Maintenance Personnel Research Articles

A Magnetic Climbing Robot for Steel Bridge Inspection

Abstract. The maintenance of traditional steel bridges presents many significant challenges due to the fact that bridges of different ages and standards vary in design, structural complexity and technical difficulty. In light of these considerations, it is clear that maintenance personnel must possess a wealth of professional expertise and experience in order to effectively address the diverse and intricate maintenance challenges that arise. Moreover, the maintenance process entails high-risk operations such as high-altitude work and welding, which pose a threat to the safety of construction personnel. Therefore, this study introduces a magnetic adsorption robot designed to address the low efficiency and high costs of traditional maintenance methods. This robot features a dual robotic arm mechanism and modeled using the Denavit Hartenberg (D-H) parameter method. Electromagnetic adsorption is utilized to achieve wall adhesion, and direct power supply is provided through DC power to control weight. The robot can perform flexible remote control operations through the HC-05 Bluetooth module. The experimental results show that the robot exhibits robust adhesion and obstacle crossing ability on steel surfaces, thereby enabling unimpeded movement within the steel structure. In addition, the robot is now capable of performing overhaul procedures via remote control.

Resilience assessment of power transmission system during wildfire disasters considering spread process

AbstractLarge‐scale wildfires can significantly reduce the air gap insulation resistance of high‐voltage transmission lines and cause chain tripping incidents. To assess the resilience of the power transmission system during wildfire, this paper proposes a resilience assessment framework for transmission system that considers the entire process of wildfire disaster. Firstly, a wildfire spread model, considering multiple influencing factors, is developed based on the cellular automaton. Based on the air gap breakdown mechanism during wildfires, the trip‐out probability of transmission lines is calculated, and various failure scenarios are obtained by using the Monte Carlo sampling. Secondly, considering the geographical location of failures, maintenance personnel schedules and restoration time, a power transmission system restoration model is established. Thus, a resilience assessment method for power transmission system during wildfire disasters is proposed. Finally, IEEE RTS‐79 transmission system is taken as an example to demonstrate the effectiveness of the proposed resilience assessment method. The results show that the proposed method can effectively calculate the wildfire spread tendency and transmission line's trip‐out probability. Furthermore, three typical resilience improvement measures are quantitatively analysed, which provides a quantifiable reference for the power sector to formulate prevention and recovery strategies for extreme wildfire disasters.

Revolutionizing superhighway traffic board lighting with remote LED spotlights

In this paper, we present a groundbreaking approach to enhance the illumination of traffic boards along superhighways, addressing significant challenges associated with conventional lighting systems. Our innovative method revolves around the strategic placement of remote spotlights at the side of the roadway to illuminate traffic signs equipped with retroreflector film (RTRF). The essence of our approach lies in remote illumination, which requires meticulous adjustment of the divergence angle of the spotlights to match the size of the signs and their distance from the projection source. To achieve the desired spotlight configuration, we have developed a hybrid optical system that incorporates a paraboloid reflector and a lens mounted on a bridge holder situated on top of the mirror. Through spot light illumination, we discovered that the initial divergence angle of the RTRF was too narrow. To improve projection angle tolerance, we recommend attaching a light diffuser film onto the surface of the RTRF. The coverage area ratio of the diffuser film can be adjusted to select the desired divergence angle for the reflected light. Our experimental measurements have yielded significant results, showcasing the half-divergent angle of the RTRF ranging from 3° to 7° for different coverage area ratios of the diffuser. In practical terms, with a target luminance of 300 nits at the white word on the traffic board, the power consumption of the spotlight fixture of the roadway was only 40 W, representing over 75% power savings when compared to traditional lighting methods. Consequently, our approach opts to utilize spotlights for illuminating specific traffic boards on superhighways, offering a more efficient and manageable lighting solution that greatly benefits both motorists and road maintenance personnel.

Magnetic Railway Sleeper Detector

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios.

Knowledge Graph Completion for High-Speed Railway Turnout Switch Machine Maintenance Based on the Multi-Level KBGC Model

The incompleteness of the existing knowledge graphs in the railway domain creates information gaps, impacting their quality and effectiveness in the operation and maintenance of high-speed railway turnout switch machines. To address this, we propose a multi-layer model (KBGC) that combines KG-BERT, graph attention network (GAT), and Convolutional Embedding Network (ConvE) for knowledge graph completion in railway maintenance. KG-BERT fine-tunes a pre-trained BERT model to extract deep semantic features from entities and relationships, converting them into graph structures. GAT captures key structural relationships between nodes using an attention mechanism, producing enriched semantic and structural embeddings. Finally, ConvE reshapes and convolves these embeddings to learn complex entity interactions, enabling accurate link prediction. Extensive experiments on the HRTOM dataset, containing triplet data from high-speed railway turnout switch machines, demonstrate the model’s effectiveness, achieving an MRR of 50.8% and a Hits@10 of 60.7%. These findings show that the KBGC model significantly improves knowledge graph completion, aiding railway maintenance personnel in decision making and preventive maintenance, and providing new tools for railway maintenance applications.

Damage to Special-Purpose Ring Wavy Springs under the Influence of Hydrogen Sulfide-Containing Media

Annular wavy springs are widely used in the oil and gas industry and most often serve either as compensators for temperature deformations or for mechanical seals of various joints in equipment transporting oil and gas media. These springs provide high load capacity with a relatively low draft, which ensures a compact design. Most often, such springs are made by stamping from sheet steel. When operating technological equipment that transport hydrogen sulfide-containing oil and gas media, hydrogen sulfide stress corrosion cracking occurs quite often, which can lead to failure of industrial equipment. In particular, damage to annular wavy springs can cause depressurization of elements of industrial equipment and cause significant harm not only to the environment, but also to maintenance personnel. The object of research was annular wavy springs made of steel type X15N7M2YU. The purpose of the study is to determine the resistance of the spring material to stress corrosion cracking. This article discusses the test results of special purpose annular wavy springs in an aggressive hydrogen sulfide-containing medium artificially prepared according to the recommendations of ANSI/NACE TM 0177. The results of hardness studies, strength calculations and metallographic studies of damage to a wavy spring are presented. The chemical composition of the metal of the spring fully corresponds to the steel type X15N7M2YU declared by the enterprise. According to the studies conducted in environments containing hydrogen sulfide, the wavy spring is not recommended for use in oil and gas equipment, since it is not resistant to hydrogen sulfide cracking under stress.

Optimizing the Transition: Strategies for Migrating On-Premise Storage to the Cloud

In recent years, Cloud Computing has had great relevance and has aroused interest in the Telecommunications industry, due to the reduction of costs in hardware acquisition, maintenance and operation personnel. One of the great advantages of cloud computing is the automation of processes that makes systems more robust and secure by limiting the margin of human and operational errors. It is of utmost importance to consider the large storage capacity management of video files that can provide solutions to media companies or related industries, this is key to think about a cost benefit analysis that can generate the migration and rental of these services in the cloud, with more reliable and accessible systems, this will help us to study hybrid technologies that offer on premise solutions. In addition to the above, when talking about companies that seek to digitally transform themselves to achieve agility and resilience, as well as to support future applications, it is necessary to mention their current infrastructures that have the capacity to host new technologies that require high performance, security and an adequate digital interconnection that allows the correct access to the acquired applications. In this research, an analysis of the existing process models for migration from on-premise storage to the cloud has been made with the objective of identifying the most important challenges, from planning, execution, factors to consider when assessing the complexity of applications and data before migrating to the cloud.

A portable test system for the radar

Abstract In order to adapt to the increasingly fierce battlefield confrontation scenarios, a portable, fast deployment and folding test system for the radar is proposed. The corresponding key test items of radar such as spectrum, waveform, lobe, target tracking and data analysis can be completed on the test system. After the Built-in-Test (BIT) information and test system results are integrated, the corresponding evaluation report and implementation recommendations can be provided to the maintenance personnel. This paper expounds the design of each subsystem step by step. The Main Control Unit (MCU) is the task scheduling and information integration center for the system. The corresponding high frequency components, processing components, storage components have been implemented systematic and miniaturized design. In the end, the improvement direction and prospect of the test system are given.

Extraction and classification of vibration features of rolling element bearing with increasing the rotational speed

Machinery components degrade over time due to continuous use. A reliable prognosis framework can improve machinery health by monitoring the behavior of its parts and providing warnings before critical failures occur. Bearings, which are essential components of rotating machinery, help maintenance personnel assess the machine’s condition during continuous wear. In this study, vibration data from roller bearings under various conditions and faults were collected. The Vibration analysis technique was employed to detect and classify different faults in bearings based on the characteristics of the vibration signals generated by the machinery. Faults can be detected, diagnosed, and classified by analyzing bearing vibration signatures using techniques such as frequency analysis, time-domain analysis, spectral analysis, and kurtogram classifiers. This enables appropriate maintenance actions to be taken in time, preventing further damage or failures.

Application of the upgraded design of the STM32F103 battery monitoring system based on hospital power

This paper presents a battery monitoring system based on an STM32F103 microcontroller for hospital power applications. The system adopts a modular design to improve reliability and maintainability. It can monitor the terminal voltage and alternating current (AC) impedance of each battery in a battery pack with up to 128 cells, as well as the ambient temperature, relative humidity and hydrogen concentration. The device includes a keyboard for parameter input, an liquid crystal display (LCD) for information display, and multiple communication interfaces. The proposed solution can automatically monitor the battery voltage and AC impedance. It also provides alarms to greatly improve the stability of hospital power system operation and reduce the workload of maintenance personnel.

Power Distribution Transformer Maintenance Skills Required byElectrical Engineering Technology Students ofPolytechnics inNorth-East Nigeria

Electric power distribution industry in North-East, Nigeria is faced with inadequate number of power transformer maintenance personnel which has led to numerous abandonment of broken down power transformers. Transformer faults have been blamed for many power outages of long duration in many towns and communities in North-East, Nigeria.It is against this backdrop that thisstudy determined the power distribution transformer maintenance skills required by Electrical Engineering Technology students of polytechnics in North-East Nigeria. Three research questions and three hypotheses guided the study which employed a descriptive survey research design. The population of the study was 144 comprising of 135 Electrical Engineering Technology Lecturers, 3 Power Transformer Maintenance Technicians and 6 Electric Power Distribution Company-Based Supervisors. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Power Distribution Transformer Maintenance Skills Required Questionnaire (PDTMSRQ) developed by the researchers. The instrument was validated by three experts and a reliability of 0.74 was obtained using Cronbach Alpha reliability method. Mean statistic was used to answer the research questions while ANOVA was used to test the two null hypotheses of the study at 0.05 level of significance. The findings of the study revealed that the Electrical Engineering Technology students of polytechnics in North-East Nigeria required preventive, predictive and corrective maintenance skills. The study recommended the following: Lecturers should encourage the students’ skill acquisition process by exposing them to various maintenance activities in order for them to imbibe the different methods of power transformer maintenance; Students should be trained to recognize sounds and sights of faulty equipment in order to predict the parts or components that needed replacement or repairs.

Fault traceability of power grid dispatching system based on DPHS-MDS and LambdaMART algorithm

Under the background of increasing system service data, it is difficult to trace the faults of power dispatching system. The current fault tracing method has some problems, such as low precision, low efficiency and difficult troubleshooting. To solve this problem, a fault tracing method based on data partition hybrid sampling method and multiple incremental regression tree algorithm is proposed. In this paper, it first uses the hybrid sampling method of data partition and dynamic selection technology to detect the business anomaly, and then applies the clustering algorithm and the information difference graph model to realize the fault tracing of system components. The experimental results showed that the F-metric value and geometric mean value of the study design method were 0.964 and 0.685, respectively. In addition, normalized discounted cumulative gains were observed in the top 10, and the mean average precision of the top 10 was 0.752 and 0.186, respectively. The proposed method can effectively improve the fault tracing efficiency of power grid operation and maintenance personnel, and provide strong data support for the safety maintenance of power grid dispatching system.

An estimation of the tower crane safety risk factors in the Vietnamese construction industry

The construction industry is among the riskiest sectors worldwide due to its involvement in complex and hazardous activities, primarily because of the use of tower cranes. Tower cranes are considered the most hazardous construction machinery, contributing to a high rate of serious accidents, including fatalities and disabling injuries, not only in Vietnam but also globally. In this paper, we identify and evaluate 42 tower crane safety risk factors specific to the Vietnamese construction industry based on their probability of occurrence, severity of impact, and associated risk levels. The paper reveals that the most probable risk factor is “Unfavorable winds (including intensity, direction, etc.)” with a mean value of 3.641. The factor with the highest severity is “Wire rope is broken” with a mean value of 4.179. Furthermore, the study shows that twenty-five out of forty-two (59.52%) factors present a moderate risk level, with a Relative Significant Index Score (RSIS) ranging from 8.019 to 13.047. Among these, the factor “Bad maintenance management of the tower crane and auxiliary aids” poses the highest risk level, with an RSIS value of 13.047. This paper aims to enhance the understanding oftower crane safety among subcontractors, government safety regulators, main contractors, maintenance personnel, and workers in the Vietnamese construction industry. Additionally, the methodology presented in this paper can be adapted for assessing safety risk factors in other industries within Vietnam.

Design of a JSP based Mobile Phone Repair Reservation System

With the development of the times, mobile phones are becoming more and more popular. The increase in the number of mobile phones will lead to an increase in the number of mobile phone repairs. Developing a mobile phone repair appointment system can be beneficial for mobile phone repairs. The mobile phone repair reservation system adopts the B/S mode, using Eclipse software as the system development tool and Java programming language for system development. The system database adopts SQL Server database. In system development, JSP and Servlet technologies are used to help users repair their phones more conveniently, while also benefiting maintenance personnel. The mobile phone repair reservation system designed in this paper consists of a front-end user module and a back-end administrator module. The front-end module implements functions such as registering and logging in for front-end maintenance personnel, modifying personal information, publishing, viewing, modifying, and deleting appointments for maintenance personnel, registering and logging in for front-end users, modifying personal information, making appointments, viewing appointments, and canceling appointments. The backend module enables administrators to modify personal information, view and delete personal information of users and maintenance personnel, view and delete appointment information, and other functions.

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.

Leveraging Large Language Models and BERT for Log Parsing and Anomaly Detection

Computer systems and applications generate large amounts of logs to measure and record information, which is vital to protect the systems from malicious attacks and useful for repairing faults, especially with the rapid development of distributed computing. Among various logs, the anomaly log is beneficial for operations and maintenance (O&M) personnel to locate faults and improve efficiency. In this paper, we utilize a large language model, ChatGPT, for the log parser task. We choose the BERT model, a self-supervised framework for log anomaly detection. BERT, an embedded transformer encoder, with a self-attention mechanism can better handle context-dependent tasks such as anomaly log detection. Meanwhile, it is based on the masked language model task and next sentence prediction task in the pretraining period to capture the normal log sequence pattern. The experimental results on two log datasets show that the BERT model combined with an LLM performed better than other classical models such as Deelog and Loganomaly.

Исследование параметров системы аспирации рельсошлифовального поезда

The rail-grinding technology is used for the railway network of the JSC RZhD to eliminate rail defects and extend the lifecycle of the railway track. In order to implement the rail-grinding technology, currently, rail-grinding trains RShP-48 are used. Rails are ground with special grinding wheels made of zirconium electro-corundum manufactured based on aluminum oxide and zirconium oxide, with bakelite organic bond as a binder. In addition, the abrasive wheel composition includes various chemical components. When an abrasive tool is destructed, grinding waste in the form of suspensions is generated during its mechanic work. The suspensions spread over long distances and negatively affect both the environment and the working conditions of the rail-grinding train maintenance personnel. Today, the company «Sinara — Transport Machines» is developing a new rail-grinding train RShP 2.0 with a pre-installed aspiration system to collect grinding wastes for further utilization. When developing the technical design of RShP 2.0, the main characteristics of the aspiration system have been determined. In order to confirm the efficiency of the obtained parameters of the aspiration system, laboratory tests have been conducted on a special rail-grinding unit. According to the results, the aspiration system with the stated technical characteristics absorbs the fine fraction of grinding products which is about 50 % of the total lost mass of the grinding wheel. Considering a large abrasive that sticks to the spark arrestors together with metal chips, the aspiration system RShP 2.0 will enable removing up to 90 % of grinding waste.

Development and Usability Evaluation of Augmented Reality Content for Light Maintenance Training of Air Spring for Electric Multiple Unit

The air spring for railway vehicles uses the air pressure inside the bellows to absorb vibration and shock to improve ride comfort and adjust the height of the underframe with a leveling valve to control stable driving of the train. This study developed augmented reality content that proposes a novel visual technology to effectively support the training of air spring maintenance tasks. In this study, a special effect algorithm that displays the dispersion and diffusion of fluid, and an algorithm that allows objects to be rotated at various angles, were proposed to increase the visual learning effect of fluid flow for maintenance. The FDG algorithm can increase the training effect by visualizing the leakage of air at a specific location when the air spring is damaged. In addition, the OAR algorithm allows an axisymmetric model, which is difficult to rotate by gestures, to be rotated at various angles, using a touch cube. Using these algorithms, maintenance personnel can effectively learn complex maintenance tasks. The UMUX and CSUQ surveys were conducted with 40 railway maintenance workers to evaluate the effectiveness of the developed educational content. The results showed that the UMUX, across 4 items, averaged as score of 81.56. Likewise, the CSUQ survey score, consisting of 19 questions in 4 categories, was very high, at 80.83. These results show that this AR content is usable for air spring maintenance and field training support.

Critical success factors in the maintenance strategies of public healthcare facilities

PurposeThe United Nations' Sustainable Development Goal of ensuring healthy lives and promoting well-being for people of all ages underscores the vital role of public healthcare facilities (PHFs) in delivering essential healthcare services. However, these facilities often suffer from inadequate maintenance, exacerbated by the insufficient implementation of maintenance strategies. Recognizing the importance of PHFs in enhancing healthcare services, this paper investigates the Critical Success Factors (CSFs) in the maintenance strategies of PHFs in South Africa.Design/methodology/approachThrough semi-structured interviews with nineteen purposively selected maintenance personnel from the Limpopo Department of Health (DoH), this study identified and analyzed the CSFs to enhance maintenance operations in PHFs. Thematic content analysis was employed to derive key insights from the collected data.FindingsThe study's findings highlight adequate maintenance planning and effective leadership as the two overarching CSFs in the maintenance of PHFs. These factors play a pivotal role in addressing challenges that hinder the current maintenance team from meeting maintenance requirements to the satisfaction of both staff and patients within PHFs.Originality/valueThe study offers valuable insights for policymakers to improve the effectiveness of maintenance operations in PHFs. By addressing the identified CSFs, policymakers can enhance maintenance operations in PHFs, positively impacting healthcare service delivery and the well-being of both staff and patients.

Safety Incidents and Situation Awareness Amongst Maintenance Personnel in the Food Manufacturing Industry

Safety Incidents and Situation Awareness Amongst Maintenance Personnel in the Food Manufacturing Industry