This application claims the benefit of U.S. patent application Ser. No. 12/572,707 filed on 2 Oct. 2009 in the U.S. Patent and Trademark Office which claims the benefit of U.S. Provisional Application Ser. No. 61/102,478 filed on 2 Oct. 2008.
This invention relates to the field of hazardous condition detectors in general and specifically to a hazardous condition detector with ambient condition compensation.
Fire detection devices such as smoke detectors and/or gas detectors are generally employed in structures or machines to monitor the environmental conditions within the living area or occupied compartments of a machine. These devices typically provide an audible or visual warning upon detection of a change in environmental conditions that are generally accepted as a precursor to a fire event.
Typically, smoke detectors include a smoke sensing chamber, exposed to the area of interest. The smoke detector's smoke sensing chamber is coupled to an ASIC or a microprocessor circuit. The smoke sensor samples the qualities of the exposed atmosphere and when a change in the atmosphere of the exposed chamber is detected by the microprocessor, an alarm is sounded.
There are two types of smoke sensors that are in common use: optical or photoelectric type smoke sensors and ionization type smoke sensors. Photoelectric-based detectors are based on sensing light intensity that is scattered from smoke particles. Light from a source (e.g. LED) is scattered and sensed by a photosensor. When the sensor detects a certain level of light intensity, an alarm is triggered.
Ionization-type smoke detectors are typically based on a radioactive material that ionizes some of the molecules in the surrounding gas environment. The current of the ions is measured. If smoke is present, then smoke particles neutralize the ions and the ion current is decreased, triggering an alarm.
The ionization smoke detectors that are currently available in the market are very sensitive to fast flaming fires. This type of fire produces considerable energy and ionized particles, which are easily detected by the sensor.
Although the ionization technology is very inexpensive compared with other technologies and has been installed in millions of homes, there is discussion regarding phasing out of this product category. It has been suggested by some members of the National Fire Protection Agency (NFPA) that ionization smoke sensors do not readily detect smoldering fires.
Smoldering fires most commonly result from cigarette ignition of materials found in homes such as sofas and beds. A smoldering fire typically produces cold smoke particles of which only a small portion is ionized. Because ionization technology focuses on detection of ionized particles, smoldering fire detection may be inconsistent.
Traditional methods of achieving consistent detection of fast flaming fires, with adequate detection of smoldering fires with ionization type smoke sensors, require the use of ionization type sensors coupled with optical or photoelectric type smoke sensors and/or gas sensors. Such a system is disclosed in U.S. Pat. No. 7,327,247 in which outputs from a plurality of different types of ambient condition sensors are cross-correlated so as to adjust a threshold value for a different, primary, sensor. The cross-correlation processing can be carried out locally in a detector or remotely. To minimize false alarming, the alarm determination may be skipped if the output from the primary sensor does not exhibit at least a predetermined variation from an average value thereof. These combination type systems are complex and therefore rather expensive, but heretofore are typical of the current solutions for consistent detection of flaming and smoldering fires.
Other approaches to achieve adequate detection of fires with low false alarm rates incorporate various filtering methods, which are typically used to prevent false or nuisance alarms. These conventional methods typically are inefficient in that they either unnecessarily delay the detection of a fire event, or they require unnecessarily processing of the signal, which delays fire event detection and significantly increases the system's power consumption. Such a system is disclosed in U.S. Pat. No. 5,736,928, which is directed to an apparatus and a method to pre-process an output signal from an ambient condition sensor. The preprocessing removes noise pulses which are not correlated with an ambient condition being sensed. The preprocessing is carried out by comparing the present output value to a prior output value and selecting a minimum value there between. The apparatus and methods incorporate storage for two prior values and the present output value is compared to the two prior values. A minimum or a maximum of the three values is selected. Additional processing is typically carried out by comparing the present output value to a nominal expected clear air output value, and if the present value exceeds the nominal expected output value, a minimum is selected among the present output value and one or more prior values. If the present output value is less than the nominally expected value, a maximum is selected from among the present output value and one or more prior output values. This approach is inefficient in that the filtering method used unnecessarily removes relevant signal information and delays the system response to a fire event.
Other systems employ multiple filtering operations. One such system is disclosed in U.S. Pat. No. 5,612,674, which describes a noise immune detection system having a plurality of detectors that generate respective indicia representative of adjacent ambient conditions. A communications link extends between the detectors. A control element is coupled to the link to receive and process the indicia and to adjust an alarm threshold level in response to noise levels in the system. Respective indicia are filtered twice by the control element. In the presence of noise, as reflected in relative values of the filtered values of the indicia, the threshold value is automatically increased. This approach tends to be inefficient and unnecessarily expends processing resources. The disclosed patent requires computational intensive multiple filtering iterations applied to a previously filtered signal.
A variety of optical gas sensors for detecting the presence of hazardous gases, especially carbon monoxide (“CO”), are also known.
Typically, optical gas sensors include a self-regenerating, chemical sensor reagent impregnated into or coated onto a semi-transparent substrate. The substrate is typically a porous monolithic material, such as silicon dioxide, aluminum oxide, aluminosilicates, etc. Upon exposure to a predetermined target gas, the optical characteristics of the sensor change, either darkening or lightening depending on the chemistry of the sensor.
Smoke and gas sensors can be affected by temperature, humidity, and dust particles. One or a combination of these ambient factors can cause a smoke or gas detector to false alarm.
Traditional methods of compensating for ambient environmental factors typically include adjusting the output of the sensors. Such an approach is disclosed in U.S. Pat. No. 5,798,701, which is directed to a self-adjusting, self-diagnostic smoke detector. The detector includes a microprocessor-based alarm control circuit that periodically checks the sensitivity of a smoke sensing element to a smoke level in a spatial region. The alarm control circuit and the smoke sensor are mounted in a discrete housing that operatively couples the smoke sensor to the region. The microprocessor implements a routine stored in memory by periodically determining a floating adjustment that is used to adjust the output of the smoke sensing element and of any sensor electronics to produce an adjusted output for comparison with an alarm threshold. The floating adjustment is not greater than a maximum value or less than a minimum value. Except at power-up or reset, each floating adjustment is within a predetermined slew limit of the immediately preceding floating adjustment. The floating adjustment is updated with the use of averages of selected signal samples taken during data gathering time intervals having a data gathering duration that is long in comparison to the smoldering time of a slow fire. The adjusted output is used for self-diagnosis.
These self adjusting systems are not optimized for the detection of traditional fires as well as smoldering fire events with a single sensor, nor do they employ multiple fire event specific thresholds from which the processor may select.
Disclosed is a microprocessor controlled hazardous condition detection system including a housing containing a sensor package; the sensor package contains sensors exposed to the ambient environment. The sensors take periodic readings of predetermined environmental conditions. The disclosed system also includes an alarm means coupled to the sensor package through a microprocessor having volatile and non-volatile memory.
The non-volatile memory features an alarm differential value stored therein and a designated clean air alarm threshold being stored in the non-volatile memory as well. Upon system power-up, the clean air alarm threshold is loaded into the volatile memory; and the microprocessor receives periodic readings of predetermined environmental conditions from the sensor package. The microprocessor preprocesses each received signal generating at least three conditioned signals for each received signal. The conditioned signals are generated by applying different levels of signal filtering to the received signals, generating a set of conditioned signals representative of the periodic reading received. Each conditioned signal in the set has a different signal to noise ratio optimized for a different signal processing task. Each set of conditioned signals is stored in the volatile memory. Based on comparisons made during the signal processing the microprocessor selects a stored alarm threshold from a plurality of stored alarm thresholds optimized to detect a certain fire profile. The microprocessor also adjusts the selected alarm threshold to compensate for changes in the ambient conditions over time by shifting the alarm threshold loaded into the non volatile memory by a small amount based on the calculated difference in the default clean air alarm threshold and the environmental readings accumulated over a period of several hours.
Also disclosed is a hazardous condition detector that is ionization-technology-based optimized to readily detect smoldering as well as traditional flash fires using a single ionization type sensor. This technology is an improvement over existing photoelectric detector technology by providing a sensor possessing enhanced detection capabilities for smoldering fires. Performance of the disclosed invention corresponds to a dual technology alarm system incorporating separate photo and ion sensors while using only the more economical ionization sensors.
The disclosed invention employs microprocessor control to analyze the character/type of smoke by tracking the rate of rise of the sensor signal over a predetermined time period. The disclosed invention pre-processes each sensor signal received, generating at least three conditioned signals representative of the received sensor signal. Each conditioned signal is optimized for a particular signal processing comparison, and is selected and employed by the microprocessor during signal processing to optimize the thresholds employed to define an alarm event. Smoldering fires yield a slow but persistent change in ionization signal and fast flaming fires will produce rapid measured signal change. Rate of rise will be different depending on the type of fire. The disclosed invention employs a plurality of distinct alarm thresholds for different types of fire events. By employing periodic sampling, and using a microprocessor to evaluate the rate of ionized particle change, and selecting a particular alarm threshold from the plurality of available thresholds based on the characteristics of the of ionized particle change, both types of fires are readily detected.
The present invention also features auto-calibration for dynamically establishing the alarm-threshold-reference based on a measurement of clear air. As such, the calibration technology of the present invention is based on the “smart” performance of a microcontroller. By relying on in situ calibration, the disclosed detector alarm units possess similar if not the same sensitivity level across different manufacturing batches and enable dynamically modified and accurate alarm sensitivity level adjustment. Alarm sensitivity may be increased when a smoldering fire is detected to allow the product to alarm faster even with small levels of detected signal. Also, the alarm sensitivity may be decreased when a fast flaming fire is detected to minimize nuisance alarms.
The present invention also discloses a smoke ASIC Wake Up feature wherein the smoke ASIC is used in conjunction with the microcontroller. The ASIC performs other necessary features of a smoke detector such as multi-station, communication, horn driving, low battery detection, signal latching, and/or buffering of the smoke sensor signal. The disclosed wake up feature minimizes power consumption by employing a microprocessor halt or active halt mode. The sensitivity pin of the ASIC is used as an external interrupt to wake up the microprocessor.
As used herein “substantially,” “generally,” and other words of degree are relative modifiers intended to indicate permissible variation from the characteristic so modified. It is not intended to be limited to the absolute value or characteristic which it modifies but rather possessing more of the physical or functional characteristic than its opposite, and preferably, approaching or approximating such a physical or functional characteristic.
In order to describe the manner in which the invention can be obtained, a more particular discussion of the invention briefly set forth above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention, and are not, therefore, to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.
Various embodiments are discussed in detail below. While specific implementations of the disclosed technology are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without departing from the spirit and scope of the invention.
Referring now to the figures, wherein like reference numbers denote like elements,
Sensor package 120 is coupled to at least one microprocessor 110 via an alarm means 130. Alarm means 130 is an ASIC optimized for hazardous condition detector use (smoke, gas, intrusion, etc.) and any supporting components including the visual, electronic, optical, magnetic and or audible signaling components. In other embodiments, the sensor package 120 may be coupled directly to the microprocessor 110 as illustrated in
In example embodiments, microprocessor 110 employs a comparison algorithm to determine the existence of a hazardous condition. A reading without smoke, dangerous levels of gas or other contaminants (clear air) is taken at the factory. This value is stored in non-volatile memory 150 which is typically in the form of an EEPROM or FLASH memory. The alarm level, or alarm threshold, is determined by the software by subtracting a predetermined alarm threshold differential from the default clear air reading. The hazardous condition detector generates an alarm when the signal of the sensor reaches or surpasses or otherwise violates the alarm threshold level. The determination of an alarm condition is governed by the following relation:
Default clean air−alarm threshold differential=X, where X is the alarm threshold, and is compared with the current environmental readings to determine the existence of an alarm condition.
Typically, if X is greater than or equal to the current environmental reading, or otherwise inconsistent with some alarm parameter, then the alarm condition is met and the system goes into alarm mode. In other embodiments, if X is less than or equal to the current environmental reading, the system goes into an alarm mode.
As denoted by the arrows in
This auto-calibration feature enables minimized alarm threshold variations between manufactured products, thereby providing for consistent alarm thresholds for a plurality of manufactured products. Also, the auto-calibration feature is useful in allowing the basic hazardous condition detector to compensate for changes in the environment that will keep the alarm conditions consistent through varying environmental conditions. This consistency also enables a manufacture or end user to dynamically vary the alarm threshold values to obtain consistent results for the different types of fires (Underwriter Laboratories—Paper, Wood, Flammable Liquid Fire Test). The ability to vary the alarm threshold values is a significant development in the field, and as employed in the instant invention breathes new life into the art of ionization sensing smoke detectors.
Specifically, this feature introduces the concept of ionization optimization, through which the performance of ionization type smoke detectors is enhanced by employing at least two distinct alarm thresholds for the ionization sensor. These include a traditional ionization alarm threshold optimized for traditional or fast flaming fires, and an enhanced alarm threshold specifically optimized for the detection of a smoldering fire event. Other alarm thresholds may be employed as well. The use of optimized alarm thresholds with the ionization sensing smoke sensor dispenses with the need for additional, multiple or supplemental sensors for consistent detection of different types of fires.
As discussed in the background section, smoke detectors typically operate by detecting a change in the environment, either in the form of light intensity or population of ionized particles sampled through a smoke chamber. In this manner an ionization type smoke sensor detects a decrease in the current flow, and ultimately voltage measured across the ion sensor electrodes disposed within the smoke detector's smoke chamber. As the smoke increases, the ionization levels in the ambient environment rise and this central electron voltage, or CEV, decreases. The resulting CEV readings are used to infer the ionization levels and ultimately the smoke present in the ambient environment. However, the sensor output voltage of ionization sensors is inherently noisy and attenuated in comparison to the sensor output of a photoelectric type smoke sensor.
Another approach is to manipulate the alarm threshold values. However, insensitive ionization type units, tend not to respond to smoldering fires even if the sensitivity level is increased. Sensitive units in which the threshold differential value is lowered, raising the alarm threshold level to aid in the detection of smoldering fires may become overly sensitive, resulting in false (nuisance) alarms.
The instant invention seeks to overcome such limitations. Depending on the type of ambient conditions detected, the alarm threshold levels are optimized to provide consistent alerts for smoldering fires and fast flaming fires, while simultaneously retaining the robustness necessary to avoid nuisance alarms.
This optimization of the alarm thresholds is accomplished via the use of a microprocessor which preprocesses the output voltage of ionization sensor and generates a set of conditioned signals for each output signal received from the sensor package. During this pre-processing step, the microprocessor employs three different levels of signal filtering, generates and stores at least three conditioned or filtered signals V1, V2 and V3 for each sensor output voltage received from the sensor package. Each level of filtering generates a conditioned signal having an optimized combination of signal to noise and ultimately signal response. During signal processing, the microprocessor selects and employs each conditioned signal at predetermined points in the ionization optimization algorithm to make optimized comparisons that are uniquely suited to the signal to noise ratio of the selected conditioned signal. This allows the microprocessor to efficiently select and or adjust the applied alarm threshold for ionization optimization.
Similarly, a gradual and consistent decrease in the CEV is a characteristic from the profile of a smoldering fire event that is efficiently detected by the ionization sensor of the inventive system and methods, without the use of additional sensors or detectors. By using the inventive system and methods, a hazardous condition detector employing a sensor package containing only an ionization sensor, coupled to a microprocessor for signal processing, can be optimized to detect both smoldering fires and fast flaming fires, thereby eliminating the need for photoelectric, gas or other supporting sensors. Coupled with microprocessor controlled ionization optimization, a smoke detector employing a single ionization type sensor may have two or more distinct and independent alarm profiles. One alarm profile may be optimized for traditional fire events, and a second alarm threshold is optimized to alert in the presence of a smoldering fire event. Each alarm profile has an independent and distinct alarm threshold associated with it. Other alarm thresholds may be specified for optimized detection of intermediate fire events. These distinctive sensitivity levels can automatically be employed by the microprocessor, based on sets of previous ionization readings.
A very consistent alarm level can now be computed for any microprocessor controlled ionization type product powered by any voltage level. The resulting equation is:
Alarm Level=CEVclear-air−Constantalarm threshold, where
CEVclear-air is given by the previous formula above and ‘Constant’ is a voltage to alarm which typically corresponds to one or more predetermined MIC readings. The Alarm Level is also referred to as the CEVALARM and the ‘Constant’ is also referred to as the alarm differential threshold or the CEVDELTA. These formulas are used by the microprocessor to compute the default alarm level. The default alarm level is dynamically varied depending on one or more of the environmental conditions, the profile or characteristics common to a particular type of fire event (for example the rate of CEV change per time).
The CEVALARM may also be considered to be the minimum acceptable CEV voltage for a non-alarm condition or CEVMIN. If at any time the CEV voltage reading falls below this CEVALARM, an alarm condition is inferred by the signal processing microprocessor and the ASIC is signaled to go into alarm mode.
Referring again to
Based on the results of these optimized comparisons, the microprocessor adjusts a selected alarm threshold by a small amount over time to compensate for changes in the ambient environment. When the system detects an ambient environmental condition outside of the alarm threshold stored in the volatile memory 140, the microprocessor 110 designates an alarm event and causes the alarm means 130 to generate an alarm.
This process of adjusting or varying the alarm threshold value within the given allowable range or selecting a new threshold optimized for the profile of the smoke detected enables the system 100 to dynamically adjust the sensitivity of the detector depending on the changes in the ambient environmental conditions in the monitored space such as heat, humidity, light, etc. In addition, in other embodiments, the alarm thresholds may be selected or altered based on predetermined variations in the type of smoke, or based on one or more particular characteristics of the smoke detected. This feature is especially useful in ionization based detectors. Typically, fast flaming fire will have a higher alarm threshold (embodied in a lower CEVALARM) and a smoldering fire will have a lower alarm threshold (embodied in a higher CEVALARM). All alarm levels are typically based on the rate of decrease of CEV reading with respect to time.
By varying the alarm thresholds via a microprocessor, based on the ambient condition variations over time, smoldering fires can now be efficiently detected with ionization type detectors acting independently without the aid of other types of sensors. Since these types of fire events typically yield a slow but persistent decrease in CEV signal while fast flaming fire events produce rapid measured signal decrease. The alarm sensitivity level may be increased when a profile suggesting the existence of a smoldering fire is detected to allow the product to alarm faster even with small levels of detected signal.
The microprocessor processes the CEV signals by employing a ionization optimization algorithm, which selects between a plurality of CEVDELTA values selected to increase or decrease the sensitivity of the ionization sensor package based on the characteristics of the smoke or smoke event detected. With each selected CEVDELTA value, the microprocessor generates a distinct CEVALARM value, or alarm level.
Signal Conditioning and Ionization Optimization
The microprocessor, when powered up, stores the previous CEVNEW value into volatile memory 140 as the CEVPREV and receives a CEVRAW value from the ASIC. The CEVRAW value is the unprocessed and unconditioned CEV reading taken from the sensor package. The microprocessor then pre-processes the CEV reading taken from the sensor package generating a current CEVNEW by applying a signal conditioning algorithm to a CEVRAW value that is retrieved from the ionization sensor package coupled to the ASIC.
The signal conditioning algorithm removes the noise and attenuation from the CEVRAW signal received from the ASIC employing low frequency digital filtering in a narrow band to generate the CEVNEW. The noise and attenuation is removed from the signal by conditioning the unprocessed CEV according to the following relation:
CEVNEW=[CEVPREV(N)+CEVRAW(1)]/[N+1] where N>>1.
The processor generates a CEVNEW by multiplying the previous stored CEV reading by a constant (N). This value is combined with the appropriate current CEVRAW and the sum is divided by the constant plus 1. The level of signal conditioning and the levels of noise and attenuation removal may be increased or decreased by changing the magnitude of this constant. As the size of the selected constant is increased, the greater the attenuation and noise removed from the signal. However, as the size of the constant is increased, time period is required to develop a meaningful trend of changing signals increases and the system response suffers. The various CEVNEW comparisons performed by the microprocessor during signal processing each require signals having different combinations of response versus attenuation for optimal performance.
The instant invention address this problem by generating a plurality of distinct CEVNEW values for each CEVRAW reading, by varying the constant (N) based on the microprocessor's signal processing requirements. Due to the varying signal requirements (response versus attenuation) the microprocessor employs at least three different N values having different magnitudes, generates and stores at least 3 distinct CEVNEW values for each CEVRAW reading received from the sensor package. In the presently described embodiment, the N value employed by the microprocessor for general ambient condition compensation approaches 214 to enhance filtering. For smoke threshold selection settings, the N value employed approaches 27. For smoke detection settings the N value employed approaches 22.
A CEV1NEW value is generated by employing a N value approaching 214.
A second CEVNEW value, CEV2NEW is generated by employing a N value approaching 27.
A third CEVNEW value, CEV3NEW is generated by employing a N value approaching 22.
The CEV3NEW value 502 is selected and used by the microprocessor for the CEV comparison step to determine if an alarm condition is present. Employing the smaller 22 constant generates a CEVNEW signal with a faster response time, making it more sensitive to abrupt changes in the conditions monitored by the ionizations sensor package. This characteristic makes the CEV3NEW value 502 most appropriate for the comparisons with the selected alarm threshold to determine the existence of a fire event.
Each set of generated CEVNEW values is stored in the volatile memory and particular CEVNEW values from the set are selected by the microprocessor depending on the comparison the microprocessor is performing. Typically, to conserve memory resources, the microprocessor will only store a set of the most recent CEVNEW values generated from a couple of detection iterations. The storage of the CEVNEW readings in volatile memory enables the system to efficiently process the CEV data, select and employ an appropriate alarm threshold from the plurality of alarm thresholds available to the microprocessor.
Referring now to
In other embodiments, the sensor package may contain a microprocessor or the hazardous condition detector may employ multiple processors in the housing such that the pre-processing step is performed by one the other microprocessors.
The microprocessor compares CEVNEW with the CEVALARM value. When the microprocessor determines that the CEVNEW<CEVALARM value, an alarm condition is inferred to be present and the microprocessor forces the ASIC into an alarm condition, generating an alarm. When the CEVNEW is determined not to be less than the CEVALARM value, the microprocessor determines if the CEVPREV>CEVNEW>CEVALARM. If the CEVPREV>CEVNEW>CEVALARM, then the microprocessor records the decreasing CEV for this cycle and increments a CEV decreasing cycle counter or similar record. In effect, the microprocessor allows this relationship to be tested during every cycle; or to conserve resources, the test may be performed at some predetermined interval.
When the microprocessor senses a decreasing trend of CEV readings lasting for some predetermined number of cycles, the microprocessor infers a smoldering fire event profile and replaces the traditional CEVALARM with a CEVALARM optimized for a smoldering fire event. This is accomplished by the microprocessor selecting and employing a smaller CEVDELTA. The smaller CEVDELTA causes the microprocessor to generate a higher CEVALARM value enhancing the smoldering fire event sensitivity.
If the CEVPREV≦CEVNEW>CEVALARM the microprocessor continues to use a traditional fire profile with a traditional alarm threshold value providing greater resistance to nuisance false alarms. If at any point after adjusting the CEVALARM to enhance smoldering event sensitivity, the CEVPREV≦CEVNEW>CEVALARM the microprocessor resets the decreasing cycle counter and selects the traditional CEVDELTA, restoring the traditional CEVALARM value for greater resistance to false alarms. The microprocessor may store, select from and employ any one of a plurality of CEVDELTA values to enhance or reduce the ionizations sensor package's or system's sensitivity to fit one or more predetermined smoke event profiles.
Referring now to
As shown in
The pre-processing step is then performed by the microprocessor. During pre-processing the microprocessor 110 generates initial V1, V2 and V3 values indicative of the readings collected from the sensor package 825 by employing the signal condition algorithm with three selected filtering constants. The filter constant used to generate V1 is typically the largest and is optimized to determine slow changes in the ambient environment and calculate the appropriate ambient condition adjustments to the selected threshold.
The filter constant used to generate V2 is optimized to generate a CEVNEW signal large enough to detect a trend of decreasing CEV signals to determine whether or not a threshold shift is appropriate. The filter constant used to generate V3 is optimized to generate a CEVNEW signal having a faster response time, making it more sensitive to abrupt changes in the conditions monitored by the ionizations sensor package.
The microprocessor selects and compares the initial pre-processed environmental reading V3 with the default alarm threshold to determine if the environmental reading is in violation of the alarm threshold 835. If the microprocessor determines that the pre-processed environmental reading V3 in violation of the default alarm threshold 835, the microprocessor with designate an alarm condition and the system will generate an alarm 840.
If the microprocessor determines that the pre-processed environmental reading V3 does not violate the default alarm threshold, the microprocessor 110 stores the initial pre-processed environmental readings V1, V2, and V3 in the volatile memory 140 as V1NEW, V2NEW and V3NEW 845.
Referring now to
A compensated default alarm threshold is generated by adjusting the default alarm threshold currently stored in the volatile memory 140 by the calculated difference DIFV1920. This compensated default alarm threshold is designated as the new default alarm threshold and stored in the volatile memory 140 as VALARM 925. This compensated alarm threshold is used by the microprocessor 110 for future comparisons to determine if an alarm condition exists.
The microprocessor 110 stores V1NEW, V2NEW and V3NEW in the volatile memory 140 as V1PREV, V2PREV and V3PREV, respectively 930. A new environmental reading is then collected from the sensor package and pre-processed by the microprocessor 110. The microprocessor 110 uses the signal conditioning algorithm to generate new readings for V1, V2 and V3935. The system microprocessor 110 stores the newest readings for V1, V2 and V3 in the volatile memory 140 as V1NEW, V2NEW and V3NEW 940.
Referring now to
When the microprocessor 110 determines that the sensitivity adjustment is not appropriate 960, the microprocessor stores V1NEW, V2NEW and V3NEW in the volatile memory 140 as V1PREV, V2PREV and V3PREV, respectively, and collects the next environmental reading to pre-process and generate V1NEW, V2NEW and V3NEW 930.
If the microprocessor 110 determines that the sensitivity adjustment is appropriate, the microprocessor 110 selects a new alarm threshold to employ, such as a smoldering threshold. The microprocessor 110 accomplishes this task by comparing V2NEW and V2PREV with the voltage profiles of a plurality of available thresholds stored in the 150 non-volatile memory, and selecting an appropriate threshold optimized for currently detected V2 profile 965. The profiles are typically associated with a threshold at the factory; however, they may be associated with a particular threshold in the field or at system initiation. The optimized threshold is stored in the volatile memory as the new default alarm threshold 970.
Referring now to
When the pre-processed environmental reading V3 does not violate the default alarm threshold 990 the system stores V1NEW, V2NEW and V3NEW as V1PREV, V2PREV and V3PREV, respectively, in volatile memory 140 and collects the next environmental reading to generate V1NEW V2NEW and V3NEW 930.
In yet another embodiment, the hazardous condition detection system incorporates an energy savings feature. Specifically, the power is conserved by employing microprocessor a sleep mode wherein a periodic wake up signal is sent to the microprocessor through the sensitivity set pin of a typical smoke ASIC. This power conservation feature extends the operational life of battery powered units by a large margin. This is very significant in view of the widespread use of battery powered systems and the failure rate of these units due to depleted battery power. This is accomplished by employing the sensitivity pin of the ASIC as an external interrupt to wake up the microprocessor. The ASIC performs all other necessary features of a smoke detector such as communication, horn driving, low battery detect, and buffering of the smoke sensor signal.
Referring now to
Upon wake up, the microprocessor 110 increments an iteration counter and sets CEVPREV=CEVNEW, as a power up initiation step 1015 prior to calculating the current CEVNEW. In setting the CEVPREV to CEVNEW the microprocessor saves the previous set of conditioned CEVNEW signals into volatile memory 140. Next, the microprocessor 110 collects a CEVRAW reading 1020 from the ASIC 130 and employs a signal conditioning algorithm 1025 to the CEVRAW signal. This pre-processing step generates a set of CEVNEW values. The set of CEVNEW values includes at least a CEV1, CEV2, and CEV3 generated by employing varying levels of filtering, optimized for different comparison tasks, when the signal is conditioned. As discussed above the CEV1 value is optimized for determining the small shifts in the thresholding that vary with the ambient condition such as temperature and humidity and is not discussed in detail in this exemplarily embodiment. The CEV2 is optimized and selected for use in comparisons to determine whether or not a new smoldering threshold or a traditional fire event threshold is appropriate. The CEV3 is optimized and selected for comparisons used to evaluate whether or not a fire event exist.
Once the microprocessor 110 generates the set of CEVNEW values, which are the conditioned signal, the microprocessor 110 periodically compares selected CEVNEW signals from the set with the current CEVALARM value. The microprocessor 110 typically stores the set of CEVNEW signals generated at the power up initiation step 1015 at periodic intervals but may store the set of CEVNEW signals at each wake up cycle.
The microprocessor 110 performs the comparison step 1030 when it compares the CEV3NEW and the CEVALARM value by employing an ionization optimization algorithm 1100. The microprocessor 110 compares the CEV3NEW with the CEVALARM at each wake up cycle or it may periodically compare the CEV3NEW and the CEVALARM. In the embodiment shown in
Referring now to
When the microprocessor 110 senses a decreasing trend of CEV2NEW readings, evidenced by the CEV2NEW decreasing for seven consecutive cycles 1124, the microprocessor 110 infers a smoldering fire, selects and employs a lower alarm threshold differential value, CEVDELTA=200 mV, 1140 to enhance the ionization detector's sensitivity.
If the CEV2PREV≦CEV2NEW>CEVALARM, the microprocessor 110 continues to use the standard alarm threshold differential value, CEVDELTA=900 mV, to maintain resistance to nuisance false alarms 1175. If the CEV2NEW does not reflect a continuous decrease at any point after selecting a CEVDELTA to enhance the detector's smoldering event sensitivity, the decreasing cycle counter is reset to one 1153, and the microprocessor reverts back to the standard alarm threshold differential value, CEVDELTA=900 mV 1175, which provides optimized detection of the traditional fast flaming fires.
When pin 13 is active, the impedance is low allowing current flow to the microprocessor coupled to the pin. The current flow in pin 13 wakes the microprocessor and the microprocessor is active during the 10 mS period. During this 10 mS period the microprocessor retrieves/receives the sensor package measurements, evaluates the results, and determines if an alarm event exist. If an alarm event is determined to exist, the microprocessor forces pin 13 to go to a high voltage overriding the deactivation signal forcing the ASIC into an alarm mode. If no alarm event is detected by the microprocessor during the active period, the microprocessor does not override pin 13 and will return to sleep mode until the ASIC's next 10 mS active period.
Since the microprocessor spends a significant amount of time, corresponding to the ASIC's inactive period, in sleep mode a substantial power savings is realized. This conservation of battery power significantly extends the system's battery life.
In other embodiments the optimization of alarm thresholds, via preprocessing of the sensor package's output and optimizing the microprocessor's signal processing comparisons, as well as the energy conservation features set forth herein, may be employed to optimize the performance of other hazardous condition detectors such as photoelectric or gas detectors. This optimization technology may be employed to improve the efficiency of stand alone detectors and/or interconnected hazardous condition detection systems employed in residential and industrial structures or other enclosed environments.
Although specific embodiments of the invention have been described herein, it is understood by those skilled in the art that many other modifications Although specific embodiments of the invention have been described herein, it is understood by those skilled in the art that many other modifications and embodiments of the invention will come to mind to which the invention pertains, having benefit of the teaching presented in the foregoing description and associated drawings.
It is therefore understood that the invention is not limited to the specific embodiments disclosed herein, and that many modifications and other embodiments of the invention are intended to be included within the scope of the invention. Moreover, although specific terms are employed herein, they are used only in generic and descriptive sense, and not for the purposes of limiting the description invention.
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Number | Date | Country | |
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20110018726 A1 | Jan 2011 | US |
Number | Date | Country | |
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61102478 | Oct 2008 | US |
Number | Date | Country | |
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Parent | 12572707 | Oct 2009 | US |
Child | 12895290 | US |