Benefits Of Maintenance Research Articles

The feasibility of transparent solar panels for high-rise building façade in Sri Lanka

Purpose The use of renewable energy has become necessary because of the harmful effects of current energy sources on the environment, limited availability and financial crisis. Transparent solar panels have emerged as a promising technology for integrating renewable energy generation into building structures. Therefore, this paper aims to explore the feasibility of transparent solar panels for high-rise building façades in Sri Lanka. Design/methodology/approach The research apprehended a qualitative approach, including two expert interview rounds adhering to the Delphi technique with 17 and 15 experts each per round. Manual content analysis was incorporated to analyse the collected data. Findings Regarding operation and maintenance, the study emphasizes the importance of regular inspection, cleaning and repair of transparent solar panels to ensure optimal performance and longevity. These activities contribute to maximizing energy generation and maintaining the aesthetic appeal of the building. The benefits of implementing transparent solar panels on building façades are manifold. They include renewable energy generation, reduced greenhouse gas emissions, improved energy efficiency and enhanced architectural aesthetics. Furthermore, the research findings underscore the potential of transparent solar panels to contribute to Sri Lanka’s sustainable development goals and address the country’s increasing energy demand. However, the study also identifies challenges that need to be addressed for successful implementation. Originality/value This study contributes to understanding the feasibility of transparent solar panels for high-rise building façades in Sri Lanka. The research findings offer valuable insights into the operation and maintenance aspects, benefits, challenges and strategies for implementing transparent solar panels effectively. This knowledge can guide policymakers, architects and developers in making informed decisions regarding the integration of transparent solar panels, thereby promoting sustainable and energy-efficient building practices in Sri Lanka.

Overdose with the anesthetic propofol causes hematological cytotoxicity and immune cell alteration in an experimental ex vivo whole blood culture model

Propofol, an anesthetic characterized by its benefits of rapid induction, maintenance, and recovery times, may cause cytotoxic effects, resulting in propofol infusion syndrome (PRIS). In addition to causing dyslipidemia in PRIS, our previous works showed that propofol overdose induced phagocyte apoptosis. This study, using an experimental ex vivo model of propofol treatment, investigated the possible cytopathology in the blood. A complete blood count examination showed the deregulating effects of propofol overdose 24 h postinoculation, characterized by mononuclear cell increase (lymphocyte and monocyte subsets) and granulocyte decrease. Advanced marker-based flow cytometric analysis confirmed these findings, although there was no change in CD14+ monocytes. Blood smear staining showed the deregulating effects of propofol overdose 24 h postinoculation, characterized by cytosolic vacuolization and cytotoxicity, particularly in neutrophils. Immune cell profiling of caspase-3 activation demonstrated the induction of cell apoptosis following propofol overdose treatment, particularly in granulocytes. Using multiparameter flow cytometry, this study further analyzed the changes in the profile of immune cells, showing a notable increase in CD4 + HLA-DR-CD62L- helper T cells. These studies explored an ex vivo model of cytopathogenic propofol overdose and its special immune-deregulating effects on peripheral blood cells.

An Improved Method for Optimizing the Timing of Preventive Maintenance of Pavement: Integrating LCA and LCCA

The optimal preventive maintenance timing of traditional asphalt pavement is based on the maximum benefit-cost ratio. For cost evaluation indicators, mainly agency cost and user cost, use life cycle cost analysis (LCCA) to determine. The environmental impact of pavement preventive maintenance activities is often overlooked. For the benefit evaluation index, only the new area under the performance curve is used to express the maintenance benefit, and the benefit evaluation index is single. This study proposes an integrated approach combining life cycle assessment (LCA) and LCCA to model preventive maintenance timing decisions considering economic, social, and environmental factors. In this paper, LCA is used to quantitatively analyze the energy consumption and emissions in the pavement operation and maintenance process, and the environmental costs are also included in the accounting scope of the costs. The costs that constitute pavement use are analyzed from three aspects: the agency cost, the user cost, and the environmental cost. At the same time, in the process of constructing the cost model, the costs caused by the maintenance construction process to users and the environment are also taken into consideration. Aiming at the benefit index and comprehensively considering the economic benefits, social benefits, and environmental benefits brought by maintenance, a comprehensive evaluation model of maintenance benefits was constructed based on the analytic hierarchy process (AHP). On the basis of accurately calculating the cost and benefit of maintenance in the life cycle, the model of the optimal preventive maintenance timing of asphalt pavement is constructed so as to calculate the optimal timing of preventive maintenance and provide certain information for the formulation of expressway pavement preventive maintenance programs. For reference. Taking a highway project in China as an example to verify the applicability of the model

Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine

Armoured Fighting Vehicles (AFVs) also called as Tanks play a critical role in modern warfare, providing mobility, protection and firepower on the battlefield. However, maintaining these complex machines and ensuring their operational readiness is a significant challenge for military organizations. Traditional maintenance practices are often reactive, resulting in unexpected failures, increased downtime, and operational inefficiencies. This paper focuses on the application of Artificial Intelligence (AI) for predictive maintenance of Armoured Fighting Vehicles. By harnessing the power of AI algorithms and advanced data analytics, predictive maintenance aims to anticipate and address potential equipment failures before they occur. This proactive approach enables military organizations to optimize resource allocation, improve operational planning and extend the lifespan of AFVs. The integration of AI in predictive maintenance involves collecting and analysing data from various sensors installed on the AFV engine. These sensors monitor key parameters, such as engine performance, temperature, vibration and fluid levels to detect anomalies and deviations from normal operating conditions. AI algorithms process this data, utilizing machine learning techniques to identify patterns, correlations, and potential failure indicators. The benefits of AI-based predictive maintenance for AFVs are multifaceted. Firstly, it enhances equipment readiness by reducing unexpected failures and maximizing operational availability. Secondly, it enables optimized resource allocation, ensuring that maintenance activities are scheduled efficiently, minimizing downtime, and improving overall operational efficiency. Thirdly, the predictive capabilities of AI help military planners in better decision-making allowing for improved mission planning and execution. However, the successful implementation of AI for predictive maintenance of AFV engine requires overcoming several challenges. These include data collection and integration from diverse sensors, ensuring data accuracy and quality, establishing robust communication infrastructure, and addressing cyber security concerns to protect sensitive vehicle data. This paper underscores the growing importance of AI in revolutionizing maintenance practices for Armoured Fighting Vehicles. By shifting from reactive maintenance to predictive strategies, military organizations can enhance their operational capabilities, reduce costs, and ensure the optimal performance and longevity of their AFV fleet.

The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures

The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures

Optimization for Asphalt Pavement Maintenance Plans at Network Level: Integrating Maintenance Funds, Pavement Performance, Road Users, and Environment

As an infrastructure project, the cost and benefits of road maintenance should be measured through social costs. In order to fully consider user interests and make road maintenance decisions more reasonable, two types of user costs that may affect road maintenance benefits have been quantified through analysis of existing literature. At the same time, environmental issues have gradually become the focus of various industries, and in optimizing road maintenance decisions, the impact of environmental issues on decision making should also be considered. This article first analyzes and quantifies the user costs that affect the effectiveness of road maintenance. Secondly, based on the concept of sustainable development of roads, the optimization of road maintenance decisions is divided into two steps: the first step is to determine the minimum maintenance budget funds, and the second step is to determine the optimal plan. The specific optimization method is to use the 0-1 mathematical programming method to establish a network-level pavement maintenance decision optimization model based on a quantitative model. The most reasonable maintenance optimization plan is determined from four aspects: maintenance funds, maintenance performance improvement value, user benefit improvement, and reducing environmental impact. Finally, a provincial road network for a case study is selected. The applicability of the new model is verified through a case study. This study can help decision makers deal with asphalt pavement maintenance arrangements at the network level with four decision objectives: maintenance funds, pavement performance, road users, and environmental impacts.

Reliable Ship Emergency Power Source: A Monte Carlo Simulation Approach to Optimize Remaining Capacity Measurement Frequency for Lead-Acid Battery Maintenance

<div>The development of predictive maintenance has become one of the most important drivers of innovation, not only in the maritime industry. The proliferation of on-board and remote sensing and diagnostic systems is creating many new opportunities to reduce maintenance costs and increase operational stability. By predicting impending system faults and failures, proactive maintenance can be initiated to prevent loss of seaworthiness or operability. The motivation of this study is to optimize predictive maintenance in the maritime industry by determining the minimum useful remaining lead-acid battery capacity measurement frequency required to achieve cost-efficiency and desired prognostic performance in a remaining battery capacity indication system. The research seeks to balance operational stability and cost-effectiveness, providing valuable insight into the practical considerations and potential benefits of predictive maintenance. The methodology employed in this study includes outlining the theoretical development of a fully automated condition monitoring system and describing data cleansing steps to account for environmental effects on system performance. A Monte Carlo simulation is used to evaluate the sensitivity of the remaining useful life prediction to varying measurement frequencies, prediction models, and parameter settings, leading to an estimate of the optimal measurement frequency for the system. The results show that a certain minimum measurement frequency is required to achieve the target prediction accuracy while balancing cost-efficiency and operational stability. Reliable failure prediction with negligible changes in prognostic accuracy can be achieved by performing useful remaining lead-acid battery capacity measurements twice a day or every 5 ship voyage cycles with the underlying utilization.</div>

PROCESSUAL BENEFITS OF PREDICTIVE MAINTENANCE IN THE MIDMARKET

Background: Industrialization has given rise to numerous new topics for companies. In the age of Industry 4.0, predictive maintenance represents an opportunity to reduce machines' downtime and enable companies to gain competitive advantages. For this purpose, sensors are installed in machines, and the data of individual parts is constantly monitored and evaluated. By detecting possible failure risks of individual parts at an early stage, they can be replaced before the downtime occurs, thus preventing a long downtime. A shorter downtime can bring further positive aspects in multiple terms. A lot is written in the literature about the general advantages of predictive maintenance, but there is no particular focus on the German midmarket sector. Aims: The aim of this paper is to find out how midsize companies perceive the processual benefits of predictive maintenance. Methods: A standardized questionnaire with four questions is developed. Sample: Answers from 104 respondents are evaluated. Results: The results show that a large proportion of the respondents expect added value. Conclusion and Implications: The research in this paper shows that the added value generated by predictive maintenance is not limited to multinational corporations but can also be realized in medium-sized companies.

Design of JKN Claim Information System for Outpatient BPJS Patients with SDLC Method

BPJS claim is the submission of all BPJS patient care costs by the Hospital to the BPJS Health, carried out collectively and billed to the BPJS Health every month through the verification process. BPJS Health is a legal entity established to administer the Health Insurance program. Health Insurance is a guarantee in the form of health protection so that participants obtain health maintenance benefits and protection in meeting basic health needs provided to everyone who has paid contributions or whose contributions are paid by the government. This study aims to design a medical record management system to support JKN claims for outpatients at Muhammadiyah Bandung Hospital. The research method used in this research is qualitative with a descriptive approach. The data collection techniques used were interview, observation and literature study. The results showed that there were problems that occurred, namely unclear resumes, illegible SEPs, missing referral letters, and there was still 1 outpatient period that was mixed up so that coding officers found it difficult to read it and became an obstacle to claiming. Based on this research, it can be concluded that there is a need for a quality audit of specific medical resume writing and incomplete BPJS patient requirement files.

Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency

Recent developments in prognostic models have led to increased use of prognostic information to improve the benefits of condition-based maintenance (CBM). Existing CBM decision-making methods are often limited by the assumption that prognostics information is available for all components and by the neglect of economic loss due to system degradation in assessing benefits. This paper aims to find optimal maintenance decisions with maximum net revenue for multi-component systems using prognostics information. In contrast to the reported works, three main contributions are made. First, considering that the remaining useful life (RUL) of only some components in the system can be predicted continuously, maintenance decisions, including planned inspection intervals and prognostic thresholds, are optimized jointly at the system-level. Second, the loss of efficiency due to system degradation is investigated when assessing the benefits of maintenance decisions. A degradation-related system efficiency model is proposed, and its parameters can be determined from historical data. Third, we extend our model with prognostic error modeling to derive insights on the effects of the prognostic model on the relative benefit of CBM. A case of maintenance optimization of wind turbine farms is provided to demonstrate and validate the proposed method.

Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines

The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the applicability of such RUL prognostics to maintenance planning, which is per definition a stochastic problem. In this paper, we therefore develop probabilistic RUL prognostics using Convolutional Neural Networks. These prognostics are further integrated into maintenance planning, both for single and multiple components. We illustrate our approach for aircraft turbofan engines. The results show that the optimal replacement time for the engines is close to the lower bound of the 99% confidence interval of the RUL estimates. We also show that our proposed maintenance approach leads to a cost reduction of 53% compared to a traditional Time-based maintenance strategy. Moreover, compared with the ideal case when the true RUL is known in advance (perfect RUL prognostics), our approach leads to a limited number of failures. Overall, this paper proposes an end-to-end framework for data-driven predictive maintenance for multiple components, and showcases the potential benefits of data-driven predictive maintenance on cost and reliability.

The WALK HOP program: a French innovative multicentric home-based cardiac rehabilitation initiative

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Fonds pour l'innovation du système de santé (FISS) - France. Background Cardiac rehabilitation (CR) has demonstrated scientific benefits in terms of morbidity and mortality. In France, 2019 national data indicates that only 22,3% of coronary patients benefit from CR. The development of alternative programs to center-based cardiac rehabilitation (CBCR) must be considered as a public health priority and requires a reorganization of ambulatory care pathways. Purpose The objective of the "Walk Hop" program is to demonstrate that home-based cardiac tele-rehabilitation (HBCT) is a complementary and safe approach to CBCR, able to increase the proportion of patients benefiting from a rehabilitation program. Methods This feasibility study started begin of 2022. 3,200 coronary patients will be recruited in 8 participating cardiac rehabilitation centers (CRC). Total duration of the experiment is 42 months. The program is financially supported by the French authorities. The HBCT solution developed for the study is proposed to patients by the CRC cardiologist. A multidisciplinary platform consisting of a secure portal and an application allows bidirectional communication. Patients are equiped with a cycle ergometer and a validated heart rate monitor. The application presents aerobic and muscle strenghtening activities and therapeutic education and collects data from the sessions carried out, adherence to the program, symptoms and quality of life. Results By october 2022, 157 active patients have been included in the program with a large majority of men (86%). Mean age is 53,9 ± 9,0. Hypercholesterolemia is noted in 36,9% of the included patients, smoking in 31%, arterial hypertension in 30,6% and diabetes in 11,1%. Obesity/overweight affects 60% of the population. Mean time from angioplasty to initiation of the rehabilitation is 81 ± 90 days. Mean number of home rehabilitation sessions is 21 and a total of 3342 home sessions have been realized. No cardiovascular event or rehospitalization have been registered since the beginnng. Mean adherence to the prescribed sessions is 80 ± 20% and the mean power gain at the end of the program is 14,6 ± 9,9 %. 94% of the patients expressed satisfaction or high satisfaction at the end of the program. Conclusion(s) The Walk Hop program is evaluating the feasibility and acceptability of HBCT with remote monitoring and digital assistance by smartphone. The preliminary data are encouraging in view of the final expected impact: greater cost-effective access to rehabilitation programs by removing obstacles like distance or resumption of work. It is also expected that HBCT will provide an improvement in the long-term maintenance benefits.

Working Parents’ Struggling to Achieve the Work-Life Balance During the Pandemic: Exploring the Benefits of Relational Maintenance Beyond Relational Outcomes

ABSTRACT Work from home mandates during the current pandemic forced people to merge personal and professional lives, thus the current study explored how relational support from a romantic partner might have helped working parents maintain satisfying work-life balance. Guided by theory of resilience and relational load, the study proposed that individuals’ perceiving greater relational maintenance from partners would report higher satisfaction balancing work-life, further associate with greater relationship and job satisfaction. 271 working parents responded to an online survey in August 2020, when work from home mandates were still in place. Results revealed that for working parents, satisfaction from work-life balance mediated the associations between receiving relational maintenance strategies and relationship/job satisfaction. The study also exposed sex differences, indicating that women carried greater burdens during the pandemic. Findings illuminate the need to cultivate supportive personal relationships and institute policies to mitigate gender inequities.

Evaluation of Probiotic Effects of Lactobacilli on Mutans Streptococci: An In Vitro Study.

The aim of the present study is to evaluate the probiotic effect of Lactobacillus acidophilus and Lactobacillus rhamnosus on clinical isolates of Mutans Streptococci (MS) and antibiotic susceptibility of these strains to commonly used antibiotics in dentistry. Plaque samples from permanent first molars were collected and transferred aseptically onto Mitis-Salivarius agar and incubated at 37°C for 24 hours in the presence of 5-10% CO2. Mutans streptococci colonies were identified biochemically using Hi-Strep identification kit. The inhibitory activity of the clinical strains of MS on Lactobacilli was investigated using agar-overlay interference technique. Positive inhibition was appreciated as a clear zone around the Lactobacilli. Disk diffusion assay was done as described by CLSI M100-S25 for antibiotic susceptibility. The zone of growth inhibition caused by Lactobacilli and antibiotics on MS clinical strains was measured directly using a vernier caliper. Statistical analysis was done using independent t-test. Mutans streptococci exhibited positive inhibition with both the probiotic strains and L. acidophilus showed more zones of inhibition than L. rhamnosus. Antibiotic susceptibility of clinical strains of MS showed sensitivity to penicillin and vancomycin, however, tetracycline and erythromycin showed very few resistant strains. The highest zone of inhibition was shown by cephalothin followed by penicillin, tetracycline, ciprofloxacin, erythromycin, and vancomycin. L. rhamnosus and L. acidophilus have strong inhibitory effects on clinical strains of MS. Lactobacillus acidophilus showed a higher zone of inhibition. All the clinical strains of MS were sensitive to penicillin and vancomycin. The highest zone of inhibition was shown by cephalothin. Dental caries remains silent epidemic and increasing antibiotic resistance is another major challenge that threatens the world. Newer methods such as whole-bacteria replacement therapy using probiotics for decreasing harmful oral pathogens and reducing the intake of antibiotics must be explored. More researches to promote use of probiotics should be initiated due to its possible preventive and health maintenance benefits providing an end to new cavities and antibiotic resistance.

Experience from implementing digital twins for maintenance in industrial processes

The capability of estimating future maintenance needs in advance and in a timely manner is a prerequisite for reliable manufacturing with high availability in a production unit. Additionally, conducting planned maintenance efforts regularly and prematurely increases the service lifetimes and utilization rates of parts, which leads to more sustainable production. The benefits of predictive maintenance are obvious, but introducing it into a facility poses various challenges. In this study, digital twins of well-functioning machines are used for predictive maintenance. The discrepancies between each physical unit and its digital twin are used to detect the maintenance needs. A thorough evaluation of the method over a period of 18 months by comparing digital twin detection results with maintenance and control system logs shows promising results. The method is successful in detecting discrepancies, and the paper describes the techniques that are used. However, not all discrepancies are related to the maintenance needs, and the evaluation identifies and discusses the most common sources of error. These are often the results of human interaction, such as parameter changes, maintenance activities and component replacement.

Effect of Model Accuracy on Maintenance and Rehabilitation Benefits

To achieve maximum benefit and cost-effectiveness, the application of pavement maintenance and rehabilitation treatments should be performed within optimal timing widows. In pavement management systems, these timing windows are primarily determined using pavement deterioration models and decision trees embedded in the system. However, owing to the prediction errors of the pavement performance models, the obtained time windows might not be optimal, making it difficult to achieve the expected maximum benefit and cost-effectiveness. To date, there is very little research quantifying the effect of model errors on the difference between the obtained benefit and the expected maximum benefit. Considering this, in this study, a reliability-based approach was developed to analyze the effect of model accuracy on the treatment benefit and extra cost. Through a case study, the effect of model accuracy on the treatment benefit was quantified. An increase in the model error was found to decrease the treatment benefit, while the extra cost needed to achieve the same benefit as the reference scenario increased. Although these findings might seem intuitive, this study has provided an analysis method to quantify such disbenefits.

The Life cycle Assessment Integrated with the Lexicographic Method for the Multi-Objective Optimization of Community-Based Rainwater Utilization.

Community-based rainwater utilization (CB-RWU) has the advantage of easy maintenance and multiple benefits. However, its promotion proves to be a complicated task due to difficulties in quantifying and evaluating external benefits. This study integrated the life cycle assessment (LCA) with a multi-objective optimization model to optimize the relationship among all stages of CB-RWU, considering the trade-offs among the benefit-cost ratio, water-saving efficiency and environmental impact. The LCA results identified abiotic depletion potential for fossil fuels (ADPF) as the key impact indicators throughout the life cycle of CB-RWU. The optimal solution from the lexicographic method was 0.3098, 28.47% and 24.68 MJ for the benefit-cost ratio, water-saving efficiency and ADPF, respectively. Compared with the traditional optimization method, the lexicographic method improved the three object functions by 26%, 43% and 14%, respectively. The uncertainty of the environmental impact was the highest (CV = 0.633) with variations in the floor area ratio, total runoff coefficient and reservoir volume. Changes in the total runoff coefficient were the main source of the uncertainty, which suggested that more attention should be paid to the area ratio of each underlying surface. In addition, economic support from the government is urgently required for the further promotion and development of CB-RWU.

Farmers’ Willingness to Pay for Tank Maintenance and Conservation – An Economic Valuation Approach

Contingent Valuation Method involves a resource by imputing a monetary value on the response of the people affected by the change in the state of the resource. In this study, contingent valuation method is used to study the factors influencing farmers decision on willingness to pay and payment levels for tank maintenance and conservation and to rank the reasons for farmers willingness to pay and non-willingness to pay. Two tanks surrounding the Noyyal river in Tirupur district of Tamil Nadu namely, Manickapuram tank and Anaipalayam tank were selected purposively. A quota of 30 farmers surrounding each tank were randomly selected and interviewed for willingness to pay after clearly explaining the benefits of tank rehabilitation, maintenance and conservation. The collected data were analyzed using Logistic, Multiple regression and Garette ranking technique. The study revealed that past benefit had a greater influence on farmers decision on Willingness to pay. Age, Education, Farm income, Farming experience and Land holding influences the payment levels. The study also identified and ranked the reasons for farmers willingness to pay and non-willingness to pay. Finally, the study recommends the farmers to construct small percolation ponds for improving the quality of groundwater.

Predictive Maintenance in Healthcare System: A Survey

Medical devices are a vital component of healthcare systems, the advantages they may give continue to grow as they are crucial for the safe and effective prevention, diagnosis, treatment, and rehabilitation of illnesses and diseases. Therefore, it is critical to maintain them in good operating order to ensure optimum availability, minimal failures, and guarantee patients’ and users’ safety. The stages involved in medical devices regulation and management are complex, but they are necessary to ensure their quality, safety, and compatibility with the settings in which they are used. Medical equipment complexity has increased due to technological advancement and the traditional maintenance strategies do not meet the needs of today’s healthcare organizations. Therefore, integrating information technology, social networking technologies, digitization and management of medical devices, and the use of big data technologies and Machine Learning (ML) techniques has the potential to significantly improve healthcare services. Integrating autonomous and intelligent systems where data and sophisticated data analytics may be employed led to enhanced equipment data collecting via the deployment of information and communication technologies, notably intelligent devices. With this advancement came an increase in Predictive Maintenance (PdM) solutions. PdM has become a commonly used approach, described as a set of procedures used to evaluate the condition of equipment and predict future failures. These estimations are then utilized to schedule maintenance activities through smart scheduling of maintenance procedures, which aids in preventing or at least minimizing the impacts of unanticipated failures. The purpose of this article is to present a Systematic Literature Review (SLR) exploring and reviewing prior research on the subject of PdM and the developments of this method, particularly in the medical field. In addition to supporting new research projects in the PdM sector, this paper offers a good foundation for understanding PdM approaches, their key findings, problems, and potential. This review focuses on two scientific databases from which a substantial number of articles dedicated solely to PdM in the medical field have been retrieved for analysis. Our research led us to conclude that, despite the many potential benefits of predictive maintenance in the medical field, the concept is still being under-exploited and faces many obstacles.

Notes: Murder and fraud for inheritance: Smit v The Master of the High Court, Western Cape

In South African law, a beneficiary may be disqualified from inheriting for killing the deceased, forging the deceased’s will, or acting in a morally reprehensible manner towards the deceased. In Smit v The Master of the High Court, Western Cape [2022] 4 All SA 146 (WCC), the court disqualified a wife from inheriting from her deceased husband because she had conspired to kill him. The court also disqualified her for forging his testamentary documents and his mother’s will. In addition, the court held that she was not entitled to claim maintenance and other benefits from his estate. This note critically evaluates the theoretical underpinnings of the court’s findings, with regard to the facts and the evidence in the case.