An Online Fault Diagnosis System for Induction Motors via Instantaneous Power Analysis

This article aims to provide a new noninvasive method for the online diagnosis of bearing-localized faults under various loading conditions of the induction motors via instantaneous power analysis. The instantaneous noise variations and sensor offsets are considered to be one of the common factors that yield erroneous fault tracking in an online condition monitoring and fault diagnosis system. An adaptive threshold scheme has been designed to tackle the sensor offsets and instantaneous noise variations for reliable decision making on the existence of fault signatures in an arbitrary environment conditions. The performance of the designed threshold scheme has been evaluated on a motor with various bearing defects operating under various loading conditions. Detailed theoretical and experimental evaluations of several bearing-localized faults are presented. The results indicate the viability and effectiveness of the proposed method.     

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.     

机械液压系统中智能故障诊断技术的研究

现代工程机械液压系统向着高性能、高精度和复杂化发展,传统液压故障诊断技术已满足不了机械维修的需要。智能故障诊断技术是20世纪90年代的前沿科学之一,其研究成果已广泛应用于生产实践中,大大提高了生产效益。介绍了在机械液压传动系统中运用智能故障诊断技术提高系统可靠性的问题。     

基于D-S证据理论的航空发动机磨损故障智能融合诊断方法

油样分析方法目前已成为航空发动机磨损故障诊断的重要手段,但单一油样分析技术的诊断准确率均有限,为了提高故障诊断的精度,本文提出了基于D-S证据理论的发动机磨损故障智能融合诊断方法。首先用BP神经网络实现发动机磨损故障的单项智能诊断,然后,充分利用神经网络诊断结果,用D-S证据理论实现了磨损故障的融合诊断。最后,算例验证了本文方法的有效性。     

设备智能故障诊断与维修支持技术研究

设备智能故障诊断和维修支持技术的研究以最大程度降低设备生产者和使用者的维修保障时间和费用为目标,其实质就是期望确定一个最低代价、最快速的故障诊断与维修的方案.提出了基于决策理论和贝叶斯网络推理模型的设备故障诊断与维修支持方法,并指出在仪器仪表领域中应用该方法的巨大效益.     

基于智能材料结构的故障诊断技术

利用智能材料锆钛酸铅(PZT)的动态响应特性,对PZT驱动器施加一定频率的激励使被测构件产生振动的同时,采集和识别PZT传感器信号,获取正常及故障状态时的响应模型并建立故障识别数据库,然后用实际系统和故障识别数据库中的数据进行比较,从而确定系统是否发生故障及故障类别.     

智能故障诊断技术在机械液压传动系统中的应用

现代工程机械液压系统向着高性能、高精度和复杂化发展,传统液压故障诊断技术满足不了机械维修的需要.智能故障诊断技术是20世纪90年代的前沿科学之一,其研究成果已广泛应用于生产实践中,大大提高了生产效益.在机械液压传动系统中运用智能故障诊断技术,大大提高了系统的可靠性.     

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。     

滚动轴承故障智能诊断系统

在小波分析理论以及现场调试的基础上,研制了滚动轴承故障智能诊断系统。该系统具有特征参数分析、时频分布、故障智能诊断等功能,能有效地诊断出轴承故障发生的部位、性能和程度等。     

感应电动机故障的综合诊断

针对辽阳石化分公司感应电动机的运行状况,在机械方面采用振动诊断,在电气方面使用MOTORMONITOR诊断,为更准确地判断电机故障提供了保障。本文介绍了感应电动机的常见故障、诊断方法和应用实例,论证了综合诊断的有效性。     

机械液压传动系统中智能故障诊断技术的应用研究

现代工程机械液压系统向着高性能、高精度和复杂化发展,传统液压故障诊断技术已满足不了机械维修的需要。智能故障诊断技术是20世纪90年代的前沿科学之一,其研究成果已广泛应用于生产实践中,大大提高了生产效益。文章在机械液压传动系统中运用了智能故障诊断技术,提高了系统的可靠性。     

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars (BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is affected. Thus, a new multiple signal classification (MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare-bones particle swarm optimization algorithm (IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO-based MUSIC, is proposed by replacing the fixed-step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the effectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10^?5, and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO-based MUSIC is applied in BRB fault detection of an induction motor, and the effectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has sufficient frequency precision to detect BRB fault in induction motors.     

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars(BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is a ected. Thus, a new multiple signal classification(MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare?bones particle swarm optimization algorithm(IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO?based MUSIC, is proposed by replacing the fixed?step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the e ectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10~(-5), and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO?based MUSIC is applied in BRB fault detection of an induction motor, and the e ectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has su cient frequency precision to detect BRB fault in induction motors.     

基于微处理器的智能化旋转机械状态监测与故障诊断系统

介绍了以准１６位徽处理器为核心的智能化旋转机械状态监测与故障诊断系统的工作原理及硬件与软件设计。系统由信号测量、数据采集、数据分析组成，能实现对旋转机械的在线监测、报警、转子振动分析和故障的模糊诊断。     

智能故障诊断中的波形识别技术

在电子设备故障诊断中,波形识别是进行故障特征提取和故障诊断的重要依据。根据某型电子设备实时监测与诊断系统的需要,提出了两种波形识别方法：一种是波形相关分析识别方法,另一种是波形模糊识别方法。相关分析主要是计算相关系数,模糊识别主要是计算监测点波形的模糊隶属度。这两种方法都是以标准渡形为依据,能够实时在线对监测波形数据进行分析,实现动态时域波形的自动识别。     

汽车发动机智能故障诊断方法综述

对汽车发动机故障诊断中的智能方法进行了分类,展现了现有汽车发动机的各种智能故障诊断方法,对基于专家系统、基于模糊数学、基于人工神经网络、基于故障树的智能诊断方法进行了逐一介绍,指出了各种方法的优缺点,最后介绍了基于上述智能技术的综合诊断方法,指出了智能故障诊断技术的发展趋势.     

基于DBN模型的航空液压管路故障诊断方法

为了解决航空发动机液压管路系统中管路故障诊断困难的问题,提出了一种基于深度置信网络(Deep Belief Networks,DBN)的航空液压管路智能故障诊断方法.首先,将采集的液压管路振动数据进行处理,提取出时频域特征参数,其次,将时频域特征参数作为输入样本,输入到深度置信网络模型中,利用深度置信网络模型进行液压管路故障的识别;最后,将本方法应用于航空液压管路模拟故障实验数据中,同时将本文方法与BPNN和SVM等方法进行对比分析,结果表明:本方法对液压管路故障的总体准确率达到99.27％,平均AUC值达到0.993 7,同时表明本文建立的分类模型不仅能够实现航空液压管路状态的准确分类,而且对于管路单一故障和多故障并发情况也能精准识别.     

感应电机转子故障特征在交直交变频器中的传播

为研究感应电机转子故障特征在交直交变频器中的传播规律,在感应电机由交直交变频器驱动的系统中,利用逆变器和整流器的开关函数,对感应电机发生转子断条故障时的逆变器直流侧电流和整流器交流侧电流进行了解析分析,发现了对应的故障特征频率分量。分别在感应电机转子一根断条和转子两根断条的情况进行试验,试验结果也证明了理论分析的正确性,这为从变频器的整流器交流侧诊断感应电机转子故障提供了理论依据。     

基于LVQ神经网络的异步电动机故障诊断

应用小波包分析法和学习矢量量化网络对异步电动机的故障进行诊断。采用小波包分析法对采集的异步电机振动信号进行小波包分解,选取特殊频段的能量特征值作为LVQ神经网络的输入样本,通过训练,使构造的学习矢量量化网络能够反应能量特征值和故障类型的映射关系,从而达到故障诊断的目的。仿真结果表明,与常规方法相比,小波包分析法与LVQ网络结合构成的故障诊断分类器能更准确、更有效地实现异步电动机故障诊断。     

液压系统故障诊断技术探讨

主要论述液压系统故障诊断方法,举例说明了主观诊断法的应用,探讨了液压系统的其它故障诊断方法.