Real-time Reliability Evaluation Research Articles

A Fully Analytical Approach for the Real-Time Dynamic Reliability Evaluation of Composite Power Systems with Renewable Energy Sources

Renewable energy sources (RES) have strong uncertainties, which significantly increase the risks of power imbalance and load shedding in composite power systems. It is thus necessary to evaluate the operational reliability for guiding economic dispatch and reducing the risks. Current methods cannot meet the requirement for the operational timeliness of reliability evaluations due to the high computational complexity of the optimal power flow (OPF) calculations of massive contingencies. This paper proposes a fully analytical approach to construct fast-to-run analytical functions of reliability indices and avoid reassessments when the load and RES change. The approach consists of uniform design (UD)-based contingency screening and a modified stochastic response surface method (mSRSM). The contingency screening method is used to select critical contingencies while considering the uncertainties. The mSRSM is used to construct the analytical functions of the load shedding to the load and RES generation for the selected contingencies. An analytical function of a smooth virtual variable that maps to the load shedding is established in such a way that, when the load and RES vary, the reliability can be assessed within a very short time rather than using laborious OPF calculations. Case studies illustrate the excellent performance of the proposed method for real-time reliability evaluation.

Enhancing vehicular platoon stability in the presence of communication Cyberattacks: A reliable longitudinal cooperative control strategy

This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles’ information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively.

An Automatic Partition Time-Varying Markov Model for Reliability Evaluation

As the service time of mechanical devices is getting longer and longer, the safe and reliability evaluation during operation is highlighted. Moreover, real-time reliability evaluation with consideration of multi-state performance degradation becomes increasingly important nowadays, since the consequences of sudden failures are more unacceptable than ever before. The Markov process is a commonly used model in multi-state reliability evaluation. However, little research of the Markov model can deal with multi-source monitoring data and time-varying properties of device performance degradation, as well as the scientific state number determination. In this article, a real-time reliability evaluation model based on automatic partition and the time-varying Markov chain is proposed to solve the problems of the scientific state number selection and time-varying properties description with the state transition matrix of the Markov process, together with taking advantage of multi-source information. The effectiveness of the proposed algorithm is demonstrated on the bearing with life-long vibration and temperature data. It shows that the proposed automatic partition time-varying Markov model can decide the state number automatically according to the trend of life-long data, and evaluate real-time reliability based on equipment operating hours and operating status. The result of predicted remaining useful life obtained by the proposed model is more accurate, and it also shows great superiority in conformity with reality.

A novel based-performance degradation Wiener process model for real-time reliability evaluation of lithium-ion battery

Real-time reliability evaluation of lithium-ion battery plays a vital role in guaranteeing the safety of energy storage system and its related products. However, it is difficult to predict and evaluate the remaining useful life and reliability of cell with accurate mathematical models, which is related to the complexity and variability of performance degradation during service. In this paper, a novel based-performance degradation Wiener process model is established based on battery degradation data and Bayesian updating algorithm. Firstly, three types of reliability evaluation models are constructed based on Wiener process degradation model, and the laboratory tests of 8 Lithium-ion cells (cylindrical 18650) are carried out to verify the validity and accuracy of the models, the results show that the binary random parameters evaluation model (BRPEM) is able to more accurately describe the degradation process of lithium-ion performance. Then, the Bayesian updating algorithm based on the BRPEM is employed to fuse the degradation data between 100 and 300 cycles of 5# cell in the test and real-time monitoring degradation data, the real-time reliability evaluation of the battery is realized. Finally, the results of life probability density function and reliability function show that the proposed model can achieve scientific and accurate reliability evaluation of Lithium-ion battery.

Research on a Real-time Reliability Evaluation Method Integrated with Online Fault Diagnosis: Subsea All-electric Christmas Tree System as a Case Study

The subsea all-electric Christmas tree is key equipment in subsea production systems. If a failure occurs, the marine environment will be seriously polluted. Therefore, strict reliability analysis and measures to improve reliability must be performed before such equipment is launched, which is crucial to safe subsea production. A real-time reliability evaluation method for the all-electric Christmas tree mechanical system integrated with the static Bayesian network fault diagnosis stage is proposed in this paper, which realizes the identification of the fault type of the components and the real-time reliability evaluation of the mechanical system under different failure rates of the components. As a supplement to the method, by using mutual information to conduct sensitivity analysis on the reliability of the mechanical system, the importance of the basic events of each component on the reliability of the system is finally given. The proposed method provides significant theoretical support for the maintenance of the subsea all-electric Christmas tree and can be extended to the reliability evaluation of general subsea production systems.

Research on anomaly detection and real-time reliability evaluation with the log of cloud platform

With the development of software, massive information systems generate large-scale data resources involving a large amount of log information, which is of lots of meaningful contents. Accurate analysis and anomaly prediction based on logs are particularly important for building safe and reliable systems. It is evident that current anomaly prediction is not accurate enough, and there are few applications to the study of real-time reliability evaluation. Therefore, this paper uses ensemble learning model to analyze and predict anomaly of the massive system logs based on the complete procedures of log processing, including log analysis, feature extraction, anomaly detection, prediction evaluation, and real-time reliability evaluation. Compared with the traditional machine learning methods, the proposed model is able to improve the accuracy, recall rate and F1 value of anomaly prediction. The evaluation results are used to correct the real-time reliability in view of the low predicted recall rate, which greatly improves the accuracy of real-time reliability and thus provides accurate data basis and anomaly location basis for Artificial Intelligence for IT Operations. In this paper we have made the following innovations and contributions in anomaly detection and reliability measurement. The whole process was established from original log to real-time reliability measurement. New methods were adopted to improve the accuracy, recall and F1 value of anomaly detection. A real-time reliability measurement method is proposed.

Research on real-time reliability evaluation of CPS system based on machine learning

This paper studies the reliability of CPS software system and proposes a real-time evaluation method of CPS software reliability based on machine learning. First, use complex networks to identify key points in the network topology of the CPS software system. Unsupervised learning classification by fast density clustering algorithm to classify the importance of nodes can be effectively applied to the importance evaluation of nodes in CPS software system and support the planning of CPS software system Secondly, a real-time CPS reliability automatic online evaluation method is proposed. This method uses machine learning ideas to build an evaluation framework, design an online queuing algorithm, and implement real-time online analysis and evaluation of CPS reliability. Preventive measures ensure that the system operates normally and without interruption, which greatly improves system reliability. Finally, simulation results verify the effectiveness of the evaluation method and its broad application prospects.

Real-time Reliability Evaluation Based on Stochastic Degradation Process and Sequential Joint Distribution Estimation

According to the different performance degradation paths, reliability curves and parameters of each product in service phase, the reliability and remaining life of components at current moment can be evaluated in real-time reliability assessment by integrating real-time status, historical information and service time of components. The realtime reliability evaluation is valued because it is more personalized, precise, real-time and lean than traditional methods. Furthermore, the results from real-time reliability assessment can also represent the health status of component at current time. In the current research, real-time reliability assessment mainly relies on regression analysis and time series analysis, but these two methods are mainly used to describe the component degradation process, and cannot reflect the influence of external random environment on the component state change. At the same time, due to these limitations of time, economy and test conditions, it is also worth studying how to obtain more accurate and practical reliability distribution and determine a detection interval under the condition of less data. Therefore, based on the analysis of real-time reliability evaluation principle, a more appropriate real-time reliability evaluation method and the detection interval decision model are proposed by means of random degradation process, parameter sequence test joint distribution estimation and failure risk. The result from this method is a quantized value, which is convenient for the direct application of the follow-up maintenance decision research. Therefore, the research in this paper has extensive reference value and practical application prospect.

Maintenance Optimization Model with Sequential Inspection Based on Real-Time Reliability Evaluation for Long-Term Storage Systems

For long-term storage systems such as rockets and missiles, most of the relevant models and algorithms for inspection and maintenance currently focus on analysis based on periodic inspection. However, considering factors such as the complexity of the degradation mechanisms of these systems, the constraints imposed by failure risk, and the uncertainty caused by environmental factors, it is preferable to dynamically determine the inspection intervals based on real-time status information. This paper investigates the issue of maintenance optimization modelling for long-term storage systems based on real-time reliability evaluation. First, the Wiener process is used to establish a performance degradation model for one critical unit of such a system, and a closed-form expression for the real-time reliability distribution is obtained by using the first-hitting-time theory. Second, sequential inspection intervals are dynamically determined by combining the real-time reliability function with a real-time reliability threshold for the system. Third, a maintenance optimization model is established for the critical unit based on update process theory. An analytical expression for the expected total cost rate is derived, and then, the real-time reliability threshold and the preventive maintenance threshold for the unit are jointly optimized by means of Monte Carlo simulation, with the lowest expected total cost rate as the optimization goal. Finally, two examples of a gyroscope and an alloy blade that are commonly used in the long-term storage systems are considered, and the validity of the proposed model is illustrated by means of a sensitivity analysis of the relevant parameters.

Sequential Inspection Model for Aviation Product Based on Real-time Reliability Evaluation

Sequential Inspection Model for Aviation Product Based on Real-time Reliability Evaluation

Trans-layer model learning: A hierarchical modeling strategy for real-time reliability evaluation of complex systems

Techniques addressing the loading condition of components in complex systems are of great significance for the real-time reliability analyses of systems. To recover component observabilities with combined condition monitoring data and empirical rules, an information criterion identifying the necessary data/rule set for the modeling of systems with the same hierarchical topologies to real-in-world realizations, referred to as trans-layer model learning (TLML), is proposed and proved. Then, with regard to general multi-component dynamic systems, a specific TLML algorithm is proposed. In this algorithm, the loss function and alternative training scheme of component models are specified for harnessing the information from sensor readings and empirical rules to serve the modeling. TLML is applied first on a simulation system to testify its ability to reveal component loading conditions, and then on an aircraft engine to test its effectiveness in improving the Residual Useful Life (RUL) prediction performance of engine turbine blades. Results show that TLML can provide real-time estimations of component loading conditions with sufficient accuracy, and thus improve the precision and reliability of the RUL estimation of system parts.

A study on the real-time reliability of on-board equipment of train control system

Real-time reliability evaluation is conducive to establishing a condition based maintenance system for the purpose of guaranteeing continuous train operation. According to the inherent characteristics of the on-board equipment, the connotation of reliability evaluation of on-board equipment is defined and the evaluation index of real-time reliability is provided in this paper. From the perspective of methodology and practical application, the real-time reliability of the on-board equipment is discussed in detail, and the method of evaluating the realtime reliability of on-board equipment at component level based on Hidden Markov Model (HMM) is proposed. In this method the performance degradation data is used directly to realize the accurate perception of the hidden state transition process of on-board equipment, which can achieve a better description of the real-time reliability of the equipment.

Oil-based maintenance interval optimization for power-shift steering transmission

The concentration of micron-size particles in used oil is generally obtained by atomic emission spectrometry. These accurate results can be considered as the most important index for real-time reliability evaluation including failure prediction and residual life assessment of power-shift steering transmission in tracked vehicles. Stochastic process and especially Wiener process can be applied in processing the oil spectral data. In this article, 50 used oil samples in total were collected and analyzed in sequence which covered 250 motor hours. The eigenvalue and mean value of elemental concentration in those samples were also revealed by linear regression analysis. Then, the increasing trends and the first hitting times for indicating elements were solved by simulating stochastic differential equations, which were based on the positive drift Wiener process. Comparing to the existing conditional maintenance time, the first hitting time increased about 27 motor hours more (13.7%). The results show that the maintenance frequency can be decreased by a longer time and finally can increase the cost-effectiveness ratio of maintenance.

Parameter auto-selection for hemispherical resonator gyroscope's long-term prediction model based on cooperative game theory

As a new vibration gyro with features of high accuracy, long lifespan, no wear-out, and great reliability, the hemispherical resonator gyroscope's (HRG's) lifespan prediction without whole lifetime test is a tough task. Dai et al, based on data driven, proposed a residual modified autoregressive grey model ARGM to predict HRG's lifespan, in which the parameters however are selected by expert experience. In order to enhance the predictive lifetime, we propose a novel approach to auto-select parameters for the multi-parametric long-term prediction model ARGM based on cooperative game theory that we call CoG-ARGM. Our idea is to map parameter auto-selection of the prediction model to coalition formation in a combined cooperative game, which is proofed convex, where each parameter is respectively considered as a sub-coalition in its own pure cooperative game. In addition, we also bring failure mode originally derived from FMEA to evaluate the real-time prediction reliability. The experiments indicate that CoG-ARGM with real-time reliability evaluation yields high-quality prediction results. Furthermore, we also demonstrate the superiority of CoG-ARGM over state-of-the-art prediction methods through detailed experiments using evaluation criteria such as MAPE, Ln(Q) and time consumption on real HRG drift data.

Real-time reliability evaluation of two-phase Wiener degradation process

ABSTRACTReliability modeling and evaluation for the two-phase Wiener degradation process are studied. For many devices, the degradation rates could possibly increase or decrease in a non smooth manner at some point in time due to the change of degradation mechanism. A two-phase Wiener degradation process with an unobserved change point is used to model the degradation process. And we assume that the change point varies randomly from device to device. Furthermore, we integrate historical data and up-to-date observation data to improve the degradation modeling and evaluation based on Bayesian method. The change point between the two phases was obtained based on the Akaike information criterion (AIC) and the criterion of the residual sum of squares. Finally, a real example of liquid coupling devices (LCDs) and a numeric example are discussed to demonstrate the effectiveness of the proposed method. The results show that the proposed method is effective and efficient.

Real-time reliability evaluation based on independent increment process with random effect

Degradation data provide useful information about the reliability assessment for highly reliable and long lifetime products. Motivated by laser degradation data, a new degradation modelling approach is proposed, in which degradation path has linear mean and linear standard deviation functions. In this article, the population degradation modelling and individual real time reliability assessment are discussed, and a Bayesian framework is proposed to integrate population degradation information and individual degradation data. The population degradation path is characterized by a random effect independent increment process where random effect captures unit to unit variation, and the Markov Chain Monte Carlo (MCMC) method is used to estimate the unknown parameters. To obtain individual real time reliability assessment, the parameters are updated iteratively using Bayesian theory. Based on updated results, the residual use life and individual real-time reliability evaluation are obtained. For an illustration of the usefulness and validity of the proposed model and method, a numerical example about laser data is given.

REAL-TIME RELIABILITY ASSESSMENT AND LIFETIME PREDICTION FOR BEARINGS USING THE INDIVIDUAL STATE DEVIATION BASED ON THE MANIFOLD DISTANCE

In recent years, the real-time reliability evaluation and life prediction for rolling bearings has attracted more attention. Most of the existing methods employ real-time transformation of traditional reliability indices, performance degradation trajectory or distribution analysis, which usually have certain limitations in terms of accuracy and applicability. This paper proposes a method for bearing real-time reliability evaluation and life prediction to avoid the negligence of real-time transformation of the monitored individual, as well as reduce the errors caused by the randomness from individual bearing operational process. The individual state deviation of a running rolling bearing geometrically measured by manifold distance is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for realtime reliability evaluation and lifetime prediction. Finally, the feasibility and efficiency of this method is validated by bearing run-to-failure experiments.

Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system

A novel real-time reliability evaluation methodology is proposed by combining root cause diagnosis phase based on Bayesian networks (BNs) and reliability evaluation phase based on dynamic BNs (DBNs). The root cause diagnosis phase exactly locates the root cause of a complex mechatronic system failure in real time to increase diagnostic coverage and is performed through backward analysis of BNs. The reliability evaluation phase calculates the real-time reliability of the entire system by forward inference of DBNs. The application of the proposed methodology is demonstrated using a case of a subsea pipe ram blowout preventer system. The value and the variation trend of real-time system reliability when the faults of components occur are studied; the importance degree sequence of components at different times is also determined using mutual information and belief variance.

Real-Time Reliability Evaluation and Life Prediction for Bearings Based on Normalized Individual State Deviation

Most of the existing methods for bearing real-time reliability evaluation employ real-time transformation of traditional reliability indices, performance degradation trajectory analysis, and performance degradation distribution, which are usually limited in terms of accuracy and applicability. A method for real-time reliability evaluation and life prediction for bearings based on normalized individual state deviation is proposed in this study. First, a self-organizing map neural network is utilized to obtain the individual state deviation of a running rolling bearing. Second, individual state deviation is normalized into a state deviation degree, which is used to formulate a modified real-time reliability model for the realization of real-time reliability evaluation and residual life prediction. The proposed method combines population information with real-time monitoring information of individual bearings, and thus avoids the negligence of the real-time transformation of the monitored individual. The errors caused by the randomness of the individual bearing operational process are also reduced. Finally, the feasibility and efficiency of the proposed method is validated by performing run-to-failure experiments on bearings.

Research on Control Method Based on Real-Time Operational Reliability Evaluation for Space Manipulator

A control method based on real-time operational reliability evaluation for space manipulator is presented for improving the success rate of a manipulator during the execution of a task. In this paper, a method for quantitative analysis of operational reliability is given when manipulator is executing a specified task; then a control model which could control the quantitative operational reliability is built. First, the control process is described by using a state space equation. Second, process parameters are estimated in real time using Bayesian method. Third, the expression of the system's real-time operational reliability is deduced based on the state space equation and process parameters which are estimated using Bayesian method. Finally, a control variable regulation strategy which considers the cost of control is given based on the Theory of Statistical Process Control. It is shown via simulations that this method effectively improves the operational reliability of space manipulator control system.