Bearing_Predictive_Maintenance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bearing Anomaly Detection using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Condition monitoring of an equipment is one of the crucial aspect one must conduct while operating a plant. The program can help identify the health state of the equipment so the plant operator can make necessary action to mitigate the equipment failure.\n",
    "\n",
    "The most common way to perform condition monitoring is to look at each sensor measurement from the machine and to set a minimum and maximum value limit on the measurement. If the measurement value is within the bounds, then the equpment is healthy. If the measurement value is outside the bouds, then the equipment is unhealthy and a warning is sent.\n",
    "\n",
    "Due to signals are measured from many sensors, the system is prone to either sending a large number of false alarms, i.e. sending unnecessary alarms or missing alarms, i.e. not sending required alarms. In order to mitigate this situation, it is necessary to have a reliable computational method which can analyze and give correct judgement from the combined data collected from various measurements of the available sensors.\n",
    "\n",
    "In recent years many novel methods have been introduced to help plant operator correctly analyze the combined signals, thanks to the rapid machine learning and deep learning application to the industry. \n",
    "\n",
    "I recently came across a great [article](https://towardsdatascience.com/how-to-use-machine-learning-for-anomaly-detection-and-condition-monitoring-6742f82900d7) written by Vegard Flovik where he highlighted two different approaches of unsupervised machine learning methods to detect anomaly of a working machine to help operator monitor its condition. In the article, he introduced two different types of unsupervised learning methods to solve the anomaly detection problem and provided practical code example;\n",
    "* Principal Component Analysis (PCA) combined with Mahalanobis distance\n",
    "* Autoencoder\n",
    "\n",
    "For the sake of understanding the introduced concept in the article, I have followed his code and tried to reproduce the results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bearing dataset was provided  provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati and is available at [Kaggle](https://www.kaggle.com/vinayak123tyagi/bearing-dataset). There exists 3 datasets in total and each dataset consists of individual files that are 1-second vibration signal snapshots recorded at specific intervals. Following the article, the second dataset was selected to perform this study.\n",
    "\n",
    "For the second dataset, there exists 984 data files with a measurement interval of 10 minutes between each two adjacent data file, and each data file has 20,480 measurements. This means a measurement exercise of the equipment was conducted every 10 minutes and in each exercise 20,480 measurements were taken.\n",
    "\n",
    "For each measurement, there are 4 different parameters recorded, representing 4 different types of bearings of the equipment. This project is essentially studying the interactions between the 4 bearings. For each data file, Vegard Flovik took an average of all 20,480 measurements and the final dataset has 984 rows and 4 columns. I believe the purpose of taking the average is for simplicity and the reduction of noise among individual measurements. However, one can actually train a more sophisticated model by using the large number of individual measurements without taking the average.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from sklearn import preprocessing\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (984, 4)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bearing 1</th>\n",
       "      <th>Bearing 2</th>\n",
       "      <th>Bearing 3</th>\n",
       "      <th>Bearing 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-02-12 10:32:39</th>\n",
       "      <td>0.058333</td>\n",
       "      <td>0.071832</td>\n",
       "      <td>0.083242</td>\n",
       "      <td>0.043067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-12 10:42:39</th>\n",
       "      <td>0.058995</td>\n",
       "      <td>0.074006</td>\n",
       "      <td>0.084435</td>\n",
       "      <td>0.044541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-12 10:52:39</th>\n",
       "      <td>0.060236</td>\n",
       "      <td>0.074227</td>\n",
       "      <td>0.083926</td>\n",
       "      <td>0.044443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-12 11:02:39</th>\n",
       "      <td>0.061455</td>\n",
       "      <td>0.073844</td>\n",
       "      <td>0.084457</td>\n",
       "      <td>0.045081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-12 11:12:39</th>\n",
       "      <td>0.061361</td>\n",
       "      <td>0.075609</td>\n",
       "      <td>0.082837</td>\n",
       "      <td>0.045118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Bearing 1  Bearing 2  Bearing 3  Bearing 4\n",
       "2004-02-12 10:32:39   0.058333   0.071832   0.083242   0.043067\n",
       "2004-02-12 10:42:39   0.058995   0.074006   0.084435   0.044541\n",
       "2004-02-12 10:52:39   0.060236   0.074227   0.083926   0.044443\n",
       "2004-02-12 11:02:39   0.061455   0.073844   0.084457   0.045081\n",
       "2004-02-12 11:12:39   0.061361   0.075609   0.082837   0.045118"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = '2nd_test'\n",
    "merged_data = pd.DataFrame()\n",
    "\n",
    "for filename in os.listdir(data_dir):\n",
    "    # each file consists of 20480 rows of data with the sampling rate set at 20 kHz\n",
    "    # data in each two adjacent files are measured 10 minutes away from each other\n",
    "    dataset = pd.read_csv(os.path.join(data_dir, filename), sep='\\t')\n",
    "    \n",
    "    # for the purpose of the anomaly detection study, we only keep the mean of the 20480 data points in each file\n",
    "    dataset_mean_abs = np.array(dataset.abs().mean())\n",
    "    dataset_mean_abs = pd.DataFrame(dataset_mean_abs.reshape(1, 4))\n",
    "    dataset_mean_abs.index = [filename]\n",
    "    merged_data = merged_data.append(dataset_mean_abs)\n",
    "\n",
    "merged_data.columns = ['Bearing 1', 'Bearing 2', 'Bearing 3', 'Bearing 4']\n",
    "merged_data.index = pd.to_datetime(\n",
    "    merged_data.index, format='%Y.%m.%d.%H.%M.%S')\n",
    "merged_data = merged_data.sort_index()\n",
    "\n",
    "print('data shape:', merged_data.shape)\n",
    "\n",
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the raw data with respect to its index (time of observation) we get the following plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_melt_merged_data = pd.melt(merged_data.reset_index(), id_vars='index')\n",
    "\n",
    "plt.figure(figsize=(14, 6))\n",
    "sns_plot = sns.lineplot(x='index', y='value', hue='variable',\n",
    "                        data=dataset_melt_merged_data).set_title('Data Raw', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above we notice that Bearing 1 started to deviate from original trend after 2004-02-16 while others still performed quite normal until after 2004-02-18. In traditional Statistical Process Control (SPC), if we are unlucky, we may not have selected Bearing 1 to be monitored and may not have detected any issue before 2004-02-19, when a breakdown event occurred."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Define train/test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before setting up the models, we need to define train/test data. To do this, we perform a simple split where we train on the first part of the dataset (which should represent normal operating conditions), and test on the remaining parts of the dataset leading up to the bearing failure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = merged_data['2004-02-12 11:02:39':'2004-02-13 23:52:39']\n",
    "test_data = merged_data['2004-02-13 23:52:39':]\n",
    "train_data.plot(figsize = (15,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, preprocessing tools of Scikit-learn will be used to scale the imput variables of the model. Here, the MinMaxScaler wil rescale the data to be in the range of [0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.MinMaxScaler()\n",
    "\n",
    "X_train = pd.DataFrame(scaler.fit_transform(train_data), \n",
    "                              columns = train_data.columns, \n",
    "                              index = train_data.index)\n",
    "# Random shuffle training data\n",
    "X_train.sample(frac=1)\n",
    "\n",
    "X_test = pd.DataFrame(scaler.transform(test_data), \n",
    "                             columns = test_data.columns, \n",
    "                             index = test_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bearing 1</th>\n",
       "      <th>Bearing 2</th>\n",
       "      <th>Bearing 3</th>\n",
       "      <th>Bearing 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-02-13 23:52:39</th>\n",
       "      <td>0.489779</td>\n",
       "      <td>0.187746</td>\n",
       "      <td>0.255713</td>\n",
       "      <td>0.298511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:02:39</th>\n",
       "      <td>0.354621</td>\n",
       "      <td>0.227843</td>\n",
       "      <td>0.244457</td>\n",
       "      <td>0.425398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:12:39</th>\n",
       "      <td>0.249681</td>\n",
       "      <td>0.309895</td>\n",
       "      <td>0.231099</td>\n",
       "      <td>0.254432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:22:39</th>\n",
       "      <td>0.303792</td>\n",
       "      <td>0.376130</td>\n",
       "      <td>0.195196</td>\n",
       "      <td>0.608553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:32:39</th>\n",
       "      <td>0.610786</td>\n",
       "      <td>0.502812</td>\n",
       "      <td>0.162301</td>\n",
       "      <td>0.380454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Bearing 1  Bearing 2  Bearing 3  Bearing 4\n",
       "2004-02-13 23:52:39   0.489779   0.187746   0.255713   0.298511\n",
       "2004-02-14 00:02:39   0.354621   0.227843   0.244457   0.425398\n",
       "2004-02-14 00:12:39   0.249681   0.309895   0.231099   0.254432\n",
       "2004-02-14 00:22:39   0.303792   0.376130   0.195196   0.608553\n",
       "2004-02-14 00:32:39   0.610786   0.502812   0.162301   0.380454"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Approach-1: Multivariate Statistical Analysis (MSA)\n",
    "\n",
    "The first approach we are going to show is to apply Principal Component Analysis (PCA) in combination with the Mahalanobis distance. Before jumping into the analysis, let's define required functions as per below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# covariance matrix calculation\n",
    "\n",
    "def cov_matrix(data, verbose=False):\n",
    "    covariance_matrix = np.cov(data, rowvar=False)\n",
    "    if is_pos_def(covariance_matrix):\n",
    "        inv_covariance_matrix = np.linalg.inv(covariance_matrix)\n",
    "        if is_pos_def(inv_covariance_matrix):\n",
    "            return covariance_matrix, inv_covariance_matrix\n",
    "        else:\n",
    "            print(\"Error: Inverse of Covariance Matrix is not positive definite!\")\n",
    "    else:\n",
    "        print(\"Error: Covariance Matrix is not positive definite!\")\n",
    "\n",
    "# Mahalanobis distance calculation\n",
    "\n",
    "\n",
    "def MahalanobisDist(inv_cov_matrix, mean_distr, data, verbose=False):\n",
    "    inv_covariance_matrix = inv_cov_matrix\n",
    "    vars_mean = mean_distr\n",
    "    diff = data - vars_mean\n",
    "    md = []\n",
    "    for i in range(len(diff)):\n",
    "        md.append(np.sqrt(diff[i].dot(inv_covariance_matrix).dot(diff[i])))\n",
    "    return md\n",
    "\n",
    "# outlier detection\n",
    "\n",
    "\n",
    "def MD_detectOutliers(dist, extreme=False, verbose=False):\n",
    "    k = 3. if extreme else 2.\n",
    "    threshold = np.mean(dist) * k\n",
    "    outliers = []\n",
    "    for i in range(len(dist)):\n",
    "        if dist[i] >= threshold:\n",
    "            outliers.append(i)  # index of the outlier\n",
    "    return np.array(outliers)\n",
    "\n",
    "# threshold value calculation for classifying datapoint as anomaly\n",
    "\n",
    "\n",
    "def MD_threshold(dist, std_dev=4, verbose=False):\n",
    "    threshold = np.mean(dist) + np.std(dist) * std_dev\n",
    "    return threshold\n",
    "\n",
    "# Check if matrix is positive definite\n",
    "\n",
    "\n",
    "def is_pos_def(A):\n",
    "    if np.allclose(A, A.T):\n",
    "        try:\n",
    "            np.linalg.cholesky(A)\n",
    "            return True\n",
    "        except np.linalg.LinAlgError:\n",
    "            return False\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having split the dataset into train and test data, we will now build a model that can detect anomaly from the bearing datasets. This section will be split into two subsection; first is building the model using PCA, second is anomaly detection using Mahalanobis distance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Principal Component Analysis (PCA) application to the MSA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dealing with high dimensional data is often computationally challenging. Luckily there are several techniques to reduce the number of variables. One of the main techniques we are going to use here is the Principal Component Analysis (PCA). \n",
    "\n",
    "Here, PCA performs a linear mapping of the data to a lower-dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. In practice, the covariance matrix of the data is constructed and the eigenvectors of this matrix are computed. The eigenvectors that correspond to the largest eigenvalues (the principal components) can now be used to reconstruct a large fraction of the variance of the original data. The original feature space has now been reduced to the space spanned by a few eigenvectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06830952 0.02739669]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# only keep the first 2 principal components\n",
    "pca = PCA(n_components=2, svd_solver='full')\n",
    "\n",
    "X_train_PCA = pca.fit_transform(X_train)\n",
    "print(pca.explained_variance_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_PCA shape: (num_training_examples x num_principal_components)\n",
    "X_train_PCA = pd.DataFrame(X_train_PCA)\n",
    "X_train_PCA.index = X_train.index\n",
    "\n",
    "X_test_PCA = pca.transform(X_test)\n",
    "# X_test_PCA shape: (num_test_examples x num_principal_components)\n",
    "X_test_PCA = pd.DataFrame(X_test_PCA)\n",
    "X_test_PCA.index = X_test.index\n",
    "\n",
    "# principal_components_train shape: (num_training_examples x num_principal_components)\n",
    "principal_components_train = np.array(X_train_PCA.values)\n",
    "# principal_components_test shape: (num_test_examples x num_principal_components)\n",
    "principal_components_test = np.array(X_test_PCA.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variation per principal component: [0.51012041 0.20459247]\n"
     ]
    }
   ],
   "source": [
    "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above output, one can observe that the principal component 1 holds 51.0% of the information while the principal component 2 holds only 20.4% of the information. Also, the other point to note is that while projecting four-dimensional data to a two-dimensional data, 28.6% information was lost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Anomaly detection using Mahalanobis distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to check that an observed point is anomaly? \n",
    "Let's consider the problem of estimating that a data point belong to a set of points. The first step would be to find the centroid or center of the set as representative of the set. Intuitively, the closer the sample point to the center of mass, the more likely it belongs to the set. \n",
    "\n",
    "However, we also need to know if the set is spread out over a large range or a small range, so that we can decide whether a given distance from the center is noteworthy or not. The simplistic approach is to estimate the standard deviation of the distances of the sample points from the center of mass. By plugging this into the normal distribution we derive the probability of the data point belonging to the same distribution.\n",
    "\n",
    "The drawback of the above approach was that we assumed the sample points are distributed around the centroid in a spherical manner. When we face a non-spherical distribution, e.g. an ellipsoidal, we would expect the probability of the test point belonging to the set to depend not only on the distance from the centroid, but also on the direction. In those directions where the ellipsoid has a short axis the test point must be closer, while in those where the axis is long the test point can be further away from the center. \n",
    "\n",
    "Putting this on a mathematical basis, the ellipsoid that best represents the set’s probability distribution can be estimated by calculating the covariance matrix of the samples. The Mahalanobis distance (MD) is the distance of the test point from the centroid divided by the width of the ellipsoid in the direction of the test point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the covariance matrix and its inverse, based principal components of training data\n",
    "# cov_matrix/inv_cov_matrix shape: (num_principal_components x num_principal_components)\n",
    "cov_matrix, inv_cov_matrix = cov_matrix(principal_components_train)\n",
    "\n",
    "# calculate the mean value for the principal components of training set, as this is used later\n",
    "# to calculate the Mahalanobis distance to datapoints in the test set\n",
    "# mean_distr shape: (num_principal_components, )\n",
    "mean_distr = principal_components_train.mean(axis=0)\n",
    "\n",
    "# calculate the Mahalanobis distance for training set\n",
    "# dist_train length: num_training_examples\n",
    "dist_train = MahalanobisDist(\n",
    "    inv_cov_matrix, mean_distr, principal_components_train, verbose=False)\n",
    "\n",
    "# find the threshold value to flag datapoints as an anomaly based on training set\n",
    "# threshold: a number, type numpy.float64\n",
    "threshold = MD_threshold(dist_train, std_dev=4)\n",
    "\n",
    "# calculate the Mahalanobis distance for test set, to be compared with the anomaly threshold\n",
    "# dist_test length: num_test_examples\n",
    "dist_test = MahalanobisDist(\n",
    "    inv_cov_matrix, mean_distr, principal_components_test, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The square of the Mahalanobis distance to the centroid of the distribution should follow chi-square (χ2) distribution if the assumption of normal distributed input variables is fulfilled. This is also the assumption behind the above calculation of the “threshold value” for flagging an anomaly. As this assumption is not necessarily fulfilled in our case, it is beneficial to visualize the distribution of the Mahalanobis distance to set a good threshold value for flagging anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "sns.distplot(np.square(dist_train),\n",
    "             bins=10,\n",
    "             kde=False).set_title('Square of the Mahalanobis distance', fontsize=15)\n",
    "plt.xlim([0.0, 15])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the Mahalanobis distance we get the following plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "sns.distplot(dist_train,\n",
    "             bins=10,\n",
    "             kde=True,\n",
    "             color='red').set_title('Mahalanobis distance', fontsize=15)\n",
    "\n",
    "plt.axvline(threshold)\n",
    "plt.text(threshold + 0.01, 0.02, '4 std dev from mean: ' +\n",
    "         str(round(threshold, 1)))\n",
    "\n",
    "plt.xlim([0.0, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the distribution above we can set the anomaly threshold equals to 4 standard deviations from mean of training data's Mahalanobis distances.\n",
    "\n",
    "Mahalanobis distance of the training data, threshold value and anomaly flag variable for both train and test data can be saved into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mob dist</th>\n",
       "      <th>Thresh</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-02-13 23:52:39</th>\n",
       "      <td>1.032676</td>\n",
       "      <td>3.724605</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:02:39</th>\n",
       "      <td>1.148163</td>\n",
       "      <td>3.724605</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:12:39</th>\n",
       "      <td>1.509998</td>\n",
       "      <td>3.724605</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:22:39</th>\n",
       "      <td>1.849725</td>\n",
       "      <td>3.724605</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:32:39</th>\n",
       "      <td>0.701075</td>\n",
       "      <td>3.724605</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Mob dist    Thresh  Anomaly\n",
       "2004-02-13 23:52:39  1.032676  3.724605    False\n",
       "2004-02-14 00:02:39  1.148163  3.724605    False\n",
       "2004-02-14 00:12:39  1.509998  3.724605    False\n",
       "2004-02-14 00:22:39  1.849725  3.724605    False\n",
       "2004-02-14 00:32:39  0.701075  3.724605    False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the Mahalanobis distance, as well as the threshold value and “anomaly flag” variable for both train and test data in a dataframe\n",
    "\n",
    "anomaly_train = pd.DataFrame()\n",
    "anomaly_train['Mob dist'] = dist_train\n",
    "anomaly_train['Thresh'] = threshold\n",
    "\n",
    "# If Mob dist above threshold: Flag as anomaly\n",
    "anomaly_train['Anomaly'] = anomaly_train['Mob dist'] > anomaly_train['Thresh']\n",
    "anomaly_train.index = X_train_PCA.index\n",
    "anomaly = pd.DataFrame()\n",
    "anomaly['Mob dist'] = dist_test\n",
    "anomaly['Thresh'] = threshold\n",
    "\n",
    "# If Mob dist above threshold: Flag as anomaly\n",
    "anomaly['Anomaly'] = anomaly['Mob dist'] > anomaly['Thresh']\n",
    "anomaly.index = X_test_PCA.index\n",
    "anomaly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "anomaly_alldata = pd.concat([anomaly_train, anomaly])\n",
    "anomaly_alldata.to_csv('Anomaly_distance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "anomaly_alldata.plot(logy=True, figsize=(10, 6), ylim=[\n",
    "                     1e-1, 1e3], color=['green', 'red'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart above shows that anomaly can be detected between 2004-02-16 to 2004-02-17 without the risk of missing Bearing 1. That's 2 days before when the breakdown actually occurred, on 2004-02-19."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Approach 2: Autoencoder model for anomaly detection\n",
    "\n",
    "An autoencoder is a type of artificial neural network used to learn efficient data encodings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input.\n",
    "\n",
    "In this case study, the distribution of the reconstruction loss of the autoencoder for training data is plotted to identify where normally reconstruction loss lies and come up with a threshold as the upper control limit. One can also compute the 3 standard deviations from the mean of reconstruction loss to determine an appropriate upper control limit, similar to what have been done in the first modeling approach above.\n",
    "\n",
    "Before building the model, let's import necessary libraries for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x128f406f0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "import copy\n",
    "from numpy.random import seed\n",
    "\n",
    "seed(10)\n",
    "torch.manual_seed(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Define model and train model\n",
    "\n",
    "Now let's define an autoencoder and train the model accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class autoencoder(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super(autoencoder, self).__init__()\n",
    "        self.encoder = nn.Sequential(\n",
    "            nn.Linear(input_dim, 10),\n",
    "            nn.ELU(),\n",
    "            nn.Linear(10, 2),\n",
    "            nn.ELU())\n",
    "        self.decoder = nn.Sequential(\n",
    "            nn.Linear(2, 10),\n",
    "            nn.ELU(),\n",
    "            nn.Linear(10, input_dim))\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ELU (Exponential Linear Unit) activation function is used, which returns x if x > 0 and exp((x)-1) if x < 0). Such choice allows faster convergence of model weigths compared to RELU because of non-null output for x < 0. No difference was observed based on variation of the initialization of weights. This may mean that model training is complete after 100 iterations, and thus results are stable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5 | Train Loss: 0.03071 | Val Loss: 0.03282\n",
      "Epoch: 10 | Train Loss: 0.0265 | Val Loss: 0.0278\n",
      "Epoch: 15 | Train Loss: 0.02258 | Val Loss: 0.0251\n",
      "Epoch: 20 | Train Loss: 0.01794 | Val Loss: 0.02038\n",
      "Epoch: 25 | Train Loss: 0.01574 | Val Loss: 0.01683\n",
      "Epoch: 30 | Train Loss: 0.01381 | Val Loss: 0.01587\n",
      "Epoch: 35 | Train Loss: 0.01174 | Val Loss: 0.01448\n",
      "Epoch: 40 | Train Loss: 0.01057 | Val Loss: 0.01358\n",
      "Epoch: 45 | Train Loss: 0.01025 | Val Loss: 0.01238\n",
      "Epoch: 50 | Train Loss: 0.009848 | Val Loss: 0.01314\n",
      "Epoch: 55 | Train Loss: 0.0101 | Val Loss: 0.01228\n",
      "Epoch: 60 | Train Loss: 0.009985 | Val Loss: 0.01284\n",
      "Epoch: 65 | Train Loss: 0.009837 | Val Loss: 0.01217\n",
      "Epoch: 70 | Train Loss: 0.00961 | Val Loss: 0.0126\n",
      "Epoch: 75 | Train Loss: 0.009716 | Val Loss: 0.01218\n",
      "Epoch: 80 | Train Loss: 0.00953 | Val Loss: 0.01196\n",
      "Epoch: 85 | Train Loss: 0.009706 | Val Loss: 0.01138\n",
      "Epoch: 90 | Train Loss: 0.009746 | Val Loss: 0.01064\n",
      "Epoch: 95 | Train Loss: 0.009624 | Val Loss: 0.01146\n",
      "Epoch: 100 | Train Loss: 0.009591 | Val Loss: 0.01067\n",
      "Training complete in 0m 2s\n",
      "Lowest validation loss: 0.01027 at epoch 92\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LEARNING_RATE = 1e-3\n",
    "NUM_EPOCHS = 100\n",
    "BATCH_SIZE = 10\n",
    "SHOW_PROGRESS = 5\n",
    "device = torch.device(\"cpu\")\n",
    "\n",
    "ae_model = autoencoder(X_train.shape[1]).to(device)\n",
    "optimiser = optim.Adam(ae_model.parameters(), lr=LEARNING_RATE)\n",
    "criterion = torch.nn.MSELoss()\n",
    "\n",
    "# train validation split\n",
    "input_train, input_val, _, _ = train_test_split(\n",
    "    X_train, X_train, test_size=0.2, random_state=10)\n",
    "\n",
    "input_train = torch.from_numpy(input_train.values).float().to(device)\n",
    "input_val = torch.from_numpy(input_val.values).float().to(device)\n",
    "\n",
    "# create torch dataset\n",
    "dataset_train = torch.utils.data.TensorDataset(input_train)\n",
    "dataset_val = torch.utils.data.TensorDataset(input_val)\n",
    "data_generator_train = torch.utils.data.DataLoader(\n",
    "    dataset_train, BATCH_SIZE, shuffle=True, drop_last=True)\n",
    "data_generator_val = torch.utils.data.DataLoader(\n",
    "    dataset_val, BATCH_SIZE, shuffle=True, drop_last=True)\n",
    "\n",
    "# training...\n",
    "\n",
    "epochs = []\n",
    "losses_train = []\n",
    "losses_val = []\n",
    "lowest_loss = 10000\n",
    "\n",
    "checkpoint = time.time()\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    epochs.append(epoch+1)\n",
    "    epoch_losses_train = []\n",
    "\n",
    "    for data_train in data_generator_train:\n",
    "\n",
    "        ae_model.train()\n",
    "        optimiser.zero_grad()\n",
    "\n",
    "        prediction = ae_model(data_train[0])\n",
    "        loss = criterion(prediction, data_train[0])\n",
    "        loss.backward()\n",
    "        optimiser.step()\n",
    "        epoch_losses_train.append(loss.item())\n",
    "\n",
    "    epoch_losses_train = np.average(np.array(epoch_losses_train))\n",
    "    losses_train.append(epoch_losses_train)\n",
    "\n",
    "    epoch_losses_val = []\n",
    "\n",
    "    for data_val in data_generator_val:\n",
    "\n",
    "        ae_model.eval()\n",
    "        with torch.no_grad():\n",
    "            prediction = ae_model(data_val[0])\n",
    "            loss = criterion(prediction, data_val[0])\n",
    "            epoch_losses_val.append(loss.item())\n",
    "\n",
    "    epoch_losses_val = np.average(np.array(epoch_losses_val))\n",
    "    losses_val.append(epoch_losses_val)\n",
    "\n",
    "    # show training progress at pre-defined interval\n",
    "    if epoch % SHOW_PROGRESS == SHOW_PROGRESS-1:\n",
    "        print('Epoch: {} | Train Loss: {:.4} | Val Loss: {:.4}'.format(\n",
    "            epoch+1, epoch_losses_train, epoch_losses_val))\n",
    "\n",
    "    # save best model\n",
    "    if epoch_losses_val < lowest_loss:\n",
    "        lowest_loss = epoch_losses_val\n",
    "        best_epoch = epoch + 1\n",
    "        best_model_wts = copy.deepcopy(ae_model.state_dict())\n",
    "\n",
    "time_elapsed = time.time() - checkpoint\n",
    "print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "    time_elapsed // 60, time_elapsed % 60))\n",
    "\n",
    "print('Lowest validation loss: {:.4} at epoch {}'.format(\n",
    "    lowest_loss, best_epoch))\n",
    "ae_model.load_state_dict(best_model_wts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training results are gathered in a dataframe and plotted against the epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_result_df = pd.DataFrame(list(zip(epochs, losses_train, losses_val)), columns=['Epochs', 'losses_train', 'losses_val'])\n",
    "\n",
    "train_result_melt_df = pd.melt(train_result_df, id_vars=['Epochs'], value_vars=['losses_train', 'losses_val'])\n",
    "\n",
    "# lineplot\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.lineplot(x='Epochs',\n",
    "             y='value',\n",
    "             hue='variable',\n",
    "             data=train_result_melt_df\n",
    "             ).set_title('Training result', fontsize=20)\n",
    "plt.ylim(0, 0.06)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Distribution of loss function in the training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By plotting the distribution of the calculated loss in the training set, one can use this to identify a suitable threshold value for identifying an anomaly. In doing this, one can make sure that this threshold is set above the “noise level”, and that any flagged anomalies should be statistically significant above the noise background.\n",
    "\n",
    "From our result, we can use a threshold of 0.3 for flagging an anomaly. We can then calculate the loss in the test set, to check when the output crosses the anomaly threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ae_model.eval()\n",
    "with torch.no_grad():\n",
    "    X_pred_train = ae_model(torch.from_numpy(X_train.values).float().to(device)).detach().cpu().numpy()\n",
    "\n",
    "X_pred_train = pd.DataFrame(X_pred_train, columns=X_train.columns)\n",
    "X_pred_train.index = X_train.index\n",
    "\n",
    "scored_train = pd.DataFrame(index=X_train.index)\n",
    "scored_train['Loss_mae'] = np.mean(np.abs(X_pred_train-X_train), axis = 1)\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.distplot(scored_train['Loss_mae'],\n",
    "             bins = 10, \n",
    "             kde= True,\n",
    "            color = 'blue').set_title('AutoEncoder reconstruction loss distribution', fontsize=20)\n",
    "plt.xlim([0.0,.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above loss distribution, let us try a threshold of 0.3 for flagging an anomaly. We can then calculate the loss in the test set, to check when the output crosses the anomaly threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loss_mae</th>\n",
       "      <th>Threshold</th>\n",
       "      <th>Anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-02-13 23:52:39</th>\n",
       "      <td>0.125746</td>\n",
       "      <td>0.3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:02:39</th>\n",
       "      <td>0.086947</td>\n",
       "      <td>0.3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:12:39</th>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:22:39</th>\n",
       "      <td>0.064879</td>\n",
       "      <td>0.3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-14 00:32:39</th>\n",
       "      <td>0.121420</td>\n",
       "      <td>0.3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Loss_mae  Threshold  Anomaly\n",
       "2004-02-13 23:52:39  0.125746        0.3    False\n",
       "2004-02-14 00:02:39  0.086947        0.3    False\n",
       "2004-02-14 00:12:39  0.026900        0.3    False\n",
       "2004-02-14 00:22:39  0.064879        0.3    False\n",
       "2004-02-14 00:32:39  0.121420        0.3    False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "THRESHOLD = 0.3\n",
    "\n",
    "ae_model.eval()\n",
    "with torch.no_grad():\n",
    "    X_pred_test = ae_model(torch.from_numpy(X_test.values).float().to(device)).detach().cpu().numpy()\n",
    "    \n",
    "X_pred_test = pd.DataFrame(X_pred_test, columns=X_test.columns)\n",
    "X_pred_test.index = X_test.index\n",
    "\n",
    "scored_test = pd.DataFrame(index=X_test.index)\n",
    "scored_test['Loss_mae'] = np.mean(np.abs(X_pred_test-X_test), axis = 1)\n",
    "scored_test['Threshold'] = THRESHOLD\n",
    "scored_test['Anomaly'] = scored_test['Loss_mae'] > scored_test['Threshold']\n",
    "display(scored_test.head())\n",
    "\n",
    "scored_train['Threshold'] = THRESHOLD\n",
    "scored_train['Threshold'] = THRESHOLD\n",
    "scored_train['Anomaly'] = scored_train['Loss_mae'] > scored_train['Threshold']\n",
    "scored = pd.concat([scored_train, scored_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot AutoEncoder reconstruction loss over time\n",
    "\n",
    "scored.plot(logy=True,  figsize = (10,6), ylim = [1e-2,1e2], color = ['blue','red'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the PCA with Mahalanobis distance method, the chart above shows that the anomaly can be detected on 2004-02-16. One can see that it detects anomaly slightly earlier than the PCA with Mahalanobis distance model. This may due to the choice of the hyperparameter of the model and may also due to Autoencoder doesn't have the assumption that the input data follows Gaussian distribution, which is a constraint of Mahalanobis distance. This requires further investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two modelling methods have been discussed to analyze data collected from various measurements of the sensors as means of machine monitoring condition. Both methods showed similar results where they are able to predict the anomaly prior to the actual failure. The main difference between two approaches lays in the definition of a suitable threshold value for anomalies. Based on the results discussed above, the approach using autoencoder of neural network is slightly superior to the one using a combination of PCA and Mahalanobis distance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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 },
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