Classical Reliability Research Articles

Accelerated Life Test and Signal Monitoring of an Industrial Pressure Sensor

Sensing pressure is widely needed technique in the industry such as plant, vehicle, aerospace, and etc. Since each pressure sensor has its own ranges of operating conditions, but the manufacturers give quite large design margins. So although the sensors has own lifetimes, they are regarded as hardly failed products. This study developed the accelerated life test system of pressure sensors, and the failure monitoring and predicting module in the system was a differentiated tool developed using a data-driven algorithm. This module were expected to save the test time since specimens which do not fail yet can be regarded as failed ones. As a preliminary commitment, 8 specimens were used in the test bench. Although classical Weibull analysis and accelerated life test method can handle these censored data, prognostic judgments of the censored data can give another analysis result. The signal-based analysis in this study showed that mean and deviation values of pressure sensor signals did not represent the failure prediction metric and so researchers should devised other approaches to substitute the classical reliability analysis.

A robust reliability redundancy allocation problem under abnormal external failures guided by a new importance measure

Besides inherent failures under normal conditions studied by classical reliability engineering, systems may also be subject to abnormal external failures, whose probabilities are hard, if not impossible, to predict, such as failures caused by natural disasters (e.g. earthquakes, floods, and tornados) and malicious attacks. Thus, abnormal external failures need to be considered when designing a system, as in reliability redundancy allocation problem. To deal with the uncertainty of abnormal external failures and based on robust optimization, this research proposes a multi-objective optimization approach that can simultaneously maximize system reliability under both normal and the worst cases of abnormal external failures for redundancy allocation problem. Since the amount of abnormal external failures is usually unknown, the worst case for each possible amount of abnormal external failures needs to be identified first. For this purpose, a new importance measure that can quantify the importance of a subset of system components with arbitrary cardinality is proposed. We also revise a widely used multi-objective evolutionary algorithm, multi-objective probabilistic solution discovery algorithm, to deal with the complexity of redundancy allocation problem. The merits of the revised algorithm are demonstrated by extensive experiments.

Research on Connection and Function Reliability of the Oil＆Gas Pipeline System

Pipeline transportation is the optimal way for energy delivery in terms of safety, efficiency and environmental protection. Because of the complexity of pipeline external system including geological hazards, social and cultural influence, it is a great challenge to operate the pipeline safely and reliable. Therefore, the pipeline reliability becomes an important issue. Based on the classical reliability theory, the analysis of pipeline system is carried out, then the reliability model of the pipeline system is built, and the calculation is addressed thereafter. Further the connection and function reliability model is applied to a practical active pipeline system, with the use of the proposed methodology of the pipeline system; the connection reliability and function reliability are obtained. This paper firstly presented to considerate the connection and function reliability separately and obtain significant contribution to establish the mathematical reliability model of pipeline system, hence provide fundamental groundwork for the pipeline reliability research in the future.

Covariate-free and Covariate-dependent Reliability.

Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part. The approach can be implemented in a single sample and can be applied to a variety of reliability coefficients.

Towards Prognostics & Health Management in Lighting Applications

Philips Lighting’s revenue is largely influenced by the change from component supplier to supplier of systems, solutions and services. Philips Lighting differentiates from competition by providing high quality and reliable products as we learned in our traditional lighting business and which we continue in our actual LED lighting business. Reliable products start with understanding the physics-of-failure by using accelerated test approaches such as (Highly) Accelerated Life Testing. A classical reliability approach is to use the results from these tests, verified by failure analysis, to obtain conservative bounds from the failure models, and predict failure rates on a system level. A next step beyond this classical approach is to use data analytics in our installed base to determine degraded performance. The data for this analysis can come either from live connections to ‘intelligent’ systems, or from actively retracted (working) products from the field. This allows us to move into the prognostics (PHM) regime where a detailed understanding of failure mechanisms, usage scenarios, technology and design come together.Until recently costs for implementing PHM in Lighting products or systems was outranging possible cost benefits. Nowadays this is reversing rapidly by the exponential increasing impact of digitization and connectivity of the Lighting Systems. The impact is far beyond the impact on single products, but extends to an ever larger amount of connected systems. Continuously, more intelligent interfacing with the technical environment and with different kind of users is being built-in by using more and different kind of sensors, (wireless) communication, and different kind of interacting or interfacing devices. Especially in professional systems, where many years of use has to be warranted and where system size, cost and complexity are continuously increasing, PHM is required. Where online debugging and adding new features or functions is already common practice, PHM should provide tools to keep the system within its quality and reliability targets. In the presentation we show our road towards prognostics and demonstrate PHM work being done in different professional Lighting applications as Public Lighting, Office & Industry Lighting and Retail Lighting. While data analytic tools are still premature, first results are achieved and improvement tracks are being defined. We will conclude with our strategy and vision on how to embed cost-effective PHM into lighting applications.

Reliability Evaluation for Clustered WSNs under Malware Propagation.

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node’s MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN.

Analysis Of MPLS VPN Resistance To External Threats

Abstract This article presents analysis and possible reactions of MPLS network on unauthorized access and on device’s failure. Five Cisco routers joined into a core network were used to conduct the project. The devices were connected into a classic reliability model with envelope shape. The key aspects of the research were incidents’ simulation in line with previously prepared scenarios as well as diagnosis of network’s reactions to a particular type of a device failure. These effects were modelled using special devices such as analysers and generators of data traffic. Additionally, the range of the project included measuring convergence time of the routing protocols acting inside the network combined with analysis of reactions to forced changes of route.

Evaluating the Psychometric Properties of the Teamwork KSA Test

The Teamwork KSA Test has been a welcome addition to the practitioner's selection toolkit as well as a useful measure for teamwork researchers. We analyzed the psychometric properties of the Teamwork KSA Test and recognized correlates of the construct using three samples of working individuals recruited through a university (N = 241, N = 230, N = 332) and found slim evidence for a factor structure of the underlying constructs. Furthermore, classical reliability estimates did not meet traditional psychometric standards. Total TKSA scores predicted self‐ratings and instructor ratings of teamwork in two of the samples; effect sizes were small.

A non-linear fuzzy degradation model for estimating reliability of a polymeric coating

Oftentimes, material components follow non-linear degradation because the material loss trough time is non-linear; the components present different levels of resistance during a degradation process. Constructing predictions of the component life span and performing reliability analyses regarding degradation is possible by non-linear regression. Generally, this method ignores the prediction error in the extrapolated time and it does not account measurement error in the observed sample paths. In this paper, it is proposed new means for predicting time to failure of the components, using a calibration regression method for measuring the error prediction in the extrapolation process. In order to consider the uncertainty, we proposed to perform the estimated times by a trapezoidal fuzzy numbers, providing a more reliable prediction without loss of information. Since the times to failure are shaped by fuzzy numbers, it was adapted the fuzzy probability theory to the classical reliability analysis for estimating the fuzzy reliability of a component. An accelerated test of a polymeric coating process was performed and also an application for a preventive maintenance frequency was proposed.

Operational Reliability Assessment of Compressor Gearboxes with Normalized Lifting Wavelet Entropy from Condition Monitoring Information

Classical reliability assessment methods have predominantly focused on probability and statistical theories, which are insufficient in assessing the operational reliability of individual mechanical equipment with time-varying characteristics. A new approach to assess machinery operational reliability with normalized lifting wavelet entropy from condition monitoring information is proposed, which is different from classical reliability assessment methods depending on probability and statistics analysis. The machinery vibration signals with time-varying operational characteristics are firstly decomposed and reconstructed by means of a lifting wavelet package transform. The relative energy of every reconstructed signal is computed as an energy percentage of the reconstructed signal in the whole signal energy. Moreover, a normalized lifting wavelet entropy is defined by the relative energy to reveal the machinery operational uncertainty. Finally, operational reliability degree is defined by the quantitative value obtained by the normalized lifting wavelet entropy belonging to the range of [0, 1]. The proposed method is applied in the operational reliability assessment of the gearbox in an oxy-generator compressor to validate the effectiveness.

Measuring positive affect and well-being after spinal cord injury: Development and psychometric characteristics of the SCI-QOL Positive Affect and Well-being bank and short form

ObjectiveTo develop an item response theory (IRT)-calibrated spinal cord injury (SCI)-specific Positive Affect and Well-being (PAWB) item bank with flexible options for administration.DesignQualitative feedback from patient and provider focus groups was used to expand on the Neurological Disorders and Quality of Life (Neuro-QOL) positive affect & well-being item bank for use in SCI. New items were created and revised based on expert review and patient feedback and were then field tested. Analyses included confirmatory factor analysis, graded response IRT modeling and evaluation of differential item functioning (DIF).SettingWe tested a 32-item pool at several rehabilitation centers across the United States, including the University of Michigan, Kessler Foundation, Rehabilitation Institute of Chicago, the University of Washington, Craig Hospital and the James J. Peters/Bronx Department of Veterans Affairs hospital.ParticipantsA total of 717 individuals with SCI answered the PAWB questions.ResultsA unidimensional model was observed (Confirmatory Fit Index = 0.947; Root Mean Square Error of Approximation = 0.094) and measurement precision was good (reliability in theta of –2.9 to 1.2 is roughly equivalent to classical reliability of 0.95 or above). Twelve items were flagged for DIF, however, after examination of effect sizes, the DIF was determined to be negligible and would have little practical impact on score estimates. The final calibrated item bank resulted in 28 retained itemsConclusionsThis study indicates that the Spinal Cord Injury – Quality of Life PAWB bank represents a psychometrically robust measurement tool. Short form items are also suggested and a computer adaptive test is available.

Availability of Stochastic Degrading Systems Subject to Imperfect Repair

A series repairable system not only is one of the classical reliability models but is also the usually used model in practice. In this paper, a method for calculating system availability for series repairable system is studied. From the states of components, this methodology uses a gamma distribution to model the material degradation, and the impact of imperfect maintenance actions on the system reliability is investigated. The state of a degrading system immediately after the imperfect maintenance action is assumed as a random variable and the maintenance time follows a geometric process. At last, a numerical example is presented to demonstrate the use of the model.

Uncertainty Quantification and Structural Reliability Estimation Considering Inspection Data Scarcity

AbstractReliability estimation of aging RC structures under environmental attack involves various sources of uncertainties. In realistic operational conditions, uncertainties due to limited number of experimental data and incomplete inspection information affect the reliability assessment of the existing RC structures. A fuzzy probabilistic methodology is proposed in this paper to consider the inspection data scarcity for the RC structures uncertainty quantification and reliability estimation. The membership function is used to describe the fuzzy characteristic of small numbers of detection results. Following this, the fuzzy variables are transformed to the equivalent random variables and the classic reliability index can be obtained. The proposed methodology is compared with the conventional probabilistic approach using the goodness-of-fit method. The effect of data scarcity is discussed in detail. Next, the developed methodology is demonstrated with a RC bridge. Parametric studies of the concrete streng...

An approach for the global reliability based optimization of the size and shape of truss structures

This paper presents a methodology to perform global reliability based design optimization (RBDO) of the size and shape of truss structures. This methodology is comprised by the use of a global constraint and the response surface method to deal with the reliability analysis together with the firefly algorithm (FA) to carry out the structural optimization. The former is responsible for the reduction of the computational cost required in the evaluation of the probabilistic constraints. The latter overcomes the issues related to the non-convexity and mixed-variables of the optimization problem. Two examples are analysed in order to show the effectiveness of the methodology. In these examples, the FA is compared to other two well-known algorithms, the harmony search (HS) and genetic algorithm (GA). As a result, the FA reached the best performance in the examples analysed. All the optima found were checked using a classical first order reliability method (FORM) approach, validating the results provided by the response surface method.

Consideration of Extreme Wind Geographical Correlation Scenarios in Reliability Assessment Studies Using Sequential Monte Carlo Simulations

Electrical generation based on the use of renewable energies is emerging in modern grids. In that way, one of the most popular solutions as well in transmission as in distribution grids is certainly coming from wind energy. However, wind resources on a given location randomly fluctuate with time and have thus a major impact on the capacity of the electrical system to continuously face the load. In order to evaluate this impact and to consequently adapt required reinforcements, Monte Carlo simulations are often used. Those approach can be either sequential or not. Nowadays, load shifting solutions (storage, demand side management…) are practically set in order to adapt consumption to time varying generation without involving too consequent investments. In that way, sequential approach is currently preferred when it comes to long-term planning evaluation and adapted time series models are developed to characterize wind generation on a given site. The consideration of the geographical correlation between those models has been recently investigated in some references. This paper proposes to complete those contributions by evaluating the impact of wind geographical correlation on classical reliability indices such as the Loss of Load Expectation (LOLE) or the Expected Energy not Served (EENS).

Research on Reliability Evaluation Methods of Distribution System

Research on reliability of distribution network has very important meaning and function to ensure the quality of power supply. This paper introduces some basic concepts of reliability in distribution network, including distribution network reliability definition, task and index. The classical reliability evaluation method was reviewed, and focused on specific distribution network, an example analysis was given, and specific reliability evaluation indexes were calculated. Finally, the future development of distribution network reliability evaluation was made a simple prospect.

Validation of the World Health Organization Disability Assessment Schedule II (WHODAS-II) in patients with multiple sclerosis

Background: The World Health Organization Disability Assessment Schedule (WHODAS-II) is a widely used generic assessment instrument for health and disability. However, a specific psychometric evaluation for this scale in multiple sclerosis (MS) is lacking. This study is aimed at the assessment of the psychometric properties of the WHODAS-II in MS with Cronbach’s α and modern Rasch-model analyses. Methods: The WHODAS-II was administered to 136 consecutively recruited MS patients. Several indexes of fit to the Rasch model were evaluated in order to assess internal construct validity. Internal consistency was assessed with Cronbach’s α and the Person Separation Index (PSI). External validity was evaluated by analyzing correlations between the WHODAS-II and the Multiple Sclerosis Quality of Life-54 (MSQoL-54). Results: Classical reliability indexes (Cronbach’s α and intraclass correlation) showed good to excellent reliability for most of the subscales and for the total scale (α = 0.93). The total scale both with (36 items) or without (32 items) work items reached good fit to the Rasch model (PSI = 0.83). However, analysis of the subscales could resolve only four subscales out of seven. Conclusions: The WHODAS-II is a reliable and valid instrument for the assessment of patient-reported disability in MS, with some limitations including some item redundancy and questionable reliability of some subscales.

Unification of Common Cause Failures’ Parametric Models Using a Generic Markovian Model

The main objective of this paper is to provide a unified Markov model which could implement any parametric models devoted to the treatment of common cause failures (CCFs), namely: beta factor, multiple greek letter, alpha factor, multiple beta factor, and binomial failure rate. The choice of the Markovian representation is motivated by the fact that classical reliability approaches (e.g., Fault trees) are not able to catch the dynamic aspect induced by CCF events. The proposed Markovian model is also capable of dealing with any M-out-of-N configuration. It is illustrated on the basis of a system made up of four identical components (N = 4) in order to quantitatively examine the differences between the first four mentioned parametric models. For that purpose, the considered performance indicators are the average unavailability (Uavg) and the average unconditional failure intensity (i.e., failure frequency) (wavg).

A New Cut-Set Distribution in Fuzzy Reliability Models of Mechanical Structures

The mechanical fuzzy reliability models based on cut-set method have been applied to many fields in engineering. The reliability analysis results are affected by the cut-set distribution. In this paper, a basic mechanical fuzzy reliability model using cut-set method was established. Based on this model, the deficiency of the three previous cut-set distributions, i.e. uniform distribution, linear distribution and truncated normal distribution, was analyzed, and a new cut-set distribution named modified truncated normal distribution was proposed. The analysis results showed that when the cut-set is long enough, the reliabilities of the fuzzy models with the three previous cut-set distributions tend to a fixed value which has nothing to do with the specific mechanical structures, which makes the models have no good convergence and unreasonable. While, the fuzzy reliability model with the new cut-set distribution proposed in this paper can converge well to the classical random reliability model. Moreover, the reliabilities of a stress-strength interference problem and a resonance problem were assessed as two examples to verify the theoretical analysis results, which indicate that with this new cut-set distribution, the fuzzy reliability models have better convergence and wider applications.

A new fault tree analysis approach based on imprecise reliability model

Fault tree analysis is a powerful and computationally efficient technique for safety analysis and reliability prediction. It decomposes an undesired failure into multiple possible root causes by constructing a sub-event tree and spreading it into basic events. Classical reliability theory using probability theory to quantify the uncertainties of basic events encounters many challenges when failure data are limited. In this case, uncertainty quantification should be carried out based on subjective information, such as experts’ assessment or engineers’ experience. As a generalization of probability theory, imprecise probability theory can quantify subjective information as the upper and lower expectations or previsions. In this article, a fault tree analysis algorithm incorporating subjective information into imprecise reliability models of basic events is proposed to calculate the failure interval of lubricating oil warning system.