System Failure Rate Research Articles

Reliability evaluation of stand-alone hybrid photovoltaic energy system for rural healthcare centre

This paper presents two methods for evaluation of reliability indices of stand-alone hybrid photovoltaic (PV) energy system. Markov model and frequency-duration (F-D) reliability techniques have been used for evaluation of reliability indices. Hybrid energy system is design and evaluated reliability indices for its configurations (Diesel only, PV/Battery and PV/Diesel/Battery). The system reliability is based on the system failure rate, interruption duration, unavailability, loss of load probability (LOLP), expected energy not supplied (EENS) mean downtime (MDT) indices, which are computed and compared different energy generation system configurations. This paper also presented hybrid energy system reliability analysis is based on monthly solar radiation and load demand data. It is observed that hybrid energy system failure rate, interruption duration, system unavailability, EENS, MDT and LOLP are least as compared to other configuration. The results show that hybrid PV/Battery/Diesel system is more efficient and reliable system as compared to other configurations. This research work will be useful for policymakers and system designers to determine optimal system in remote areas before installation.

Optimization and Reliability Evaluation of Hybrid Solar-Wind Energy Systems for Remote Areas

T his paper presents optimization and reliability analysis of renewable energy system for farm house electricity and irrigation load demand located at remote area. Optimum sizing of solar PV, wind turbine and battery energy system has been obtained to fully utilize the capacity of the system. The economic analysis have been carried out for different configuration of hybrid energy system in terms of net present cost (NPC), operating cost (OC), levelized cost of energy (LCOE). This paper proposes a recent evolutionary technique based on meta-heuristic optimization algorithm called grasshopper optimization algorithm (GOA), to be used for selecting optimal energy system configuration. Markov method has been employed for evaluating the reliability indices i.e. system failure rate, repair rate, availability, unavailability, mean up time (MUT), mean down time (MDT), loss of load probability (LOLP) and expected energy not supplied (EENS). Economic analysis, GOA and Markov method-based result shows that hybrid PV/wind/battery energy system is more reliable and cost effective system as compared to other configuration.Â

Reliability Modeling of PV Systems Based on Time-Varying Failure Rates

The penetration of photovoltaic (PV) systems in power grids has substantially increased since the recognition of renewable energies. In a high solar-integrated network environment, an accurate forecast of the expected solar energy output is vital. One of the important factors that influence such forecast is the failure rates of PV systems. Therefore, a new and realistic reliability model of the PV system is proposed in this study. In contrast to the conventional reliability model, which uses fixed values of failure rates in a year, the proposed model considers various weather conditions, detailed PV system architecture, manufacturing quality and other necessary materials to determine the time-varying failure rates of the PV system. Results reveal that the proposed model produces monthly failure rates that are considerably different from the fixed yearly failure rate in which the difference in high latitude regions is more significant than that in tropical climate regions.

Joint Optimization of Reliability Design and Level of Repair Analysis Considering Time Dependent Failure Rate of Fleet System

Joint Optimization of Reliability Design and Level of Repair Analysis Considering Time Dependent Failure Rate of Fleet System

Solving the Problem of Determining the Maximum Fault Tolerance of Distributed Information Processing Systems with a Limit on Their Reactivity

The use of exponential Queuing networks for calculating the reliability characteristics of distributed in-formation processing systems is proposed. This allows you to naturally tie the reliability characteristics of the system (failure rates) with its technical and operational characteristics (the average response time of the system to user requests). And also reduce the problem to calculating stationary probabilities of Queuing network States. The article formulates and solves the optimization problem of finding the maximum fault tolerance of distributed information processing systems implemented on the basis of a two-level and three-level client-server architecture with a fixed value of the average system response time to user requests. The problem of determining the fault tolerance indicators of distributed information processing systems is reduced to the problem of deter-mining the stationary probabilities of Queuing network States. The article presents the results of numerical ex-periments. The analysis of the obtained results shows the prospects of using these models to obtain estimates of the reliability characteristics distributed information processing systems in various subject areas.

Immunogenic cell death by neoadjuvant oxaliplatin and radiation protects against metastatic failure in high-risk rectal cancer

ObjectiveHigh rates of systemic failure in locally advanced rectal cancer call for a rational use of conventional therapies to foster tumor-defeating immunity.MethodsWe analyzed the high-mobility group box-1 (HMGB1) protein, a measure of immunogenic cell death (ICD), in plasma sampled from 50 patients at the time of diagnosis and following 4 weeks of induction chemotherapy and 5 weeks of sequential chemoradiotherapy, both neoadjuvant modalities containing oxaliplatin. The patients had the residual tumor resected and were followed for long-term outcome.ResultsPatients who met the main study end point—freedom from distant recurrence—showed a significant rise in HMGB1 during the induction chemotherapy and consolidation over the chemoradiotherapy. The higher the ICD increase, the lower was the metastatic failure risk (hazard ratio 0.26, 95% confidence interval 0.11–0.62, P = 0.002). However, patients who received the full-planned oxaliplatin dose of the chemoradiotherapy regimen had poorer metastasis-free survival (P = 0.020) than those who had the oxaliplatin dose reduced to avert breach of the radiation delivery, which is critical to maintain efficient tumor cell kill and in the present case, probably also protected the ongoing radiation-dependent ICD response from systemic oxaliplatin toxicity.ConclusionThe findings indicated that full-dose induction oxaliplatin followed by an adapted oxaliplatin dose that was compliant with full-intensity radiation caused induction and maintenance of ICD and as a result, durable disease-free outcome for a patient population prone to metastatic progression.

An Importance Analysis–Based Weight Evaluation Framework for Identifying Key Components of Multi-Configuration Off-Grid Wind Power Generation Systems under Stochastic Data Inputs

Wind power systems have great potential due to its inexhaustible nature and benign environmental impacts. Especially in remote areas, where wind is plentiful, but it is difficult to get grid-connected power, an off-grid wind power system is an effective alternative for power supply. Reliable and safe operation of the generating system are essential for electricity production and supply. Importance analysis to identify key components of the system is a critical part of reliability assessment. This paper proposes an importance analysis–based weight evaluation framework for identifying key components of multi-configuration off-grid wind power generation systems under stochastic inputs. In the framework, the joint importance analysis based on Birnbaum importance and Criticality importance are introduced to analyze the system reliability and failure rate. Wind speed with stochastic characteristics, load demand with multiple scenarios, and energy transfer with different paths are also merged into the evaluation framework. The results reveal that the rectifier, battery, discharge load, and valve controller dominate the reliability of the off-grid wind power generation system. High priority should be placed on these components during the design phase and maintenance stage. The proposed approach is a positive step forward in promoting component importance analysis and providing more theoretical supports in system design, reliability analysis, and monitoring scheme formulation.

ERP issues and challenges: a research synthesis

PurposeDespite more than two decades of experience regarding the adoption and implementation of enterprise resource planning (ERP) systems in organizations, ERPs success is questionable. Though ERPs success stories are published in past research studies, the failure rate of ERP systems is relatively high. The purpose of this study was to find issues and challenges and assess the degree of criticality of these issues/challenges faced by organizations during ERP implementation.Design/methodology/approachFor doing systematic review/research synthesis systematic literature review (SLR) was carried out considering research studies published within the time period, i.e. 1999-2018. Three major steps such as planning, conducting and reporting were followed to proceed further in this study. This study attempted to accomplish a critical review of 53 studies out of 103 studies identified, which were published in reputable journals to synthesize the existing literature in the ERP domain. The studies selected have almost addressed different challenges/issues faced by small and large organizations during ERP implementation.FindingsResearch synthesis/SLR led to the identification of 31 issues/challenges, which may be termed as most critical based on their occurrence/frequency in past studies included. The topmost ten issues/challenges amongst 31 identified include top management approach, change management, training and development, effective communication, system integration, business process reengineering, consultants/vendors selection, project management, project team formation, team empowerment/skilled people and data conversing/migration. However, other issues/challenges identified such as security risks/data security, cloud awareness, functionality limitations, service level agreements and subscription expenses are more related to cloud ERPs.Originality/valueThe current study is unique in its kind, focusing on the issues and challenges faced by organization during implementing ERP projects. Moreover, this study contributes to understanding and further analyzing management capabilities for developing remedial measures while planning the implementation of an enterprise system in their organizations prior to the occurrence of different issues and challenges ahead. The study also led to understanding and explaining socio-technical issues and their severity.

Analyzing Sanitation Sustainability Assessment Frameworks for Resource-Limited Communities.

Diverse and numerous sanitation sustainability assessment frameworks have been created to enhance the ability of systems to provide safe sanitation services, especially in resource-limited contexts. However, many go unused while new frameworks are developed and high sanitation system failure rates persist. To better support the sustainable development goal around global sanitation, there is a need to better understand how sanitation sustainability is defined and measured and the potential advantages and disadvantages of existing assessment frameworks. A subset of existing sanitation sustainability assessment frameworks was reviewed after applying each to evaluate multiple successful and failed community sanitation systems in India. Overall, the evaluated frameworks did not share a sanitation sustainability definition or core set of essential indicators. Many indicators lacked clear definitions and guidance on data collection and analysis. When evaluating framework effectiveness, differentiations between successful and failed cases varied greatly between frameworks. Potential improvements include indicator pilot testing to verify measurement feasibility and that they provide expected results; context-specific weightings; and project-specific framework selection. Clarifying and improving sanitation sustainability assessment frameworks could increase their effectiveness and use, leading to better decision-making and improved public and environmental health, economic viability, and sanitation use and acceptance.

Inteligentne prognozowanie intensywności uszkodzeń automatycznego systemu ochrony pociągów kolei dużych prędkości w Chinach

Intelligent and personalized dynamic maintenance and spare parts configuration of high-speed railway have been the main trend to guarantee the safety capability of trains. In this paper, a new Automatic Train Protection (ATP) system failure rate calculation method is proposed, and the delay time and embedded dimension are determined by C-C algorithm. Then the phase space is reconstructed from one-dimensional time series to high-dimensional space. Based on chaotic characteristics of failure rate, a short-term intelligent forecasting model of failure rate of ATP system is established. The actual failure statistics from 2010 to 2018 are used as samples to train and test the validity of the model. From prediction results, it shows that the proposed chaos prediction model has an accuracy of 99.71%, which is better than the support vector machine model. Through the intelligent prediction of failure rate, this paper solves the maintenance inflexibility and imbalance of supply and demand of spare parts configuration.

Optimal Computing Budget Allocation for Stochastic N–$k$ Problem in the Power Grid System

The $N$ − $k$ problem is very well known in the power industry and it tries to answer the question whether there exists a set of $k$ lines in a power network with $N$ elements whose removal would cause the failure of the system. In practice, it is common to evaluate a system according to an $N$ −1 criterion, i.e., $k = 1$ . While this problem has traditionally been considered in a deterministic setting, stochastic behavior within the system is important especially in the context of extreme events. A number of stochastic Monte Carlo models have been proposed to estimate the probability of cascading failures. In this paper, we deal with simulation budget allocation of the stochastic $N$ –1 problem. More specifically, we assume that a simulation model is able to provide us an estimate of the system failure rate when any line is tripped. It is not difficult to see how simulation of all configurations to some certain accuracy can become computationally expensive with the growth of $N$ . Under such a setting, we transform the $N$ –1 problem into a stochastic selection process with optimal computing budget allocation (OCBA): given $N$ configurations, we would like to sequentially allocate a certain number of simulation replications in order to answer the question whether the system is reliable or not. We show through theoretical analysis and numerical experiments that the probability of correctly identifying the system reliability state can be increased by applying OCBA allocation rules in the simulation budget allocation process.

Automatic detection of electric meter position in automated assembly line turnover box

Abstract: In the process of metering and automatic production system in the measurement and verification of the loading robot, if there is a positional shift of the electric meter, the gripper of the loading robot will mechanically collide with the electric meter. Due to the large force of the loading robot, the appearance of the electric meter is damaged, and the mechanical arm of the loading robot is broken, which reduces the service life of the measuring and verifying equipment, which is not conducive to the continuous and stable operation of the automatic verification system. In this paper, an automatic detection system of electric meter position in the turnaround box is proposed, which can reduce the failure rate of the automatic verification system, improve the verification efficiency, reduce the loss of metering assets and the occurrence of safety accidents by realizing the offset detection and correction of the electric meter position in the turnover box.

Human reliability analysis and optimization of manufacturing systems through Bayesian networks and human factors experiments: A case study in a flexible intermediate bulk container manufacturing plant

Human reliability analysis (HRA) and optimization in manufacturing systems are effective to reduce system failure. The purpose of this study is to examine the HRA and optimization through a Bayesian network (BN) model and human factors experiments (HFEs). This study was applied to a flexible intermediate bulk container manufacturing plant. The human physiological and psychological factors consisting of personal abilities of flexibility, coordination, memory, and attention were regarded as the only performance shaping factors in this study. With the BN model, the relationship between human factors and human errors was described qualitatively and the impact of the human factor on system failures was judged quantitatively. Then the workers’ abilities training with HFEs based on the fault diagnosis results was carried out. The total numbers of errors have been decreased by 69.06% and the system failure rate has been reduced significantly after training.

Approximate Yield Estimation of Correlated Failure Events for Near-threshold SRAM

In advanced manufacturing technology, SRAM yield estimation with correlation is a significant challenge due to process variations and structures especially at low voltage. In this paper, Double Asymptotic Probability Approximation (DAPA) method with Subset Analytical Model (SAM) is proposed to effectively estimate the probability of correlated failure event. SAM models SRAM failure events based on simulation of subsets of the equivalent circuit. DAPA obtains the recursive function of partial failure rate. The key idea is to approximate the failure rate of the entire system based on a linear combination of partial failure rates by decoupling failure correlation. Under TSMC 28 nm process, (M=N=8) SRAM failure rate is estimated at 500 mV with an error of less than 5% by the proposed method. Besides 2.5 hours of modeling consumption, the calculation can be completed in 7 seconds, which greatly reduces SRAM failure estimation time, compared with Monte Carlo method.

Quality and reliability oriented maintenance for multistage manufacturing systems subject to condition monitoring

For manufacturing systems, product quality and machine reliability are two key health indicators, which usually deteriorate as a result of machine deterioration. Maintenance can mitigate the machine deterioration and consequently improve product quality and machine reliability. In this context, we propose a quality and reliability oriented condition-based maintenance (CBM) policy for the serial multistage manufacturing systems. In the policy, the conditions of quality-related components are monitored to evaluate the quality loss of final products and the system failure rate, which are further compared with the corresponding thresholds to decide whether to trigger preventive maintenance (PM). When PM is triggered, a cost-based improvement factor is introduced to identify the importance ranking of PM groups and then determine a group of machines for PM. This factor is developed based on importance measure and jointly considers the improvement of quality and reliability and the reduction of maintenance costs. A multi-level CBM decision-making process is developed to evaluate the total cost over the planning horizon for decision optimization. The effectiveness and superior performance of the proposed CBM policy is demonstrated through a case study of a serial four-stage machining system producing shaft sleeves.

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

The advertising of any smart product is crucial in generating customer demand, along with reducing sale prices. Naturally, a decrease in price always increases the demand for any smart product. This study introduces a multi-product production process, taking into consideration the advertising- and price-dependent demands of products, where the failure rate of the production system is reduced under the optimum energy consumption. For long-run production systems, unusual energy consumption and machine failures occur frequently, which are reduced in this study. All costs related with the production system are included in the optimum energy costs. The unit production cost is dependent on the production rate of the machine and its failure rate. The aim of this study is to obtain the optimum profit with a reduced failure rate, under the optimum advertising costs and the optimum sale price. The total profit of the model becomes a complex, non-linear function, with respect to the decision variables. For this reason, the model is solved numerically by an iterative method. However, the global optimality is proved numerically, by using the Hessian matrix. The numerical results obtained show that for smart production, the maximum profit always occurs at the optimum values of the decision variables.

Multi-stage models for the failure of complex systems, cascading disasters, and the onset of disease

Complex systems can fail through different routes, often progressing through a series of (rate-limiting) steps and modified by environmental exposures. The onset of disease, cancer in particular, is no different. Multi-stage models provide a simple but very general mathematical framework for studying the failure of complex systems, or equivalently, the onset of disease. They include the Armitage-Doll multi-stage cancer model as a particular case, and have potential to provide new insights into how failures and disease, arise and progress. A method described by E.T. Jaynes is developed to provide an analytical solution for a large class of these models, and highlights connections between the convolution of Laplace transforms, sums of random variables, and Schwinger/Feynman parameterisations. Examples include: exact solutions to the Armitage-Doll model, the sum of Gamma-distributed variables with integer-valued shape parameters, a clonal-growth cancer model, and a model for cascading disasters. Applications and limitations of the approach are discussed in the context of recent cancer research. The model is sufficiently general to be used in many contexts, such as engineering, project management, disease progression, and disaster risk for example, allowing the estimation of failure rates in complex systems and projects. The intended result is a mathematical toolkit for applying multi-stage models to the study of failure rates in complex systems and to the onset of disease, cancer in particular.

Reliability and Availability Models of Belt Drive Systems Considering Failure Dependence

Conventional reliability models of belt drive systems in the failure mode of fatigue are mainly based on the static stress strength interference model and its extended models, which cannot consider dynamic factors in the operational duration and be used for further availability analysis. In this paper, time-dependent reliability models, failure rate models and availability models of belt drive systems are developed based on the system dynamic equations with the dynamic stress and the material property degradation taken into account. In the proposed models, dynamic failure dependence and imperfect maintenance are taken into consideration. Furthermore, the issue of time scale inconsistency between system failure rate and system availability is proposed and addressed in the proposed system availability models. Besides, Monte Carlo simulations are carried out to validate the established models. The results from the proposed models and those from the Monte Carlo simulations show a consistency. Furthermore, the case studies show that the failure dependence, imperfect maintenance and the time scale inconsistency have significant influences on system availability. The independence assumption about the belt drive systems results in underestimations of both reliability and availability. Moreover, the neglect of the time scale inconsistency causes the underestimate of the system availability. Meanwhile, these influences show obvious time-dependent characteristics.

Temporal trends and disease characteristics associated with total neoadjuvant therapy (TNT) usage.

642 Background: Neoadjuvant chemoradiotherapy (nCRT) followed by resection and postoperative multi-agent chemotherapy (maChT) is the standard of care for locally advanced rectal cancer (LARC). Using this approach, maChT administration is delayed several months, leading to concern for high rates of distant failure. To reduce the rate of systemic failure, a novel treatment approach known as total neoadjuvant therapy (TNT) has been increasingly employed, in which patients receive both maChT and nCRT prior to resection. We utilized the National Cancer Database (NCDB) to examine temporal trends in TNT usage, as well, as any potential effect on survival. Methods: We queried the NCDB for patients diagnosed with LARC (Stage II-III) from 2004–2015 treated with nCRT or TNT. TNT was defined as maChT initiated ≥ 90 days prior to the start of nCRT. Overall survival (OS) was calculated from the date of diagnosis to the date of last contact or death using Kaplan Meier curves to present the cumulative probability of survival, with log-rank statistics used to assess statistical significance between groups. Multivariable cox regression was used to identify predictors of survival and propensity score analysis was used to account for indication bias. Results: We identified 9,066 eligible patients, with 8,812 and 254 patients receiving nCRT and TNT, respectively. Nodal involvement and Stage III disease were predictive of TNT use, as well as, treatment in in more recent year. TNT and nCRT had similar 5-year survival, 76% and 78%, respectively. MVA identified age > 58, increased income, urban location, academic treatment facility, and lower co-morbidity score as predictors of worse OS. Multivariable analysis with propensity score demonstrated increased age, higher comorbidity score, higher grade, African American race, and gender as predictive of worse OS. Conclusions: Our data demonstrates a trend toward increasing TNT use, particularly in patients with greater nodal involvement or clinical stage. Despite worse disease characteristics, patients treated with TNT had similar survival to those receiving standard nCRT. Randomized trials are underway to further define the clinical benefit of TNT.

Reliability evaluation of MMC system considering working conditions

The reliability is a key issue of the high-voltage high-power modular multilevel converter (MMC) system due to the usage of fragile insulated gate bipolar transistor (IGBT) modules. In this study, it is proposed that the reliability of the MMC system can be modelled and calculated considering practical working conditions. Firstly, based on the MMC power level, the power loss and the junction temperature are analysed. Then considering the aging process of IGBT, the failure rate is calculated with temperature coefficient. Finally, the MMC system failure rate and its relationships with some key parameters are found.