Equipment PHM using non-stationary segmental hidden semi-Markov model

Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains where safety, reliability, and availability of the systems are considered mission critical. As a key complement to CBM, prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient faults. Conducting successful prognosis, however, is more difficult than conducting fault diagnosis. A much broader range of asset health related data, especially those related to the failures, shall be collected. The asset health progression can then be possibly extracted from the congregated data, which has proved to be very challenging. This paper presents a non-stationary segmental hidden semi-Markov model (NSHSMM) based prognosis method to predict equipment health. Unlike previous HSMMs, the proposed NSHSMM no longer assumes that the state-dependent transition probabilities keep the same value all the time. That is, the probability of transiting to a less healthy state does not increase with the age. “Non-stationary” means the transition probabilities will change with time. In the proposed method, in order to characterize a deteriorating equipment, three kinds of aging factor that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are introduced. The performances of these aging factors are compared by using historical data colleted from three hydraulic pumps. The hazard function (h.f.) has been introduced to analyze the distribution of lifetime with a combination of historical failure data and on-line condition monitoring data. Using h.f., PHM is based on a failure rate that is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in PHM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The estimated state duration probability distributions can be used to predict the remaining useful life of the systems. With the equipment PHM, the behavior of the equipment condition can be predicted.     

巷道掘进机健康管理研究现状及展望

掘进机健康管理技术通过对大量监测数据进行分析处理,动态掌握掘进机运行状况,并对各类故障进行预测预报,从而提高掘进机运行的安全性,降低事故发生率及损失,减少设备维护成本。指出巷道掘进机健康管理的关键技术为工作状态参数提取、全状态健康管理、剩余使用寿命估计和远程监测,总结了4项关键技术的研究现状,指出目前对于复杂机械设备剩余使用寿命预测的研究基本不考虑工况变化,仍停留在理论仿真和实验室试验阶段,若使该技术得到应用,必须考虑变工况条件;提出了掘进机健康管理的研究方向,包括掘进机微弱故障诊断方法、掘进机监测多信息融合技术、掘进机关重件与保养件寿命估计方法及油液污染度评估、数字孪生技术在掘进机健康管理中的应用。     

A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition

Remaining useful life prediction methods are extensively researched based on failure or suspension histories. However, for some applications, failure or suspension histories are hard to obtain due to high reliability requirement or expensive experiment cost. In addition, some systems’ work condition cannot be simulated. According to current research, remaining useful life prediction without failure or suspension histories is challenging. To solve this problem, an individual-based inference method is developed using recorded condition monitoring data to date. Features extracted from condition data are divided by adaptive time windows. The time window size is adjusted according to increasing rate. Features in two adjacent selected windows are regarded as the inputs and outputs to train an artificial neural network. Multi-step ahead rolling prediction is employed, predicted features are post-processed and regarded as inputs in the next prediction iteration. Rolling prediction is stopped until a prediction value exceeds failure threshold. The proposed method is validated by simulation bearing data and PHM-2012 Competition data. Results demonstrate that the proposed method is a promising intelligent prognostics approach.     

Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation

Most of the reported prognostic techniques use a small number of condition indicators and/or use a thresholding strategies in order to predict the remaining useful life (RUL). In this paper, we propose a reliability-based prognostic methodology that uses condition monitoring (CM) data which can deal with any number of condition indicators, without selecting the most significant ones, as many methods propose. Moreover, it does not depend on any thresholding strategies provided by the maintenance experts to separate normal and abnormal values of condition indicators. The proposed prognostic methodology uses both the age and CM data as inputs to estimate the RUL. The key idea behind this methodology is that, it uses Kaplan–Meier as a time-driven estimation technique, and logical analysis of data as an event-driven diagnostic technique to reflect the effect of the operating conditions on the age of the monitored equipment. The performance of the estimated RUL is measured in terms of the difference between the predicted and the actual RUL of the monitored equipment. A comparison between the proposed methodology and one of the common RUL prediction technique; Cox proportional hazard model, is given in this paper. A common dataset in the field of prognostics is employed to evaluate the proposed methodology.     

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.     

不完美维护下基于剩余寿命预测信息的设备维护决策模型

基于剩余寿命预测信息进行设备维护决策的研究中,现有方法通常仅考虑不完美维护对退化量或退化率的单一影响,忽略了不完美维护对两者的双重影响.鉴于此,针对随机退化设备,提出一种考虑不完美维护影响的性能退化模型与维护决策模型,融合了维护活动对设备退化量和退化率的双重影响.首先基于Wiener过程分阶段构建存在不完美维护干预的随机退化模型,在首达时间的意义下推导出剩余寿命的解析概率分布;然后基于剩余寿命的预测结果,以检测间隔和预防性维护阈值为决策变量建立维护决策模型;最后数值仿真实验验证了本文模型的有效性,并对费用参数进行了敏感性分析.实验结果表明本文模型具有潜在的工程应用价值.     

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。     

A multi-head neural network with unsymmetrical constraints for remaining useful life prediction

This paper proposes a multi-head neural network (MHNN) model with unsymmetrical constraints for remaining useful life (RUL) prediction of industrial equipment. Generally, the existing deep learning methods proposed for RUL prediction utilize symmetrical constraint loss functions such as the mean squared error function to calculate training errors. However, if the predicted RUL is much larger than the actual value in some safety–critical applications, severe damage may occur. To address this issue, an unsymmetrical constraint function is proposed as the loss function in this work that penalizes the late predictions (i.e., the predicted RUL is larger than the actual RUL) more strongly. In addition, an adjustable parameter is added to this function to adjust the model’s attention to the late predictions. In MHNN model, the bidirectional gated recurrent units (BGRU) and self-attention mechanism are employed to extract temporal features from the condition monitoring data. In addition, the structure of the multi-head neural network is adopted in the proposed model, helping to capture more degradation information by means of multiple identical and parallel networks. The proposed method is validated against a commonly used turbofan engine dataset. Compared with other latest methods on the same dataset, the proposed method is proven to be superior. Taking the FD004 dataset as an example, the score obtained by MHNN is 24.09% lower than that obtained by the best existing method.     

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.     

基于遗传算法选优的集成手段与时序卷积网络的涡扇发动机剩余寿命预测

涡扇发动机作为航空航天领域的核心设备之一,其健康状况决定了航空器能否稳定可靠地运行。而对涡扇发动机的剩余寿命（RUL）进行判断,是设备监测与维护的重要一环。针对涡扇发动机监测过程中存在的工况复杂、监测数据多样、时间跨度长等特点,提出了一种遗传算法优选时序卷积网络（TCN）基模型的集成方法（GASEN-TCN）的涡扇发动机剩余寿命预测模型。首先,利用TCN捕获长跨度下的数据内在关系,从而对RUL作出预测;然后,应用GASEN集成多个独立的TCN,以增强模型的泛化性能;最后,在通用的商用模块化航空推进系统模拟模型（C-MAPSS）数据集上,对所提模型与当下流行的机器学习方法和其他的深度神经网络进行了比较。实验结果表明,在多种不同的运行模式和故障条件下,与流行的双向长短期记忆（Bi-LSTM）网络相比,所提模型都有着更高的预测准确率与更低的预测误差。以FD001数据集为例,在该数据集上所提模型的均方根误差（RMSE）相较Bi-LSTM低17.08%,相对准确率（Accuracy）相较Bi-LSTM高12.16%。所提模型在设备的智能检修与维护方面有着较好的应用前景。     

基于遗传算法选优的集成手段与时序卷积网络的涡扇发动机剩余寿命预测

涡扇发动机作为航空航天领域的核心设备之一，其健康状况决定了航空器能否稳定可靠地运行。而对涡扇发动机的剩余寿命（RUL）进行判断，是设备监测与维护的重要一环。针对涡扇发动机监测过程中存在的工况复杂、监测数据多样、时间跨度长等特点，提出了一种遗传算法优选时序卷积网络（TCN）基模型的集成方法（GASEN-TCN）的涡扇发动机剩余寿命预测模型。首先，利用TCN捕获长跨度下的数据内在关系，从而对RUL作出预测；然后，应用GASEN集成多个独立的TCN，以增强模型的泛化性能；最后，在通用的商用模块化航空推进系统模拟模型（C-MAPSS）数据集上，对所提模型与当下流行的机器学习方法和其他的深度神经网络进行了比较。实验结果表明，在多种不同的运行模式和故障条件下，与流行的双向长短期记忆（Bi-LSTM）网络相比，所提模型都有着更高的预测准确率与更低的预测误差。以FD001数据集为例，在该数据集上所提模型的均方根误差（RMSE）相较Bi-LSTM低17.08%，相对准确率（Accuracy）相较Bi-LSTM高12.16%。所提模型在设备的智能检修与维护方面有着较好的应用前景。     

基于非线性数据融合的设备多阶段寿命预测

为解决设备监测数据具有维数高、非线性且退化过程中存在多阶段的问题,提出了一种基于非线性数据融合和多阶段退化的设备寿命预测模型.首先,利用神经网络理论中的自编码器对表征设备退化的多维参数进行了融合,构建出设备的退化指示量;然后,利用CUSUM算法提取出设备退化过程中的分段点;最后,构建了多阶段维纳退化模型,从而实现对设备寿命的预测.利用航空发动机状态监测数据对所提模型进行了验证,剩余寿命预测的平均误差为0.254 5,低于传统的基于线性数据融合方法和基于单阶段维纳过程退化模型的寿命预测方法.结果证明,基于非线性数据融合的多阶段退化模型具有很好的鲁棒性,对设备的寿命预测更加精准.     

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。     

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.     

适用于小子样时间序列预测的动态回归极端学习机

针对设备状态在线监测中的小子样建模问题,提出一种基于动态回归极端学习机(dynamic regression extreme learning machine,DR-ELM)的设备状态在线监测方法.该方法利用设备状态数据训练基于DR-ELM的预测模型,通过逐次增加新数据与删减旧数据的方式,对DR-ELM预测模型进行在线训练,从而实现对设备状态的准确预测.混沌时间序列预测仿真与基于时间序列预测的风机状态监测实例表明,相比于极端学习机(extreme learning machine,ELM)与在线贯序极端学习机(on-line sequential extreme learning machine,OS-ELM),该方法的计算效率与预测精度更高.     

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.     

Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems.     

基于CNN-WaveNet的滚动轴承剩余寿命预测

为保证设备正常运行并准确预测轴承剩余寿命,提出二维卷积神经网络与改进WaveNet组合的寿命预测模型.为克服未优化的递归网络在预测训练过程中易出现梯度消失问题,该模型引入了WaveNet时序网络结构.针对原始WaveNet结构不适用滚动轴承振动数据情况,将WaveNet结构改进与二维卷积神经网络结合应用于滚动轴承寿命预测.模型利用二维卷积网络提取一维振动序列的特征,随后特征输入WaveNet并进行滚动轴承的预测寿命.改进模型相比于深度循环网络计算效率更高、结果更准确,相比于原始CNN-WaveNet-O模型预测结果更准确.相比于深度长短期记忆网络模型,改进方法预测结果均方根误差降低了11.04％,评分函数降低了11.34％.     

基于建康管理技术的机载计算机智能故障诊断方法

为了满足飞机机载电子设备以状态监控为基础的视情维修保障策略,提升设备可维护性,提出了一种基于在线检测、故障预测、辅助决策的健康监控管理故障诊断方法,支持对机载电子设备的健康状态进行预测和评估。通过划分机载电子设备子功能的敏感威胁区域,对这些区域设计专门的威胁预警监控电路,进行功能危害监控,建立推理监控模型对监控电路故障进行预警监控,结合辅助决策的方式对预警到的故障进行定位,实现对电子设备的智能故障诊断。通过FMEA的分析与故障注入测试验证,该预警电路、推理模型和辅助决策能有效的预测故障及定位,具有较高的故障预测覆盖率,可提高机载计算机的维修性、降低维修时间,在电子设备视情维修策略上具备工程应用价值。     

基于可自动扩展的LSTM模型的航空发动机剩余寿命预测方法

对航空发动机进行实时状态监测和健康管理可以有效降低发动机故障风险,确保飞机飞行安全。准确预测航空发动机的剩余寿命是有效监测发动机运行状态的一种重要手段,其中长短期记忆（long-short term memory,LSTM）网络常被使用。但由于航空发动机复杂的机械结构与运行模式,使用传统的LSTM网络对航空发动机的剩余寿命进行单次预测后,所得预测结果的准确率不足以满足其寿命预测的精度要求。基于LSTM网络的广泛使用以及它对时间序列数据的有效预测能力,考虑到采用多级预测的方法能够有效降低预测误差,提出了一种新型的可自动扩展的长短期记忆（automatically expandable LSTM,AELSTM）预测模型。AELSTM模型依托多个子模块逐级连接的网络结构,不断地提取前一级模块的输出误差作为后一级模块的训练值,形成了误差的多级预测机制,有效降低了模型的预测误差,提升了预测结果的准确性。基于美国国家航空航天局发布的C-MAPSS数据集的四个子集对AELSTM模型的预测效果进行了测试,实验结果表明,与传统的LSTM网络相比,AELSTM模型在四个子集上的均方根误差平均减少了95.44%,同时它的预测效果也优于现有的一些先进算法。实验充分验证了AELSTM模型在提升航空发动机剩余寿命预测准确度方面的有效性及优势。