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In response to the issue of neglecting the inherent differences in samples in the traditional degradation analysis of long-life aerospace products, a degradation modeling method combining a frailty model is proposed to quantify the unobservable heterogeneity in random degradation processes. Firstly, the frailty term is described using a generalized inverse Gaussian distribution to more comprehensively capture random effects. Secondly, an inverse Gaussian degradation model is established, combined with the frailty model to consider the unobservable heterogeneity in practical use. Through maximum likelihood estimation, parameter estimation is carried out, and Bayesian theory is used to infer an independent frailty term in order to quantify differences between individual products. Finally, reliability analysis is conducted on a core chamber cooling control valve of a certain aerospace engine. The results indicate that neglecting the unobservable heterogeneity will lead to overly ideal reliability estimates, and quantifying random effects reasonably can make the model’s estimation closer to the actual situation.

The accurate modeling of multi-temporal correlation of photovoltaic stations output is important to achieve the precise power system reliability. However, the existing studies is mainly focused on the hourly temporal correlation between adjacent interval outputs of photovoltaic station. In this paper, a novel multi-temporal correlation modeling method for photovoltaic station output is proposed by using an improved nonparametric disaggregation (INPD) technique, and the multi-temporal characteristic representing the interactive correlation between the total daily output and hourly temporal outputs are deeply explored. Moreover, a three-stage sampling method for the INPD based power system reliability evaluation is further presented considering the multi-temporal correlation of photovoltaic stations output, which includes 1) the random sampling of daily photovoltaic station output; 2) the random disaggregation of daily output to the hourly temporal outputs and 3) the random sampling of power device. Finally, several numerical tests are conducted for a modified IEEE-RTS79 to validate the proposed method.

To overcome the slow-running drawback of the Monte Carlo simulation method for bulk power system reliability assessment, this paper develops an end-to-end machine learning approach to directly predict the targeted reliability index considering grid topology changes caused by emergent line maintenance. Three machine learning models, i.e., Support Vector Machine, Boosting Trees, and Graph Neural Network, are considered and compared. The grid topology information is embedded into the above models via two feature engineering schemes. Dataset creation and data preprocessing are also described. Then, two case studies with different experimental settings and prediction targets are performed on the IEEE RTS-79 system to inspect the proposed approach’s adaptability. Results demonstrate the proposed approach’s effectiveness and speed advantage. Finally, an analysis is presented regarding the Support Vector Machine’s generalizability against the varying dataset size based on the empirical-risk theory from the machine learning community.

The stability control device is an important part of the safety and stability control system of the power system. The stability control devices cooperate through certain control logic, so that the stability control system can realize the functions of prevention control, emergency control, and recovery control, to guarantee the safe and stable operation of the power system. The existing reliability assessment methods of the stability control devices mainly rely on the analysis of the failure principle of the devices and the statistics of the historical operation data, which cannot make an effective prediction of the changing trend of the failure rate of the stability control devices. In this paper, we propose a Weibull fault rate fitting-based reliability assessment method for power grid control devices, which is based on a function fitting method to fit the parameters of the fault rate function of the control devices and use the obtained parameters to calculate the reliability evaluation index of the control devices. The method has important engineering significance for the production and deployment of stabilization control devices and the design improvement of grid stabilization control systems.

The generation and transmission capacities of many power systems in the world are significantly increasing due to the escalating global electricity demand. Therefore, the adequacy evaluation of power systems has become a computationally challenging and time-consuming task. Recently, population-based intelligent search methods such as Genetic Algorithms (GAs) and Binary Particle Swarm Optimization (BPSO) have been successfully employed for evaluating the adequacy of power generation systems. In this work, the authors propose a novel Evolutionary Swarm Algorithm (ESA) for the adequacy evaluation of composite generation and transmission systems. The random search guiding mechanism of the ESA is based on the underlying philosophies of GAs and BPSO. The main objective of the ESA is to find out the most probable system failure states that significantly affect the adequacy of composite systems. The identified system failure states can be directly used to estimate the system adequacy indices. The proposed ESA-based framework is used to evaluate the adequacy of the IEEE Reliability Test System (RTS). The estimated annualized and annual adequacy indices such as Probability of Load Curtailments (PLC), Expected Duration of Load Curtailments (EDLC), Expected Energy Not Supplied (EENS) and Expected Frequency of Load Curtailments (EFLC) are compared with those obtained using Sequential Monte Carlo Simulation (SMCS), GA and BPSO. The results show that the accuracy, computational efficiency, convergence characteristics, and precision of the ESA outperform those of GA and BPSO. Moreover, compared to SMCS, the ESA can estimate the adequacy indices in a more time-efficient manner.

In this paper, a method of assessing and improving the reliability of power distribution systems based on Monte Carlo simulation and a novel risk priority index is proposed. The initialization of the assessment process is carried out by using Multinomial Monte Carlo simulation with a nonsequential technique to assess system reliability in the form of SAIFI and SAIDI indices. Then, the novel per-time-based component reliability indices representing the insights obtained from root-cause analysis for each component in the system are evaluated to make suitable decisions on improvement measures. The proposed indices are derived as a component risk priority index based on the principle of the failure mode and an effect analysis to prioritize and select the implementation points by the Pareto principle. By applying the proposed method, a reliability improvement should be achieved at the correct point with minimal operations. In addition, the proposed method can be used to study the effect of uncertainty regarding some device operations on the system reliability. To verify the performance of the proposed method and demonstrate its application, three case studies were performed on the IEEE RBTS Bus-2 test system. From the first case study, the results of the proposed assessment process were validated by comparison with a standard benchmark. The second case study showed the performance of applying the entire process to improve system reliability, and the results showed that system reliability can be improved significantly. The third case study was performed to determine the effect of uncertainty in protective device operations. The results of the third case showed that there was a significant decrease in overall reliability in terms of a higher level of power outages, while the performance of the protective components was slightly reduced.

Transmission congestion identification and expansion planning methods for power systems with wind power are proposed in this paper. First, wind farm model is established based on Copula theory considering the uncertainty and correlation of wind speed and wind turbine’s failure. Next, generation rescheduling model with wind farm and TCSC is established and a transmission congestion identification method based on reliability evaluation is proposed. Then, alternative schemes of expansion planning have been proposed according to the results of congestion identification, and the final scheme is determined by probabilistic economic analysis method. Finally, case studies have been carried out on modified RBTS test system. For the mRBTS system, when wind farms are integrated into system from bus 1 and 2, the best expansion solution is to build new transmission line; while when wind farms are integrated from bus 3 and 5, the best expansion solution is to install TCSC. Results of case studies verify the effectiveness of the proposed congestion identification and expansion planning methods of transmission system, and reveal that the location of wind power affects the decision making of transmission system expansion planning scheme.

The energy interactions and uncertain factors of integrated energy systems (IES) have brought risks to the reliable energy supply. A large number of states need to be analyzed to obtain a stable reliability value. However, different operating characteristics complicate the optimal energy flow (OEF) model, which brings tremendous computational cost. To address that, a deep-learning-based approach is proposed as an alternative way to solve the OEF problems. This approach constructs the mapping between system state and energy allocation to directly obtain the optimal load curtailment. Thereafter, the deep-learning-based reliability assessment framework for IES is proposed to improve efficiency. Additionally, the Gaussian noise and data-processing strategies are involved to achieve higher accuracy. Compared to the model-based approach, the proposed method increases the reliability assessment efficiency by 6 orders of time. With an accuracy of over 95%, it outperforms other autoencoder and random forest methods. Method accuracy has remained above 90% in various scenarios.

The generalized renewal process considers repair efficiency of imperfect repair in reliability assessment of repairable systems; therefore, its evaluation results are close to real repair circumstance than the ordinary renewal process or the non-homogeneous Poisson process. Based on the asymptotic distribution of maximum likelihood estimation, a calculating method of reliability indices for repairable systems with imperfect repair is proposed. The point and approximate interval estimators of model parameters for Kijima type Weibull generalized renewal processes Models I and II, as well as reliability indices of repairable systems, such as reliability, cumulative failure number and failure intensity, etc., are all presented. Two real cases are studied using generalized renewal processes Models I and II respectively to show the validity of our method. The results show that imperfect repair makes the instantaneous failure intensity of a repairable system discretely jump either up or down at the time of each failure, and the method proposed in this paper agrees well with the other exiting methods, and can also reduce the complexity of calculation.

Reliable external power supplies guarantee the safe and reliable operation of electrified rail-ways. Once the external power system fails, it disrupts the normal operation of trains and, especially for railways supplied by weak external power sources, may well incur serious accidents. Therefore, this paper proposes a reliability evaluation method of external power supplies of electrified railways with weak external power sources. Firstly, a weak external power system of electrified railways is modelled. Then, system operational states are analyzed by integrating Monte Carlo simulation with the DC optimal power flow (OPF) algorithm considering system node weight coefficients. Finally, reliability evaluation metrics are defined to quantify the reliability of both power system nodes and traction substations. A case study of a typical weak external power system supplying the electrified railways is presented to demonstrate the proposed method. Results show that the method can effectively quantify the reliability of the external power supply of electrified railways. Meanwhile, the DC OPF algorithm considering weight coefficients can significantly improve the reliability of critical loads, and thus improving the reliability of traction substations and electrical trains. The proposed method can provide valuable guidelines for the planning and designing of electrified railways supplied by weak power sources.

Technological innovation in modern systems has significantly improved their performance. However, fault characteristics such as epistemic uncertainty and dynamic failure modes often occur when these systems break down, which greatly raises some new challenges in fault diagnosis. A new fault diagnosis strategy for complex systems is presented based on multi-source heterogeneous information considering epistemic uncertainty in this paper. Specifically, in view of the epistemic uncertainty, the failure distribution parameters of basic events are described with interval numbers and test cost of these events are evaluated using domain experts and intuitionistic fuzzy linguistic set; Aiming at the problem of dynamic failure modes, a dynamic fault tree (DFT) is used to establish the dynamic failure model and is converted into a dynamic evidential network to calculate some reliability parameters; Furthermore, a diagnostic decision table is constructed based on multi-attribute heterogeneous information such as test cost and some reliability results; Finally, a novel fault diagnosis strategy is designed based on distance-based VIKOR algorithm, which can provide some decision support for fault diagnosis and locate the fault as quickly as possible.

Owing to spatiotemporal flexibility of electrical vehicles (EVs), EVs will introduce new synergies between the urban traffic system (UTS) and power distribution system (PDS). As a mobile charging load, arranged EV charging can not only provide more flexibility for power system optimization, but also influence the traveling time and other mobility-related characteristics of EVs. However, there are few studies that consider the impact of the coupling facility (such as parking lot with charging facilities) on UTS reliability, as well as the reliability of UTS and PDS (referred to as PTS). Therefore, it is necessary to extend the original research scope to incorporate the traffic system simulation into the collaborative analysis of the coordinated PTS. This paper first develops a new simulation framework for the coordinated UTS and PDS coupled with commercial charging lots (CLs). Then, a new method is illustrated for measuring the reliability of a coordinated UTS and PDS. At last, a novel unified PTS reliability index considering both UTS and PDS reliability indices are presented.

The integration of large-scale renewable energy significantly increases the computation time of reliability assessment and tracing. To solve this problem, the traditional methods cluster the scenarios directly based on renewable energy data. However, it could lead to errors in reliability assessment due to scenarios with similar risks. In this paper, a multi-scenario risk-oriented clustering algorithm considering renewable energy is proposed. The enumeration method is used to calculate the risk for different scenarios. According to the risk of each scenario, the Fuzzy C-means clustering method is adopted to cluster the scenarios, which maximizes the similarity of scenarios in the same cluster. The high-risk scenarios that contribute more to the reliability index are retained. The system reliability assessment and tracing are conducted based on the clustered scenarios. Case studies on the IEEE RBTS-6 and RTS-79 systems verify the accuracy and efficiency of the proposed method.

To address the problem that high-reliability and long-life complex products are susceptible to coupled fault mechanisms, and to address the challenge of constructing a macro fault behavior model for reliability evaluation, the reliability of a smart meter based on a Phase-Type (PH) distribution under a degradation-shock load is examined. This reliability assessment method considers the impacts of environmental stress and lightning shock on a smart meter. The performance degradation process caused by environmental stress is described by a Wiener model, which is converted into a representation of a PH distribution in a subsequent step. A lightning impulse can cause performance degradation or direct failure of a smart meter. The arrival time of the lightning, the magnitude of the lightning, and the damage caused by the lightning are analyzed separately, and then, the obtained model is expressed in the form of a PH distribution. Finally, a reliability evaluation model of a smart meter based on the PH model under a degradation-shock load is established. The accuracy of the model is verified by comparing the model results with Monte Carlo simulation results.

Cyber-attacks on a cyber-physical power system could lead to significant data failure, false data injection and cascading failure of physical power system components. This paper proposes an advanced approach based on a ternary Markovian model of cyber-physical components interactions to capture the subsystem layers' interactions of the cyber-physical power system and to quantify the interdependency impacts on physical power system security. The approach models cyber-physical interactive operation based on interactions and characteristics of three subsystem layers of the system with the presence of random and unforeseen contingencies, load demand variations and then quantify the impacts with Monte Carlo simulation. The viability of the approach is investigated by simulating a set of scenarios, representing realistic physical power system operating conditions with the cyber network interactions. Findings justify the presence of cyber-attacks in a cyber-physical power system components operation could lead to severe insecurities. However, the impacts on physical power system security does not always correlate with the severity of cyber-attacks.

Reliability engineering plays an important role in the design, manufacture, maintenance, and replacement of industrial products. Over the last few decades, accelerated degradation testing (ADT) has been largely utilized to shorten test durations, reduce the samples needed, and provide sufficient degradation data to ensure the effective reliability assessment of the concerned products. Meanwhile, performance degradation modeling has been recognized as an essential approach to help researchers and producers understand the health conditions of the deteriorating systems. However, the diversity in reliability tests, degradation models, and statistical analysis techniques has increased the difficulty in selecting appropriate reliability assessment methods in specific scenarios. Besides, there are no systematic reviews focused on modeling and analysis of performance degradation data. Therefore, this paper aims to (1) present ADT fundamentals, including the basic theory, ADT methods, accelerated stress variables, type of acceleration models, as well as ADT optimization, (2) comprehensively review current states and future challenges in degradation modeling, (3) discuss the problem of model mis-specification and compare different approaches for parameter estimation, (4) highlight future opportunities and possible directions deserving further research.

Integration of non-conventional renewables such as wind and solar to the power system may affect the system reliability, especially when the proportion of renewable power in the system is large. Therefore, with a significant level of renewable penetration, the intermittency and both diurnal and seasonal variations of renewable power generation should be deliberately modeled in order to accurately quantify the power system reliability. This paper presents a novel method based on Kernel Density Estimation (KDE) for modeling intermittency and both diurnal and seasonal variations of wind and solar power generation using historical renewable power generation data. The proposed KDE based renewable power models are used with non-sequential Monte Carlo simulation to evaluate the generation system adequacy. Several case studies are conducted on IEEE reliability test system to analyze the impact of increasing renewables on the generation system adequacy. The results show that the generation system adequacy tends to decay exponentially when the renewable integration is increased. It is shown that the reliability values obtained using the proposed approach are very close to those provided by the time-consuming sequential simulations. Importance of modeling seasonal variations of wind and solar is also investigated.

Due to the polymorphic uncertainties in microgrids (MGs), prohibitive computational burden is produced in reliability assessment. In this work, a novel sequential sampling algorithm (NSSA) compatible with sequential Monte Carlo (SMC) simulation is developed to overcome the computational burden. First, optimal probability density functions (PDFs) of random variables are worked out based on variation method. Then, optimal PDFs are employed to chronologically simulate the random states of microturbine (MT), photovoltaics (PV) and time varying load with improved computational efficiency. Therefore, the convergence of reliability assessment is accelerated accordingly. A series of case studies have been conducted, and the computational results show that NSSA provides a favorable sampling efficiency and adaptability to system conditions in reliability assessment of MGs. At last, based on optimal PDFs produced by NSSA, dominant joint PDF (DJ-PDF) is defined and employed to quantify the contributions of different scenarios to the reliability indices. Case studies have confirmed that DJ-PDF can provide detailed information for scenario-based reliability analysis.

Reliability of safety instrumented systems (SISs) is a critical measure to ensure production safety of many industries. This paper focus on low-demand SISs. The reliability of these SISs is quantified by evaluating their probability of failure on demand (PFD). However, due to lack of knowledge, and/or vague judgments from experts, epistemic uncertainty associated with the parameters of components' degradation models is inevitable. Meanwhile, common cause failure (CCF) of two or more components caused by shared environments, often exists in SISs, reliability assessment for a SIS, therefore, becomes a challenging task. In this paper, fuzzy reliability assessment for SISs is conducted by taking account of the CCF among components of a SIS. The fuzzy Markov model is utilized to characterize the degradation process of components under fuzzy environment. The fuzzy state probability distribution of components is, then, calculated by formulating a set of constrained optimization models. Based on the fault tree of a SIS, the fuzzy PFD of the entire SIS with CCFs is formulated by using the $\beta $ -factor to quantify the CCFs. The fuzzy PFD at any $\alpha $ -cut level is, therefore, computed by a constrained optimization model. According to the optimization of parameters of SIS, the lower bound and upper bound of fuzzy PFD of SIS can be determined. Finally, we can quantify the effectiveness of CCF event for assessing the fuzzy PFD of SIS.

The creep-fatigue resistance of turbine rotor seriously affects the reliability performance and service lifetime of aircraft engine. Creep-fatigue reliability assessment is an effective measure to quantify the uncertain creep-fatigue damage and evaluate the creep-fatigue reliable life for turbine rotor. To improve the modeling accuracy and simulation efficiency of creep-fatigue reliability assessment, a multi-surrogate collaboration approach (MSCA) is proposed by absorbing the strengths of the proposed dynamic neural network surrogate (DNNS) into distributed collaborative strategy. The creep-fatigue reliability assessment of a typical turbine rotor is regarded as one case to estimate the presented MSCA with respect to the fluctuations of multi-physical variables and the variabilities of multi-model parameters. The assessment results reveal that the creep-fatigue reliable life of turbine rotor under reliability degree of 0.998 7 is 629 cycles, and the fatigue strength coefficient and holding creep time play a leading role on creep-fatigue reliable life since their effect probabilities of 27 % and 19 %, respectively. Comparison of various methods (direct Monte Carlo simulation, response surface, neural network surrogate, DNNS) shows that the presented MSCA holds high efficiency and accuracy in creep-fatigue reliability assessment of turbine rotor.

Increasingly complex system environments have put forward higher requirements for information fusion. Under this background, the application prospects of evidence theory which can function well in uncertain information representation and information fusion is promising. The effect of evidence theory on information fusion is closely related to evidence distance measurement method and evidence combination rules. Considering the difference of evidence information in belief level and that in decision level, this paper redefines the evidence distance and puts forward the measurement method of evidence credibility, thus realizing the correction of the original basic probability assignment function (BPA). Based on the evidence credibility, the evidence weight is generated, which leads to an efficient distribution of evidence conflict in the process of evidence combination. So, a new evidence combination rule is generated. According to a series of examples, the evidence distance proposed in this paper can effectively measure the evidence differences, and the new combination rules can reasonably distribute evidence conflicts thus avoiding evidence paradox. At the end of this paper, the new evidence distance and combination rule, combing with the Transferable Belief Model (TBM), are used to fuse the multi-source reliability test information, then the life cycle evaluation of a product is obtained. In addition, through the comparative analysis of the results, the feasibility and validity of this method being applied to practice are verified.

In order to cooperate the wind farm operators with grasping the operation status of wind power converter, a novel reliability assessment strategy is proposed based on supervisory control and data acquisition (SCADA) multistate parameters prediction of permanent magnet synchronous generator (PMSG) wind turbine. The strategy considers “off-line training, on-line matching and assessment”. The operation reliability of wind power converter is obtained via the analysis and weight computing of confidence level, prediction value and actual value of SCADA multistate parameters. In the “off-line training” part, first, the FP-Growth association algorithm is employed to analyze the confidence levels of SCADA variables to the faults of wind power converter. The variables with high confidence level are defined as SCADA multistate parameters. Afterwards, wavelet packet transform (WPT) and K-means algorithm are employed to decompose, reconstruct, normalize and cluster the time series (off-line data) of multistate parameters under normal operation of wind turbine, to improve the generalization capability of long short term memory (LSTM) prediction model. In the part of “on-line matching and assessment”, the actual value time series (on-line data) of multistate parameters are decomposed and reconstructed by WPT. Each normalized sample is matched to the closest cluster centroid, which is generated in the part of “off-line training”. Then the corresponding LSTM model is conducted to predict based on the each sample. The final prediction value is sum of prediction results of entire samples in clusters. Finally, the reliability of wind power converter is assessed by the proposed strategy. The effectiveness of proposed assessment strategy is verified by the experimental results on a PMSG wind power converter.

The study of a metro traction power supply system reliability makes a significant contribution to researching on new power supply principles and configuration, and also providing a reference for system design and operation. According to the power supply structure and operation mode of the metro traction substation power supply system, based on the internal equipment, the reliability evaluation model of the traction substation is established by the block processing fault tree. An improved sequential Monte Carlo method is used to realize the quantitative reliability evaluation of the metro traction substation power supply system. The conclusions show that the reliability model and the sequential Monte Carlo simulation method proposed in this paper can obtain rich reliability indexes. Through visual simulation and theoretical calculation, the reliability of the metro traction power supply system can be effectively evaluated.

Recently, the AC/DC hybrid distribution network has become a general trend in energy research. To accommodate the addition of renewable energy, this paper proposes a reliability assessment approach for AC/DC hybrid distribution network considering the new features introduced by the DC distribution technique. First, the probability models of renewable generations and loads are established. Second, the control modes of the voltage source converter (VSC) yielding to different fault scenarios are modeled based on its operating characteristics. Thereafter, an optimal load shedding model that considers the control modes of the VSC is developed, and it can be solved using a second-order cone algorithm. Finally, a reliability assessment method for the AC/DC hybrid distribution network is proposed that is based on the nonsequential Monte Carlo method. Case studies based on a modified AC/DC distribution network are applied to verify the effectiveness of the proposed method. The operational characteristics and reliability performance of the AC/DC distribution network with high integration of renewable energy are demonstrated by comparing it to a corresponding AC distribution network.

Frequency control of isolated microgrid (MG) relies heavily on a safe and reliable cyber system. Random failures of cyber elements may cause the malfunction of the frequency control, resulting in reliability issues, e.g., load shedding or generation curtailment. A reliability modeling and assessment method for isolated MG is proposed, considering the influence of frequency control from cyber physical system perspectives. The frequency control process is modeled to establish the interdependencies between a cyber system and physical system within MGs. An assessment method is proposed to quantify the impacts of cyber element failures based on the frequency control models. A set of novel reliability index is defined to illustrate impacts of frequency control malfunction on the MG reliability. The sequential Monte Carlo simulation method is adopted to assess the reliability of MGs considering the malfunction of frequency control. The effectiveness of proposed models and indices is demonstrated by the case studies on an isolated MG. The test results indicate that the frequency control malfunction caused by the failures of cyber elements has a great impact on MG reliability. This research can provide technical support for the planning and operation of isolated MGs.

As photovoltaic (PV) generation has been one of the major renewable energy sources around the world, its PV capacity has also increased. When the large-scale PV systems are integrated into the distribution network, the complexity of the assessment process of the distribution network reliability will increase hazardously. In order to accurately assess this reliability in the distribution network combined with the PV generation, a reliability assessment procedure is proposed. In order to accurately evaluate the impact of the failure of conventional power equipment on reliability, the time-varying failure rate of conventional power equipment is modeled, taking into account the aging period. Then, in order to accurately evaluate the reliability improvement with PV systems integration, the new procedure is proposed highlighting the following contributions: 1) five new indices are added. 2) PV output is modeled so that not only the radiation intensity but also the failure and degradation of PV modules are represented. 3) time-varying islanding operation is considered and integrated. A case study using real-life distribution network topology and data in China is applied to verify that the newly proposed reliability indices display more sensitivity, and the proposed procedure significantly improves the accuracy of the reliability assessment.

Life is an important monitoring index for prognostic and health management systems. This paper proposes a new life expectancy assessment method for high-value complex systems with multi-characteristics based on goal-oriented (GO) method. First, the new life expectancy assessment method is expounded in detail, and its process is formulated. Then, the control system of power-shift steering transmission for a heavy vehicle, which is a typical mechatronics control system with multi-characteristics, such as multi-state, multi-function, and correlations, is taken as an example to evaluate its life expectancy by this paper's method. In order to verify the advantages, feasibility, and rationality of the new life expectancy assessment method, the results are compared with the results by Monte Carlo method. All in all, this life expectancy assessment method not only improves the theory of GO method, so that GO method is applied to evaluate the life expectancy of complex systems with multi-characteristics first, but also provides a new approach for life expectancy assessment of complex systems, which can take into consideration various system characteristics and only use unit life data, so that it is low evaluation cost, higher estimating efficiency and accuracy, and a more stable assessment result.

To properly depict the heteroscedasticity when analyzing strain-based fatigue reliability, this paper proposes a novel P-ε-N model from both viewpoints of geometric properties and statistical characteristics. The constructed approach deals with the elastic strain and plastic strain separately, where the logarithmic fatigue life is considered as normally distributed with linear mean and standard deviation (std) functions of logarithmic elastic strain or plastic strain, respectively. P-ε-N curve with any percentile can be derived to facilitate the reliability analysis. An improved parameter estimation method based on maximum likelihood estimation (MLE) via genetic algorithm (GA) is further established. The presented method is illustrated and verified via a comprehensive simulation study and two real applications by comparing with the conventional homoscedasticity model. Corresponding results indicate that our proposed method can enhance the modeling accuracy and provide an extensive adaptation.

Reliability assessments of long-life and high-reliability products often face the difficulty of lack of failure time data. Meanwhile, there existing individual differences in produces due to the uncertainties during their whole lifetime, such as manufacturing, assembly, work environment and work load. This paper develops a reliability modeling and assessment method considering individual differences based on performance degradation data. Firstly, the model selection method is employed into the analysis of degradation process. And according to the model selection results, the appropriate degradation model for the degradation process can be chosen. Then, the Bayesian method is applied to establish the parameter estimation method and reliability assessment framework based on the appropriate model. At last, a real engineer example is presented to illustrate the effectiveness of the proposed degradation analysis method with individual differences.

The GO methodology is an important technique for reliability assessment and evaluation of complex systems, especially for systematic timing and dynamic characteristics. However, the treatment of the shared signal and multi-state issues has increased the calculation burden in GO mode. Multi-valued decision diagrams (MDDs) have the merit of effectively performing qualitative and quantitative analysis via Boolean function forms directly; hence, in this paper, we propose a novel efficient algorithm for the multi-state GO model based on MDD, which combines the advantages of the two techniques. Moreover, the algorithm avoids the complex separate process of handling shared signals in the probability formulas method and the problem of complicated calculation in the state compounding method. In addition, the path and cut sets of the system are obtained through qualitative calculation based on the new algorithm. Finally, the correctness, simplicity, and accuracy of the new algorithm for analyzing systems that contain shared signals are verified by a case study.

Cloud manufacturing (CMfg) is an innovative manufacturing paradigm in which geographically distributed various manufacturing resources are encapsulated into manufacturing services (MSs), and also these MSs can provide users with various services. Users can find and choose qualified MSs to form cliques to help them complete their manufacturing tasks via Internet. Trust makes cooperative endeavors happen. Hence, it is an important problem to evaluate the trustworthiness of MSs in CMfg. In this paper, we present a comprehensive assessment approach to evaluate the trustworthiness of a MS in CMfg. The approach evaluates a MS from both objective and subjective perspectives, named as credit assessment and reliability assessment, respectively. Multinomial logistic regression analysis is presented to assess the credit of a MS, while fuzzy analytic hierarchy process is used to assess its reliability. Finally, a case study is presented and the results show the comprehensive assessment approach is efficacious at the trustworthiness evaluation of a MS.

Recently, the analysis of remaining useful life (RUL), which is central to the reliability assessment of lasers under various environment stresses, has become one of the most crucial issues in the field of laser reliability. In this paper, a similarity-based difference analysis (SbDA) approach is proposed to estimate the RUL of GaAs-based semiconductor lasers. SbDA utilizes the inherent relation between historical samples and the on-site sample in which we calculate the inherent difference to acquire a more exact performance degradation value. This method can make adequate use of the limited historical data sets to more accurately estimate the lifetime of an operating GaAs laser. In addition, we present several significant results of studies of model parameters, such as random disturbance, weight distribution factor, and similarity. Experimental results show the effectiveness and advantages of the proposed SbDA method.