Condition monitoring studies on spindle bearing of a lathe

In modern industry, machinery must become increasingly flexible and automatic. In order to increase productivity, enhance quality and reduce cost, machine tools have to work free of any failure. When a failure occurs in a machine tool, it is necessary to identify the causes as early as possible. Machine tool condition monitoring is very important to achieve this goal. Condition monitoring is generally used on the critical subsystem of any machine tool. This paper endeavors to focus on the condition monitoring aspects on the machine tool element. In the present study, a critical subsystem has been identified based on the failure data analysis. Condition monitoring techniques like vibration monitoring, acoustic emission, Shock Pulse Method (SPM) and surface roughness have been successfully used for fault identification.     

机械设备中黑箱部件的状态监测与故障诊断

利用小波包分解、Yule—Walker AR谱密度分析算法和概率神经网络技术研究开发了一套状态监测和故障诊断系统,该系统是用于类似卷烟厂卷接包机八工位转塔的黑箱部件。利用仿真信号对系统的状态监测部分进行了测试,并应用到实践中去。在状态监测系统的基础上开发的基于概率神经网络的故障诊断系统,用仿真信号进行了测试,结果证明该系统是可行的。该系统的研制开发对类似黑箱部件的状态监测和故障诊断具有一定的实用价值,对其他类似机构的状态监测和故障诊断也具有参考意义。     

Nonlinear adaptive backstepping control for variable-speed wind energy conversion system-based permanent magnet synchronous generator

Tool wear degradation and working status of slotting cutter have a great effect on the surface quality of rotor slot; therefore, tool condition monitoring and its degradation estimation are needed for guaranteeing slot machining quality. This paper proposes a two-phase method based on acoustic emission (AE) signal classification and logistic regression model for slotting cutter condition monitoring and its degradation estimation. In the first phase, the failure reliability estimation models corresponding to different machining processes are established considering the variability of process system like tool regrinding times and material randomness of workpiece. In the second phase, the most appropriate estimation model corresponding to the optimum cluster is selected and used for failure reliability estimation and status determination of slotting cutter. This approach has been validated on a CNC rotor slot machine in a factory. Experimental results show that the proposed method can be effectively used for cutting tool degradation estimation and status determination of slotting cutter with high accuracy.     

Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models

Condition monitoring and classification of machinery state is of great practical significance in manufacturing industry, because it provides updated information regarding machine status on-line, thus avoiding the production loss and minimising the chances of catastrophic machine failure. In this paper, the condition classification is based on hidden Markov models (HMMs) processing information obtained from vibration signals. We present an on-line fault classification system with an adaptive model re-estimation algorithm. The machinery condition is identified by selecting the HMM which maximises the probability of a given observation sequence. The proper selection of the observation sequence is a key step in the development of an HMM-based classification system. In this paper, the classification system is validated using observation sequences based on the wavelet modulus maxima distribution obtained from real vibration signals, which has been proved to be effective in fault detection in previous research.     

Assessment of the tool post reliability of a high-stiffness turning machine

Reliability refers to the ability of a part, device or system to conduct an intended function in a given condition for a certain period of time. A mechanical system or structure such as a machine tool exercises the capacity of the entire system with regard to the various constituent parts that are connected to each other; as such, the reliability of the parts constituting the system determines the reliability of the entire system. A tool post is a device designed to efficiently provide the tools necessary for the processing of a turning machine: the parts used in a hard turning machine which requires higher stiffness must provide greater reliability. For the purposes of this study, the reliability of a tool post, which has the highest failure rate of a turning machine system, was assessed. In order to conduct a reliability assessment of a given tool post, reliability prediction using a failure rate database, weak point analysis, the manufacture of a reliability tester and the calculation of reliability testing and quantitative reliability criteria were also carried out. By so doing, the failure rate, the MTBF (Mean time between failures) and other factors could be calculated. Furthermore, the results can also be applied to other parts of the turning machine or to a reliability assessment of a subsystem by using the suggested assessment method.     

车削中心刀具磨破损的监控

论述了用主轴切削电流作为监控参量，采用自学习法对车削中心进行刀具磨破损监控的技术。     

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.     

Investigation on chatter stability of thin-walled parts considering its flexibility based on finite element analysis

High-speed milling of thin-walled part is a widely used application for aerospace industry. The low rigidity components, large quantities of material removed in machining progress, are in the risk of the instability of the progress. In this paper, the thin-walled parts have the similar characteristics with the tools. Therefore, the dynamic model and the stability critical condition determined by the relative dynamic behavior between tool subsystem and workpiece subsystem are put forward. The thin-walled parts’ dynamic character varies greatly with time when machining. The whole workpiece has been divided into several stages by finite element analysis (FEA) so that its various modal parameters in the milling progress can be obtained gradually; thus, the variation due to metal removal has been accurately taken into account. The stability critical condition is predicted by frequency domain method based on the dynamic behavior of the two subsystems. With the respect to time-varying critical stability condition, a three-dimensional lobe diagram has been developed to show the changing conditions of chatter. Finally, the proposed methods and models were proven by series milling experiments.     

Surface Texture Indicators of Tool Wear - A Machine Vision Approach

There has been much research on the automated monitoring of cutting tool wear. This research has tended to focus on three main areas that attempt to quantify the cutting tool condition: monitoring of specific machine tool parameters in order to infer tool condition, direct observations made on the cutting tool; and measurements taken from the chips produced by the tool. However, considerably less work has been performed on the development of surface texture sensors that provide information on the condition of the tool employed in machining the surface. A preliminary experimental study is presented for accomplishing this texture analysis using a machine vision-based sensor system. In particular, an investigation of the condition of a two-flute end mill used in a standard face milling operation is presented. The degree of tool wear is estimated by extracting three parameters from video camera images of the machined surface. The performance of three image-processing algorithms, in estimating the tool condition, is presented: analysis of the intensity histogram; image frequency domain content; and spatial domain surface texture.     

[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。     

自动化生产线中关键设备的预维护策略研究

为了避免由于设备故障导致的生产线停产,首先研究生产线故障维修成本、预防性维护成本和停产损失的计算方法,建立生产线平均可靠度与维护总成本多目标优化模型,从而获得了最优的生产线周期性维护计划。针对关键设备,如数控机床、工业机器人,运行状态突然严重劣化的情况,基于时间延迟理论得到了关键设备子系统可靠度随时间变化的规律,根据设备运行状态的监测数据,采用支持向量机模型对设备子系统潜在故障的发生概率进行预测。由此建立了关键设备延迟维护最佳时刻优化模型,并通过粒子群优化算法求解关键设备的最佳维护时刻。最后,通过实例仿真分析验证了文本预维护策略的有效性,能够在保证可靠度要求的同时,有效降低维护成本。     

THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS

The process of metal cutting is a complex phenomenon that has been researched for many years but the aim of practical cutting tool condition monitoring has yet to be achieved. Previous work by the current authors using two neural networks (to classify acquired data) moderated by an Expert System (based on Taylor's tool life equation) has shown that it is possible to accurately monitor tool wear with a single machine/tool/material/cutting condition combination and to identify any inconsistencies between the predictions of the neural networks and engineering practice. This paper investigates the effects that minor inconsistencies in cutting conditions might have on such a system by determining the ‘zone of influence’ of this working system by systematically varying the cutting conditions whilst keeping all other variables fixed. The investigation has found that the zone of influence is small but usable, and an approach to the utilisation of the system in a machine shop is suggested.     

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.     

Generalized singular value decomposition in multidimensional condition monitoring of machines—A proposal of comparative diagnostics

With the modern metrology, we can measure almost all variables in the phenomenon field of working machine, and many of measuring quantities can be symptoms of machine condition. On this basis, we can form the symptom observation matrix (SOM) for condition monitoring. From the other side we know that contemporary complex machines may have many modes of failure, so-called faults, which form the fault space. Even if we apply some modern tool like singular value decomposition (SVD) for the fault extraction purpose, this multidimensional problem is not a simple one. Therefore, the question remains if one can learn considering similar problem when having SOM of similar machine observed just before. In this way, we can consider the application of generalized singular value decomposition (GSVD) to the machine condition monitoring problems, and uncover some new possibilities.     

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.     

基于组态的曲轴数控加工过程监控系统的研究

在分析了曲轴数控加工技术的基础上,采用组态软件开发了曲轴加工在线监控系统。该系统包括机床状态监测、曲轴加工状态实时监控、刀具状态监测以及测头检测状态实时监控等功能模块,同时建立了实时和历史数据库,可实现特定条件下的监控数据查询,并且可以生成数据报表,便于操作人员管理、查询、打印历史记录。该系统具有良好的可扩充性、可修改性、可移植性和可维护性,操作简便为同类零件加工过程监控系统的研究提供了新的思路具有一定的实用价值和推广前景。     

Symmetrical component filter based on line condition monitoring instrumentation system for mine winder motor

As the capital investment in underground coal mining is huge enough by the standards of any conventional industry hence coal production process has to be very efficient to make commercially viable. In a situation of intensive and massive investment, the economics of production would primarily depend on machine utilization indicated by machine availability. Thus machine available time i.e. the time that a machine is available to do productive work, has to be maximized, for best returns on capital invested and utilization of manpower.In this present research work an online condition monitoring instrumentation system has been developed for condition monitoring of mine winder motor. The instrumentation system has been developed based on current monitoring technique. The symmetrical current component present in the unbalanced motor current is sensed with the help of current transformer, current to voltage converter, all pass filters and adders. Any electrical fault in mine winder motor will produce unbalancing in the motor circuit and will cause for the development of symmetrical current component. The type of electrical fault can be determined by sensing the symmetrical current component. One important advantage of this condition monitoring technique is that the instrument can be made hand held and the same hand held instrument may be used for the fault diagnosis of other motors also.A novel condition monitoring instrumentation system based on symmetrical component filter has been developed for on-line condition monitoring of mine winder motor. The instrumentation system would be able to diagnose various incipient faults of mine winder motor and will increase the safety as well as availability of mine winder.The result obtained from symmetrical current component filter based motor diagnostic technique has been verified with the result obtained by axial leakage flux based motor diagnostic technique for similar simulated motor fault condition to pinpoint the exact faulty of condition of the model mine winder motor.     

用振动信号的双谱估计法监测刀具状态的可行性实验

以铣削加工为研究对象探讨了振动信号的双谱分析在刀具状态监测中的应用问题，研究结果表明：应用振动信号的双相干系数法可以有效地监测铣削加工过程中的刀具状态。     

Experimental analysis of change detection algorithms for multitooth machine tool fault detection

This paper describes an industrial application of fault diagnosis method for a multitooth machine tool. Different statistical approaches have been used to detect and diagnose insert breakage in multitooth tools based on the analysis of electrical power consumption of the tool drives. Great effort has been made to obtain a robust method, able to avoid any needed re-calibration process, after, for example, a maintenance operation. From the point of view of maintenance costs, these multitooth tools are the most critical part of the machine tools used for mass production in the car industry. These tools integrate different kinds of machining operations and cutting conditions.     

Failure Analysis and Condition Monitoring of an Open-Loop Oil System Using Ferrography

Condition monitoring of dynamic systems based on oil analysis is well known for closed-loop systems. The motivation for this work stemmed from repeating failures of Wankel engines. Failure analysis identified contact fatigue as the failure mechanism, but could not identify the cause. Thus, the objective of the work was to develop a method for condition monitoring of open-loop oil systems. A variety of analytical techniques was evaluated, including direct-reading ferrography, analytical ferrography combined with computational image analysis, atomic emission spectroscopy, and scanning electron microscopy combined with energy dispersive X-ray spectroscopy. Procedures for collection and separation of oil samples were developed. Analytical ferrography was found most useful in condition monitoring. Six engines were detected in their early failure stage. Those engines were disassembled, and contact fatigue failures in the bearing needles were observed. The quantitative image analysis allowed for a fairly objective rating of the wear level. The method developed in this work has already been implemented on a daily basis for monitoring the health of Wankel engines, with much success.