Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J1/00—Photometry, e.g. photographic exposure meter
  • G01J1/02—Details
  • G01J1/0228—Control of working procedures; Failure detection; Spectral bandwidth calculation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J1/00—Photometry, e.g. photographic exposure meter
  • G01J1/42—Photometry, e.g. photographic exposure meter using electric radiation detectors
  • G01J1/4204—Photometry, e.g. photographic exposure meter using electric radiation detectors with determination of ambient light

Definitions

• the present invention relates to a method of detecting a defect light sensor.
• Luminaires are being wirelessly connected and integrated into lighting systems. Combined with light sensors, and possibly other sensors like PIR sensors, these lighting systems are designed to provide advanced functions like daylight adaptation for energy saving. However, proper functioning of the lighting system depends on the correct functioning and calibration of the sensors. It is known that these may degrade and drift over time. Hence, proper calibration techniques must be employed to detect the behaviour of the sensors, so that recalibration or replacement can occur when sensor faults are detected.
• a defect light sensor comprising:
• said performing a detection procedure comprising:
• the present method relies on more passively recording sensor information from the lighting system. By selecting data which has been collected during similar or comparable circumstances, it is possible to compare the data and discover defect behaviour of the light sensor.
• the time period is at night. This is advantageous in that light from other sources than the lighting system which the light sensor refers to are either negligible or relatively constant.
• the collection of data further comprises collecting outdoor weather data in conjunction with said light sensor data
• the determination of a template behavior of the light data comprises determining a template of a relation between the light sensor data and the outdoor weather data collected during said time period.
• the operation of performing a detection procedure comprises collecting outdoor weather data in conjunction with said light sensor data
• the operation of determining a corresponding behavior comprises determining a corresponding relation for each selected day
• the operation of comparing the corresponding behavior with the template comprises comparing the relations with the template to detect any defect of the light sensor. It is advantageous to consider also outdoor weather data, and to relate the light sensor data to that data.
• the light sensor data is indoor light sensor data
• the operation of determining a template of a relation comprises:
• the operation of determining a coefficient comprises fitting a linear dependence of the indoor light sensor data on the outdoor weather data.
• the operation of comparing the set of coefficients with the template comprising displaying the coefficients in a control chart and applying one or more of the Nelson rules to the set of coefficients and said template.
• it comprises determining said well-defined indoor conditions by means of presence data.
• the method comprises determining said well-defined indoor conditions by means of at least one type of data out of a set of data consisting of data on window blinds, data on switching or dimming status of a lighting system, or data on energy consumption by a lighting system.
• the operation of selecting further sequences comprising determining if the outdoor weather data is within predetermined limits of the model sequence data by applying a distance function to the outdoor weather data and the model sequence data.
• the weather data comprises solar irradiation data.
• Fig. 1 is a block diagram of an example system for performing the present method
• Figs. 2 to 5 are diagrams showing indoor light sensor data versus outdoor weather data for different ranges of time
• Figs. 6 and 7 are diagrams showing selected data from Fig. 2 illustrated by curves connecting the data points;
• Fig. 8 is a flow chart illustrating a preparation procedure according to an embodiment of the present method.
• Fig. 9 is a flow chart illustrating a detection procedure according to an embodiment of the present method.
• Fig. 10 is a chart illustrating one way of determining a defect
• Figs. 1 1 and 12 are flow charts illustrating procedures according to another embodiment of the method.
• Figs. 13 and 14 are diagrams showing results obtained by an embodiment of the method. DESCRIPTION OF EMBODIMENTS
• An example monitoring system 1 in which the present method of detecting a defect light sensor is implementable comprises a controller 2 connected, wireless or by wire, to a lighting system 3 having several sets of luminaires 4, 5 arranged in different rooms of a building. More particularly, the controller 2 is connected with an indoor light sensor 6, 7, or with several indoor light sensors, in each of the rooms, detecting indoor illumination.
• the monitoring system 1 further comprises an outdoor weather sensor 8, arranged outdoor of the building.
• the outdoor weather sensor 8 typically is also a light sensor detecting outdoor illumination.
• the controller 2 is connected to a display 9.
• the monitoring system 1 can be connected to several light sensors 6, 7, which can be arranged in one or more lighting systems 3. However, if nothing else is expressed below, the description refers to a single light sensor, but is equally valid for every light sensor 6, 7 when the monitoring system 1 is connected with several light sensors 6, 7.
• the method according to the present invention can be regarded as being based on a passive recording of sensor data, and processing of the data in order to find a diverging behavior of a light sensor. This is in contrast to prior art methods where the luminaires are actively operated in conjunction with the data recording. According to a first
• indoor light sensor data and outdoor weather data in this embodiment being light sensor data as well, is collected by means of the indoor light sensors 6, 7 and the outdoor weather sensor 8.
• the data collection is performed for several days during at least a part of each day.
• a preparation procedure on the collected data is performed in order to determine a template, which represents the behavior of a fully functioning light sensor.
• the conditions when data are collected must be stable and repeatable.
• the preparation procedure involves determining, by means of the controller 2, a template of a relation between the indoor light sensor data and the outdoor weather data collected during a time period constituting a part of the day with well-defined indoor and outdoor conditions.
• the detection procedure comprises using the controller 2 for collecting outdoor weather data, from the weather sensor 8, and indoor light sensor data, from the light sensor 6, 7, for several further days during the corresponding time period; selecting representative days thereof; determining a corresponding relation for each selected day; and comparing the relations with the template to detect any defect of the light sensor 6, 7.
• the preparation procedure comprises collecting light sensor data and weather data for several consecutive days, all day around, see box 80. For instance the sensor outputs are sampled every 5-10 minutes.
• the pairs For a linear dependence, or high correlation, between the indoor light level and the outdoor light level, the pairs should be on a straight line. However, as can be seen in Fig. 2, the pairs are far from concentrated on a straight line and, in fact, the overall correlation is only about 0.3.
• the dependence is of a more complicated nature, which is caused by a number of environmental conditions.
• occupants of the building will interfere with the indoor light levels as derived from the outdoor light levels in a number of ways. They may interfere directly by opening or closing blinds and switching luminaires on and off.
• reflections can considerably change the measured illumination levels in a room.
• the room orientation and the shading have significant influence.
• data from weekends was selected. Plots of the indoor light sensor data versus the outdoor weather data are shown in Figs. 3 to 5 for three different days on weekends. Fig.
• FIG. 3 shows a day at the end of April
• Fig. 4 shows the following day
• Fig. 5 shows a day at the beginning of January.
• successive observations have been interconnected by means of a directed line.
• arrows indicate the temporal behavior of the observations. The observation starts in the lower left corner of the plot, where it was dark both outdoor and indoor in the beginning of the day. Subsequently, both outdoor and indoor light levels rise and decay. It can be seen from Fig. 3 that there is a functional dependence of indoor light level on outdoor light level, which is distinctly non-linear throughout the day, and depends on the angle with which the sunlight enters the room. From Fig. 4, it can be concluded that this dependence is more or less deterministic.
• Fig. 5 illustrates the dependence on season. Again, the data reveal a clear, be it non-linear, dependence on a day on which there was no presence in the building. However, the shape of the trajectory is different, and, as outdoor light levels are low in January relative to April, only a small part of the illumination space is traversed.
• the sharp turn at the lower right corner, at A represents the sun light starting to enter the room; and the sharp turn to the upper left, at B, represents the sun being hidden behind a building.
• a distance function d(M, W) ⁇ 5, box 83 If the distance is too large, then a next sequence is tested. Those sequences W falling within predetermined limits, determined by choosing the size of ⁇ , of the model sequence data M are selected.
• Figs. 6 and 7 illustrate this selection, where the curves of Fig. 6 represent the data collected all days during the selected time period. The curves of Fig. 7 represent the curves remaining after having applied the distance function on the weather data and the selection on basis of presence.
• box 85 determines whether or not the indoor light sensor data S has been collected during well-defined indoor conditions, box 85, which in this embodiment is performed by determining whether or not someone has been present in the room during the collection of the data. If no one has been present the light sensor data S is accepted. Presence data can be obtained in different ways. In an office presence data is typically available from the enterprise using the office. As an alternative, a particular presence sensor can be added to the monitoring system 1. Then the relation between the indoor light sensor data and the outdoor weather data is determined, by determining a coefficient b representing the relation, and more particularly b is calculated such that the distance d(S, bW) is minimized, box 86.
• the determination of the coefficient consists of fitting a linear dependence of the indoor light sensor data S on the outdoor weather data W.
• the coefficients b for several selected sequences of light sensor data are stored, box 87, and then it is determined whether or not a sufficient number of selected sequences of light sensor data, and thus corresponding coefficients b, have been found, box 88.
• statistical values for the stored coefficients b are determined.
• the statistical values constitute said template.
• the statistical values are the mean and the standard deviation of b, i.e. mean(b) and o(b).
• the continuous monitoring i.e. the detection procedure
• the detection procedure according to the first embodiment of the method, is illustrated with the flow chart of Fig. 9.
• the detection procedure involves the operations of collecting outdoor weather data and indoor light sensor data for several further days during the corresponding time period; selecting representative days thereof; determining a corresponding relation for each selected day; and comparing the relations with the template to detect any defect of the light sensor.
• each new day, light sensor data and weather data are selected during the time period, i.e. during the two and a half hours in the morning, box 90.
• the model sequence M is retrieved, box 91 , and it is determined if the weather data is within predetermined limits of the model sequence, i.e. if the distance between the sequence of weather data W and the model sequence M is smaller than the predetermined limit value ⁇ , expressed by d(W, M) ⁇ ⁇ , box 92.
• ⁇ the predetermined limit value
• box 94 also similar to the preparation procedure. If not then the data of this day is rejected. If the test is passed, then the light sensor data S and the weather data W for that specific day are selected. Next, similar to the preparation procedure, a relation between the light sensor data S and the weather data W is determined by fitting a linear dependence of the light sensor data S on the weather data W, i.e. by calculating a coefficient c such that d(S, cW) is minimized, box 95. The coefficient c is stored in a database, box 96. Thus, after a while the database will hold a set of coefficients c for several days, for which the criteria for the selection have been met.
• the set of coefficients c is compared with the template, i.e. mean(b) and o(b), and appropriate quality measures for determining deviations beyond what is considered as normal behavior of the light sensor are applied, box 97.
• the template i.e. mean(b) and o(b)
• appropriate quality measures for determining deviations beyond what is considered as normal behavior of the light sensor are applied, box 97.
• the mean value and the standard deviation of the template constitute a basis of a chart where the following coefficients c are added. For instance, a trend among the values can be detected as illustrated in Fig. 10.
• One rule for defining a trend can be that the value of more than six
• coefficients in a row increase or decrease.
• such a trend can mean a defect.
• a value that falls outside an interval defined by a mean value plus or minus three standard deviations is indicative of a defect sensor.
• a defect is discovered a flag is raised to an operator, boxes 98 and 99, and the control chart is displayed on the display 9, box 100. Alternatively, the very determination of whether there is a defect or not is made manually. Then the control chart is displayed and the operator looks for patterns that can indicate a defect.
• the method is performed at night when there is no daylight to consider.
• the template then consists of the direct light sensor values, instead of the above described relation.
• a template is generated with light sensor data for well-defined conditions which, in addition to the night time of the day include non-presence and data defining whether the luminaires contributing to the illumination sensed by the light sensor are on or off.
• the preparation procedure comprises collecting light sensor data during a time period constituting a part of the night, as shown in box 101 ; determining whether or not the well-defined conditions are met, box 102. If not, new data is collected next night. If the conditions are met, a template of the behavior of the light sensor data is determined, box 103.
• the detection procedure comprises collecting light sensor data for several further days during the corresponding time period, as shown in Fig. 12, box 104. Then representative days thereof are selected, box 105. The selection is done by identifying similar well-defined conditions.
• corresponding behavior is determined for each selected day, box 106; and the corresponding behavior is compared with the template to detect any defect of the light sensor, box 107. If a defect is found, box 108, this is flagged to the operator, box 109.
• the final defect detection can be made manually by displaying the template and the light sensor data in a chart.
• FIG. 14 An example of collected light sensor values ranging over several nights is shown in Fig. 14, displayed by light level (y axis) versus a sequential number of the collected light sensor sample.
• the intermediate light levels stems from the averaging behavior of a light sensor, and correspond to time slot during which a switch between on and off occurred, so that the environment was respectively dark and illuminated for only part of the time slot.
• a similar embodiment consists of making the operations for detecting a defect for one day at a time, such as continuously once a day. Then the operation of selecting representative days is exchange for determining whether the current day is a representative day. If not the procedure is ended there.
• further input data for determining well-defined indoor conditions may include data on window blinds, data on switching or dimming status of the lighting system, or data on energy consumption by the lighting system. Furthermore, additional determinations are possible to perform on basis of the further information obtained by such further input data.
• the method can be performed both indoor and in other environments, as long as repeatable well-defined conditions can be established.
• Another part of the day, such as the night or a part thereof can be chosen for determining the function of the light sensors, etc.

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• Physics & Mathematics (AREA)
• Spectroscopy & Molecular Physics (AREA)
• General Physics & Mathematics (AREA)
• Life Sciences & Earth Sciences (AREA)
• Sustainable Development (AREA)
• Photometry And Measurement Of Optical Pulse Characteristics (AREA)
• Circuit Arrangement For Electric Light Sources In General (AREA)

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• 2015
  • 2015-02-16 US US15/121,789 patent/US20170016761A1/en not_active Abandoned
  • 2015-02-16 WO PCT/EP2015/053211 patent/WO2015128214A1/en not_active Ceased
  • 2015-02-16 EP EP15706401.5A patent/EP3111177A1/en not_active Withdrawn
  • 2015-02-16 JP JP2016553904A patent/JP2017508152A/ja active Pending
  • 2015-02-16 CN CN201580011014.6A patent/CN106164631A/zh active Pending

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