initial commit (File Exchange to GitHub)

Author: minoue-xx

README.md

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+# DEMO: アプリを使用した特徴量調査
+
+このプログラムでは、デモに使用するデータファイル sampleData.mat を作成します。もともとの1 日当たり 6 秒間の振動信号を連続 50 日間取得したものですが、ここでは計算量軽減のため、1 日当たり 1 秒間、1 日おき 25 日分のデータに削減します。
+
+# Dataset
+
+20 歯のピニオンギヤで駆動する 2 MW 風力タービン高速シャフトから収集されたデータを使用します[1]。まず、[https://github.com/mathworks/WindTurbineHighSpeedBearingPrognosis-Data](https://github.com/mathworks/WindTurbineHighSpeedBearingPrognosis-Data) からリポジトリ全体を zip ファイルとしてダウンロードして、本スクリプトと同じディレクトリに保存してください。以下のコマンドを使用してファイルを解凍します。こちらのデータでは計測間隔は 1 日間隔です。
+
+```matlab:Code
+if exist('WindTurbineHighSpeedBearingPrognosis-Data-master.zip', 'file')
+    unzip('WindTurbineHighSpeedBearingPrognosis-Data-master.zip')
+end
+```
+
+# Data Import
+
+まず風力タービンデータに対して fileDatastore を作成します。使用するデータには振動とタコメータの信号が含まれています。各 mat ファイルからcutomreader 関数で読み取りますが、1 秒分の振動データに加えてファイル名から日付情報も合わせて取得します。
+
+```matlab:Code
+dir = 'WindTurbineHighSpeedBearingPrognosis-Data-master';
+ds = fileDatastore(fullfile('.', dir,'*.mat'),'ReadFcn',@customreader,'UniformRead',true);
+data = readall(ds)
+```
+
+| |Date|vibration|
+|:--:|:--:|:--:|
+|1|2013/03/07|97656x1 timetable|
+|2|2013/03/08|97656x1 timetable|
+|3|2013/03/09|97656x1 timetable|
+|4|2013/03/10|97656x1 timetable|
+|5|2013/03/11|97656x1 timetable|
+|6|2013/03/12|97656x1 timetable|
+|7|2013/03/13|97656x1 timetable|
+|8|2013/03/14|97656x1 timetable|
+|9|2013/03/15|97656x1 timetable|
+|10|2013/03/16|97656x1 timetable|
+
+# Data Reduction
+
+ここでは計算量軽減のため、さらに1 日おき 25 日分のデータに削減します。
+
+```matlab:Code
+sampleData = data;
+sampleData(1:2:end,:) = [];
+```
+
+さらに前半のデータには faultCode = 0、後半のデータについては faultcode = 1 とラベル付けします。
+
+```matlab:Code
+sampleData.faultcode = [zeros(13,1); ones(12,1)];
+save('sampleData.mat','sampleData');
+```
+
+任意に変更して、他にもアプリで使用するサンプルデータを作成して試してみてください。
+
+# References
+
+[1] [<http://data-acoustics.com/measurements/bearing-faults/bearing-3/](http://data-acoustics.com/measurements/bearing-faults/bearing-3/)> Bechhoefer, Eric, Brandon Van Hecke, and David He. "Processing for improved spectral analysis." *Annual Conference of the Prognostics and Health Management Society, New Orleans, LA, Oct*. 2013.
+
+# Helper Function
+
+```matlab:Code
+function data = customreader(filename)
+% Read variables for the fileEnsemble
+% Inputs:
+% filename  - a string of the mat file name to read from.
+% Output:
+% data      - return a table with a single row
+% Copyright 2018 The MathWorks, Inc.
+
+data = table;
+% Extract the datetime information from the file names
+[~, fname] = fileparts(filename);
+token = regexp(fname, 'data-(\d+)', 'tokens');
+data.Date = datetime(token{1}{1}, 'InputFormat', 'yyyyMMdd');
+
+% Load the vibration signal from the mat file
+mfile = matfile(filename); % Allows partial loading
+vib = mfile.vibration;
+
+% Extract the first 1s of signal and convert it to timetable
+fs = 97656; % Sampling rate
+tVibration = timetable(vib(1:fs),'SamplingRate',fs);
+data.vibration = {tVibration};
+
+end
+
+```
+
+*Copyright 2018 The MathWorks, Inc.*

SECURITY.md

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+# Reporting Security Vulnerabilities 
+
+If you believe you have discovered a security vulnerability, please report it to 
+[security@mathworks.com](mailto:security@mathworks.com). Please see 
+[MathWorks Vulnerability Disclosure Policy for Security Researchers](https://www.mathworks.com/company/aboutus/policies_statements/vulnerability-disclosure-policy.html) 
+for additional information.  

dataPreparation_FeatureExtraction.mlx

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