realize the structured application of fault diagnosis knowledge of CNC machine tools. This paper first fully analyzes the content and structural characteristics of these fault cases, and optimizes the design of a set of domain ontology for machine tool fault diagnosis, which contains 8 types of ontoly and 7 types of relation. Using the rule-based knowledge extraction method combined with the BiLSTM-CRF information extraction algorithm to extract effective information from this knowledge. Finally, the structured knowledge is stored through the Neo4J graph database for visualization. Under the application of a good ontology, the accuracy and effectiveness of the composite extraction algorithm have been proved to be greatly improved compared with the single information extraction algorithm in the experimental verification process.

A cable health assessment method based on multi-agent and matter-element extension model

It is of great significance for the stable operation of the power system to accurately evaluate the health status of the cable, and then provide a reference for the implementation of condition maintenance. Based on the matter-element extension model, this paper divides different cable health levels, selects the important state quantity of the cable as the evaluation index, assigns different weights to it, and finally obtains the health level of the cable evaluation. The multi-agent plays its role to make the cable health assessment system run well. The calculation example results show that the health assessment model in this paper can effectively assess the health status of cables in different stages of operation, and the assessment results can be used as a reference for the formulation of on-site maintenance strategies. Compared with other assessment methods, the assessment results are more comprehensive and more reliable.

RETRACTED ARTICLE: Research on reliability evolutionary optimization of relays device based on Multi-Markov model

As a critical component of the present day power system, a few studies have been conducted on the optimization and future prediction for quality reliability of relay protection devices during their production stage of intelligent testing. Therefore, in this study, a Markov model of multimodal hierarchical spatial states is proposed (1) to calculate the stationary probability and comprehensive availability of different spatial state transfers (2) to predict the quality reliability of the device during production intelligence testing and (3) to infer and calculated the integrated availability of the relay protection device for its future operation during testing. Statistical results from the theoretical findings of this study are highly relevant when compared to the actual operation of relay protection device systems. The correctness of the model calculation method proposed in this study is verified, which provides a feasible method for reliability assessment of relay protection device intelligence tests. In addition, it was also found that the failure rate of the device internal module CPU has a relatively large impact on the comprehensive availability. It is therefore recommended to focus on CPU module detection and timely replacement during the maintenance cycle of on-site operations and maintenance. Finally, the CPU module failure rate threshold for production intelligence testing was amended, which is a valuable indicator for the evolutionary optimization of quality problems in actual smart manufacturing and testing.

Enhanced adhesive and anti-corrosive performances of polymer composite coating for rusted metallic substrates by capillary filling

At present, mechanical de-rust technologies (grinding and sandblasting) are forbidden in some specific engineering fields such as oil drilling platforms equipped with storage tanks, chemical pipelines and power transmission system. It is passivated rust with cracks and holes that is known to hinder adhesion between anticorrosive coating and metallic substrate. This paper reported an organic-organic fluoropolymer/polyurethane (PF/PU) polymer coating, showing strong adhesion strength of 18.2 MPa with rust and 16.5 MPa after 240 h salt spray test, which is 2–3 times higher than conventional inorganic-organic epoxy (EP) coating (6–9 MPa). The PF/PU coating have protected rusted chemical pipelines from marine atmosphere corrosion for 3 years. Due to the micro/nano-channels in the rust layer, PF/PU precursor of appropriate surface tension and viscosity spontaneously adsorb/adhesive, wet, spread and fill with the channel by capillary force. A composite polymer-rust‑iron substrate with three-dimensional interpenetrating network is fabricated via mechanical interlocking/chemical bonding and PU cross-linking. The physical model of “capillary filling of polymer coating in micro/nano-rust-channels (μ/nRCs)” is proposed and demonstrated by “spinning by air cushion escaping” phenomenon as one end of the channel sealed. Based on researches and practical engineering applications on chemical pipelines and subway/railway tracks, a new polymer coating technology with rust towards on-site maintenance has been developed, aiming to realize the surface function remanufacturing of high-tech equipment served in severe corrosive medium and improve reliability of the equipment.

Wearable Device-Based Intelligent Patrol Inspection System Design and Implementation

The traditional on-site operation of power stations includes inspection and maintenance. However, it heavily relies on experience for maintenance. Most on-site operation and maintenance data are text records. On the one hand, the data processing is tedious for experience to affect the safe on-site operation. On the other hand, we usually cannot give full consideration to the value of maintenance experience, so that the corresponding efficiency is very low. Therefore, this paper proposes a wearable device based remote and intelligent patrol inspection system that uses the cloud video transmission mode of both public and private clouds to realize the video connection between the power stations and the remote diagnosis center and uses the wearable devices for real experience. In this way, the authors can simulate real operation guidance and safety supervision, etc. so as to realize the remote management patrol operations, improve the fault detection efficiency, and improve equipment reliability.

Assessing relationships between onsite wastewater treatment system maintenance patterns and system-level variables

Globally, millions of households rely on onsite wastewater treatment systems (OWTSs), such as septic systems, to safely treat and dispose of wastewater. Conventional subsurface OWTSs are a common and affordable option for many landowners, and effectively remove pathogenic and nutrient pollution from wastewater when properly sited and maintained. However, OWTSs can also be a source of nonpoint pollution in watersheds when they are not functioning properly. To better understand the drivers of OWTS maintenance and failure, we explored relationships between OWTS age, environmental characteristics (edaphic conditions, topographic wetness index, and distance to stream), and repair and pumping records for OWTSs in Athens-Clarke County, Georgia, USA. Repair records indicated that 7.8 % of the 8826 OWTSs in the study were repaired over a 78-year period and that the median age of a repaired OWTSs was 65 years old. Pumping records showed that 12.2 % of the OWTSs were pumped in a 38-month period (an annualized rate of 5.7 %). The suite of widely available environmental variables we used as predictors were likely not granular enough to detect patterns of individual system maintenance at this scale. However, we found that the oldest OWTSs (>50 years) had the highest probabilities of being repaired and exhibiting signs of hydraulic failure. Notably, new OWTSs (2–10 years) were nearly as likely as the oldest systems to exhibit signs of hydraulic failure. These findings suggest that repair and replacement efforts should target older systems that are at or near the end of their serviceable life, and, in addition to continually monitoring older systems, all OWTSs should be inspected one year after installation. By leveraging data that may already exist, practitioners in other localities can use this reproducible approach to estimate the performance of OWTSs. Our data and methods will support efforts to prioritize wastewater infrastructure investments and policies.

Research progress and development tendency of polymer drilling fluid technology for unconventional gas drilling

Unconventional gas includes tight sandstone gas, shale gas, coalbed methane, and natural gas hydrate. With huge reserves, unconventional gas has become the most important natural gas resource successor after the end of the “Easy Oil era.” The drilling fluid is an indispensable wellbore working fluid for unconventional gas drilling with multiple functions. The polymer drilling fluid (PDF) is the most common, longest developed, and most diverse drilling fluid type. With advantages of easily controlled rheology, convenient on-site performance maintenance, and specifically low cost and weak environment pollution, the PDF is gradually replacing the oil-based drilling fluid as the first choice for unconventional gas drilling. The invention of the non-disperse low-solid-content PDF in the 1960s shows that PDF technology has entered the stage of scientific development, and until now, its development has generally experienced five stages: beginning, developing, improving, re-developing, and re-improving. Dozens of polymer additives and PDF systems have been invented and applied, which have solved severe drilling problems, greatly improved drilling efficiency, and promoted exploration and development in difficult oil and gas resources. This paper first reviews the research progress of PDF technology according to the timeline by introducing the composition, feature, advantages, and disadvantages of some representative polymer additives and PDF systems, emphatically the function and mechanism of stabilizing wellbores, lubricating drilling tools, and protecting reservoirs of the biomimetic wellbore-strengthening PDF and amphiphobic high-efficiency PDF in unconventional gas drilling. Then, combining future global demands, especially China’s strategic needs of oil and gas exploration and development, the development tendency of PDF technology is critically illustrated by introducing several potential research directions including intelligent PDF, ecological PDF, and PDF for natural gas hydrate and deep layer gas resources.

Research on gas production law of free gas in oil-immersed power transformer under discharge fault of different severity

Dissolved gas analysis (DGA) is a common technology used in the on-site maintenance of oil-immersed power transformers in the power industry at present. However, when the content of dissolved gas in the oil reaches the attention value DGA method can effectively diagnose the operating state of the transformer. Due to the lack of gas production data of free gas which was detected when the faults occur, DGA method cannot timely diagnose the transformer status. To solve the above problem, an experimental platform is built for studying the free gas generation law in oil-immersed transformers under discharge faults, and the characteristic free gas information under discharge fault of transformer is obtained through the experiment. It is found that the existing DGA method cannot accurately analyze the types and severity of sudden serious insulation faults. When high-energy partial discharge fault occurred in the equipment, CO, CO2, CH4, and H2 will be collected in large quantities on the oil surface. These four gases can be used as the basis for characterizing high-energy partial discharge faults. When spark discharge occurred in the equipment, C2H6, C2H4, and C2H2 also be collected on the oil surface which can be used as a diagnostic basis for spark discharge. Moreover, it is found that the existing three-ratio method cannot be used for accurate analysis of oil free characteristic gas, so it is necessary to explore new diagnostic methods. The aim of this study is to explore the pattern of free gas production law by experiments when discharge faults occur and to provide data for a new diagnostic method.

Research on Key Technologies for Measuring the Recovery Rate and Gas Measurement of SF6 Gas On-site Maintenance Surface

Carrying out full lifecycle management of SF6 gas recovery, purification, and recycling, developing new digital control technologies, and eliminating SF6 gas emissions from the source are of great significance for achieving the “dual carbon” goal. Given the low level of digital control of SF6 gas at present, especially in the situation, the SF6 gas recovery rate, the effective volume of the gas chamber, and gas weight cannot be accurately measured at an on-site inspection surface, this paper carries out research on SF6 gas recovery rate measurement at on-site inspection surface and key technologies of gas measurement. An intelligent online monitoring device such as SF6 gas recovery rate and effective chamber volume of high voltage electrical equipment was developed, which can quickly identify SF6 gas density, recovery rate, effective chamber volume, weight, and other parameters in high voltage electrical equipment, and evaluate the detection methods such as gas recovery rate and effective chamber volume. The results show that when the recovery rate was about 96.5%, the maximum positive deviation between the recovery rate measured by the detection device and that measured by the weighing method was 0.32% and the maximum negative deviation was -0.24%, both of which are less than 0.50%. When the container to be tested was 1000 L, the maximum indication error was -1.51%, which can meet the requirements of digital control in the SF6 gas recycling and reuse process of high-voltage electrical equipment.

An Optimization Method for Maintenance Resource Allocation in Electrified Railway Catenary Systems

The traditional preventive maintenance plans of electrified railways are mainly formulated according to historical maintenance records and the experience of maintenance technicians. With the increase of the maintenance workload of catenary systems, traditional maintenance decision-making modes are hard to accommodate the demand for the high-efficiency and economical operation of catenary systems. To cater for the on-site maintenance demands, a conceptual framework towards the cost-effective optimization of catenary maintenance is proposed in this paper. The framework includes investigating and sorting the physical quantities in practical maintenance operations, modeling the maintenance operation process, establishing and solving the optimization model of the maintenance operator allocation with multiple optional objectives, and developing the human-machine application system, namely, the resource allocation system (RAS) with both hardware and software for electrified railway catenary maintenance. Field application shows that the implementation of RAS is superior to the current maintenance scheme in maintenance efficiency and cost, which can provide decision support for the scientific formulation of catenary system maintenance scheme, and promote the intelligent development of current maintenance modes.

STOCHOS: Stochastic opportunistic maintenance scheduling for offshore wind farms

Despite the promising outlook, the numerous economic and environmental benefits of offshore wind energy are still compromised by its high Operations and Maintenance (O&M) expenditures. On one hand, offshore-specific challenges such as site remoteness, harsh weather, transportation requirements, and production losses, significantly inflate the O&M costs relative to land-based wind farms. On the other hand, the uncertainties in weather conditions, asset degradation, and electricity prices largely constrain the farm operator’s ability to identify the time windows at which maintenance is possible, let alone optimal. In response, we propose STOCHOS, short for the stochastic holistic opportunistic scheduler—a maintenance scheduling approach tailored to address the unique challenges and uncertainties in offshore wind farms. Given probabilistic forecasts of key environmental and operational parameters, STOCHOS optimally schedules the offshore maintenance tasks by harnessing the opportunities that arise due to favorable weather conditions, on-site maintenance resources, and maximal operating revenues. STOCHOS is formulated as a two-stage stochastic mixed-integer linear program, which we solve using a scenario-based rolling horizon algorithm that aligns with the industrial practice. Tested on real-world data from the U.S. North Atlantic where several offshore wind farms are in-development, STOCHOS demonstrates considerable improvements relative to prevalent maintenance benchmarks, across various O&M metrics, including total cost, downtime, resource utilization, and maintenance interruptions.

Proposals and Evaluations of Robotic Attendance at On-Site Network Maintenance Works

Proposals and Evaluations of Robotic Attendance at On-Site Network Maintenance Works

Quantitative tools for maintenance planning for in-situ service entities

The company under study is dedicated to providing in-situ maintenance services, its objective is to provide comprehensive security technical goods and services to national and foreign entities, according to current standards; with professionalism and advanced technologies, guaranteeing the primacy in the national market and promoting exports. These services involve moving employees and equipment to the physical locations of customers. The companies that are dedicated to guaranteeing Security Systems, need to always keep the equipment installed available to the clients, so it is essential for them to periodically restore the capacities of these systems. The objective of this work is to propose the use of a set of quantitative tools that allow to increase the efficiency and effectiveness in the planning of on-site maintenance services. The security and protection company under study for the application of the proposal, improved considerably in terms of availability of the systems installed to customers and in the use of the resources. The work includes tools for the planning phases: long-term, which will be carried out annually, short-term, to decide on a monthly basis which clients will be served and the daily one in which it is determined on a daily basis which clients will be served.

Multiperiod integrated spare parts and tour planning for on-site maintenance activities with stochastic repair requests

Motivated by the problem facing many home-attended maintenance service providers, this paper considers the joint optimization problem of spare parts planning and the scheduling of a single technician to visit geographically distributed customers for on-site maintenance activities. We present and formulate the problem using stochastic mixed-integer programs, encompassing many important real-life characteristics, such as stochastic spare parts demand, stochastic customer requests, and return visits. We also propose an anticipatory solution method, which uses a look-ahead procedure to integrate stochastic information into the planning process and compare this with two myopic planning approaches. With a detailed numerical study, we show that anticipation can lead to cost reductions and service level improvements, especially when customers are sparely distributed. Furthermore, our results reveal that the repair kit and technician tour integrated planning helps to substantially reduce penalty costs for late service. We also provide insights into the value of having perfect information about spare part demand and future request locations.

Performance degradation detection method of aeroengine fuel metering device

Abstract In order to realize the safety status monitoring and health management of aeroengine fuel system, a performance degradation detection method of aeroengine fuel metering device was proposed. Aiming at the internal leakage, external leakage, static friction increase, dynamic friction increase, differential pressure controller degradation, and other common performance degradation modes of fuel metering devices, a residual life estimation method based on random forest support vector regression (RF-SVR) was proposed. The SVR model optimized by RF feature selection is used to estimate the remaining life of components. The simulation results show that the mean square error of remaining useful life (RUL) estimation is less than 1.8, the average percentage error is less than 3%, and it has high prediction accuracy. Therefore, the evaluation and verification of the internal leakage health indicators proposed in this article screen out the health indicators that are sensitive to changes in performance degradation parameters but insensitive to changes in environmental and structural parameters and provide decision-making reference for onsite maintenance of engine fuel metering devices.

Study on Obtaining Real Power Curve of Wind Turbines Using SCADA Data

The key problem to be solved in the process of wind turbine (WT) operation and maintenance is to obtain the wind turbine performance accurately. The power curve is an important indicator to evaluate the performance of wind turbines. How to model and obtain the power curve of wind turbines has always been one of the hot topics in research. This paper proposes a novel idea to get the actual power curve of wind turbines. Firstly, the basic data preprocessing algorithm is designed to process the zero value and null value in the original supervisory control and data acquisition (SCADA) data. The moving average filtering (MAF) method is employed to deal with the wind speed, the purpose of which is to consider the comprehensive result of wind on the wind turbine power in a certain period. According to the momentum theory of the ideal wind turbine and combined with the characteristics of the anemometer installation position, the deviation between the measured wind speed and the actual wind speed is approximately corrected. Here, the influence of dynamic changes in air density is also considered. Then, the Gaussian fitting algorithm is used to fit the wind-power curve. The characteristics of the power curve before and after wind speed correction are compared and analyzed. At the same time, the influence of the parameter uncertainty on the reliability of the power curve is considered and investigated. Finally, the characteristics of the power curves of four wind turbines are compared and analyzed. The research results show that among these power curves, WT3 and WT4 are the closest, WT2 is the next, and WT1 has the farthest deviation from the others. The research work provides a valuable basis for on-site performance evaluation, overhaul, and maintenance of wind turbines.

Railway Fault Text Clustering Method Using an Improved Dirichlet Multinomial Mixture Model

Railway signal equipment fault data (RSEFD) are one of the issues with in-depth traffic big data analysis throughout the life cycle of intelligent transportation. In the course of daily operation and maintenance, the railway electrical maintenance department records equipment malfunction information in a natural language. The data have the characteristics of strong professionalism, short text, unbalanced category, and low efficiency of manual analysis and processing. How to effectively mine the information contained in these fault texts to provide help for on-site operation and maintenance plays an important role. Therefore, we propose a railway fault text clustering method using an improved Dirichlet multinomial mixture model called ICH-GSDMM. In this method, first, the railway signal terminology thesaurus is established to overcome the inaccurate problem of RSEFD segmentation. Second, the traditional Chi square statistics is improved to overcome the learning difficulties caused by the imbalance of RSEFD. Finally, the Gibbs sampling algorithm for Dirichlet multinomial mixture model (GSDMM) is modified using an improved chi-square statistical method (ICH) to overcome the symmetry problem of the word Dirichlet prior parameters in the traditional GSDMM. Compared to the traditional GSDMM model and the GSDMM model based on chi-square statistics (CH-GSDMM), the quantitative experimental results show that the GSDMM model based on improved chi-square statistics (ICH-GSDMM internal)’s evaluation index of clustering performance has greatly improved, and its external evaluation indices are also the best, with the exception of external index NMI of data set DS2. Simultaneously, the diagnostic accuracy of a select few categories in RSEFD has considerably improved, demonstrating its efficacy.

Intelligent Contactless Current Measurement for Overhead Transmission Lines

Traditional current monitoring devices (e.g. current transformers) for transmission and distribution (T&D) networks are mostly installed at substations, so real-time and accurate information on current is almost unavailable for on-site load current monitoring, imbalance assessment, and equipment maintenance. This increases the fault risk in T&D networks and degrades the reliability and quality of the power supply. To address such a situation, this study proposes an intelligent and contactless current measurement method. The proposed method uses only a small number of magnetic-field sensors and the conductor geometry specification of an overhead line obtained from utilities. The simulation results show that the proposed method can accurately calculate each phase current using only three magnetic field sensors. In addition, a scaled-down overhead line measurement system was built to test the proposed method. The experimental results verify the feasibility of the proposed contactless method for overhead-line current measurement with less than 10 % maximum measurement error.