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**.

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## 📌 **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**

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## 📁 1. **Data Preprocessing**

“`python
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

“`python
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.

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## 🛠️ 2. **Feature Engineering**

“`python
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.

“`python
def prep_data(start, end):
“`

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

“`python
load_all()
“`

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

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## 🧮 3. **Metric Definition**

“`python
def matthews_correlation(…)
“`

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

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## 🧠 4. **Custom Layers**

### 🧲 Attention Layer

“`python
class Attention(Layer):
“`

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

### 🎯 Capsule Layer

“`python
class Capsule(Layer):
“`

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

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## 🏗️ 5. **Model Architecture**

“`python
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**

“`python
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.

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## 🎯 7. **Threshold Optimization**

“`python
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**

“`python
meta_test = pd.read_csv(‘../input/metadata_test.csv’)
“`

* Test metadata is read and transformed using same pipeline.

“`python
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.

“`python
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.

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## 📤 9. **Submission**

“`python
submission[‘target’] = preds_test
submission.to_csv(‘submission.csv’, index=False)
“`

* Saves the final predicted labels into submission format.

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## 📌 Summary Diagram of Model

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

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## ✅ 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.