Multi-Criteria State Machines for Hazard Detection
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Solution Overview
Problem
Conventional hazard detection systems are limited in their ability to accurately assess environmental conditions, often resulting in false alarms or failure to alert occupants to potentially hazardous situations due to their binary operation based on single-sensor readings and simplistic thresholds.
Innovation Solution
The implementation of multi-criteria state machines that manage alarming and pre-alarming states by integrating data from multiple sensors, using filtered sensor values and transition conditions to transition between states, allowing for advanced warning and prevention of unnecessary alarms through voice messages and adjustable thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional binary threshold-based alarm systems are used, then the system operation is simple, but the measurement precision and reliability of hazard detection deteriorate due to false alarms and missed detections
Solution Approach 1:
The alarm system is segmented into multiple independent sensor state machines (smoke, heat, CO, humidity) and system state machines that each handle specific detection criteria. This segmentation allows complex multi-criteria evaluation without requiring a single monolithic complex system, improving reliability through specialized detection while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system transitions from binary threshold detection to multi-dimensional state machine evaluation. Instead of simple on/off thresholds, the system evaluates multiple sensor readings across different dimensions (smoke density, temperature rate of change, CO concentration, humidity levels) and transitions through multiple alarm states (monitoring, pre-alarm, alarm, hush) based on combined criteria, thereby improving detection reliability.
2Measurement precision
If single-sensor binary thresholds are used, then the device complexity is low, but the measurement precision deteriorates leading to false alarms or missed hazards
Solution Approach 1:
Multiple sensor readings (smoke, heat, CO, humidity) are merged within the state machine evaluation framework. The system combines data from different sensor types to make alarm decisions, improving measurement precision by considering multiple hazard indicators simultaneously rather than relying on a single sensor threshold.
Solution Approach 2:
The state machine framework serves multiple functions: it monitors individual sensor readings, evaluates combined criteria, determines alarm states, and manages pre-alarm and hush conditions. This universal evaluation mechanism improves detection precision by handling diverse sensor inputs through a unified multi-functional system rather than separate single-purpose components.
3Reliability
If pre-alarm states with multiple criteria are implemented, then the reliability and advanced warning capability improve, but the device complexity increases
Solution Approach 1:
The system implements pre-alarm states that trigger before full alarm conditions are met. When sensor readings approach hazardous thresholds but do not yet satisfy complete alarm criteria, the system activates pre-alarm warnings to provide advance notice. This preliminary action improves reliability by enabling early intervention while the state machine framework manages the added complexity through structured transition conditions.
Solution Approach 2:
The alarm system dynamically transitions between multiple states (monitoring, pre-alarm, alarm, hush) based on real-time sensor evaluations. The state machines adapt their behavior according to current hazard levels, providing dynamic response rather than static threshold triggering. This dynamic approach improves reliability by matching system response to actual hazard severity while the modular state machine structure contains the complexity.
Data Source
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AI summary
Systems and methods for using state machines to manage alarming states and pre-alarming states of a hazard detection system are described herein. The state machines can include one or more sensor state machines that can control the alarming states and one or more system state machines that can control the pre-alarming states. Each state machine can transition among any one of its states based on raw sensor data values, filtered sensor data values, and transition conditions. Filters may be used to transform raw sensor values into filtered values that can be used by one or more state machines. Such filters may improve accuracy of data interpretation by filtering out readings that may distort data interpretation or cause false positives. For example, smoke sensor readings may be filtered by a smoke alarm filter to mitigate presence of steam.