The air quality inside protected areas is always changing. Various types of aerosols are present in the air, and factory emissions, vehicle exhaust, wind-blown dust, agricultural activities, and even smoggy weather can cause changes in the particulate concentration, or air quality, in the air and eventually enter buildings and affect indoor air quality. Similarly, the operation of indoor production line machinery and equipment, human activities, air conditioning and ventilation systems can further deteriorate or improve indoor air quality. These various airborne particles, whether they enter from the outside or are generated indoors, become the background smoke level variation that is normally detected by smoke detectors.
Problems with absolute scale system alarm levels
The graph below shows a typical trend of the background smoke value, which is high and low at different times of the day. For an absolute scale system, the alarm threshold is a fixed value, so for a system with an alarm threshold of 0.05%/m, a smoke background value of 0.003%/m would require a smoke concentration of 0.047%/m to generate an alarm in the event of a fire. When the background value is 0.048%/m, a fire would only require 0.002%/m to generate an alarm. Therefore, for an absolute scale system, the ability to detect a fire is constantly fluctuating due to fluctuations in the background smoke value. Absolute scale systems are less sensitive in relatively clean air and require more smoke to detect a fire, but are too sensitive in dirty air and are susceptible to false alarms due to fluctuations in the background smoke value.
SSL - Smart Smoke Level Algorithm
False alarms are always a concern for ASD systems because of the high sensitivity. Unless ASD is used in cleanrooms or IDC, which air is well controlled, the variation of smoke background level can sometimes trigger unwanted alarms. There are many factors contributed to the smoke background level of a ASD. For instance, the indoor activities like production & human activities, the existance and efficiency of air-conditioning will affect the background smoke level. The outside air will go inside more or less so the outdoor activities like the air quality, near by traffics, construction, agriculturing will also affect the smoke background.For an abolute or fixed sensitivity ASD system, the alarm threshold setup during the commsionning will not guarantee it's proper for the long term operation. It becomes too sensitive and generate false alarms if the environment become dirty. Or it become less sensitive and require more smoke to trigger alarms when the environment become clean. On the other hand, a relative sensitivity ASD system see the problem of a fixed sensitivity system. So it continuously collects the data of smoke background level and calculate its mean and standard deviation and set the alarm level base on mean + n x standard deviation. Although it seems like it can overcome the environment change and automatically set the alarm level to a proper level. However, it still gererates false alarms in the practical operation. The reason is the standard deviation that represent the variation of the collected data. When there's period of time the environment becomes clean, the standard deviation is small and the system becomes very sensitive. It will raise the possibility of false alarms when there's indoor or outdoor activities that increase the smoke level of the protected area. The other downside of thsi relative sensitivity system is the smoke performance can not be predicted because the standard deviation is controlled by the environment and calculation. One will not know what is the smoke sensitivity required to generate the alarm.
AVA see both problems of the absolute/fixed and the relative sensitivity ASD and provide a solution call Relatively Fixed Sensitivity, RFS. The RFS only also continuously collect the data of smoke background level. However, the only the mean is calculated. The alarm threshold is a fixed smoke level required or increased from the mean to generate a alarm and will not varis over time. By the RFS algorithm, the AVA ASD reduce false alarm significantly with predictable performance.
