safety-interlocks

•

9382423 months ago

Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.

Quick Install

npxskills add benchflow-ai/skillsbench--skill safety-interlocks

Instructions

Safety Interlocks for Control Systems

Overview

Safety interlocks are protective mechanisms that prevent equipment damage and ensure safe operation. In control systems, the primary risks are output saturation and exceeding safe operating limits.

Implementation Pattern

Always check safety conditions BEFORE applying control outputs:

def apply_safety_limits(measurement, command, max_limit, min_limit, max_output, min_output):
    """
    Apply safety checks and return safe command.

    Args:
        measurement: Current sensor reading
        command: Requested control output
        max_limit: Maximum safe measurement value
        min_limit: Minimum safe measurement value
        max_output: Maximum output command
        min_output: Minimum output command

    Returns:
        tuple: (safe_command, safety_triggered)
    """
    safety_triggered = False

    # Check for over-limit - HIGHEST PRIORITY
    if measurement >= max_limit:
        command = min_output  # Emergency cutoff
        safety_triggered = True

    # Clamp output to valid range
    command = max(min_output, min(max_output, command))

    return command, safety_triggered

Integration with Control Loop

class SafeController:
    def __init__(self, controller, max_limit, min_output=0.0, max_output=100.0):
        self.controller = controller
        self.max_limit = max_limit
        self.min_output = min_output
        self.max_output = max_output
        self.safety_events = []

    def compute(self, measurement, dt):
        """Compute safe control output."""
        # Check safety FIRST
        if measurement >= self.max_limit:
            self.safety_events.append({
                "measurement": measurement,
                "action": "emergency_cutoff"
            })
            return self.min_output

        # Normal control
        output = self.controller.compute(measurement, dt)

        # Clamp to valid range
        return max(self.min_output, min(self.max_output, output))

Safety During Open-Loop Testing

During calibration/excitation, safety is especially important because there's no feedback control:

def run_test_with_safety(system, input_value, duration, dt, max_limit):
    """Run open-loop test while monitoring safety limits."""
    data = []
    current_input = input_value

    for step in range(int(duration / dt)):
        result = system.step(current_input)
        data.append(result)

        # Safety check
        if result["output"] >= max_limit:
            current_input = 0.0  # Cut input

    return data

Logging Safety Events

Always log safety events for analysis:

safety_log = {
    "limit": max_limit,
    "events": []
}

if measurement >= max_limit:
    safety_log["events"].append({
        "time": current_time,
        "measurement": measurement,
        "command_before": command,
        "command_after": 0.0,
        "event_type": "limit_exceeded"
    })

Pre-Control Checklist

Before starting any control operation:

Verify sensor reading is reasonable
- Not NaN or infinite
- Within physical bounds
Check initial conditions
- Measurement should be at expected starting point
- Output should start at safe value
Confirm safety limits are configured
- Maximum limit threshold set
- Output clamping enabled

def pre_control_checks(measurement, config):
    """Run pre-control safety verification."""
    assert not np.isnan(measurement), "Measurement is NaN"
    assert config.get("max_limit") is not None, "Safety limit not configured"
    return True

Best Practices

Defense in depth: Multiple layers of protection
Fail safe: When in doubt, reduce output
Log everything: Record all safety events
Never bypass: Safety code should not be conditionally disabled
Test safety: Verify interlocks work before normal operation

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