An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

Applied Intelligence - Rotating machinery is a very important mechanical device widely used in critical industrial applications. Efficient fault detection and diagnosis are key challenges in the...     

A rotating machinery fault diagnosis method based on local mean decomposition

Local mean decomposition (LMD) is a novel self-adaptive time–frequency analysis method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals. By using LMD, any complicated signal can be decomposed into a number of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated signal from which physically meaningful instantaneous frequencies can be obtained. In fact, each PF is just a mono-component AM–FM signal. Therefore, the procedure of LMD may be regarded as the process of demodulation. While fault occurs in gear or roller bearing, the vibration signals picked up would exactly display AM–FM characteristics. So it is possible to diagnose gear and roller bearing fault by LMD. Targeting the modulation features of the gear or roller bearing fault vibration signal, a rotating machinery fault diagnosis method based on LMD is proposed. In this paper, firstly the LMD method is introduced; secondly, the LMD method is compared with another competing time–frequency analysis approach, namely, empirical mode decomposition (EMD) method and the results show the superiority of the LMD method; finally, the LMD method is applied to the gear and roller bearing fault diagnosis. The analysis results from the practical gearbox vibration signal demonstrate that the diagnosis approach based on LMD could identify gear and roller bearing work condition accurately and effectively.     

Generalized sparse filtering for rotating machinery fault diagnosis

The Journal of Supercomputing - This paper develops generalized sparse filtering (GSF) by applying general norm normalization to improve the feature learning ability. A rotating machinery fault...     

基于初始路径优化的蚁群算法及应用

为解决蚁群算法在初始阶段执行效率低、信息素随机分布、路径杂乱无章的缺点,提出将正交设计方法引入初始优化中.创建正交离散过程,形成正交优化的路径设置;优化初始化过程,形成初始解;以动态概率转移规则来构造路径;精练的选路策略等4项改进措施的初始路径优化模型.该模型提高了算法的执行效率,模拟算例成功应用于连续域问题的饲料配方设计方面,表明该算法有效且可行,开辟了一条饲料配方设计的新途径,同时对蚁群算法解决连续域问题提供可参考技的模型和求解方法.     

Intelligent fault diagnosis of rotating machinery using infrared thermal image

This study presents a new intelligent diagnosis system for classification of different machine conditions using data obtained from infrared thermography. In the first stage of this proposed system, two-dimensional discrete wavelet transform is used to decompose the thermal image. However, the data attained from this stage are ordinarily high dimensionality which leads to the reduction of performance. To surmount this problem, feature selection tool based on Mahalanobis distance and relief algorithm is employed in the second stage to select the salient features which can characterize the machine conditions for enhancing the classification accuracy. The data received from the second stage are subsequently utilized to intelligent diagnosis system in which support vector machines and linear discriminant analysis methods are used as classifiers. The results of the proposed system are able to assist in diagnosing of different machine conditions.     

An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines

In this paper, a new intelligent method for the fault diagnosis of the rotating machinery is proposed based on wavelet packet analysis (WPA) and hybrid support machine (hybrid SVM). In fault diagnosis for mechanical systems, information about stability and mutability can be further acquired through WPA from original signal. The faulty vibration signals obtained from a rotating machinery are decomposed by WPA via Dmeyer wavelet. A new multi-class fault diagnosis algorithm based on 1-v-r SVM approach is proposed and applied to rotating machinery. The extracted features are applied to hybrid SVM for estimating fault type. Compared to conventional back-propagation network (BPN), the superiority of the hybrid SVM method is shown in the success of fault diagnosis. The test results of hybrid SVM demonstrate that the applying of energy criterion to vibration signals after WPA is a very powerful and reliable method and hence estimating fault type on rotating machinery accurately and quickly.     

A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems

Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects the performance of fault diagnosis. In this paper, by synthetically considering feature extraction and time delay of occurrence of faults, we propose a novel fault diagnosis method that consists of two parts, namely, sliding window processing and CNN-LSTM model based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, samples obtained from multivariate time series by the sliding window processing integrates feature information and time delay information. Then, the obtained samples are fed into the proposed CNN-LSTM model including CNN layers and LSTM layers. The CNN layers perform feature learning without relying on prior knowledge. Time delay information is captured with the use of the LSTM layers. The fault diagnosis of the Tennessee Eastman chemical process is addressed, and it is verified that the predictive accuracy and noise sensitivity of fault diagnosis can be greatly improved when the proposed method is applied. Comparisons with five existing fault diagnosis methods show the superiority of the proposed method.

     

电动机联接性故障诊断方法及其应用

针对电动机联接性故障特征识别困难的问题,阐述了不对中故障、联接螺栓松动故障、基础刚度不足故障这3种典型电动机联接性故障的数学模型及其频谱特征,提出了利用经验模式分解方法对电动机的振动信号进行滤波处理,根据故障特征频率得出诊断结果。现场应用结果验证了该方法的有效性。     

基于VB的蚁群算法及其在网络路由中的应用

蚁群优化算法是一种新型的用于求解复杂组合优化问题的模拟进化算法,已广泛应用于多目标优化、聚类分析、数据挖掘、模糊系统控制等,特别适合网络路由应用.目前该算法的伪码一般均采用MATLAB语言实现,针对MATLAB语言实时效率差,不能脱离环境运行,并且不利于算法和数据的保密.本文在阐述了该算法的基本原理、算法模型的基础上,采用面向对象的VB语言实现该算法的伪码,并对该算法在网络路由问题中的应用进行了总结.     

Application of an intelligent classification method to mechanical fault diagnosis

A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively.     

一种煤矿机械轴承故障诊断方法

针对现有煤矿机械轴承故障自适应诊断方法易受高频噪声和间断噪声干扰而导致原始信号分解和特征提取精度较低的问题,提出了一种基于改进局部均值分解的煤矿机械轴承故障诊断方法。该方法在局部均值分解方法的自适应分解部分采用噪声辅助分解方法,将高斯白噪声加入原始信号,然后进行局部均值分解,以抑制高频噪声及间断噪声对信号分解的影响;在特征参数提取部分对乘积函数分量进行Hilbert变换,然后进行特征参数提取,以实现在全部取值范围内提取特征参数。仿真及测试结果表明,该方法对轴承故障信号分解和特征参数提取的效果较好,对轴承内外圈故障诊断的准确性较高。     

基于支持向量机的旋转机械故障诊断

为了解决旋转机械故障的在线诊断识别问题，用小波包从旋转机械的震动信号中提取特征向量，给出了一种基于支持向量机的故障诊断分类方法。该方法通过有限的学习样本，建立旋转机械故障特征与其运行状态之间的关系。利用获得的矿井提升机减速箱齿轮数据建立了多级故障分类器，通过对样本的分类输出检验，验证了该故障诊断方法的可行性。     

煤矿旋转机械在线故障诊断及预警系统设计

针对现有煤矿机械在线监测与诊断技术未实现故障特征在线提取及故障类型自动识别的问题,设计了一种基于LabVIEW的煤矿旋转机械故障在线诊断及预警系统。该系统采用频谱分析、功率谱分析、包络谱分析、倒频谱分析等方法分析振动信号,得到旋转机械运行过程中各部件的特征参数,与故障类型数据库里的特征参数进行对比,实现故障诊断。设计了精细诊断和粗略诊断2种故障诊断模式,通过互锁的方式将2种模式关联起来,若旋转机械各主要部件结构参数已知,可选用精细诊断模式,否则选用粗略诊断模式。通过模拟旋转机械转子不平衡故障验证系统性能,结果表明,该系统能够准确识别故障并发出提示,且操作简单、可靠性高。     

Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition

A clustering method, called HACO (Hyperbox clustering with Ant Colony Optimization), is proposed for classifying unlabeled data using hyperboxes and an ant colony meta-heuristic. It acknowledges the topological information (inherently associated to classification) of the data while looking in a small search space, providing results with high precision in a short time. It is validated using artificial 2D data sets and then applied to a real medical data set, automatically extracting medical risk profiles, a laborious operation for doctors. Clustering results show an improvement of 36% in accuracy and 7 times faster processing time when compared to the usual ant colony optimization approach. It can be further extended to hyperbox shape optimization (fine tune accuracy), automatic parameter setting (improve usability), and applied to diagnosis decision support systems.     

Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

Features extracted from real world applications increase dramatically, while machine learning methods decrease their performance given the previous scenario, and feature reduction is required. Particularly, for fault diagnosis in rotating machinery, the number of extracted features are sizable in order to collect all the available information from several monitored signals. Several approaches lead to data reduction using supervised or unsupervised strategies, where the supervised ones are the most reliable and its main disadvantage is the beforehand knowledge of the fault condition. This work proposes a new unsupervised algorithm for feature selection based on attribute clustering and rough set theory. Rough set theory is used to compute similarities between features through the relative dependency. The clustering approach combines classification based on distance with clustering based on prototype to group similar features, without requiring the number of clusters as an input. Additionally, the algorithm has an evolving property that allows the dynamic adjustment of the cluster structure during the clustering process, even when a new set of attributes feeds the algorithm. That gives to the algorithm an incremental learning property, avoiding a retraining process. These properties define the main contribution and significance of the proposed algorithm. Two fault diagnosis problems of fault severity classification in gears and bearings are studied to test the algorithm. Classification results show that the proposed algorithm is able to select adequate features as accurate as other feature selection and reduction approaches.     

An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies

Journal of Intelligent Manufacturing - The fault diagnostics of rotating components are crucial for most mechanical systems since the rotating components faults are the main form of failures of...     

A semi-supervised transferable LSTM with feature evaluation for fault diagnosis of rotating machinery

Applied Intelligence - Aiming at the issue of impracticality or costliness of collecting enough labeled signals under all working conditions, the performance of a method usually suffers a...     

智能传感器故障诊断系统数据预处理方法

发动机故障诊断系统中传感器采集到的信号是实际信号和随机噪声的合成,这给故障检测和诊断带来许多不利的影响.将中值滤波和小波分析结合起来使用,中值滤波用于消除数据中的脉冲噪声,小波滤波用于消除数据中的其他平稳随机噪声,仿真结果表明:混合滤波能够有效去除信号中的脉冲噪声与随机噪声,较好地还原了原始信号,将该方法应用于发动机的试车数据,结果表明:该方法具有理想的去除噪声效果.