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BA-WPHM的滚动轴承两阶段剩余寿命预测方法

为了提高滚动轴承剩余寿命预测的准确性,根据滚动轴承运行过程的两阶段性特点,提出了一种基于蝙蝠算法(BA)和威布尔比例风险模型(WPHM)的滚动轴承两阶段剩余寿命预测方法。首先,构建基于WPHM的剩余寿命预测模型;其次,提出了两阶段极大似然估计法,建立新的似然函数,并利用BA算法进行求解,以提高参数估计的准确性;最后,建立BA-WPHM模型对滚动轴承进行剩余寿命预测。案例分析表明,相比于Newton-Raphson算法、自组织分层猴群算法(SHMA)和独特的自适应粒子群算法(UAPSO),提出的方法参数估计的准确性更高,剩余寿命的预测精度优于支持向量回归(SVR)方法,验证了所提方法的有效性,为滚动轴承维修决策的可行性提供了依据。     

Remaining useful life prediction of bearing based on stacked autoencoder and recurrent neural network

Remaining Useful Life (RUL) prediction play a crucial part in bearing maintenance, which directly affects the production efficiency and safety of equipment. Moreover, the accuracy of the prediction model is constrained by the feature extraction process and full life data of bearings. In this paper, the life prediction method of faulty rolling bearing with limited data is presented including degradation state model and RUL prediction model. In order to obtain health indication (HI) without human interference in the degradation state modeling stage, the bottleneck structure of Stacked Autoencoder (SAE) is utilized to fuse the four selected features into one HI using Intelligent Maintenance Systems (IMS) bearing dataset as training sample. In RUL prediction model, the Long Short-Term Memory (LSTM) neural network is carried out to establish the model with Standard deviation (Std) input and HI training label. In order to solve the problem of large training error caused by insufficient data in the failure stage of bearing acceleration test, the third-order spline curve interpolation is utilized to enhance the data points. Through parameter analysis, the RMSE and MAE of the test set on the prediction model are 0.032582 and 0.024038, respectively. Furthermore, the effectiveness of the proposed method is further validated by dataset from Case Western Reserve University (CWRU) with different bearing fault degrees. The analysis indicates that the RUL prediction of bearing fault data is consistent with the size of artificial added faults, that is,the more severe the fault the shorter the time of remaining life. The results validate that the proposed method can effectively extract the bearing health state by incorporating feature fusion and establish accurately prediction model for bearing remaining life.     

Machine health prognostics using survival probability and support vector machine

Prognostic of machine health estimates the remaining useful life of machine components. It deals with prediction of machine health condition based on past measured data from condition monitoring (CM). It has benefits to reduce the production downtime, spare-parts inventory, maintenance cost, and safety hazards. Many papers have reported the valuable models and methods of prognostics systems. However, it was rarely found the papers deal with censored data, which is common in machine condition monitoring practice. This work concerns with developing intelligent machine prognostics system using survival analysis and support vector machine (SVM). SA utilizes censored and uncensored data collected from CM routine and then estimates the survival probability of failure time of machine components. SVM is trained by data input from CM histories data that corresponds to target vectors of estimated survival probability. After validation process, SVM is employed to predict failure time of individual unit of machine component. Simulation and experimental bearing degradation data are employed to validate the proposed method. The result shows that the proposed method is promising to be a probability-based machine prognostics system.     

动车组转向架轴箱剩余寿命预测方法研究

动车组转向架轴箱的寿命作为衡量转向架性能的重要指标,主要受材料、工艺、质量、载荷、保养、工况等因素影响。为解决单一工况预测轴箱故障发生时间不准确问题,需充分考虑多工况因素,基于全生命周期构建转向架轴箱剩余寿命预测模型。本文通过对比分析多工况与轴箱的相互影响关系,采用大数据和机器学习算法,设计出一种基于长短记忆神经网络(LSTM)的轴箱相对温升与里程的剩余寿命预测方法。该方法能精确地刻画轴箱性能退化特征模型,可在动车运行过程中实时预测转向架轴箱故障发生率,较大幅度地提高动车组转向架轴箱剩余寿命预测的实效性、准确性。     

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.      

A Novel Product Remaining Useful Life Prediction Approach Considering Fault Effects

In this paper, a novel remaining useful life prediction approach considering fault effects is proposed. The Wiener process is used to construct the degradation process of single performance characteristic with the fault effects. The first passage time based remaining useful life distribution is calculated by assuming fault occurrence moment is a random variable and follows a certain distribution. Expectation maximization algorithm is employed to estimate model parameters, where the fault occurrence moment is considered as a missing data. Finally, a Copula function is used to describe the dependence between the multiple performance characteristics and derive joint remaining useful life (RUL) distribution of product with the fault effects. The effectiveness of the proposed approach is verified by the experiments of turbofan engines.      

基于深度时序特征迁移的轴承剩余寿命预测方法

不同工况下轴承退化数据分布不一致导致深度学习等方法对剩余寿命预测效果有限,而已有迁移学习预测方法未能充分挖掘不同工况退化序列的内在趋势性,为此,提出一种基于深度时序特征迁移的轴承剩余寿命预测方法.首先,提出一种深度时序特征融合的健康指标构建模型,利用时间卷积网络挖掘退化趋势的内在时序特征,得到源域多轴承的健康指标;然后,提出一种最小化序列相似度的领域自适应算法,利用源域健康指标作为退化趋势元信息,选取目标域与源域之间的公共敏感特征;最后,采用支持向量机构建预测模型.在IEEE PHM Challenge 2012 轴承全寿命数据集上进行实验,结果表明,所提出方法构建的健康指标可更有效地反映退化趋势,同时明显提升剩余寿命预测的准确度.     

Remaining useful life estimation based on nonlinear feature reduction and support vector regression

Prognostics and health management (PHM) of rotating machines is gaining importance in industry and allows increasing reliability and decreasing machines’ breakdowns. Bearings are one of the most components present in mechanical equipments and one of their most common failures. So, to assess machines’ degradations, fault prognostic of bearings is developed in this paper. The proposed method relies on two steps (an offline step and an online step) to track the health state and predict the remaining useful life (RUL) of the bearings. The offline step is used to learn the degradation models of the bearings whereas the online step uses these models to assess the current health state of the bearings and predict their RUL. During the offline step, vibration signals acquired on the bearings are processed to extract features, which are then exploited to learn models that represent the evolution of the degradations. For this purpose, the isometric feature mapping reduction technique (ISOMAP) and support vector regression (SVR) are used.The method is applied on a laboratory experimental degradations related to bearings. The obtained results show that the method can effectively model the evolution of the degradations and predict the RUL of the bearings.     

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.

     

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.     

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.     

Residual life prediction based on dynamic weighted Markov model and particle filtering

In order to improve the prediction accuracy of non-Gaussian data and build reasonably the prediction model, a novel residual life prediction method is proposed. A dynamic weighted Markov model is constructed by real time data and historical data, and the residual life is predicted by particle filter. The particles of the state vector are predicted and updated instantaneously using particle filter. The probability distribution of the predicted value is estimated by the updated particles. The residual life can be predicted using the set threshold of the state. This method improves the accuracy of residual life prediction. Finally, the advantage of this proposed method was shown experimentally using the bearings’ full cycle data.     

基于APSO-LSSVM的航空发动机轴承故障诊断及寿命预测

航空发动机轴承在高速、高温、高载荷等极端工况下易发生机械故障,为了提前预警,提出了一种基于自适应粒子群优化(Adaptive Particle Swarm Optimization, APSO)算法的最小二乘支持向量机(APSO Least Squares Support Vector Machine, APSO-LSSVM)对滑油系统中轴承磨屑进行在线监测的故障诊断及寿命预测。通过主成分分析法(Principal Components Analysis, PCA)对滑油磨屑信息进行降维处理,构建特征向量,并将特征向量输入APSO-LSSVM模型,对轴承故障状态进行分类并对轴承剩余寿命进行预测。结果表明：使用PCA可以保留数据样本99.9%的信息,同时还能极大地降低数据维度;与遗传算法(Genetic Algorithm, GA)、灰狼优化(Grey Wolf Optimization, GWO)算法、粒子群优化(Particle Swarm Optimization, PSO)算法的支持向量机相比,所提算法因采用了自适应调节粒子移动步幅,在进行轴承状态分类时准确率更高,分类正确率可达...     

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.     

Hybrid scheme through read-first-LSTM encoder-decoder and broad learning system for bearings degradation monitoring and remaining useful life estimation

This paper proposes a novel hybrid scheme through read-first-LSTM (RLSTM) encoder-decoder and broad learning system (BLS) for bearings degradation monitoring and remaining useful life (RUL) estimation, which aims to describe the nonlinear characteristics of the degradation process. Firstly, the raw signals are processed premier by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a novel dimensionality reduction method composed of t-distribution stochastic neighbor embedding (t-SNE) and density-based spatial clustering of application with noise algorithm (DBSCAN). Then, the health indicator is constructed with the Hilbert-Huang transform (HHT) corresponding to the bearings’ natural fault frequency, which can be employed as the hybrid scheme training label. Linear rectification technology (LRT) and exponentially weighted moving average (EWMA) control chart are adapted to define the exact process of the degradation. Secondly, a novel RLSTM is proposed. And simultaneously, an encoder-decoder model, where RLSTM is utilized as an encoder, and LSTM is adopted as a decoder, is designed for degradation monitoring. Finally, a broad learning system (BLS), which differs from deep learning with a deeper structure, is established in a flat network to estimate the RUL of bearings. Compared with the state-of-the-art techniques, the better efficacy of the proposed hybrid scheme is illustrated using the PRONOSTIA platform dataset.     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.     

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 over-estimated 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.      

Remaining useful life prediction of bearings by a new reinforced memory GRU network

The remaining useful life (RUL) prediction of bearings has great significance in the predictive maintenance of mechanical equipment. Owing to the difficulty of collecting abundant lifecycle datasets with correct labels, it is quite necessary to explore a prediction method with high precision and robustness in the case of small samples. It follows that a novel RUL prediction approach is put forward to overcome this problem. First, for reducing the man-made interference and the demand for expert knowledge, an unsupervised health indicator (HI) is constructed by Gaussian mixture model (GMM) and Kullback-Leibler divergence (KLD), which is named as KLD-based HI. Then because of the rapid forgetting of historical trend information in the current RNN-based prediction models, a novel reinforced memory gated recurrent unit (RMGRU) network is proposed by reusing the state information at the previous moment. According to the constructed KLD-based HI vector, the unknown HIs are successively predicted by RMGRU until the predicted HI value exceeds the failure threshold, and then RUL is calculated. The contrast experiment on IEEE 2012PHM bearing datasets shows the superiority of the bearing RUL prediction approach based on RMGRU over the classical time series forecasting methods. It can be concluded that this method has great application potential in bearing RUL prediction.