Implementation Guide: Building AI That Predicts Equipment Failures

# Equipment Health Monitoring System
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import asyncio
import aiohttp

class EquipmentHealthMonitor:
    def __init__(self, equipment_id, sensor_config):
        self.equipment_id = equipment_id
        self.sensors = sensor_config
        self.health_model = None
        self.baseline_data = None
        
    async def collect_sensor_data(self):
        """Collect real-time data from all sensors"""
        sensor_data = {}
        
        for sensor_type, sensor_config in self.sensors.items():
            # Simulated sensor reading - replace with actual sensor APIs
            if sensor_type == 'vibration':
                # Accelerometer data collection
                sensor_data['vibration_x'] = await self.read_sensor(sensor_config['x_axis'])
                sensor_data['vibration_y'] = await self.read_sensor(sensor_config['y_axis'])
                sensor_data['vibration_z'] = await self.read_sensor(sensor_config['z_axis'])
                
            elif sensor_type == 'thermal':
                # Thermal camera or temperature sensors
                sensor_data['temperature'] = await self.read_sensor(sensor_config['temp_probe'])
                sensor_data['hot_spots'] = await self.detect_thermal_anomalies(sensor_config)
                
            elif sensor_type == 'acoustic':
                # Sound analysis for bearing wear, electrical arcing
                audio_data = await self.read_sensor(sensor_config['microphone'])
                sensor_data['acoustic_signature'] = self.analyze_acoustic_patterns(audio_data)
                
            elif sensor_type == 'electrical':
                # Current, voltage, power factor measurements
                sensor_data['current'] = await self.read_sensor(sensor_config['current_transformer'])
                sensor_data['voltage'] = await self.read_sensor(sensor_config['voltage_probe'])
                sensor_data['power_factor'] = await self.calculate_power_factor()
                
        sensor_data['timestamp'] = pd.Timestamp.now()
        return sensor_data
    
    async def read_sensor(self, sensor_endpoint):
        """Generic sensor reading function"""
        # Replace with actual sensor communication
        # This could be Modbus, OPC-UA, HTTP API, etc.
        async with aiohttp.ClientSession() as session:
            async with session.get(sensor_endpoint) as response:
                return await response.json()
    
    def analyze_acoustic_patterns(self, audio_data):
        """Extract acoustic signatures using FFT analysis"""
        # Fast Fourier Transform to identify frequency patterns
        frequencies = np.fft.fft(audio_data)
        
        # Look for specific frequency bands that indicate problems
        bearing_frequencies = self.extract_bearing_frequencies(frequencies)
        electrical_frequencies = self.extract_electrical_frequencies(frequencies)
        
        return {
            'bearing_signature': bearing_frequencies,
            'electrical_signature': electrical_frequencies,
            'overall_noise_level': np.mean(np.abs(frequencies))
        }
    
    def train_baseline_model(self, historical_data):
        """Train the AI to understand normal equipment behavior"""
        # This is where the AI learns what "healthy" looks like
        scaler = StandardScaler()
        normalized_data = scaler.fit_transform(historical_data)
        
        # Use Isolation Forest to learn normal patterns
        self.health_model = IsolationForest(
            contamination=0.1,  # Expect 10% of data to be anomalous
            random_state=42,
            n_estimators=200
        )
        
        self.health_model.fit(normalized_data)
        self.baseline_data = scaler
        
        print(f"AI learned normal patterns from {len(historical_data)} data points")
        
    def predict_equipment_health(self, current_data):
        """AI predicts if current behavior indicates developing problems"""
        if self.health_model is None:
            raise ValueError("AI hasn't been trained yet - call train_baseline_model first")
            
        # Normalize current data using baseline patterns
        normalized_current = self.baseline_data.transform([current_data])
        
        # Get anomaly score (-1 = anomaly, 1 = normal)
        anomaly_score = self.health_model.decision_function(normalized_current)[0]
        is_anomaly = self.health_model.predict(normalized_current)[0] == -1
        
        # Convert to health percentage (0-100%)
        health_percentage = max(0, min(100, (anomaly_score + 0.5) * 100))
        
        return {
            'health_percentage': health_percentage,
            'is_anomalous': is_anomaly,
            'anomaly_score': anomaly_score,
            'confidence': abs(anomaly_score) * 100
        }

# Advanced Predictive Models
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

class AdvancedFailurePrediction:
    def __init__(self):
        self.failure_models = {}
        self.time_series_model = None
        
    def create_failure_specific_models(self, training_data):
        """Train AI to recognize specific failure patterns"""
        
        # Different AI models for different failure types
        failure_types = ['bearing_wear', 'electrical_fault', 'thermal_degradation', 
                        'alignment_issues', 'lubrication_problems']
        
        for failure_type in failure_types:
            print(f"Training AI to recognize {failure_type}...")
            
            # Prepare data for this specific failure type
            X, y = self.prepare_failure_data(training_data, failure_type)
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
            
            # Random Forest for interpretable predictions
            model = RandomForestClassifier(
                n_estimators=200,
                max_depth=15,
                min_samples_split=5,
                random_state=42
            )
            
            model.fit(X_train, y_train)
            accuracy = model.score(X_test, y_test)
            
            self.failure_models[failure_type] = model
            print(f"{failure_type} prediction accuracy: {accuracy:.2%}")
            
            # Show what the AI learned
            feature_importance = model.feature_importances_
            feature_names = X.columns
            
            print(f"Top 3 patterns AI uses to predict {failure_type}:")
            for i, importance in enumerate(feature_importance.argsort()[-3:][::-1]):
                print(f"  - {feature_names[importance]}: {feature_importance[importance]:.3f}")
    
    def create_time_series_model(self, time_series_data):
        """LSTM model for time-based failure prediction"""
        
        # Prepare sequences (use past 24 hours to predict next 7 days)
        sequence_length = 24 * 4  # 15-minute intervals for 24 hours
        prediction_horizon = 7 * 24 * 4  # 7 days ahead
        
        X, y = self.create_sequences(time_series_data, sequence_length, prediction_horizon)
        
        # Build LSTM neural network
        model = Sequential([
            LSTM(128, return_sequences=True, input_shape=(sequence_length, X.shape[2])),
            Dropout(0.2),
            LSTM(64, return_sequences=True),
            Dropout(0.2),
            LSTM(32),
            Dropout(0.2),
            Dense(16, activation='relu'),
            Dense(1, activation='sigmoid')  # Probability of failure
        ])
        
        model.compile(
            optimizer='adam',
            loss='binary_crossentropy',
            metrics=['accuracy', 'precision', 'recall']
        )
        
        # Train the time series model
        history = model.fit(
            X, y,
            epochs=50,
            batch_size=32,
            validation_split=0.2,
            verbose=1
        )
        
        self.time_series_model = model
        return history
    
    def predict_failure_probability(self, current_data, equipment_history):
        """Combine all AI models for comprehensive failure prediction"""
        
        predictions = {}
        
        # Get predictions from each failure-specific model
        for failure_type, model in self.failure_models.items():
            prob = model.predict_proba([current_data])[0][1]  # Probability of failure
            predictions[failure_type] = {
                'probability': prob,
                'risk_level': 'HIGH' if prob > 0.7 else 'MEDIUM' if prob > 0.4 else 'LOW',
                'days_to_failure': self.estimate_time_to_failure(prob, failure_type)
            }
        
        # Get time series prediction
        if self.time_series_model and len(equipment_history) >= 96:  # Need 24 hours of data
            sequence = equipment_history[-96:].values.reshape(1, 96, -1)
            time_series_prob = self.time_series_model.predict(sequence)[0][0]
            
            predictions['time_series'] = {
                'probability': time_series_prob,
                'trend': 'INCREASING' if time_series_prob > 0.5 else 'STABLE'
            }
        
        return predictions
    
    def estimate_time_to_failure(self, probability, failure_type):
        """Estimate days until failure based on probability and failure type"""
        
        # Different failure types progress at different rates
        progression_rates = {
            'bearing_wear': 30,      # Bearings typically fail over weeks
            'electrical_fault': 7,   # Electrical issues can be sudden
            'thermal_degradation': 45, # Thermal damage builds slowly
            'alignment_issues': 60,   # Alignment problems develop gradually
            'lubrication_problems': 14 # Lubrication issues progress quickly
        }
        
        base_days = progression_rates.get(failure_type, 30)
        
        # Higher probability = less time until failure
        estimated_days = base_days * (1 - probability)
        
        return max(1, int(estimated_days))

# Enterprise Integration
class MaintenanceWorkflowIntegration:
    def __init__(self, cmms_api, erp_api, notification_service):
        self.cmms = cmms_api
        self.erp = erp_api
        self.notifications = notification_service
        
    async def process_ai_prediction(self, equipment_id, prediction_results):
        """Automatically create maintenance workflows from AI predictions"""
        
        for failure_type, prediction in prediction_results.items():
            if prediction['risk_level'] == 'HIGH':
                
                # Create maintenance work order
                work_order = await self.cmms.create_work_order({
                    'equipment_id': equipment_id,
                    'predicted_failure': failure_type,
                    'priority': 'URGENT',
                    'estimated_completion_date': prediction['days_to_failure'],
                    'description': f"AI predicted {failure_type} with {prediction['probability']:.1%} confidence",
                    'recommended_actions': self.get_recommended_actions(failure_type)
                })
                
                # Check parts availability
                required_parts = self.get_required_parts(failure_type)
                parts_status = await self.erp.check_inventory(required_parts)
                
                # Send notifications
                if any(part['quantity'] < part['required'] for part in parts_status):
                    await self.notifications.send_urgent_alert(
                        f"Parts shortage detected for predicted {failure_type} on {equipment_id}"
                    )
                
                print(f"Created work order {work_order['id']} for predicted {failure_type}")

# Digital Twin Implementation
class EquipmentDigitalTwin:
    def __init__(self, equipment_specs, physics_model):
        self.physical_properties = equipment_specs
        self.physics_model = physics_model
        self.virtual_state = {}
        
    def simulate_equipment_behavior(self, operating_conditions):
        """Simulate how equipment responds to different conditions"""
        
        # Physics-based simulation
        stress_levels = self.physics_model.calculate_stress(
            load=operating_conditions['load'],
            temperature=operating_conditions['temperature'],
            speed=operating_conditions['speed']
        )
        
        # Predict wear progression
        wear_rate = self.calculate_wear_progression(stress_levels)
        
        # Virtual equipment aging
        self.virtual_state['bearing_wear'] += wear_rate['bearing']
        self.virtual_state['insulation_degradation'] += wear_rate['electrical']
        
        return {
            'predicted_health': 100 - max(self.virtual_state.values()) * 100,
            'stress_hotspots': stress_levels,
            'remaining_life_estimate': self.estimate_remaining_life()
        }
    
    def test_maintenance_strategies(self, strategy_list):
        """Test different maintenance approaches virtually"""
        
        results = {}
        
        for strategy in strategy_list:
            # Create virtual copy for testing
            test_twin = self.create_copy()
            
            # Simulate strategy over time
            costs, downtime, reliability = test_twin.simulate_strategy(strategy)
            
            results[strategy['name']] = {
                'total_cost': costs,
                'downtime_hours': downtime,
                'reliability_score': reliability,
                'roi': self.calculate_roi(costs, downtime)
            }
        
        return results