Vibration — A tool for machine diagnostics and condition monitoring

Vibration is an effective tool in detecting and diagnosing some of the incipient failures of machines and equipment. The present paper deals with the basic principles, which may help in identifying its diagnostic ability, the scope of its diagnostic capabilities, the instrumentation in vogue for its monitoring and the state-of-the-art of the monitoring techniques and programs. A few case studies are also given to illustrate how machine troubles/failures are diagnosed with the help of vibration signatures.     

Systems for monitoring technological and production processes of commercial enterprises

The problems that arise in comprehensive automation of production are considered. Characteristics are given and examples are provided of intellectual measurement provisions and control, information measurement systems, and software/hardware suites used in enterprises of the chemical, mining, petrochemical, metallurgical, and oil processing industries. A version of implementing a system for production management is demonstrated. __________ Translated from Izmeritel’naya Tekhnika, No. 7. pp. 59–62, July, 2007.     

Dynamic processes monitoring using predictive PCA

Abstract

Principal component analysis (PCA) has been extensively used for the monitoring of industrial systems when the measurements are highly correlated or corrupted with noise. The basic assumptions for monitoring using PCA are that the considered processes are stationary and their operating regions are unchanged. In an actual plant, the operation conditions might sometimes vary with time because of its dynamic behavior or the effects of disturbance. In this paper, a new predictive monitoring method is proposed that is composed of three parts: (1) the training data is divided into several parts, and each part stands for the operating region at that stage (2) PCA is applied to the first part of the divided raw data sets, and the other parts will be projected by the same PCA model (3) time series models are built to interpret the operating centers obtained in step 2, and the operating region can be estimated for future monitoring. From these, a more reasonable monitoring region and future process deviations can be built. Based on this monitoring scheme, false alarms will be reduced. Moreover, a measure of the difference of the principal component directions between the training data set and the monitored data set is used to check whether a process fault occurs in processes. The effectiveness of this proposed method is demonstrated with simulation results.     

远程测振系统在群楼拆除爆破振动监测中的应用

为了探索数字爆破测振系统在工程爆破危害效应实时监测中的应用效果,以汉口滨江商务区拆除爆破19栋群楼的工程为例,按照公安主管部门在审批拆除爆破方案时的要求,该工程必须采用控制爆破的方法,同时加强拆除爆破产生危害效应的实时监测。为此,本次群楼拆除爆破采用的是广州中爆数字公司研发的远程测振系统,对群楼拆除爆破过程中产生的爆破振动及塌落振动进行了全程、在线监测。通过远程测振系统,能够实现爆破测振数据的自动记录、远程传输和精细处理,进而推动爆破行业的快速发展,同时作为爆破安全监管的数字化、信息化手段,可对拆除爆破危害效应进行实时在线监测,可有效提升公安主管部门对复杂环境下爆破工程项目作业安全的远程监管能力。应用结果表明,远程测振系统可对爆破振动进行实时有效监测。     

Diagnostics and prognostics for complex systems: A review of methods and challenges

Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever-increasing complexity makes fault diagnostics and prognostics challenging for the system-level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on application to complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper first presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems. Afterwards, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state-of-the-art and case studies with a reference value for scholars and practitioners.     

基于液压泵复数信号的log-SAM故障诊断方法研究

针对基于液压泵单源振动信号易受噪声污染、无法有效表征故障特征信息的问题,提出了一种基于复数信号的log-SAM新方法.在液压泵端盖同一截面上,采集两个互相正交传感器上的振动信号;基于全矢思想,对上述双源信号融合为一个复数信号;利用log-SAM对复数信号进行包络分析,充分地突出故障特征信息,抑制噪声干扰;基于总体和最优...     

电子雷管近村庄爆破振动信号的小波分析

福建明溪兰圈采石场距村庄较近,为了最大限度地降低采石爆破振动对周围村庄的影响,采用电子雷管进行毫秒延时爆破,降振效果明显;并对电子雷管爆破振动进行监测,针对爆破振动信号具有非平稳随机的特性,利用小波变换分析技术,将爆破振动信号分解在不同的频率带上,获得爆破振动信号的细节信息,较好地反映了爆破振动信号的非平稳特性。     

电子雷管近村庄爆破振动信号的小波分析

福建明溪兰圈采石场距村庄较近,为了最大限度地降低采石爆破振动对周围村庄的影响,采用电子雷管进行毫秒延时爆破,降振效果明显;并对电子雷管爆破振动进行监测,针对爆破振动信号具有非平稳随机的特性,利用小波变换分析技术,将爆破振动信号分解在不同的频率带上,获得爆破振动信号的细节信息,较好地反映了爆破振动信号的非平稳特性。     

切削稳定性的在线监测

根据切削过程失稳前后振动信号在时域的变化特性，提出用过零频数比Ｒ作为监测切削稳定性的特征参数。当Ｒ值超过设定的监测门限值时，微机控制系统转入执行控制程序。试验在一数控车床上进行。     

On-line tool condition monitoring in face milling using current and power signals

The vast majority of tool condition monitoring systems use the cutting force as the predictor signal. However, due to prohibitive cost to performance ratios and maintenance and operational problems, such methods are not favoured by industries. In this paper, a method for continuous on-line estimation of tool wear, based on the inexpensive spindle motor current and voltage measurements, is proposed for the complex and intermittent cutting face milling operation. Sensors for these signals are free from problems associated with the cutting forces and the vibration signals. Novel signal processing strategies have been proposed for on-line computation of useful features from the measured signals. Feature space filtering is introduced to obtain robust and improved predictors from the extracted features. A multiple linear regression model, built on the filtered features, is then used to estimate tool wear in real-time. Very accurate predictions are achieved for both laboratory and industrial experiments, surpassing earlier results using cutting forces and estimation methods based on complex methodologies such as artificial neural networks.     

Monitoring Bernoulli processes considering measurement errors and learning effect

This paper presents the effect of measurement errors and learning on monitoring processes with individual Bernoulli observations. A cumulative sum control chart is considered to evaluate the possible impacts of measurement errors and learning. We propose a time‐dependent learning effect model along with measurement errors and incorporate them into the Bernoulli CUSUM control chart statistic. The performance of the Bernoulli CUSUM control chart is then merely assessed by comparing the average number of observations to signal (ANOS) under two proposed conditions with the condition of no possible errors. Thus, the ANOS values are obtained under different proportions of non‐conforming items, once considering errors due to measurement by inspectors, and once considering both errors and learning effect together. The experimental results show that the efficiency of the control chart to detect assignable causes deteriorates in the presence of measurement errors and enhances when learning affects operators' performance. The proposed approach has a potential to be used in monitoring high‐quality Bernoulli processes as well as disease diagnosis, and other health care applications with Bernoulli observations.     

Optical monitoring of thin film properties using in-situ measurement of transmittance and reflectance

Having a good reproducibility and uniformity of the coating properties is a mutual challenge for all coating processes. To face this challenge adequately, it is not only necessary to have accurate control of the coating process but also to have the capability to monitor the optical properties of the coating layers during or directly after deposition. Especially in sensitive multi-layer products produced by large area coating technology, small uniformity variations may give rise to a variation in the visual appearance or other deviations from the product requirements. It becomes necessary to monitor the individual layer thicknesses, requiring sensitive and accurate optical measurement techniques that offer nanoscale precision over large areas. This demand for sensitivity and accuracy puts a strain on the limits of existing in-line measurement capability. The objective of this paper is to discuss some of the measurement problems and give practical solutions to improve accuracy and repeatability of in-situ transmittance and reflectance measurements for optical monitoring of thin film properties.     

Process monitoring using statistical stability metrics: Applications to biopharmaceutical processes

Industrial manufacturing commonly employs control charts to monitor process performance, where special-cause variation is identified with runs rules. While the sensitivity of a control chart to detect non-random variation is increased as the number of rules increases, the false positive rate also increases. Accordingly, there is no way to discern a false signal from a true shift by simply observing runs rule violations on a control chart alone. In this article, two stability metrics are utilized to identify underlying variation as “common cause” or “special cause,” when Nelson rule violations are observed in an XmR control chart in biopharmaceutical processes.     

Flexibility and productivity in complex production processes

Drastic changes in market demands and rapid technological development have created a need for more flexible production systems and more complex products with a larger degree of variation.

There is strong pressure towards the use of more and more mechanized and automated equipment, from single NC-machines to complete manufacturing systems. At the same time, there is a need for flexibility towards changes in the products. These changes have to be made in a limited time, and without the need for large reinvestments in the production system.

This means that more often there must be discussion regarding flexibility versus productivity before the production system is designed. T will discuss methods for calculation of different flexibility levels, strategies for a more flexible view upon products and processes, and examples and results from different areas within the Swedish industry.     

复杂环境沉井施工爆破振动远程综合监测系统

由于沉井施工埋置深度较大,施工技术要求较高,复杂环境下常需进行多次爆破作业,且施工工艺不合理时容易导致工程事故的发生,故需对沉井施工爆破振动信号进行监测与分析处理,以便预测和控制爆破振动效应。针对复杂环境下沉井施工爆破振动监测,利用虚拟仪器技术、数据库技术、无线通信技术、Web技术,建立一个集爆破振动数据采集、存储、传输、多终端查看于一体的多传感器爆破振动远程综合监测系统。系统由数据采集层、数据传输层、Web应用层、客户端四部分组成,能够实现多传感器信号的同时采集、显示与传输,并实现远程多终端实时快速查看爆破振动监测结果,便于及时对数据进行处理。     

复杂环境沉井施工爆破振动远程综合监测系统

由于沉井施工埋置深度较大,施工技术要求较高,复杂环境下常需进行多次爆破作业,且施工工艺不合理时容易导致工程事故的发生,故需对沉井施工爆破振动信号进行监测与分析处理,以便预测和控制爆破振动效应。针对复杂环境下沉井施工爆破振动监测,利用虚拟仪器技术、数据库技术、无线通信技术、Web技术,建立一个集爆破振动数据采集、存储、传输、多终端查看于一体的多传感器爆破振动远程综合监测系统。系统由数据采集层、数据传输层、Web应用层、客户端四部分组成,能够实现多传感器信号的同时采集、显示与传输,并实现远程多终端实时快速查看爆破振动监测结果,便于及时对数据进行处理。     

Formulation of a novel production line monitoring technique

Despite the effectiveness of current developments in modern computerised manufacturing, little attention has been paid to monitoring the material transfer system (e.g. conveyor), which covers most malfunctions in practice and greatly affects production efficiency. The proposed methodology observes the characteristics of flow components using time-stamped counting devices for monitoring purposes. The simulation results showed that blocking in a production line could be timely diagnosed. It is anticipated that the proposed research could contribute to the development of new production monitoring philosophy and potentially, its applications can also be extended to other material transportation systems.     

Methodology for monitoring and measurement of complex broadband networks

A robustly engineered monitoring and measurement capability provides comprehensive and timely performance information that allows broadband network operators and service providers to observe and proactively respond to events that occur during deployment and operation of their respective networks. As network complexity increases, so do the requirements for monitoring and measurement to ensure the high-quality service to internal and external customers. A methodology for monitoring and measurement of complex broadband networks is presented. This methodology includes a framework and reference architecture, essential metrics categories to refine and focus the collection of information and extensions to support net-centric enterprise systems driven by service-oriented architecture (SOA). Examples of the application of the methodology to complex broadband networks are provided along with suggestions for further work.     

Phase II monitoring of covariance stationary autocorrelated processes

Statistical process control charts are intended to assist operators in detecting process changes. If a process change does occur, the control chart should detect the change quickly. Owing to the recent advancements in data retrieval and storage technologies, today's industrial processes are becoming increasingly autocorrelated. As a result, in this paper we investigate a process‐monitoring tool for autocorrelated processes that quickly responds to process mean shifts regardless of the magnitude of the change, while supplying useful diagnostic information upon signaling. A likelihood ratio approach was used to develop a phase II control chart for a permanent step change in the mean of an ARMA (p, q) (autoregressive‐moving average) process. Monte Carlo simulation was used to evaluate the average run length (ARL) performance of this chart relative to that of the more recently proposed ARMA chart. Results indicate that the proposed chart responds more quickly to process mean shifts, relative to the ARMA chart, while supplying useful diagnostic information, including the maximum likelihood estimates of the time and the magnitude of the process shift. These crucial change point diagnostics can greatly enhance the special cause investigation. Copyright © 2010 John Wiley & Sons, Ltd.