刀具磨损破损监控技术

刀具磨损和破损给自动化加工带来诸多坏处。为保证加工系统的正常运行,需实时监控刀具磨损状态。回顾国内外刀具监控技术发展过程,分析刀具监控方法,阐述刀具监控中的关键技术及其发展趋势,最后指出刀具监测系统存在的问题,并提出相应对策。     

高速加工切削热产生机理及监控技术研究综述

在概括总结了高速加工主要特点的基础上,分析了高速加工切削热产生机理及目前的研究状况,构建了基于光学、软测量技术和网络通信的计算机刀具综合监控系统,重点从高速加工切削温度场建模和数字仿真、切削温度测量和实时监控等方面论述了切削热产生机理及监控技术的研究思路,最后指出了高速切削刀具种类和刀具参数的选择方法,以及高速切削虚拟仿真软件研制等方面存在的技术难点,这些工作为高速加工应用的基础理论研究提供了技术支撑.     

运用刀具在线监控技术优化汽轮机轮槽刀具寿命

通过刀具在线监控技术监控分析汽轮机轮槽铣刀在切削加工镍基高温合金中的状态,研究刀具在切削加工中的磨损规律,得出优化刀具寿命的可行性方法,以提高刀具的切削寿命。试验结果表明,通过调整刀具的切削参数可提高刀具的切削寿命。     

基于SIEMENS840D刀具管理系统的刀具监控应用

高速加工是现代制造技术核心,加工过程表现出的安全要求更为迫切,刀具监控正在发展成为现代先进制造理论的支撑技术之一。加工中心的高速加工特性带来了柔性制造系统生产效率的大幅提高,同时它也提升了加工产品的精度和加工状态稳定性。由于采用了高速加工,随之而来的对加工刀具的材料、性能、可靠性提出了较高的要求,在高速加工的过程中也伴随着制造风险。通过对零件在高速加工过程中刀具管理和对刀具进行实时在线监控的研究,提出了监控刀具使用寿命的方法和策略,促进企业的刀具监控管理与应用水平。     

立铣加工刀具状态监控技术评述

综述了立铣加工刀具状态监控技术的研究现状.刀具状态监控技术在工业上的应用涉及到两种关键系统:一是具有不同智能水平的各种传感器系统,另一个是能把机床控制器所用的控制信号综合成能发觉刀具破损和反映刀具磨损程度信号的过程监控系统.这两种系统相结合,就能实现刀具状态监控系统在工业加工环境的应用.     

《第四次全国金属切削刀具学术讨论会》纪要

<正> 为了总结交流近年来在数控机床刀具、刀具监控技术,陶瓷刀具和超硬材料刀具,以及难加工材料的切削加工等领域的研究、开发和应用方面的新成果,促进切削加工技术的发展进步,中国机械工     

高速加工切削热产生机理及监控技术研究综述

在概括总结了高速加工主要特点的基础上，分析了高速加工切削热产生机理及目前的研究状况，构建了基于光学、软测量技术和网络通信的计算机刀具综合监控系统，重点从高速加工切削温度场建模和数字仿真、切削温度测量和实时监控等方面论述了切削热产生机理及监控技术的研究思路，最后指出了高速切削刀具种类和刀具参数的选择方法，以及高速切削虚拟仿真软件研制等方面存在的技术难点，这些工作为高速加工应用的基础理论研究提供了技术支撑。     

高速加工中的传感与检测技术应用

根据高速加工的特点，从位置检测、刀具状态监测、工件状态检测以及机床工况监控等方面全面讨论了传感检测技术在高速加工中的应用情况。指出高速加工中的传感检测技术将向高速化、高精度化、非接触化和经济化方向发展。     

展望我国切削加工与刀具技术的发展趋势

介绍国内外先进制造技术与高速加工的发展趋势,认识刀具在切削加工中的重要性,刀具新材料、刀具新结构、与刀具相关新技术的应用,以及几点启示和建议.     

高速加工中的传感与检测技术应用

根据高速加工的特点,从位置检测,刀具状态监测,工件状态检测以及机床工况监控等方面全面讨论了传感检测技术在高速加工中的应用情况,指出高速加工中的传感检测技术将向高速化,高精度化,非接触化和经济化方向的发展。     

大型电动机的状态监测及诊断

电动机故障包括绝缘故障、定子故障、转子故障、轴承故障等。各种故障都会以一定的故障信号方式表现出来，而通过对信号中故障特征信号的提取分析可以对电动机故障进行判断。本文对电动机的多种基于信号监测的故障分析方法进行了原理分析，包括对定子电流信号的多种分析、轴承振动的频谱分析、电动机转速的波动分析等，对其他的多种故障监测方法也进行了介绍，并对每种分析方法所适用的故障诊断类型及优缺点给予了说明，最后指出了今后的发展趋势，为电动机故障诊断方法的应用提供了参考依据。     

Induction machine condition monitoring using notch-filtered motor current

This paper presents a new approach to induction motor condition monitoring using notch-filtered motor current signature analysis (NFMCSA). Unlike most of the previous work utilizing motor current signature analysis (MCSA) using spectral methods to extract required features for detecting motor fault conditions, here NFMCSA is performed in time-domain to extract features of energy, sample extrema, and third and fourth cumulants evaluated from data within sliding time window. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with different synthetic faults were used for condition detection and classification. Extracted features obtained from NFMCSA of all motors were employed in three different and popular classifiers. The proposed motor current analysis and the performance of the features used for fault detection and classification are examined at various motor load levels and it is shown that a successful induction motor condition monitoring system is developed. Developed system is also able to indicate the load level and the type of a fault in multi-dimensional feature space representation. In order to test the generality and applicability of the developed method to other induction motors, data acquired from another healthy induction motor with different number of poles and rated power is also incorporated into the system. In spite of the above difference, the proposed feature set successfully locates the healthy motor within the classification cluster of “healthy motors” on the feature space.     

An on-line condition monitoring system for induction motors via instantaneous power analysis

Condition monitoring is an important factor in assuring well-being of motors. Existing approaches of condition monitoring are dependent on expensive special sensors. This paper reviews various forms of existing condition monitoring methods and highlights the need for an economical intelligent fault diagnosis system. In this study, the methodology taken in developing a condition monitoring system for the motor bearing faults identification, utilizing the commonly available motor stator current and voltage is demonstrated. This unique diagnostic condition monitoring system provides continuous real time tracking of the various bearing defects and determines the severity which can be adopted for fast decision making. The study on different bearing faults under no-load and full-load conditions is carried out experimentally and analyzed, and the results on the real hardware implementation confirm the effectiveness of the proposed approach.     

Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method

In this study, a new method was presented for the detection of a static eccentricity fault in a closed loop operating induction motor driven by inverter. Contrary to the motors supplied by the line, if the speed and load, and therefore the amplitude and frequency, of the current constantly change then this also causes a continuous change in the location of fault harmonics in the frequency spectrum. Angular Domain Order Tracking analysis (AD-OT) is one of the most frequently used fault diagnosis methods in the monitoring of rotating machines and the analysis of dynamic vibration signals. In the presented experimental study, motor phase current and rotor speed were monitored at various speeds and load levels with a healthy and static eccentricity fault in the closed loop driven induction motor with vector control. The AD-OT method was applied to the motor current and the results were compared with the traditional FFT and Fourier Transform based Order Tracking (FT-OT) methods. The experimental results demonstrate that AD-OT method is more efficient than the FFT and FT-OT methods for fault diagnosis, especially while the motor is operating run-up and run-down. Also the AD-OT does not incur any additional cost for the user because in inverter driven systems, current and speed sensor coexist in the system. The main innovative parts of this study are that AD-OT method was implemented on the motor current signal for the first time.     

状态监测在电动机维护中的应用

电动机是炼油化工装置中最主要的原动力和驱动设备,针对电动机在运行过程中出现的各种故障,介绍了目前状态监测的发展概况,通过简述状态监测在电动机维护中的实际应用经验,以及取得的显著效果,说明电动机状态监测在电动机维护中所起的重要作用。     

现代电机设备智能故障诊断综述

分析了电机设备故障诊断的基本原理,系统地阐述了电机设备的现代故障诊断方法,并分析了已有的各种故障诊断方法的特点,最后指出了电机故障诊断方法的发展趋势。     

关于电机设备故障诊断的分析

分析了电机设备故障诊断的基本原理，系统地阐述了电机设备的现代故障诊断方法，并概括了已有的各种故障诊断方法的特点，指出了电机故障诊断方法的发展趋势。     

On-line breakage monitoring of small drills with input impedance of driving motor

Input current of driving motor has been employed with success as monitoring signature for tool breakage and wear detection in manufacturing processes for more than a decade. In micro-drilling, however, the accuracy of current signature downgrades significantly owing to the disturbances often seen in electrical power supply. This paper reports the investigation results on the effectiveness of using the input impedance of the spindle motor as monitoring signature for detecting drill breakage in micro-drilling. As input impedance is an inherent property of a dynamic system and independent upon the system input such as voltage or current fluctuations and thus avoids the difficulties faced by the methods using current signature. Experimental results show that impedance signature reflects torque variations well and indicates health conditions of drills properly during micro-drilling processes. When associated with an artificial neural network to recognise its waveform, impedance signature is capable of identifying drill breakages promptly and accurately.     

A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed

Instantaneous angular speed (IAS)-based condition monitoring is an area in which significant progress has been achieved over the recent years. This condition monitoring technique is less known compared to the existing conventional methods. This paper presents model-predicted simulation and experimental results of broken rotor bar faults in a three-phase induction motor using IAS variations. The simulation was performed under normal, and a broken rotor bar fault. The present paper evaluates through simulating and measuring the IAS of an induction motor at broken rotor bar faults in both time and frequency domains. Experimental results show a good agreement with the model-predicted simulation results. Three vital key features were extracted from the angular speed variations. One feature is the modulating contour of pole pass frequency periods in time domain. The other two features are in frequency domain. The primary feature is the presence of the pole pass frequency component at the low-frequency region of the IAS spectrum. The secondary feature which are the multiple of pole pass frequency sideband components around the rotor speed frequency component. Experimental results confirm the validity of the simulation results for the proposed method. The IAS has demonstrated more sensitivity than current signature analysis in detecting the fault. This research also shows the power of angular speed features as a useful tool to detect broken rotor bar deteriorations using any economical transducer such as low-resolution rotary shaft encoders; which may well be already installed for process control purposes.     

基于谐波阻抗的感应电机系统无传感器监测方法研究

从信号处理的观点出发，结合阻抗理论和波分析，提出了感应电机系统无传感器监测的谐波阻抗和谐波导纳法，给出了获取和计算谐波阻抗和谐波导纳的方法。在鼠笼式三相感应电机上用提出的方法进行了转子推衡的实验研究，实验结果证实了方法的可行性，它与传统方法相比有更强的抗干扰能力。