Constant Failure Rate Research Articles

Sensitivity analysis for joint optimization of reliability design and LOR considering CFR and TDFR approach

This analysis is conducted to test sensitivity of the developed integrated model for reliability and maintenance decisions for fleet system. Sensitivity analysis is an important analysis to justify the model sensitivity considering the different model parameters. The parameters of integrated model are mean time in transportation delay, maintenance, and repair time at depot and at original equipment manufacturer. Sensitivity analysis of integrated model parameters is carried out to establish the minimum fleet maintenance life cycle cost. This analysis uses maintenance equipment cost, transportation cost and consumable cost. In this analysis, the different failure rates of the component approach are used i.e., Constant Failure Rate CFR and Time-dependent Failure Rate (TDFR). Therefore, to calculate the time to failure of the parts, Exponential distribution and Weibull distribution are used, respectively. The result shows that consumables cost, and transportation cost are sensitive for integrated model.

Evidence of the Effectiveness of Cyclic Technical Safety Mechanisms

At the vehicle powernet level, technical safety mechanisms, e.g., smart switching modules, mainly operate in a discrete-time or cyclic manner. In accordance with the functional safety standard ISO 26262, safety-related E/E systems require a safety validation process. Therefore, evidence of the effectiveness of technical safety mechanisms shall be made available. Particularly for cyclic safety mechanisms (cyclic - regarding their fault handling performance), this is an open issue. In this regarding, a novel modeling approach for cyclic safety mechanisms is introduced to provide evidence of their effectiveness. Thus, cyclic diagnoses, e.g., vehicle start-up checks, or new dedicated measures could be utilized in the quantitative validation of residual faults. The novel approach enhances the current state of research in a way that makes the interpretation of a piecewise constant failure rate irrelevant. In addition, this approach is not solely based on the exponential distribution; therefore, it is universally adaptable, and it is demonstrated for the use case of alternating safety mechanisms. This degree of freedom is accompanied by more conservative quantitative results in comparison to those obtained in recent studies. Nevertheless, the current state-of-the-art method totally excludes the evaluation of cyclic safety mechanisms because of a lack of evidence of effectiveness. This elementary concept is based on the derivation, elaboration, and separation of time dependencies in a safety scenario while extracting and evaluating the partial system states with effective continuous behavior. The derived mathematical model and its approximation are based on fundamental statistics and are indicated as the final recommended cyclic approach.

Coordinating an ExtendedWarranty Supply Chain under Increasing, Constant and Decreasing Product Failure Rates

A product typically exhibits three different failure rates across its lifetime: increasing, decreasing, or constant. This paper studies how the characteristics of failure rate impact the supply chain coordination for an extendedwarranty programinvolving a manufacturer and a retailer. Atwo-stage Stackelberg game is utilized to model the interaction between these two players. Two extended warranty channel structures are compared depending on whether the manufacturer or the retailer offers the warranty service. The analysis shows that the failure rate trend during the warranty period has different effects on the coordination of the service supply chain. When a product has an increasing or constant failure rate, the optimal length of extended warranty offered by the retailer is longer than that of the manufacturer, while the optimal length is shorter for a product with a decreasing failure rate. If a product during the warranty coverage has an increasing or constant failure rate, a longer extended warranty period will motivate customers to buy the product without the warranty, whereas more customers will buy both the product and the warranty if the product experiences a decreasing failure rate. It is concluded that, if the manufacturer and the retailer incur the same warranty service cost, the total profit in the supply chain is higher when the manufacturer offers the extended warranty. From the game participants’ perspective, the one which sells the extended warranty will obtain more profit.

Accurate capacity factor calculation of waste-to-energy power plants based on availability analysis and design/off-design performance

The aim of this study is to obtain more realistic estimations of the achievable capacity factor and availability of waste-to-energy power plants to better understand their economics, and hence facilitate appropriate deployment. Towards this aim, we introduce Markov analysis and off-design considerations into the calculation of plant performance. Parameters such as increasing failure rates as a result of aging, infant mortality failures, and overhauls are all taken into account with our approach. Meanwhile, the effect of failures on such plant’s operating mode is evaluated, and Stodola’s cone law is applied to calculate the actual power generation in off-design mode. A system divided into three main sub-components, two steam generation blocks and a power generation block, each of which has its own failure and repair rates, is proposed for this study. The state-space diagram of the proposed system is constructed and simplified, and the transient differential equations governing the state-space diagram are solved numerically. Having obtained the availability of the system and off-design performance, the capacity factor is calculated and compared with actual field data. Also, a new availability-based formulation is presented to obtain the net present value of the cash flow. Results show that by using a time-varying failure rate, the availability of the power plant declines about 23% over 20 years, a significant difference to estimated availability if a constant failure rate was assumed. Our findings highlight that the present price of electricity, $0.057 per kWh, for generation from the plant is insufficient to encourage investment in this technology in Iran at present, and greater deployment will require some form of policy intervention.

Simulation-based joint optimization of fleet system modularity and level of repair decisions considering different failure rates of components

PurposeModularization and level of repair analysis for fleet system influences every phase of the system life cycle. Modular based fleet system design raises new issues since the maintenance/repair services introduces further requirements than traditional product engineering. The decision of modular system and level of repair plays an important role to reduce the Life Cycle Costs (LCC) of fleet maintenance system. The concept of modularity has been extended to services in maintenance for the varieties of fleet systems such as wind turbines, gas turbines, advance machine tools and aircrafts etc. System modularity allows the designers to use of different design alternatives and ease of fault diagnosis, repair and services. The purpose of this paper to develop a joint optimization approach for optimal selection of modular design and level of repair decisions. Usually these two decisions are taken separately.Design/methodology/approachIn the proposed joint approach, level of repair analysis is used to obtain the optimal modular design decisions with reduced life cycle cost. In the existing research, the effect of system modularity on the level of repair decisions is investigated. The simulation-based approach is used to solve this joint problem. Which is rarely seen in the existing literature. A genetic algorithm-based simulation is used to investigate the joint problem. The proposed approach also evaluates all the possible configurations of modular design to justify the integrated effect of modularity and maintenance decisions, that is Level of Repair (LOR).FindingsThis paper highlights interactive effect of system modularity and level of repair decisions for the system operated in multi-echelon maintenance network. A comparative study is provided on effect of system modularity and level of repair decisions considering the time dependent failure rate and constant failure rate of the system components. A simulation based joint approach is used to solve this problem. The results obtained from the investigation are shown that modularity plays an important role to allocate modularity and level of repair decisions for the fleet system. The novelty of this research work is to identify the role of modularization to optimizing the level of repair decisions. The models, that is time-dependent failure rate and constant failure rate presented in this study provides more practical approach to deal the modularity and level of repair analysis.Research limitations/implicationsThe proposed joint approach illustrates using a numerical case of a mechanical system operated at fleet level. More modular structure in terms of number of modules in the machine may be presented for an industrial case. Additionally, the joint approach can also be extended for the any other consumer product and system. But, the prime motive of the paper is to highlights the importance of the modular design while selecting the level of repair decisions.Originality/valueThis is the first work which consider the joint optimization of modular design and level of repair analysis to the best of authors knowledge. Present paper is a more practical approach for identifying the modular design and level of repair decisions for the system operated at fleet level.

A Sensitivity Analysis of a New NHPP Software Reliability Model with the Generalized Exponential Fault Detection Rate Function Considering the Uncertainty of Operating Environments

Early in the development of the software reliability model, it was assumed that the operating environment of the software was used in the same or similar field environment as the test environment. However, they may also be used in many different locations that may differ from the environment in which they were developed and tested. And also, in general, software systems are known to degrade performance and increase failure rate as their service life increases. Therefore, efforts have been made to expand the exponential distribution assuming a constant failure rate. In this paper, we propose a new software reliability model with the generalized exponential (GE) fault detection rate function considering the uncertainty of operation environments based on a non-homogeneous Poisson process (NHPP). We examine the goodness-of-fit of a new NHPP software reliability model and other existing models based on dataset. As a result of comparison of goodness-of-fit, it can be confirmed that the new proposed model is significantly better by showing that all the criteria of the new proposed model are the lowest compared to other models. In addition, the results for the sensitivity analysis show that the parameters of a new NHPP software reliability model affect the mean value function.

Availability-based predictive maintenance scheduling for vibrating-grate biomass boilers

This paper proposes an availability-based maintenance scheduling for a vibrating-grate biomass boiler. The main objective is to minimise the maintenance programme cost while maintaining the system availability at a targeted level. A modified failure density function was employed to deliberate the effect of maintenance on the reliability of the components. The constant failure rate of the components which yields constant hazard functions were converted to time-dependent hazard functions by means of Weibull regression analysis. The updated failure parameters were examined by Kolmogorov Smirnov tests and the results showed a good agreement with the primary data. A critical factor named maintenance impact plays an important role in optimised scheduling and determines which components have a higher influence on system availability. Fault tree analysis method was engaged to estimate system availability. The results showed 32% reduction in the number of maintenance requirements with the help of modified predictive maintenance management.

A Software reliability model with a fault detection rate function of the generalized exponential distribution

The software reliability model is modeled on the basis of the number of software failures and the interval time between failures, and predicts how failures will occur in the future by estimating reliability evaluation measures such as failure interval time, software reliability and failure rate, In general, software systems are known to degrade performance and increase failure rate as their service life increases. Therefore, efforts have been made to expand the exponential distribution assuming a constant failure rate. In this paper, we propose a new software reliability model with a fault detection rate function of the generalized exponential distribution based on a non-homogeneous Poisson process (NHPP). We examine the goodness-of-fit of a new NHPP software reliability model and other existing models based on two data sets. The comparative results for the goodness-of-fit show that the proposed model does significantly better than the existing models.

A study of microprocessor systems using RAMD approach

The aim of the present study is to offer results of reliability, availability, maintainability and dependability analysis of a microprocessor system. A microprocessor is designed with the help of seven subsystems including input, terminal, processor, main memory disk, addressing modes, disk controller and disk drives. Reliability, availability, maintainability and dependability analysis has been performed for all the subsystems. For analysis purpose, state transition diagram has been designed for all subsystems and using Markov birth–death process governing equations has been derived. All random variables are exponentially distributed, i.e., follows constant failure rate. Mean time between failures, mean time to repair, availability, reliability, maintainability and dependability ratio parameters for all the subsystems are computed. The reliability and maintainability are estimated at various time instants.

Availability Equivalence Analysis for the Simulation of Repairable Bridge Network System

The performance of a repairable bridge network system is improved by using the availability equivalence factors. All components for the bridge system have constant failure and repair rates. The system is improved through the use of five methods: reduction, increase, hot duplication, warm duplication, and cold duplication methods. The availability of the original and improved systems is derived. Two types of availability equivalence factors of the system are obtained to compare different system designs. Numerical example to interpret how to utilize the obtained results is provided.

Project Evaluation and Selection with Task Failures

We consider a company that schedules the tasks of its projects to maximize their expected net present value (ENPV) when tasks may fail. The failure of any task terminates the project immediately. We show that for projects with certain decreasing failure rates, the ENPV optimization problem can be solved using a linear program. The main focus of our work is on how constant task failure rates contribute to decreasing project risk as tasks are completed. Under constant task failure rate, earlier completion of a task improves its probability of success and the risk profile of the project. However, it may also accelerate costs, which worsen discounted cash flow. We show the equivalence of cash flow discount rate and failure rate. Further, if task failures are independent, then the failure rates are additive. We develop a model that (a) recognizes the reduction in project risk when a task is completed, (b) implements this risk reduction into the ENPV calculation, and (c) permits optimization of the ENPV through sequencing and timing decisions for the tasks. We design an algorithm to solve the problem optimally. This enables us to validate the contributions of our work using two computational studies. The first study demonstrates a significant increase in maximum project ENPV from improved project scheduling. The second study demonstrates a significant increase in total project portfolio value as a result of better informed project selection. Our work motivates companies to develop more precise information about the failure risks of their project tasks.

Why a run-to-fail strategy is not good practice for SIS?

The Maintenance & Reliability engineers understand pain area that the portion before the “flat” part of the “Bathtub” is where a high number of premature failures can occur: commonly referred to as “infant mortality”. One of the fundamentals of the Probability of Failure on Demand (PFDavg) calculations in SIL verification analysis, is the concept of a constant failure-rate during useful life of the equipment. Probabilistic calculations assume that the failure rate of the devices used in a Safety Instrumented System (SIS) remains constant during the “flat” (constant failure rate) portion of the “bathtub” curve. Most manufacturers will perform stringent testing to weed-out, weaker units that could fail prematurely, leading to unwanted warranty claims. When equipment reaches to flat portion of it’s lifetime called as “wear-out” phase then the failures start to rise dramatically & then the integrity of the Safety Instrumented Functions (SIF) is compromised. During the Operation and Maintenance phase of the SIS, periodic performance assessments need to be made in order to ensure the as-designed, installed and commission SIS and its SIFs, still meet the performance requirements of the Safety Requirement Specification (SRS) [1]. Using a run-to-fail strategy will not enable the SIFs to meet the performance requirements once wear-out occurs. This paper will explain why a run-to-fail strategy is not good practice for a SIS, which will lead to the de-graded performance of the SIFs. This paper addresses both the design and operational impacts of a run-to-fail strategy.

Reliability Assessment of Photoelectric Smoke Detector, Ionization Smoke Detector and a Fire Alarm Control Panel with Both Detectors AS Notification Device

Notification device like smoke detectors are critical and important part of a Fire Alarm Control Panel (FACP). Popularly used smoke alarms in commercial establishments in India are photoelectric smoke alarms (PESD) and ionization smoke alarms (ISD). In this study reliability assessments of PESD with Integrated Circuit (IC) – MC145010 and ISD with IC – MC145017 have been carried out on the basis of reliabilities of their respective electronic components. The cases considered are: (I) Failure rates of all components are equal and constant over time (II) Failure rates of all components are equal and follow Weibull distribution and (III) Failure rates of all components are different. To determine failure rates of 9 volt battery of both detectors additional assumptions taken are, (a) battery life is 10 years with constant failure rate, (b) battery life follows Weibull distribution. In this paper the reliability and failure rate of two types of smoke alarms have been calculated based on failure rates of their electronic parts like resistors, capacitors, Integrated Circuits etc. These failure rates have been subsequently used for reliability assessment of a non-addressable FACP containing four PESDs and four ISDs as notification device. A comparison of failure rates was also performed on the basis of two quality factors of electronic components – military specific and lower than military specific. Mean Time To Failure (MTTF) of PESD and ISD have been calculated in all the cases. Subsequently, MTTF values obtained in case III were used to approximate failure rates for case I case II.

ON USE OF GAMMA DISTRIBUTION FOR EVALUATION OF RELIABILITY AND AVAILABILITY OF A SINGLE UNIT SYSTEM SUBJECT TO ARRIVAL TIME OF THE SERVER

The preference to the use of single unit systems over the redundant systems has been given due to their intrinsic reliability and affordability. And, stochastic modeling of repairable systems of one or more unit has been done by assuming negative exponential distribution for failure and repair times. In fact, the repairable systems may or may not have constant failure and repair rates. In such situations some other distributions possessing monotonic nature of the random variables associated with different time points may be considered. Gamma distribution is one of the distributions that may offer a good fit to some set of failure data. Also, negative exponential distribution is a special case of this distribution. Hence, in this paper reliability and availability of a single unit system by considering Gamma distribution for the random variables associated with failure and repair times of the system have been evaluated. A single server is employed to carry out the repair activities. The server is allowed to take some time to arrive at the system (called arrival time). The system has all the transit points as regenerative and so regenerative point has been used to derive the expressions for reliability measures. The values of reliability and availability are obtained for particular situations of the parameters. The behavior of these measures has been observed for the arbitrary values of the parameters.

Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks

ABSTRACT Flaring is a combustion process commonly used in the oil and gas industry to dispose flammable waste gases. Flare flameout occurs when these gases escape unburnt from the flare tip causing the discharge of flammable and/or toxic vapor clouds. The toxic gases released during this process have the potential to initiate safety hazards and cause serious harm to the ecosystem and human health. Flare flameout could be caused by environmental conditions, equipment failure, and human error. However, to better understand the causes of flare flameout, a rigorous analysis of the behavior of flare systems under failure conditions is required. In this article, we used fault tree analysis (FTA) and the dynamic Bayesian network (DBN) to assess the reliability of flare systems. In this study, we analyzed 40 different combinations of basic events that can cause flare flameout to determine the event with the highest impact on system failure. In the quantitative analysis, we use both constant and time-dependent failure rates of system components. The results show that combining these two approaches allows for robust probabilistic reasoning on flare system reliability, which can help improving the safety and asset integrity of process facilities. The proposed DBN model constitutes a significant step to improve the safety and reliability of flare systems in the oil and gas industry.

Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures

The need to increase the energy efficiency of buildings, as well as the use of local renewable heat sources has caused heat meters to be used not only to calculate the consumed energy, but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyze a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption, and unnecessary maintenance. The reliability analysis of heat meters, based on historical data collected over several years, shows some regularities, which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past, but is a non-linear combination of state variables. To predict meters’ failures in the next billing period, three independent machine learning models are implemented and compared with selected metrics, because even the high performance of a single model (87% true positive for neural network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameter optimization boosts the models’ performance by a few percent. Finally, three improved models are used to build an ensemble classifier, which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilized to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

Performance analysis of redundant safety-instrumented systems subject to degradation and external demands

Safety-instrumented systems (SISs) play a vital role in preventing hazardous events in the offshore facilities. Many of existing performance analysis of SISs are based on the constant failure rate assumption, which is however doubtful when it is applied to actuator sub-systems or mechanical final elements of a SIS. These mechanical SIS components can become vulnerable with time and with upcoming demands given the past exposures to shocks/demands. In this paper, we analyze SIS reliability and unavailability by considering that a failure occurs when total degradation of a SIS component, including continuous degradation and increments caused by random demands, exceeds to a predefined critical threshold. The dependency of two components in a redundant structure of mechanical actuators caused by random demands is also taken into account in the analysis. Approximation formulas for reliability and unavailability of the redundant SIS sub-system under a degradation process are developed. Finally, a numerical example is conducted to illustrate effects of degradation parameters on SIS performance.

PRIORITIZATION OF PIPELINE REPLACEMENT BASED ON FAILURE RATE

The purpose of this paper is to prioritize water pipelines for replacement through the analysis of pipeline failure rates and reliability. By using actual pipe condition record data from Honolulu Board of Water Supply (BWS), the constant failure rate is recognized through Bathtub profile and used to calculate reliability. This paper provides the utility manager a quantitative method to evaluate and arrange pipeline renewal. It assists in determining maintenance schedules depending on the age and type of the pipe by plotting the corresponding failure rate against time. Reliability helps determine whether a certain group of pipes are within the acceptable practice threshold to signal replacement. In this paper, the analysis of four major types of pipe systems from BWS are conducted. With the data from fiscal year (FY) 2008, the failure rates are calculated, and the reliability of all pipelines are seen to be above 80%.

Prediction of Safety Level of Low-Head and Ownerless Hydraulic Structures

Methods of systems reliability theory are applied to the results of an expert inspection of more than 1000 low-head hydraulic power systems in the Moscow region. The rates of failure of homogeneous earth dams are estimated. A state graph of a hydraulic structure, represented as a system that passes through discrete states from a normal to a dangerous level without maintenance or repair, is proposed. The constant failure rates for these states are calculated. The probabilistic characteristics of the safety level of low-head earth dams are predicted.

Stochastic Characterization of Voltage Sag Occurrence Based on Field Data

Computational methods for predicting voltage sags consider their occurrence as a Poisson process, although without confirmation from monitoring data, until now. In this paper, the stochastic nature of voltage sags is analyzed and discussed using monitoring data from 60 sites of the Portuguese Transmission Network, covering the years 2011–2015 (a total of 17 157 recorded voltage sags). A mathematical model to describe the voltage sag occurrence as a stochastic process is presented. The assumption of constant failure rates of the network elements implies that the occurrence of voltage sags is a Poisson process. However, that assumption is not valid if voltage sag clusters are included, as these imply considering time-dependent failure rates. Then, the time between voltage sags is described by an exponential distribution, if clusters are not included, and may be described by the gamma distribution, if including clusters. The boundaries of the adequacy of exponential and gamma distributions are assessed, based on monitoring data. The time of occurrence of monitored voltage sags are analyzed and results confirm that the Poisson process describes the occurrence of voltage sags when voltage sag clusters are disregarded. The gamma distribution fitting is also confirmed when clusters are included in the analysis.