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.     

基于AE-LSTM的多目标硬盘故障预测方法

硬盘故障预测是在故障发生前发出预警,避免数据丢失或服务中断,提高数据中心的可靠性和安全性。然而,大多数故障预测模型将硬盘故障问题转化为二分类任务,忽略了硬盘故障是渐变过程的,并且缺乏故障诊断功能。因此,提出了一种基于AE-LSTM的硬盘故障预测框架,实现多目标任务：硬盘健康状态分级、硬盘剩余使用寿命预测、硬盘故障诊断。首先,采用回归决策树模型智能化对硬盘健康状态进行标记;其次,通过AE-LSTM模型提取鲁棒的隐藏变量,并构建剩余使用寿命预测模型和硬盘健康状态分级模块;最后,根据AE模块的输入输出差异进行硬盘故障诊断。在Backblaze公开数据集上,对比了RF、LSTM和AE-LSTM三种算法,实验结果证实了AE-LSTM算法在多目标硬盘故障预测中的有效性和优势。     

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.      

Visual analytics towards axle health of high-speed train based on large-scale scatter image

Axle is an important part of high-speed train. The axle is the key component connecting the train wheelset, which has a great impact on the train safety. The health monitoring of axles is very important for the safe and smooth operation of trains. The axle health detection is a complex process of multi-factor coupling, which faces the problems of health model construction. From the perspective of big data visual analysis, this paper helps people find the information behind the big data of high-speed railway axle monitoring, and makes a prediction and analysis of the health status of high-speed railway axle operation. Starting from the present situation of scatter plot presentation of multi-dimensional data visual analysis, this paper proposes a visual analysis and processing method for high-speed train axle health monitoring, aiming at the problems of intensive rendering, visual mutation and trend prediction when drawing large data scatter plot. Firstly, a new method of the axle data fusion model is proposed in this paper, which can effectively clean the axle health monitoring data and construct the data acquisition and expression mode of axle temperature of high-speed train. Then, visualization of axle data and prediction of axle health trend provide a new analysis model for axle health monitoring. In addition, the visual analysis method of scatter density map data can eliminate the dependence of the original complex mechanical model, and can be used to analyze different working conditions and axle types. Compared with the existing axle health monitoring methods, this method has high accuracy and practicability.

     

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.     

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.     

基于动作周期退化相似性度量的机械轴健康指标构建与剩余寿命预测

针对工业机器人机械轴健康管理中检测效率和精准度较低的问题,提出了一种机械轴运行监控大数据背景下的基于动作周期退化相似性度量的健康指标（HI）构建方法,并结合长短时记忆（LSTM）网络进行机器人剩余寿命（RUL）的自动预测。首先,利用MPdist关注机械轴不同动作周期之间子周期序列相似性的特点,并计算正常周期数据与退化周期数据之间的偏离程度,进而构建HI;然后,利用HI集训练LSTM网络模型并建立HI与RUL之间的映射关系;最后,通过MPdist-LSTM混合模型自动计算RUL并适时预警。使用某公司六轴工业机器人进行实验,采集了加速老化数据约1 500万条,对HI单调性、鲁棒性和趋势性以及RUL预测的平均绝对误差（MAE）、均方根误差（RMSE）、决定系数（R^{\mathrm{2}}）、误差区间（ER）、早预测（EP）和晚预测（LP）等指标进行了实验测试,将该方法分别与动态时间规整（DTW）、欧氏距离（ED）、时域特征值（TDE）结合LSTM的方法,MPdist结合循环神经网络（RNN）和LSTM等方法进行比较。实验结果表明,相较于其他对比方法,所提方法所构建HI的单调性和趋势性分别至少提高了0.07和0.13,RUL预测准确率更高,ER更小,验证了所提方法的有效性。     

锂离子电池剩余寿命预测方法研究综述

电池故障预测和健康管理(Prediction and Health Management, PHM)评价的主要方法是确定电池的健康状态和剩余使用寿命(Remaining Useful Life, RUL),以此保证锂离子电池安全可靠地工作和实现寿命优化。锂电池RUL预测不仅是PHM中的热点问题和挑战问题,其预测方法的准确性也会直接影响电池管理系统(Battery Management System, BMS)的整体性能。介绍了单体电芯测评标准,对影响锂电池循环寿命的主要因素进行详细分析。简述电池日历寿命和循环寿命。概括和总结了近几年锂离子电池剩余寿命预测方法,比较不同方法的优缺点。提出了当前实际应用中预测锂电池RUL仍存在的关键问题并进行探讨。     

Temporal convolutional network with soft thresholding and attention mechanism for machinery prognostics

Remaining useful life (RUL) prediction is a challenging task for prognostics and health management (PHM). Due to the complexity physics involved for precisely modeling the machine degradation process, learning-based data-driven methods, which learn the degradation pattern solely from the historical data without referring to physical models, have become promising alternatives to model-based prognostic methods. In this paper, a new temporal convolutional neural network (TCN) with soft threshold and attention mechanism is proposed for machinery prognostics. Multi-channel sensor data are directly used as inputs to the prognostic network without feature extraction as a pre-processing step. A soft thresholding mechanism is embedded in the network, serving as a flexible activation function for certain layers to preserve useful features. The threshold value is adaptively learned by a subnetwork trained with the attention mechanism instead of assigning a deterministic value to the threshold. As a result, each feature map is assigned a customized threshold value such that the network training process can focus on features that are more critical to RUL prediction. To verify the generalization ability of the proposed method, three benchmark datasets related to rolling bearings and cutting tools are tested, and the performance of the developed method is compared with several state-of-the-art prognostic approaches. The results show that for all the three case studies, the developed method has produced accurate RUL prediction with good robustness and generalization ability.     

考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计

剩余寿命估计是预测与健康管理的基础,是降低系统运行风险、提高系统安全性与可靠性的有效途径.针对工程实际中大量存在的非线性随机性退化系统,现有方法仅单独考虑了不确定测量或系统间个体差异对剩余寿命的影响,尚未实现同时考虑不确定测量和个体差异的剩余寿命估计.因此,本文首先建立了一种基于扩散过程的非线性退化模型,进一步通过建立的状态空间模型和Kalman滤波实现了同时考虑不确定测量和个体差异下的随机退化系统剩余寿命自适应估计,同时对漂移系数进行自适应估计,以获取非线性退化系统更加精确的剩余寿命估计.最后,将所提方法应用于疲劳裂纹和陀螺仪的监测数据,结果表明本文方法显著优于仅考虑不确定测量或仅考虑个体差异的寿命估计方法,具有潜在的工程应用价值.     

Transformer-based hierarchical latent space VAE for interpretable remaining useful life prediction

Data-driven prediction of remaining useful life (RUL) has emerged as one of the most sought-after research in prognostics and health management (PHM). Nevertheless, most RUL prediction methods based on deep learning are black-box models that lack a visual interpretation to understand the RUL degradation process. To remedy the deficiency, we propose an intrinsically interpretable RUL prediction method based on three main modules: a temporal fusion separable convolutional network (TF-SCN), a hierarchical latent space variational auto-encoder (HLS-VAE), and a regressor. TF-SCN is used to extract the local feature information of the temporal signal. HLS-VAE is based on a transformer backbone that mines long-term temporal dependencies and compresses features into a hierarchical latent space. To enhance the streaming representation of the latent space, the temporal degradation information, i.e., health indicators (HI), is incorporated into the latent space in the form of inductive bias by using intermediate latent variables. The latent space can be used as a visual representation with self-interpretation to evaluate RUL degradation patterns visually. Experiments based on turbine engines show that the proposed approach achieves the same high-quality RUL prediction as black-box models while providing a latent space in which degradation rate can be captured to provide the interpretable evaluation.     

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.     

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.     

基于两阶段自适应Wiener过程的剩余寿命预测方法

针对退化过程呈现两阶段特征的一类随机退化设备, 现有剩余寿命预测方法不适用于测量间隔分布不均匀、监测数据的测量频率与历史数据频率不一致的情况, 并且忽略了自适应漂移的可变性. 鉴于此, 提出了一种新的考虑个体差异性的两阶段自适应Wiener过程剩余寿命预测模型与方法. 首先, 基于自适应Wiener过程分阶段构建随机退化模型, 在首达时间意义下推导出寿命和剩余寿命解析式. 然后, 结合Kalman滤波技术和期望最大化算法进行参数自适应更新, 同时利用赤池信息准则实现退化模型变点的辨识. 最后, 通过蒙特卡洛仿真和锂电池实例, 验证了本文所提方法的有效性和实用价值.     

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.     

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...     

基于优化混合模型的航空发动机剩余寿命预测方法

针对航空发动机剩余使用寿命（RUL）预测方法没有同时加权不同时间步下的数据,包括原始数据和所提取的特征,导致RUL预测准确性较低的问题,提出了一种基于优化混合模型的RUL预测方法。首先,选用三种不同的路径提取特征：1）将原始数据的均值和趋势系数输入至全连接网络;2）将原始数据输入双向长短期记忆（Bi-LSTM）网络,并采用注意力机制处理得到的特征;3）使用注意力机制处理原始数据,并将加权特征输入至卷积神经网络（CNN）和Bi-LSTM网络中。然后,采用融合多路径特征预测的思想,将上述提取到的特征融合后输入至全连接网络获得RUL预测结果。最后,使用商用模块化航空推进系统仿真（C-MAPSS）数据集验证方法的有效性。实验结果显示,所提方法在4个数据集上均有较好的表现。以FD001数据集为例,所提方法的均方根误差（RMSE）比Bi-LSTM网络降低了9.01%。     

基于改进CNN-LSTM的飞控系统剩余寿命预测

数据驱动的剩余寿命（remaining useful life,RUL）预测是复杂系统健康管理的重点研究内容,然而数据集的缺乏制约了不同系统上RUL预测的研究。针对这一问题,以飞控系统为例,提出一种仿真模型和数据混合驱动的RUL预测方法。该方法通过模型仿真提供充足的故障数据,并结合改进CNN-LSTM网络实现高质量的故障信息提取。首先对系统及其故障模式建立仿真模型,利用蒙特卡罗方法生成随机故障时间序列并依次注入故障,根据仿真响应和失效阈值确定序列的寿命标签,即可生成包含多组随机序列的系统失效数据集;其次利用长短时记忆网络（long short-term memory,LSTM）提取系统状态参数时间序列的故障信息,结合一维卷积神经网络（1D-CNN）提取不同状态参数之间的关联特征,从而形成时序-空间相结合的剩余寿命预测网络。充分的实验结果证明了所提方法对不同系统均能帮助达到动态和准确的剩余寿命预测。     

基于自注意力长短期记忆网络的Web软件系统实时剩余寿命预测方法

为了能够实时准确对Web软件系统的剩余使用寿命（RUL）进行预测,考虑Web系统健康状态性能指标的时序特性和指标间的相互依赖特性,提出了一种基于自注意力长短期记忆（Self-Attention-LSTM）网络的Web软件系统实时剩余寿命预测方法。首先,搭建加速寿命测试实验平台来收集反映Web软件系统老化趋势的性能指标数据;然后,根据该性能指标数据的时序特性来构建长短期记忆（LSTM）循环神经网络以提取性能指标的隐含层特征,并使用自注意力机制建模特征间的依赖关系;最后,得到系统RUL的实时预测值。在三组测试集上,把所提模型与反向传播（BP）网络和常规的循环神经网络（RNN）做了对比。实验结果表明,所提模型的平均绝对误差（MAE）比长短期记忆（LSTM）网络平均低16.92%,相对准确率（Accuracy）比LSTM网络平均高5.53%,验证了Self-Attention-LSTM网络剩余寿命预测模型的有效性。可见所提方法能为优化系统抗衰决策提供技术支撑。