液压驱动压裂泵液压系统设计及其脉动的抑制

针对传统机械式压裂泵冲次高、易损件寿命短等缺点,提出了液压驱动式压裂泵,设计了"半开半闭式液压系统",建立了仿真模型,搭建了试验样机,结果表明:仿真模型准确,液压驱动式压裂泵也可以实现高压输出,受液压缸泄露、魏氏效应、系统固有换向时间的影响,压裂泵存在压力和流量脉动,且随负载压力的增大而增大。根据压力和流量脉动特点,设计了一套由柱塞缸、气缸和蓄能器组等构成的气缸式蓄能器脉动抑制装置,结果表明:该装置能显著抑制压力和流量脉动;且脉动随蓄能器容积或预充气压力的增大而减小;在该装置的作用下,液压驱动式三缸压裂泵的压力脉动等效于机械式五缸压裂泵。     

Neue Vorvakuumpumpe: Schnelles Evakuieren dank Membran‐ Stabilisierungssystem. New roughing pump: Fast Evacuation thanks to Diaphragm Stabilization System

Diaphragm pumps are used at turbomolecular pumps to generate rough vacuum. They are characterized by high suction speed, which however deteriorate when the working pressure decreases. This is caused by the difference between the working pressure of the pump and the ambient pressure. The larger the pressure difference, the more the elastic diaphragm bulges, lowering the effective input volume of the pump. This problem is alleviated by a newly developed diaphragm stabilization system. Diaphragm roughing pumps equipped with this system pump down faster than pumps without the system. Due to the enhanced suction speed, they also ensure greater process reliability. The first application of the diaphragm stabilization system (patent applied for) is in a newly developed diaphragm roughing pump. This pump is driven by a compact, brushless DC motor with very great efficiency. The pump is available in 24‐volt DC and 90‐ to 264‐volt, 50/60‐Hz AC versions. OEM and portable versions are available.     

Reliability assessment for failure-dependent and uncertain systems: A Bayesian network based on copula method and probability-box

Managing failure dependence of complex systems with hybrid uncertainty is one of the hot problems in reliability assessment. Epistemic uncertainty is attributed to complex working environment, system structure, human factors, imperfect knowledge, etc. Probability-box has powerful characteristics for uncertainty analysis and can be effectively adopted to represent epistemic uncertainty. However, arithmetic rules on probability-box structures are mostly used among structures representing independent random variables. In most practical engineering applications, failure dependence is always introduced in system reliability analysis. Therefore, this paper proposes a developed Bayesian network combining copula method with probability-box for system reliability assessment. There are four main steps involved in the reliability computation process: marginal distribution identification and estimation, copula function selection and parameter estimation, reliability analysis of components with correlations and Bayesian forward analysis. The benefits derived from the proposed approach are used to overcome the computational limitations of n-dimensional integral operation, and the advantages of useful properties of copula function in reliability analysis of systems with correlations are adopted. To demonstrate the effectiveness of the developed Bayesian network, the proposed method is applied to a real large piston compressor.     

Analysis of correlated multivariate degradation data in accelerated reliability growth

Modern engineering systems have become increasingly complex and at the same time are expected to be developed faster. To shorten the product development time, organizations commonly conduct accelerated testing on a small number of units to help identify failure modes and assess reliability. Many times design changes are made to mitigate or reduce the likelihood of such failure modes. Since failure-time data are often scarce in reliability growth programs, existing statistical approaches used for predicting the reliability of a system about to enter the field are faced with significant challenges. In this work, a statistical model is proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program. The model allows the components in the system to have multiple failure modes, each associated with a monotone stochastic degradation process. To take into account unit-to-unit variation, the random effects of degradation parameters are explicitly modeled. Moreover, a mean-degradation-stress relationship is introduced to quantify the effects of different accelerating variables on the degradation processes, and a copula function is utilized to model the dependency among different degradation processes. Both a maximum likelihood (ML) procedure and a Bayesian alternative are developed for parameter estimation in a two-stage process. A numerical study illustrates the use of the proposed model and identifies the cases where the Bayesian method is preferred and where it is better to use the ML alternative.     

基于GaAs激光器性能退化的可靠性度量方法

针对传统可靠性度量方法对高可靠性及长寿命的激光产品进行可靠性度量存在因模型假设不准确而出现可靠性度量错误风险的问题,基于性能退化轨迹提出了利用非参数局部线性回归估计对实际的退化模型进行直接估计的方法.该方法在确定实际模型后,利用失效阈值外推获得伪失效寿命时间,进而采用完全寿命时间数据进行可靠性度量.最后通过对GaAs激光器的退化数据进行可靠性验证分析,结果表明此方法提高了可靠性的预测精度,拟合程度高,稳健性好.采用非参数局部线性回归估计方法得到的结果合理、准确.     

Estimation of time‐to‐failure distribution derived from a degradation model using fuzzy clustering

Some life tests are terminated with few or no failures. In such cases, a recent approach is to obtain degradation measurements of product performance that may contain some useful information about product reliability. Generally degradation paths of products are modeled by a nonlinear regression model with random coefficients. If we can obtain the estimates of parameters under the model, then the time‐to‐failure distribution can be estimated. In some cases, the patterns of a few degradation paths are different from those of most degradation paths in a test. Therefore, this study develops a weighted method based on fuzzy clustering procedure to robust estimation of the underlying parameters and time‐to‐failure distribution. The method will be studied on a real data set. Copyright © 2000 John Wiley & Sons, Ltd.     

基于完备总体经验模态分解和模糊熵结合的液压泵退化特征提取方法

针对液压泵振动信号具有非线性、非平稳性,以及信噪比低等特点,提出了基于完备总体经验模态分解和模糊熵结合的液压泵性能退化特征提取方法。首先,使用完备总体经验模态分解方法对液压泵振动信号进行分解,得到若干个固有模态函数分量。其次,求取各个分量与原始信号的相关性,选取相关性较高的前几个分量作为有效分量并求其模糊熵,实现液压泵的退化特征提取,形成特征向量。最后,以液压泵不同退化状态下的实测数据为例,使用基于变量预测模型的模式识别方法对提取的特征向量进行验证。实验结果表明,该液压泵退化特征提取方法具有较高的精度,使退化状态识别的准确率提高到了100%。     

A Method for Performance Degradation Modeling Based upon the Accelerated Experiment

Performance degradation modeling plays an important role in prognostics and health management of mechanical system. Influenced by the complex structure of the hydraulic pump and the limited experiment standards, it is hard to establish an appropriate performance degradation model. To fulfill current requirements, a method for establishing the performance degradation model based on accelerated experiment is proposed. In order to describe the general trend of the degradation, the double-stress exponential model is firstly established as the theoretical degradation model. On this basement, combined with the characteristics of the experiment, the accelerating coefficient is settled; meanwhile, the procedures for assuring the model parameters are presented. Furthermore, based on the accelerated experiment of the hydraulic pump under various stresses, the performance degradation model is finally established. Result of the experimental analysis indicates that the proposed method is applicable and the presented model is effective to measure the performance degradation of pump.     

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.     

相依失效生命线工程系统抗震可靠度估计的统一RDA算法

在网络连通可靠度分析的递推分解算法(Recursive Decomposition Algorithm,简称RDA)的基础上,先整合原RDA算法中分别用于不同网络赋权形式的三部分程序,发展出统一RDA算法。应用顺序复合法(Sequential Compounding Method,简称SCM)和Gumbel Copula函数分别计算RDA算法中不交最小路和不交最小割事件的联合失效概率,解决元件相依失效生命线工程系统的抗震可靠度估计问题,最终发展出相依失效生命线工程系统抗震可靠度估计的统一RDA算法。36节点格栅型网络可靠度案例计算结果表明:此方法可以用于相依失效网络系统的动力抗震可靠度估计,计算精度较高。     

Rotational torsion fatigue failure of an engine driven fuel pump coupling

Between 2005 and 2007 serious flight safety concerns arose when a small fleet of single engine basic trainer aircraft experienced five critical failures of engine driven fuel pump (EDFP) couplings, and a further instance of a cracked coupling. Consequent investigations showed that the coupling fractures were likely to have been caused by a combination of reverse torsional loading and bending. Engine ground runs were then carried out to evaluate linear and torsional engine vibration levels in the drive train affecting the fuel pumps. Analysis of the vibration data indicated that engines, which had caused pump‐coupling failures exhibited high‐amplitude torsional vibration at certain power settings. The excessive vibration level was eventually attributed to detuning of the crankshaft counterweight dampers. This investigation resulted in a change to the maintenance practises on the aircraft in question and no fuel pump failures have occurred since December 2008.     

Comparison of Methods to Estimate the Time‐to‐failure Distribution in Degradation Tests

Degradation tests are alternative approaches to lifetime tests and accelerated lifetime tests in reliability studies. Based on a degradation process of a product quality characteristic over time, degradation tests provide enough information to estimate the time‐to‐failure distribution. Some estimation methods, such as analytical, the numerical or the approximated, can be used to obtain the time‐to‐failure distribution. They are chosen according to the complexity of the degradation model used in the data analysis. An example of the application and analysis of degradation tests is presented in this paper to characterize the durability of a product and compare the various estimation methods of the time‐to‐failure distribution. The example refers to a degradation process related to an automobile's tyre, and was carried out to estimate its average distance covered and some percentiles of interest. Copyright © 2004 John Wiley & Sons, Ltd.     

Remaining useful life prediction for hard failures using joint model with extended hazard

In this paper, we investigate a joint modeling method for hard failures where both degradation signals and time‐to‐event data are available. The mixed‐effects model is used to model degradation signals, and extended hazard model is used for the time‐to‐event data. The extended hazard is a general model which includes two well‐known hazard rate models, the Cox proportional hazards model and accelerated failure time model, as special cases. A two‐stage estimation approach is used to obtain model parameters, based on which remaining useful life for the in‐service unit can be predicted. The performance of the method is demonstrated through both simulation studies and a real case study.     

A Degradation Feature Extraction Method for Hydraulic Pumps Based Upon MUWDF and MF-DFA

Hydraulic pump degradation feature extraction is a key step of condition-based maintenance. Since vibration signals of hydraulic pumps during degradation are strongly nonlinear and the feature information is too weak to be effectively extracted, a method based upon MUWDF and MF-DFA is proposed. Initially, the MUWDF is presented to reduce disturbances and improve feature information. Approximate signals of various decomposition layers are selected by feature energy factor and fused according to the presented fusion rules. On this basis, the fused signal is further processed by MF-DFA with a sliding window. Multi-fractal spectrum sensitive factors are selected to be the degradation feature vector of the hydraulic pump. The proposed method is verified by vibration signals sampled in a hydraulic pump degradation experiment.     

A Wiener‐based degradation model with logistic distributed measurement errors and remaining useful life estimation

Degradation data analysis, which investigates degradation processes of products to extrapolate the lifetime properties, is an effective method for reliability analysis. But degradation data that reflect a product's inherent randomness of degradation are often contaminated by measurement errors. To deal with the problem, this paper proposes a Wiener‐based model with an assumption of logistic distributed measurement errors and adopts the Monte Carlo expectation‐maximization method together with the Gibbs sampling for parameter estimation. Based on the model and parameter estimates, an efficient algorithm is proposed for a quick calculation of maximum likelihood value. Also, the estimation of remaining useful lifetime is discussed. Simulation results show that the proposed model is relatively better and more robust in comparison with the Wiener process with Gaussian noises. Finally, the application of the proposed model is illustrated by an example.     

A two‐stage degradation model considering the stage‐varying of dispersity regulation

In reliability analysis, degradation test has been recognized as an effective method for high reliable products and complex systems when key performance indicators can be observed. Then, a reasonable degradation model becomes a key issue to guarantee a reasonable reliability assessment. Motivated by practical needs, this paper proposes a novel two‐stage degradation model considering the different dispersity regulations corresponding to the two stages. A maximum likelihood estimation (MLE) method for unknown parameters is established, and an initial guess procedure is given to improve the efficiency of optimization algorithm. Then, the reliability inference regarding the product population is discussed. A comprehensive simulation study is conducted to validate the proposed approach where the two‐stage Wiener process model is adopted as a reference for a better understanding. Finally, the constructed model is further verified by two real applications. Comparative results clearly demonstrate the reasonability and effectiveness of the proposed model.     

基于双方差随机过程的半导体激光器寿命评估

针对高可靠、长寿命半导体激光器的寿命评估问题,提出了基于双方差随机过程的性能退化评估方法。该方法不仅考虑了半导体激光器内部失效机理的固有随机性,还考虑了由人为因素、测量仪器等引起的测量随机误差。首先,建立了半导体激光器性能退化模型及其未知参数的极大似然估计方法。然后,基于首达时的概念给出了失效时间分布函数和概率密度函数的解析表达式,以对半导体激光器的可靠性和寿命进行评估。最后,通过半导体激光器寿命评估工程实例验证了所提出方法的适用性和有效性。结果表明：与现有的性能退化模型相比,所构建模型的拟合效果更好,能够提高寿命评估精度。这可为半导体激光器及其整机系统最优维修决策的制定提供有力支撑。     

Real‐time Reliability Evaluation for an Individual Product Based on Change‐point Gamma and Wiener Process

Reliability evaluation based on degradation data has received significant attentions in recent years. However, existing works often assume that the degradation evolution over time is governed by a single stochastic process, which may not be realistic if change points exist. Here, for cases of degradation with change points, this paper attempts to capture the degradation process with a multi‐phase degradation model and find the method to evaluate the real‐time reliability of the product being monitored. Once new degradation information becomes available, the evaluation results are adaptively updated through the Bayesian method. In particular, for a two‐stage degradation process of liquid coupling devices (LCDs), a model named as change‐point gamma and Wiener process is developed, after which issues of real‐time reliability evaluation and parameters’ estimation are addressed in detail. Finally, the proposed method is illustrated by a case study of LCDs, and the corresponding results indicate that trustful evaluation results depend on the fitting accuracy in cases of multi‐phase degradation process. Copyright © 2013 John Wiley & Sons, Ltd.     

Lifetime prediction and reliability estimation methodology for Stirling-type pulse tube refrigerators by gaseous contamination accelerated degradation testing

Lifetime and reliability are the two performance parameters of premium importance for modern space Stirling-type pulse tube refrigerators (SPTRs), which are required to operate in excess of 10 years. Demonstration of these parameters provides a significant challenge. This paper proposes a lifetime prediction and reliability estimation method that utilizes accelerated degradation testing (ADT) for SPTRs related to gaseous contamination failure. The method was experimentally validated via three groups of gaseous contamination ADT. First, the performance degradation model based on mechanism of contamination failure and material outgassing characteristics of SPTRs was established. Next, a preliminary test was performed to determine whether the mechanism of contamination failure of the SPTRs during ADT is consistent with normal life testing. Subsequently, the experimental program of ADT was designed for SPTRs. Then, three groups of gaseous contamination ADT were performed at elevated ambient temperatures of 40 °C, 50 °C, and 60 °C, respectively and the estimated lifetimes of the SPTRs under normal condition were obtained through acceleration model (Arrhenius model). The results show good fitting of the degradation model with the experimental data. Finally, we obtained the reliability estimation of SPTRs through using the Weibull distribution. The proposed novel methodology enables us to take less than one year time to estimate the reliability of the SPTRs designed for more than 10 years.     

Failure analysis of an aircraft nose landing gear piston rod end

A failure investigation has been conducted on a piston rod end used in a hydraulic actuating cylinder of an aircraft landing gear. The failed piston rod end was found to be broken. An evaluation of the failed piston rod end was undertaken to assess its integrity that included a visual examination, photo documentation, chemical analysis, hardness measurement, tensile testing, and metallographic examination. The failure zones were examined with the help of a scanning electron microscope (SEM) equipped with EDX facility. A stress analysis is also carried out by the finite element technique for the determination of highly stressed regions on the piston rod end. The results indicated that the piston rod end failed by fatigue with cracks initiated at the surface close to the mechanically damaged region due to high stress concentrations.