[go: up one dir, main page]

WO2019002001A1 - Procédé et système de localisation d'actifs, évaluation de performance, et détection de défaut - Google Patents

Procédé et système de localisation d'actifs, évaluation de performance, et détection de défaut Download PDF

Info

Publication number
WO2019002001A1
WO2019002001A1 PCT/EP2018/066097 EP2018066097W WO2019002001A1 WO 2019002001 A1 WO2019002001 A1 WO 2019002001A1 EP 2018066097 W EP2018066097 W EP 2018066097W WO 2019002001 A1 WO2019002001 A1 WO 2019002001A1
Authority
WO
WIPO (PCT)
Prior art keywords
lighting
data
output
observed data
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2018/066097
Other languages
English (en)
Inventor
Yuting Zhang
Dong Han
Sirisha RANGAVAJHALA
Talmai BRANDÃO DE OLIVEIRA
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Signify Holding BV
Original Assignee
Philips Lighting Holding BV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Philips Lighting Holding BV filed Critical Philips Lighting Holding BV
Priority to JP2019571499A priority Critical patent/JP6804671B2/ja
Priority to CN201880043528.3A priority patent/CN110832955B/zh
Priority to EP18730010.8A priority patent/EP3646675B1/fr
Priority to US16/624,329 priority patent/US11445589B2/en
Publication of WO2019002001A1 publication Critical patent/WO2019002001A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
    • H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
    • H05B47/20—Responsive to malfunctions or to light source life; for protection

Definitions

  • the present disclosure is directed generally to methods and lighting systems configured to localize lighting unit assets, and to assess performance and detect faults of lighting units within the lighting system.
  • Street lights and other municipal lighting units are important assets that provide critical liveability functions and services, including roadway lighting, night visibility, and public safety. Historically, these lighting units have been visually observed to obtain performance assessment and detect faults. Human inspection of each lighting unit within a lighting network, or recorded data such as geo-located illuminance data or other lighting unit images, is both subjective and labor intensive.
  • vehicle- and/or drone-mounted sensing devices can collect geolocation and illuminance measurements, which can include timestamp, illuminance and geolocation data, and the information can be reviewed to measure performance of a lighting unit or to detect a fault with the lighting unit.
  • existing lighting asset localization methods usually analyze two-dimensional data and attempt to localize the lighting units by comparing geo-located assets to local maximum illuminance values detected from observed data. This method is noisy due to the irregularity of local maxima, the asynchrony between different sensors, and the post-hoc association from the observed data to the geolocation data.
  • the present disclosure is directed to inventive methods and apparatus for a lighting network configured to localize and analyze lighting units within the network.
  • Various embodiments and implementations herein are directed to a system configured to obtain data about a lighting system comprising multiple distributed lighting units, such as streetlights.
  • the system utilizes a simulation platform to simulate the output of the lighting system utilizing a photometric database populated with data about the lighting units and/or the environment.
  • the system also utilizes a database populated with prior measurements of obtained lighting unit output.
  • the system localizes lighting unit assets by fitting prior measurements of obtained lighting unit output using a Gaussian mixture model (GMM), and building a global background model (GBM).
  • GMM Gaussian mixture model
  • GBM global background model
  • the system compares the global background model to observational data about the lighting unit assets, to assess performance and detect faults.
  • the simulated data may also be utilized to augment the observational data, for example if data acquisition is sparse or below a certain threshold.
  • a method for analyzing output of one or more lighting units in a lighting system includes the steps of: (i) simulating, based on data from a photometric database, the output of a lighting unit; (ii) receiving, from a database of historical information, historical observed data about the output of the lighting unit; (iii) receiving observed data about the output of the lighting unit; (iv) generating a model of the lighting system based at least in part on the simulated output of the lighting unit and the historical observed data about the output of the lighting unit, wherein the model comprises localization information for the lighting unit; (v) comparing the received observed data about the output of the lighting unit to the generated model, wherein a fault is detected if the received observed data varies from the generated model by a predetermined amount.
  • the method further includes the step of notifying a user that a fault is detected.
  • the method further includes the step of storing, only if no fault is detected, the received observed data about the output of the lighting unit in the database of historical output information.
  • the predetermined amount is based on a lighting standard, a luminaire specification, and/or a service level agreement.
  • the data stored in the photometric database comprises photometric information about one or more of the lighting units.
  • the model of the lighting system comprises topographical information about the lighting environment.
  • the model of the lighting system comprises geolocation information about one or more of the lighting units.
  • the method further includes the step of enhancing, using the generated model, the receiving observed data.
  • the generated model comprises a weight for each of the one or more lighting units
  • the received observed data comprises a weight for each of the one or more lighting units
  • the step of comparing the received observed data to the generated model comprises comparing the model weight to the observed weight
  • a system configured to analyze the output of one or more lighting units in a lighting system.
  • the system includes: (i) a historical database comprising historical observed data about the output of the one or more lighting units; (ii) a photometric database comprising photometric information about the one or more lighting units; (iii) a simulation module configured to simulate, based on the data from the photometric database, the output of the one or more lighting units; (iv) a training module configured to generate a model of the lighting system based at least in part on the simulated output of the one or more lighting units and the historical observed data about the output of the one or more lighting units, wherein the model comprises localization information for the one or more lighting units; and (v) an assessment module configured to compare received observed data to the generated model, wherein a fault is detected if the received observed data for one or more of the lighting units varies from the generated model for that lighting unit by a predetermined amount.
  • system further includes a user interface configured to provide information to a user about a detected fault.
  • the system further includes an enhancement module configured to enhance, using the generated model, the receiving observed data.
  • the generated model comprises a weight for each of the one or more lighting units
  • the observed data comprises a weight for each of the one or more lighting units
  • the assessment module is configured to compare the model weight to the observed weight.
  • light source should be understood to refer to any one or more of a variety of radiation sources, including, but not limited to, LED-based sources (including one or more LEDs as defined above), incandescent sources (e.g., filament lamps, halogen lamps), fluorescent sources, phosphorescent sources, high-intensity discharge sources (e.g., sodium vapor, mercury vapor, and metal halide lamps), lasers, other types of electroluminescent sources, pyro-luminescent sources (e.g., flames), candle-luminescent sources (e.g., gas mantles, carbon arc radiation sources), photo-luminescent sources (e.g., gaseous discharge sources), cathode luminescent sources using electronic satiation, galvano-luminescent sources, crystallo- luminescent sources, kine- luminescent sources, thermo-luminescent sources, tribo luminescent sources, sonoluminescent sources, radio luminescent sources, and luminescent polymers.
  • LED-based sources including one
  • the term "lighting fixture” is used herein to refer to an implementation or arrangement of one or more lighting units in a particular form factor, assembly, or package.
  • the term “lighting unit” is used herein to refer to an apparatus including one or more light sources of same or different types.
  • a given lighting unit may have any one of a variety of mounting arrangements for the light source(s), enclosure/housing arrangements and shapes, and/or electrical and mechanical connection configurations. Additionally, a given lighting unit optionally may be associated with (e.g., include, be coupled to and/or packaged together with) various other components (e.g., control circuitry) relating to the operation of the light source(s).
  • An "LED-based lighting unit” refers to a lighting unit that includes one or more LED-based light sources as discussed above, alone or in combination with other non LED- based light sources.
  • a processor or controller may be associated with one or more storage media (generically referred to herein as "memory,” e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.).
  • the storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein.
  • program or “computer program” are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.
  • one or more devices coupled to a network may serve as a controller for one or more other devices coupled to the network (e.g., in a master/slave relationship).
  • a networked environment may include one or more dedicated controllers that are configured to control one or more of the devices coupled to the network.
  • multiple devices coupled to the network each may have access to data that is present on the communications medium or media; however, a given device may be "addressable" in that it is configured to selectively exchange data with (i.e., receive data from and/or transmit data to) the network, based, for example, on one or more particular identifiers (e.g., "addresses") assigned to it.
  • network refers to any interconnection of two or more devices (including controllers or processors) that facilitates the transport of information (e.g. for device control, data storage, data exchange, etc.) between any two or more devices and/or among multiple devices coupled to the network.
  • various implementations of networks suitable for interconnecting multiple devices may include any of a variety of network topologies and employ any of a variety of communication protocols.
  • any one connection between two devices may represent a dedicated connection between the two systems, or alternatively a non-dedicated connection. In addition to carrying information intended for the two devices, such a non-dedicated connection may carry information not necessarily intended for either of the two devices (e.g., an open network connection).
  • networks of devices as discussed herein may employ one or more wireless, wire/cable, and/or fiber optic links to facilitate information transport throughout the network.
  • FIG. 1 is a schematic representation of a lighting system comprising a plurality of lighting units, in accordance with an embodiment.
  • FIG. 2 is a schematic representation of a lighting unit, in accordance with an embodiment.
  • FIG. 3 is a schematic representation of an assessment system for a lighting system, in accordance with an embodiment.
  • FIG. 4 is a flowchart of a method for analyzing output of a lighting unit in a lighting network, in accordance with an embodiment.
  • the present disclosure describes various embodiments of a system configured to localize lighting units within a lighting system, and to assess the performance of those lighting units. More generally, Applicant has recognized and appreciated that it would be beneficial to provide an automated lighting network monitoring system. A particular goal of utilization of certain embodiments of the present disclosure is to notify a user that a lighting unit within the lighting system is or may be experiencing a fault that should be corrected.
  • various embodiments and implementations are directed to an automated method and system configured to combine simulated data, historical data, and observational data into a model that localizes lighting units and assesses the performance of those lighting units.
  • the system analyzes lighting unit assets by building a global background model about the lighting system and comparing the model to
  • observational data about lighting unit assets If the global background model and the observational data vary by a predetermined amount or above a predetermined threshold, a fault is identified and a user is notified. The user can then analyze the lighting unit and repair the fault if necessary.
  • a lighting system 100 comprising a plurality of lighting units 10.
  • the lighting system can be any lighting system or network.
  • the lighting system can be a city, town, village, or other municipality lighting system.
  • the lighting system may be a centralized system such as a parking lot, or may be a distributed system such as a city- wide lighting system.
  • the lighting system is an interior lighting system such as an office building, a mall, a stadium, or other structure or interior space.
  • the lighting units 10 within the lighting system may be networked to each other or to a remote or central server, hub, or location.
  • the lighting units may comprise a wired and/or wireless communications module configured to communicate via a network 101 to the remote or central server, hub, or location.
  • Wireless communication with the network can be, for example, cellular, Wi-Fi, Bluetooth, IR, radio, or near field communication, among many others.
  • the lighting unit 10 configured to emit light.
  • the lighting unit includes one or more light sources 12, where one or more of the light sources may be an LED-based light source.
  • the LED-based light source may have one or more LEDs.
  • the light source can be driven to emit light of predetermined character (i.e., color intensity, color temperature) by one or more light source drivers 14.
  • light source drivers 14 Many different numbers and various types of light sources (all LED-based light sources, LED-based and non-LED-based light sources alone or in combination, etc.) adapted to generate radiation of a variety of different colors may be employed in the lighting unit 10.
  • lighting unit 10 can be any type of lighting fixture, including but not limited to a street light or any other interior or exterior lighting fixture.
  • lighting unit 10 is configured to illuminate all or a portion of a target surface within a lighting environment, wherein the lighting environment can be a room, building, campus, street, city, portion of a city, or any other lighting environment.
  • the lighting environment is a city comprising a plurality of lighting units 10.
  • lighting unit 10 includes a controller 16 that is configured or programmed to output one or more signals to drive the one or more light sources 12a-d and generate varying intensities, directions, and/or colors of light from the light sources.
  • Lighting unit 10 also includes a source of power 18, most typically AC power, although other power sources are possible including DC power sources, solar-based power sources, or mechanical-based power sources, among others.
  • Lighting unit 10 can also include a communications module 20 configured for wired and/or wireless communication with other lighting units, a remote or central server or hub, or other devices.
  • One or more of the lighting units may be unique compared to the others within the system. According to one embodiment, each of the lighting units may be unique.
  • the lighting units may be different sizes or shapes, may have different lighting angles, different outputs, different intensities, different colors, and/or different heights, among many other types of customizations. This complexity is often found in lighting systems within a city or municipality where the topology of the environment necessitates many lighting units each comprising a unique position within the environment.
  • FIG. 3 in one embodiment, is a schematic representation of an assessment system 300 configured to combine simulated data, historical data, and
  • Assessment system 300 may be co-localized with a lighting network 100 that it analyzes, or may be remote from the lighting network.
  • the assessment system may service or analyze one lighting network or multiple different lighting networks.
  • assessment system 300 comprises a photometric database 310.
  • Photometric database 310 comprises data about performance and other parameters of the lighting units 10 within lighting system 100.
  • the photometric database comprises information about the performance of a lighting unit in one or more situations, such as different temperatures, different weather conditions, the spatial
  • Performance may include parameters about output, color, and other data about the lighting unit.
  • photometric data may be supplied by the manufacturer and/or supplier of the lighting unit.
  • assessment system 300 may be configured to automatically retrieve and store photometric data within photometric database 310, such as when lighting units are purchased or commissioned within the lighting system.
  • a simulation module 320 of assessment system 300 accesses data within photometric database 310 to simulate a spatial lighting distribution of the lighting network within the lighting environment.
  • the simulation module may simulate the spatial lighting distribution of a plurality of streetlights located along a street, or a plurality of ceiling lights within a mall.
  • the simulation module can consider a family of lighting simulation software and apply comprehensive simulation methodologies across all software to simulate the spatial lighting distribution.
  • the simulation module may therefore simulate multiple lighting units, different street morphologies, mixed types of lighting units, and many other photometric characteristics.
  • Simulation module 320 may optionally comprise information about the lighting environment, such as the positioning of the lighting units within the environment, among many other types of information and parameters about the lighting environment.
  • the simulation module may comprise a three-dimensional map or topology information about the lighting environment and/or the lighting units within the lighting environment, localization information for the lighting units, the orientation and angling of the lighting units, timing schedules for the lighting units, ambient light, and many other types of information and parameters.
  • simulation module 320 may comprise information about which side(s) of a street comprises lighting units, the height of the lighting units on that street, the topology and shape of the street, and other factors. Using this information, simulation module 320 generates a simulated spatial lighting distribution of the lighting network within the lighting environment.
  • assessment system 300 comprises a historical database 330.
  • the historical database comprises observational data collected within the lighting environment, such as illuminance data about the one or more lighting units within the lighting system.
  • the data is geo-located, and thus the illuminance data about the one or more lighting units comprises information about the location of the lighting unit within the lighting system.
  • the observational data may be collected by a dynamic light measurement system or device, such as an automated or manual light-detecting and/or image-capturing device that moves through the lighting system.
  • the dynamic light measurement system or device may be a sensor or sensor array mounted on a vehicle, drone, or other device configured to automatically or manually manoeuvre through the lighting environment.
  • assessment system 300 comprises a training module 340 configured to receive or request data from the historical database 330 and to receive or request simulation data from simulation module 320.
  • Training module 340 utilizes the data from the historical database and the simulation module to create a data model using one or more methods or algorithms.
  • training module 340 estimates a global background model (GBM) from the historical database using the Gaussian mixture model (GMM) learning principle, and utilizing the simulated data from the simulation module as reference.
  • GBM global background model
  • GMM Gaussian mixture model
  • the Gaussian mixture model is a parametric probability density function represented as weighted sum of Gaussian component densities, in a scalar form as: ( ) ⁇ 1 ⁇ ( ; ⁇ , ⁇ ⁇ ) (Eq. 1) or in a vector form as
  • GMM is statistically parameterized by the mean vectors ⁇ ? covariance matrices ⁇ i? and mixture weights ⁇ £ . According to an embodiment, these parameters can be estimated by the expectation maximization, maximum likelihood, and maximum a posteriori algorithms.
  • the Gaussian densities can be in scalar and vector to fit different nature of data distributions.
  • the estimated mean vectors ⁇ can represent the light geolocation coordinates, and the covariance matrices ⁇ j can characterize street light illuminance outputs.
  • the geo-located illuminance data can be characterized by independent Gaussian distributions, which by nature represent both first and higher order statistics. These parameters can calculate aspects such as street lighting metrics and benchmark light auditing with high order of fidelity.
  • the output of training module 340 is asset localization.
  • assessment system 300 comprises an assessment module 350 configured to compare real-time measurement data 360 to the generated GBM for performance assessment and fault detection.
  • Real-time measurement or observational data 360 may be analyzed in realtime or may be analyzed after collection.
  • the observational data may be analyzed as it is collected, or it may be stored for analysis at a later time or date.
  • the real-time measurement or observational data may be obtained using any system for obtaining illuminance or other observational data about the one or more lighting units within the lighting system.
  • the real-time measurement or observational data may be obtained using a dynamic light measurement system or device, such as an automated or manual light-detecting and/or image-capturing device that moves through the lighting system.
  • the dynamic light measurement system or device may be a sensor or sensor array mounted on a vehicle, drone, or other device configured to automatically or manually manoeuvre through the lighting environment.
  • real-time measurement or observational data 360 may be augmented or enhanced by historical data.
  • a data enhancement module or algorithm 370 analyzes the fidelity of the observed data, such as parameters of amount and/or distribution, and determines that enhancement is necessary.
  • enhancement module 370 may comprise a predetermined threshold or other mechanism to determine that the fidelity of the obtained observational data is insufficient.
  • the enhancement module can then receive or request data from the simulation module 320, and/or the historical database 330, and can augment or enhance the obtained observational data, with interpolation and/or fitting methods.
  • the enhancement may be based on the reference model generated by the simulation module.
  • assessment module 350 fits the real-time measurement or observational data, which may or may not be enhanced by enhancement module 370, to the GBM Gaussian densities from the training module 340, and estimates the observed weights ⁇ . The module then compares the observed weights ⁇ - to the GBM weights ( ⁇ for the performance assessment and the fault detection. According to an embodiment, assessment module 350 may utilize one or more of the following comparison scenarios and decisions:
  • the assessment module 350 may utilize one or more predetermined thresholds to quantify performance and/or detect a fault.
  • the one or more predetermined thresholds may be based on one or more of a user-set threshold, thresholds set forth in a service-level agreement, machine- learned thresholds, or other thresholds.
  • one or more of the predetermined thresholds may be based on requirements for lighting compliance, roadway safety, city sustainability, and other parameters, settings, or requirements.
  • one or more of the predetermined thresholds may be based on information from the photometric database. For example, the performance of a lighting unit may degrade over time in a known manner as set forth in the photometric database.
  • the observational data 360 can optionally be fed into the historical database 330 to enrich the training module.
  • the performance assessment can re-estimate one or more lighting metrics to check the anomaly. Fault detection can lead to further actions, such as transferring the fault information to the entities or authorities who are in charge of the light assets.
  • assessment system 300 comprises a user interface 380 configured to receive information from and/or provide information to a user.
  • the assessment system 300 may provide information to the user about one or more outcomes of the analysis, such as information about one or more lighting units, information about lack of faults, information about identified faults, two- or three- dimensional maps of simulated and/or observational data, or other information.
  • the user interface may be co-located with one or more other components of the assessment system, or may be located remote from the other components of the assessment system.
  • user interface 380 may be a computer, monitor, smartphone, wearable, or other device in wired and/or wireless communication with one or more other components of the assessment system.
  • FIG. 4 in one embodiment, is a flowchart illustrating a method 400 for analyzing the output of a lighting unit in a lighting network.
  • the method is configured to combine simulated data, historical data, and observational data into a model that localizes lighting units and assesses the performance of those lighting units.
  • the method may utilize an assessment system 300 comprising one or more of a photometric database 310, simulation module 320, historical database 330, training module 340, assessment module 350, observational data 360, enhancement module 370, and/or user interface 380.
  • the method is configured to work with a lighting system 100 comprising a plurality of lighting units 10.
  • Lighting system can be any of the embodiments described herein or otherwise envisioned, and the plurality of lighting units can include any of the components of the lighting units described in conjunction with FIG. 2.
  • each lighting unit 10 is configured to illuminate all or a portion of a target surface within the lighting environment.
  • the lighting unit is an outdoor lighting fixture such as a streetlight, parking lot light, or other lamp post or external lighting fixture configured to illuminate an exterior target surface.
  • the lighting unit is an indoor lighting fixture such as a ceiling light or other internal lighting fixture configured to illuminate an interior target surface.
  • the assessment system receives or retrieves photometric information about one or more of the lighting units within the lighting system, and stores it in photometric database 310.
  • Photometric data may be supplied by the manufacturer and/or supplier of the lighting unit.
  • assessment system 300 may be configured to automatically retrieve and store photometric data within photometric database 310, such as when lighting units are purchased or commissioned within the lighting system.
  • the photometric information may comprise information about the performance of a lighting unit in one or more situations, such as different temperatures, different weather conditions, the spatial distribution of light output from the lighting unit, and other information.
  • the assessment system receives or retrieves observational data collected within the lighting environment and stores it in historical database 330.
  • the observational data collected about one or more of the plurality of lighting units within the lighting system may comprise illuminance data, among other data.
  • the data is geo-located and thus the illuminance data about the one or more lighting units comprises information about the location of the lighting unit within the lighting system.
  • the observational data may be collected by a dynamic light measurement system or device, such as an automated or manual light-detecting and/or image-capturing device that moves through the lighting system.
  • a simulation module 320 of the assessment system accesses data within photometric database 310 and simulates a spatial lighting distribution of one or more of the lighting units within the lighting network of the lighting environment.
  • the simulation module may simulate the spatial lighting distribution of a plurality of streetlights located along a street, or a plurality of ceiling lights within a mall.
  • the simulation module can consider a family of lighting simulation software and apply comprehensive simulation methodologies across all software to simulate the spatial lighting distribution. The simulation module may therefore simulate multiple lighting units, different street morphologies, mixed types of lighting units, and many other photometric characteristics.
  • the training module 340 of the assessment system generates a model of the lighting system based at least in part on the simulated output of the lighting unit and the historical observed data about the output of the lighting unit.
  • the model comprises localization information for one or more of the lighting units within the system.
  • training module 340 estimates a global background model (GBM) from the historical database using the Gaussian mixture model (GMM) learning principle, and utilizing the simulated data from the simulation module as reference.
  • the Gaussian densities can be in scalar and vector to fit different nature of data distributions.
  • the estimated mean vectors ⁇ can represent the light geolocation coordinates, and the covariance matrices ⁇ j can characterize street light illuminance outputs.
  • the geo-located illuminance data can be characterized by independent Gaussian distributions, which by nature represent both first and higher order statistics. These parameters can calculate aspects such as street lighting metrics and benchmark light auditing with high order of fidelity.
  • the output of training module 340 is asset localization.
  • observational data about one or more lighting units in the lighting system is received by the assessment system.
  • the real-time measurement or observational data may be obtained using any system for obtaining illuminance or other observational data about the lighting units.
  • the real-time measurement or observational data may be obtained using a dynamic light measurement system or device, such as an automated or manual light-detecting and/or image-capturing device that moves through the lighting system.
  • Observational data may be analyzed as it is collected, or it may be stored for analysis at a later time or date.
  • the observational data may be stored in the assessment system, or may be stored remotely and provided to or accessed by the system when needed during performance of the method.
  • a data enhancement module 370 enhances the received observational data using the model generated by the training module.
  • the data enhancement module analyzes the fidelity of the observed data, such as parameters of amount and/or distribution, and determines that enhancement is necessary.
  • the enhancement module can then receive or request data from the simulation module 320, and/or the historical database 330, and can augment or enhance the obtained observational data, with interpolation and/or fitting methods.
  • the assessment module 350 of the assessment system compares the observational data to the generated model to create a performance assessment and for fault detection.
  • assessment module 350 fits the real-time measurement or observational data, which may or may not be enhanced by enhancement module 370, to the GBM Gaussian densities from the training module 340, and estimates the observed weights ⁇ .
  • the module compares the observed weights ⁇ - to the GBM weights for the performance assessment and the fault detection.
  • the assessment module 350 may utilize one or more predetermined thresholds to quantify performance and/or detect a fault.
  • the one or more predetermined thresholds may be based on one or more of a user-set threshold, thresholds set forth in a service-level agreement, machine- learned thresholds, or other thresholds.
  • the system notifies a user of the fault using the user interface 380.
  • the notification may comprise information about the fault, such as the location of the lighting unit, the nature of the fault, the observational and/or historical data, the comparison to the generated model, and/or other information.
  • the notification may be a visual warning, a light, a sound, a text, or any other notification.
  • the user interface may comprise a map of the lighting environment, and may display the location of the lighting unit comprising the fault. Many other methods for notifying the user of the fault are possible.
  • the received observational data is stored in the historical database 330, and the data may be utilized in future iterations of the assessment method.
  • inventive embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed.
  • inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein.
  • At least one of A and B can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

Landscapes

  • Circuit Arrangement For Electric Light Sources In General (AREA)

Abstract

La présente invention concerne un procédé (400) servant à analyser la sortie d'unités d'éclairage (10) dans un système d'éclairage (100), comprenant les étapes consistant : (i) à simuler (430), sur la base de données provenant d'une base de données photométrique (310), la sortie d'une unité d'éclairage ; (ii) à recevoir et stocker (420), à partir d'une base de données (330) d'informations historiques, des données historiques observées concernant la sortie de l'unité d'éclairage ; (iii) à recevoir (450) des données observées (36) concernant la sortie de l'unité d'éclairage ; (iv) à générer (440) un modèle du système d'éclairage sur la base, au moins en partie, de la sortie simulée de l'unité d'éclairage et des données historiques observées concernant la sortie de l'unité d'éclairage, le modèle comprenant des informations de localisation de l'unité d'éclairage ; et (v) à comparer (470) les données observées reçues concernant la sortie de l'unité d'éclairage au modèle généré, un défaut étant détecté si les données observées diffèrent du modèle généré d'une ampleur prédéterminée.
PCT/EP2018/066097 2017-06-27 2018-06-18 Procédé et système de localisation d'actifs, évaluation de performance, et détection de défaut Ceased WO2019002001A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2019571499A JP6804671B2 (ja) 2017-06-27 2018-06-18 資産位置特定、性能評価及び障害検出のための方法及びシステム
CN201880043528.3A CN110832955B (zh) 2017-06-27 2018-06-18 用于资产定位、性能评估和故障检测的方法和系统
EP18730010.8A EP3646675B1 (fr) 2017-06-27 2018-06-18 Procédé et système de localisation de biens, d'évaluation des performances et de détection de défaillances
US16/624,329 US11445589B2 (en) 2017-06-27 2018-06-18 Method and system for asset localization, performance assessment, and fault detection

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201762525234P 2017-06-27 2017-06-27
US62/525234 2017-06-27
EP17179933.1 2017-07-06
EP17179933 2017-07-06

Publications (1)

Publication Number Publication Date
WO2019002001A1 true WO2019002001A1 (fr) 2019-01-03

Family

ID=59409150

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2018/066097 Ceased WO2019002001A1 (fr) 2017-06-27 2018-06-18 Procédé et système de localisation d'actifs, évaluation de performance, et détection de défaut

Country Status (1)

Country Link
WO (1) WO2019002001A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119203059A (zh) * 2024-11-25 2024-12-27 盛隆电气集团有限公司 基于数字孪生技术的智慧楼宇管理方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120200660A1 (en) * 2011-02-04 2012-08-09 Microsoft Corporation Techniques and system for active lighting control in video conferencing
WO2015132717A1 (fr) * 2014-03-06 2015-09-11 Koninklijke Philips N.V. Système d'éclairage intelligent à programmation de maintenance prédictive et son procédé de fonctionnement
US20160258209A1 (en) * 2015-03-03 2016-09-08 Mechoshade Systems, Inc. Shade adjustment notification system and method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120200660A1 (en) * 2011-02-04 2012-08-09 Microsoft Corporation Techniques and system for active lighting control in video conferencing
WO2015132717A1 (fr) * 2014-03-06 2015-09-11 Koninklijke Philips N.V. Système d'éclairage intelligent à programmation de maintenance prédictive et son procédé de fonctionnement
US20160258209A1 (en) * 2015-03-03 2016-09-08 Mechoshade Systems, Inc. Shade adjustment notification system and method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119203059A (zh) * 2024-11-25 2024-12-27 盛隆电气集团有限公司 基于数字孪生技术的智慧楼宇管理方法

Similar Documents

Publication Publication Date Title
CN103548417B (zh) Oln光变化/优化系统
US10707959B2 (en) Locating assets using auto-commissioned light fixtures in a lighting system
US11445589B2 (en) Method and system for asset localization, performance assessment, and fault detection
US20200051038A1 (en) Calibration of cloud-based information for lifetime prediction of luminaires
US10813194B2 (en) Noise-flow monitoring and sound localization via intelligent lighting
TW201531164A (zh) 使用複合照明模型之動態空間分辨照明
JP2014517981A5 (ja) 照明変更／最適化システム
US20080037241A1 (en) Light fixture
JP6584716B2 (ja) 照明センサ分析
US11064591B2 (en) Flooding localization and signalling via intelligent lighting
US20190182930A1 (en) Method for calibration of lighting system sensors
US10674581B2 (en) Optimizing multichannel luminaire control using a color efficient matrix
WO2015104603A2 (fr) Système de mise en service d'éclairage utilisant des détecteurs de position
WO2019002001A1 (fr) Procédé et système de localisation d'actifs, évaluation de performance, et détection de défaut
US10645773B2 (en) System and method for managing lighting based on population mobility patterns
WO2018091315A1 (fr) Système et procédé de gestion d'éclairage en fonction de modèles de mobilité de population
EP3577609A1 (fr) Système activé par éclairage et procédés pour construire une planification d'évacuation
WO2019020431A1 (fr) Systèmes, procédés et appareil de compensation de données de signal analogique provenant d'un luminaire à l'aide d'estimations de température ambiante

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18730010

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019571499

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2018730010

Country of ref document: EP

Effective date: 20200127