Transmission Line Fault Detection using Deep Learning

Description

   Here’s a structured flow and explanation of the entire code you provided. This is a solution pipeline for time-series classification, specifically applied to power grid fault detection using a neural network combining **LSTM**, **Attention**, and **Capsule layers**.

📌 Overview

The pipeline consists of:

1. Data Preprocessing
2. Feature Engineering
3. Model Design (LSTM + Attention + Capsule)
4. Cross-Validation Training
5. Threshold Optimization
6. Test Prediction & Submission

📁 1. Data Preprocessing

df_train = pd.read_csv('../input/metadata_train.csv')

• Reads metadata (labels and signal mapping) and sets a multi-index (id_measurement, phase).

Min-max Transformation

def min_max_transf(ts, min_data, max_data, range_needed=(-1,1)):

• Scales raw signal values to a specified range (usually [-1, 1]) using linear scaling.

🛠️ 2. Feature Engineering

def transform_ts(ts, n_dim=160, min_max=(-1,1)):

• Splits the time series into buckets.

• For each bucket, it computes:

  • Mean, Std, Std bounds, Percentiles, Relative Percentiles
• Output: Feature vector of shape [n_dim, 17] for each signal.

def prep_data(start, end):

• Loads signal data from Parquet files.
• Transforms all 3 phases of each measurement into features.
• Concatenates features across phases.

load_all()

• Loads the entire training dataset in two parts to manage memory.

🧮 3. Metric Definition

def matthews_correlation(...)

• Implements Matthews Correlation Coefficient (MCC) as a metric.
• MCC is ideal for binary imbalanced classification.

🧠 4. Custom Layers

🧲 Attention Layer

class Attention(Layer):

• Learns attention weights to focus on important time steps.
• Outputs a context vector by weighted sum over time.

🎯 Capsule Layer

class Capsule(Layer):

• Captures spatial relationships via dynamic routing.
• Applies squashing activation to constrain output vector length.

🏗️ 5. Model Architecture

def model_lstm(input_shape):

Model Structure:

• Input: Shape (160, 51) — 3 phases × 17 features per phase

• Layers:

  • 2 BiLSTM (CuDNNLSTM) layers
  • Attention Layer
  • Capsule Layer (on BiLSTM output)
  • CNN + Attention + Capsule (parallel path)
  • Concatenate → Dense → Output (sigmoid)
• Compiled with binary crossentropy loss and MCC metric

🔁 6. Cross-Validation

splits = list(StratifiedKFold(n_splits=5)...)

• Applies Stratified 5-fold Cross-Validation.

• For each fold:

  • Clears Keras session.
  • Trains the model.
  • Saves best weights using ModelCheckpoint.
  • Stores validation predictions and scores.

🎯 7. Threshold Optimization

def threshold_search(y_true, y_proba):

• Searches for best threshold for MCC.
• MCC is computed for thresholds in [0.00, 0.99] using matthews_correlation.

🧪 8. Test Data Prediction

meta_test = pd.read_csv('../input/metadata_test.csv')

• Test metadata is read and transformed using same pipeline.

for start, end in start_end:

• Loads test data in chunks for memory efficiency.
• Each test instance is represented by 3 phase signals → features → concatenated.

preds_test = []

• Uses 5 trained models (from cross-validation).
• Averages predictions and applies optimal threshold.
• Maps the same scalar prediction to all 3 signals in a measurement.

📤 9. Submission

submission['target'] = preds_test
submission.to_csv('submission.csv', index=False)

• Saves the final predicted labels into submission format.

📌 Summary Diagram of Model

                     Input (160 x 51)
                          │
             ┌────────────┼────────────┐
             │                           │
    [BiLSTM → BiLSTM]         [1D Conv]
             │                           │
    [Attention + Dropout]   [Attention] [Capsule + Flatten]
       │         │               │          │
   [Capsule + Flatten]           └────┬─────┘
             │                        │
             └─────[Concatenate]─────┘
                         │
                    [Dense + Dropout]
                         │
                       Sigmoid

✅ Key Points

• Efficient time-series transformation using statistical features.
• Hybrid deep model combining sequence modeling (LSTM), spatial focus (Attention), and hierarchical encoding (Capsules).
• MCC used as evaluation metric due to dataset imbalance.
• Cross-validation ensures robustness and threshold tuning optimizes final performance.

abhishek gupta

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