Impact of Safety-Maintenance Practices on the Overall Performance of an Industry

The study aims to evaluate how safety-maintenance practices affect the mechanical engineering industry’s overall performance in Ghana. The study used a descriptive survey design technique to ascertain the type of maintenance engineering that was practiced in Ghanaian mechanical engineering workshops at the time of the study. In the mechanical engineering workshops, respondents provided both qualitative and quantitative data using a variety of data collecting instruments, with the quantitative approach being more common. The study employed Kumasi, Tamale, and Accra’s mechanical engineering workshops as a case study. The number of mechanical engineering workshop enterprises that made up the sample size for the questionnaire administration was sixty (60), chosen at random from the AGI membership registry. Primary data was gathered using interview guides and questionnaires. To analyse the data, descriptive statistics were employed. According to the study’s findings, mechanical engineering companies combined different maintenance techniques in order to best fit their organisational culture and equipment. Preventive shut-down, with a mean score of 4.78 and RII = 0.98, placing first (1st) in the Likert rating order, is the most frequently used maintenance system by respondents. The maintenance procedures employed by mechanical engineering organisations were influenced not only by their equipment and organisational culture but also by other factors such as cost, personnel expertise and external partnerships.

Design and pricing of maintenance service contract based on Nash non-cooperative game approach

Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

In this study,we introduce a novel multi-objective optimization model tailored for modern manufacturing,aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance.Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel,addressing a crucial gap in the integration of maintenance personnel dispatching and station selection.Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness.The core of our methodology is the NSGA Ⅲ+Dispatch,an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ),meticulously designed for the selection of maintenance stations and effective operator dispatching.This method integrates a comprehensive coding process,crossover operator,and mutation operator to efficiently manage multiple objectives.Rigorous empirical testing,including a detailed analysis from a taiwan region electronic equipment manufacturer,validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations.The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23%in low-frequency and 28.23%in high-frequency machine failure scenarios,leading to notable improvements in efficiency and cost reduction.Additionally,it demonstrates significant improvements in oper-ational efficiency,particularly in selective high-frequency failure contexts,while ensuring substantial manpower cost savings without compromising on operational effectiveness.This research significantly advances maintenance strategies in production environments,providing the manufacturing industry with practical,optimized solutions for diverse machine malfunction situations.Furthermore,the methodologies and principles developed in this study have potential applications in various other sectors,including healthcare,transportation,and energy,where maintenance efficiency and resource optimization are equally critical.

Reliability-BasedModel for Incomplete Preventive ReplacementMaintenance of Photovoltaic Power Systems

At present,the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance,breakdown maintenance,and condition-based maintenance,which is very likely to lead to over-or under-repair of equipment.Therefore,a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed.First,a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment,and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle.Then,based on the reliability as a constraint,the average maintenance cost and availability of the equipment are considered,and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs,each maintenance cycle and the replacement cycle of the PV power generation system components.Next,the inverter of a PV power plant is used as a research object.The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold,Through model comparison,when the optimal maintenance strategy is(0.80,4),the average maintenance cost of this paper’s model are decreased by 20.3%and 5.54%and the availability is increased by 0.2395% and 0.0337%,respectively,compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold.Therefore,this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy.

Study on Fracture Delay of High-Strength Bolts in Road Bridge Maintenance

In the maintenance work of highway and bridge engineering structures,the fracture delay of high-strength bolts is a content that needs to be focused on and researched.Based on this,the paper analyzes the fracture delay of high-strength bolts in highway bridge maintenance,including an overview of the fundamental research on fracture delay and related specific studies.It is hoped that this study can provide scientific reference for the reasonable maintenance of high-strength bolts,so as to ensure the overall maintenance effect of highway bridge projects.

Remaining Useful Life Prediction Method for Multi-Component System Considering Maintenance:Subsea Christmas Tree System as A Case Study

Maintenance is an important technical measure to maintain and restore the performance status of equipment and ensure the safety of the production process in industrial production,and is an indispensable part of prediction and health management.However,most of the existing remaining useful life(RUL)prediction methods assume that there is no maintenance or only perfect maintenance during the whole life cycle;thus,the predicted RUL value of the system is obviously lower than its actual operating value.The complex environment of the system further increases the difficulty of maintenance,and its maintenance nodes and maintenance degree are limited by the construction period and working conditions,which increases the difficulty of RUL prediction.An RUL prediction method for a multi-omponent system based on the Wiener process considering maintenance is proposed.The performance degradation model of components is established by a dynamic Bayesian network as the initial model,which solves the uncertainty of insufficient data problems.Based on the experience of experts,the degree of degradation is divided according to Poisson process simulation random failure,and different maintenance strategies are used to estimate a variety of condition maintenance factors.An example of a subsea tree system is given to verify the effectiveness of the proposed method.

Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance:A Review

Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns.Due to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and maintainability.While Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is unavailable.Therefore,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques.We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular subsystems.We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars.The review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI applications.Indeed,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic support.By synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable vehicles.The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis.

A Two-Stage Scenario-Based Robust Optimization Model and a Column-Row Generation Method for Integrated Aircraft Maintenance-Routing and Crew Rostering

Motivated by a critical issue of airline planning process,this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions.Following the route network scheme and generated flight timetables,aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management.This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints,rules,and regulations.Considering multiple locations for airline maintenance and crew bases,we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering(AMRCR)to achieve the minimum airline cost.One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights.Due to the fact that disruption scenarios are expressed discretely with a specified probability,and we provide adjustable decisions under disruption to deal with this disruption risk,we provide a Two-Stage Scenario-Based Robust Optimization(TSRO)model.In this model,here-and-now or first-stage variables are the initial resource assignment.Furthermore,to adapt itself to different disruption scenarios,the model considers some adjustable variables,such as the decision to cancel the flight in case of disruption,as wait-and-see or second-stage variables.Considering the complexity of integrated models,and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance,we apply the column and row generation(CRG)method that iteratively considers the disruption scenarios.The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability.To evaluate the proposed TSRO model,which solves the AMRCR problem in an integrated and robust manner,five Key Performance Indicators(KPIs)like Number of delayed/canceled flights,Average delay time,and Average profit are taken into account.As key results driven by conducting a case study,we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models.The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems.However,for large-scale instances the proposed TSRO model falls short in terms of computational efficiency.Conversely,the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level.

Predictive maintenance and its applications in civil engineering structures:A review

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.

Azacitidine maintenance therapy for blastic plasmacytoid dendritic cell neoplasm allograft: A case report

BACKGROUND Blastic plasmacytoid dendritic cell neoplasm(BPDCN)is a rare,highly invasive malignant neoplasm.There is no universally accepted standard of care because of its rarity and the dearth of prospective research.It is still challenging for some patients to achieve persistent clinical remission or cure,despite the success of allogeneic hematopoietic stem cell transplantation(allo-HSCT),indicating that there is still a significant recurrence rate.We report a case of prevention of BPDCN allograft recurrence by azacitidine maintenance therapy and review the relevant literature.CASE SUMMARY We report a 41-year-old man with BPDCN who was admitted to hospital due to skin sclerosis for>5 mo’duration.BPDCN was diagnosed by combined clinical assessment and laboratory examinations.Following diagnosis,the patients underwent induction consolidation chemotherapy to achieve the first complete remission,followed by bridging allo-HSCT.Post-transplantation,azacitidine(75 mg/m2 for 7 d)was administered as maintenance therapy,with repeat administration every 4–6 wk and appropriate extension of the chemotherapy cycle.After 10 cycles,the patient has been disease free for 26 mo after transplantation.Regular assessments of bone marrow morphology,minimal residual disease,full donor chimerism,Epstein–Barr virus,and cytomegalovirus all yielded normal results with no abnormalities detected.CONCLUSION Azacitidine may be a safe and effective maintenance treatment for BPDCN following transplantation because there were no overt adverse events during the course of treatment.

Mediating role of social support in dysphoria,despondency,and quality of life in patients undergoing maintenance hemodialysis

BACKGROUND Dysphoria and despondency are prevalent psychological issues in patients undergoing Maintenance Hemodialysis(MHD)that significantly affect their quality of life(QOL).High levels of social support can significantly improve the physical and mental well-being of patients undergoing MHD.Currently,there is limited research on how social support mediates the relationship between dysphoria,despondency,and overall QOL in patients undergoing MHD.It is imperative to investigate this mediating effect to mitigate dysphoria and despondency in patients undergoing MHD,ultimately enhancing their overall QOL.AIM To investigate the mediating role of social support in relationships between dysphoria,despondency,and QOL among patients undergoing MHD.METHODS Participants comprised 289 patients undergoing MHD,who were selected using a random sampling approach.The Social Support Rating Scale,Self-Rating Anxiety Scale,Self-Rating Depression Scale,and QOL Scale were administered.Correlation analysis was performed to examine the associations between social support,dysphoria,despondency,and QOL in patients undergoing MHD.To assess the mediating impact of social support on dysphoria,despondency,and QOL in patients undergoing MHD,a bootstrap method was applied.RESULTS Significant correlations among social support,dysphoria,despondency,and quality in patients undergoing MHD were observed(all P<0.01).Dysphoria and despondency negatively correlated with social support and QOL(P<0.01).Dysphoria and despondency had negative predictive impacts on the QOL of patients undergoing MHD(P<0.05).The direct effect of dysphoria on QOL was statistically significant(P<0.05).Social support mediated the relationship between dysphoria and QOL,and this mediating effect was significant(P<0.05).Similarly,the direct effect of despondency on QOL was significant(P<0.05).Moreover,social support played a mediating role between despondency and QOL,with a significant mediating effect(P<0.05).CONCLUSION These findings suggest that social support plays a significant mediating role in the relationship between dysphoria,despondency,and QOL in patients undergoing MHD.

Research on Carbon Emission for Preventive Maintenance of Wind Turbine Gearbox Based on Stochastic Differential Equation

Time based maintenance(TBM)and condition based maintenance(CBM)are widely applied in many large wind farms to optimize the maintenance issues of wind turbine gearboxes,however,these maintenance strategies do not take into account environmental benefits during full life cycle such as carbon emissions issues.Hence,this article proposes a carbon emissions computing model for preventive maintenance activities of wind turbine gearboxes to solve the issue.Based on the change of the gearbox state during operation and the influence of external random factors on the gearbox state,a stochastic differential equation model(SDE)and corresponding carbon emission model are established,wherein SDE is applied to model the evolution of the device state,whereas carbon emission is used to implement carbon emissions computing.The simulation results indicate that the proposed preventive maintenance cannot ensure reliable operation of wind turbine gearboxes but reduce carbon emissions during their lifespan.Compared with TBM,CBM minimizes unit carbon emissions without influencing reliable operation,making it an effective maintenance method.

Exploration of Operation and Maintenance Management Mode and Innovation Mechanism of Pilot Base of Chemical Park

Based on the current situation of the operation and maintenance management of the pilot base in many chemical parks in China,this paper conducts an in-depth exploration of the operation and maintenance management and innovation mode of the pilot base from the aspects of the evaluation of the pilot project entering the park,safety and environmental protection supervision,pilot mode,and industrialization mode of the pilot test results,so as to provide solutions for accelerating the process and industrialization of the pilot project and realizing the innovation value of the pilot base.

Analysis of current status of and barriers to exercise participation among patients on maintenance hemodialysis

Objective:The objective of this study was to analyze the current status of barriers to exercise participation(EP)among patients on maintenance hemodialysis(MHD).Materials and Methods:A cross-sectional study was conducted on 277 outpatients undergoing MHD in 2 tertiary first-class hospitals in Beijing from February 2023 to June 2023 who were selected using convenience sampling method.The data of patients on MHD were collected using the general information questionnaire,Physical Activity Rating Scale,Exercise Benefits/Barriers Scale(EBBS),and Exercise Self-Efficacy Scale(ESES).The relationship between EP and barriers to EP was analyzed through univariate and multivariate linear regression models.Results:Patients on MHD had a low exercise volume score of 13.71±0.68 points and a medium EBBS score of 63.36±0.40 points.Multivariate logistic analysis showed that exercise volume was significantly related to the following four aspects,including low monthly household income per capita(odds ratio[OR]=86.741,95%confidence interval[CI][1.164-6.465],P=0.042),primary underlying disease of diabetic nephropathy(OR=45.993,95%CI[1.353-1.564],P=0.033),the belief that“fatigue in lower extremities hinders exercise”(OR=4.697,95%CI[1.127-19.585],P=0.034),and the belief that“physical exercise bringing optimistic and positive life attitude”(OR=0.074,95%CI[0.007-0.830],P=0.035).Conclusions:Since patients on MHD had low physical exercise volume,the health-care provider should pay more attention on the controllable factors that affect the EP of patients on MHD.Therefore,feasible and effective intervention measures can be formulated based on ESES in clinical nursing.

Translation Theory and Techniques in Optometry Literature: A Case Study of the “Optoform 80 Installation and Maintenance Manual”

Translation of specialized documents in optometry presents unique challenges,requiring a deep understanding of the professional context,terminology,and adherence to specific translation styles.This paper delves into the translation theory and techniques employed in the English-to-Chinese translation of the“Optoform 80 Installation and Maintenance Manual”,serving as a case study.Emphasizing Nida’s theory of functional equivalence,the paper explores the complexities of translating professional optometry literature,focusing on key points and common issues in translating installation and maintenance manuals for optometric devices.Special attention is given to the translation of professional terminology,employing methods such as addition,omission,conversion,and repetition.The translation process aims to meet specific requirements,ensuring accuracy and coherence while aligning with Chinese language conventions.The paper also analyzes translation methods and techniques concerning terminology,prepositions,conjunctions,and handling parallel elements based on practical insights from the Optoform 80 Manual.

A Maintenance Service Improvement Approach Based on Queue Networks: Application

The quest to increase the performance of production systems that have become complex leads to the transfer to the maintenance function of the responsibility of guaranteeing the availability of such systems. Also, we will never stop saying that maintenance must integrate into all of the company’s initiatives, to affirm its role, which is to ensure greater availability and sustainability of the means of production. The objective of this paper is to evaluate the reliability and availability of a system without knowing the distribution law of the operating times. Among the methods for evaluating dependability criteria (Fault Trees, Petri Nets, etc.), we are interested in queues that have the advantage of taking into account functional dependencies, thus allowing a quantified optimization of maintenance. Indeed, queues make it possible to model parallel or sequential processes, implementing operations taking place at the same time or one after the other, meeting the needs of modeling production systems. The main result of this paper is the study of the influence of availability on the reliability of a multi-state production system.

Analysis of Detection and Monitoring Technology in the Construction and Maintenance of Large Bridges

Currently,there is significant attention placed on the construction,management,and maintenance of large service bridges.Within the realm of bridge maintenance management,the utilization of detection and monitoring technology is indispensable.By employing these technologies,we can effectively identify any structural defects within the bridge,promptly uncover unknown risks,proactively establish maintenance strategies,and prevent the rapid deterioration of bridge conditions.This article aims to explore the advantages of applying bridge monitoring and testing technology and to discuss various methods for implementing detection and monitoring technology throughout the construction,management,and maintenance phases of large bridges.Ultimately,this will contribute to ensuring the safe operation of large bridges.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Evaluation of the Application Effect of Psychological Nursing Intervention on Maintenance Hemodialysis Patients with Uremia

Objective:To evaluate the application effect of psychological nursing intervention on maintenance hemodialysis(MHD)patients with uremia.Methods:Sixty cases of uremic patients admitted to the hospital between May 2023 and May 2024 were selected for MHD treatment and divided using the random number table method into 30 cases in each group.The observation group implemented psychological nursing intervention,while the reference group received conventional nursing intervention,after which the nursing effects were compared.Results:After nursing,the psychological state score of the observation group was lower than that of the reference group;the treatment compliance of the observation group was higher than that of the reference group;the self-care ability score of the observation group was higher than that of the reference group,and the quality-of-life score of the observation group was higher than that of the reference group(P<0.05).Conclusion:Psychological nursing intervention for uremic MHD patients can improve their negative psychology,enhance treatment compliance,and comprehensively improve patients’self-care ability and quality of life.

Instrument Automation Control System Faults and Maintenance

This article explores the topic of fault diagnosis and maintenance strategies for instrument automation control systems,analyzing them through specific cases.The aim of this research is to improve the stability and reliability of the system by conducting a thorough investigation of faults and maintenance in instrument automation control systems.By doing so,this research hopes to provide a strong guarantee for the smooth progress of industrial production.