Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.     

基于状态空间模型的飞机APU在翼RUL预测方法

为解决利用飞机辅助动力装置(APU)在翼监测数据难以表征其性能状态而造成的性能评估以及剩余使用寿命预测(RUL)难的问题,本文提出一种基于状态空间模型(SSM)与卡尔曼滤波融合的APU在翼RUL预测方法.首先,通过在翼监测数据构造含噪声的性能指标(PI)来表征APU的性能状态,借助维纳过程与建立的含噪声的PI构建状态方...     

基于Wiener过程的万能式断路器附件剩余寿命预测*

万能式断路器作为一个复杂的机械系统,其操作附件的剩余寿命预测对于维护断路器的可靠性至关重要。为准确掌握操作附件剩余寿命情况,提出了一种基于Wiener过程的万能式断路器操作附件剩余机械寿命预测方法。首先,通过对操作附件动作过程中线圈电流波形的分析选取了动作时间作为性能退化特征量;其次,考虑到断路器操作附件性能退化过程具有线性非单调的特点,采用Wiener过程建立了操作附件的性能退化模型,并利用极大似然估计法对退化模型参数进行估计;然后,基于首达时间的概念建立了剩余寿命预测模型,推导出剩余寿命概率密度函数解析式。最后对安装于万能式断路器上的分励脱扣器和释能电磁铁两种操作附件进行全寿命试验及其剩余寿命预测,预测结果验证了所提方法的有效性。     

基于KPCA和WPHM的滚动轴承可靠性评估与寿命预测

为了评估滚动轴承的可靠性和预测剩余使用寿命,选取能够反映性能退化过程的特征参数作为寿命预测模型的输入参数,提出一种基于核主元分析(kernel principal component analysis,简称KPCA)和威布尔比例故障率模型(Weibull proportional hazards model,简称WPHM)的方法。首先,提取滚动轴承全寿命周期的时域、频域及时频域等多特征参数,从中筛选出有效的特征参数,构建高维相对特征集;其次,进行核主元分析,选取能够反映轴承全寿命周期性能退化过程的核主元,进而作为WPHM的协变量来进行可靠性评估和剩余寿命预测。通过滚动轴承全寿命试验,验证了该方法能够对轴承进行准确的可靠性评估和剩余寿命预测,以提供及时的维修决策。同时,由于提取的是相对特征,降低了同种轴承间在制造、安装及工况的差异,增强了该方法的适用性和稳定性。     

Path classification and estimation model based prognosis of pneumatic cylinder lifetime

Prognosis is a key technology to improve reliability,safety and maintainability of products,a lot of researchers have been devoted to this technology.But to improve the predict accuracy of remaining li...     

Methodology to Estimate Remaining Service Life of Steel Structure by Possibilistic Reliability Theory

Steel structure system of crane deteriorates over time due to environmental effects,material fatigue,and overloading.System structural reliability and remaining service life assessment methods are developed during the few decades.But until now estimating remaining service life methods of crane steel system by reliability theory begin to develop.Safety assessment of existing steel structure system requires the development of a methodology that allows for an accurate evaluation of reliability and prediction of the remaining life.Steel structures are the supporting elements in the special equipment such as hoisting machinery.Structure reliability and remaining service life safe assessment are important for steel structures.For finding the reason which caused the failure modes(such as fatigue strength failure,stiffness failure and stability failure),incremental loading method based on possibilistic reliability is applied into dynamic structure failure path research.Through reliability analyzing and calculating for crane,it is demonstrated that fatigue damage is the most common failure mode.Fuzzy fatigue damage accumulation theory is used for basis theory and Paris-Eadogan equations are used for mathematical modeling.All fatigue parameter values of the welding box girder of bridge cranes are determined and fatigue remaining life formulas are deduced.After field test and collecting working parameters of numerous cranes,typical fatigue load spectrum was compiled for the dangerous point of box girders used in the area.Fatigue remaining life is assessed for different types and lifting capacities.Safety for steel structure system of bridge crane is assessed by two quantitative indexs:reliability and remaining life.Therefore,the evaluation means is more comprehensive and reasonable.The example shows that the two quantitative indexs are mutually correlated.Through analyzing the 120 t-22.5 m bridge crane of a certain enterprise,a new methodology to estimate remaining service life of steel structure by possibilistic reliability theory is introduced for safety evaluation of structure system.     

基于设备状态振动特征的比例故障率模型可靠性评估

基于概率统计的传统可靠性评估方法一般依赖于失效寿命数据。但失效寿命数据需要通过寿命试验和加速寿命试验获取,对于大量的实时工作设备采用这种方法是不适宜的。这种情况下,基于设备状态的性能退化数据能够提供可靠性评估的重要信息。在集成失效数据可靠性建模技术和基于设备状态的振动信号特征提取的基础上,提出设备状态振动特征的比例故障率模型可靠性评估的新方法。该方法可以把底层信息(如设备运行状态特征的方均根、峭度)与高层信息(如可靠性统计学)之间建立联系,适合设备运行可靠性参数的估计,并能够提供有效的可靠性评估,为设备以可靠性为中心的基于状态的预防维修提供技术支持。通过在铁路机车轮对轴承上的应用实例说明该方法的合理性和有效性。     

数字孪生驱动的航空发动机涡轮盘剩余寿命预测

为解决航空发动机涡轮盘剩余寿命在线预测难题,提出一种数字孪生驱动的涡轮盘剩余寿命预测方法。在建立数字孪生模型的过程中,首先,分析涡轮盘疲劳裂纹损伤机理,构建性能退化指标,建立涡轮盘性能退化过程的共性表征模型;其次,分析多种不确定性因素,采用状态空间模型建立涡轮盘性能退化过程的个性表征模型;然后,通过动态贝叶斯网络描述状态空间模型随时间的演化规律,建立涡轮盘性能退化过程的动态演化模型;最后,采用粒子滤波算法实现涡轮盘退化状态追踪和剩余寿命预测,从而完成涡轮盘性能退化数字孪生模型的建立。融合涡轮盘实时传感数据,通过贝叶斯推理实现对该数字孪生模型的动态更新。通过某型涡轮盘试验数据对该方法进行验证,结果表明该数字孪生模型能够较好地解决涡轮盘剩余寿命在线预测问题。     

滚动轴承剩余使用寿命预测综述

滚动轴承作为旋转机械的关键零部件,其剩余使用寿命(RUL)预测对生产维修和人身安全具有重要意义。由于滚动轴承复杂多变的工作环境,使得同工况的参考样本少而变工况的参考样本较多,具有不平衡、不完整、无标签及噪声干扰等特性,增加了滚动轴承RUL预测的困难。随着大数据时代的来临和人工智能的发展,滚动轴承RUL预测方法也变得更加丰富。因此,在故障预测与健康管理(PHM)的框架下,对滚动轴承失效模式和故障数据特点进行阐述,对故障特征提取、降维和融合方法以及得到的性能退化指标分别进行了分类和对比分析。结合数据驱动算法,对滚动轴承RUL的预测方法、模型选择和评估标准进行了梳理和对比。最后对滚动轴承RUL预测未来的发展趋势进行了展望。     

基于性能退化模型的万能式断路器操作附件实时剩余寿命预测

针对万能式断路器操作附件的个体差异性以及在实际使用过程中动作不频繁的特性,提出一种基于性能退化模型的万能式断路器操作附件实时机械剩余寿命(RUL)预测方法。不同于传统的RUL预测方法,该方法融合了操作附件的历史退化数据与实时更新的状态监测(CM)数据。首先,考虑到操作附件性能退化过程具有线性非单调的特点,建立基于Wiener过程的操作附件性能退化模型;其次,对操作附件的历史退化数据采用极大似然估计法和一维搜索法确定模型参数的先验分布;然后,运用贝叶斯方法并结合操作附件实时更新的CM信息对模型参数进行迭代更新;基于首达时间的概念建立了RUL预测模型,以实现对断路器操作附件实时RUL的预测。最后,通过操作附件的寿命数据对本文所提方法进行验证,结果表明本文方法不仅可实现操作附件的实时剩余机械寿命预测,同时相较于其他文献方法具有更高的预测精度。     

基于复合非齐次泊松过程的不完美维修设备剩余寿命预测

针对现有不完美维修设备剩余寿命预测方法难以准确反映设备真实维修规律的问题,提出一种基于复合非齐次泊松过程的不完美维修设备剩余寿命预测方法。基于非线性Wiener过程构建设备随机退化模型;假设不完美维修次数存在上限值,并据此建立基于复合非齐次泊松过程的不完美维修模型;然后,基于设备的随机退化模型与不完美维修模型构建综合退化模型,并采用极大似然方法估计模型参数;基于首达时间的概念,推导出不完美维修设备剩余寿命的概率密度函数。实例分析表明,所提方法能够有效提升不完美维修设备剩余寿命预测的准确性,具备工程应用前景。     

一种融合多传感器数据的数模联动机械剩余寿命预测方法

随着传感和信息技术的发展,各式各样的传感器获取了机械装备海量的监测数据,让剩余寿命预测有"据"可依,推动机械剩余寿命预测进入了大数据时代。但由于数据类型多样、量大面广,如何利用丰富的多传感器数据,从中快速挖掘健康状态退化信息,指导寿命预测,成为大数据时代下机械寿命预测的全新挑战。基于模型的寿命预测方法大多仅针对单一监测数据进行建模分析,无法有效利用丰富的大数据资源。数据驱动的方法则过分依赖训练数据,缺乏必要的经验指引,方法的可解释性差。为了有效利用多传感器数据指导寿命预测,从数模联动的思路出发,建立了一种融合多传感器数据的数模联动寿命预测方法。采用一种通用的Wiener过程模型对健康状态退化过程进行描述,分别建立多源观测函数和多源映射函数对状态与数据之间的因果关系和关联关系进行描述,采用粒子滤波算法将多传感器数据与模型进行动态匹配,预测剩余寿命。在提出方法的统一框架指导下,选取三种特定模型对铣刀剩余寿命进行预测,验证了提出方法的有效性。     

锂离子电池循环寿命的融合预测方法

针对传统基于粒子滤波的锂离子电池剩余使用寿命预测方法的不足:过度依赖电池经验退化模型和模型输入变量单一的问题,提出了一种相关向量机、粒子滤波和自回归模型融合的锂离子电池剩余寿命预测的方法。通过相关向量机提取电池历史数据的退化趋势,构建趋势方程替换以往的电池经验退化模型,作为粒子滤波算法的状态转换方程。引入自回归模型的长期趋势预测值,替换观测值构建粒子滤波算法的观测方程。将3种方法相融合估计电池剩余寿命。实验结果表明:融合方法不仅预测精度高而且采用数据驱动的方法避免了构建复杂的电池机理退化模型,通用性强。     

Data-driven prognostics method for turbofan engine degradation using hybrid deep neural network

Journal of Mechanical Science and Technology - Powerful sequence modeling capability for massive multi-sensor data enables deep-learning-based methods to obtain accurate remaining useful life (RUL)...     

动态贝叶斯网络在设备剩余寿命预测中的应用研究

采用动态贝叶斯网络对设备剩余寿命进行预测,建立了基于动态贝叶斯网络模型的设备剩余寿命预测框架模型,运用动态贝叶斯网络的粒子滤波近似推理算法对加工过程中钻头寿命预测进行实例研究,结果表明了该方法的有效性.     

基于变分模态分解与集成深度模型的 锂电池剩余寿命预测方法

锂电池剩余寿命(RUL)预测对于锂电池安全使用至关重要。由于锂电池使用过程中存在容量再生现象和随机干扰等因素,导致单一尺度信号下单一模型的预测精度及泛化性能较差。针对上述问题,提出一种新的基于变分模态分解(VMD)与集成深度模型的锂电池剩余寿命预测方法。首先,采用变分模态分解将锂电池容量数据进行多尺度分解,得到信号的全局退化趋势和局部随机波动分量;然后,分别采用多层感知机(MLP)和长短期记忆神经网络(LSTM)对全局退化趋势和各波动分量进行建模;最后,将各个分量子模型的预测结果进行集成,获得最终的锂电池剩余寿命预测结果。实验结果表明,该方法具有较高的预测精度与稳定性。     

Deep Spatiotemporal Convolutional-Neural-Network-Based Remaining Useful Life Estimation of Bearings

The remaining useful life(RUL) estimation of bearings is critical for ensuring the reliability of mechanical systems. Owing to the rapid development of deep learning methods, a multitude of data-driven RUL estimation approaches have been proposed recently. However, the following problems remain in existing methods: 1) Most network models use raw data or statistical features as input, which renders it di cult to extract complex fault-related information hidden in signals; 2) for current observations, the dependence between current states is emphasized, but their complex dependence on previous states is often disregarded; 3) the output of neural networks is directly used as the estimated RUL in most studies, resulting in extremely volatile prediction results that lack robustness. Hence, a novel prognostics approach is proposed based on a time–frequency representation(TFR) subsequence, three-dimensional convolutional neural network(3 DCNN), and Gaussian process regression(GPR). The approach primarily comprises two aspects: construction of a health indicator(HI) using the TFR-subsequence–3 DCNN model, and RUL estimation based on the GPR model. The raw signals of the bearings are converted into TFR-subsequences by continuous wavelet transform and a dislocated overlapping strategy. Subsequently, the 3 DCNN is applied to extract the hidden spatiotemporal features from the TFR-subsequences and construct HIs. Finally, the RUL of the bearings is estimated using the GPR model, which can also define the probability distribution of the potential function and prediction confidence. Experiments on the PRONOSTIA platform demonstrate the superiority of the proposed TFR-subsequence–3 DCNN–GPR approach. The use of degradation-related spatiotemporal features in signals is proposed herein to achieve a highly accurate bearing RUL prediction with uncertainty quantification.     

Set-membership methodology for model-based prognosis

This paper addresses model-based prognosis to predict Remaining Useful Life (RUL) of a class of dynamical systems. The methodology is based on singular perturbed techniques to take into account the slow behavior of degradations. The full-order system is firstly decoupled into slow and fast subsystems. An interval observer is designed for both subsystems under the assumption that the measurement noise and the disturbances are bounded. Then, the degradation is modeled as a polynomial whose parameters are estimated using ellipsoid algorithms. Finally, the RUL is predicted based on an interval evaluation of the degradation model over a time horizon. A numerical example illustrates the proposed technique.     

Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

A timing decision-making method for predecisional remanufacturing is presented. The method can effectively solve the uncertainty problem of remanufacturing blanks. From the perspective of reliability, this study analyzes the timing decision-making interval for predecisional remanufacturing of mechanical products during the service period and constructs an optimal timing model based on energy consumption and cost. The mapping relationships between time and energy consumption are predicted by using the characteristic values of performance degradation of products combined with the least squares support vector regression algorithm. Application of game theory reveals that when the energy consumption and cost are comprehensively optimal, this moment is the best time for predecisional remanufacturing. Used engine blades are utilized as an example to demonstrate the validity and effectiveness of the proposed method.

     

基于支持向量机工具的性能劣化建模方法

针对基于故障数据的数控装备可靠性研究中的小样本问题,提出了建立基于支持向量机的性能劣化模型.在研究支持向量机的建模理论和参数优化方法的基础上,将最小二乘法支持向量机工具LSSVM.M应用于性能退化数据处理,提出一种改进的参数选择方法,以提高拟合和预测准确性.通过实例,验证了该方法的可行性,并建立了数控机床加工精度的性能劣化模型,为可靠性评估奠定了基础.