Reliability Modeling Method Research Articles

Mission Reliability Modeling and Analysis Methods for Reconfigurable Ship Electronic Information Systems

With the increasing intensity of ship missions, the mission reliability requirements of various ship systems are getting higher. Especially the new reconfigurable ship electronic information systems, which have the characteristics of resource sharing and reuse, function reconfiguration on demand, software and hardware loose coupling, and business integration, etc., is difficult to be taken into account by traditional reliability design and analysis methods. Therefore, it is imperative to design a reliability modeling and analysis method with careful consideration and strong applicability. Taking the new reconfigurable ship electronic information system as the research subject, this paper firstly adopts the architecture analysis and design language (AADL) to establish a reliability model from three perspectives, namely, system structure, system behavior, and failure impact; then, a model of mission reliability allocation under multiple constraints is developed and solved using the hybrid algorithm of Slime Mode Optimization Differential Evolution (SMA-DE) based on the optimal allocation model of reliability; next, a method for analyzing the mission reliability of ship electronic information systems based on the Goal Oriented (GO) method is proposed, and based on the state probability algorithm therein, a correction algorithm oriented to the co-causal failure modes is derived to realize the quantitative calculation of the mission reliability of ship electronic information systems; finally, an application validation was performed in conjunction with a typical ship electronic information system task. The results show that the method proposed in this study can successfully achieve the mission reliability allocation as well as quantitative analysis of reconfigurable ship electronic information systems under multi-mission requirements.

Reliability modeling of multi-state phased mission systems with random phase durations and dynamic combined phases

Random phase durations and dynamic combined phases challenge the application of existing reliability models in the reliability analysis of multistate-phased mission systems (MS-PMSs). To this end, this paper presents a new reliability modeling method for multi-state phased mission systems with random phase durations and dynamic combined phases. Initially, a multi-state multi-valued decision diagram-based (MMDD-based) reliability modeling method is created to efficiently map random phase durations and the dynamic combined phase nature of MS-PMSs into the reliability model. To solve the MMDD-based reliability model, a path probability evaluation method is subsequently constructed with the assistance of the Markov regenerative function. The effectiveness and the superior performance of the proposed MMDD-based reliability model and its solving algorithm are validated by the application to the reliability modeling and analysis of an attitude and orbit control system with multiple modes. Overall, this paper provides the reliability sector with a new reliability model and its solving algorithm to enhance the reliability and safety of multi-state phased mission systems.

A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning.

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%.

Experimental investigation of collapsible soils under oedometric loading: effect of internal erosion

Collapsible soils, geological formations characterized by pronounced structural instability upon saturation, present a significant challenge in geotechnical engineering. This experimental study aims to deepen our understanding of the mechanisms governing the behavior of these soils, which are particularly prevalent in arid and semi-arid regions subject to extreme hydrological cycles. Collapsible soils, sensitive to climatic variability, undergo profound alterations in their hydromechanical properties due to alternating periods of prolonged drought and intense rainfall events. These fluctuations induce significant modifications in the soil's pore structure, promoting the development of internal erosion phenomena such as suffusion. The results obtained from this experimental investigation on reconstituted samples highlight the combined influence of various factors, such as water content, dry density, particle size, and flow rate, on the collapse potential. It appears that fine particles play a predominant role in the formation of fragile structures, susceptible to collapse upon saturation. Moreover, the intensity and duration of infiltration significantly influence the propagation velocity of ultrasonic waves, thus offering a promising tool for monitoring the evolution of damage within the soil mass. Furthermore, this study provides essential insights for assessing the risks associated with collapsible soils and contributes to the development of more reliable characterization and modeling methods. It emphasizes the need to consider the complex interactions between the physical, chemical, and mechanical properties of the soil, as well as the influence of environmental conditions, for better control of the risks associated with these materials.

Research on Equipment Reliability Modeling and Periodic Maintenance Strategies in Dynamic Environment

Equipment along the railway has different degradation paths and reliability in different environments. It is of great significance to study the reliability and maintenance strategy of equipment in dynamic environment to ensure the safety of train operation. In this paper, a method of equipment reliability modeling and periodic maintenance decision-making in dynamic environment is proposed. A reliability model considering the degradation and impact of environmental changes and considering the characteristics of the equipment is established. A new concept of maintenance cycle is defined, and a decision model of equipment periodic maintenance is established. Finally, through numerical simulation, it is concluded that under severe environment, the maintenance time is shortened by 29508h and 31639h respectively, and the maintenance cost is increased by 41.4% and 28.2% respectively. Furthermore, the change trend and adjustment scheme of the strategy under different operating environments are analyzed, which verifies the correctness and effectiveness of the model in this paper.

A modular model for reliability analysis model of PMS with multiple K/N subsystems and mixed shocks

AbstractIn this article, a modular model is proposed for the reliability modeling of phased mission systems (PMSs) with complicated system behaviors. By the modular method, the system is divided into several levels: system, subsystem, and components. At the component level, the shock effect, including self‐degradation, additional wear and damage caused by shocks, is considered and the component reliability is evaluated. Then, the reliability modeling method of the K/N system consisting of multiple K/N subsystems is proposed, by a mathematics reasoning method. The correctness of the proposed method is also verified by a Monte Carlo (MC) simulation procedure. At last, the modular method is applied at the system level, and the reliability of a practical engineering case, the attitude and orbit control system (AOCS), is evaluated for illustration. Meanwhile, the parameter sensitivity analysis is also carried out for implementation.

Belief Reliability Modeling Method for Wind Farms Considering Two-Directional Rotor Equivalent Wind Speed

Compared to conventional energy sources, wind power is a clean energy source with high intermittence and uncertainty. As a system that converts wind energy into electricity, wind farms inevitably face severe reliability issues. In this paper, based on reliability theory, a new reliability modeling method for wind farms is proposed. Firstly, a belief reliability model for wind farms is constructed. Then, a power generation model based on two-directional rotor equivalent wind speed is established to represent the wind farm performance in the belief reliability model. Finally, several numerical studies are conducted to verify the power generation model under different wind speeds and directions, to demonstrate the belief reliability model with different levels of uncertainty, and to compare the belief reliability considering two-directional rotor equivalent wind speed with other methods.

Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process

AbstractEstablishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.

Reliability-Based Preventive Maintenance Strategy for Subsea Control System

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the complex interplay between components under variable loads, improving model accuracy. This study also examines the effects of imperfect maintenance on degradation paths and introduces a holistic lifecycle cost model for preventive maintenance (PM), optimized against reliability and economic considerations. Numerical simulations on a Subsea Control Module demonstrate the effectiveness of the developed models.

Reliability assessment of man‐machine systems subject to probabilistic common cause errors

AbstractThe occurrence of probabilistic common cause errors (PCCEs) in man‐machine systems can lead to multiple human errors being affected by common causes and will contribute greatly to the reliability of the system. In this paper, A reliability modeling method based on event tree (ET)‐fault tree (FT) model for man‐machine systems subjected to PCCEs is proposed. Under the influence of PCCEs, risk factors in the system are not independent, and human errors affected by the same common cause will occur with different probabilities. To describe the dependencies among risk factors, a PCCE gate is proposed to extend FTs in the ET‐FT model. To analyze the impact of common causes on the human error probabilities and quantify the extended FT, an explicit method and an implicit method based on the Cognitive Reliability and Error Analysis Method are proposed. The proposed quantification methods consider the different relationships among multiple common causes in a single model. Finally, the proposed methods are validated in the reliability assessment of the emergency response system under malfunction of the solar wing mechanism.

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Animal models of brain and spinal cord metastases of NSCLC established using a brain stereotactic instrument

ObjectiveAnimal models of brain and spinal cord metastases of non-small cell lung cancer were established through the intracranial injection of PC-9 Luc cells with a brain stereotaxic device. This method provides a reliable modeling method for studying brain and spinal cord metastases of non-small cell lung cancer. MethodsPC-9 Luc cells at logarithmic growth stage were injected into the skulls of 5-week-old BALB/c nude mice at different cell volumes (30 × 104, 80 × 104) and different locations (using anterior fontanel as a location point, 1 mm from the coronal suture, and 1.5 mm from the sagittal suture on the right upper and right lower side of the skull). After 1 week of cell inoculation, fluorescence signals of tumor cells in the brain and spinal were detected using the IVIS Xenogen Imaging system. After 4 weeks, brain and spinal tissues from the nude mice were harvested. Following paraffin-embedded sectioning, HE staining was performed on the tissues. ResultsThe fluorescence signals revealed that both brain and spinal cord metastasis occurred in the mice where the cells were injected at the lower right side of the skull. There was only brain metastasis in the nude mice injected with 30 × 104 cells at the upper right side of the skull. Both brain and spinal cord metastasis occurred in the nude mice injected with 80 × 104 cells. The HE staining revealed that both brain and spinal cord metastasis occurred in the mice injected with different amounts of PC-9 Luc cells, consistent with the results detected using the IVIS Xenogen Imaging system, thereby demonstrating the reliability of detecting fluorescent signals in vivo to determine tumor growth. ConclusionIt is a reliable method to establish the animal model of brain and spinal cord metastases of non-small cell lung cancer by injecting different quantities of cells from different positions with a brain stereotaxic device. The IVIS Xenogen Imaging system has high reliability in detecting the fluorescence signals of brain and spinal cord metastatic tumors.

An Open-Source Software Reliability Model Considering Learning Factors and Stochastically Introduced Faults

In recent years, software development models have undergone changes. In order to meet user needs and functional changes, open-source software continuously improves its software quality through successive releases. Due to the iterative development process of open-source software, open-source software testing also requires continuous learning to understand the changes in the software. Therefore, the fault detection process of open-source software involves a learning process. Additionally, the complexity and uncertainty of the open-source software development process also lead to stochastically introduced faults when troubleshooting in the open-source software debugging process. Considering the phenomenon of learning factors and the random introduction of faults during the testing process of open-source software, this paper proposes a reliability modeling method for open-source software that considers learning factors and the random introduction of faults. Least square estimation and maximal likelihood estimation are used to determine the model parameters. Four fault data sets from Apache open-source software projects are used to compare the model performances. Experimental results indicate that the proposed model is superior to other models. The proposed model can accurately predict the number of remaining faults in the open-source software and be used for actual open-source software reliability evaluation.

Reactor reliability modeling and reliable life analysis method for multi-state space reactor systems based on DBN and interval estimation

Space nuclear reactor power (space reactor) is of the highest energy density and the most promising long-term self-sustaining and high-power level space power source. Achieving high reliability during the years-long maintenance-free service process is one of the bottlenecks that constrain the application of space reactors, which brings a challenging task on the quantitative evaluation of reliability indicators (life and reliability) of space reactors during the design phase. On the one hand, traditional binary reliability theory cannot model the system functional structure accurately, due to diverse failure modes and complex propagation paths; on the other hand, the lack of reliability data introduces large epistemic uncertainty, resulting from high costs and time-consuming ground reliability tests. To tackle these problems, this paper takes the Stirling integrated space reactor (ACMIR) as a specific research object and proposes a general reactor reliability modeling and reliable life analysis method for space reactors with large prior epistemic uncertainty which is robust to manipulation and can obtain system reliability indicators with small confidence intervals for engineering decisions. First, based on experts’ experience, the functional logic relationship between components and system is sorted out by the failure mode and effect analysis (FMEA) and the prior failure rates of components are evaluated according to the failure grade of the industrial standard. Then, the FMEA model is mapped to a discrete-time Bayesian network (DBN) to model the system reliable life, based on which system reliability indicators with low epistemic uncertainty are analyzed by interval estimation. Finally, the practical and engineering value of the proposed method is demonstrated with the example of ACMIR in two application cases.

Mission Reliability Modeling of Multistation Manufacturing Systems Considering Cascading Functional Failure

The core function of the manufacturing system is to output high-quality products at a stable production rhythm. However, there are complex interactions between physical and functional failures of machines in the station. Functional failures can be propagated to other stations through material flow, resulting in the increasing risk of system performance degradation, which deserves further study. Therefore, in this article, a mission reliability modeling method considering the cascading functional failure of multistation manufacturing systems is presented. First, from the perspective of functional output, the cascading failure mechanism of a manufacturing system is discussed, and the connotation of functional failure and mission reliability is proposed. Second, the interaction model between machine performance and product quality is established to analyze the production rhythm considering the imperfect inspection. Third, the cascading functional failure model is constructed based on the mechanism of failure propagation. The integrated mission reliability modeling method of a multistation manufacturing system is proposed. Finally, a case study of a ferrite phase-shifting unit manufacturing system is conducted to illustrate the effectiveness and advancement of the proposed method.

Integrated mission reliability modeling for multistate manufacturing systems considering heterogeneous feedstocks based on extended stochastic flow manufacturing network

Mission reliability is a crucial health indicator for systematically characterizing the operational state and degradation process of manufacturing systems. However, the impact of heterogeneous feedstocks (qualified feedstocks and unqualified feedstocks) on processing machine degradation and the reliability of buffers have not received much attention in the existing research on mission reliability modeling of manufacturing systems. Accordingly, this study proposes a novel integrated mission reliability modeling method for multistate manufacturing systems considering heterogeneous feedstocks. Specifically, based on the operational mechanism of a multistate manufacturing system considering heterogeneous feedstocks and the connotation of the integrated mission reliability of the manufacturing system, an extended stochastic flow manufacturing network (ESFMN) model is constructed to quantify the interaction among processing machines, inspection machines, buffers, and heterogeneous feedstocks. By analyzing the mixture degradation of the processing machine under the impact of heterogeneous feedstock, an integrated mission reliability model for a multistate manufacturing system considering heterogeneous feedstocks based on ESFMN is established. Finally, an industrial example of the valve spool manufacturing system is given to illustrate the effectiveness and superiority of the proposed method.

System reliability-based robust design of deep foundation pit considering multiple failure modes

Recently, reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical engineering. However, for deep foundation pit, evaluating the system safety of retaining structures and finding cost-effective design points are main challenges. To address this, this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou, China. The proposed method included two parts: system reliability model and robust design method. Back Propagation Neural Network (BPNN) is used to fit limit state functions and conduct efficient reliability analysis. The common source random variable (CSRV) model are used to evaluate correlation between failure modes and determine the system reliability. Furthermore, based on the system reliability model, a robust design method is developed. This method aims to find cost-effective design points. To solve this problem, the third generation non-dominated genetic algorithm (NSGA-III) is adopted. The efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III algorithm. The proposed method has a good performance in locating the balanced design point between safety and construction cost. Moreover, the proposed method can provide design points with reasonable stiffness distribution.

Reliability analysis of reusable turbine rotor blisk: An application of parametric modelling method under multi-field coupling

As a critical component of reusable rocket engines (RRE), the cyclic life of the turbine rotor blisk often varies due to uncertainties such as machining errors and other factors. However, considering the difficulty of structural reconstruction and the large number of parameters related to the blade configuration, few studies have considered the blade configuration variation due to machining errors in the reliability analysis process. To investigate the influence of factors in reliability analysis, a reliability analysis framework is established by combining parametric modelling methods with finite element analysis methods under fluid-thermal-solid sequential coupling. The influence of blade lattice changes due to machining errors on the reliability of turbine rotor blisks in reusable rocket engines (RRE) is investigated. The effects of machining errors, material, and load variability on the cyclic life reliability of rotor blisks are effectively quantified. With the suggested method, a reliability analysis is conducted for an RRE turbine rotor blisk, and the effect of the machining error range on the analysis results is investigated. It is found that the tolerance level to meet the high reliability requirements is at least medium for the basic cyclic life requirements of the model. In terms of cyclic life reliability, the more influential random factors are blade axial chord, low-cycle fatigue plasticity coefficient, and low-cycle fatigue ductility coefficient, with sensitivity factors of 29.09%, 24.4% and 22.13%, respectively. This study provides an efficient and reliable modelling method for the reliability analysis of reusable turbine rotor blisks based on machining errors, investigates the influence of the range of machining errors, quantifies the ability of the random factors to influence, and provides valuable insights and recommendations for the reliability design and optimization of reusable turbine blisks.

System-level creep-fatigue reliability evaluation by engineering damage mechanics incorporating cumulative damage-damage threshold interference

Reliability assessment for structural system is of great significance in ensuring system safety, providing a scientific guidance for life management. In this work, a creep-fatigue reliability modeling method is developed for system-level perspective on the basis of cumulative damage-damage threshold interference. Probabilistic damage accumulations to given loading cycles are associated with the creep-fatigue models considering multi-source uncertainties and the damage threshold is regarded as a random variable. Particularly, a numerical procedure is proposed for practical application in engineering by combining the Monte Carlo simulation with Gaussian process regression-based surrogate model. Furthermore, a low-pressure turbine disk under creep-fatigue loading condition is taken as an instance to evaluate its reliability considering multiple weakness sites. Results show that the proposed method is better than the independent treatment because it is capable of avoiding the overly-conservative reliability estimation. In addition, the influence of damage summation rules and damage threshold on reliability is quantitatively analyzed in terms of the developed method. The effect of random parameters in structural system on computational efficiency of the proposed numerical procedure is investigated.

A Predictive Damage-Tolerant Approach for Fatigue Life Estimation of Additive Manufactured Metal Materials

Metal Additive Manufacturing (AM) allows the fabrication of intricate shaped parts that cannot be produced with conventional manufacturing techniques. Despite the advantages of this novel manufacturing technology, the main drawback is the inferior fatigue performance of AM metal materials and parts due to the presence of process-induced defects that act as initial cracks. Reliable fatigue modeling methods that can assist the design and characterization of AM components must be developed. In this work, a computational damage-tolerance framework for the fatigue analysis of the AM metals and parts is presented. First, thermal modeling of the AM process for the part fabrication is performed to predict the susceptible areas for defect formation in the parts. From the processing of results, the characteristics of the critical defect are determined and used as input in a fracture mechanics-based model for the prediction of fatigue life of AM metals and parts. For validation purposes, the framework is utilized for the fatigue modeling and analysis of AM Ti-6Al-4V and 316L SS metals of relative experimental test cases found in the literature. The predicted results exhibit good correlation with the available experimental data, demonstrating the predictive capability of the modeling procedure.