Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran

The performance of mining machines depends on the reliability of the equipment used, the operating environment, the maintenance efficiency, the operation process, the technical expertise of the miners, etc. As the size and complexity of mining equipments continue to increase, the implications of equipment failure become ever more critical. Therefore, reliability analysis is required to identify the bottlenecks in the system and to find the components or subsystems with low reliability for a given designed performance. It is important to select a suitable method for data collection as well as for reliability analysis. This paper presents a case study describing reliability and availability analysis of the crushing plant number 3 at Jajarm Bauxite Mine in Iran. In this study, the crushing plant number 3 is divided into six subsystems. The parameters of some probability distributions, such as Weibull, Exponential, and Lognormal distributions have been estimated by using ReliaSoft's Weibull++6 software. The results of the analysis show that the conveyer subsystem and secondary screen subsystem are critical from a reliability point of view, and the secondary crusher subsystem and conveyer subsystem are critical from an availability point of view. The study also shows that the reliability analysis is very useful for deciding maintenance intervals.     

Multi‐system reliability trend analysis model using incomplete data with application to tank maintenance

This paper focuses on the estimation of power law type reliability trend analysis model given incomplete failure data from multiple homogeneous machines. In many real situations, we encounter data having missing parts especially in the initial recording stage, so‐called left censored data. We provide a method to estimate the failure intensity function of power law process using left censored data from multiple machines. The method consists of two folds: initially estimate parameters via the law of large numbers theory and revise the estimated parameters recursively through the EM algorithm. The validity of our method is confirmed by simulation experiments. We also apply our method to the real‐world case, Korean ARMY tank maintenance data, and show that our proposed method is applicable to practical maintenance planning.     

A Framework for Reliability Prediction During Product Development Process Incorporating Engineering Judgments

This paper presents a comprehensive framework for reliability prediction during the product development process. Early in the product development process, there is typically little or no quantitative evidence to predict the reliability of the new concept except indirect or qualitative information. The proposed framework addresses the issue of utilizing qualitative information in the reliability analysis. The framework is based on the Bayesian approach. The fuzzy logic theory is used to enhance the capability of the Bayesian approach to deal with qualitative information. This paper proposes to extract the information from various design tools and design review records and incorporate it into the Bayesian framework through a fuzzy inference system. The Weibull distribution is considered as failure/survival time distribution with the assumption of a known value of shape factor. Initial parameters of the Weibull distribution are estimated from warranty data of prior systems to estimate the initial Bayesian parameter ( λ_t). The applicability of the framework is illustrated via an example.     

Age replacement policy in the case of no data: the effect of Weibull parameter estimation

Age replacement is a common maintenance policy when wear-out failures occur, and it is characterised by periodic replacement of components. Data on time to failure (TTF), often modelled with the Weibull function, are necessary for estimating optimal replacement intervals to minimise the total maintenance costs. In many cases, such as new components, new machines or new installations, no TTF data are available, so the Weibull parameters and optimal replacement interval cannot be estimated. To overcome this problem, these parameters can be assessed from the experience of the maintenance engineers and technicians. The aim of this study is investigating the relationship between the error in parameter estimation and additional maintenance costs related to this error. Analysis of variance (ANOVA) and multifactorial analysis are carried out for investigating the influence of these estimations on the final costs. Economic decision maps are introduced for supporting maintenance engineering in defining the maintenance policy with minimal additional cost in the case of no data being available. The analysis shows that, when no data are available, the application of the age replacement policy can result in a global saving of more than 50% compared with corrective maintenance.     

Establishment and Analysis of the Fault Tree for the Oil Transfer Pump System in CNPC Work Zone

The reliability of the equipment is very important for the large petrochemical industry, especially for oil pump as the core component of driving equipment. In order to reduce the loss of the enterprise brought by equipment failure, it is need to find those reasons which may lead to equipment failure and take some preventive measures as early as possible. This article analyzes the failure of the oil transfer pump system in CNPC work zone systematically, qualitatively and quantitatively, using the fault tree analysis method. Then 105 groups of minimal cut sets are found, and the probability of system failure after a certain time operation is calculated by using Weibull distribution. Combined with specific requirements of reliability, the work zone may make a scientific decision of plant maintenance cycle according to the conclusion.     

三参数威布尔分布参数估计及在可靠性分析中的应用

威布尔分布是可靠性中应用最广泛的分布之一。三参数威布尔分布尤其适用于在开始使用时有一时间段内不发生故障的情况。由于该分布的位置参数不等于0,在参数估计时不能采用简单的参数估计方法实现,限制了该分布形式在可靠性分析中的应用。根据三参数威布尔分布的特点提出了一种综合图解法和遗传算法的参数估计方法,应用该方法可以获得更精确的参数估计值。随后应用于某系列数控车床计算机数控系统的故障分析中,验证了本文提出的参数估计方法的可行性。     

Reliability analysis of hydraulic systems of LHD machines using the power law process model

Earlier, a preliminary study of the reliability characteristics of a fleet of load-haul-dump (LHD) machines deployed at Kiruna mine showed that the engine and the hydraulics are the two most critical subsystems. Hydraulic systems are selected for further study because such systems are still under a development phase. Maintenance data for two years for these machines are analyzed. The tests for trends and serial correlation showed that times between successive failures for the hydraulic systems are in most cases not independent and identically distributed. Goodness-of-fit tests showed that the power law process model provides a good fit to the failure data of the hydraulic systems. Methods for parameter estimation in the power law process model and estimation of optimal maintenance intervals for such systems are presented. Emphasis is on the use of graphical methods for data analysis.     

Reliability analysis for electronic devices using beta‐Weibull distribution

Today, in reliability analysis, the most used distribution to describe the behavior of electronic products under voltage profiles is the Weibull distribution. Nevertheless, the Weibull distribution does not provide a good fit to lifetime datasets that exhibit bathtub‐shaped or upside‐down bathtub–shaped (unimodal) failure rates, which are often encountered in the reliability analysis of electronic devices. In this paper, a reliability model based on the beta‐Weibull distribution and the inverse power law is proposed. This new model provides a better approach to model the performance and fit of the lifetimes of electronic devices. To estimate the parameters of the proposed model, a Bayesian analysis is used. A case study based on the lifetime of a surface mounted electrolytic capacitor is presented, the results showed that the estimation of the proposed model differs from the inverse power law–Weibull and that it affects directly the mean time to failure, the failure rate, the behavior, and the performance of the capacitor under analysis.     

System reliability of a manufacturing network with reworking action and different failure rates

From the perspective of network analysis, the manufacturing system can be constructed as a stochastic-flow network, since the capacity of each machine is stochastic (i.e. multistate) owing to the failure, partial failure, and maintenance. Considering reworking action and different failure rates of machines, the input flow (raw materials/work in process) processed by each machine might be defective, and therefore the output flow (work in process/products) would be less than the input amount. To evaluate the capability of the manufacturing system, we measure the probability that the manufacturing network can satisfy demand. Such a probability is defined as the system reliability. A decomposition method is first proposed to divide the manufacturing network into one general processing path and one reworking path. Subsequently, two algorithms are utilised for different network models to generate the lower boundary vector of machine capacity to guarantee that the manufacturing network is able to produce sufficient products fulfilling the demand. The system reliability of the manufacturing network can be derived in terms of such a capacity vector afterwards.     

Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant

Power generators are critical assets in wastewater treatment plants (WWTPs) in Australia and many countries. Better managing the lifetime, minimising failures, improving reliability and availability, and reducing operating and maintenance costs of the power generation assets are still challenging topics for water utilities. This case study aims to develop power generation system reliability and availability modelling considering redundancy to minimise operation and maintenance costs. The two-parameter Weibull model was used to assess system reliability and availability to power generation engines in WWTPs. The Kaplan-Meier method (a time-driven estimation technique) and the log beta-Weibull model (which is suitable for modelling censored and uncensored data) were used to analyse and validate the modelling results. Shape and scale parameters of the Weibull models were estimated by maximising the log-likelihood function using non-linear optimisation. Hazard and reliability functions were calculated using the Weibull model. Results using two-parameter Weibull, Kaplan-Meier, and log beta-Weibull models display low reliability and high hazard rate over time, which was associated with spark plug failure due to a suboptimal start and stop operation strategy.     

A modified Weibull extension with bathtub-shaped failure rate function

Models with bathtub-shaped failure rate function are useful in reliability analysis, and particularly in reliability related decision making and cost analysis. The traditional Weibull distribution is, however, unable to model the complete lifetime of systems with a bathtub-shaped failure rate function. In this paper, a new model, which is useful for modeling this type of failure rate function, is presented. The model can also be seen as a generalization of the Weibull distribution. Parameter estimation methods are studied for this new distribution. Examples and results of comparison are shown to illustrate the applicability of this new model.     

Probability distribution of machining center failures

Through field tracing research for 24 Chinese cutter-changeable CNC machine tools (machining centers) over a period of one year, a database of operation and maintenance for machining centers was built, the failure data was fitted to the Weibull distribution and the exponential distribution, the effectiveness was tested, and the failure distribution pattern of machining centers was found. Finally, the reliability characterizations for machining centers are proposed.     

Reliability analysis using exponentiated Weibull distribution and inverse power law

Today in reliability analysis, the most used distribution to describe the behavior of devices is the Weibull distribution. Nonetheless, the Weibull distribution does not provide an excellent fit to lifetime datasets that exhibit bathtub shaped or upside‐down bathtub shaped (unimodal) failure rates, which are often encountered in the performance of products such as electronic devices (ED). In this paper, a reliability model based on the exponentiated Weibull distribution and the inverse power law model is proposed, this new model provides a better approach to model the performance and fit of the lifetimes of electronic devices. A case study based on the lifetime of a surface‐mounted electrolytic capacitor is presented in this paper. Besides, it was found that the estimation of the proposed model differs from the Weibull classical model and that affects the mean time to failure (MTTF) of the capacitor under analysis.     

An exploratory study of a neural network approach for reliability data analysis

The results of this paper show that neural networks could be a very promising tool for reliability data analysis. Identifying the underlying distribution of a set of failure data and estimating its distribution parameters are necessary in reliability engineering studies. In general, either a chi-square or a non-parametric goodness-of-fit test is used in the distribution identification process which includes the pattern interpretation of the failure data histograms. However, those procedures can guarantee neither an accurate distribution identification nor a robust parameter estimation when small data samples are available. Basically, the graphical approach of distribution fitting is a pattern recognition problem and parameter estimation is a classification problem where neural networks have been proved to be a suitable tool. This paper presents an exploratory study of a neural network approach, validated by simulated experiments, for analysing small-sample reliability data. A counter-propagation network is used in classifying normal, uniform, exponential and Weibull distributions. A back-propagation network is used in the parameter estimation of a two-parameter Weibull distribution.     

A study of time‐between‐events control chart for the monitoring of regularly maintained systems

Owing to usage, environment and aging, the condition of a system deteriorates over time. Regular maintenance is often conducted to restore its condition and to prevent failures from occurring. In this kind of a situation, the process is considered to be stable, thus statistical process control charts can be used to monitor the process. The monitoring can help in making a decision on whether further maintenance is worthwhile or whether the system has deteriorated to a state where regular maintenance is no longer effective. When modeling a deteriorating system, lifetime distributions with increasing failure rate are more appropriate. However, for a regularly maintained system, the failure time distribution can be approximated by the exponential distribution with an average failure rate that depends on the maintenance interval. In this paper, we adopt a modification for a time‐between‐events control chart, i.e. the exponential chart for monitoring the failure process of a maintained Weibull distributed system. We study the effect of changes on the scale parameter of the Weibull distribution while the shape parameter remains at the same level on the sensitivity of the exponential chart. This paper illustrates an approach of integrating maintenance decision with statistical process monitoring methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation

Reliability has an impact on wind energy project costs and benefits. Both life test data and field failure data can be used for reliability analysis. In wind energy industry, wind farm operators have greater interest in recording wind turbine operating data. However, field failure data may be tainted or incomplete, and therefore it needs a more general mathematical model and algorithms to solve the model. The aim of this paper is to provide a solution to this problem. A three-parameter Weibull failure rate function is discussed for wind turbines and the parameters are estimated by maximum likelihood and least squares. Two populations of German and Danish wind turbines are analyzed. The traditional Weibull failure rate function is also employed for comparison. Analysis shows that the three-parameter Weibull function can obtain more accuracy on reliability growth of wind turbines. This work will be helpful in the understanding of the reliability growth of wind energy systems as wind energy technologies evolving. The proposed three-parameter Weibull function is also applicable to the life test of the components that have been used for a period of time, not only in wind energy but also in other industries.     

Experimental report on the reliability of ammonia-water absorption chillers

The analysis of a large amount of data on the failures of absorption refrigeration machines of the ammonia-water type was carried out. The data derive from the records of a 10-year maintenance contract under which every machine was installed. The records were computerized for the period 1989–1992, and they refer to sales starting from 1980 (i.e. to 73 778 machines). The failures are classified, identifying the most serious (generator and solution pump). The rate of early failures is rather high, but the failures are generally minor and easily repaired. The production quality was not constant, with a very bad year in 1984. The failure rate is evaluated for the first 10 years of life of the equipment. In general terms, one machine in two does not require any intervention during that period.     

基于故障数据的设备运行可靠性分析与评估

基于故障数据,对设备运行可靠性进行了分析与评估。对某汽车制造企业的一台卧式加工中心的故障数据进行了统计与分析,形成观测样本,并拟合出了设备故障间隔时间的概率密度分布函数和累计分布函数曲线,从而推断得出其分布规律可能服从威布尔分布。然后通过对威布尔分布函数相关性进行检验,验证了该设备的故障间隔时间分布服从威布尔分布。最后根据统计结果计算得出了该设备的各项可靠性评估指标。     

A study of Weibull shape parameter: Properties and significance

The two-parameter Weibull distribution has been widely used for modelling the lifetime of products and components. In this paper we study the effect of the shape parameter on the failure rate and three variables of importance in the context of maintenance and reliability improvement. These variables are (i) time to failure, (ii) age at replacement based on risk and (iii) residual life. We propose a classification scheme for the distribution based on the shape parameter and discuss the application of the results.     

Reliability estimation with Weibull inter failure times

In this paper we present three models for the behavior of software failures. By applying these models an attempt has been made to predict reliability growth by predicting failure rates and mean time to next failure of software with Weibull inter failure times at different stages. The changes in the performance of the software as a result of the error removal are described as a Bayes empirical-Bayes prediction in Model I. Model II considers a fully Bayesian analysis with non informative priority of Weibull parameters. An approximation due to Lindley is used in this model as the expressions do not appear in close forms. The M.L. approach is used in Model III. Finally we apply these three models to actual failure data and compare their predictive performances. The comparison of the proposed models is also made in terms of the ratio of likelihoods of observed values based on their predictive distributions.

Among these three models, Model I seems to be quite reasonable as it shows higher reliability growth in all stages. It is noted that this model may be useful to measure the current reliability at any particular stage of the testing process and viewed as a measure of software quality.