Skill: Calculate Machinery Maintenance Schedule

Domain

machinery_equipment

Description

Predicts optimal maintenance intervals using equipment telemetry, failure history, and reliability models to minimize downtime and maintenance costs.

Tags

machinery, predictive-maintenance, reliability, equipment-management, industrial

Use Cases

• •Preventive maintenance scheduling
• •Spare parts planning
• •Equipment lifecycle optimization
• •Downtime prediction

Proprietary Business Rules

Rule 1: Condition-Based Triggers

Maintenance triggered by sensor threshold exceedances with equipment-specific parameters.

Rule 2: Failure Probability Modeling

Weibull distribution modeling for remaining useful life estimation.

Rule 3: Maintenance Optimization

Cost-benefit optimization between preventive and corrective maintenance.

Rule 4: Parts Lead Time Integration

Maintenance scheduling considers spare parts availability.

Input Parameters

• •equipment_id (string): Equipment identifier
• •equipment_type (string): Equipment classification
• •operating_hours (float): Current operating hours
• •sensor_data (dict): Current sensor readings
• •maintenance_history (list): Past maintenance records
• •parts_inventory (dict): Available spare parts

Output

• •next_maintenance (dict): Recommended next maintenance
• •failure_probability (float): Probability of failure before maintenance
• •remaining_useful_life (dict): RUL estimate
• •parts_needed (list): Required spare parts
• •cost_analysis (dict): Maintenance cost comparison

Implementation

The maintenance logic is implemented in maintenance_scheduler.py and references parameters from CSV files:

• •equipment_types.csv - Reference data
• •parameters.csv - Reference data.

Usage Example

from maintenance_scheduler import calculate_maintenance

result = calculate_maintenance(
    equipment_id="PUMP-001",
    equipment_type="centrifugal_pump",
    operating_hours=8500,
    sensor_data={"vibration_mm_s": 4.2, "temperature_c": 65, "pressure_bar": 8.5},
    maintenance_history=[{"date": "2025-06-15", "type": "bearing_replacement"}],
    parts_inventory={"bearings": 2, "seals": 5}
)

print(f"Next Maintenance: {result['next_maintenance']}")

Test Execution

from maintenance_scheduler import calculate_maintenance

result = calculate_maintenance(
    equipment_id=input_data.get('equipment_id'),
    equipment_type=input_data.get('equipment_type'),
    operating_hours=input_data.get('operating_hours', 0),
    sensor_data=input_data.get('sensor_data', {}),
    maintenance_history=input_data.get('maintenance_history', []),
    parts_inventory=input_data.get('parts_inventory', {})
)