Rotating machinery prognostics: State of the art,challenges and opportunities

Machinery prognosis is the forecast of the remaining operational life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on rotating machinery prognostics in the past few years. These publications covered a wide spectrum of prognostics techniques. This review article first synthesises and places these individual pieces of information in context, while identifying their merits and weaknesses. It then discusses the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field. Current methods for predicting rotating machinery failures are summarised and classified as conventional reliability models, condition-based prognostics models and models integrating reliability and prognostics. Areas in need of development or improvement include the integration of condition monitoring and reliability, utilisation of incomplete trending data, consideration of effects from maintenance actions and variable operating conditions, derivation of the non-linear relationship between measured data and actual asset health, consideration of failure interactions, practicability of requirements and assumptions, as well as development of performance evaluation frameworks.     

Condition monitoring scheme via one-class support vector machine and multivariate control charts

A condition-based maintenance (CBM) has been widely employed to reduce maintenance cost by predicting the health status of many complex systems in prognostics and health management (PHM) framework. Recently, multivariate control charts used in statistical process control (SPC) have been actively introduced as monitoring technology. In this paper, we propose a condition monitoring scheme to monitor the health status of the system of interest. In our condition monitoring scheme, we first define reference data set using one-class support vector machine (OC-SVM) to construct the control limit of multivariate control charts in phase I. Then, parametric control chart or non-parametric control chart is selected according to the results from multivariate normality tests. The proposed condition monitoring scheme is applied to sensor data of two anemometers to evaluate the performance of fault detection power.

     

Support vector machine in machine condition monitoring and fault diagnosis

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.     

Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system

Machine condition plays an important role in machining performance. A machine condition monitoring system will provide significant economic benefits when applied to machine tools and machining processes. Development of such a system requires reliable machining data that can reflect machining processes. This study demonstrates a tool condition monitoring approach in an end-milling operation based on the vibration signal collected through a low-cost, microcontroller-based data acquisition system. A data acquisition system has been built through interfacing a microcontroller with a signal transducer for collecting cutting vibration. The examination tests of this developed system have been carried out on a CNC milling machine. Experimental studies and data analysis have been performed to validate the proposed system. The onsite tests show the developed system can perform properly as proposed.     

A smart machine supervisory system framework

Machine tools and machining systems have gone through significant improvements in the past several decades. Recent advance in information technology made it possible to collect and analyze a large amount of data in real time. This brings about the concept of a smart machine tool, enabled by process monitoring and control technologies, to produce the first and all subsequent parts correctly. This paper presents a system framework for a smart machine supervisory system. The supervisory system integrates individual technologies and makes overall intelligent decisions to improve machining performance. The communication mechanism of the supervisory system is discussed in detail. Its decision-making mechanism is illustrated through an example that integrates process planning, health maintenance, and tool condition monitoring.     

支持向量数据描述用于机械设备状态评估研究

本文提出了对机械设备运行状态进行评估的新方法———支持向量数据描述方法。该方法应用在机械故障诊断和状态监测中,仅仅依靠正常运行时的数据信号,而不需要故障数据,就可以监测机器的运行状态。给出了机组运行状态优劣的定量指标,从而为设备管理和预知维修提供科学的决策依据。将该方法应用于某炼油厂关键设备的运行状态评估中,及时、正确地评价出设备状态异常,为成功诊断出螺栓裂纹的早期故障提供帮助。     

[设备健康管理]基于机床信息的加工过程刀具磨损状态在线监测

为实现刀具磨损状态的在线监测,提高监测系统的实用性,提出一种基于机床信息的加工过程刀具磨损状态在线监测方法。采用OPC UA通信技术在线采集与存储数控机床信息,得到与磨损相关的机床内部过程信息,并基于这类信息与相应的刀具磨损信息,利用卷积神经网络建立了刀具磨损状态识别模型。应用案例证明了该方法的监测性能,与其他传统监测方法相比,该方法更适用于实际的生产加工。     

Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

Under the only hypothesis of independent sources, blind source separation (BSS) consists of recovering these sources from several observed mixtures of them. As it extracts the contributions of the sources independently of the propagation medium, this approach is usually used when it is too difficult to modelise the transfer from the sources to the sensors. In that way, BSS is a promising tool for non-destructive machine condition monitoring by vibration analysis. Principal component analysis (PCA) is applied as a first step in the separation procedure to filter out the noise and whiten the observations. The crucial point in PCA and BSS methods remains that the observations are generally assumed to be noise-free or corrupted with spatially white noises. However, vibration signals issued from electro-mechanical systems as rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA.This paper extends a robust-to-noise technique earlier developed for the separation of rotating machine signals. It exploited spectral matrices of delayed observations to eliminate the noise influence. In this paper, we focus on the modulated sources and prove that the proposed PCA is available to denoise such sources as well as sinusoidal ones. Finally, performance of the algorithm is investigated with experimental vibration data issued from a complex electro-mechanical system.     

Fault detection using transient machine signals

This paper describes the development and testing of a strategy for vibration-based online detection of faults in a particular class of machinery. This machinery is defined by two basic characteristics that preclude it from the application of standard online condition monitoring systems. The first characteristic is the absence of historical fault data. The second characteristic is that the machine is in a constant state of transient operation. An example of such a machine is the swing machinery of an electromechanical excavator. The monitoring strategy presented here employs an anomaly detection scheme together with various methods of signal processing and feature extraction. Experiments are carried out using a laboratory apparatus to show the how various configurations of the system are able to detect different types of faults. The results indicate that this approach is effective and merits further investigation.     

基于铁谱分析的颗粒分类识别方法与应用

铁谱颗粒分析是机器磨损状态监测与维修决策制定最有效的油液分析方法。通过近年来开展工业企业机器油液监测积累的大量铁谱磨粒图像,进行基于不同的颗粒特征的分类识别探究,并基于不同颗粒形成机制与原因提出切合工业现场的润滑管理维保策略。应用实践表明,铁谱分析方法在机器磨损状态监测、润滑磨损诊断机制判别以及企业润滑管理提升活动中仍发挥着积极作用。     

智能化状态监测技术研究

Agent技术是目前人工智能研究的前沿领域。基于Agent技术的状态监测技术是制造与维护技术的发展趋势。运用Agent技术的状态监测系统将在未来的数字化制造和数字化维护中起着重要作用。在介绍目前状态监测技术发展概况的基础上，提出了一种基于多Agent的状态监测系统模型，讨论了模型的结构、功能特性及模型的工作原理，并给出了一个应用实例。     

Empirical mode decomposition of pressure signal for health condition monitoring in waterjet cutting

Waterjet/abrasive waterjet cutting is a flexible technology that can be exploited for different operations on a wide range of materials. Due to challenging pressure conditions, cyclic pressure loadings, and aggressiveness of abrasives, most of the components of the ultra-high pressure (UHP) pump and the cutting head are subject to wear and faults that are difficult to predict. Therefore, the continuous monitoring of machine health conditions is of great industrial interest, as it allows implementing condition-based maintenance strategies, and providing an automatic reaction to critical faults, as far as unattended processes are concerned. Most of the literature in this frame is focused on indirect workpiece quality monitoring and on fault detection for critical cutting head components (e.g., orifices and mixing tubes). A very limited attention has been devoted to the condition monitoring of critical UHP pump components, including cylinders and valves. The paper investigates the suitability of the water pressure signal as a source of information to detect different kinds of fault that may affect both the cutting head and the UHP pump components. We propose a condition monitoring approach that couples empirical mode decomposition (EMD) with principal component analysis to detect any pattern deviation with respect to a reference model, based on training data. The EMD technique is used to separate high-frequency transient patterns from low-frequency pressure ripples, and the computation of combined mode functions is applied to cope with the mode-mixing effect. Real industrial data, acquired under normal working conditions and in the presence of actual faults, are used to demonstrate the performances provided by the proposed approach.     

Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review

Nowadays, manufacturing companies are making great efforts to implement an effective machinery maintenance program, which provides incipient fault detection. The machine problem and its irregularity can be detected at an early stage by employing a suitable condition monitoring accompanied with powerful signal processing technique. Among various defects occurred in machines, rotor faults are of significant importance as they cause secondary failures that lead to a serious motor malfunction. Diagnosis of rotor failures has long been an important but complicated task in the area of motor faults detection. This paper intends to review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection. The aim of this article is to provide a broad outlook on rotor fault monitoring techniques for the researchers and engineers.     

喷管式翼缝对垂直轴风力机气动性能影响研究

具有良好动态性能是垂直轴风力机叶片高效捕获风能的关键,合理的流动控制方法可有效改善叶片气动性能,提高风能利用率。基于二维H型垂直轴模型研究不同椭圆形渐缩式翼缝及其开口宽度对垂直轴风力机动态失速的影响。结果表明,翼缝的主要作用机理是通过控制流动分离以延缓动态失速,较之原始翼型,椭圆形翼缝翼型在尖速比为0.5时转矩系数提高53.8%,显著增强了垂直轴风力机的起动转矩,但在尖速比大于1.5时因流动分离并不明显,从而作用效果并不明显;与传统渐缩式直翼缝相比,椭圆形翼缝因出口处流体与翼型表面相切而未显著影响外流场,从而使其在尖速比较大时功率系数相对更高。此外,椭圆形翼缝因减弱吸力面逆压梯度使叶片失速相位角推迟,从而有效抑制了流动分离,提高了风力机运行稳定性。     

An intelligent approach to machine component health prognostics by utilizing only truncated histories

Numerous techniques and methods have been proposed to reduce the production downtime, spare-part inventory, maintenance cost, and safety hazards of machineries and equipment. Prognostics are regarded as a significant and promising tool for achieving these benefits for machine maintenance. However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances. In practice, engineering assets are rarely being permitted to run to failure. Many studies have reported valuable models and methods that engage in maximizing both truncated and failure data. However, limited studies have focused on cases where only truncated data are available, which is common in machine condition monitoring. Therefore, this study develops an intelligent machine component prognostics system by utilizing only truncated histories. First, the truncated Minimum Quantization Error (MQE) histories were obtained by Self-organizing Map network after feature extraction. The chaos-based parallel multilayer perceptron network and polynomial fitting for residual errors were adopted to generate the predicted MQEs and failure times following the truncation times. The feed-forward neural network (FFNN) was trained with inputs both from the truncated MQE histories and from the predicted MQEs. The target vectors of survival probabilities were estimated by intelligent product limit estimator using the truncation times and generated failure times. After validation, the FFNN was applied to predict the machine component health of individual units. To validate the proposed method, two cases were considered by using the degradation data generated by bearing testing rig. Results demonstrate that the proposed method is a promising intelligent prognostics approach for machine component health.     

Data-driven approach to machine condition prognosis using least square regression tree

Machine fault prognosis techniques have been profoundly considered in the recent time due to their substantial profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are precisely forecasted before they reach the failure thresholds. In this work, we propose the least square regression tree (LSRT) approach, which is an extension of the classification and regression tree (CART), in association with one-step-ahead prediction of time-series forecasting techniques to predict the future machine condition. In this technique, the number of available observations is first determined by using Cao’s method and LSRT is employed as a prediction model in the next step. The proposed approach is evaluated by real data of a low methane compressor. Furthermore, a comparative study of the predicted results obtained from CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers the potential for machine condition prognosis. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin Van Tung Tran is a lecturer at the Hochiminh City University of Technology in Vietnam. He received his B.S. and M.S. degrees in mechanical engineering from Hochiminh City University of Technology, Vietnam, in 1997 and 2003, respectively, and Ph.D. from Pukyong National University, South Korea in 2009. His research interests include machine fault diagnosis and condition prognosis. Bo-Suk Yang is a professor at the Puyong National University in Korea. He received his Ph.D. degree in mechanical engineering from Kobe University, Japan in 1985. His main research fields cover machine dynamics and vibration engineering, intelligent optimum design, and condition monitoring and diagnostics in rotating machinery. He has published well over 190 research papers in the research areas of vibration analysis, intelligent optimum design and diagnosis of rotating machinery. He is listed in Who’s Who in the World, Who’s Who in Science and Engineering, among others.     

压缩机状态实时监测系统的研制

以压缩机为监测对象提出了由数据采集模块、状态监测模块和故障诊断模块组成的分布式信息处理系统的构思 ,阐明了基于结构层次化和功能模块化的状态监测系统的软、硬件设计方案。整个系统具有高速度、高精度、多通道、大容量和高性能价格比等特点 ,可广泛应用于各类大型旋转机械的状态监测。     

Condition-based tool management for small batch production

This paper presents a novel tool management concept for cutting processes which integrates tool relevant information, such as distribution data, tool orders, tool condition, and allocation data, within a centralized information cycle. The developed tool management approach uses decentralized identification and storage technologies, enabling an autonomous cooperation of tools and machine tools within a production. The first part of the paper is focused on the assessment of tool condition in a flexible job shop production. A tool wear monitoring system based on cutting force coefficients is developed and demonstrated by an exemplary milling operation. Thereby, it is shown that cutting force coefficients are suitable for wear monitoring and prediction, even for varying cutting conditions. For the online assessment of the current tool condition and for the prediction of residual tool life, an empirical tool wear model is demonstrated. This is applied to a novel condition-based tool management strategy which enables the optimum exploitation of the life time and performance of the cutting tool. The developed condition-based tool management concept is finally demonstrated by a software demonstrator.     

Estimation and forecasting of machine health condition using ARMA/GARCH model

This paper proposes the hybrid model of autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedasticity (GARCH) to estimate and forecast the machine state based on vibration signal. The main idea in this study is to employ the linear ARMA model and the nonlinear GARCH model to explain the wear and fault condition of machine, respectively. The successful outcomes of the ARMA/GARCH prediction model can give obvious explanation for future states of machine, which enhance the worth of machine condition monitoring as well as condition-based maintenance in practical applications. The advance of the proposed model is verified in empirical results as applying for a real system of a methane compressor in a petrochemical plant.