Predictive Maintenance

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Overview

This project aims to develop a predictive maintenance system for electric motors using machine learning algorithms and sensor data. The system will analyze various parameters to detect potential malfunctions before they occur, allowing for proactive maintenance and reducing downtime.

Key Features

  1. Vibration Analysis: Detect unusual vibrations that may indicate bearing wear.
  2. Temperature Monitoring: Track heat evolution in the motor to identify potential issues.
  3. Current Analysis: Monitor electric current and its correlation with heat development.
  4. Speed and Acceleration Tracking: Utilize encoder information in combination with other data for comprehensive analysis.

Technical Implementation

  • Data Collection: Gather sensor data from electric motors, including vibration, temperature, current, and encoder readings.
  • Data Processing: Clean and preprocess the collected data for analysis.
  • Feature Engineering: Extract relevant features from the processed data.
  • Model Development: Create and train machine learning models to detect anomalies and predict potential failures.
  • Real-time Monitoring: Implement a system for continuous monitoring and analysis of motor performance.
  • Cloud Integration: Report detected malfunctions and predictions to a cloud-based AI Supported Control Network.

Expected Outcomes

  • Early detection of potential motor failures
  • Reduced downtime and maintenance costs
  • Improved motor efficiency and lifespan
  • Enhanced understanding of motor performance under various conditions

Future Enhancements

  • Integration with additional sensor types for more comprehensive analysis
  • Development of a user-friendly dashboard for monitoring and reporting
  • Implementation of advanced machine learning techniques, such as deep learning, for improved prediction accuracy

Example Python code for vibration analysis

Vibration Analysis
<script>
import numpy as np
import pandas as pd
from scipy.fft import fft
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import IsolationForest
import matplotlib.pyplot as plt

# Load and preprocess data
def load_data(file_path):
    df = pd.read_csv(file_path)
    return df

# Feature extraction from vibration data
def extract_features(vibration_data):
    # Time-domain features
    mean = np.mean(vibration_data)
    std = np.std(vibration_data)
    rms = np.sqrt(np.mean(vibration_data**2))
    
    # Frequency-domain features
    fft_values = fft(vibration_data)
    fft_freq = np.fft.fftfreq(len(vibration_data))
    dominant_freq = fft_freq[np.argmax(np.abs(fft_values))]
    
    return pd.DataFrame({
        'mean': [mean],
        'std': [std],
        'rms': [rms],
        'dominant_freq': [dominant_freq]
    })

# Train anomaly detection model
def train_model(X_train):
    model = IsolationForest(contamination=0.1, random_state=42)
    model.fit(X_train)
    return model

# Predict anomalies
def predict_anomalies(model, X):
    predictions = model.predict(X)
    return predictions

# Visualize results
def visualize_results(vibration_data, predictions):
    plt.figure(figsize=(12, 6))
    plt.plot(vibration_data, label='Vibration Data')
    anomalies = np.where(predictions == -1)[0]
    plt.scatter(anomalies, vibration_data[anomalies], color='red', label='Anomalies')
    plt.title('Vibration Data with Detected Anomalies')
    plt.xlabel('Time')
    plt.ylabel('Vibration Amplitude')
    plt.legend()
    plt.show()

# Main function
def main():
    # Load data (replace with your actual data file)
    df = load_data('vibration_data.csv')
    
    # Extract features
    features = df['vibration'].apply(extract_features)
    X = pd.concat(features.to_list(), ignore_index=True)
    
    # Split data
    X_train, X_test = train_test_split(X, test_size=0.2, random_state=42)
    
    # Scale features
    scaler = StandardScaler()
    X_train_scaled = scaler.fit_transform(X_train)
    X_test_scaled = scaler.transform(X_test)
    
    # Train model
    model = train_model(X_train_scaled)
    
    # Predict anomalies
    predictions = predict_anomalies(model, X_test_scaled)
    
    # Visualize results
    visualize_results(df['vibration'].iloc[X_test.index], predictions)

if __name__ == "__main__":
    main()
</script>
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