System Failure Rate Research Articles

Practical drought risk assessment and management framework: A step toward sustainable modernization in agricultural water management

A proposed framework to assess and manage risks associated with Agricultural Water Off-farm Conveyance and Distribution Systems (AWOCDS), quantify potential failure due to drought-induced scenarios, and provide short to long-term recovery plans to mitigate drought impacts. It makes an intellectual contribution by creating a new proactive risk assessment focusing specifically on AWOCDS as a crucial part of agricultural water management. The risk assessment comprises two components − risk probability and consequence. The former is calculated by analyzing droughts via the streamflow drought index using the DrinC calculator. The latter is determined through AWOCDS’ performance evaluation using hydraulic simulation via HEC-RAS. Additionally, water distribution is assessed spatiotemporally using adequacy and equity indicators, and an integrated consequence is developed by applying the simple additive weighting method. AWOCDS’ rehabilitation and modernization projects worldwide include three practical practices in its risk management component. These practices involve modifying standard operating procedures (SOPs) by updating maintenance, upgrading manual-based operations, and developing a centralized Model Predictive Controller (MPC) for AWOCDS automatic operation. The proposed framework underwent testing in an arid region. Based on the risk assessment results, the AWOCDS operating system failure rate ranges from 70% to 96% during extreme-severe drought scenarios and 53% to 65% during moderate-mild drought scenarios. Risk management analysis shows that a short-term recovery plan reduces the drought risk by 4% monthly and 3% yearly. However, the risk of failure ranges from 50 to 94% in mild to extreme drought scenarios. Upgrading SOPs decreases impact by 4–11% in extreme, 4–7% in moderate-severe and 7–9% in mild-moderate drought. Automating the operating system with MPC reduces impact by 40–43% and 30–56% in mild-moderate and severe-extreme drought scenarios. This proactive-based strategy can help water managers plan for rehabilitation and improve surface water distribution reliability.

Optimizing System Efficiency and Reliability: Integrating Semi-Markov Processes and Regenerative Point Techniques for Maintenance Strategies in Plate Manufacturing

This paper offers a modeling methodology to evaluate similar systems’ performance and optimize system effectiveness with customized maintenance plans. This strategy, which combines regeneration point techniques with semi-Markov processes, is used to assess a plate manufacturing company’s system. Key insights are obtained using a thorough cost-benefit analysis that includes numerical analysis, graphical interpretations, and system efficacy indicators. The study clarifies the dynamics of the system’s reliability under different repair rates by showing an inverse relationship between Mean Time to System Failures (MTSF) and failure rate. Additionally, the analysis clarifies how profit is affected by failure rate, highlighting the necessity of efficient maintenance plans from an economic standpoint. This research highlights the growing significance of availability and dependability in technology-driven sectors and emphasizes the need for dependable systems. This paper makes a substantial contribution to the knowledge already available in the field of reliability engineering by providing a thorough framework that combines regeneration point approaches and semi-Markov processes. The metrics that are obtained offer a comprehensive understanding of the behavior of the system, which in turn helps industry practitioners make well-informed decisions.

Design and optimisation of hybrid solar PV energy system for rural areas in the Narmada Valley, India

This study aims to design and select an optimal hybrid photovoltaic (PV) energy system for rural areas in the Narmada Valley (21.68°N, 74.92°E), Madhya Pradesh (MP) state, India. Two case studies in Narmada Valley (farmhouse and healthcare) were analysed for technical, economic, environmental, and reliability parameters, using MATLAB/Simulink and HOMER Pro software. The result indicates that the PV/BB/DG farmhouse energy system has capital cost of ₹2643200 (CC), an operating cost (OC) of ₹955763, fuel cost (FC) of ₹7240725, net present cost (NPC) of ₹3496135, an annualized cost (AC) of ₹257246 and cost of energy (COE) of ₹12.05. The PV/BB/DG healthcare energy system is CC ₹528640, OC ₹221191, FC ₹1979132, NPC ₹802055, AC ₹53213 and COE ₹12.34. The failure rate, expected energy not supplied (EENS) and loss of load probability (LOLP) of the farmhouse hybrid PV/BB/DG system are 0.0501/yrs, 0.0927 hrs/yrs, 0.4356 kWh/hrs and 1.05822E-05, respectively. The healthcare hybrid PV/BB/DG system failure rate (0.0117199), interruption duration (0.09009), system unavailability (2.16 hours.), EENS (0.234234 kWh/yrs), and LOLP (1.02842E–05) are all much lower than those of other configurations. Hybrid solar PV/BB/DG energy systems provide reliable, cost-effective, reduced environmental impact, sustainability, and job creation in rural areas.

The Role of Mycobacterium indicus pranii in Sepsis Management: A Comprehensive Review of Clinical Outcomes and Therapeutic Potential.

Sepsis is a critical condition characterized by a dysregulated immune response to infection, leading to systemic inflammation, multi-organ failure, and high mortality rates. Current treatments primarily involve antibiotics and supportive care, which address the infection and stabilize hemodynamics but do not directly modulate the inflammatory response. This limitation highlights the need for novel therapeutic approaches. This review aims to evaluate the role of Mycobacterium indicus pranii (MIP) in sepsis management, focusing on its clinical outcomes and therapeutic potential. By examining preclinical and clinical evidence, we seek to understand the efficacy, safety, and practical applications of MIP in treating sepsis. A comprehensive review of existing literature was conducted, including preclinical studies, clinical trials, and case reports involving MIP. The review synthesizes findings related to its mechanism of action, therapeutic efficacy, and safety profile. MIP has demonstrated significant immunomodulatory effects, including enhancing innate and adaptive immune responses and reducing excessive inflammation. Clinical trials have shown promising results, with MIP improving clinical outcomes and reducing sepsis-related complications. The agent's unique ability to modulate the cytokine storm associated with sepsis positions it as a potential adjunctive therapy. MIP offers a novel approach to managing sepsis by addressing immune dysregulation and inflammation. The evidence suggests that MIP could be a valuable adjunct to current treatments, improving patient outcomes and addressing some limitations of conventional therapies. Further research is needed to establish its role in clinical practice and to optimize treatment protocols.

Визначення оптимальних норм надійності космічного апарата з урахуванням економічних показників

The goal of this paper is to develop a method for determining the values of system failure rates optimal in terms of net profit maximization with account for spacecraft mass limitations. It is adopted that the spacecraft systems are independent in terms of reliability, each of them can be only in two states (a functioning state or a failure state), and each equipment type of the special complex makes an independent contribution to the overall effect. The paper considers the case where the spacecraft system failure time obeys the exponential law. Use is made of the Lagrange multiplier method and numerical optimization methods. The problem is solved using the mass ? failure rate and cost ? failure rate relationships of the spacecraft systems. For the supporting complex, the dimension of the mathematical simulation problem is reduced to two: a formula is derived to find the optimal failure rates of all the systems of the supporting complex using the optimal failure rates of two systems of it. The variables for the special complex and the supporting complex are separated. Due to the fact that each special complex system makes an independent contribution to the overall effect, the problem for the whole spacecraft is reduced to a system of two equations for the special complex and the supporting complex and ncn equations in one variable for the special complex with two coupling variables between the supporting complex and the special complex where ncn is the number of the special complex systems. The paper presents a numerical-and-analytical method for spacecraft system failure rate optimization where the initial guess is specified indirectly: by specifying the supporting complex mass and probability of no-failure operation. The method may be used in the development of a space hardware design methodology accounting for economic factors. From the optimal values of the spacecraft system failure rates found using the mass ? failure rate and cost ? failure rate relationships, one can find the masses and costs of the spacecraft systems to be used in optimizing the parameters and development cost of the systems. The method is expected to increase the profitability and competitiveness of spacecraft under development.

Reliability Analysis of Hinged Hydraulic System of Remanufactured EPB Shield Machine

Abstract To verify the reliability of the Earth Pressure Balance (EPB shield machine) hinged system, firstly, this paper analyses the reliability of the remanufactured EPB shield machine hinged system and then aims at the problem of the high failure rate of the hinged system in EPB shield machine. A fault tree model was established, and the qualitative and quantitative analysis of the fault tree was carried out. Finally, it is determined that the main cause of the oil cylinder malfunction in the hinged system is the insufficient pressure caused by the malfunction of the solenoid valve’s directional switching.

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network.

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

Reliability and Residual Life of Cold Standby Systems

In this study, we conduct a reliability characterisation study of cold standby systems. Utilising synthetic rectangular formulas and cold preparedness equivalent models for cold standby systems, we analyse the lifetimes of several typical configurations, including series, parallel, and k/n:m voting systems. This study proposes system equivalent models for various types of cold standby systems, all composed of components that follow the same exponential distribution. We use the equivalent model to determine the optimal timing for the use of cold spares and derive the reliability function and residual lifetime function for each type of system. To demonstrate the validity of our model, the Monte Carlo simulation is strategically designed based on the system failure rate function. The experimental results are then compared with those obtained from the numerical model, highlighting that the numerical method incurs a lower time cost.

Study on the method of transfer alignment based on the distributed inertial network of guided submunition

Aiming at the problem of poor transfer alignment accuracy between missiles with a high length-to-diameter ratio as a carrier missile and the internally carried guided submunition, this study proposes a transfer alignment method based on the distributed inertial network of the guided submunition. Initially, considering the influence of the deflection deformation of the carrier missile body and the dynamic nonlinear deformation of the lever arm on the transfer alignment accuracy, the corresponding error model is established and compensated for the sub-inertial guide velocity to improve the accuracy of the observed quantities. Additionally, in response to the high failure rate of low-cost inertial guidance systems equipped with guided submunition, we used an inertial network data fusion architecture based on the isolation of device failure monitoring, which effectively isolates the faulty data through the fusion of inertial measurements at the sensor level and optimal fusion of filtering to further improve the reliability of transmission alignment. The effectiveness of the method proposed in this paper is verified through a large number of simulation experiments. Compared with the traditional transfer alignment method between a single master inertial guide and a sub inertial guide, the method in the paper has obvious improvement in the transfer alignment accuracy. In addition, this method is not affected by the failure of inertial devices in the distributed inertial network, and can continuously maintain a high transfer alignment accuracy. For this kind of missile-borne distributed inertial network with a sub-node sets layer, it is found after comparison that: the transmission alignment error is basically the same for different slave nodes in the same sub-node sets; between different sub-node sets, the closer to the master inertial guide the transmission alignment error is smaller. This study provides a new solution idea and certain reference value for solving the problem of poor transfer alignment accuracy between missiles with a high length-to-diameter ratio as a carrier missile and the internally carried guided submunition.

The Impact of Climate Change on the Failure of Water Supply Infrastructure: A Bibliometric Analysis of the Current State of Knowledge

With ongoing climate change, new threats appear to the operation of water supply systems (WSSs), which are related to the amount of available drinking water resources, its quality, the operation of existing water supply infrastructure and changes in consumer behavior. The paper presents a bibliometric analysis of the state of knowledge on the impact of climate change on the failure of water supply infrastructure. The bibliometric analysis was performed based on the VOSviewer program. The results of the analysis indicate current research trends in this area around the world and allow the identification of strengths and weaknesses. Most research concerns the identification of factors related to the impact of climate on the failure rate of water distribution systems. A popular research topic was also the prediction of water supply network failures, taking into account the impact of climatic factors. The main research gap is determining the impact of climate change on water quality. The acquired knowledge can be used by water companies, policy-makers and other researchers to plan adaptation strategies to climate change, which pose new challenges for the operation of water supply systems. The conducted bibliometric analysis also allowed for identifying research gaps.

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Abstract The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition.

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

Optimal selection and analysis of microgrid energy system using Markov process

This study investigated the reliability of a microgrid (MG) energy system using the Markov process reliability method. Twenty possible MG configurations based on photovoltaic (PV), wind turbine generator (WTG), biomass generator (BMG), battery bank (BB), diesel generator (DG) and an electric vehicle (EVs) have been considered. This study also selects an optimal configuration based on reliability indices (failure rate, repair time, unavailability, expected energy not supplied (EENS) and loss of load probability (LOLP)), annually load demand, availability of the system and its components. The results show that the hybrid PV/WTG/BMG/BB/EVs/DG microgrid system has the lowest system failure rate (0.01012 /year), repair time (1.1858 hrs), unavailability (0.0120 hrs/year), EENS (4.5 kWh/year) and LOLP (1.36986E-06 /year). This MG configuration has an availability of 99%. The PV array (97.55%) and the changeover switch (99.5%) are the parts of the MG system that are the most reliable. The least stable elements of an MG system are battery (63.64%), diesel generator (80%) and biomass generator (83.89%). PV (62%) and WTG (28% of total) are the main contributors to electricity generation, with the battery, electric vehicles, and diesel generator acting as backup sources. Under different weather and load conditions, the hybrid PV/WTG/BMG/EVs/BB/DG microgrid was found to be a reliable and efficient system for remote areas. This research carries a significant impact for policymakers, government, energy planners, and researchers working on stand-alone microgrid systems as it provides valuable insights into optimizing system configuration for reliability and efficiency.

Design and Development of IoT based Smart System for Monitoring Laboratory Environment

This study was conducted to address the critical issue of the poor safety management system in laboratories, through the design of a smart laboratory management facility based on the Internet of Things (IoT). In this design, three major safety parameters-fire, temperature and carbon (ii) oxide (CO) levels were monitored by appropriate sensors, which transmit data to the microcontroller (Arduino) for interpretation. The Arduino microprocessor processed the data received from the sensor(s), makes decisions based on the predefined algorithms. Based on the decisions made by the Arduino, the microprocessor sends instructions to a relay module triggered the necessary actions to be taken by the output hardware devices-fire extinguisher, air conditioning system and exhaust fan unit. The temperature monitoring system was designed at an operational range of 18°C to 25°C, the CO control unit was designed to maintain the CO concentration inside the laboratory at a level not exceeding 4 parts per million (ppm), as approved by the World Health Organization; while fire control unit was designed to detect the presence of smoke of naked fire inside the building. In the event that any of these parameters breach safety thresholds, the smart structure's safety system will trigger the appropriate responses. The designed structure was built in compliance with international safety standards. Results obtained through the testing and evaluation of the system revealed that the smart system had overall performance efficiency of 91% and false output of 9%. The system's failure rate of 9% can be reduced by employing advanced sensors and adjusting the delay rate. The findings of this study revealed that IoT and automation can successfully monitor and protect the working environment inside laboratories.

Reliability Analysis of a Building Real Fire Simulation Training System

Real fire simulation training systems have gradually become an important method of emergency rescue team training and improvement. However, the failure rate of such systems is high, which threatens the safety of the trainers. Therefore, this study takes a real fire simulation training scenario as the research object and analyzes the system structure of the real fire simulation training base. The system structure of the real fire simulation training base is analyzed and divided into three systems: a smoke and heat training room, a combustion training room, and a water, oil, and gas supply. Then, a reliability model is established, and the reliability is determined. The main structures affecting the reliability of the system are identified, and an optimization plan for improving the structure is proposed. The results show that the combustion training room is the least reliable of the three parts in the real fire simulation training base. The series link in the system structure should be reduced as much as possible to meet the training requirements while the parallel link should be increased, and a reserve system should be added if necessary.

Post ERP implementation issues and challenges: exploratory case studies in the context of Saudi Arabia

PurposeThe purpose of this study is to investigate the post–enterprise resource planning (ERP) issues and challenges in the context of Saudi Arabia. There is a lack of research in the context of developing countries regarding post-ERP implementation issues and challenges. The high failure rate of the ERP system is a reflection of many management issues that occurred at different phases of ERP implementation. Previous research indicated that even after a successful implementation, the ERP system was unable to sustain itself in the organizations.Design/methodology/approachAn exploratory case study design was adopted to proceed with this research process at two organizations in Saudi Arabia. The interviews of the top and middle management are conducted and transcribed. These case studies were further analyzed using the Creswell approach to generate several themes, and descriptions provided a deeper understanding of the post ERP implementation issues and challenges.FindingsResearch findings show that for successful ERP implementation, identified factors are top management support, integration, strategy, employee resistance, BPR, change management, vendor selection, team formation and culture. Moreover, factors for the post-ERP implementation that led to sustainability are top management support, training, system adoption, system testing, data migration, cost overrun, employee retention and post-implementation support.Originality/valueThis study is unique in its type to examine the issues and challenges organizations face after deploying ERP initiatives. This research's findings were useful and supportive for the senior management interested in successfully sustaining such an initiative in the organization.

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

The education industry considers quality to be a crucial factor in its development. Nevertheless, the quality of many institutions is far from perfect, as there is a high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, the literature lacks a reasonable number of systematic reviews that classify research that applied natural language processing and machine learning solutions for students’ sentiment analysis and quality assurance feedback. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in a high-impact journal-indexed database. The work extracted 59 relevant papers from the 3392 initially found using exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in education and the prevalent supervised machine learning algorithms, lexicon-based approaches, and evaluation metrics in assessing feedback in the education domain.

Tolerable random interruption duration based reliability estimation of stand alone hybrid renewable energy system by network reduction and sequential Monte Carlo simulation

AbstractThis paper presents a novel tolerable random interruption duration (TRID) concept for estimation of reliability indices of stand‐alone hybrid renewable energy system (SAHRES) during down time. This concept utilizes the ignorance of interruption duration if it is tolerable in event of failure. It uses a framework using network reduction technique and sequential Monte Carlo simulation (SMCS) suitable for present research whereas the solution in such cases is not feasible with analytical method. Thus reliability indices such as mean up time, mean down time, failure frequency, system failure rate, interruption duration and system unavailability are estimated using SMCS. Modelling aspects are considered for load, capacity, renewable energy system and TRID due to network outage. The impact of considering random tolerable interruption duration has been demonstrated on the reliability indices and case study is presented to show the effect of change of TRID on reliability indices. A sample SAHRES has been considered for the study.

System reliability optimization with two-sided power distributed component failure times

Many systems connected in series are subject to failure earlier in their design life. The failure rate of such systems has been described by a two-sided power distribution, which enables modeling the entirety of the bathtub curve failure modes with a single distribution. We propose a time-dependent reliability optimization model for improving a system’s reliability through the optimal allocation of redundant components whose failure distribution follows a two-sided power distribution. To solve the multi-criteria optimization model, we propose an algorithm that (i) identifies the scale tolerance at which a selected component will have a higher mean residual life, (ii) identifies the level of redundancy for enhancing system reliability, and (iii) identifies other optimal system characteristics, including minimized cost and minimized volume.

Fuzzy DEMATEL-ANP-Based Approach for Determining the CRM Readiness Factors

Research in Customer Relationship Management (CRM) in the healthcare sector is in a developing stage and demands further research to get more in-depth insight. We present a comprehensive model to develop and prioritize CRM readiness factors in hospitals using fuzzy DEMATEL-ANP, which helps top managers allocate their limited resources to enhance the required infrastructure for a CRM system. After extracting proper readiness factors, multiple criteria decision-making (MCDM) techniques are applied to assess CRM readiness. First, using the fuzzy decision-making trial and evaluation laboratory approach (DEMATEL), the interdependent relations amongst criteria are designated. Second, the fuzzy analytic network process (ANP) is applied to weigh the sub-criteria. Top management support and structure are the most critical factors which play an essential role in the CRM readiness concept. The importance of top management’s factor has been investigated in many previous works. The structure has been neglected in the previous studies; however, our results demonstrate that it should be considered a crucial factor. This study’s findings can facilitate the CRM system’s adoption process to be employed by decision-makers within hospitals to mitigate the failure rate of the CRM system’s implementation, leading to providing plenty of advantages to the patient association and hospitals. The results of this paper can also have a contribution to the implementation of CRM and artificial intelligence (AI) as an innovative strategy in organizations, particularly hospitals.