A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine

Over the increasing number of charging and discharging cycling processes of lithium-ion batteries, the aging and even failure of lithium-ion batteries may occur. If anomalies are not detected in time, lithium-ion batteries could cause major safety accidents. In this paper, a prognostics method integrating the sample entropies and relevance vector machine (RVM) is proposed to estimate the remaining useful life (RUL) of lithium-ion batteries. First, RUL prediction using multiple inputs, including the voltage sample entropy and the current sample entropy, are compared with prediction methods based on a single entropy input. The multiple entropy input method indicates better capability of describing the battery degradation process. In addition, the wavelet denoising method is used to pre-process the inputs to remove sudden and unusual changes in the battery capacity degradation data. A prediction model using the denoised entropy inputs is constructed through linearly weighting the entropy inputs in the RVM model. The weight for each input is assigned according to the individual contribution to the prediction accuracy. Experimental data from lithium-ion battery testing are applied to three prediction models with different entropy inputs. The results indicate that the proposed method has higher prediction accuracy than those in existing models only using a single sample entropy. The proposed method has potentials for the RUL estimation of industrial machinery in manufacturing.     

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.     

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing datafusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur in real industries especially for machines in harsh operating environments. In this paper, a deep lea...     

Bayesian更新与EM算法协作下退化数据驱动的剩余寿命估计方法

设备的剩余寿命(RUL)估计是对设备进行视情维护、预测与健康管理的关键问题之一.为实现对于单个服役设备退化过程的建模以及RUL的估计,文中提出一种Bayesian更新与期望最大化算法协作下退化数据驱动的RUL估计方法.首先利用指数退化模型来描述设备的退化过程,基于监测的退化数据,利用Bayesian方法对模型的随机参数进行更新,进而得到RUL的概率分布函数和点估计.其次,利用运行设备到当前时刻的监测数据,基于EM算法给出退化模型中非随机未知参数的估计方法,并证明参数迭代估计中每步得到的结果是唯一最优解.最后通过数值仿真和实际数据应用研究,表明文中方法可对单个设备退化过程进行建模,有效估计退化模型中的未知参数,进而得到更好的RUL估计结果.     

Discrete Event Logistics Systems (DELS) simulation modeling incorporating two-step Remaining Useful Life (RUL) estimation

Prognostics and Health Management (PHM) exerts an essential influence on the spare supply process and the maintenance activities. Discrete Event Logistics Systems (DELS) simulation model facilitates a better understanding of the maintenance and logistics/support systems. Previous DELS models treat the RUL estimation as a one shot event. However, the treatment would be rough to coordinate the logistics and maintenance activities, and the estimated RUL result would not be sufficiently reliable. In this paper, we propose the principle and operational technique of two-step RUL estimation for the DELS simulation model. Two-step RUL estimation starts with the component RUL modeling subject to a continuous accumulation of degradation. The component deterioration is modeled using a time-dependent stochastic process, which combines the linear degradation path with a random effect. Besides, the sequential logics of the DELS simulation model incorporating two-step RUL estimation is exploited in the local behavior study. Finally, the proposed technique is testified with a case study via the DELS simulation implementation, showing that the performance using two-step RUL estimation outperforms traditional one-step RUL estimation.     

A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings

The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.     

Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach

Predicting remaining useful life (RUL) is crucial for system maintenance. Condition monitoring makes not only degradation data available for RUL estimation but also categorized health status data for health state identification. However, RUL prediction has been treated as an independent process in most cases even though potential relevance exists with health status detection process. In this paper, we propose a convolution neural network based multi-task learning method to reflect the relatedness of RUL estimation with health status detection process. The proposed method applied to the C-MAPSS dataset for aero-engine unit prognostics supported superior performances to existing baseline models.

     

双时间尺度下的设备随机退化建模与剩余寿命预测方法

基于退化建模的剩余寿命预测（Remaining useful life,RUL）是当前可靠性领域研究的热点.现有的退化模型都是针对单个时间尺度下的退化设备,缺少对设备性能变化与多个时间尺度相关的退化建模与剩余寿命预测方法.鉴于此,本文基于Wiener过程提出了一种双时间尺度随机退化建模与剩余寿命预测方法,用随机比例系数描述不同时间尺度之间的不确定关系,推导出丫首达时间意义下设备的双时间尺度剩余寿命分布,讨论了其与基于单时间尺度退化模型得到的剩余寿命分布之间的关系,并给出了基于历史退化数据的未知参数极大似然估计方法.最后,将所提方法应用到惯性平台关键器件陀螺仪的退化建模与剩余寿命预测中,验证了方法的有效性.     

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life (RUL) of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage. A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development. However, it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries, as well as dynamic operating conditions in practical applications. Moreover, due to insignificant capacity degradation in early stages, early prediction of battery life with early cycle data can be more difficult. In this paper, we propose a hybrid deep learning model for early prediction of battery RUL. The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction. We also design a non-linear correlation-based method to select effective domain knowledge-based features. Moreover, a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost. Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set, but also generalizes well to the secondary test set having a clearly different distribution with the training set. The PyTorch implementation of our proposed approach is available athttps://github.com/batteryrul/battery_rul_early_prediction.      

基于时间序列迁移递归预测的未知工况下滚动轴承在线剩余寿命评估

深度迁移学习技术已经成功应用于跨工况的滚动轴承剩余寿命(remaining useful life,RUL)预测问题,但针对在线场景的RUL评估仍存在如下不足:在线工况存在漂移,无法确定同工况的历史数据,不能直接构建回归预测模型;在线目标轴承只有正常状态和早期故障数据,无法直接与离线轴承的快速退化期数据进行迁移学习.鉴于此,从时序退化信息迁移的角度提出一种面向未知工况的轴承在线RUL评估方法.首先,构建融合第三方退化信息的时间序列迁移递归预测模型,利用张量长短时记忆网络提取离线工况全寿命数据的单调性和趋势性等时序信息,并迁移到在线递归预测过程;然后,利用迁移成分分析对所预测的在线退化序列和已有离线序列进行公共特征空间适配,提取域无关特征,并构建支持向量机回归模型,实现在线轴承剩余寿命评估.在IEEE PHM Challenge 2012轴承数据集上的实验结果表明,所提出方法可在只有早期故障数据的情况下准确预测退化趋势,为未知工况下的轴承在线RUL评估提供一种有效的解决方案.     

基于并联CNN-SE-Bi-LSTM的轴承剩余使用寿命预测

滚动轴承作为一种机械标准件,广泛应用于各类旋转机械设备,其健康状况对机器设备的正常运行至关重要,掌握其剩余使用寿命(RUL)可以更好地保证生产活动安全有效的进行.针对目前基于深度学习的机器RUL预测方法普遍存在:a)预测性能很大程度依赖手工特征设计;b)模型不能够充分提取数据中的有用特征;c)学习过程中没有明确考虑多传感器数据等缺点,提出了一种新的深度预测网络——并联多个带有压缩激励机制的卷积神经网络和双向长短期记忆网络集成网络(CNN-SE-Bi-LSTM),用于设备的RUL预测.在该预测网络中,不同传感器采集的监测数据直接作为预测网络的输入.然后,在改进的压缩激励卷积网络(CNN-SE-Net)提取空间特征的基础上进一步通过双向长短期记忆网络(Bi-LSTM)提取时序特征,建立起多个独立的可以自动从输入数据中学习高级表示的RU L预测模型分支.最后,将各独立分支学习到的特征通过全连接层并联获得最终的RU L预测模型.通过滚动轴承加速退化实验的数据,验证了所提网络的有效性并与现有的一些改进算法进行了对比实验.结果表明,面对原始多传感器数据,该算法能够自适应地提供准确的RU L预测结果,且预测表现优于现有一些预测方法.     

带测量误差的非线性退化过程建模与剩余寿命估计

剩余寿命(Remaining useful lifetime, RUL)估计是设备视情维护和预测与健康管理(Prognostics and health management, PHM)中的一项关键问题. 采用退化过程建模进行剩余寿命估计的研究中,现有方法仅考虑了具有线性或可以线性化的退化轨迹的问题.本 文提出了一种基于扩散过程的非线性退化过程建模方法,在首达时间的意义下,推导出了剩余寿命的分布.该方法可以描述一般的非线性退化轨迹, 现有的线性退化建模方法是其特例.在参数的推断中,考虑到真实的退化过程受到测量误差的影响,难以直接测量得到, 因此,在退化建模的过程中引入了测量误差对退化观测数据的影响,通过观测数据,提出了一种退化模型未知参数的极大似然估计方法. 最后,通过激光发生器和陀螺仪的退化测量数据验证了本文方法明显优于线性建模方法,具有潜在的工程应用价值.     

An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System

Remaining useful life(RUL) estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed. Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL. Most current researches focus on age-dependent degradation models, but it has been found that some degradation processes in engineering are also related to the degradation states themselves. In addition, due to different ...     

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.     

Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo

The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the application of deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially critical in applications, in which unplanned failures lead to high costs or even to human harm. Therefore, the determination of the uncertainty associated with the RUL estimate is important for the applicability of DL models in practice. In this article, Bayesian DL models, that naturally quantify uncertainty, were applied to the task of RUL estimation of simulated turbo fan engines. Inference is carried out via Hamiltonian Monte Carlo (HMC) and variational inference (VI). The experiments show, that the performance of Bayesian DL models is similar and in many cases even beneficial compared to classical DL models. Furthermore, an approach for utilizing the uncertainty information generated by Bayesian DL models is presented. The approach was applied and showed how to further enhance the predictive performance.     

考虑执行器性能退化的控制系统剩余寿命预测方法

工程控制系统在运行过程中，由于内外部应力的综合作用以及外部环境等的影响，其部件性能将逐渐退化，最终会导致控制系统失效.然而，由于控制系统中闭环反馈的作用，系统的输出残差可能仍在较小范围内变动，使得早期性能退化这种微小故障难以被检测到，呈现隐含退化的特点.现有文献中，针对此类在闭环反馈控制作用下部件存在隐含退化过程的控制系统剩余寿命（Remaining useful lifetime，RUL）预测问题，鲜有研究.为此，本文针对一类仅考虑执行器性能退化的确定闭环控制系统，提出一种基于解析模型的剩余寿命预测方法.该方法首先基于权值优选粒子滤波算法，利用系统的监测数据在线估计出执行器的隐含退化量，然后在每一个预测时刻通过蒙特卡洛（Monte Carlo，MC）仿真计算得到合理的失效阈值，建立基于该失效阈值的系统失效判断准则，最后将隐含退化量的估计值代入退化模型中外推出剩余寿命分布.惯性平台稳定回路控制系统的仿真实验结果验证了该方法的可行性、有效性.     

Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process

This paper considers the development of multivariate statistical soft sensors for the online estimation of product quality in a real-world industrial batch polymerization process. The batches are characterized by uneven length, non-reproducible sequence of processing steps, and scarce number of measurements for the quality indicators with uneven sampling of (and lag on) these variables. It is shown that, for the purpose of quality estimation, the complex series of operating steps characterizing a batch can be simplified to a sequence of three estimation phases. The switching from one phase to the other one can be triggered by easily detectable events occurring in the batch. For each estimation phase, PLS software sensors are designed, and their performance is evaluated against plant data. The estimation accuracy can be substantially improved if some form of dynamic information is included into the models, either by augmenting the process data matrix with lagged measurements, or by averaging the process measurements values on a moving window of fixed length. In particular, the moving average three-phase PLS estimator shows the best overall performance, providing accurate estimations also during estimation Phase 2, which is characterized by a very large variability between batches.     

Anomaly detection and critical attributes identification for products with multiple operating conditions based on isolation forest

Performance analysis of the existing mechanical products is critical to identifying design defects and improving product reliability. With the advances of information technologies, product operating data collected through continuous condition monitoring (CM) serve as main sources for analysis of performance and detection of anomaly. Most of the existing anomaly detection methods, however, are not effective when CM data are very high dimensional, leading to poor quality of assessment results. Besides, the effects of multiple operating conditions on anomaly detection are seldom considered in these existing methods. To solve these problems, an integrated approach for anomaly detection and critical behavioral attributes identification based on CM data is developed in this research. Gaussian mixed model GMM) is employed to develop a method for clustering of operating conditions. Isolation forest (iForest) method is used to detect anomaly instances, and further to identify the critical attributes related to product performance degradation. The effectiveness of the developed approach is demonstrated by an application with collected operating data of a wind turbine.     

Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction

Reliability of prognostics and health management systems relies upon accurate understanding of critical components’ degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of physical or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HIs) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline HI, to the online HI, using k-nearest neighbors classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian filter. The method is verified using battery and turbofan engine degradation simulation data acquired from NASA data repository. The results show the effectiveness of the method in predicting the RUL for both applications.     

Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks

Accurate estimation of the remaining useful life (RUL) and health state for rollers is of great significance to hot rolling production. It can provide decision support for roller management so as to improve the productivity of the hot rolling process. In addition, the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance. Therefore, a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper. Firstly, a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator (HI) is developed, where the HI is able to indicate the health state of the roller. Following that, a state-space model is constructed to describe the HI, and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold. Finally, application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site, and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.