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.     

基于电动机电流的数控机床主轴状态监测系统研究

提出了一种基于主轴电动机电流的数控机床主轴状态监测系统,建立了主轴交流电动机转矩输出模型,深入研究了主轴组件故障诊断和主轴当前运行能力评估原理,搭建了主轴状态监测系统;实验证明提出的主轴状态监测方法简单、准确、有效,具有很高的性价比,从而为数控机床状态在线监测提供了技术支撑.     

Web-based remote monitoring and fault diagnosis system

This paper proposes an approved Web-based multilayer distributed software architecture solution for remote monitoring and fault diagnosis. To tightly integrate legacy monitor systems, a component framework model based on COM has been proposed, which is very suitable for remote monitoring and fault diagnosis applications. This system has the potential for exchanging a data acquisition system by using wrapper service components. A mixed thick Web client architecture is proposed to implement real-time remote monitoring. A Web-based remote monitoring and fault diagnosis system is developed by using modeling technology, Web application technology, component technology and VME extension for instrumentation (VXI) bus technology, which publishes a fault diagnosis algorithm package, a basic monitoring package and an advanced analysis package on the Internet. This system provides authorized users with an effective and instant way to access new monitoring and diagnosis component packages in time. This paper gives researchers a paradigm to accomplish similar systems. The technique developed may be used for other fields, such as power engineering, manufacture and transportation system.     

矿井大型设备远程监测及性能测定系统探究

在对矿井大型设备监测现状进行深入调查,对故障机理及故障分类深入研究的基础上,结合传感与测量技术、数字信号处理技术、系统控制理论、人工智能故障诊断等现代科学技术,确定了矿井大型设备远程监测预警系统的监测监控预警模型,系统采用基于工业以太网的环形网络结构,建立了完整的数据采集系统,将矿井大型设备的运行状态参数集中采集,通过网络传输至监测预警中心,实现了矿井大型设备运行的远程实时状态监测。     

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.     

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。     

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.     

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。     

Motor square current signature analysis for induction motor rotor diagnosis

Induction machines play an important role in today’s industry. Thus, preventive maintenance combined with fault diagnosis techniques have become an essential issue. One of the most used techniques for the diagnosis of faults in the induction machine is motor current signature analysis (MCSA). This approach presents some limitations for induction motor rotor diagnosis, particularly for small faults. In this paper, a new motor square current signature analysis (MSCSA) fault diagnosis methodology is presented. The proposed technique is based on three main steps: first, the induction motor current is measured; secondly, the square of the current is computed; and finally a frequency analysis of the square current is performed. This technique allows more information to be obtained from a motor with a rotor fault than the classical MCSA approach. Simulation and experimental results are presented in order to confirm the theoretical assumptions. This methodology has also been tested for the identification of two distinct faults (broken bars and rotor eccentricity).     

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.     

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.     

基于小波抗体记忆克隆算法的电机故障诊断

针对电机定子、转子、轴承偏心、气隙偏心等故障,提出了一种将小波分析和抗体克隆算法相结合的故障诊断新方法。使用小波技术对电机定子电流监控数据进行预处理,对采样信号进行小波分解,提取各频段的能量,归一化后将能量作为故障诊断的特征向量。将得到的故障特征向量作为抗原,由算法建立的聚类中心作为免疫系统的抗体,然后利用抗体记忆克隆算法对故障样本进行故障识别分类。试验和应用结果表明,用小波记忆克隆算法能很好地分类出电机的各种工作状态,使电机故障诊断具有较高的正确率和浓缩率。     

Machine condition monitoring using principal component representations

The purpose of this paper is to find the low-dimensional principal component (PC) representations from the statistical features of the measured signals to characterize and hence, monitor machine conditions. The PC representations can be automatically extracted using the principal component analysis (PCA) technique from the time- and frequency-domains statistical features of the measured signals. First, a mean correlation rule is proposed to evaluate the capability of each of the PCs in characterizing machine conditions and to select the most representative PCs to classify machine fault patterns. Then a procedure that uses the low-dimensional PC representations for machine condition monitoring is proposed. The experimental results from an internal-combustion engine sound analysis and an automobile gearbox vibration analysis show that the proposed method is effective for machine condition monitoring.     

基于MCSA和SVM的异步电机转子故障诊断

本文提出一种基于电机电流信号频谱分析和支持向量机的异步电机转子故障诊断方法，该方法可以利用支持向量机对电机电流频谱信号的特征信息和故障模式进行关联。对电机定子电流采样后，其信号经FFT变换后提取故障特征量作为支持向量机的输入，基于1对1算法构造了感应电机转子故障多类分类器。实验结果表明，该方法具有很好的分类和泛化能力，可以提高电机故障诊断的准确性。     

Detection of faults in gearboxes using acoustic emission signal

Vibration analysis is widely used in machinery diagnosis, and wavelet transform and envelope analysis have also been implemented in many applications to monitor machinery condition. Envelope analysis is well known as a useful tool for the detection of rolling element bearing faults, and wavelet transform is used in research to detect faults in gearboxes. These are applied for the development of the condition monitoring system for early detection of the faults generated in several key components of machinery. Early detection of the faults is a very important factor for condition monitoring and a basic component to extend CBM (Condition-Based Maintenance) to PM (Prediction Maintenance). The AE (acoustic emission) sensor has a specific characteristic on the high sensitivity of the signal, high frequency and low energy. Recently, AE technique has been applied in some studies for the early detection of machine fault. In this paper, a signal processing method for AE signal by envelope analysis with discrete wavelet transforms is proposed. Through the 15 days test using AE sensor, misalignment and bearing faults were observed and early fault stage was detected. Also, in order to find the advantage of the proposed signal processing method, the result was compared to that of the traditional envelope analysis and the accelerometer signal.     

基于DSP工况监测系统的设计

设计一个基于数字信号处理(DSP)的数据采集与工况监测系统,用以实现对监测对象连续监测,为监测设备的运行情况提供实时、有效的手段.介绍DSP芯片TMS320LF2407A特点,重点介绍监测系统的软件、硬件设计.     

基于内置传感器的大型数控机床状态监测技术

提出一种基于光栅尺、编码器、伺服进给电机电流(转矩)等内置传感器的机床状态监测系统,深入研究转矩、位置、润滑特性等内置传感器测试原理,就开放式和商业数控系统给出不同采集机床状态信息的策略;并进行多种工况下的恒速、润滑特性测试,试验分析表明电机输出转矩(电流)、位置、瞬时速度、瞬时加速度等内置传感器信息被用于机械传动部件故障诊断和伺服控制特性评估的可行性和有效性,从而为大型数控机床状态在线监测和故障快速溯源,提供技术支撑.     

In situ assessment of independent wire rope core ropes in cage winders by a nondestructive method

The most widely used method to study the condition of winder ropes is the magnetic nondestructive method. Localized and distributed flaws in winder ropes can be detected by this method. This paper is intended to highlight the findings using such a technique in the case of independent wire rope core rope in two cage winders in a coal mine. The text was submitted by the authors in English.     

集成传感器的液压系统状态监测设计与应用

采用嵌入加载阀的集成式液压系统压力、温度和流量测量传感器组．配合相应的信号调理与接口箱,构建了工程机械液压系统状态监测与故障诊断的硬件框架结构体系。利用虚拟仪器开发平台LabWindows／CVI开发了测试仪的信号采集、状态监测和故障诊断系统。软件开发中将神经网络技术与传统的专家系统技术有机地融合起来,可实现工程机械液压系统的状态监测和故障诊断。使用结果表明,该测试仪可以准确地测量液压系统的工作状态参数。诊断工程机械液压系统的常见故障。     

基于交流传动的轧机机电耦合系统振动特性分析

建立恰当的轧机系统模型,对其动态特性进行正确完备的描述是进行轧机系统设计、控制、状态监测和故障诊断的关键。随着交流调速技术的发展,交流电机已广泛用于轧机传动系统中。通过考虑驱动电机的影响,建立异步电机一轧机主传动系统的机电耦合模型,并利用该模型对轧机系统由串联补偿电容、摩擦、间隙和负载扰动等因素引起的机电耦合动态过程进行数值研究？研究结果表明,建立的机电耦合模型可以方便地分析轧机系统机电耦合动力学规律,为进一步控制轧机振动特性奠定基础。