├── Estimation of RUL using CNN.ipynb ├── LICENSE ├── PHM08.csv └── README.md /Estimation of RUL using CNN.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd\n", 12 | "import numpy as np\n", 13 | "import tensorflow as tf\n", 14 | "from sklearn.preprocessing import scale" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": { 21 | "collapsed": false, 22 | "scrolled": true 23 | }, 24 | "outputs": [], 25 | "source": [ 26 | "def windows(nrows, size):\n", 27 | " start,step = 0, 2\n", 28 | " while start < nrows:\n", 29 | " yield start, start + size\n", 30 | " start += step\n", 31 | "\n", 32 | "def segment_signal(features,labels,window_size = 15):\n", 33 | " segments = np.empty((0,window_size))\n", 34 | " segment_labels = np.empty((0))\n", 35 | " nrows = len(features)\n", 36 | " for (start, end) in windows(nrows,window_size):\n", 37 | " if(len(data.iloc[start:end]) == window_size):\n", 38 | " segment = features[start:end].T #Transpose to get segment of size 24 x 15 \n", 39 | " label = labels[(end-1)]\n", 40 | " segments = np.vstack([segments,segment]) \n", 41 | " segment_labels = np.append(segment_labels,label)\n", 42 | " segments = segments.reshape(-1,24,window_size,1) # number of features = 24 \n", 43 | " segment_labels = segment_labels.reshape(-1,1)\n", 44 | " return segments,segment_labels" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "collapsed": false, 52 | "scrolled": true 53 | }, 54 | "outputs": [], 55 | "source": [ 56 | "data = pd.read_csv(\"PHM08.csv\")\n", 57 | "features = scale(data.iloc[:,2:26]) # select required columns and scale them\n", 58 | "labels = data.iloc[:,26] # select RUL " 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": null, 64 | "metadata": { 65 | "collapsed": false, 66 | "scrolled": false 67 | }, 68 | "outputs": [], 69 | "source": [ 70 | "segments, labels = segment_signal(features,labels)" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": null, 76 | "metadata": { 77 | "collapsed": true 78 | }, 79 | "outputs": [], 80 | "source": [ 81 | "train_test_split = np.random.rand(len(segments)) < 0.70\n", 82 | "train_x = segments[train_test_split]\n", 83 | "train_y = labels[train_test_split]\n", 84 | "test_x = segments[~train_test_split]\n", 85 | "test_y = labels[~train_test_split]" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "--------------------------------------------------------------------------------------------------------------------------------" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": null, 98 | "metadata": { 99 | "collapsed": false 100 | }, 101 | "outputs": [], 102 | "source": [ 103 | "def weight_variable(shape):\n", 104 | " initial = tf.truncated_normal(shape, stddev = 0.1)\n", 105 | " return tf.Variable(initial)\n", 106 | "\n", 107 | "def bias_variable(shape):\n", 108 | " initial = tf.constant(1.0, shape = shape)\n", 109 | " return tf.Variable(initial)\n", 110 | "\n", 111 | "def apply_conv(x,kernel_height,kernel_width,num_channels,depth):\n", 112 | " weights = weight_variable([kernel_height, kernel_width, num_channels, depth])\n", 113 | " biases = bias_variable([depth])\n", 114 | " return tf.nn.relu(tf.add(tf.nn.conv2d(x, weights,[1,1,1,1],padding=\"VALID\"),biases))\n", 115 | " \n", 116 | "def apply_max_pool(x,kernel_height,kernel_width,stride_size):\n", 117 | " return tf.nn.max_pool(x, ksize=[1, kernel_height, kernel_width, 1], strides=[1, 1, stride_size, 1], padding = \"VALID\")" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": null, 123 | "metadata": { 124 | "collapsed": true 125 | }, 126 | "outputs": [], 127 | "source": [ 128 | "num_labels = 1\n", 129 | "batch_size = 10\n", 130 | "num_hidden = 800\n", 131 | "learning_rate = 0.0001\n", 132 | "training_epochs = 30\n", 133 | "input_height = 24\n", 134 | "input_width = 15\n", 135 | "num_channels = 1\n", 136 | "total_batches = train_x.shape[0] // batch_size" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": null, 142 | "metadata": { 143 | "collapsed": true 144 | }, 145 | "outputs": [], 146 | "source": [ 147 | "X = tf.placeholder(tf.float32, shape=[None,input_height,input_width,num_channels])\n", 148 | "Y = tf.placeholder(tf.float32, shape=[None,num_labels])\n", 149 | "\n", 150 | "c = apply_conv(X, kernel_height = 24, kernel_width = 4, num_channels = 1, depth = 8) \n", 151 | "p = apply_max_pool(c,kernel_height = 1, kernel_width = 2, stride_size = 2) \n", 152 | "c = apply_conv(p, kernel_height = 1, kernel_width = 3, num_channels = 8, depth = 14) \n", 153 | "p = apply_max_pool(c,kernel_height = 1, kernel_width = 2, stride_size = 2) \n", 154 | "\n", 155 | "shape = p.get_shape().as_list()\n", 156 | "flat = tf.reshape(p, [-1, shape[1] * shape[2] * shape[3]])\n", 157 | "\n", 158 | "f_weights = weight_variable([shape[1] * shape[2] * shape[3], num_hidden])\n", 159 | "f_biases = bias_variable([num_hidden])\n", 160 | "f = tf.nn.tanh(tf.add(tf.matmul(flat, f_weights),f_biases))\n", 161 | "\n", 162 | "out_weights = weight_variable([num_hidden, num_labels])\n", 163 | "out_biases = bias_variable([num_labels])\n", 164 | "y_ = tf.add(tf.matmul(f, out_weights),out_biases)" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": null, 170 | "metadata": { 171 | "collapsed": true 172 | }, 173 | "outputs": [], 174 | "source": [ 175 | "cost_function = tf.reduce_mean(tf.square(y_- Y))\n", 176 | "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost_function)" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 75, 182 | "metadata": { 183 | "collapsed": false 184 | }, 185 | "outputs": [ 186 | { 187 | "name": "stdout", 188 | "output_type": "stream", 189 | "text": [ 190 | "Training set MSE\n", 191 | "4752.72697916\n", 192 | "4677.79731598\n", 193 | "4675.55796569\n", 194 | "4668.99871858\n", 195 | "4646.43073364\n", 196 | "4606.30021167\n", 197 | "4544.63535847\n", 198 | "4453.55062054\n", 199 | "4320.07571005\n", 200 | "4142.02065626\n", 201 | "3939.75333712\n", 202 | "3732.80059479\n", 203 | "3540.9024371\n", 204 | "3373.17483584\n", 205 | "3231.20530547\n", 206 | "3112.15695777\n", 207 | "3011.5041224\n", 208 | "2925.24642685\n", 209 | "2851.78353692\n", 210 | "2789.1534664\n", 211 | "2736.44654112\n", 212 | "2687.96121346\n", 213 | "2644.67107264\n", 214 | "2605.52678315\n", 215 | "2568.49537431\n", 216 | "2533.52371995\n", 217 | "2503.20061828\n", 218 | "2473.34282306\n", 219 | "2445.49239087\n", 220 | "2419.93309399\n", 221 | "Test set MSE: 2607.9456\n" 222 | ] 223 | } 224 | ], 225 | "source": [ 226 | "with tf.Session() as session:\n", 227 | " tf.global_variables_initializer().run()\n", 228 | " print(\"Training set MSE\")\n", 229 | " for epoch in range(training_epochs):\n", 230 | " for b in range(total_batches): \n", 231 | " offset = (b * batch_size) % (train_x.shape[0] - batch_size)\n", 232 | " batch_x = train_x[offset:(offset + batch_size), :, :, :]\n", 233 | " batch_y = train_y[offset:(offset + batch_size),:]\n", 234 | " _, c = session.run([optimizer, cost_function],feed_dict={X: batch_x, Y : batch_y})\n", 235 | " \n", 236 | " p_tr = session.run(y_, feed_dict={X: train_x})\n", 237 | " tr_mse = tf.reduce_mean(tf.square(p_tr - train_y))\n", 238 | " print(session.run(tr_mse))\n", 239 | "\n", 240 | " p_ts = session.run(y_, feed_dict={X: test_x})\n", 241 | " ts_mse = tf.reduce_mean(tf.square(p_ts - test_y))\n", 242 | " print(\"Test set MSE: %.4f\" % session.run(ts_mse)) " 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": null, 248 | "metadata": { 249 | "collapsed": true 250 | }, 251 | "outputs": [], 252 | "source": [] 253 | } 254 | ], 255 | "metadata": { 256 | "anaconda-cloud": {}, 257 | "kernelspec": { 258 | "display_name": "Python [conda root]", 259 | "language": "python", 260 | "name": "conda-root-py" 261 | }, 262 | "language_info": { 263 | "codemirror_mode": { 264 | "name": "ipython", 265 | "version": 3 266 | }, 267 | "file_extension": ".py", 268 | "mimetype": "text/x-python", 269 | "name": "python", 270 | "nbconvert_exporter": "python", 271 | "pygments_lexer": "ipython3", 272 | "version": "3.5.2" 273 | } 274 | }, 275 | "nbformat": 4, 276 | "nbformat_minor": 1 277 | } 278 | -------------------------------------------------------------------------------- /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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11 | 12 | #### Tools Required 13 | Python 3.5 is used during development and following libraries are required to run the code provided in the notebook: 14 | * Tensorflow 15 | * Numpy 16 | * Pandas 17 | 18 | #### Dataset 19 | PHM08 challenge dataset is available in the repository or can be downloaded from the following link. 20 | --------------------------------------------------------------------------------