Loon Ching Tang

FUZZY ASSESSMENT OF FMEA FOR ENGINE SYSTEMS

Abstract When performing failure mode and effects analysis (FMEA) for quality assurance and reliability improvement, interdependencies among various failure modes with uncertain and imprecise information are very difficult to be incorporated for failure analysis. Consequently, the validity of the results may be questionable. This paper presents a fuzzy-logic-based method for FMEA to address this issue. A platform for a fuzzy expert assessment is integrated with the proposed system to overcome the potential difficulty in sharing information among experts from various disciplines. The FMEA of diesel engines turbocharger system is presented to illustrate the feasibility of such techniques.

Application of neural networks in forecasting engine systems reliability

Abstract This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.

Travel time analysis for general item location assignment in a rectangular warehouse

A travel time model with general item location assignment in a rectangular warehouse system is presented. We give the exact probability mass functions that characterise the tour of an order picker and derive the first and second moments associated with the tour. We apply the model to analysing order batching and storage allocation strategies in an order picking system. The order picking system is modelled as a queueing system with customer batching. The results are compared and validated via simulation. The effects of batching and batch size on the delay time are discussed with consideration to the picking and sorting times for each batch of orders.

Computing process capability indices for non‐normal data: a review and comparative study

When the distribution of a process characteristic is non-normal, Cp and Cpk calculated using conventional methods often lead to erroneous interpretation of the processs capability. Though various methods have been proposed for computing surrogate process capability indices (PCIs) under non-normality, there is a lack of literature that covers a comprehensive evaluation and comparison of these methods. In particular, under mild and severe departures from normality, do these surrogate PCIs adequately capture process capability, and which is the best method(s) in reflecting the true capability under each of these circumstances? In this paper we review seven methods that are chosen for performance comparison in their ability to handle non-normality in PCIs. For illustration purposes the comparison is done through simulating Weibull and lognormal data, and the results are presented using box plots. Simulation results show that the performance of a method is dependent on its capability to capture the tail behaviour of the underlying distributions. Finally we give a practitioners guide that suggests applicable methods for each defined range of skewness and kurtosis under mild and severe departures from normality. Copyright

A Distribution-Based Systems Reliability Model Under Extreme Shocks and Natural Degradation

Degradation, and shock are two common mechanisms accounting for product failures. This paper presents a convenient means of capturing both shock and degradation in a single model when the extent of degradation and the magnitude of shocks are not observable, but only the failure times and the corresponding failure modes are recorded. We assume that the lifetime of a degradation-oriented failure, which is regarded as some initial random resource, belongs to some distribution family. Shocks arrive according to a non-homogeneous Poisson process, and the destructive probability depends on the transformed remaining resource of the system. Under these assumptions, we propose the single failure time model, and the recurrent event model. This study complements the well-known Brown-Proschan model. The single failure time model has successfully been applied to a real time data set. We also conduct a simulation study to examine the accuracy of our model.

FORTIFICATION OF SIX SIGMA: EXPANDING THE DMAIC TOOLSET

Six Sigma as a quality improvement framework cannot remain static if it is to sustain its value for businesses beyond the first waves of applications. This paper explores the possibilities of enhancing the usefulness and effectiveness of Six Sigma by the integration of established Operations Research/Management Science (OR/MS) techniques. In this paper, we elucidate the needs for OR/MS techniques to enhance Six Sigma deployment in operational and transactional environments and propose a new training roadmap for core Six Sigma professionals (Six Sigma Black Belts) which incorporates these techniques. A matrix relating the components of the proposed training curriculum to the actual deliverables during implementation for a hybrid of operational and transactional environments is also presented. A practical case study is also presented to demonstrate the usefulness of the OR/MS tools in a typical transactional environment. Copyright

Nonparametric CUSUM and EWMA Control Charts for Detecting Mean Shifts

Nonparametric control charts are useful when the underlying process distribution is not likely to be normal or is unknown. In this paper, we propose two nonparametric analogs of the CUSUM and EWMA control charts based on the Wilcoxon rank-sum test for detecting process mean shifts. We first derive the run-length distributions of the proposed control charts and then compare the performance of the proposed nonparametric charts to (1) CUSUM and EWMA control charts on subgroup means and (2) the median chart and the Shewhart-type nonparametric control chart based on Mann–Whitney test. We show that the charts proposed herein perform well in detecting step mean shifts and perform almost the same as the parametric counterparts when the underlying process output follows a normal distribution and better when the output is nonnormal. We also study the effect of the reference sample size and the subgroup size on the performance of the proposed charts. A numerical example is also given as an illustration of the design and implementation of the proposed charts.

Planning of step-stress accelerated degradation test

Estimating the long term performance of highly reliable products has been a difficult problem as accelerated life testing (ALT), which involves testing at highly elevated stresses, often results in too few failures for drawing useful inferences. To overcome this problem, accelerated degradation testing (ADT) has been proposed as a means to predict performance for highly reliable products. It requires one to identify a performance measure that would exhibit degradation and to monitor it over time. Product reliability can then be inferred from the degradation paths without the need of observing actual failures. Although physical failures are not needed in ADT, one usually defines failure as the first time when the degradation process exceeds a pre-specified threshold, so that the degradation path can be correlated to product reliability. As a result, reliability information of a product is embedded in degradation paths of units tested under ADT. In this paper, we look into planning of an ADT in which the test stress is increased in steps from lower stress to higher stress during the test, so that specimens are gradually conditioned to the stressed environment thus avoiding over-stressing. Our objective is to minimize the cost of testing, which is a function of sample size, test duration and number of inspections, as well as obtaining a reliability estimate of a requisite level of precision. Data from degradation paths are used to characterize the appropriate stochastic model underlying the product degradation process. We then derive the maximum likelihood estimators and the mean life at the use stress and its asymptotic variance. This variance is then used as a constraint, in a test plan which minimizes the testing cost. The optimal test plan gives the optimal sample size, number of inspections at each intermediate stress level and number of total inspections.

Reliability bounds and critical time for the Birnbaum-Saunders distribution

The Birnbaum-Saunders distribution, under certain conditions, can be used to model the fatigue failure-time caused by the catastrophic crack size. Reliability bounds for the Birnbaum-Saunders distribution and point and interval estimates for the critical time of the failure (hazard)-rate are discussed. The confidence intervals are constructed under the assumption that both parameters are unknown. This method is not affected by censoring, as long as confidence intervals for the parameters can be established. Numerical examples illustrate the procedure. >

Accelerated Degradation Test Planning Using the Inverse Gaussian Process

The IG process models have been shown to be an important family in degradation analysis. In this paper, we are interested in optimal constant-stress accelerated degradation tests (ADTs) planning when the underlying degradation follows the inverse Gaussian (IG) process. We first consider ADT planning for the IG process without random effects. Asymptotic variance of the estimate of a lower quantile is derived, and the objective of the planning is to minimize this variance by properly choosing the testing stresses, and the number of samples allocated to each stress. Next, ADT planning for a random-effects IG process model is considered. We then applied the IG process to fit the stress relaxation data of a component, and use the developed methods to help with the optimal ADT design.