Create nnpm.py

models/nnpm/nnpm.py

@@ -0,0 +1,32 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class NNPM(nn.Module):
+    def __init__(self):
+        super(NNPM, self).__init__()
+        self.fc1 = nn.Linear(128, 256)  # Input layer (128) -> Hidden layer (256)
+        self.fc2 = nn.Linear(256, 128)  # Hidden layer (256) -> Output layer (128)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))  # Activation function for hidden layer
+        x = self.fc2(x)
+        return x
+
+model = NNPM()
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Train the model
+for epoch in range(100):
+    optimizer.zero_grad()
+    outputs = model(inputs)
+    loss = criterion(outputs, labels)
+    loss.backward()
+    optimizer.step()
+    print(f'Epoch {epoch+1}, Loss: {loss.item()}')
+
+# Use the trained model for predictive maintenance
+def predict_maintenance(inputs):
+    outputs = model(inputs)
+    return outputs