Building_a_Digital_Twin_with_Foundation_Models.ipynb

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      "authorship_tag": "ABX9TyN7mv806QeIvf5H8G6WWPPB",
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      "name": "python"
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/Building_a_Digital_Twin_with_Foundation_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install torch scikit-learn"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Example function to get sensor data from machinery (replace with actual data fetching logic)\n",
        "def get_sensor_data_from_machinery():\n",
        "    return np.random.rand(10, 5)  # Replace with actual sensor data\n",
        "\n",
        "# Define a simple neural network for simulation\n",
        "class TwinModel(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(TwinModel, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_size, hidden_size)\n",
        "        self.fc2 = nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "# Initialize and train the model (example data and training process)\n",
        "def train_model():\n",
        "    input_size = 5  # Number of sensor features\n",
        "    hidden_size = 10\n",
        "    output_size = 1  # Maintenance suggestion (binary: 0 or 1)\n",
        "\n",
        "    model = TwinModel(input_size, hidden_size, output_size)\n",
        "    criterion = nn.BCEWithLogitsLoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "    # Example training data (replace with actual sensor data and labels)\n",
        "    X_train = np.random.rand(100, input_size)\n",
        "    y_train = np.random.randint(0, 2, (100, 1)).astype(np.float32)\n",
        "\n",
        "    # Standardize the data\n",
        "    scaler = StandardScaler()\n",
        "    X_train = scaler.fit_transform(X_train)\n",
        "\n",
        "    # Convert data to PyTorch tensors\n",
        "    X_train = torch.tensor(X_train, dtype=torch.float32)\n",
        "    y_train = torch.tensor(y_train, dtype=torch.float32)\n",
        "\n",
        "    # Train the model\n",
        "    model.train()\n",
        "    for epoch in range(100):  # Example: 100 epochs\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(X_train)\n",
        "        loss = criterion(outputs, y_train)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(\"Training complete!\")\n",
        "    return model, scaler\n",
        "\n",
        "# Simulate and make predictions\n",
        "model, scaler = train_model()\n",
        "sensor_data = get_sensor_data_from_machinery()\n",
        "sensor_data = scaler.transform(sensor_data)  # Standardize the sensor data\n",
        "sensor_data = torch.tensor(sensor_data, dtype=torch.float32)\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    predictions = model(sensor_data)\n",
        "    maintenance_suggestions = torch.sigmoid(predictions).round().numpy()\n",
        "\n",
        "print(\"Maintenance suggestions:\", maintenance_suggestions)"
      ],
      "metadata": {
        "id": "GJ1mRJzrorgt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
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