Manufacturing AI: Ushering in the Era of Industry 4.0

Equipment Health Management Platform

class PredictiveMaintenanceAI:
    """Predictive Maintenance AI System"""
    
    def analyze_equipment_health(self, sensor_data):
        """Analyze equipment health status"""
        
        # 1. Multi-dimensional data fusion
        features = {
            'vibration': sensor_data['vibration_patterns'],
            'temperature': sensor_data['temp_history'],
            'pressure': sensor_data['pressure_readings'],
            'acoustic': sensor_data['sound_spectrum'],
            'current': sensor_data['power_consumption']
        }
        
        # 2. Anomaly detection
        anomalies = self.detect_anomalies(features)
        
        # 3. Failure prediction
        failure_prediction = self.llm.predict(f"""
        Predict failures based on the following equipment data:
        Vibration patterns: {features['vibration']}
        Temperature trends: {features['temperature']}
        Anomaly indicators: {anomalies}
        
        Analysis:
        1. Failure types and probabilities
        2. Estimated time to failure
        3. Impact scope
        4. Maintenance recommendations
        """)
        
        # 4. Optimize maintenance plan
        maintenance_plan = self.optimize_maintenance(
            failure_prediction,
            production_schedule,
            spare_parts_inventory
        )
        
        return {
            'health_score': self.calculate_health_score(features),
            'predictions': failure_prediction,
            'maintenance_plan': maintenance_plan,
            'cost_saving': self.estimate_cost_saving()
        }