铣刀磨损量监测和剩余寿命预测方法研究

研究在线诊断端面铣刀磨损量和预测铣刀剩余寿命的方法。首先通过实验采集Y向铣削力作为监测信号,分析铣削力与刀具磨损量VB和刀具磨损时间之间的关系。然后,通过铣削力信号分析提取出有效监测特征向量,此特征向量作为BP神经网络的输入,用于刀具磨损量的监测和剩余寿命的预计。最后,通过实验证明,该神经网络模型误差很小,利用该方法能够正确地进行在线监测和预测。     

Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability

The International Journal of Advanced Manufacturing Technology - Remaining useful life prediction is essential for cutting tool utilization evaluation and replacement decision-making. However, it...     

深度学习在装备剩余使用寿命预测技术中的研究现状与挑战

有效的装备剩余使用寿命(RUL)预测有助于及时规避严重生产事故,并为视情维护提供技术支持,在现代工业中发挥着重要作用.近年来,深度学习凭借其在大数据处理和特征提取方面的独特优势与潜力,在RU L预测领域得到了广泛应用.鉴于此,综述了深度学习在装备RU L预测领域的最新研究.首先介绍几种应用于RU L预测的典型深度学习方...     

基于迁移学习的刀具剩余寿命预测方法

在新加工工艺条件下,针对历史工艺条件下的刀具剩余寿命预测模型失效,且新工艺条件下缺乏足够的训练样本构建新预测模型的问题,提出一种基于动态对抗域适应的迁移学习方法,以快速构建新工艺条件下的刀具剩余寿命预测模型.首先,利用历史工艺条件下带寿命标签的过程监控数据样本,预训练源域的刀具剩余寿命预测模型.其次,通过对抗域适应训练...     

自动扶梯轴承健康指标提取及剩余寿命预测方法研究

针对小样本情况下滚动轴承的健康指标提取及寿命预测问题,提出了一种结合变分模态分解(VMD)和门控循环网络(GRU)的轴承健康指标提取及寿命预测方法.首先,针对现场设备轴承故障全寿命数据稀缺问题,对样本数据进行扩增,通过VMD分解将一维数据分解为多维数据;然后,对多维数据进行时域特征提取及归一化处理;最后,以归一化特征为...     

A neural network approach for remaining useful life prediction utilizing both failure and suspension histories

Artificial neural network (ANN) methods have shown great promise in achieving more accurate equipment remaining useful life prediction. However, most reported ANN methods only utilize condition monitoring data from failure histories, and ignore data obtained from suspension histories in which equipments are taken out of service before they fail. Suspension history condition monitoring data contain useful information revealing the degradation of equipment, and will help to achieve more accurate remaining useful life prediction if properly used, particularly when there are very limited failure histories, which is the case in many applications. In this paper, we develop an ANN approach utilizing both failure and suspension condition monitoring histories. The ANN model uses age and condition monitoring data as inputs and the life percentage as output. For each suspension history, the optimal predicted life is determined which can minimize the validation mean square error in the training process using the suspension history and the failure histories. Then the ANN is trained using the failure histories and all the suspension histories with the obtained optimal predicted life values, and the trained ANN can be used for remaining useful life prediction of other equipments. The key idea behind this approach is that the underlying relationship between the inputs and output of ANN is the same for all failure and suspension histories, and thus the optimal life for a suspension history is the one resulting in the lowest ANN validation error. The proposed approach is validated using vibration monitoring data collected from pump bearings in the field.     

A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction

Tool wear is one of the important indicators to reflect the health status of a machining system. In order to obtain tool’s wear status, tool condition monitoring (TCM) utilizes advanced sensor techniques, hoping to find out the wear status through those sensor signals. In this paper, a novel weighted hidden Markov model (HMM)-based approach is proposed for tool wear monitoring and tool life prediction, using the signals provided by TCM techniques. To describe the dynamic nature of wear evolution, a weighted HMM is first developed, which takes wear rate as the hidden state and formulates multiple HMMs in a weighted manner to include sufficient historical information. Explicit formulas to estimate the model parameters are also provided. Then, a particular probabilistic approach using the weighted HMM is proposed to estimate tool wear and predict tool’s remaining useful life during tool operation. The proposed weighted HMM-based approach is tested on a real dataset of a high-speed CNC milling machine cutters. The experimental results show that this approach is effective in estimating tool wear and predicting tool life, and it outperforms the conventional HMM approach.     

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.     

Prognostic modelling options for remaining useful life estimation by industry

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs.This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.     

基于深度学习的铣刀剩余寿命预测方法研究

刀具剩余寿命预测对保证设备正常运行和提高生产效率具有重要意义.建立了一种改进的基于一维卷积神经网络(one-Dimensional Convolutional Neural Network,1DCNN)和双向门控循环单元(Bidirectional Gated Recurrent Unit,BiGRU)的铣刀剩余寿命预测混合模型.借助评分函数能对误差进行不同程度的惩罚,在均方误差函数的基础上引入评分函数,构造了一种基于均方误差和评分函数(MSE-Score)的调和平均(Harmonic Mean-MSE-Score,HM-MSE-Score)损失函数;利用卷积层和池化层代替BiGRUs处理后的全连接层,设计了1DCNN-BiGRUs-CP混合模型,实现铣刀剩余寿命预测.结合铣刀磨损实验,验证了该预测混合模型具有较高的预测精度和较快的运行速度,研究结果能为数控加工过程中铣刀剩余寿命预测提供理论依据.     

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VMD-NARX的MOSFET剩余使用寿命预测方法

金属氧化物半导体场效应晶体管(MOSFET)剩余使用寿命预测能够防止因器件长时间导通出现性能逐渐退化或失效,但传统预测模型易忽略MOSFET退化参数的非线性细节特征而导致预测精度较差。本文提出一种基于变分模态分解与带外源输入的非线性自回归神经网络的MOSFET剩余使用寿命预测方法。首先采用变分模态分解将退化参数序列分解为多组含有非线性变化信息的特征分量。然后分别利用贝叶斯正则和Levenberg-Marquardt算法对预测网络进行优化。最终集成多组预测分量值获得MOSFET剩余使用寿命预测结果。实验结果表明,本文所提方法的均方根误差小于0.003,平均绝对百分比误差小于5%,均优于对比方法,剩余使用寿命预测平均偏差小于5 min,验证了该方法的有效性.     

A review of flank wear prediction methods for tool condition monitoring in a turning process

Flank wear is the most commonly observed and unavoidable phenomenon in metal cutting which is also a major source of economic loss resulting due to material loss and machine down time. A wide variety of monitoring techniques have been developed for the online detection of flank wear. In order to provide a broad view of flank wear monitoring techniques and their implementation in tool condition monitoring system (TCMS), this paper reviews three key features of a TCMS, namely (1) signal acquisition, (2) signal processing and feature extraction, and (3) artificial intelligence techniques for decision making.     

Prediction of remaining useful life under different conditions using accelerated life testing data

Prognostics methods model the degradation of system performance and predict remaining useful life using degradation data measured during service. However, obtaining degradation data from in-service systems in practice is either difficult or expensive. Therefore, accelerated life testing (ALT) is instead frequently performed for validating designs using considerably heavy loads. This work discusses the methods and effectiveness of utilizing ALT degradation data for the prognostics of a system. Depending on the degradation model and loading conditions, four different ways of utilizing ALT data for prognostics are discussed. A similar transformation method used in ALT is adopted to convert accelerated loading conditions to field loading conditions. To demonstrate the proposed approach, synthetic data are generated for crack growth under accelerated loading conditions; these data are used for training a neural network model or identifying model parameters in a particle filter. The applied example shows that the use of ALT data increases the accuracy of prognostics in the early stages in all four cases and compensates for the problem posed by data insufficiency through the proposed method.     

可重构卫星锂离子电池剩余寿命预测系统研究

针对卫星锂离子电池剩余寿命预测问题,提出一种基于FPGA的可重构卫星锂离子电池剩余寿命预测系统设计方法.首先利用具备不确定性表达能力的相关向量机实现锂离子电池的RUL预测,进而采用FPGA动态重构技术,实现了基于相关向量机的预测算法的嵌入式计算,解决了核函数矩阵和矩阵求逆的计算方法和结构设计等关键问题,为解决硬件计算资源有限条件下的机器学习算法计算问题提供了一种新颖的思路.实验结果表明,在与PC平台保持相近计算精度的条件下,利用FPGA实现的剩余寿命预测计算效率提升了4倍,同时证明了机器学习的可重构计算方法在嵌入式计算体系中的应用具有良好的前景.     

A dynamic multi-scale Markov model based methodology for remaining life prediction

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.