Monitoring vehicle and equipment operations at an airport

A sensor network for monitoring vehicle operations comprises a set of wireless gateways, a plurality of wireless sensors, a plurality of wireless routers, and data processing system. The set of wireless gateways is capable of receiving emissions data from the sensor network. The plurality of wireless sensor units has sensors capable of monitoring vehicle emissions and is capable of generating the emissions data in response to monitoring the vehicle emissions. The plurality of wireless routers is capable of receiving emissions data received from the plurality of wireless sensor units and routing the emissions data received from the plurality of sensors to the set of wireless gateways. The data processing system is capable of receiving the operations data from the set of wireless gateways and capable of processing the operations data. The operations data may include data related to emissions from the vehicle or equipment.

BACKGROUND INFORMATION

The present disclosure relates generally to monitoring vehicles and equipment and in particular to monitoring operations of vehicles and equipment. Still more particularly, the present disclosure relates to a method and apparatus for monitoring operations of vehicles and equipment in a facility.

An airport is a facility at which aircraft, such as airplanes and helicopters, may operate. An airport typically includes at least one surface, such as a runway or helipad for take offs and landings. Airports often include other structures. These structures may include, for example, hangers and terminal buildings.

In performing operations for air traffic, different vehicles may be used to provide support for these operations. These support vehicles may include, for example, mobile air conditioning vehicles, cargo transportation vehicles, shuttle buses, fuel trucks, fire trucks, deicing vehicles, catering vehicles, push back tugs, baggage loaders, and other suitable vehicles. These vehicles may be involved in ground power operations, aircraft mobility, loading operations, and other suitable operations to support aircraft flights

The different operations performed at an airport, keep traffic moving both in the air and on the surface. The operations also may be a source of noise and air pollution. These types of pollution and their effect on the environment are of concern. Airports may generate environmental reports to show how they consider environmental concerns, and how they protect the environment from airport operations in various airport management reports. These reports may include, for example, environmental protection measures that are put in place by the airport. These measures may include ones to reduce water, air, soil, and noise pollution.

One area of particular concern with respect to pollution at airports is the production of green house gas emissions. Emissions of interest with respect to the environment may include the emission of carbon dioxide and nitrogen oxide generated by airport operations. One source of these types of emissions includes support vehicles at the airport.

Currently, these types of emissions are estimated using manufacture's specifications. Current methodologies for identifying emissions use the total fuel consumption and the manufacturer's specifications to identify emissions generated by vehicles over a selected period of time, such as a year. The granularity of these estimates may be set based on the granularity at which fuel consumption estimates can be obtained. The fuel consumption is currently identified from fuel purchase reports.

These types of reports provide a monthly or yearly amount of fuel purchased for use by support vehicles. These types of reports do not provide information of sufficient granularity to reveal specific use patterns of specific vehicles or equipment that might be useful in discovering emission reduction opportunities.

Therefore, it would be advantageous to have a method and apparatus for identifying emissions of vehicles at a facility that overcomes the problems described above.

SUMMARY

In one advantageous embodiment, a sensor network for monitoring vehicle emissions comprises a set of wireless gateways, a plurality of wireless sensors, a plurality of wireless routers, and data processing system. The set of wireless gateways is capable of receiving emissions data from the sensor network. The plurality of wireless sensor units has sensors capable of monitoring parameters indicative of vehicle emissions and is capable of generating the emissions data in response to monitoring the vehicle emissions. The plurality of wireless routers is capable of receiving emissions data received from the plurality of wireless sensor units and routing the emissions data received from the plurality of sensors to the set of wireless gateways. The data processing system is capable of receiving the emissions data from the set of wireless gateways and capable of processing the emissions data.

In another advantageous embodiment, an apparatus comprises a set of wireless gateways, a plurality of wireless sensor units, and a plurality of routers. The set of wireless gateways is capable of routing operations data to a data processing system. The plurality of wireless sensor units is capable of being attached to a plurality of fuel operated equipment and has sensors capable of monitoring operations of the plurality of vehicles. The set of wireless sensor units is capable of generating the operations data in response to monitoring the operations of the plurality of vehicles. The plurality of wireless routers is capable of receiving operations data from the plurality of wireless sensor units, and routing the operations data received from the plurality of wireless sensor units to the set of wireless gateways.

In still another advantageous embodiment, a method is present for monitoring operations for a plurality of vehicles at a facility. The operations for the plurality of vehicles at the facility are monitored in real time using a plurality of wireless sensor units attached to the plurality of vehicles to generate operations data for the plurality of vehicles. Operations data for the plurality of vehicles from the plurality of wireless sensor units is transmitted to a plurality of wireless routers located within the facility. The operations data is routed through the plurality of wireless routers to a wireless gateway. The operations data is sent from the wireless gateway to a data processing system for processing.

DETAILED DESCRIPTION

With reference now to the Figures and in particular with reference toFIG. 1, a diagram of a sensor network monitoring vehicle operations is depicted in accordance with an advantageous embodiment. In this example, operations monitoring system100is employed to monitor operations of fuel operated equipment101, such as, vehicles102, at facility104. In these examples, facility104takes the form of airport106.

Vehicles102may include support vehicles108, which may take the form of ground support equipment117. In these examples, the operations of vehicles102are monitored by using sensor network112. Sensor network112is capable of providing real time data gathering as opposed to the currently used manual data from reports or estimates. In this example, sensor network112includes gateways114, wireless routers116, and wireless sensor units118.

Support vehicles108are designed to support operations at airport106. Ground support equipment117is not typically designed for on road use outside of airport106in these illustrative examples. Support vehicles108may take various forms. For example, support vehicles108may include, without limitation, at least one of fire trucks, shuttle buses, fuel trucks, deicing vehicles, push back tugs, catering vehicles, cargo transport vehicles, mobile air conditioning vehicles, ground power carts, and other suitable types of vehicles.

In these illustrative examples, wireless sensor units118monitor operations120performed by vehicles102. Operations120may include, for example, transporting cargo from a terminal to an aircraft, pushing an aircraft back away from a gate, refueling an aircraft, moving barriers, and other suitable operations.

Wireless sensor units118are attached to vehicles102in these examples. Wireless sensor units118may detect various physical quantities relating to use patterns122and emissions124in monitoring operations data126of vehicles102. These physical quantities include, for example, exhaust temperature, current in an electrical system, ambient air temperature, location of a vehicle, and other suitable physical quantities.

In monitoring these physical quantities, wireless sensor units118generate operations data126. In these examples, operations data126may be any data relating to the operation of vehicles102. Operations data126may be signals or data generated by the sensors without processing. In other advantageous embodiments, some preprocessing may be included in generating operations data126. An example for subset of operations data126is emissions data. This type of data is any data that may be used for identifying emissions generated by vehicles102. The emissions data may include data used to derive or estimate emissions as well as direct measurements of emissions from vehicles102. In turn, operations data126is transmitted wirelessly to wireless routers116.

Wireless routers116route operations data126from one wireless router to another wireless router until gateways114is reached. In some embodiments, operations data126may be sent by wireless sensor unit in wireless sensor units118to gateways114rather than using wireless routers116.

Gateways114may transmit operations data126to computer128through network130. Network130may include one or more networks such as, for example, a local area network, a wide area network, an intranet, the Internet, or some other network. These networks may include both wireless and wire connections. In these examples, computer128and network130are shown as being located outside of facility104.

Computer128may process operations data126to perform analysis132to identify emissions124in use patterns122. From this data, an identification of emissions with respect to use patterns122may be identified. Further, emissions for particular vehicles within vehicles102also may be identified. This information may be used to generate reports that accurately reflect emissions124generated by vehicles102. This information may be identified accurately for granular periods of time.

For example, emissions and patterns may be identified for time periods, such as days, hours, minutes, or some other suitable time period. This type of reporting is in contrast to the currently available systems, which only generate estimates for a fleet of vehicles based on aggregate fuel usage. With analysis132, facility104may be managed. In these examples, the management may be to reduce emissions124.

Emissions124may be reduced by, for example, changing use patterns122, changing the make up of vehicles102, changing maintenance operations for vehicles102, identifying needed repairs for vehicles102, and other suitable steps or operations. Further, this analysis also may be used for other purposes, such as identifying efficiency for fuel usage in operations120.

This type of monitoring system may be easily attached to vehicles and use wireless transmissions. In this manner, impact on the infrastructure of airport106and the equipment may be minimized. With an identification of use patterns122and emissions124, this information may be used to identify where reductions in emission may be made. For example, this information may identify that one manufacturer of a cargo transport vehicle results in less emissions than another manufacturer for the same type of usage. As a result, better selections of manufacturers or vehicles may be made.

Further, this monitoring may identify that certain vehicles may generate more emissions. This identification along with other data may identify vehicles that may need maintenance or repairs. Further, changes in repair schedules and other operations may occur based on the identification of this information. Additionally, adjustments to vehicle operating procedures or adjustments to the facility infrastructure may be initiated to reduce vehicle operation based on the identification of this information.

Moreover, with the identification of emissions data124over a period of time both before and after emissions reduction improvements are made, airport and/or airline operators may become able to document the quantifiable results of their emission improvement efforts.

Such documentation may enable them to demonstrate compliance to the requirements of regulatory authorities, obtain carbon offset credits, demonstrate an environment control system in compliance with ISO 14001, and earn points in programs, such as, the Leadership in Energy and Environmental Design (LEED) program by demonstrating energy performance measurement and providing emissions reduction reporting. The above may allow airport and/or airline operators to improve their public relations.

Illustration of operations monitoring system100inFIG. 1is not meant to imply architectural limitations to the manner in which different advantageous embodiments may be implemented. Illustration provides functional components and examples of some components for purposes of illustrating one manner in which different advantageous embodiments may be implemented.

For example, in some advantageous embodiments, computer128and network130may be part of sensor network112. In other advantageous embodiments, sensor network112may be deployed across multiple facilities rather than just facility104. In other advantageous embodiments, other facilities may be monitored other than airport106. Facilities, such as, for example, a trucking depot, a shipping dock, a manufacturing facility, or some other suitable facility may be monitored in which vehicles are operated.

Further, different advantageous embodiments may employ operations monitoring system100to monitor other types of fuel operated equipment101other than vehicles102. For example, operations monitoring system100may monitor operations of generators, fuel powered work lights, pumps, ground power carts, and other portable equipment. In these examples, fuel operated equipment101may be any equipment that has an engine powered using fuel that generates emissions. The types of fuel may include, for example, gasoline, diesel, and other suitable fuels.

For the purpose now ofFIG. 2, a diagram illustrating a sensor network is depicted in accordance with an advantageous embodiment. In this example, sensor network200is an example of one implementation of sensor network112inFIG. 1. As illustrated, sensor network200includes wireless sensor units202,204,206,208,210, and212. These wireless sensor units are examples of wireless sensor units118inFIG. 1and may be attached to support vehicles located at a facility such as airport106inFIG. 1. Sensor network200also includes wireless routers214,216,218,220,222,224,226,228,230,232,234, and236, which are examples of wireless routers116inFIG. 1.

These wireless routers are located in various locations at a facility. Wireless routers214,216,218,220,222,224,226,228,230,232,234, and236route operations data detected by the different wireless sensor units towards gateway238. In these examples, gateway238may be a set of gateways. A set as used herein refers to one or more items. For example, a set of gateways is one or more gateways. Gateway238may then send the operations data to a remote data processing system for processing. In this example, the operations data takes the form of emissions data for monitoring emissions from vehicles within a facility.

The different components in sensor network200are wireless components in these examples. By using wireless transmissions, the impact to operations and equipment at a facility may be minimized.

In these examples, sensor network200may be implemented using a number of different architectures, protocols, and/or other designs. In this particular example, sensor network200may be implemented using a wireless mesh network. A wireless mesh network is made up of radio nodes in which at least two pathways of communication are typically present to each node. The coverage area of the radio nodes working as a single network becomes a mesh cloud. Zigbee is an example specification of communication protocols for use in a mesh network that may be implemented in sensor network200in these depicted examples. This specification is available from the Zigbee Alliance.

Gateway238may be implemented using a Zigbee coordinator while the different routers may be implemented using Zigbee routers. The different wireless sensor units may be implemented as a Zigbee end device. A Zigbee end device contains functionality to talk to nodes such as gateway238or wireless router218. A Zigbee router may act as a router passing data from other devices. A Zigbee coordinator forms the root of sensor network200and may provide a bridge to other networks. With this type of architecture, only a single gateway is present. Of course, with other implementations, more than one gateway may be used.

With reference now toFIG. 3, a diagram illustrating locations for components in a sensor network is depicted in accordance with an advantageous embodiment. In this example, sensor network300illustrates an example of one manner in which different components may be located or placed in a facility. Sensor network300also includes a gateway, which is an example of a gateway within gateways114inFIG. 1. Sensor network300shows one manner in which different components in sensor network300may be configured.

In this example, sensor network300is located at airport302. Wireless sensor unit304is attached to ground support equipment306. Wireless router308, wireless router310, router312, and gateway314are located in or on a structure, such as terminal316in airport302. The components are placed on rooftop318of terminal316to provide better coverage for wireless sensor units, such as wireless sensor unit304. Further, by placing these components on rooftop318, these components my not interfere with operations and equipment at airport302.

As seen in this example, wireless sensor unit304may transmit operations data to wireless router308. In turn, wireless router308routes the operations data to wireless router310. From there, the operations data may be sent to wireless router312, which sends the operations data to gateway314. Gateway314may then transmit the data to a remote computer for processing. Gateway314also may be a wireless gateway in which the operations data is transported to the network through a wireless communications link. In some advantageous embodiments, gateway314may provide a wired link or connection to the network.

In addition to the locations illustrated on rooftop318, wireless routers and/or gateways may be positioned in any location around a facility to provide wireless communication coverage over locations that support vehicles may commonly operate. These different components may be located on other structures in addition to or in place of terminal316. For example, wireless routers and gateways may be located in other locations, such as jet way rooftops, light poles, near ground support equipment fueling stations, and other suitable locations.

With reference now toFIG. 4, a diagram illustrating a wireless sensor unit on a support vehicle is depicted in accordance with an advantageous embodiment. In this example, ground support equipment400is an example of ground support equipment117inFIG. 1on which wireless sensor unit402may be located. Wireless sensor unit402includes housing404in which various electronics for wireless sensor unit402are present. Additionally, in this example, energy harvesting device406is located on surface408of ground support equipment400.

In these examples, wireless sensor unit304collects data and associates data with time stamps. Typically, operations wireless sensor unit304may store data for periods of time such as, for example, hours or days before transmitting the data to a router. The operations data then moves through the router and may be collected at gateway238. The operations data may be stored at gateway238for some periods of time before reporting it or sending the data for further processing.

In other advantageous embodiments, in these examples, operations data may move in a real time manner. In these examples, “real time” means that the operation data is moved as quickly as possible as opposed to holding the operations data and sending it at different periods of time when the operations data could be sent earlier.

Energy harvesting device406, in this example, takes the form of one or more solar cells. Of course, in other advantageous embodiments, other types of energy harvesting devices may be used. For example, energy harvesting device406may be, for example, without limitation, a vibration harvesting device, a thermal electrical device, or some other energy harvesting device.

As a vibration harvesting device, electrical power may be generated when exposed to vibrations, such as operational vibrations. When energy harvesting device406takes the form of a thermal electric device, electrical power may be generated when energy harvesting device406is exposed to thermal gradient. This thermal gradient may be, for example, a hot hydraulic line in ambient air or an exhaust pipe in ambient air.

Wireless sensor unit402also includes sensors, which are connected to housing404. In this example, these sensors include current sensor410and temperature sensor412. Current sensor410may be, for example, a current sensor and may clamp onto a wire in ground support equipment400. Temperature sensor412may be, for example, a thermocouple and may be located in a stainless steel housing positioned in the exhaust pipe414for ground support equipment400. Temperature sensor412also may be, for example, a thermistor and/or a bi-metal thermometer.

Turning now toFIG. 5, a diagram of a data processing system is depicted in accordance with an illustrative embodiment of the present invention. Data processing system500is an example of the data processing system that may be used to implement different components within operations monitoring system100inFIG. 1. For example, data processing system500may be used to implement computer128and/or gateways114inFIG. 1. In this illustrative example, data processing system500includes communications fabric502, which provides communications between processor unit504, memory506, persistent storage508, communications unit510, input/output (I/O) unit512, and display514.

Memory506and persistent storage508are examples of storage devices. A storage device is any piece of hardware that is capable of storing information either on a temporary basis and/or a permanent basis. Memory506, in these examples, may be, for example, a random access memory or any other suitable volatile or non-volatile storage device. Persistent storage508may take various forms depending on the particular implementation.

For example, persistent storage508may contain one or more components or devices. For example, persistent storage508may be a hard drive, a flash memory, a rewritable optical disk, a rewritable magnetic tape, or some combination of the above. The media used by persistent storage508also may be removable. For example, a removable hard drive may be used for persistent storage508.

Communications unit510, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit510is a network interface card. Communications unit510may provide communications through the use of either or both physical and wireless communications links.

Input/output unit512allows for input and output of data with other devices that may be connected to data processing system500. For example, input/output unit512may provide a connection for user input through a keyboard and mouse. Further, input/output unit512may send output to a printer. Display514provides a mechanism to display information to a user.

Instructions for the operating system and applications or programs are located on persistent storage508. These instructions may be loaded into memory506for execution by processor unit504. The processes of the different embodiments may be performed by processor unit504using computer implemented instructions, which may be located in a memory, such as memory506. These instructions are referred to as program code, computer usable program code, or computer readable program code that may be read and executed by a processor in processor unit504. The program code in the different embodiments may be embodied on different physical or tangible computer readable media, such as memory506or persistent storage508.

Program code516is located in a functional form on computer readable media518that is selectively removable and may be loaded onto or transferred to data processing system500for execution by processor unit504. Program code516and computer readable media518form computer program product520in these examples. In one example, computer readable media518may be in a tangible form, such as, for example, an optical or magnetic disc that is inserted or placed into a drive or other device that is part of persistent storage508for transfer onto a storage device, such as a hard drive that is part of persistent storage508.

In a tangible form, computer readable media518also may take the form of a persistent storage, such as a hard drive, a thumb drive, or a flash memory that is connected to data processing system500. The tangible form of computer readable media518is also referred to as computer recordable storage media. In some instances, computer readable media518may not be removable.

Alternatively, program code516may be transferred to data processing system500from computer readable media518through a communications link to communications unit510and/or through a connection to input/output unit512. The communications link and/or the connection may be physical or wireless in the illustrative examples. The computer readable media also may take the form of non-tangible media, such as communications links or wireless transmissions containing the program code.

The different components illustrated for data processing system500are not meant to provide architectural limitations to the manner in which different embodiments may be implemented. The different illustrative embodiments may be implemented in a data processing system including components in addition to or in place of those illustrated for data processing system500. Other components shown inFIG. 5can be varied from the illustrative examples shown.

With reference now toFIG. 6, a block diagram of a router is depicted in accordance with an advantageous embodiment. In this example, wireless router600is an example of a router in wireless routers116inFIG. 1. Wireless router600includes container602, which provides a housing for components in wireless router600. In this example, wireless router600also includes receiver604, router unit606, memory608, battery610, and energy harvesting device612.

Container602may be, for example, a plastic container or some other suitable container to protect components of wireless router600from the elements. Container602may be sealed in some implementations.

Energy harvesting device612and battery610provide power to router unit606, receiver604, and memory608. In these examples, energy harvesting device612generates and sends electrical current to charge and power battery610. Energy harvesting device612in these examples may be, for example, a solar cell. Of course, other types of energy harvesting devices may be used in place of or in addition to energy harvesting device612depending on the particular implementation.

Receiver604may receive wireless transmissions from wireless sensor units located on support vehicles. The operations data in the wireless transmissions may be stored in memory608for transmission by router unit606to another router and/or gateway. Router unit606provides a capability to transmit operational data towards a gateway in the sensor network. When receiver604receives operations data, this information may be stored in memory608. The storage of data in memory608may be temporary until router unit606is capable of routing the data to a gateway or another router.

In these examples, routers are powered by an energy harvesting device, which minimizes infrastructure complexity, installation time and costs. Alternatively, routers may be powered by other means, such as mains power, a primary battery, or a rechargeable battery that is remotely recharged or is recharged by an engine alternator.

With reference now toFIG. 7, a diagram of a wireless sensor unit is depicted in accordance with an advantageous embodiment. In this example, wireless sensor unit700is an example of a wireless sensor unit within wireless sensor units118inFIG. 1. As illustrated, wireless sensor unit700includes energy harvester702, DC-to-DC converter704, battery fuel gauge706, battery708, processor unit710, memory712, transceiver714, time receiver716, and sensors718.

Energy harvester702may be, for example, a solar cell and provides energy to charge battery708. DC-to-DC converter704may boost or buck the current and/or voltage generated by energy harvester702. Battery fuel gauge706provides processor unit710a capability of identifying the state of charge present in battery708. Further, processor unit710may monitor battery708to obtain statistics as to power usage. Memory712stores operations data detected by sensors718.

Sensors718may include, for example, a current sensor, a thermocouple, and a thermistor. The current sensor may be used to identify electrical current usage in the support vehicle. The thermistor may be used to detect ambient air temperature. The thermocouple may be used to detect the temperature in an exhaust pipe. With this type of implementation, engine power may be estimated using information about the exhaust temperature of the vehicle. From engine power, exhaust may be identified. The exhaust temperature and the rate of change of exhaust temperature may be used to identify engine power. From engine power, an identification of emissions may be identified.

In other advantageous embodiments, sensors718may include a NOx sensor. A NOx sensor may be a high temperature device designed to detect nitrogen oxides in combustion environments, such as in an exhaust of a vehicle. Nitrogen oxide sensors may be available from Siemens VDO/NGK. This type of sensor is an example of one type of sensor that may be used to directly detect emissions from a vehicle. Of course, sensors718in different advantageous embodiments may include other types of sensors in place of or in addition to the ones described in this example.

Transceiver714transmits operations data stored in memory712to a router. Time receiver716is used to obtain the current time. The current time may be obtained through a signal transmitted from locations, such as, for example, WWVB (Fort Collins, Colo.), DCF77 (Germany), JJY (Japan), MSF (Britan) and HBG (Switzerland). This time information may be used to provide time stamps for the operations data. Further, sensor718also may include, for example, a global positioning receiver to obtain location and/or time information for the sensor.

Wireless sensor unit700may provide the ability to wake up on demand. In other words, many of the components in wireless sensor unit700may be shut down with transceiver714waking up the rest of the system when incoming transmissions are detected.

In these examples, processor unit710may be one or more processors. Processor unit710in this particular example may be implemented using a micro controller from Texas Instruments. In particular, a MSP430 micro controller from Texas Instruments, Inc. may be used. Memory712in these examples may be implemented using a flash memory. In particular, the flash memory may be a four megabyte flash memory. Of course, other types of memory and other sizes of memory may be used for memory712depending on the particular implementation.

In this example, transceiver714may be implemented using a CC2500RTK transceiver chip, which is available from Texas Instruments, Inc. Time receiver716may be implemented using a CME800 analog/digital receiver integrated circuit, which is available from C-MAX Time Solutions GmbH.

The wireless sensor unit700depicted inFIG. 7is shown using an energy harvesting device and a battery as a power source, which may allow rapid installation of the sensor with minimal modification to existing vehicle systems. However, the wireless sensor unit700may instead be powered by any battery or power supply already on-board the vehicle, such as an engine start battery.

With reference now toFIG. 8, a diagram illustrating an example of operations data is depicted in accordance with an advantageous embodiment. In this example, message800is an example of a message that may be used to transmit operations data. As depicted, message800includes vehicle identifier802, timestamp804, sensor data806, and status808.

In the illustrative examples, vehicle identifier802is a unique identifier used to identify the vehicle in which the sensor unit generating message800is located. Vehicle identifier802may take various forms. For example, this may be an identifier that is unique within a facility or unique within an entire monitoring system. Vehicle identifier802may be, for example, a media access control address for a processor in a sensor unit, an identifier assigned by the monitoring system, a serial number or other identifier for the vehicle itself, or some other suitable identifier.

Timestamp804identifies the time when sensor data806was detected. Sensor data806is data for physical quantities detected by sensors in the wireless sensor unit. Status808may be the status of a wireless sensor unit. Status808includes an identification of the health or condition of the wireless sensor unit, such as condition of the battery, energy harvester, memory, or time receiver. In these different advantageous embodiments, operations data may be sensor data806alone or may include other data within message800.

Further, the illustration of message800is only provided as one example of the manner in which operations data may be packaged and/or transmitted. Of course, in other implementations, message800may take other forms and may include other fields in addition to or in place of the ones illustrated in message800. For example, message800also may include information identifying a path of routers used to route the data, an identification of the facility, and other suitable information.

With reference now toFIG. 9, a flowchart of a process for monitoring of operations data is depicted in accordance with an advantageous embodiment. The process illustrated inFIG. 9may be implemented in an operations monitoring system, such as operations monitoring system100inFIG. 1. In particular, this process may be implemented in a component, such as computer128inFIG. 1.

The process begins by monitoring for operations data (operation900). A determination is made as to whether operations data has been received (operation902). In these examples, the data may be received from gateways114inFIG. 1. If operations data has not been received, the process returns to operation900. Otherwise, the vehicle associated with the operations data is identified (operation904). This identification may be made through a unique identifier located in the message containing the operations data. The process then stores the operations data (operation906). Operation906may store this data in the database for analysis. In this example, the monitoring system waits for data to be sent by the gateways. In other advantageous embodiments, the monitoring system may actively establish communications with the gateway and request the data.

Turning toFIG. 10, a flowchart of a process for collecting operations data in a wireless sensor unit is depicted in accordance with an advantageous embodiment. In this example, the flowchart inFIG. 10may be implemented in a wireless sensor unit, such as wireless sensor unit700inFIG. 7. In particular, this process may be implemented or executed by processor unit710inFIG. 7.

The process begins by waiting in a sleep mode (operation1000). The wait time in the sleep mode in operation1000may have various time periods, depending on the particular implementation. For example, the sleep mode may be for twenty seconds, one minute, or ten minutes.

During the sleep mode, power usage may be reduced by shutting down various components that may not be needed. Thereafter, the process monitors a set of sensors for data (operation1002). A determination is made as to whether data has been detected (operation1004). If data has not been detected, the process returns to operation1002. Otherwise, the data is stored in association with the timestamp (operation1006).

A determination is made as to whether the data should be sent (operation1008). This determination may be made in other different ways depending on the particular implementation. For example, a determination may be made as to whether a connection can be established or is established with a wireless router.

In other advantageous embodiments, the determination may be whether some period of time has passed. For example, data may be sent every minute, every half hour, every five hours, every day, or once a week depending on the particular implementation. In other advantageous embodiments, this determination may be whether a particular event has occurred. The event may be a request from the monitoring system for data, whether the amount of data in the memory exceeds some threshold, or some other suitable event.

If data is not to be sent, the process returns to operation1000. If data is to be sent, the set of messages is created for all the stored sensor data (operation1010). These messages may take the form of a message, such as message800inFIG. 8. The process then transmits the set of messages (operation1012). Thereafter, the process erases the stored data (operation1014). In this manner, transmitted data may be removed to provide for more storage room for new data. Thereafter, the process returns to operation1000as described above.

Turning now toFIG. 11, a flowchart of a process for managing a facility is depicted in accordance with an advantageous embodiment. The process illustrated inFIG. 11may be implemented using operation monitoring system100inFIG. 1. These operations may include computer implemented steps, as well as human or user implemented steps.

The process begins by selecting operations data for analysis (operation1100). This operations data may be for a single facility or multiple facilities. Further, the data may be for certain vehicles within a facility, a group or class of vehicles within a facility, or all of the vehicles. The process then identifies patterns of use (operation1102). These patterns of use are for the different vehicles selected for operations data1100.

The process then identifies emissions for the vehicles (operation1104). With the patterns of use and emissions with the vehicles, trends in emissions are identified (operation1106). These trends may be based on the comparison of the patterns with the emissions as well as the type of vehicles and maintenance histories for these vehicles. Of course, other information may be considered depending on the implementation. The trends in operation1106may be generated using various known statistical algorithms for analyzing data. Additionally, artificial intelligence and neural network systems also may be implemented to identify trends.

Based on the trends, changes in the operation of the vehicles inside the facility may be identified (operation1106). These changes may include, for example, changes in the patterns of use, changes in maintenance schedules, changes in the selection or makeup of vehicles, changes in the facility infrastructure and other suitable changes. The vehicles in the facility are then managed using one or more of the identified changes (operation1108), with the process terminating thereafter.

In the different advantageous embodiments, an identification of emissions may be made based on estimating the engine load factor. A load factor is a measurement of the amount of power generated by an engine on a scale between the least or zero amount of power and the maximum amount of power that can be generated by the engine.

For example, a load factor may be from 0 percent of the engine power to 100 percent of the engine power. In other advantageous embodiments, other scales may be used. For example, a scale of 0 may represent no engine power while a scale of 10 may represent the maximum engine power. Databases and tables are currently available for many vehicles in which these data sources provide an identification of exhaust based on engine load factor.

The different advantageous embodiments recognize that current processes for measuring engine load factors require modifications of systems in the vehicles or other types of fuel operated equipment. These changes may be expensive and time consuming. Further, some methods may interfere with the operation of fuel operated equipment or cause the equipment owner or operator to be concerned about making these modifications. These current methods may measure parameters, such as manifold pressure as an indication of power to identify load factor.

Current methods include, for example, measuring vacuum pressure for gasoline engines and fuel pump activity. These types of methods may require modifications or alterations to the engine or exhaust system. The different advantageous embodiments provide a method and apparatus for measuring engine load factors by monitoring exhaust temperatures within the fuel operated equipment. The monitoring in the different advantageous embodiments may be less invasive and easier to perform as compared to currently available methods.

In the different advantageous embodiments, engine load factor may be estimated using a thermal time constant for the exhaust system and measuring the temperature and rate of change of temperature for the exhaust system. The thermal time constant is for a location in the exhaust system at which the temperature and rate of temperature change may be measured. This type of measurement method requires less intrusion and/or modification of fuel operated equipment.

Turning toFIG. 12, a diagram illustrating locations for taking measurements in an engine system is depicted in accordance with an advantageous embodiment. In this example, engine system1200includes engine1202and exhaust system1204. Engine1202may use fuel1206and air1208at an ambient temperature to turn shaft1210. In turning shaft1210, engine1202generates heat that may be exhausted from the engine at least partially through exhaust system1204. This exhaust heat is located at point1212in these examples. Heat also may be lost by an engine through a cooling system in these examples.

Section1214represents a lumped thermal capacitance region in which temperatures may be taken to identify a load factor of engine1202. In these examples, these measurements may be taken using a sensor such as, for example, sensor1216.

In these examples, the heat exhausted into exhaust system1204may be roughly proportional to the power generated by engine1202. As heat flows into exhaust system1204, some of the heat may dissipate in ambient surroundings along exhaust system1204. The heat that may dissipate may vary depending on the ambient air temperature and the heat exhausted into exhaust system1204.

The temperature measured by sensor1216may rise and fall as the heat exhausted into exhaust system1204rises and falls. A response time lag, however, may occur, which is caused by the length of exhaust system1204and the lumped capacitance of exhaust system1204. The different advantageous embodiments take these factors into account to identify the heat exhausted from the engine at point1212. In these examples, sensor1216may be located within or on exhaust system1204.

In these examples, when a steady state condition is present, the difference between the temperature at sensor1216, Tsensor,ss, and the temperature of air1208, Tamb, is proportional to the temperature of the heat exhausted at point1212. As a result, since the heat exhausted from the engine is roughly proportional engine power, the engine power may be identified as being roughly proportional to the difference between these two temperatures (Tsensor,ss−Tamb). Thus, with the thermal dynamic concept of lumped capacitance, an estimate at any moment in time of the temperature at sensor1216may be identified if that sensor were allowed to reach a steady state temperature.

With reference now toFIG. 13, a flowchart of a process for estimating engine loads through monitoring exhaust system temperatures is depicted in accordance with an advantageous embodiment. The process illustrated inFIG. 13may be implemented in operations monitoring system100inFIG. 1.

The process begins by placing a first temperature sensor in a location with respect to the exhaust system (operation1300). In some advantageous embodiments, in placing the first temperature sensor in a location with respect to the exhaust system, the temperature sensor may be placed in or on the exhaust system. The particular location selected is one in which the temperature sensor is capable of measuring temperature generated by the exhaust system. This sensor may be sensor1216inFIG. 12.

A second temperature sensor is placed in a location with exposure to ambient air (operation1302). The measurement of ambient air using the second temperature sensor may be used to take in to account changes in the ambient environment around the engine and exhaust system. Changes in ambient air temperature conditions may be a source of air for the engine combustion and also may be the heat sink to which the exhaust system is transferring heat. The ambient air temperature may cancel out in many of the different calculations.

The process then performs a calibration of the first temperature sensor (operation1304). This calibration involves identifying a thermal time constant for the particular location of the first temperature sensor in the exhaust system. The process for calibrating the temperature sensors is described in more detail inFIG. 14below.

After calibration has been performed, the load factor of the engine may be estimated (operation1306) with the process terminating thereafter. The estimation of engine load factor is described in more detail inFIG. 15below. In operation1306, the load factor may be estimated for different times based on the temperature measured by the first sensor to obtain the temperature of the exhaust and the rate of change in temperature of the exhaust.

In other words, the temperature of the exhaust may be hotter or cooler than its eventual steady state temperature. The different advantageous embodiments provide a capability to identify this difference at any moment in time. This capability allows the steady state temperature to be more accurately estimated at a particular point in time for a location in or on the exhaust system.

With reference now toFIG. 14, a flowchart of a process for calibrating a temperature sensor is depicted in accordance with an advantageous embodiment. In this example,FIG. 14is a more detailed illustration of operation1304inFIG. 13.

In calibrating a temperature sensor, it is assumed that a temperature at a given location in or on the exhaust system may vary with engine load, ambient temperature and time. An assumption is also made that for a given engine load, a steady state temperature rise above the ambient temperature is eventually reached in or on the exhaust system. This steady state temperature rise above ambient temperature is assumed to be proportional to the engine load. As a result, a temperature sensor in a location with respect to the exhaust system may register or detect one value for the temperature in the exhaust. If the engine power factor changes at that point in time, the temperature of the exhaust system and of the sensor may require some period of time to register the new corresponding steady state temperature value. This period of time is the lag in these examples. In these examples, the lag is the time for the exhaust system at the sensor location to respond to a new amount of power or power factor generated by the engine.

The different advantageous embodiments employ a thermal concept of “lumped capacitance” used to predict the temperature of the exhaust at the sensor location. From the lumped capacitance method, the temperature at a point within a body exposed to a new environment may change with time according to the following:

ⅆTⅆt=(T-T∞)τEquation⁢⁢1⁢(T-T∞)(Ti-T∞)=ⅇ(-tτ)Equation⁢⁢2
Where T is the temperature at time t, T∞is the final steady state temperature, and Tiis the initial temperature before exposure to the new environment.

The new environment indicates a change in the exhaust flow that may occur. The initial temperature before exposure to the new environment is the temperature measured by the exhaust at one moment in time. T∞is the steady state of the temperature after the engine has been running at idle for sufficient time to approach steady state. τ is the thermal time constant. τ may be identified as follows:
τ=RtCt

The system thermal time constant is equal RtCt, where Rtis the system lumped thermal resistance to convection heat transfer and Ctis the system lumped thermal capacitance.

Solving equation 2 for various values results in the following:

The time constant τ may be system specific in these examples and may be fairly stable over a variety of engine run conditions and ambient temperatures. As a result, an idle to warm up procedure may be all that is needed to calculate the time constant τ for a given exhaust system of the fuel operated equipment.

Additionally, from equation 1, the following may be obtained;

As can be seen in equation 5, the ambient temperature represented by T∞is the eventual steady state temperature of the exhaust system at the sensor. T is the temperature that is measured and dT/dt is the rate of change of the temperature. For example, if two temperature readings are taken at 0.5 seconds apart, the temperature T is the average of those two readings. The rate of change is the difference between the two readings divided by 0.5 to obtain the rate of change. Equation 5 may then be used to obtain the steady state temperature. Equation 5 may be rewritten as follows:

In equation 6, Tsensor,ssis the temperature of the sensor when it reaches steady state, Tsensoris the temperature actually measured by the sensor, and dTsensoris the rate of change of the temperature for the sensor. In other words, equation 6 allows an identification of the steady state temperature that the temperature sensor would eventually reach if nothing else changed from the engine running conditions at the moment of time when a particular temperature is detected by the sensor. In this example, the sensor may be, for example, sensor1216inFIG. 12.

With reference still toFIG. 14, the process begins by starting the engine (operation1400). Temperature data from the temperature sensor is stored (operation1402). A determination is made as to whether the exhaust temperature sensor has approached a steady state temperature (operation1404). This determination may be made in a number of different ways. For example, the engine may be allowed to run until the exhaust temperature sensor does not increase more than a specified amount after some selected period of time.

If the temperature is not at this steady state temperature, the process returns to operation1402to store temperature data. When the exhaust temperature sensor finally approaches the steady state temperature, the process then fits a time constant curve to the stored temperature data (operation1406).

The process then identifies the thermal time constant from the curve (operation1408). This thermal time constant may be used with measured temperatures and rates of change of temperature to estimate the engine load factor. The stored temperature data provides temperatures over different periods of time. This temperature data may be associated with time based on time stamps. The time constant may be fit to a curve through empirical processes using different values in equation 4 until the curve fits the data. Of course, other curve fitting methods also may be used depending on the particular implementation.

Next, the process operates the engine at a maximum load factor (operation1410). In operation1410, the engine is operated at its maximum power or capability. In other words, the engine may be operated at 100 percent of its capable power. The temperature data is stored while operating the engine at this load factor (operation1412). A determination is then made as to whether sufficient temperature data has been collected to estimate the steady state temperature corresponding to a 100 percent load factor for the engine (operation1414). If insufficient data has been collected, the process returns to operation1410.

If sufficient data has been collected, the process then identifies the steady state temperature corresponding to the 100 percent load factor for the engine from the temperature data stored while operating the engine at the maximum load factor (operation1416), with the process terminating thereafter. The data stored when operating the engine at the maximum load factor may be used to extrapolate the steady state temperature at the sensor when the load factor is 100 percent. This temperature is calculated as Tsensor,ss100%. Note that Tsensor,ss100%−Tamb, where Tsensor,ss100%is the temperature at steady state with 100 percent load factor and Tambis the ambient air temperature corresponds to 100 percent load factor and may then be used to identify the load factor for other percent levels of power for stead state temperatures.

With reference now toFIG. 15, a flowchart of a process for estimating load factor of an engine is depicted in accordance with an advantageous embodiment. The process illustrated inFIG. 15is a more detailed description of operation1306inFIG. 13.

The process begins by obtaining temperature data from the temperature sensor (operation1500). In these examples, the temperature sensor is the temperature sensor that is placed in a location with respect to the exhaust system. This temperature sensor is used to measure the temperature in or on an exhaust system at a point at or downstream of the engine.

The process then estimates the rate of change of the temperature (operation1502). This change may be estimated by comparing the current temperature with previous values. The process then calculates the steady state temperature (operation1504). This calculation may be made using equation 6 as shown above.

Next, the engine load factor is estimated from the steady state temperature (operation1506) with the process terminating thereafter. In operation1506, engine load factor may be estimated in a number of different ways.

In this example, the power level at the moment in time for a particular steady state temperature identified in step1504may be calculated as follows:

P∝Tsensor,ss-Tamb=Tsensor-τ⁢ⅆTsensorⅆt-TambEquation⁢⁢7
where P is equal to power, Tsensor,ssis the steady state temperature at the sensor, Tambis the ambient temperature, Tsensoris the temperature measured by the sensor, τ is the thermal time constant, dTsensor/dt is the change in temperature over time, also referred to as the rate of temperature change. From identifying the power, a load factor for the engine at a moment in time may be estimated as follows:

In this equation, LF is the load factor, P100%is 100 percent power, and Tsensor,ss,100%is when the engine is operating at a 100 percent load factor. In this example, the estimated engine power P may be calculated from the load factor by multiplying the load factor by the specified maximum power for the engine:
P=LF·(max rated power)
where P is equal to power, LF is the load factor, and max rated power is the maximum power specified for the engine.
As an alternate method for determining (Tsensor,ss,100%−Tamb), the GSE may be operated over a long period of time with the maximum (Tsensor,ss−Tamb) detected assumed to (Tsensor,ss,100%−Tamb). Alternatively, the engine or equipment manufacturer may specify the engine's idle load factor. This would allow calculation of

Once the engine has been instrumented and calibrated, the exhaust temperature may be used at any later moment in time to estimate (Tsensor,ss−Tamb) using Equation 6. The corresponding engine load factor at that moment may be calculated as

LF=⁢Tsensor,ss-TambTsensor,ss,100⁢%-Tamb=⁢(Tsensor-τ⁢ⅆTsensorⅆt-Tamb)Tsensor,ss,100⁢%-TambEquation⁢⁢10
where Tsensorand (dTsensor/dt) and Tambare now the only variables, which are easy to instrument and measure.

Another alternative involves collecting both (Tsensor,ss−Tamb) data and actual fuel utilization data over a period of time. Integrating (Tsensor,ss−Tamb) over a time period allows a calculation of a fuel burn rate as a function of (Tsensor,ss−Tamb) as follows:

(TotalFuelBurn)=∫c·(Tsensor,ss-Tamb)⁢ⅆt⁢⁢or⁢⁢ⅆ(fuel)ⅆt=c·(Tsensor,ss-Tamb)Equation⁢⁢11
where c is a conversion constant which may be determined by running the engine over a period of time, observing the actual fuel burned of that period of time and dividing by the area under the curve for (Tsensor,ss−Tamb) plotted over the same period of time.

With reference now toFIG. 16, a diagram illustrating a curve fitted to temperature data is depicted in accordance with an advantageous embodiment. Graph1600illustrates temperature in the Y axis and time in the X axis. Curve1602in graph1600represents the ambient temperature. Curve1604represents the measured temperature in the exhaust system and curve1606represents a fitted curve from which a time constant may be identified.

With reference now toFIG. 17, a diagram illustrating a graph used to obtain power levels is depicted in accordance with an advantageous embodiment. Points on graph1700may be derived from data obtained inFIG. 14. In particular, the data inFIG. 14may be used to identify the temperature of the sensor at steady state when the engine is at 100 percent load factor. The ambient temperature may be subtracted from this temperature to identify 100 percent load factor for use in generating graph1700. Similarly, the data inFIG. 14may be used to identify the temperature at the sensor at steady state when the engine is at idle load factor. In graph1700, a percent power level is represented on the Y axis while the steady state temperature rise above ambient is represented on the X axis. This percent power level is a representation of the load factor for the engine. The percent power level may be identified from the steady state temperature using the curve1702.

In this manner, the different advantageous embodiments provide a method and apparatus for monitoring vehicle emissions. These vehicles emissions may be monitored for one or more facilities and may involve using a set of wireless gateways, wireless sensor units, wireless routers, and a data processing system. The wireless sensor units are capable or monitoring operations of the vehicles.

These operations may include the generation of emissions. This data is routed through the wireless routers to a gateway. The gateway then sends the operations data to a data processing system which is capable of processing this emissions data. The process in these examples may include identifying operational use patterns and/or emissions generated by the vehicles as a group. Further, trends and information used to manage the facility also may be generated.

Additionally in some advantageous embodiments, the engine load factor may be estimated based on the exhaust temperatures measured in the exhaust system. This information along with the thermal time constant, the rate of change of temperature in the exhaust system and ambient air temperature may be used to estimate the engine load factor. In the engine load factor, the correlation or estimate may be made of the exhaust generated by the engine.

Further, it is recognized that the depicted method for estimating engine load factor from exhaust temperatures is an approximate method. For example, no consideration is made for the flow rate of ambient air over the exhaust system from wind or vehicle motion, which may have an impact on the estimate of load factor. Further, the relationship between steady state exhaust temperature rise and power level, as depicted inFIG. 17, may not be linear. Still further, sophisticated engines may operate in various modes including, for example, re-circulating some of the exhaust gases through the engine to speed its warm-up cycle, which may alter the relationships between steady state temperature and load factor.

However, one or more of these factors may be corrected through additional data obtained from the equipment specifications in the different advantageous embodiments. Further, useful trends may still be revealed by observing the time history of the collected data, such as equipment operating patterns. Further, data accumulated over time may be correlated or normalized to more precisely collected data, such as total fuel use over a period of time as given by Equation 11. Still further, changes in operating patterns are likely to be observed from the data over time.

The flowcharts and block diagrams in the different depicted embodiments illustrate the architecture, functionality, and operation of some possible implementations of apparatus, methods, and computer program products. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of computer usable or readable program code, which comprises one or more executable instructions for implementing the specified function or functions.

In some alternative implementations, the function or functions noted in the block may occur out of the order noted in the figures. For example, in some cases, two blocks shown in succession may be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

The description of the different advantageous embodiments has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. Although the illustrative embodiments are described with respect to monitoring emissions and vehicle/equipment operations, the advantageous embodiments may be applied to monitoring other things. For example, use patterns may be monitored and compared to maintenance performed on vehicles to identify ways to increase reliability or reduce needed maintenance for vehicles at a facility.