搜索资源列表
svd
- svd算法用于滚动轴承故障诊断中，该算法通过对故障信号进行重构，能够有效提高故障频率。-svd algorithm for ball bearing fault diagnosis, fault signal by the algorithm to reconstruct, can effectively improve the fault frequency.
harmony-search
- 使用和声搜索算法是需要编写的代码，由此可实现其故障定位的功能-The use of harmony search algorithm is the need to write code, which can achieve the function of its fault location
222222222222
- 遗传算法是一种新的优化方法，在计算科学、模式识别和智能故障诊断中得到了广泛的应用。它是用来解决复杂的非线性和多维空间的优化问题，近年来也得到了更广泛的应用。-Genetic algorithm is a new optimization method, extensively used in computational science, pattern recognition and intelligent fault diagnosis. It is used in solving the c
MP
- 此程序为稀疏分解匹配追踪的算法，应用于轴承信号故障处理中-This program is a sparse decomposition matching pursuit algorithm, applied to the bearing signal fault processing
Clustering-Algorithm
- 聚类分析又称群分析，它是研究（样品或指标）分类问题的一种统计分析方法，同时也是数据挖掘的一个重要算法。本代码主要用于过程监控故障检测-Clustering analysis, also called group analysis, which is a statistical analysis method of the research (sample or index) classification problem, is also an important algorithm of dat
kpca
- 基于核函数的非线性维数约简方法有基于核函数的主成分分(KPCA),本算法主要应用于过程监测、故障诊断等领域。-Kernel function based nonlinear dimensionality reduction method is based on kernel function (KPCA), which is mainly used in process monitoring, fault diagnosis and so on.
KICA
- 核独立主元分析(KICA算法)在模式识别、过程监测、故障诊断等不同领域的应用中都表现了很好的性能。-Kernel independent principal component analysis (KICA algorithm) has shown good performance in pattern recognition, process monitoring, fault diagnosis and other fields.
Research-on-Ad-Hoc-based-on-QBD
- 基于拟生灭过程的无线Ad Hoc网络若干技术研究, 利用生灭过程和拟生灭过程对Ad Hoc网络的分簇算法、节点故障维护策略以及MAC层接入协议技术进行了建模和分析-Research on technologies in wireless Ad Hoc network based on Quasi-birth-and-death process, based on birth-and-death and quasi-birth-and-death stochastic process, resea
jc
- 电机状态检测与故障诊断的matlab算法原理与代码，包含数据-Matlab algorithm principle and code, including the data of motor state detection and fault diagnosis
ELM_dialog
- 本算法主要采用的是极限学习机对电机故障诊断的研究，通过训练精度来诊断该算法的效果-This algorithm is mainly used in research ELM motor fault diagnosis, through training to diagnose accuracy of the algorithm effect
ICA
- ICA的具体算法流程及对TE过程进行故障检测的应用-ICA and fault detection on TE
cva3
- 基于CVA+som的故障诊断,0为simple SOM，1为PCA+SOM, 2为CCA+SOM(CCA与PLS算法相同),3为LDA+SOM, -Based on CVA+ som fault diagnosis, 0 for the simple SOM, 1 for the PCA+ SOM, 2 for the CCA+ SOM (CCA and PLS algorithm the same), 3 for the LDA+ SOM,
SOM-dataget
- 基于SOM算法的采油机故障检测，对比其他短发有很大的优点，适合初学者学习！-Production machine fault detection based on SOM algorithm, compared with other hair has a lot of advantages, for beginners learning!
FSWT-and-Spectrum-Kurtosis
- 本文提出一种基于频率切片小波变换和谱峭度的综合算法。首先对轴承端的振动信号时频分析，采用FFT、包络谱、频率切片小波变换对其频域性能进行分析，再求其峭度谱与对应包络谱，结合其时域、频域性能，综合分析轴承故障。-This paper presents a synthesis algorithm based on frequency slice wavelet transform and spectral kurtosis. Firstly, the time-frequency analysis
Parameter-optimization
- 针对滚动轴承早期故障特征提取困难的问题，提出一种基于参数优化变分模态分解的轴承早期故障诊断方法。首先利用粒子群优化算法对变分模态分解算法的最佳影响参数组合进行搜索，搜索结束后根据所得结果设定变分模态分解算法的惩罚参数和分量个数，并利用参数优化变分模态分解算法对故障信号进行处理。-Aiming at the difficult problem of early fault feature extraction of rolling bearing, an early fault diagnosis
LMD
- 局部均值分解算法，用于多分量信号的自适应分解，用于脑电信号处理、故障诊断等方面。-LMD method
shiyutezheng
- 振动信号时域特征提取，用于故障诊断，包括十余种特征提取算法-Time domain features vibration signals for fault diagnosis, including more than ten kinds of feature extraction algorithm
RSSD
- 该代码针对滚动轴承故障振动信号呈现出非线性、非平稳性及噪声背景较强等特点，为了有效提取故障特征，使用的一种共振稀疏分解（Resonance-based sparse signal decomposition，RSSD）与小波变换相结合的振动信号特征提取技术的相关仿真实验程序和轴承数据分解程序。其中，共振稀疏分解是基于品质因子可调小波变换与形态分量分析的一种新的信号分解方法，与常规的基于频带划分的信号分解方法不同，它依据信号各分量的振荡形态不同对信号进行分解。 同时还提供了可调谐 Q 因子小波
SVM
- 支持向量机(SVM)分类支持向量机的故障诊断,主要可以处理多维数据,该算法能否实现故障和故障类型的判断-For support vector machine (SVM) classification of SVM fault diagnosis, the main can handle multidimensional data, whether the algorithm can realize fault and failure types of judgment
GA_BP
- 基于遗传算法的BP神经网络优化算法，以某拖拉机的齿轮箱为工程背景，介绍使用基于遗传算法的BP神经网络进行齿轮箱故障的诊断。-BP neural network optimization algorithm based on genetic algorithm, in the engineering background of a tractor gearbox, introduces using BP neural network based on genetic algorithm for g