Aspirating Smoke Detector Dust Rejection via Differential Airflow
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Solution Overview
Problem
Existing smoke detection systems, particularly aspirating smoke detectors, face challenges in distinguishing between dust and smoke particles, leading to false alarms due to the merging of spikes from multiple dust particles at high dust levels, which existing methods fail to effectively address.
Innovation Solution
A particle detection system that differentially detects particles by splitting air samples into two subsets, one with dust reduction means and another without, allowing for a comparative analysis to determine the presence of smoke by distinguishing between particle size distributions, using methods like electrostatic precipitation, mechanical filters, or inertial separation, and adjusting alarm logic based on signal comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dust discrimination is implemented by spike detection and removal in light-scatter-based smoke detection systems, then dust detection capability is improved, but at high dust levels the spikes due to dust merge and the method becomes ineffective
Solution Approach 1:
The air sample flow is divided into two separate air samples: a first air sample that passes through dust reduction means and a second air sample that does not pass through dust reduction means. This segmentation allows the system to compare particle signals from both samples to distinguish dust from smoke, solving the problem of ineffective spike detection at high dust levels by providing a reference sample that contains both dust and smoke particles.
Solution Approach 2:
The dust reduction means acts as an intermediary component that selectively removes dust particles from the first air sample before detection. This intermediary element enables differential measurement between the filtered first sample and the unfiltered second sample, allowing the system to identify and reject dust-related signals while maintaining smoke detection capability.
2Reliability
If multiple light wavelengths, multiple polarisations, or mechanical filtering are used to reduce dust signal, then dust rejection is improved, but device complexity increases
Solution Approach 1:
Rather than using complex multi-wavelength or multi-polarisation optical systems, the invention segments the air sample flow into two paths: one with dust reduction and one without. This simpler segmentation approach achieves dust rejection through comparative measurement, avoiding the need for complex optical components while maintaining effective dust discrimination.
Solution Approach 2:
The system changes the particle concentration parameter by creating two different air samples with different dust content (one filtered, one unfiltered). This parameter change approach allows dust rejection through signal comparison rather than through complex optical parameter adjustments, simplifying the overall system design.
3Quantity of substance
If dust reduction means such as electrostatic precipitation or mechanical filters are applied to the air sample, then dust particle removal is improved, but some dust particles may still pass through due to statistical nature of filtration
Solution Approach 1:
The system uses feedback from comparing the signals of the first air sample (with dust reduction) and the second air sample (without dust reduction) to determine whether detected particles are dust or smoke. This feedback mechanism allows the system to account for incomplete dust removal and still achieve reliable dust discrimination by identifying the characteristic signal pattern of dust particles that pass through the reduction means.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively reduces false smoke alarms by accurately attributing particle intensity to dust, enabling the system to modify its behavior and differentiate between dust and smoke, even at high dust concentrations, thereby improving the reliability of smoke detection.
Implementation Method 1
using methods like electrostatic precipitation, mechanical filters, or inertial separation
Implementation Method 2
using methods like electrostatic precipitation, mechanical filters, or inertial separation
Implementation Method 3
using methods like electrostatic precipitation, mechanical filters, or inertial separation
Implementation Method 4
In light-scatter-based smoke detection systems, dust discrimination or rejection may be implemented
Data Source
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AI summary
A system and method of reducing the incidence of false alarms attributable to dust in smoke detection apparatus. The method includes obtaining at least two sample air flows, subjecting a first airflow to particle reduction and measuring the level of particles in the first airflow and generating a first signal indicative of the intensity. The method also includes measuring the level of particles in the second airflow and generating a second signal indicative of the intensity. The first signal is compared to a predetermined alarm level and, if the alarm level is achieved, the first and second signals are subsequently compared and an output signal is generated based on the relative difference between the first and second signals.