nasa-turbofan-engine-predictive-maintenance-lstm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0e48197e",
   "metadata": {},
   "source": [
    "## Nasa Turbofan Engine Predictive Maintenance with LSTM\n",
    "\n",
    "In deep learning, Long Short-Term Memory (LSTM) networks is powerful tool for sequence prediction problems. LSTM is a type of recurrent neural network (RNN) architecture that is well-suited for sequential data, such as time series data generated by sensors in turbofan engines. In this project, we will utilize LSTM to develop an accurate and reliable predictive maintenance model to predict when maintenance is required before a failure occurs, thereby reducing downtime and preventing catastrophic failures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b953f59",
   "metadata": {},
   "source": [
    "### 1. Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44f41826",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix, recall_score, precision_score, ConfusionMatrixDisplay\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM, Activation\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f4ba12",
   "metadata": {},
   "source": [
    "### 2. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f13efa9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import the pre-proceed dataset of engine FD0001\n",
    "\n",
    "train_df = pd.read_csv('data/train_fd001_with_rul.csv')\n",
    "test_df = pd.read_csv('data/test_fd001_with_rul.csv')\n",
    "\n",
    "cycle = 30\n",
    "\n",
    "train_df['target'] = np.where(train_df['rul'] <= cycle, 1, 0)\n",
    "test_df['target'] = np.where(test_df['rul'] <= cycle, 1, 0)\n",
    "\n",
    "train_df['norm_time_cycles'] = train_df['time_cycles']\n",
    "test_df['norm_time_cycles'] = test_df['time_cycles']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1ab4980",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Apply mix max normalization\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "normalization_columns = train_df.columns.difference(['unit_number', 'time_cycles', 'rul', 'target'])\n",
    "\n",
    "train_df[normalization_columns] = min_max_scaler.fit_transform(train_df[normalization_columns])\n",
    "test_df[normalization_columns] = min_max_scaler.fit_transform(test_df[normalization_columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b96240a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_sequence(data, sequence_length, sequence_columns):\n",
    "    vals = data[sequence_columns].values\n",
    "    row_number = vals.shape[0]\n",
    "    \n",
    "    ### Initialize range for start \n",
    "    start_range = range(0, row_number - sequence_length)\n",
    "    \n",
    "    ### Initialize range for end\n",
    "    end_range = range(sequence_length, row_number)\n",
    "    \n",
    "    ### Start slicing for rows\n",
    "    for start, stop in zip(start_range, end_range):\n",
    "        yield vals[start:stop, :]\n",
    "\n",
    "def generate_label(data, sequence_length):\n",
    "    vals = data[['target']].values\n",
    "    row_number = vals.shape[0]\n",
    "    \n",
    "    return vals[sequence_length:row_number, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39960318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train sequence data:  (15631, 50, 25)\n",
      "Shape of train label data:  (15631, 1)\n"
     ]
    }
   ],
   "source": [
    "selected_columns = ['norm_time_cycles', 'setting_1', 'setting_2', 'setting_3',\n",
    "       'sensor_1', 'sensor_2', 'sensor_3', 'sensor_4', 'sensor_5', 'sensor_6',\n",
    "       'sensor_7', 'sensor_8', 'sensor_9', 'sensor_10', 'sensor_11',\n",
    "       'sensor_12', 'sensor_13', 'sensor_14', 'sensor_15', 'sensor_16',\n",
    "       'sensor_17', 'sensor_18', 'sensor_19', 'sensor_20', 'sensor_21']\n",
    "\n",
    "sequence_length = 50\n",
    "\n",
    "### Generator for sequence data\n",
    "train_sequence_data = (\n",
    "    list(generate_sequence(train_df[train_df['unit_number'] == unit_number], sequence_length, selected_columns)) \n",
    "        for unit_number in train_df['unit_number'].unique()\n",
    ")\n",
    "\n",
    "### Generator for label data\n",
    "train_label_data = [\n",
    "    generate_label(train_df[train_df['unit_number'] == unit_number], sequence_length) \n",
    "        for unit_number in train_df['unit_number'].unique()\n",
    "]\n",
    "\n",
    "### Concatenate all the sequences generated for different units into a single numpy array\n",
    "### Convert the data type of the array to float32\n",
    "train_sequence_data = np.concatenate(list(train_sequence_data)).astype(np.float32)\n",
    "\n",
    "### Concatenate all list of array into a single numpy array\n",
    "### Convert the data type of the array to float32\n",
    "train_label_data = np.concatenate(train_label_data).astype(np.float32)\n",
    "\n",
    "print(\"Shape of train sequence data: \", train_sequence_data.shape)\n",
    "print(\"Shape of train label data: \", train_label_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c854a9f7",
   "metadata": {},
   "source": [
    "### Model Initialization/ Training/ Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d842f00e",
   "metadata": {},
   "source": [
    "Every LSTM Layar should be accompanied by dropout layer to prevent overfitting by ignoring randomly selected neurons during training. 20% is common drop out percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a50d05e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 50, 100)           50400     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 50, 100)           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 50)                30200     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 50)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 80,651\n",
      "Trainable params: 80,651\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "number_of_feature = train_sequence_data.shape[2]\n",
    "number_of_output = train_label_data.shape[1]\n",
    "\n",
    "### Initialization of model\n",
    "model = Sequential()\n",
    "\n",
    "### First layer\n",
    "### Input shape based on sequence length and number of feature, eg 50, 25.\n",
    "### 100 unit of neuron\n",
    "### 'return_sequences = true' process sequences and produce sequences as output to next layer\n",
    "model.add(LSTM(input_shape = (sequence_length, number_of_feature), units = 100, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "### Second layer\n",
    "### 50 unit of neuron\n",
    "### 'return_sequences = false' will not process sequences to last layer\n",
    "model.add(LSTM(units = 50, return_sequences = False))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "### Final layer with activation 'sigmoid'\n",
    "### Since it is binary classification, the units of layer layer is 1\n",
    "model.add(Dense(units = number_of_output, activation = 'sigmoid'))\n",
    "\n",
    "### Model summary detail\n",
    "model.summary()\n",
    "\n",
    "### Final setting for training\n",
    "### loss = 'binary_crossentropy' for classification of  two possible classe, and final layer with 'sigmoid'\n",
    "### 'adam' optimization updating the model's weights during training based on the calculated gradients of the loss function\n",
    "### 'accuracy' metric because it is classification tasks\n",
    "model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66e50c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "63/63 [==============================] - 12s 123ms/step - loss: 0.2772 - accuracy: 0.8784 - val_loss: 0.1720 - val_accuracy: 0.9261\n",
      "Epoch 2/15\n",
      "63/63 [==============================] - 6s 102ms/step - loss: 0.1184 - accuracy: 0.9520 - val_loss: 0.0685 - val_accuracy: 0.9703\n",
      "Epoch 3/15\n",
      "63/63 [==============================] - 7s 111ms/step - loss: 0.0862 - accuracy: 0.9660 - val_loss: 0.0812 - val_accuracy: 0.9687\n",
      "Epoch 4/15\n",
      "63/63 [==============================] - 9s 137ms/step - loss: 0.0842 - accuracy: 0.9643 - val_loss: 0.1137 - val_accuracy: 0.9469\n",
      "Epoch 5/15\n",
      "63/63 [==============================] - 9s 149ms/step - loss: 0.0823 - accuracy: 0.9650 - val_loss: 0.0677 - val_accuracy: 0.9709\n",
      "Epoch 6/15\n",
      "63/63 [==============================] - 9s 139ms/step - loss: 0.0665 - accuracy: 0.9722 - val_loss: 0.0636 - val_accuracy: 0.9754\n",
      "Epoch 7/15\n",
      "63/63 [==============================] - 8s 129ms/step - loss: 0.0665 - accuracy: 0.9719 - val_loss: 0.0556 - val_accuracy: 0.9757\n",
      "Epoch 8/15\n",
      "63/63 [==============================] - 8s 124ms/step - loss: 0.0582 - accuracy: 0.9758 - val_loss: 0.0521 - val_accuracy: 0.9789\n",
      "Epoch 9/15\n",
      "63/63 [==============================] - 8s 126ms/step - loss: 0.0569 - accuracy: 0.9766 - val_loss: 0.0552 - val_accuracy: 0.9754\n",
      "Epoch 10/15\n",
      "63/63 [==============================] - 8s 124ms/step - loss: 0.0571 - accuracy: 0.9756 - val_loss: 0.0505 - val_accuracy: 0.9770\n",
      "Epoch 11/15\n",
      "63/63 [==============================] - 8s 121ms/step - loss: 0.0561 - accuracy: 0.9761 - val_loss: 0.0474 - val_accuracy: 0.9821\n",
      "Epoch 12/15\n",
      "63/63 [==============================] - 8s 120ms/step - loss: 0.0618 - accuracy: 0.9742 - val_loss: 0.0681 - val_accuracy: 0.9671\n",
      "Epoch 13/15\n",
      "63/63 [==============================] - 7s 119ms/step - loss: 0.0524 - accuracy: 0.9784 - val_loss: 0.0561 - val_accuracy: 0.9760\n",
      "Epoch 14/15\n",
      "63/63 [==============================] - 7s 119ms/step - loss: 0.0528 - accuracy: 0.9776 - val_loss: 0.0494 - val_accuracy: 0.9795\n",
      "Epoch 15/15\n",
      "63/63 [==============================] - 8s 119ms/step - loss: 0.0526 - accuracy: 0.9775 - val_loss: 0.0654 - val_accuracy: 0.9722\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_sequence_data, train_label_data, epochs = 15, batch_size = 200, validation_split = 0.2, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2a9ca90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84ec099f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44880f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 4s 46ms/step - loss: 0.0487 - accuracy: 0.9801\n"
     ]
    }
   ],
   "source": [
    "### Training matrix\n",
    "\n",
    "training_acc_score = model.evaluate(train_sequence_data, train_label_data, verbose = 1, batch_size = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55b650fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "489/489 [==============================] - 9s 16ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = np.where(model.predict(train_sequence_data) > 0.5, 1, 0)\n",
    "y_true = train_label_data\n",
    "\n",
    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "disp_conf_matrix = ConfusionMatrixDisplay(confusion_matrix = conf_matrix)\n",
    "disp_conf_matrix.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abf24cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurracy: 0.980103611946106, Precision: 0.9836975372875477, Recall: 0.9148387096774193, F1: 0.9480193882667557\n"
     ]
    }
   ],
   "source": [
    "train_precision = precision_score(y_true, y_pred)\n",
    "train_recall = recall_score(y_true, y_pred)\n",
    "train_f1 = 2 * (train_precision * train_recall) / (train_precision + train_recall)\n",
    "\n",
    "print('Accurracy: {0}, Precision: {1}, Recall: {2}, F1: {3}'.format(training_acc_score[1], train_precision, train_recall, train_f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358d55e0",
   "metadata": {},
   "source": [
    "Last sequence data for each engine id in the test data will be used for testing purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "68f5c93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sequence_data = [\n",
    "    test_df[test_df['unit_number'] == unit_number][selected_columns].values[-sequence_length:] \n",
    "    for unit_number in test_df['unit_number'].unique()\n",
    "    ### Get selected test data based on engine number with more and equal to 50, otherwise omit \n",
    "    if len(test_df[test_df['unit_number'] == unit_number]) >= sequence_length\n",
    "]\n",
    "\n",
    "### Convert the list of arrays into a numpy array\n",
    "test_sequence_data = np.asarray(test_sequence_data).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cbfd13f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of train sequence data:  (93, 50, 25)\n",
      "Shape of train label data:  (93, 1)\n"
     ]
    }
   ],
   "source": [
    "label_filter = [len(test_df[test_df['unit_number'] == unit_number]) >= sequence_length for unit_number in test_df['unit_number'].unique()]\n",
    "\n",
    "### Group then extract the last value of target\n",
    "test_label_data = test_df.groupby('unit_number')['target'].nth(-1)[label_filter].values\n",
    "\n",
    "###  Reshape numpy array to have a single column and convert to 32-bit floating-point data type\n",
    "test_label_data = test_label_data.reshape(test_label_data.shape[0], 1).astype(np.float32)\n",
    "\n",
    "print(\"Shape of train sequence data: \", test_sequence_data.shape)\n",
    "print(\"Shape of train label data: \", test_label_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63806759",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 17ms/step - loss: 0.5325 - accuracy: 0.8710\n"
     ]
    }
   ],
   "source": [
    "### Testing matrix\n",
    "\n",
    "test_acc_score = model.evaluate(test_sequence_data, test_label_data, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b70ee56f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 15ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = np.where(model.predict(test_sequence_data) > 0.5, 1, 0)\n",
    "y_true = test_label_data\n",
    "\n",
    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "disp_conf_matrix = ConfusionMatrixDisplay(confusion_matrix = conf_matrix)\n",
    "disp_conf_matrix.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88d1ec51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurracy: 0.8709677457809448, Precision: 0.6756756756756757, Recall: 1.0, F1: 0.8064516129032258\n"
     ]
    }
   ],
   "source": [
    "test_precision = precision_score(y_true, y_pred)\n",
    "test_recall = recall_score(y_true, y_pred)\n",
    "test_f1 = 2 * (test_precision * test_recall) / (test_precision + test_recall)\n",
    "\n",
    "print('Accurracy: {0}, Precision: {1}, Recall: {2}, F1: {3}'.format(test_acc_score[1], test_precision, test_recall, test_f1))"
   ]
  }
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