Run length quantiles of EWMA control charts monitoring normal mean or/and variance

Exponentially weighted moving average (EWMA) control charts are well-established devices for monitoring process stability. Typically, control charts are evaluated by considering their Average Run Length (ARL), that is the expected number of observations or samples until the chart signals. Because of the limitations of an average, various papers also dealt with the run length distribution and quantiles. Going beyond these papers, we develop algorithms for and evaluate the quantile performance of EWMA control charts with variance adjusted control limits and with fast initial response features, of EWMA charts based on the sample variance, and of EWMA charts simultaneously monitoring mean and variance. Additionally, for the mean charts we consider medium, late and very late process changes and their impact on appropriately conditioned run length quantiles. It is demonstrated that considering run length quantiles can protect from constructing distorted EWMA designs while optimising their zero-state ARL performance. The implementation of all the considered measures in the R package ‘spc’ allows any control chart user to consider EWMA schemes from the run length quantile prospective in an easy way.     

Parameter Selection for a Robust Tracking Signal

Tracking signals use past forecast errors to monitor and control a forecasting process. In this study, the cumulative‐sum tracking signal and the smoothed‐error tracking signal are evaluated on their ability to aid in shift (process upset) detection. The moving‐centerline EWMA control chart technique is coupled with these tracking signals to enhance the monitoring of autocorrelated processes. The analysis characterizes two prevalent time series models: AR(1) and ARMA(1,1). The goal of this paper is to explore the capabilities of the tracking signals and the moving‐centerline EWMA when the smoothing constants are varied and a shift is introduced into the process. The tracking signals are evaluated based on average run length (ARL) and false alarm rate (FA). Copyright © 2005 John Wiley & Sons, Ltd.     

齿轮故障振动信号非高斯性特征趋势分析

以齿轮箱实测振动信号为对象,对齿轮点蚀故障发展过程深入研究。通过Gabor滤波仅保留振动信号的边带成分与随机成分;据双谱分析结果研究信号非线性、非高斯性变化,并提取非高斯性强度特征值;在故障趋势分析中利用“3σ准则”设定故障阈值。结果表明,非高斯性强度特征值对齿轮点蚀故障较敏感,可揭示故障发展变化趋势,有利于齿轮故障报警及寿命预测,对齿轮传动系统状态监测与故障诊断具有实际意义。     

Assessment of the effectiveness of a spindle power signal for tool condition monitoring in machining processes

Less expensive and ‘readily available’ process monitoring techniques are needed to be effective in industrial machining processes. Spindle motors on modern computer numerical control machine tools allow easy access to the monitoring of spindle power. Whilst a spindle power signal fulfils the requirements for simple process monitoring, such a signal can trigger ‘machine alarms’ when process malfunctions occur. Little analysis has been done to assess the sensitivity of a spindle power signal relative to interrupted/continuous cutting processes. This paper aims to assess the effectiveness of a spindle power signal for tool condition monitoring in three machining processes: milling, drilling and turning. Based on cutting force/torque, the cutting power was calculated and a comparison between the theoretical cutting power and the spindle power signal was performed. Tool condition monitoring using spindle power could be successful in continuous machining processes (turning and drilling), while for discontinuous machining operations (milling), the spindle power signal showed reduced sensitivity to detect small uneven events such as chipping of one tooth. The results were used to define the sensitivity limitations when using a spindle power signal for tool condition monitoring on different computer numerical control machining centres where continuous and discontinuous machining operations are performed.     

On‐line reliability estimation for individual components using statistical degradation signal models

With very few exceptions, most contemporary reliability engineering methods are geared towards estimating a population characteristic(s) of a system, subsystem or component. The information so extracted is extremely valuable for manufacturers and others that deal with product in relatively large volumes. In contrast, end users are typically more interested in the behavior of a ‘particular’ component used in their system to arrive at optimal component replacement or maintenance strategies leading to improved system utilization, while reducing risk and maintenance costs. The traditional approach to addressing this need is to monitor the component through degradation signals and ‘classifying’ the state of a component into discrete classes, say ‘good’, ‘bad’ and ‘in‐between’ categories. In the event, one can develop effective degradation signal forecasting models and precisely define component failure in the degradation signal space, then, one can move beyond the classification approach to a more vigorous reliability estimation and forecasting scheme for the individual unit. This paper demonstrates the feasibility of such an approach using ‘general’ polynomial regression models for degradation signal modeling. The proposed methods allow first‐order autocorrelation in the residuals as well as weighted regression. Parametric bootstrap techniques are used for calculating confidence intervals for the estimated reliability. The proposed method is evaluated on a cutting tool monitoring problem. In particular, the method is used to monitor high‐speed steel drill‐bits used for drilling holes in stainless‐steel metal plates. A second study involves modeling and forecasting fatigue‐crack‐growth data from the literature. The task involved estimating and forecasting the reliability of plates expected to fail due to fatigue‐crack‐growth. Both studies reveal very promising results. Copyright © 2002 John Wiley & Sons, Ltd.     

Distribution‐free Lepage Type Circular‐grid Charts for Joint Monitoring of Location and Scale Parameters of a Process

In the last 5 years, research works on distribution‐free (nonparametric) process monitoring have registered a phenomenal growth. A Google Scholar database search on early September 2015 reveals 246 articles on distribution‐free control charts during 2000–2009 and 466 articles in the following years. These figures are about 1400 and 2860 respectively if the word ‘nonparametric’ is used in place of ‘distribution‐free’. Distribution‐free charts do not require any prior knowledge about the process parameters. Consequently, they are very effective in monitoring various non‐normal and complex processes. Traditional process monitoring schemes use two separate charts, one for monitoring process location and the other for process scale. Recently, various schemes have been introduced to monitor the process location and process scale simultaneously using a single chart. Performance advantages of such charts have been clearly established. In this paper, we introduce a new graphical device, namely, circular‐grid charts, for simultaneous monitoring of process location and process scale based on Lepage‐type statistics. We also discuss general form of Lepage statistics and show that a new modified Lepage statistic is often better than the traditional of Lepage statistic. We offer a new and attractive post‐signal follow‐up analysis. A detailed numerical study based on Monte‐Carlo simulations is performed, and some illustrations are provided. A clear guideline for practitioners is offered to facilitate the best selection of charts among various alternatives for simultaneous monitoring of location‐scale. The practical application of the charts is illustrated. Copyright © 2016 John Wiley & Sons, Ltd.     

深度学习在机械设备诊断中的应用

传统的机械设备状态监测是根据经验通过提取现场采集的振动信号特征值构建特征空间,采用多种方法对特征值进行聚类、分类,从而实现对设备状态的分类。但这种方法严重依赖于专家经验,并且效果受到信号噪声等众多因素的影响。分别在经典一维和二维卷积神经网络的的基础上,提出两种机械设备智能故障诊断方法,并通过凯斯西储大学轴承数据中心发布的数据集比较两种模型的性能,实验结果表明,基于一维卷积神经网络的智能诊断方法更适用于一维振动信号。将基于一维卷积神经网络的智能诊断方法应用于石化厂的机泵设备,证明其能实现特征自适应提取,可取得较好诊断效果。     

Evaluation of the use of envelope analysis and DWT on AE signals generated from degrading shafts

Vibration analysis is widely used in machinery diagnosis. Wavelet transforms and envelope analysis, which have been implemented in many applications in the condition monitoring of machinery, are applied in the development of a condition monitoring system for early detection of faults generated in several key components of machinery. Early fault detection is a very important factor in condition monitoring and a basic component for the application of condition-based maintenance (CBM) and predictive maintenance (PM). In addition, acoustic emission (AE) sensors have specific characteristics that are highly sensitive to high-frequency and low-energy signals. Therefore, the AE technique has been applied recently in studies on the early detection of failure. In this paper, AE signals caused by crack growth on a rotating shaft were captured through an AE sensor. The AE signatures were pre-processed using the proposed signal processing method, after which power spectrums were generated from the FFT results. In the power spectrum, some peaks from fault frequencies were presented. According to the results, crack growth in rotating machinery can be considered and detected using an AE sensor and the signal processing method.     

基于粒子滤波的多模态振动信号在线跟踪

针对多模态振动信号的在线监测和跟踪,提出基于随机子空间（SSI）和粒子滤波（PF）算法的仿真振动信号在线监测和跟踪方法。通过SSI算法提取得到振动系统的模态主频和阻尼比,根据振动系统模型模态主频和阻尼比的计算公式,得到系统的状态矩阵和输出矩阵。将计算所得状态矩阵和输出矩阵代入状态方程,利用PF算法进行信号的在线监测和跟踪,实现信号的降噪处理和预测分析。对于大型机械、桥梁等建筑物,对其进行在线监测保障其正常营运对社会经济发展具有深远影响。文中利用SSI算法提取系统的模态参数,进一步构建振动系统的状态矩阵和输出矩阵,并利用PF算法进行信号滤波抑噪和预测,在此基础上可以对结构状态实施在线监测及预警控制,实际大桥斜拉索振动信号测试也表明本文算法可以提供稳定可靠的信号跟踪与预测技术。     

Research on the Signal Reconstruction of the Phased Array Structural Health Monitoring Based Using the Basis Pursuit Algorithm

The signal processing problem has become increasingly complex and demand high acquisition system, this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal. The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals. According to the principles of the compressive sensing and signal processing method, non-sparse ultrasonic signals are converted to sparse signals by using sparse transform. The sparse coefficients are obtained by sparse decomposition of the original signal, and then the observation matrix is constructed according to the corresponding sparse coefficients. Finally, the original signal is reconstructed by using basis pursuit algorithm, and error analysis is carried on. Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.     

小波尺度谱在AE信号特征提取中的应用

通过分析典型声发射信号及其特征提取,将小波尺度谱引入到声发射故障诊断领域,首次提出了声发射信号的小波尺度谱分析法。给出了小波基函数及其参数的选取,克服了声发射信号小波尺度谱的时、频分辨率不能同时达到最好的缺陷。将小波尺度谱用于声发射检测的滚动轴承损伤类型及部件的识别,诊断结果十分直观、清晰、准确。仿真分析和实验研究均表明小波尺度谱能有效应用于基于声发射技术的状态监测与故障诊断。     

Misleading signals in joint schemes for the mean vector and covariance matrix

In multivariate statistical process control, it is recommendable to run two individual charts: one for the process mean vector and another one for the covariance matrix. The resulting joint scheme provides a way to satisfy Shewhart's dictum that proper process control implies monitoring both process location and spread. The multivariate quality characteristic is deemed to be out of control whenever a signal is triggered by either individual chart of the joint scheme. Consequently, a shift in the mean vector can be misinterpreted as a shift in the covariance matrix and vice versa. Compelling results are provided to give the quality control practitioner an idea of how joint schemes for the mean vector and covariance matrix are prone to trigger misleading signals that will likely lead to a incorrect diagnostic of which parameter has changed.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Wavelet-based SPC procedure for complicated functional data

Functional data characterize the quality or reliability performance of many manufacturing processes. As can be seen in the literature, such data are informative in process monitoring and control for nanomachining, for ultra-thin semiconductor fabrication, and for antenna, steel-stamping, or chemical manufacturing processes. Many functional data in manufacturing applications show complicated transient patterns such as peaks representing important process characteristics. Wavelet transforms are popular in the computing and engineering fields for handling these types of complicated functional data. This article develops a wavelet-based statistical process control (SPC) procedure for detecting ‘out-of-control’ events that signal process abnormalities. Simulation-based evaluations of average run length indicate that our new procedure performs better than extensions from well-known methods in the literature. More importantly, unlike recent SPC research on linear profile data for monitoring global changes of data patterns, our methods focus on local changes in data segments. In contrast to most of the SPC procedures developed for detecting a known type of process change, our idea of updating the selected parameters adaptively can handle many types of process changes whether known or unknown. Finally, due to the data-reduction efficiency of wavelet thresholding, our procedure can deal effectively with large data sets.     

基于DCT的微弱信号提取和识别

微弱信号提取一直是故障诊断领域的难点。结合离散余弦变换(DCT),将离散时间序列经过离散余弦变换处理成对应的系数向量,在阈值处理的基础上,重构信号提取出微弱故障信息。与小波降噪和低通滤波方法进行对比分析,该算法突出了信号的微弱故障特征信息,较好的再现了夹杂在信号中的微弱成分,参数设定简单,结果对参数不敏感。最后通过实验证实该方法的有效性。本算法速度快,简单易行,可用于实时故障监测。     

Automatic feature selection for unsupervised clustering of cycle-based signals in manufacturing processes

Recent developments in sensing and computer technology have resulted in most manufacturing processes becoming a data-rich environment. A cycle-based signal refers to an analog or digital signal that is obtained during each repetition of an operation cycle in a manufacturing process. It is a very important class of in-process sensing signals for manufacturing processes because it contains extensive information on the process condition and product quality (e.g., the forming force signal in forging processes). In contrast with currently available supervised classification approaches that heavily depend on the training dataset or engineering field knowledge, this paper aims to develop an automatic feature selection method for the unsupervised clustering of cycle-base signals. First, principal component analysis is applied to the raw signals. Then a new method is proposed to select information containing principal components to allow clustering to be performed. The dimension of the problem can be significantly reduced through the use of these two steps. Finally, a model-based clustering method is applied to the selected principal components to find the clusters in the cycle-based signals. A numerical example and a real-world example of a forging process are used to illustrate the effectiveness of the proposed method. The proposed technique is an important data pre-processing technique for the monitoring and diagnostic system development using cycle-based signals for manufacturing processes.     

Optically induced non-radiative fast pulses in metals

Abstract

We describe new experiments in which the signals induced inside a solid conductor by external excitation using nanosecond long pulsed laser radiation are detected using a pyro-electric material. From the data obtained, it is shown that there is a previously unreported ‘early’ or ‘fast’ signal which propagates inside the solid at a speed greater than that reported for sound in the same media. This signal is observed in addition to the usually observed thermal and acoustic waves that travel at the speed of sound in the same media. We demonstrate that this signal cannot be adequately accounted for by existing theories of thermo-elastic wave propagation in solids.     

Cycle-based signal monitoring using a directionally variant multivariate control chart system

Cycle-based signals are generally obtained through the automatic sensing of critical process variables during each repetitive operation cycle of a manufacturing process, and they thus contain a significant amount of information about the process condition. Increasing attention has been paid recently to the problem of effectively monitoring these signals as an aid to the detection of process changes. In general, either based on process engineering knowledge or on historical data analysis, it is possible to obtain process faults and the corresponding signal patterns (the direction and magnitude of a mean shift). In order to fully utilize such fault pattern information in process monitoring, this paper proposes a directionally variant control chart obtained through the effective combination of a multivariate χ² chart and a univariate projection chart. It is shown that the addition of the univariate projection chart can improve the detection power for pre-known process faults, however, this may be at the cost of a deterioration in the detection power for unknown faults. A detailed quantitative analysis is provided to justify the application conditions of the proposed chart. A case study of cycle-based tonnage monitoring of a forging process is presented to illustrate the design procedures and the effectiveness of the proposed control chart system.     

Tool condition monitoring by acoustic analysis through a solid path

A sensing method using an acoustic signal obtained in a relative low frequency range through a solid path for the monitoring of tool wear has been investigated. Such acoustic signals could be in the form of stress waves that are released during a machining process, which can be picked up by a regular ferroelectric microphone. Data analysis was conducted in both time and frequency domains. A clear pattern in such signals corresponding to the tool wear conditions has been identified. Several components in spectra were found in the pattern for indicating sudden changes of tool wear or breakage occurring at major cutting edges. It was also observed that the RMS and variance values of the signals could indicate the specific wear condition of the tool. Therefore, this kind of acoustic signal carries sensitive information about the progress of tool wear and can be implemented on line for monitoring tool wear.