├── Data_File ├── DevelopmentData_File ├── TestingData_File └── TrainingData_File ├── Extract14Features.m ├── FeatureExtractor.m ├── FeatureSelection.m ├── LICENSE ├── LogisticMap.m ├── Logistic_ELM.m ├── MainLogisticELM.m └── README.md /Extract14Features.m: -------------------------------------------------------------------------------- 1 | 2 | function feature = Extract14Features(Sample_File) 3 | 4 | % Input: 5 | % Sample_File - Filename of sample data set(the original vibration signals and their labels) 6 | % 7 | % Output: 8 | % feature - 14 features of samples 9 | % 10 | % "feature" is a matrix, the number of rows is the number of samples in "Sample_File", and the number of columns is 14. 11 | % For example, there are 100 samples in "Sample_File", then "feature" is a matrix of 100*14. 12 | % 13 | %%%% Authors: PROF. ZHEN-HUA TAN AND DR JING-YU NING 14 | %%%% NORTHEASTERN UNIVERSITY, CHINA 15 | %%%% EMAIL: tanzh@mail.neu.edu.cn ningjy@mail.neu.edu.cn 16 | %%%% DATE: JANUARY 2021 17 | 18 | NumberofFeatures=14; 19 | Sample=load(Sample_File); 20 | Class(:,1)=Sample(:,1)'; 21 | Sample(:,1)=[]; 22 | [NumberofSample,~]=size(Sample); 23 | feature = zeros( NumberofSample,NumberofFeatures); 24 | for j = 1 : NumberofSample 25 | u = mean(Sample(j,:)); 26 | feature(j,1) = u; %Mean Value 27 | stdvalue = std(Sample(j,:)); 28 | feature(j,2) = stdvalue; %Standard Deviation 29 | sigm = var(Sample(j,:)); 30 | feature(j,3) = sigm; %Variance 31 | P_Pvalue = max(Sample(j,:))-min(Sample(j,:)); 32 | feature(j,4) = P_Pvalue; %Peak-to-Peak Value 33 | Xr = mean(sqrt(abs(Sample(j,:))))*mean(sqrt(abs(Sample(j,:)))); 34 | feature(j,5) = Xr; %Square Root Amplitude 35 | Xmean = mean(abs(Sample(j,:))); 36 | feature(j,6) = Xmean; %Average Amplitude 37 | Xrms = sqrt(mean(Sample(j,:).*Sample(j,:))) ; 38 | feature(j,7) = Xrms ; %Mean Square Amplitude 39 | Xp = max(max(Sample(j,:)),-max(Sample(j,:))); 40 | feature(j,8) = Xp; %Peak Value 41 | W = Xrms/Xmean; 42 | feature(j,9) = W; %Waveform Index 43 | C = Xp/Xrms; 44 | feature(j,10) = C; %Peak Index(non-dimensional) 45 | I = Xp/Xmean; 46 | feature(j,11) = I; %Impulsion Index(non-dimensional) 47 | L = Xp/Xr; 48 | feature(j,12) = L; %Clearance Factor(non-dimensional) 49 | S = skewness(Sample(j,:)); 50 | feature(j,13) = S; %Degree of Skewness(non-dimensional) 51 | K = kurtosis(Sample(j,:)); 52 | feature(j,14) = K; %Kurtosis Value(non-dimensional) 53 | end 54 | feature=[Class feature]; 55 | end -------------------------------------------------------------------------------- /FeatureExtractor.m: -------------------------------------------------------------------------------- 1 | function [Feature,NumberofFeatures] = FeatureExtractor(Sample_File, Position) 2 | 3 | % Input: 4 | % Sample_File - Filename of sample data set(the original vibration signals and their labels) 5 | % Position - The serial numbers of selected features 6 | % 7 | % Output: 8 | % Feature - The feature matrix of samples 9 | % NumberofFeatures - The number of features for each sample 10 | % 11 | % "Feature" is a matrix, the number of rows is the number of samples in "Sample_File", 12 | % and the number of columns is the number of features for each sample. 13 | % For example, there are 100 samples in "Sample_File", and "FeatureExtractor" extracts 2 features, 14 | % then "NumberofFeatures" is 5, and "Feature" is a matrix of 100*5. 15 | % 16 | %%%% Authors: PROF. ZHEN-HUA TAN AND DR JING-YU NING 17 | %%%% NORTHEASTERN UNIVERSITY, CHINA 18 | %%%% EMAIL: tanzh@mail.neu.edu.cn ningjy@mail.neu.edu.cn 19 | %%%% DATE: JANUARY 2021 20 | 21 | feature = Extract14Features(Sample_File); 22 | %tic; 23 | Class(:,1)=feature(:,1)'; 24 | feature(:,1)=[]; 25 | [NumberofSample,NumberofFeatures]=size(feature); 26 | Feature = zeros( NumberofSample,NumberofFeatures); 27 | i=1; 28 | while Position(i,1)~=0 29 | Feature(:,i)=feature(:,Position(i,1)); 30 | i=i+1; 31 | if i==15 32 | break; 33 | end 34 | end 35 | Feature=[Class Feature]; 36 | Feature(:,all(Feature==0,1))= []; 37 | NumberofFeatures = i-1; 38 | %toc; 39 | end -------------------------------------------------------------------------------- /FeatureSelection.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TAN-OpenLab/logistic-ELM/48c359391429cda0d21e910a7d2a7f792a83e1b6/FeatureSelection.m -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /LogisticMap.m: -------------------------------------------------------------------------------- 1 | 2 | 3 | function W =LogisticMap(mu,z1,NumberofHiddenNeurons,NumberofFeatures) 4 | 5 | % Input: 6 | % mu - Initial value of logistic mapping(range from 3.56995 to 4) 7 | % z1 - Initial value of logistic mapping(range from 0 to 1) 8 | % NumberofHiddenNeurons - Number of hidden neurons assigned to the ELM 9 | % NumberofFeatures - Number of features to every sample 10 | % 11 | % Output: 12 | % W - The input weights to the ELM 13 | % 14 | %%%% Authors: PROF. ZHEN-HUA TAN AND DR JING-YU NING 15 | %%%% NORTHEASTERN UNIVERSITY, CHINA 16 | %%%% EMAIL: tanzh@mail.neu.edu.cn ningjy@mail.neu.edu.cn 17 | %%%% DATE: JANUARY 2021 18 | 19 | z=zeros(1,NumberofHiddenNeurons*NumberofFeatures); 20 | z(1,1)=z1; 21 | 22 | W=zeros(NumberofHiddenNeurons,NumberofFeatures); 23 | for i=2:1:NumberofHiddenNeurons*NumberofFeatures 24 | z(1,i)=z(1,i-1)*mu*(1-z(1,i-1)); 25 | end 26 | for i=1:NumberofHiddenNeurons 27 | for j=1:NumberofFeatures 28 | W(i,j)=z(1,(i-1)*NumberofFeatures+j); 29 | end 30 | end -------------------------------------------------------------------------------- /Logistic_ELM.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TAN-OpenLab/logistic-ELM/48c359391429cda0d21e910a7d2a7f792a83e1b6/Logistic_ELM.m -------------------------------------------------------------------------------- /MainLogisticELM.m: -------------------------------------------------------------------------------- 1 | function [ TestingAccuracy] = MainLogisticELM(TrainingData_File,DevelopmentData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction,mu,z1) 2 | 3 | % Input: 4 | % TrainingData_File - Filename of training data set 5 | % DevelopmentData_File - Filename of development data set 6 | % TestingData_File - Filename of testing data set 7 | % Elm_Type - 0 for regression; 1 for (both binary and multi-classes) classification 8 | % NumberofHiddenNeurons - Number of hidden neurons assigned to the ELM 9 | % ActivationFunction - Type of activation function 10 | % mu - Initial value of logistic mapping(range from 3.56995 to 4) 11 | % z1 - Initial value of logistic mapping(range from 0 to 1) 12 | % 13 | % Output: 14 | % TrainingAccuracy - Training accuracy 15 | % TestingAccuracy - Testing accuracy 16 | % 17 | %%%% Authors: PROF. ZHEN-HUA TAN AND DR JING-YU NING 18 | %%%% NORTHEASTERN UNIVERSITY, CHINA 19 | %%%% EMAIL: tanzh@mail.neu.edu.cn ningjy@mail.neu.edu.cn 20 | %%%% DATE: JANUARY 2021 21 | 22 | Position = FeatureSelection(TrainingData_File,DevelopmentData_File,mu,z1,NumberofHiddenNeurons) ; 23 | [Train,~] = FeatureExtractor(TrainingData_File, Position); 24 | [Test,NumberofFeatures] = FeatureExtractor(TestingData_File, Position); 25 | W =LogisticMap(mu,z1,NumberofHiddenNeurons,NumberofFeatures); 26 | [~, ~, ~, TestingAccuracy] = Logistic_ELM(Train, Test, Elm_Type, NumberofHiddenNeurons, ActivationFunction, W); 27 | end -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # logistic-ELM 2 | A fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping. 3 | ## Introduction 4 | 5 | Considering both accuracy and the real-time requirement, we propose a novel fast fault diagnosis method for rolling bearings. First, we extract 14 kinds of time-domain features from the original vibration signals and adopt the sequential forward selection (SFS) strategy to select features to ensure a further reduction in dimensionality. Next, we utilize logistic-ELM for fast fault classification, and replace the random input weights in ELM by the logistic mapping sequence. 6 | 7 | ## Function Description 8 | 9 | **Extract14Features.m**: extract 14 features from the sample data. 10 | 11 | **FeatureSelection.m**: select features which have best performance for fault diagnosis by the sequential forward selection (SFS), and combine into the feature matrix. 12 | 13 | **FeatureExtractor.m**: extract the feature matrix from the sample data. 14 | 15 | **LogisticMap.m** : generate the input weights of ELM by Logistic Mapping. 16 | 17 | **Logistic_ELM.m** : diagnose fault type from the feature matrix. 18 | 19 | **MainLogisticELM.m** : the main funcion, diagnose fault type from the sample data. 20 | 21 | ## Dataset 22 | 23 | We use the rolling bearing vibration signal dataset prepared by the Case Western Reserve University (CWRU) Bearing Data Centre, and you can get if from http://csegroups.case.edu/bearingdatacenter/home. 24 | 25 | We also upload the preprocessed data files here as examples, named **Data_File**. 26 | --------------------------------------------------------------------------------