Maintenance Staff Research Articles

Real-Time Leakage Current Classification of 15kV and 25kV Distribution Insulators Based on Bidirectional Long Short-Term Memory Networks With Deep Learning Machine

This paper presents an online monitoring system to classify the leakage/discharge current of the insulator in coastal sites using Bidirectional Long short-term memory (Bi-LSTM) models on a web-based service. The remote monitoring methodology uses environmental parameters to classify the peak level of leakage/discharge current of the 15kV and 25kV distribution insulators. The sequential weather data, the humidity, temperature, rainfall, dew point, solar illumination, wind speed, air pressure, and wind direction are automatically collected hourly in real-time and transferred to data servers. The hyperparameter optimization for the structure of Bi-LSTM is utilized through the grid search capability in a deep learning machine. The optimized design of Bi-LSTMs improves the performance and accuracy in predicting the leakage/discharge current classification for HDPE and SR of 15kV and 25kV insulators. Compared with persistent methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU), the Bi-LSTM has better performance and higher accuracy in predicting the leakage/discharge levels, which are utilized to evaluate the surface pollution of insulators. The optimized structure of the proposed Bi-LSTM model could achieve a maximum improvement of 49.529% error, 12.761% accuracy, 72.736% error, and 36.641% accuracy for training and validating data compared with other models. Moreover, a web-based service is developed for maintenance staff to interact with all current and predicted status of insulators. This online leakage current classification has been installed in Mailiao Township in Taiwan and could establish a reasonable maintenance mechanism for 15kV and 25kV distribution insulators.

Effects of Horizontal Multiskilling of Tradesmen on Employee Performance for the Zimbabwean Manufacturing companies in Harare

One of the key concerns in the manufacturing industry is technical skill acquisition. The present incompetence and poor performance among many tradesmen in their specialized work environment expose them among other factors, the inadequacy in; diversity of knowledge, different practical know-how, diverse skills and approaches. This is against the backdrop that many factors have created uncertain working environments due to rapid changes in technology, economics, globalisation and internationalisation. Consequently, they created an unbalance between staffing and staff skills as well as skills demand. It also significantly increased both under and over staffing costs. However, the use of polyvalent employees who initially had one skill minimizes such mismatches. The purpose of this study was to examine the effect of multi-skilling on employee performance for tradesmen in manufacturing companies in Zimbabwe. The study adopted a survey research design. The target population was one thousand one hundred and eighty-seven tradesmen from all large manufacturing Zimbabwean companies based in Harare, from which a sample of four hundred was drawn through stratified random sampling technique. Then two hundred and ninety-nine questionnaires were dully filled, returned and analysed using Statistical Package for Social Sciences to facilitate descriptive and inferential analysis of the study variables. Linear regression results indicated that multi-skilling of tradesmen significantly explained employee versatility which intrinsically motivates them to enhancing their performance and consequently organisational performance. Pearson correlation results showed strong positive relationship between horizontal multi-skilling and employee performance. The study concluded that multiskilling plays a significant role in employee performance in the manufacturing sector. The study recommended manufacturing companies in Zimbabwe to rigorously implement multiskilling training programs for their technical maintenance staff.

Significance of Personnel Hygiene Practices in Pharmaceutical Industry

Personnel hygiene is very important in the pharmaceutical industry to prevent or reduce any kind of contamination which can affect the quality of medicinal products. When it comes to delivering highest quality standards of pharmaceutical products, key hygiene factors must to be upheld. Good personnel hygiene policies and practices are the foundation for successful product safety and purity. They are the backbone of a meaningful Good manufacturing Practices (GMP). Proper personnel training and sanitation are of prime importance. Personnel engaged in the manufacture, processing, packing, or holding of a drug product shall wear clean clothing appropriate for the duties they perform. All personnel should undergo medical check-up which may be a prerequisite for safe working condition. Personnel hygiene policies and procedures are to be followed by all personnel including management, visitors, production, sanitation and maintenance staff, for reducing the chance of product contamination. Personnel hygiene practices and procedures play a vital role in pharmaceutical industry in order to achieve a good quality and safe product.

Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data.

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

PERSPECTIVES ON MALAYSIA'S LACK OF IMPLEMENTATION OF GREEN BACK LANES

Malaysia has been utilising green back lanes since the ‘90s. In spite of the several benefits of these lanes, such as the formation of active spaces, decrease in runoff water, and visitor attractions, green back lanes are yet to be deployed in the residential regions of Malaysia, prompting the question as to why these lanes have not yet been installed extensively. The objectives of this research work were: to determine what experts opined regarding the installation of green back lanes in the country, study the scenarios which caused the absence of implementation, produce a report showing the outcomes and analysis, and offer suggestions. The researcher deployed the interview strategy for ascertaining the views and standpoints of architects, town planners, maintenance staffs, project managers, and marketers on the dearth of green back lane installation. The research emphasises on the aspects of cost, maintenance, and safety.

Fault detection method based on an automated operating envelope during transient states for the large turbomachinery

In the energy generation business steam powered turbo-generators still play an important role in electrical power generation all over the world. Every facility using steam turbines considers them as the critical machinery. Such machines should be well-maintained, properly handled, and precisely diagnosed in order to achieve the best performance and safety. The most valued data about the technical health are collected during machine’s shut-downs and run-ups. These data are more than seldom and hard to assess without expert’s knowledge with strong theoretical background and experience. Main novelty of the paper is the automated method for novelty detection of machine’s vibration. Most proposed methods apply to smaller machines with rolling bearings, whereas we propose the method for large machines with sliding bearings, which have much different behavior. The application of the method is support of the plant maintenance staff to evaluate deviations of turbo-sets from a healthy state based on the concept which we called the Operating Envelope. The envelope is created based on the data from a vibration sensor during the transient state. In this paper we consider a single vibration sensor and only the first harmonic amplitude of this signal. To set the acceptance limits within which turbo-set’s dynamic response will be considered as acceptable, we used the cubic spline interpolation coupled with expert judgement. Beyond these limits the state of the turbo-set is considered as unhealthy, so it is an automated fault detection method. In such a case a machine should be a subject to further and deeper diagnostic analysis. The method was validated on the data from the 13K242 type (a 200 MW class turbine) steam turbine. We also proposed a set of parameters to evaluate the severity of malfunction.

Decommissioning procedure and induced activation levels, calculations and measurements in an 18 MeV medical cyclotron

The present article describes the decommissioning of a self-shielded 18 MeV medical cyclotron IBA Cyclone 18/9 after 14 years of operation. A Monte Carlo simulation of the possible nuclear reactions was performed in order to plan the decommissioning activities. During the cyclotron dismantling, the activities of the cyclotron components, concrete wall and floor samples were measured. Residual activities were analysed by means of an HPGe detector and liquid scintillation counting, and compared with simulation data. Dosimetry of the staff involved in the decommissioning procedure was monitored by individual TL dosimeters and/or digital dosimeter. The cyclotron component analysis confirmed the presence of gamma and pure beta emitters, 22Na, 54Mn, 60Co, 65Zn, 207Bi, 55Fe, 63Ni at different values of specific activity, depending on the positioning of the sample point and on the alloy of the sampled part. In these components the presence of gamma and pure beta emitters was measured 5 years after the shutdown at levels far above clearance limits as defined by the ‘Recommended radiological protection criteria for the recycling of metals from the dismantling of nuclear installations’ (RP89) guidelines. The simulation, carried out by FLUKA Code (version 2020.0.5) on the cyclotron components, provided good agreement with measurements, with a maximum discrepancy of the same order as the uncertainties. Four engineers of the cyclotron maintenance staff were involved in the dismounting of the hottest components and rigging of the cyclotron in the deposit 6 months after shutdown and two engineers were involved during the drilling phase 3.5 years after shutdown. The measured dose from external exposure of the involved staff was lower than 100 μSv person−1 during the first phase and lower than 20 μSv person−1 during the final drilling phase. Measured doses from intake were negligible. In conclusion, the decommissioning of the 18 MeV cyclotron does not represent a risk for the involved staff, but, due to the presence of long-lived radioisotopes, the cyclotron components are to be treated as low level radioactive waste, and stored in an authorised storage area for at least 25 years after shutdown.

A program to improve the quality of dental unit water in a medical center.

The water quality of dental unit waterlines (DUWLs) is associated with patient safety. No program for DUWL water quality improvement has been formulated since the time they were established 20 years ago. This study provides an improvement program for the quality of dental unit water. The improvement program was implemented step by step: discharge of DUWLs for 5 minutes in the morning before clinical service to flush out the water left in the pipeline overnight; weekly disinfection of the handpiece connector with 75% alcohol and replacement of the old connector when the water quality of the same dental chair unit (DCU) was continuously found to be unqualified; monthly disinfection of the water supply system and pipeline; and establishment of DCU maintenance work standards and staff education and training. From 2016 to 2018, the water quality of 18 DCUs was tested by microorganism culture. The colonies >200 colony forming unit were categorized as unqualified. This program was divided into a pre-test phase, Phase 1, a maintenance phase, and Phase 2. A Chi-square test was used to calculate the difference of unqualified water quality numbers between each phase of the improvement program. In the pre-test phase, the water quality rate (high quality number/high-quality number + low-quality number) was 58.3%. In Phase 1, the quality rate before and after the intervention was 64.8% (35/54) and 92.2% (83/90) (P < .001), respectively. After Phase 1, the quality rate reached 100%. However, the quality rate dropped to 75% during the maintenance phase. Then, we proceeded into Phase 2 of the improvement program by further monthly disinfection to DUWLs. In Phase 2, the quality rate was 62/73 (84.9%) and improved to 142/144 (98.6%) after the intervention (P < .001). The quality rate reached 100% once again and was maintained at 100% thereafter. In conclusion, the 4 steps of the improvement program improved the water quality of the DUWL, which is important for patient safety.

Designing and managing biodiverse streetscapes: key lessons from the City of Melbourne

Underutilised public spaces such as streetscapes offer substantial opportunities to integrate habitats that increase biodiversity into existing urban landscapes and create more ecologically connected cities. Cooperation and collaboration from diverse stakeholders are paramount to achieving this because growing conditions for plants in streetscapes are often much harsher than remnant habitats or urban parks and little is known about the horticultural performance of many native understorey species in these novel urban environments. This paper describes how the City of Melbourne collaborated with researchers from the University of Melbourne to develop and test a suite of understorey plant species to increase streetscape biodiversity. To do so, we selected species using criteria from a horticultural planting guide which guided the design and creation of four streetscape plantings within the municipality. Here, we document the process and discuss lessons learnt from this project to assist other cities to design, construct and maintain streetscapes with successful, cost-effective plantings that improve urban biodiversity and aesthetic value. Key to the long-term success of these biodiverse plantings was thorough soil preparation and weed management before planting, and the implementation of a clear, ecologically sensitive management plan. To support this plan, suitably qualified and experienced landscape maintenance staff were essential, particularly those with horticultural knowledge and experience with indigenous and native plant species. Our project highlights the often conflicting needs of local authorities and ecological researchers and the necessary trade-offs needed to meet realistic goals and achieve successful project outcomes for creating more biodiverse urban landscapes.

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.

Research on Visual Detection Algorithm of 500kV High Voltage Live Operating Robot

The 500kV transmission line is exposed to the outdoor for a long time. It is affected by complex climate charge change and other factors which leads to line connection parts bolt loosening, wire breaking and fixture damage and other line failures. In order to ensure the stable transmission of electric energy, power operators need to wear shielding suits and work in the high-risk and high-voltage environment. Use of electric power operation robot instead of manual operation is an effective way to liberate maintenance staff labor. But robots still have some problems such as low degree of automation and low efficiency of power operation. Machine vision detection technology in recent years has been widely used in major areas including deep learning as emerging visual detection technology shows excellent performance. In this paper, the vision detection algorithm is studied respectively for the bolt fastening end working device and wire repairing end working device of the live transmission line robot to improve the operating efficiency of the robot.

Surface flashover patterns of GIS basin insulator under impulse voltage

Basin insulator is an important component of SF6 gas insulated switchgear (GIS), and its fault is also one of the major causes of GIS equipment failure. Due to the large capacity of high voltage power system, the faulty basin insulators are generally burned severely by the high energy arc, so the fault diagnosis is very difficult. In this paper, the surface flashover pictures of several full-scaled GIS basin insulators under impulse voltage are collected, and the morphology features of flashover traces initiating from different positions are summarized. As a result, the surface flashover patterns are divided into three types: 1) surface of shielding electrode initiated flashover, 2) triple junction initiated flashover and 3) surface of insulator initiated flashover. The inception and development process of flashover are also summarized. Besides, the diagnosis method of discharge inception position is proposed. Firstly, check the ablation points at high voltage terminal. If there is a ablation point on the surface of shielding electrode, the flashover initiates from there. If there is only ablation point at the triple junction, the inception position depends on the flashover trace morphology: when the flashover trace disperses in a single direction towards grounding electrode or high voltage electrode, the flashover initiates from the opposite triple junction; when the flashover trace disperses towards both electrodes, the flashover initiates from the insulator surface. The dispersion direction could be directly distinguished from the black carbonized dendritic traces, or from the “white dendritic traces” with the auxiliary methods. The conclusions could help the substation maintenance staff do the root cause analysis of the surface flashover, and assist the researchers in the structural optimization design of basin insulator.

Ontology-based modelling of lifecycle underground utility information to support operation and maintenance

Operation and maintenance (O&M) of underground utilities is crucial to ensure normal functionality of utilities by performing different inspection and maintenance activities. A variety of data are required by maintenance staff to evaluate utility condition and make decisions in planning maintenance works. Nevertheless, data required for utility O&M are usually originated from different sources, with various formats or representations. Interpreting and integrating such heterogenous data for utility O&M remains a significant challenge. Therefore, this paper proposes an ontology-based framework for modelling lifecycle utility information such as to facilitate activities and support decision making during utility O&M. The framework consists of (1) a data layer to collect various types of utility lifecycle data, (2) a knowledge layer to extract common mechanisms and methods for utility O&M, (3) an ontology layer where a domain ontology, Utility Operation and Maintenance Ontology (UOMO), is developed with entities and semantic rules to represent different categories of information related to utility projects, (4) an application layer to automate manual tasks and support decision making by leveraging the query and reasoning capability of ontology. To validate the framework, a case study is performed using real-world inspection data where a user interface is developed to allow users to implement different functions without technical knowledge. The case study demonstrates that the framework can, efficiently and automatically, extract and integrate various information to assist different activities, e.g. defect rating, condition assessment and validation, as well as to support decision making in prioritizing maintenance works, and selecting appropriate maintenance methods. This study contributes by proposing the first ontology-based framework for improving efficiency in utility O&M, where the query and semantic reasoning capabilities of the ontology are fully utilized. The developed ontology with lifecycle utility information is expected to facilitate data interoperability in the utility domain and can be extended with other knowledge for large-scale infrastructure management.

Determination PV Module Technical Condition

The paper focuses on the determination of technical conditions and degradation of photovoltaic modules installed in grid-on operated photovoltaic systems in Ukraine and the Czech Republic. Detected malfunctions of PV modules are classified and Fault Tree Analyses (FTA) is created for particular PV modules. For assessment of the technical condition of the PV modules it is proposed to use residual resource index (RRI), that is being successfully used for determination of technical condition of transformers, breakers and synchronous generators. The main contribution is the method for the detection of photovoltaic modules condition with the use of a neuro-fuzzy network based on the experience of operational (maintenance) staff of particular plants.

Analyzing and Detecting Drifts in a Flowmeter by Discrete Fourier Transform

Large flowmeters are used in many industrial facilities, including power plants, cooling-water stations for refineries, and petrochemical plants. These flowmeters are employed for various purposes, including billing. Just like all machines, flowmeters are subject to failure. Drift is a particular type of failure in which the flowmeter produces an error in measurement that would incrementally increase with time. Maintenance technicians calibrate and fix all measuring equipment, including flowmeters. Nevertheless, downsizing policies and budget cuts in most contemporary industrial facilities have made these technicians overwhelmed with work. A mathematical and computer-based drift-detection scheme is developed to reduce the burden of the maintenance staff. The detection scheme only uses the flowmeter's flow data and the discrete Fourier transform (DFT). The detection scheme was applied over the flow data from an actual flowmeter that drifted during its operation. DFT application over the data produced by the flowmeter led to expected results and other unexpected results. This paper discusses both results and suggests areas for further study. Practically speaking, the scheme would facilitate the early detection of drifts in flowmeters having seasonal flow regardless of type or manufacturer.

Lineman Safety

Electrical accidents to lineman are rising during electric line repair due to lack of communication between the maintenance staff and electric line man. This proposed system provides a solution that ensures safety of electric lineman i.e., line man on detecting a fault in electric line the line man senses his finger in fingerprint scanner and the main line is switched off which is again switched on after solving the fault by again sensing his finger, thus it saves the life of lineman working on electric line. The proposed system is fully operated on Arduino.

Application of 5G communication technology on intelligent inspection in 750kV substation

Ultra-high voltage AC substation has the characteristics of high voltage level, large area, complex equipment structure, heavy inspection task and high operation risk. The existing video surveillance system is mainly used for substation security, but the distribution of points is insufficient and the clarity is low, so it can’t achieve full coverage of inspection points. UHV AC substation needs to develop remote intelligent inspection system to assist operation and maintenance staffs to carry out daily inspection work, reduce the workload of operation and maintenance staffs, improve the speed of emergency response and ensure the safety of operation. In this paper, remote intelligent inspection system of substation based on 5G communication transmission technology is proposed in 750kV substation, so as to achieve the purpose of intelligent inspection of substation, and expand the idea for the application of 5G technology in substation.

Visualization concept for maintenance information using wearable devices

PurposeThe purpose of this paper is to show the current state of methods for information visualization on wearable devices and demonstrate how these devices and methods can be used to support the work of aircraft maintenance engineers. This paper develops a path to make aircraft maintenance tasks more efficient and safe by providing relevant and important information to maintenance personnel matching the task they are currently working on.Design/methodology/approachThe current researchers elaborate prerequisites that wearable computing devices have to meet to use them in aircraft maintenance. Based on these prerequisites the researchers evaluate such existing devices and visualization techniques with regard to how they can be used to visualize information in aircraft maintenance processes.FindingsThis paper elaborates a roadmap how wearable devices can be applied in future aircraft maintenance scenarios. It also shows that augmented reality (AR) is a technology that shows great potential to support aircraft maintenance staff. The current researchers develop a scenario, how AR applications on wearable devices support such tasks.Practical implicationsThe implementation of the maintenance scenario that is developed in this paper will vastly reduce the possibilities of mistakes by maintenance personnel. This leads to more efficient maintenance processes and further increases the safety of aviation in general.Originality/valueAlthough AR today already is used in fields of application like gaming, an application of this technology in a safety-critical environment is not known to the author. This paper provides a step towards extending the application domains of this technology to aircraft maintenance.

Fuzzy reliability centered maintenance considering personnel experience and only censored data

The automotive industry uses Reliability Centered Maintenance (RCM) to establish efficient and cost-effective maintenance schedules of equipment (IATF 16949). The RCM methodology is based on equipment reliability which requires collection of failure times. However, in some cases it is not possible to record enough information. Sometimes in the field, only service times are available (these can be classified as “only censored data”). The most commonly found situation waiting until the component fails. In that sense, the RCM methodology mainly uses the classic Weibull model for estimating reliability indexes, which cannot be performed if there are only censored data. The maximum entropy can be used to propose an alternative reliability model that is able to work under the limited information conditions. Nevertheless, the uncertainty of only censored data and the limited information also affect the maximum entropy model. The information available, such as maintenance staff experience, must help provide accuracy to reliability estimation, which can be addressed through fuzzy numbers. Hence, a fuzzy maximum entropy approach is proposed to determine the maximum entropy reliability function considering the uncertainty and the maintenance staff knowledge; therefore, the replacement frequency. Finally, the proposed method was applied to an injection molding machine. Outcomes proved that the alternative model is a useful and reliable option if there are only censored data and personnel experience as available information. The RCM maintenance plan was scheduled using the proposed methodology and its results showed 40% savings in the annual maintenance costs, which represent an important benefit for company.

Research on Status Monitoring and Fault Alarm Device of Drop-out Fuse

Drop-out fuse is a common circuit protection device. When a short fault occurs in the circuit, the fusion pipe will drop and the fuse will be disconnected, so as to isolate the fault line. However, in practical application, there is no reliable means and device for remote monitoring, resulting in the failure information can not be found in time. Although the traditional drop-out fuse alarm device can play the role of fault alarm. It has the disadvantages of large time difference and high cost. Based on this, a set of status monitoring and alarm device according to the drop characteristics of drop-out fuse after failure has been developed. The inclination and temperature of the fusion pipe are monitored by sensors in real time, and the data is sent to the master station through wireless communication. When a fault occurs, the operation and maintenance staff can be notified in time to repair the fault point. Through experimental demonstration, it can be concluded that the fuse status monitoring and fault alarm device has stable performance, convenient control and high cost performance. If it is applied to the distribution network, it can shorten the outage time, reduce economic losses and increase user satisfaction.