Determining runway conditions

Abstract

In an example embodiment, there is disclosed herein a methodology for determining runway condition. The methodology comprises receiving sensor data from a sensor associated with a light fixture. The method further comprises determining a heat transfer rate for heat generated by the light source, an expected dry heat transfer rate for the fixture, and determining if there is a deviation from the heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate for the fixture. The deviation between the expected dry heat transfer rate and the heat transfer rate for heat generated by the light. The deviation from the dry heat transfer rate is correlated with various models and / or a trained Artificial Intelligence (AI) inference model to determine a runway condition. Other embodiments include a system and a computer readable medium of instructions upon which the methodology can be implemented.

Description

Determining Runway ConditionsCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit under 35 U.S.C. § 119 of U.S. Provisional Application No. 63 / 607,403, filed December 7, 2023.TECHNICAL FIELD

[0002] The present disclosure relates generally to employing data from airfield equipment, such as for example, lighting fixtures, to determine runway conditions.BACKGROUND

[0003] The presence of contaminants such as rain, snow, ice, or slush on airfield pavements causes hazardous conditions that may contribute to airplane incidents and accidents. Without accurate real time information pilots cannot safely assess takeoff or landing performance.SUMMARY

[0004] The following presents a simplified overview of the example embodiments in order to provide a basic understanding of some aspects of the example embodiments. This overview is not an extensive overview of the example embodiments. It is intended to neither identify key or critical elements of the example embodiments nor delineate the scope of the appended claims. Its sole purpose is to present some concepts of the example embodiments in a simplified form as a prelude to the more detailed description that is presented later.

[0005] In accordance with an example embodiment, there is disclosed herein an airfield lighting system that employs sensor data from a sensor associated with a light fixture and an ambient temperature for determining a runway condition. A dry heat transfer rate is calculated for the fixture and a variation of the heat transfer rate of thefixture from the dry heat transfer rate is determined. The variation from the dry heat transfer rate is correlated with a predetermined model and / or a trained Artificial Intelligence (Al) inference model to determine a runway condition (RC).

[0006] In accordance with an example embodiment, there is disclosed herein a methodology for determining runway condition. The methodology comprises receiving sensor data from airfield equipment, such as a sensor associated with a light fixture and / or other sources. The method comprises determining a heat transfer rate for the light fixture based on the sensor data and an ambient temperature. The method further comprises determining a dry heat transfer rate for the fixture and determining a deviation from the dry heat transfer rate for the fixture and the determined heat transfer rate for the light fixture. The method further comprises correlating the variation of the determined heat transfer rate from the dry heat transfer rate with predetermined models and / or a trained Artificial Intelligence (Al) inference model to determine a runway condition.

[0007] In accordance with an example embodiment, there is disclosed herein a computer readable medium of instructions comprising instructions that when executed are operable to determine a runway condition. The instructions are operable to obtain sensor data from a sensor associated with a light fixture and obtain data representative of an ambient temperature and determine a determined heat transfer rate for the light fixture. The instructions are further operable to determine a dry heat transfer rate for the fixture and determine a variation of the determined heat transfer rate for the light fixture from the dry heat transfer rate for the fixture. The instructions are further operable to correlate the variation from the dry heat transfer rate with a predetermined model and / or a trained Artificial Intelligence (Al) inference model to determine a runway condition.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings incorporated herein and forming a part of the specification illustrate the example embodiments.

[0009] FIG. 1 is a block diagram illustrating a portion of a runway with a runwaycenterline insert light.

[0010] FIG. 2 is a block diagram of an airfield system that employs data from a fixture 102 for determining runway conditions.

[0011] FIG. 3 is a block diagram illustrating an example of a methodology for determining runway condition.

[0012] FIG. 4 is a is a block diagram illustrating a more complex example of a methodology for determining runway condition.

[0013] FIG. 5 is a computer system upon which an example embodiment can be implemented.DESCRIPTION OF EXAMPLE EMBODIMENTS

[0014] This description provides examples not intended to limit the scope of the appended claims. The figures generally indicate the features of the examples, where it is understood and appreciated that like reference numerals are used to refer to like elements. Reference in the specification to "one embodiment" or "an embodiment" or “an example embodiment” means that a particular feature, structure, or characteristic described is included in at least one embodiment described herein and does not imply that the feature, structure, or characteristic is present in all embodiments described herein.

[0015] FIG. 1 is a block diagram illustrating a portion of a runway 100 with a runway centerline inset light fixture 102. Although the example embodiment in FIG. 1 illustrates a system with a runway centerline inset light, those skilled in the art can readily appreciate this was selected merely for ease of illustration as any suitable light, or other airfield equipment, can be employed. Examples of other lights that can be employed for light 102 includes, but is not limited to, Runway Touchdown Zone Inset, Runway Edge Inset, Runway Edge Inset, Runway Threshold And End Inset, Runway Threshold Wingbar Inset, Rapid Exit Taxiway Indicator (RETIL) Inset, Stopway Inset, Runway Entrance Light Inset, Takeoff And Hold (THL) And Runway Intersection (RIL), Runway Entrance Light (REL), Taxiway Centerline Inset, Stop Bar Inset, Taxiway Edge Inset,Runway Guard Light Inset, Stop Bar Inset, Taxiway Edge, Protected Taxiway Edge, Protected Apron Maneuvering Guidance Inset, Triple Line Taxiway Centerline Inset, and Taxiway Centerline Stop Bar.

[0016] In an example embodiment, as will be described in further detail herein, infra, (see e.g., FIGS 2-4) the light fixture 102 comprises logic for performing calculations to facilitate a rules based method for determining a runway condition as described herein. “Logic”, as used herein, includes but is not limited to hardware, firmware, software and / or combinations of each to perform a function(s) or an action(s), and / or to cause a function or action from another component. For example, based on a desired application or need, logic may include a software controlled microprocessor, discrete logic such as an application specific integrated circuit (ASIC), a programmable / programmed logic device, memory device containing instructions, or the like, or combinational logic embodied in hardware. Logic may also be fully implemented in software that is embodied on a tangible, non-transitory computer- readable medium that performs the described functionality when executed by one or more processors.

[0017] The light fixture 102 is coupled with a controller 104. The controller 104 comprises logic that communicates with logic in the light fixture 102 for controlling the operation of the light fixture 102. For example, the controller can provide commands instructing the light fixture to turn its light on or off, flash, blink rate, color, or any other operational parameter. The controller 104 can be located at the airfield, or in some embodiments in the cloud.

[0018] In an example embodiment, the controller 104 and / or logic in light fixture 102 are operable to communicate with external data sources 106. Examples of external data sources, includes, but is not limited to, sources of weather data, ambient data around the light fixture such as temperature, pressure, humidity at the fixture as well as the temperature of the tarmac. In other embodiments, the logic in the light fixture 102 is operable to communicate with the external data sources 106.

[0019] In an example embodiment, as will be described in further detail herein infra (see e.g., FIG. 2), the logic associated with the light source 102 and / or controller 104obtains data from a sensor associated with light fixture 102 and in particular embodiments from external sources 106. Logic associated with the controller 104 and / or the light fixture 102 is further operable to perform intermediate calculations such as signal processing, filtering, modeling employed in determining runway condition as described herein. In some embodiments, at least some of the intermediate calculations can be performed at the light fixture 102. For example, the junction temperature (Tj) of a light emitting diode (LED) can be calculated. The power consumed by the fixture’s LED and heater, which may be operated by Pulse Width Modulation (PWM) can be calculated. As used herein, logic associated with the light fixture can refer to either logic associated with the light fixture 102, logic associated with the controller 104, or a combination of the logic associated with the light fixture 102 and the logic associated with the controller 104.

[0020] In an example embodiment, energy balancing calculations are performed. These calculations include, but are not limited, to, heat transfer rate for heat generated by the fixture (e.g., power for light and heat) (Qgenerated), The heat transfer rate based on temperatures associated with the fixture (for example a temperature internal to the light fixture and / or a temperature of an external surface of the light temperature) and an ambient temperatures (Qambient), the heat transfer rate based on heat from the sun (Qsun), the heat transfer rate for heat from other sources (Qother). Examples of these calculations will be further described herein infra. The ‘dry’ heat balance is the heat transfer rates that are expected if there is no precipitation at the fixture, and, is calculated as a sum of the aforementioned heat transfer rates. The logic associated with the light fixture 102 calculates a time varying (differential) signal (referred to herein as “Sigma”), where Sigma is the deviation from the known inputs (such as the aforementioned heat transfer rates). The controller 104 correlates Sigma using various models and / or a trained Artificial Intelligence (Al) inference model to a runway condition (RC). In an example embodiment, the logic associated with the light fixture 102 combines other sensor data, such as video camera information and / or user input to create a runway condition report (RCR).

[0021] As those skilled in the art can readily appreciate the calculations can be performed by other devices, such as a remote server (not shown). In an exampleembodiment, the calculations can be split amongst multiple computing devices, e.g., some calculations are performed by the logic at the light fixture 102 and other calculations are performed by the controller 104.

[0022] FIG. 2 is a block diagram of an airfield system 200 that employs data from a fixture 102 for determining runway condition. In an example embodiment, the airfield lighting fixture 102 obtains data for any one or a combination of sources, including but not limited to, one or more sensors 202 (e.g., sensors associated with the light fixture, such as a sensor for obtaining internal measurements 204 and / or a sensor for obtaining an external measurement of the fixture 102), virtual data 206, inferred data 208, a camera 210, a multi-spectral camera 212, an infra-red (IR) camera 214, and / or connected feedback 216. The virtual data and inferred data are based on calculations on data obtained from the sensors 202. Examples of data obtained from sensors 202 include, but are not limited to, any one or combination of air temperature inside fixture 102, a surface temperature of an interior surface of the fixture 102, a surface temperature of an external surface of the fixture 102, sunlight, pressure, humidity, current, voltage, pulse width modulation duty cycle, and acceleration.

[0023] In an example embodiment, the fixture 102 is communicatively coupled with a Communication, Monitoring, and Control module 217. The fixture 102 and communications, monitoring, and control module 217 are operable to provide two-way communications between them. The communications, monitoring, and control module 217 is operable to send commands to the fixture 102 to control it’s operation, and is operable to receive data from the fixture 102 to monitor the fixture 102. In an example embodiment, the communications, monitoring and control module 217 is operable to communicate with other systems, such as, for example, system connected information systems 220 and ambient information control settings 222. An example of a module suitable for performing the functionality of the communications, monitoring, and control module 217 is the LNC 360 Communication Platform available from ADB Safegate, the Applicant of this application, which provides powerline communications.

[0024] In an example embodiment, the communications, monitoring and control module 217 is operable to communicate with light activation controller 218. The lightactivation controller 218 is operable to receive data from the fixture 102 via the communications, monitoring and control module 217 and is further operable to send commands for controlling the operation of the fixture 102 via the communications, monitoring and control module 217. The light activation controller 218 can be located at any suitable location. In an example embodiment, the light activation controller 218 is a cloud based controller that further comprises a database.

[0025] In other embodiments, the fixture 102 can communicate directly with the light activation controller 218. For example, the fixture 102 can employ a wireless communications protocol such as Long Range (LoRa), Light Fidelity (LiFi), BLUETOOTH, WIFI, 4G, or 5G networks for communicating with the light activation controller 218 as represented link 224.

[0026] In an example embodiment, the light activation controller 218 is operable to obtain data from additional sources, such as, for example, external data sources, a Web Application Programming Interface (API), and / or streaming external inputs represented by 226 and / or sources for ambient weather information, such as weather stations, which in particular embodiment are local to the airfield, control tower, relay stations or any other suitable source of weather information for the airfield.

[0027] In an example embodiment, the light activation controller 218 is operable to determine he runway condition based on data obtained from the light fixture 102 and data from other sources such as any one, or combination of, system connected information streams 220, ambient information and control settings 222, web application programming interface (API), streaming and / or external inputs 226, and / or ambient weather information from weather stations, control towers, relay stations 228, such as for example Automatic Terminal Information Service (ATIS), Automated Surface Observing Systems (ASOS), and / or Automated Weather Observing System (AWOS).

[0028] As those skilled in the art can readily appreciate, the energy balance calculations described herein can be performed by any device within the system that has adequate data. For example, the calculations can be performed by logic in the fixture 102. In an example embodiment, the communications monitoring and control module 217 calculates energy balance calculations (heat transfer rates) for the fixture102. The energy balance calculations include ‘dry’ heat balance which comprises the heat transfer rates. In another example embodiment, the light activation controller 218 calculates energy balance calculations (heat transfer rates) for the fixture 102.

[0029] In an example embodiment, the system 200 further comprises a user interface 232. In user interface comprises an output device 234 for reporting runway conditions or other relevant information and an input 236 enabling a user 238 to confirm the accuracy of the determined runway conditions. The user feedback can be employed for machine learning and / or Al for improving the results.

[0030] The example illustrated on FIG. 2 employs a single light fixture 102 configured for providing data for determining runway conditions. However, as those skilled in the art can readily appreciate, any physically realizable number of light fixtures 102 can be configured for providing data for determining runway conditions. For example a single light fixture 102, a plurality of light fixtures 102, or all of the light fixtures 102 of a runway can be configured to provide data for determining runway conditions. As mentioned herein, supra, in particular embodiments, determining runway conditions can be performed by logic within light fixture 102.

[0031] In view of the foregoing structural and functional features described above, methodologies in accordance with an example embodiment will be better appreciated with reference to FIGS 3 and 4 . While, for purposes of simplicity of explanation, the methodologies of FIGS 3 and 4. are shown and described as executing serially, it is to be understood and appreciated that the example embodiments are not limited by the illustrated orders, as some acts could occur in different orders and / or concurrently with other acts from that shown and described herein. Moreover, not all illustrated features may be required to implement any of the methodologies illustrated herein. The methodologies described herein are suitable to be implemented in logic, such as hardware, software stored on a computer readable medium when executed by a processor, or a combination thereof.

[0032] FIG. 3 is a block diagram illustrating an example of a methodology 300 for determining runway condition. This method can be implemented logic at a light fixture (e.g., light fixture 102 in FIGS. 1 and 2), at the communications monitoring and controlmodule 217 (FIG. 2), and / or the light activation controller 218 (FIG. 2). In particular embodiments, the light activation controller 218 can be located in the cloud.

[0033] At 302, data (inputs) from one or more sensors associated with the fixture are obtained. Sensor data may include, but is not limited to any one or combination of Temperature (internal to the fixture or external, such as an external surface), Pressure, Humidity, current, voltage, pulse width modulation (PWM) duty cycle,, and / or accelerometer (which can be employed to detect snow plowing). In other example embodiments, snow and / or ice removal can be detected via changes in the heat transfer rate. In some embodiments, data such as voltage, current, PWM can be obtained from controller set points instead of being measured. In addition to sensor data, the inputs can also include data from external sources, such as for example, weather information, visual data (camera, IR camera, and / or multi-spectrum camera), and / or observed user inputs (e.g., slush observed on surface). Other sources can also include Automatic Terminal Information Service (ATIS), Automated Surface Observing Systems (ASOS), and / or Automated Weather Observing System (AWOS).

[0034] At 304, the energy balancing calculations are performed. These calculations include, but are not limited, to, heat transfer rate for heat generated by the fixture (e.g., power for light and heat) (Qgenerated), The determined heat transfer rate is based on a temperatures associated with the fixture and ambient temperatures (Qambient), the heat transfer rate can also include heat from the sunlight (Qsun), and / or the heat transfer rate for heat from other sources (Qother).

[0035] At 306, the ‘dry’ heat balance is determined. The ‘dry’ heat balance is the expected heat transfer rate when there is no precipitation present. The dry heat balance Can be determined from Qgenerated, Qambient, Qsun, Qother, Qtbd.

[0036] At 308, a time varying deviation, or unexpected stimulus, model (referred to herein as “Sigma”) is determined from the known inputs (e.g., dry heat balance). For example, the deviation can be determined as Sigma = dE / dt + Qgenerated, Qambient, Qsun, Qother, Qtbd, where dE / dt = m*cp*dT / dt. Mass and cp can be determined on a per design unit basis or experimentally determined. dT / dt is measured and then calculated.

[0037] When Sigma equals zero, the runway is dry. If Sigma does not equal zero,then precipitation is present.

[0038] At 310, the deviation is correlated to a runway condition (RC). In an example embodiment, Sigma is correlated to a runway condition (RC) using various predetermined models and / or using a trained Artificial Intelligence (Al) inference model to a runway condition (RC). The Sigma value, rate, shape, trend, frequency, etc. reveals the relevant information, it is not just dependent on a singular value. The runway condition (RC)=F(sigma, Tambient, Ttarmac, ... ), or is a function of Ttarmac, Tambient, rain, snow, slush, ice, and their rates of accumulation, etc.

[0039] FIG. 4 is a is a block diagram illustrating a more complex example of a methodology 400 for determining runway condition. This method can be implemented at a light fixture (e.g., light fixture 102 in FIGS. 1 and 2), at the communications, monitoring and control module 217, and / or at the light activation controller 218 (FIG. 2), In particular embodiments, the light activation controller 218 can be located in the cloud.

[0040] At 402, inputs from sensors associated with the fixture are obtained. Sensor data may include, but is not limited to any one or combination of Temperature, Pressure, Humidity, current, voltage, pulse width modulation (PWM) duty cycle, and / or accelerometer (which can be employed to detect snow plowing). In addition to sensor data, the inputs can also include data from external sources, such as for example, weather information, visual data (camera, IR camera, and / or multi-spectrum camera), and / or observed user inputs (e.g., slush observed on surface). Other sources can also include Automatic Terminal Information Service (ATIS), Automated Surface Observing Systems (ASOS), and / or Automated Weather Observing System (AWOS).

[0041] At 404, virtual and inferred data values are calculated. For example, the junction temperature (Tj) of a light emitting diode (LED) can be calculated. The power consumed by the fixture’s LED and heater, which may be operated by Pulse Width Modulation (PWM) can be calculated.

[0042] At 406, the energy balancing calculations are performed. These calculations include, but are not limited, to, heat transfer rate for heat generated by the fixture (e.g., power for light and heat) (Qgenerated), The heat transfer rate based on temperatureseither inside the fixture and / external to the fixture such as an external surface, and ambient temperatures (Qambient), the determined heat transfer rate based on heat from the sun (Qsun), the heat transfer rate for heat from other sources (Qother), and the heat transfer rate from any other sources of heat(Qtbd).

[0043] The heat transfer rate generated by the light fixture can be calculated as □generated = F(V0ltage, Current, PWMLED, PWMheater, Tinternal, Tjuncton, LE Dchannels, Heaterchannels, internal pressure, ... )

[0044] The heat transfer rate based on temperatures inside the fixture and ambient temperatures can be calculated as Qambient=F(Tintemai, Tjunction, Tambient, wind velocity, wind direction, internal pressure, ambient pressure, ambient humidity, ... ). The ambient heat transfer rate is location specific; if using an external source of data, the Global Positioning Satellite (GPS) coordinates are employed. If using on-site monitoring, location information is not relevant.

[0045] The heat transfer rate caused by the sun can be calculated as Qsun = F(Tintemai, fixture type, cloud cover, cloud opacity, solar flux, DNI, DHI, GHI, ... ), where DNI = Direct Normal Radiance, DHI Diffuse Horizontal Irradiance, and GHI - Global Horizontal Irradiance. The sun, or solar, heat transfer rate is location specific; if using an external source of data, the GPS coordinates are employed. If using on-site monitoring, location information is not relevant.

[0046] The heat transfer rate from other sources can be calculated as Qother = F(Ttarmac, TwalIJnternal, TwelIJnternal, .. . ). In an example embodiment, Ttarmac is the temperature of the tarmac adjacent to the light fixture, Twaiijnternai is the temperature of the wall inside the fixture, and Tweiijnternai is the temperature of the well inside the fixture.

[0047] At 408, the ‘dry’ heat balance is determined. The ‘dry’ heat balance is the expected heat transfer rate when there is no precipitation present. The dry heat balance can be determined from Qgenerated, Qambient, Qsun, Qother, at steady-state or when no changes are present

[0048] At 410, a time varying deviation, or unexpected stimulus, model (referred to herein as “Sigma”) is determined from the known inputs (e.g., dry heat balance). Forexample, the deviation can be determined as Sigma = dE / dt + Qgenerated, Qambient, Qsun, Qother, Qtbd, where dE / dt = m*cp*dT / dt. Mass and cp can be determined on a per design unit basis or experimentally determined. dT / dt is measured and then calculated. When Sigma equals zero, the runway is dry. If Sigma does not equal zero, then precipitation is present.

[0049] At 412, the deviation is correlated to a runway condition (RC). In an example embodiment, Sigma is correlated to a runway condition (RC) using various predetermined models and / or using a trained Artificial Intelligence (Al) inference model to a runway condition (RC). The Sigma value, rate, shape, trend, frequency, etc. reveals the relevant information, it is not just dependent on a singular value. The runway condition (RC)=F(sigma, Tambient, Ttarmac, ... ), or is a function of Ttarmac, Tambient, rain, snow, slush, ice, and their rates of accumulation, etc.

[0050] At 414, In an example embodiment, the deviation is combined with other data, such as for example, video camera information, IR data, and / or user input, etc. At 416, the combined deviation and other data to create a runway condition report (RCR).

[0051] In an example embodiment, the RCR is output on a user interface, such as a display. A user can employ an input at the user interface to verify the accuracy of the RCR. If there are in accuracies, this can be input into a machine learning module to improve future results.

[0052] FIG. 5 is a block diagram that illustrates an example of a computer system 500 upon which an example embodiment can be implemented. The computer system 500 can be employed to implement the functionality (or logic) of the light fixture 102 (FIGS. 1 and 2), the controller 104 (FIGS. 1 and 2), methodology 300 (FIG. 3), and / or methodology 400 (FIG. 4).

[0053] Computer system 500 includes a bus 502 or other communication mechanism for communicating information and a processor 504 coupled with bus 502 for processing information. Computer system 500 also includes a main memory 506, such as random access memory (RAM) or other dynamic storage device coupled to bus 502 for storing information and instructions to be executed by processor 504. Main memory 506 also may be used for storing a temporary variable or other intermediateinformation during execution of instructions to be executed by processor 504. Computer system 500 further includes a read only memory (ROM) 508 or other static storage device coupled to bus 502 for storing static information and instructions for processor 504. A storage device 510, such as a magnetic disk or optical disk, is provided and coupled to bus 502 for storing information and instructions.

[0054] The Computer system 500 may be coupled via bus 502 to a display 512 such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device 514, such as a keyboard including alphanumeric and other keys is coupled to bus 502 for communicating information and command selections to processor 504. Another type of user input device is cursor control 516, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 504 and for controlling cursor movement on display 512. This input device typically has two degrees of freedom in two axes, a first axis (e.g. x) and a second axis (e.g. y) that allows the device to specify positions in a plane. In an example embodiment, the input device 514 is a touch screen.

[0055] An aspect of an example embodiment is related to the use of computer system 500 for determining runway conditions. According to one embodiment, determining runway conditions is provided by computer system 500 in response to processor 504 executing one or more sequences of one or more instructions contained in main memory 506. Such instructions may be read into main memory 506 from another computer-readable medium, such as storage device 510. Execution of the sequence of instructions contained in main memory 506 causes processor 504 to perform the process steps described herein. One or more processors in a multiprocessing arrangement may also be employed to execute the sequences of instructions contained in main memory 506. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement an example embodiment. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0056] The term "computer-readable medium" as used herein refers to any mediumthat participates in providing instructions to processor 704 for execution. Such a medium may take many forms, including but not limited to non-volatile media. Nonvolatile media include for example optical or magnetic disks, such as storage device 710. Common forms of computer-readable media include for example RAM, PROM, EPROM, FLASHPROM, CD, DVD, SSD or any other memory chip or cartridge, or other medium from which a computer can read.

[0057] Computer system 500 also includes a communication interface 518 coupled to bus 502. Communication interface 518 provides a two-way data communication coupling to a network link 520 that is coupled with a local network 522. For example, communication interface 518 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 518 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 518 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

[0058] Network link 520 typically provides data communication through one or more networks to other data devices. For example, network link 520 may provide a connection through local network 522 to a host computer 524 or to data equipment operated by an Internet Service Provider (ISP) 526. ISP 526 in turn provides data communications through the worldwide packet data communication network, now commonly referred to as the "Internet" 528. Local networks 522 and Internet 528 both use electrical, electromagnetic, or optical signals that carry the digital data to and from computer system 500, are example forms of carrier waves for transporting the information.

[0059] Described above are example embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the example embodiments, but one of ordinary skill in the art will recognize that many further combinations and permutations of the example embodiments are possible. Accordingly, it is intended to embrace all such alterations,modifications and variations that fall within the spirit and scope of any claims filed in applications claiming priority hereto interpreted in accordance with the breadth to which they are fairly, legally and equitably entitled.

Claims

CLAIMS1. An apparatus, comprising: a sensor associated with a light fixture that provides data representative of a measurement, the data representative of the measurement comprises temperature associated with the light fixture; a light source associated with the light fixture; logic associated with the light fixture that is coupled with the sensor and the light source, the logic is operable to obtain data from the sensor and operable to control operation of the light source; the logic is operable to obtain data representative of a temperature associated with the light fixture from the sensor; the logic is further operable to obtain data representative of an ambient temperature; the logic is operable to determine a determined heat transfer rate for heat generated by the light fixture based on data obtained from the sensor and the ambient temperature; the light fixture logic is operable to determine an expected dry heat transfer rate based on data received from the sensor and the ambient temperature; the light fixture logic is operable to determine a deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate; and the logic is operable to correlate the deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate to a runway condition based on a predetermined model for a runway associated with the light fixture.

2. The apparatus set forth in claim 1 , further comprising: a second sensor for measuring light received from a sun coupled with the light fixturelogic; the logic is further operable to determine a heat transfer rate for light received from the sun; and the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate are further based on the heat transfer rate from the sun.

3. The apparatus set forth in claim 1 , further comprising: the light source comprises a light emitting diode; the logic is further operable to determine a junction temperature the light emitting diode; and the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate that are further based on the junction temperature.

4. The apparatus set forth in claim 1 , further comprising: the logic is operable to determine power generated by the light fixture; and the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate are further based on the power generated by the light fixture5. The apparatus set forth in claim 4, further comprising: determining the power generated by the light source by the logic further comprises determining power consumed by a heater in the light fixture; and the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate are further based at least in part on the power consumed by the heater.

6. The apparatus set forth in claim 4, the light fixture logic is further operable to employ one of a group consisting of artificial intelligence, machine learning, and statistical models to correlate the deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate to a runway condition based on the predetermined model for the runway associated with the light fixture.

7. The apparatus set forth in claim 1 , further comprising: a communication interface coupled with the light fixture logic; and the logic is further operable to obtain the ambient temperature from an external source via the communication interface.

8. The apparatus set forth in claim 1 , further comprising at least one additional sensor associated with the light fixture that is coupled with the light fixture logic and is operable for measuring one of a group consisting of pressure, humidity, current, voltage, pulse width modulation duty cycle, and acceleration.

9. The apparatus set forth in claim 1 , further comprising: a camera selected from a group consisting of a video camera, an infrared camera, and a multi-spectral camera coupled with the logic; the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate are further based on data obtained from the camera.

10. A method, comprising: obtaining data from a sensor associated with a light fixture, the data comprises data representative of a temperature associated with the light fixture;obtaining data representative of an ambient temperature; determining, by logic associated with the light fixture, a determined heat transfer rate for heat generated by the light fixture based on data obtained from the sensor and an ambient temperature; determining, by the logic, an expected dry heat transfer rate based on data received from the sensor and the ambient temperature; determining, by the logic, a deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate; and correlating, by the logic, the deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate to a runway condition based on a predetermined model.11 . The method of claim 10, further comprises: measuring light obtained from an external heat source selected from a group consisting of sunlight, infrared, and other electromagnetic heat source; wherein the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate are further based on the heat from the external heat source..

12. The method of claim 10, further comprising: determining a junction temperature of a light emitting diode of the light fixtures; wherein the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate are further based on the junction temperature.

13. The method of claim 10, further comprising: determining power generated by the light fixture; wherein the determined heat transfer rate for heat generated by the light fixture anddetermining the expected dry heat transfer rate are further based on the power generated by the light fixture.

14. The method of claim 13, further comprising; determining power consumed by a heater in the light fixture; wherein the determined heat transfer rate for heat generated by the light fixture and determining the expected dry heat transfer rate are further based on the power consumed by the heater.

15. The method of claim 14, further comprising determining a pulse width modulation duty cycle for determining power generated by the light fixture.

16. The method of claim 15, further comprising determining a heater pulse width modulation duty cycle for determining power consumed by the heater.

17. The method of claim 10, wherein ambient temperature is measured by a sensor coupled with the light fixture.

18. The method of claim 10, wherein the ambient temperature is obtained from an external source.

19. The method of claim 10, further comprising obtaining data from at least one additional sensor, the data obtained is selected from a group consisting of Pressure, Humidity, current, voltage, pulse width modulation duty cycle, and acceleration.

20. A tangible, non-transitory computer readable medium with instructions encodedthereon for execution by a processor, and when executed operable to: obtain data from a sensor associated with a light fixture, the data comprises data representative of a temperature associated with the light fixture; obtain data representative of an ambient temperature; determine a determined heat transfer rate for heat generated by the light fixture based on data obtained from the sensor and the ambient temperature; determine an expected dry heat transfer rate based on data received from the sensor and the ambient temperature; determine a deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate; and correlate the deviation between the determined heat transfer rate for heat generated by the light fixture and the expected dry heat transfer rate to a runway condition based on a predetermined model.

Images