A Robust Calibration Methodology for an On-Board Diagnostic Car System

New car models are now by law equipped with on-board diagnostic (OBD) systems aimed at monitoring the state of health of strategic components that ensure low levels of polluting exhaust emissions. During development phases, for each new car model, the OBD system must be finely calibrated. This article presents a robust calibration methodology taking into account sources of variability mainly due to production process, operating, and environmental conditions. The methodology enables us to evaluate the false alarm and failure to detect risks intrinsically related to the adopted calibration. An application concerning an upstream oxygen sensor monitored by the OBD is presented.     

Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems

In this paper, we presented a continuous‐time Markov process‐based model for evaluating time‐dependent reliability indices of multi‐state degraded systems, particularly for some automotive subsystems and components subject to minimal repairs and negative repair effects. The minimal repair policy, which restores the system back to an “as bad as old” functioning state just before failure, is widely used for automotive systems repair because of its low cost of maintenance. The current study distinguishes with others that the negative repair effects, such as unpredictable human error during repair work and negative effects caused by propagated failures, are considered in the model. The negative repair effects may transfer the system to a degraded operational state that is worse than before due to an imperfect repair. Additionally, a special condition that a system under repair may be directly transferred to a complete failure state is also considered. Using the continuous‐time Markov process approach, we obtained the general solutions to the time‐dependent probabilities of each system state. Moreover, we also provided the expressions for several reliability measures include availability, unavailability, reliability, mean life time, and mean time to first failure. An illustrative numerical example of reliability assessment of an electric car battery system is provided. Finally, we use the proposed multi‐state system model to model a vehicle sub‐frame fatigue degradation process. The proposed model can be applied for many practical systems, especially for the systems that are designed with finite service life.     

An optimized technique for reliability analysis of safety‐critical systems: A case study of nuclear power plant

Stochastic models are extensively used in quantifying the reliability of safety critical systems. These models use the state‐space model for reliability quantification. Markov chain is comprehensively used in describing a sequence of possible events of any system in which the probability of each event depends only on the state attained in the previous event. Markov chains are convenient to model the software system of the SCS with the help of Petri Nets, a directed bipartite graph widely used for the verification and validation of real‐time systems. However, the stochastic model suffers from the state‐space explosion problem. In this paper, we proposed a technique for reliability analysis of safety critical systems, excavating into the coherent optimization of Markov chain. The approach has been validated on 17 safety critical systems of nuclear power plants.     

Reliability evaluation and opportunistic maintenance policy based on a novel delay time model

The delay time concept is widely adopted in literature to model the two‐stage failure process of most industrial systems which can be divided into normal stage (from new to an initial point of a defect) and defective stage (from defect arrival point to failure). Most existing delay time models assume that the normal and defective stages are independent. A generalized delay time model is proposed in this paper by considering the dependence between the normal and defective stages which is reflected in the fact that they share the same external shock process. According to the definition of shot‐noise process, external shocks will incur random hazard rate increments in the two stages. The failure state is self‐announcing, whereas the defective state can only be detected by block‐based inspection or opportunistic inspection offered by unexpected shutdown due to unavoidable external factors. The system is correctively replaced upon the occurrence of a system failure or preventively replaced at the detection of a defective state. Based on the stochastic failure model and maintenance policy, this paper evaluates system reliability performance and average long‐run cost rate via a Markov‐chain based approach. Finally, a case study on a steel convertor plant is given to demonstrate the applicability of the proposed model.     

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

In recent years, much attention has been given to monitoring multistage processes in order to effectively improve the product reliability. To this end, the output of the process is investigated under special circumstances, and the values corresponding to reliability‐related quality characteristic are measured. However, analyzing reliability data is quite complicated because of their unique features such as being censored and obeying nonnormal distributions. A more sophisticated picture arises when the observations of the process are autocorrelated in some cases, which makes the application of previous control procedures futile. In this paper, the accelerated failure time (AFT) regression models have been modified in order to account for autocorrelated data. Then, a cumulative sum (CUSUM) control chart and an exponentially weighted moving average (EWMA) control chart based on conditional expected values have been proposed to monitor the quality variable with Weibull distribution while taking the effective covariates into consideration. Extensive simulation studies reveal that the CUSUM control chart outperforms its counterpart in detecting out‐of‐control conditions. Finally, a real case study in a textile industry has been provided to investigate the application of the CUSUM control scheme.     

Improved CUSUM monitoring of Markov counting process with frequent zeros

There has been a growing interest in monitoring processes featuring serial dependence and zero inflation. The phenomenon of excessive zeros often occurs in count time series because of the advancement of quality in manufacturing process. In this study, we propose three control charts, such as the cumulative sum chart with delay rule (CUSUM‐DR), conforming run length (CRL)‐CUSUM chart, and combined Shewhart CRL‐CUSUM chart, to enhance the performance of monitoring Markov counting processes with excessive zeros. Numerical experiments are conducted based on integer‐valued autoregressive time series models, for example, zero‐inflated Poisson INAR and INARCH, to evaluate the performance of the proposed charts designed for the detection of mean increase. A real example is also illustrated to demonstrate the usability of our proposed charts.     

System Reliability with Multiple Failure Modes and Time Scales

Lifetime of some systems can be measured based on multiple time scales. For instance, the lifetime of an airplane may be affected by its mileage or number of landings. Furthermore, most systems are exposed to competing risks. In this regard, time scales can accelerate the failure mechanism of these systems. In this paper, the behavior of systems is investigated under competing risks and multiple time scales. The time scales follow independent Poisson processes. As it is not straight forward obtaining closed‐form relations for the system reliability, we have provided a parametric upper bound. Also, the upper bound can be tightened by considering an error function. The error function can be built by regression on a sample containing real values of system reliability for given time units. Performance of the upper bound is studied in two numerical examples and a case study. Results show that the obtained upper bound is very tight. Copyright © 2015 John Wiley & Sons, Ltd.     

基于RTK GPS系统的结构安全 监测系统研究

利用GPS系统在线监测获取的大量结构响应数据,完成了大量数据处理工作,得到了结构响应的时间序列。在此基础上,完成了结构响应的谱分析,初步建立了结构响应的状态空间模型及ARMA(autoregres-sive moving average method)模型。计算分析结果与大桥通车试验的实测结果及有限元模型预测结果相当吻合。研究表明,基于结构响应的状态空间模型及ARMA模型是评估结构动力性能的有力工具,同时进一步验证了通过RTK GPS系统建立结构安全监测系统的可能性。     

Reliability and maintenance strategy for systems with aging‐dependent components

An innovative approach is presented for the reliability analysis of aging multistate systems that considers the subsystems and their components' dependency. A reliability function is determined for an aging series system with the component dependency following the local load‐sharing rule, and a reliability function is determined for an aging “m out of n” system with the component dependency following the equal load‐sharing rule. Linking the results of those load‐sharing models, a mixed‐dependency model for multistate “m out of l”‐series systems is constructed by assuming the dependence between subsystems connected in series under the local load‐sharing rule and the dependence between their components under the equal load‐sharing rule. As a special case, the reliability of this system, modeled using piecewise exponential reliability functions, is considered, and the results are applied to characterize shipyard rope elevator reliability. Finally, the maintenance of this elevator as a repairable multistate system is analyzed with the time of renovation ignored.     

Real‐time Reliability Self‐assessment in Milling Tools Operation

To ensure reliable operations, online reliability assessment based on the system monitoring is essential, especially for the critical machineries or components with high safety requirements. The real‐time reliability of the milling cutters in practice is one of the examples that decide the total manufacturing effectiveness and the quality of products. The research on how to best estimate cutters' reliability has gained popularity in recent years due to the need in prognostics and health management. The state space model (SSM), employed to recognize the underlying degradation state as a first order Markov chain, is widely used to model the residual life and reliability evaluation. In this paper, non‐linear and non‐Gaussian SSM are established based on the tool wear condition. The degrading tendency is predicted by the particle filter algorithm, and then the conditional reliability is calculated based on the degradation state and a pre‐set threshold. The effectiveness of this approach was proven by a real case study provided. Copyright © 2015 John Wiley & Sons, Ltd.     

Set theoretic formulation of performance reliability of multiple response time‐variant systems due to degradations in system components

This paper presents a design stage method for assessing performance reliability of systems with multiple time‐variant responses due to component degradation. Herein the system component degradation profiles over time are assumed to be known and the degradation of the system is related to component degradation using mechanistic models. Selected performance measures (e.g. responses) are related to their critical levels by time‐dependent limit‐state functions. System failure is defined as the non‐conformance of any response and unions of the multiple failure regions are required. For discrete time, set theory establishes the minimum union size needed to identify a true incremental failure region. A cumulative failure distribution function is built by summing incremental failure probabilities. A practical implementation of the theory can be manifest by approximating the probability of the unions by second‐order bounds. Further, for numerical efficiency probabilities are evaluated by first‐order reliability methods (FORM). The presented method is quite different from Monte Carlo sampling methods. The proposed method can be used to assess mean and tolerance design through simultaneous evaluation of quality and performance reliability. The work herein sets the foundation for an optimization method to control both quality and performance reliability and thus, for example, estimate warranty costs and product recall. An example from power engineering shows the details of the proposed method and the potential of the approach. Copyright © 2006 John Wiley & Sons, Ltd.     

A modeling framework for assessing the impact of new time/mileage warranty limits on the number and cost of automotive warranty claims

An automobile with over 7000 parts is a highly complex product. In spite of employing the best quality and reliability practices during product development, manufacturing, and assembly, unexpected failures during warranty period do occur and cost automobile companies billions of dollars annually in warranty alone. Warranty coverage for an automobile is generally stated in terms of mileage (in miles) and time (in months or years). The coverage expires when any of the two limits is crossed. Any change in warranty coverage too, influences warranty cost significantly. However, changes made to warranty coverage are often market driven. In either case, a company needs to plan for maintaining a large cash reserve to pay for the warranty services on their products.In this paper, we present a simple method to assess the impact of new time/mileage warranty limits on the number and cost of warranty claims for components/sub-systems of a new product. We highlight the use of mileage accumulation rates of a population of vehicles to arrive at claims per thousand vehicles, sold with new time/mileage warranty limits. We also discuss the bias in warranty cost estimates that may result in using cumulative cost per repair information. We recommend the use of incremental cost per repair especially when populations with different mileage accumulation rates are under consideration. Application examples are included to illustrate the use of the proposed methodology.     

Mission Reliability Assessment of Space TT&C System by Discrete Event System Simulation

Based on the analysis of system characteristics and mission process, space tracking, telemetry and command (TT&C) system can be viewed as a phased‐mission system (PMS). A general methodology using discrete event system simulation is proposed to quantitatively assess mission reliability of space TT&C system, because the traditional method is difficult to solve such complex problem. By dividing the time sequence of TT&C mission profile into several phases, the fault tree model of PMS is built to represent the system logical structure in each phase. In order to efficiently build simulation models, unified modeling language static class diagram is used to describe simulation model architecture. Extensible markup language is adopted to represent the mission reliability model in standard format for simulation input. By randomly generating the failure and repair events of the system components, the changes of the system state are simulated. The logic structure function of fault tree and observation data of the system state change jointly determines the mission reliability. A case study is given to illustrate the approach and validate its effectiveness. Copyright © 2013 John Wiley & Sons, Ltd.     

On misinterpretation of the asymptotic property of system time‐between‐failures

If a system, not necessarily one in series, is composed of components having various time‐to‐failure distributions and components are replaced good‐as‐new as they fail, then the system time‐between‐failure distribution tends toward the exponential. Many practicing reliability engineers, incorrectly invoking this property, model their systems with an exponential time‐to‐failure. We show, under two conditions, using a hypothetical fleet of vehicles, the severity of this error. Modeling time‐to‐failure as exponential results in gross over‐sparing and high unavailability costs as well. Copyright © 2001 John Wiley & Sons, Ltd.     

A non-homogeneous Poisson process predictive model for automobile warranty claims

Automobile warranties and thus lifetimes are characterized in the two-dimensional space of time and mileage. This paper presents a non-homogenous Poisson process (NHPP) predictive model for automobile warranty claims consisting of two components: a population size function and a failure or warranty claim rate. The population size function tracks the population in the time domain and accounts for mileage by removing vehicles from the population when they exceed the warranty mileage limitation. The model uses the intensity function of a NHPP—the instantaneous probability of failure—to model the occurrence of warranty claims. The approach was developed to support automobile manufacturers’ process of using claims observed during the early portion (first 7 months) of vehicle life to predict claims for the remainder of coverage, typically between 3 and 5 years. This paper uses manufacturer provided warranty data from a luxury car to demonstrate the NHPP model by predicting claims for three vehicle subsystems. Warranty predictions are then compared with the actual observed values and predictions made with a standard forecasting technique.     

Structural damage detection using ARMAX time series models and cepstral distances

A novel damage detection algorithm for structural health monitoring using time series model is presented. The proposed algorithm uses output-only acceleration time series obtained from sensors on the structure which are fitted using Auto-regressive moving-average with exogenous inputs (ARMAX) model. The algorithm uses Cepstral distances between the ARMAX models of decorrelated data obtained from healthy and any other current condition of the structure as the damage indicator. A numerical model of a simply supported beam with variations due to temperature and operating conditions along with measurement noise is used to demonstrate the effectiveness of the proposed damage diagnostic technique using the ARMAX time series models and their Cepstral distances with novelty indices. The effectiveness of the proposed method is validated using the benchmark data of the 8-DOF system made available to public by the Engineering Institute of LANL and the simulated vibration data obtained from the FEM model of IASC-ASCE 12-DOF steel frame. The results of the studies indicate that the proposed algorithm is robust in identifying the damage from the acceleration data contaminated with noise under varied environmental and operational conditions.     

Application of discrete‐time Bayesian network on reliability analysis of uncertain system with common cause failure

With the increasing complexity of engineering systems, reliability analysis and evaluation of systems with traditional methods can't meet practical engineering requirements. Based on limited experimental conditions, lack of data, complex structure models, insufficient cognitive abilities, and many other issues, people have to consider many uncertain factors in system reliability research. Besides, common cause failure (CCF) has become an important factor of system failure. In this paper, a discrete‐time Bayesian network (DTBN) associated with an eight‐rotor unmanned aerial vehicle (UAV) system is presented to discuss above problems. In this approach, the system is assumed as a two‐state system. After that, interval analysis theory is employed to deal with uncertainty. We consider the four sets of auxiliary propellers in the auxiliary power group as a 3/8 voting system, and β factor model is used to process CCF in the auxiliary power group. The proposed methods prove the validity of proposing interval analysis theory to solve uncertain problems and it is necessary to consider reducing or avoiding CCFs in system.     

Automotive exhaust gas sensing systems

Gas sensors have become an integral component of control systems for internal combustion engines to provide information for feedback control of air-to-fuel ratio (A/F) to achieve improved vehicle performance and fuel economy as well as decreased levels of emission. Increasingly stringent limits on evaporative emissions as well as the requirement of having on-board diagnostics (OBD), which includes catalyst monitoring, necessitate the monitoring of exhaust gas constituents [i.e., carbon monoxide (CO), hydrocarbons (HCs), and oxides of nitrogen (NO_x)]. The different sensing requirements, testing procedures, environmental parameters, and need for microsystem-based realizations are discussed     

Multivariate performance reliability prediction in real-time

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state–space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique.     

Improving the Product Reliability in Multistage Manufacturing and Service Operations

Monitoring and improving the product reliability is of main concern in a large number of multistage manufacturing processes. The process output is commonly inspected under limited load conditions, and the tensile strength of reliability‐related quality characteristic is measured. This brings about censored observations that make the direct application of traditional control charts futile. The monitoring procedure becomes aggravated when the influence of variable competing risk is pronounced during the conducted test. To deal with this critical issue, we propose a regression‐adjusted cumulative sum (CUSUM) chart to effectively monitor a quality characteristic that may be right censored because of both fixed and variable competing risks. Moreover, two exponentially weighted moving average (EWMA) control charts on the basis of conditional expected values are devised to detect decreases in the tensile mean. The comparison of the three competing monitoring schemes confirms the superiority of the regression‐adjusted CUSUM procedure. Not only is the proposed control chart applicable to manufacturing processes with the aim of monitoring reliability‐related quality variables, it is also appropriate for monitoring similar quality measurements in service operations such as survivability measures in healthcare services. Copyright © 2011 John Wiley & Sons, Ltd.