tingting-0901

tingting-0901's Stars

• Lucky-Loek/ieee-phm-2012-data-challenge-dataset

  Dataset that was used during the IEEE PHM 2012 Data Challenge, built by the FEMTO-ST Institute

  119157

• SecWiki/sec-chart

  安全思维导图集合

  6.2k2.3k

• CSVdivya21/Human-Activity-Recognition-using-Random-Forest

  Human Activity Recognition using Wearable devices like Accelerometer is an useful application of ML in ioT which is explored in this repo

  Language:Jupyter Notebook1

• ianalad/random-forest-classifier

  Application of Random Forest Classifier in predicting students’ grades

  Language:Jupyter Notebook1

• LucDemortier/RandomForests

  An application of random forests to the problem of predicting flight delays

  Language:Jupyter Notebook54

• gsyrrakos/-Airq-Thesis-SKlearn-Timeseries-classification-on-Imbalanced-Dataset

  Timeseries Classification For polution detection using PMS7003 with SVM,Random Forest and KNN algorithms,imbalanced Data,Smote Application

  Language:Python1

• youssefsaeed97/RandomForest_algorithm

  Applying Random Forest algorithm to predict a label of 'slow' or 'fast' as an application of self driving cars + VISUALIZATION - class project.

  Language:Python1

• wangfin/SSLDPCA-IL-FaultDetection

  Semi-Supervised Density Peak Clustering Algorithm, Incremental Learning, Fault Detection（基于半监督密度聚类+增量学习的故障诊断）

  Language:Python669

• doubleplusplus/incremental_decision_tree-CART-Random_Forest

  incremental CART decision tree, based on the hoeffding tree i.e. very fast decision tree (VFDT), which is proposed in this paper "Mining High-Speed Data Streams" by Domingos & Hulten (2000). And a newly extended model "Extremely Fast Decision Tree" (EFDT) by Manapragada, Webb & Salehi (2018). Added new implementation of Random Forest

  Language:Python9927

• wangfin/1DCNN_Fault_Detection

  1DCNN Fault Detection（1DCNN的轴承故障诊断）

  Language:Python12830

• malyvsen/bearing-fault-detection

  Improving on NASA's work with induction motor bearing fault detection using RNN-powered smart sensors.

  Language:Python287

• ZhaoZhibin/UDTL

  Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM

  Language:Python406104

• iqiukp/SVDD-Python

  Python code for abnormal detection using Support Vector Data Description (SVDD)

  Language:Python18358

• lyst/rpforest

  It is a forest of random projection trees

  Language:Python22343

• serengil/chefboost

  A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

  Language:Python453101

• RaaviSoni/Fault-Detection-in-Wireless-Sensor-Networks-using-Statistical-Machine-Learning-Methods-and-Neural-Ne

  I have used different machine learning algorithm such as Support Vector Machine (SVM), Random forest, K-Nearest Neighbor (KNN), Decision Tree, Gradient Boost, XG Boost and Recurrent Neural Network (RNN) for classification.

  Language:Jupyter Notebook72

• xinlianghu/svm

  用Python实现SVM多分类器

  Language:Python350100

• kLabUM/rrcf

  🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

  Language:Python494112

• zhaoxingfeng/RandomForest

  随机森林，Random Forest（RF）

  Language:Python354135

• zhangjiali1201/Keras_bearing_fault_diagnosis

  Language:Python18953

• ChileWang0228/Deep-Learning-With-Python

  《Python深度学习》书籍代码

  Language:Jupyter Notebook615203

• nathanhubens/Autoencoders

  Implementation of simple autoencoders networks with Keras

  Language:Jupyter Notebook23692

• Georacer/fault-diagnosis

  PhD research-related software on fault diagnosis

  Language:MATLAB1313

• HustWolfzzb/Fault-diagnosis-based-on-industrial-big-data

  这是我的毕业设计，本人本科机械，但是读直博到计算机，所以毕业设计双向都有涉及。权当是练手之作了，请勿见笑

  Language:HTML6722

• ddrrrr/bearing-fault-diagnosis

  domain adaption with LSGAN for bearing fault diagnosis

  Language:Python6622

• wargod797/Fault_diagnosis_ballbearing_wavelet

  Bearing fault diagnosis is important in condition monitoring of any rotating machine. Early fault detection in machinery can save millions of dollars in emergency maintenance cost. Different techniques are used for fault analysis such as short time Fourier transforms (STFT), Wavelet analysis (WA), cepstrum analysis, Model based analysis, etc. we have doing detecting bearing faults using FFT and by using Wavelet analysis more specifically wavelet Analysis up to two levels of approximations and detail components. The analysis is carried out offline in MATLAB. Diagnosing the faults before in hand can save the millions of dollars of industry and can save the time as well. It has been found that Condition monitoring of rolling element bearings has enabled cost saving of over 50% as compared with the old traditional methods. The most common method of monitoring the condition of rolling element bearing is by using vibration signal analysis. Measure the vibrations of machine recorded by velocity

  Language:MATLAB8920

• SNBQT/Limited-Data-Rolling-Bearing-Fault-Diagnosis-with-Few-shot-Learning

  This is the corresponding repository of paper Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning

  Language:Jupyter Notebook28345

• songyer/Fault_Diagnosis

  基于深度学习的滚动轴承故障诊断方法

  Language:Python16331

• raady07/CNN-for-bearing-fault-diagnosis

  CNN applied to bearing signals for analysis

  Language:Python8729

• iqiukp/KPCA-MATLAB

  MATLAB code for dimensionality reduction, feature extraction, fault detection, and fault diagnosis using Kernel Principal Component Analysis (KPCA).

  Language:MATLAB23180