System-Reliability Prediction in Impulsive-Noise Environments, Based on Wear-Dependent Failure Rates, Using Response “Peak” Statistics

Modern products are complicated and standards of reliability are higher than ever before. Therefore, prediction of system reliability is essential, especially as the time available for developing products has decreased. Most of the previous studies assumed that the causes of failure are random. That is, the failure rates of components follow exponential distributions. To reflect various types of failure such as initial, random, or wear-out failure, we developed a novel procedure for estimating system reliability based on the Weibull distribution. We analyzed the effect of component selection on the types of system failure. Finally, we verified the proposed method by analyzing warranty data on actual Korean products.

Nowadays, products are complicated and standards of reliability are higher than ever before. In contrast, the time available for developing products has gradually decreased. In this context, assessment of the system reliability of products is an active field of study. In this paper, an experimental design was used to investigate the effect of the failure type of components on the whole system’s failure type. For this, a general procedure for estimating system reliability based on the Weibull distribution was adopted.

Flexible manufacturing cells (FMC) are used to process a variety of products in the system and provide higher productivity. The components of the system such as robots and machines are more likely to fail when compared with the traditional manufacturing system due to their higher operating rate. Literature studied reveals that the failures of the machines have a high impact on the production rate of the system. In this work, two types of failures are considered i.e. wear-out failure and random failure. Wear-out failure can be eliminated by applying some maintenance plans but random failures are difficult to eliminate. So simulation experiments along with maintenance plans are studied to eliminate such failures. We also discussed two types of failure rate distributions i.e. exponential, and Weibull distribution and their effect on the throughput of the FMC. In this analysis, ARENA simulation model is made and simulation experiments were performed to analyze the throughput of FMC that consists of 2 machines in parallel and a robot for material handling. We assumed that failure is occurring in machines only and the robot is reliable throughout the simulation.

Structural engineering makes a giant stride forward with this symposium. This book represents an international sym­ posium held in Stockholm honoring Prof. W. Weibull. It drew participants from 21 countries with a total of 55 invited papers. There were 7 scientific sessions. Most of our studies in school revolved about deterministic happenings. Random loading becomes more and more important when we equate events in our world with physical and mathematical reasoning. Probabilistic failure model and stochastic fatigue crack growth play important roles in the safety of nuclear structures, random loading due to wind turbulence, earthquake spectra, offshore structural design, and aircraft design. The initial paper summarizes Prof. Weibull's life and ac­ complishments including a mention of Weibull distribution used in fatigue studies. The initial session deals with extreme value theory (EVT). The first paper reviewed applications of extreme value theory to material behavior and indicates the importance of the accompanying statistical theory. The next paper generalizes Weibull distribution in matrix form. The following papers proclaim (a) extreme value theory as com­ patible to random field theory, (b) incomplete samples can be transformed to a mixed-exponential survival for a more com­ prehensive Weibull distribution, (c) EVT in endurance testing of ball and roller bearings, (d) use of the Slepian model identi­ fying the behavior of Gaussian noise near or between its zero crossing, (e) plastic movement of SDOF elastic plastic oscillator subjected to stationary Gaussian process excitation, and (/) generalized Hermitian polynomial used in the pertur­ bation method for nonlinear random vibrations. The final paper reports on Weibull distribution in large earthquake modelling. Session 2 reports on fatigue crack growth. Beginning with an analysis of stochastic equation models of crack growth, it continues with stochastic models of fatigue crack growth and propagation, prediction of crack growth under spectrum loading employing a cycle-by-cycle technique and fatigue life distribution in a random loading sense with interacting failures. The final paper considers a new approach to fatigue crack propagation under random loading and the use of Monte Carlo simulation in correlating micro fracture process and fatigue crack propagation. This session indicates that more experimental work is required in crack propagation to reach the same level as crack initiation. Session 3 comprises a total of 13 papers and dwells upon probabilistic failure models. The initial paper proposes a generalized probabilistic model for the fatigue life and reliability prediction of a structure containing noninteracting cracks and employs Poisson random sets. The next group of papers focuses upon (a) structural reliability treated as a single stochastic model, (Jb) fatigue life and reliability estimates of mineral pipe lines using probability theory, (c) Weibull-based equation employed in predicting the failure of a stressed brittle material, (d) a preliminary suggestion that specimens failed in proof tests may be used to estimate the parameters of the reliability function for the survivors, and (e) brittle material design using three parametric Weibull distributions. The next set of papers considers ring-in-ring tests on strength of cladded glass using the three-parameter Weibull distribution, prob­ abilistic analysis of plastic plates utilizing yield-line theory plus lower bound reliability analysis of plastic plates evaluated by inclusion-exc lusion theory. The final set of papers ques­ tions the possibility of flaws in the present probabilistic models for unidirectional fibrous composites, strength rela­ tions for cracks in unidirectiona l long fiber composites employing Weibull distribution and a stochastic approach in studying the fracture and fatigue of concrete. Session 4 delves into probabilistic fracture mechanics. The initial paper estimates the probable failure of PWR pressure vessels employing Weibull distribution and updates the wellknown Marshall A(a) and B(a) functions. The final set of papers covers probabilistic assessment of structures with weld defects, probability of fracture in main coolant pipes of PWR, and statistical modelling of shaft predictions (as measured by Charpy impact curve) in the reference temperature of pressure vessel welds. The authors suggested that U.S. Nuclear Regulatory Guide 1.99 be updated to remove excessive conservatism.

The reliability of circular electrical connectors was usually assessed according to standards like MIL-HDBK-217 (Reliability Prediction of Electronic Equipment). Given to their limitations and mislead results, a new assessment method needs to be presented. Some concerns on the physics of failure (PoF) modeling and reliability assessment of circular electrical connectors are addressed in this paper to overcome this defect. An original approach combining laboratory accelerated degradation testing (ADT) and PoF modeling is proposed for effectively assessing connector reliability. Random vibration and current stress are selected as acceleration factors that affect the contact life of electrical connectors. Then, an appropriate multiple-stresses ADT scheme is derived. The PoF model for electrical connectors under multiple stresses yielded the generalized Eyring relationship. According to the system reliability model for multiposition connectors, connectors' life follows Weibull distribution, which is derived from extreme-value distribution theory. Through ADT and data gained from tests, statistical analysis result yielded the estimated value of reliability character of MIL-C-38999 I series electrical connectors under the action of vibration and current stresses.

Weibull distribution is widely used in various areas such as life tables, failure rates, and definition of wind speed distribution. Therefore, parameter estimation for the Weibull distribution is an important problem in many real data applications. The least square (LS), the weighted least square (WLS) and the maximum likelihood (ML) are the most popular methods for the parameter estimation in the Weibull distribution. In this study, based on the LS, WLS and ML estimation methods, a multi-objective programming approach is proposed for the parameter estimation of two-parameter Weibull distribution. This new approach evaluates together LS, WLS and ML methods in the estimation process. NSGA-II method, which is a multi-objective heuristic optimization method, is used to solve the proposed multi-objective estimation model. To evaluate the applicability and performance of the proposed approach, a detailed Monte Carlo simulation study based on deficiency criteria and a real data application are designed. The results illustrated that the proposed multi-objective programming approach provides quite accurate parameter estimates for the two parameter Weibull distribution with respect to deficiency criteria.

The development in technology is taking place with an accelerating pace across the globe. The increasing expansion and advancement in the field of information technology and the modern teaching system provide a technical support for the development of a distance teaching system to learn English courses. Multimedia teaching system of English course based on B/S framework (system 1) and English teaching system based on MVC architecture (system 2) were the two most prominent and widely used approaches for the distance teaching system of English learning courses. These systems comprehensively consider the current English teaching needs, develop the existing architectures, discuss the system architecture and functions, and establish the corresponding development environment. However, the mentioned systems have the problem of high proportion of memory resource consumption and high failure rate of the communicating nodes. In order to reduce the proportion of memory resource consumption and node failure rate of distance teaching system and effectively improve the teaching effect, this study designed a distance teaching system of English course based on wireless network technology. In order to analyze the functionality and stability of the wireless network technology in distance teaching of English course, the server-side and client-side modules of the system are designed. The server side is mainly used to maintain and control the overall functions of the system, while the client side is used to access/request the contents from the server. On this basis, the system software module is designed. The memory consumption results are accounted for under 30%, which is significantly lower than the earlier-mentioned systems, and the node failure rate of the system proposed in this paper does not increase significantly and remains below 4% all the time which indeed is a very low amount of failure rate. The experimental results show that the memory resource consumption ratio and node failure rate of the proposed system are very low, and the application feedback effect is significantly better than the other systems.

A new three-parameter generalized distribution, namely, half-logistic generalized Weibull (HLGW) distribution, is proposed. The proposed distribution exhibits increasing, decreasing, bathtub-shaped, unimodal, and decreasing-increasing-decreasing hazard rates. The distribution is a compound distribution of type I half-logistic-G and Dimitrakopoulou distribution. The new model includes half-logistic Weibull distribution, half-logistic exponential distribution, and half-logistic Nadarajah-Haghighi distribution as submodels. Some distributional properties of the new model are investigated which include the density function shapes and the failure rate function, raw moments, moment generating function, order statistics, L-moments, and quantile function. The parameters involved in the model are estimated using the method of maximum likelihood estimation. The asymptotic distribution of the estimators is also investigated via Fisher’s information matrix. The likelihood ratio (LR) test is used to compare the HLGW distribution with its submodels. Some applications of the proposed distribution using real data sets are included to examine the usefulness of the distribution.

Runtime noise management systems typically respond to on-chip noise sensors to accurately capture voltage emergencies. As such, the threshold voltage for noise sensors to report emergencies serves as a critical tuning knob between the system failure rate and the runtime performance loss (RPL) due to false alarms. Unfortunately, the problem of optimal threshold voltage computation remains open in literature despite its importance. The problem is further complicated by process variations, which introduce significant variations in load currents and thus in noise across different chips. A uniform noise margin may not work optimally for all the chips. In this paper, we first formulate the problem of minimizing the system failure rate subject to a given RPL constraint. We then put forward a uniform scheme to find an optimal solution for all chips. Compared to a seemingly more intuitive approach which is too conservative, experimental results over a set of industrial designs show an average of 32.1% reduction in system failure rate under the same RPL constraint. We further show that with the help of I ddq measurements during testing which reveals process variation information, it is possible and efficient to compute a per-chip optimal threshold voltage. Such an approach further reduces the system failure rate by 25.0% on average compared with the uniform threshold approach, under the same RPL constraint. To the best of the authors knowledge, this is the first in-depth study on optimal threshold voltage computation for noise sensors. We hope that it shall point out new directions for systematic studies of on-chip noise sensor utilization.

This paper takes an Alpha-stable distribution as the noise model to solve the parameter estimation problem of bistatic multiple-input multiple-output (MIMO) radar system in the impulsive noise environment. For a moving target, its echo often contains a time-varying Doppler frequency. Furthermore, the echo signal may be corrupted by a non-Gaussian noise. It causes the conventional algorithms and signal models degenerating severely in this case. Thus, this paper proposes a new signal model and a novel method for parameter estimation in bistatic MIMO radar system in the impulsive noise environment. It combines the fractional lower-order statistics (FLOS) and fractional power spectrum density (FPSD), for suppressing the impulse noise and estimating parameters of the target in fractional Fourier transform domain. Firstly, a new signal array model is constructed based on the $$\alpha $$ź-stable distribution model. Secondly, Doppler parameters are jointly estimated by peak searching of the FLOS---FPSD. Furthermore, two modified algorithms are proposed for the estimation of direction-of-departure and direction-of-arrival (DOA), including the fractional power spectrum density based on MUSIC algorithm (FLOS---FPSD---MUSIC) and the fractional lower-order ambiguity function based on ESPRIT algorithm (FLOS---FPSD---ESPRIT). Simulation results are presented to verity the effectiveness of the proposed method.

The burr XII and weibull distributions are the most important and the most widely used distributions for the life time distribution as well as the wealth distribution. Burr XII distribution is mainly used to explain the allocation of wealth and wages among the people of the particular society. And weibull distribution is mostly used in the extreme value theory, failure analysis and the reliability engineering. In this paper, the classical properties of the mixture burr XII weibull distribution have been discussed. The estimation of the parameters and the construction of the information matrix are introduced. The var-cov matrix and the interval estimation of the parameters are also proposed in the paper. Cumulative distribution function, hazard rate, failure rate, inverse hazard function, odd function and the cumulative hazard function, rth moment, moment generating function characteristic function, moments, mean and variance, Renyi and Beta entropies, mean deviation from mean and mean time between failures(MTBF)have also been discussed.

The reliability of the electric power system is very important in the continuity of the supply of electrical energy to the load. One of the important parts in the electric power system is the transformer. Maintaining the reliability of the transformer is an effort to make electrical energy channeled properly. For this reason, efforts are needed to evaluate whether the transformer is in good condition or not by using the Weibull distribution. By looking at the average failure rate, the MTTF value is used as a benchmark for reliability. It is based on the number of disturbances that cause the transformer to fail to operate. The results of the reliability analysis with the Weibull distribution on distribution transformers in the Rungkut ULP area during the time span from 2019, 2020, 2021 obtained an average failure rate of 18.321894% per year, with an MTTF value of 0.054579 per year, so the value reliability obtained 0.981. So according to Croanbach alpha, this value belongs to a very reliable level.

PurposeThe purpose of this paper is to understand the consequence of the use of mixed Weibull distribution in the cell formation problem. In reliability theory, a mixed distribution is used for more than one hazard cause, and the Weibull distribution can be used for ascendant, monotonous and descendant failure rate. Here, the authors mixed these two theme and use it in a common problem in group technology.Design/methodology/approachIn this paper, the authors made a non-polynomial-hard mathematical model based on past research and solved it with an exact algorithm. The algorithm is coded and solved in GAMS to illustrate the model, and the authors use simulation. A common numerical example is solved with the model, and the results are compared.FindingsReliability analysis model based on the mixed Weibull distribution approach will give options to a user to select the suitable failure rate and modes for a specific situation. If the user uses the exponential or Weibull distribution instead of the mixed Weibull distribution, the calculated cost and reliability are wrong; therefore, it leads to user making wrong decisions.Originality/valueThe model the authors use is the one used in past research, but in the past, researchers did not use the mixed distribution for explaining failure time. Therefore, the model can be considered as a new and more complete model.

In the era of advancement in technology and global competitiveness the traditional maintenance practices have become obsolete. Also in recent years sophisticated and more advanced equipments have been augmented in the armed forces hence, the role of maintenance workshops/ engineers has become very challenging. Therefore, analyzing various maintenance issues and the associated risk has become very important in the armed forces. Preventive maintenance practices assist in improving the reliability of assemblies and how it will be effective for the equipments of armed forces is illustrated in this article. Gear box assembly used in the medium size vehicles are considered for the analysis and the failure data in various operating conditions is taken from the logbooks of the vehicles. For modeling purposes the Weibull distribution has been chosen (since it has practical utility-ability to model products/systems consisting of large number of components and may have increasing/decreasing or a constant failure rate). The result will be useful to the maintenance engineer to find the unreliability of the gear box assemblies and also for future strategic decision making.

This paper discusses the problem of system reliability prediction by accelerated testing results on its components. This problem appears when the testing of a system as a whole either has high cost or may be impossible in a short period of time, especially at the beginning of development. A multi-variate Weibull model is proposed to utilize testing results of components in predicting system reliability with reduced test length and minimized cost. Prediction of system reliability in a given situation means that we must know how to calculate not only the point estimation of reliability index, but also the lower confidence bound (LCB) with given confidence probability q (q=0.8-0.95). In this case, the authors' goal is to give the algorithm for calculating not by approximation for LCB (based as a rule on normal distribution of point reliability estimation), but exact LCB corresponding to a given confidence probability.