JP2017033753A - Illumination lamp deterioration prediction method and system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make it possible to accurately predict cleaning timing and replacement timing for an individual illumination lamp.SOLUTION: An illumination lamp deterioration prediction method includes the steps of: previously setting correspondence among an illumination lamp's turbidity, the illumination lamp's illumination output, and an illuminance ratio as a ratio of the illumination lamp's illuminance to a reference value; imaging an illumination lamp, a deterioration prediction target, by using imaging means; acquiring a brightness distribution curve from the imaged picture, calculating a variation coefficient as a slope at the time of shifting to the brightness maximum value on the brightness distribution curve, and calculating a turbidity estimation value for the deterioration prediction target illumination lamp on the basis of the variation coefficient; calculating an illumination output estimation value for the deterioration prediction target illumination lamp on the basis of the brightness maximum value on the brightness distribution curve and the turbidity estimation value; and calculating an illuminance ratio estimation value for the deterioration prediction target illumination lamp by applying the turbidity estimation value and the illumination output estimation value to the set correspondence.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a method and system for predicting deterioration of an illuminating lamp, and in particular, a method of predicting the degree of deterioration of each illuminating lamp by photographing an illuminating lamp on a tunnel lining surface with a photographing means mounted on a vehicle traveling in the tunnel. And the system.

  The driver's visual environment is poor in tunnels such as highways. For this reason, lighting equipment and interior work are constructed according to the traffic volume, inspection of various facilities, and cleaning of tunnel accessories.

A major factor that degrades the visual environment in the tunnel is the deterioration of lighting equipment in the tunnel.

For this reason, attempts have been made to evaluate the degree of deterioration of the lighting equipment in the tunnel. Conventionally, the illuminance measurement vehicle is periodically run to measure the illuminance near the road surface and evaluate the degree of deterioration of the lighting equipment.

Patent Document 1 listed below describes an invention in which a lighting fixture replacement time is determined based on data on lighting fixture replacement costs and power consumption costs.

Further, in Patent Document 2 below, an illuminance measuring unit is provided in a cleaning robot that cleans a plurality of lighting fixtures arranged along a tunnel, and the illuminance measurement is performed when the cleaning robot is located in the vicinity of the lighting fixture to be measured. Describes an invention in which the light output of a lighting fixture to be measured is measured in the unit.

JP 2007-59133 A JP 2005-339920 A

However, in the past, it was impossible to evaluate the deterioration of each illumination lamp.

Therefore, the present invention evaluates the degree of deterioration of each illuminating lamp, that is, the decrease in illumination output and the turbidity of the glass surface, for example, by running the tunnel lining surface photographing vehicle and analyzing the photographed image, thereby The problem to be solved is to be able to accurately predict the timing of cleaning individual lamps and the timing of replacing individual lamps.

The first invention is
Correspondence setting step for presetting the correspondence between the turbidity of the illuminating lamp, the illumination output of the illuminating lamp, and the illuminance ratio as a ratio to the reference value of the illuminance of the illuminating lamp;
A shooting step for shooting the illumination light subject to deterioration prediction;
Obtaining a luminance distribution curve from the photographed image, obtaining a variation coefficient as a slope when shifting to the maximum luminance value on the luminance distribution curve, and estimating the turbidity of the deterioration prediction target illumination lamp based on the variation coefficient A turbidity estimation step for calculating a value;
Illumination output estimation step for calculating an estimated value of the illumination output of the deterioration prediction target illumination lamp based on the luminance maximum value on the luminance distribution curve and the turbidity estimated value;
Illuminance ratio estimation step of applying the turbidity estimated value and the illumination output estimated value to the set correspondence and obtaining an estimated value of the illuminance ratio of the deterioration prediction target illumination lamp. It is a method.

The second invention is the first invention,
In the turbidity estimation step, a luminance distribution curve is obtained for each turbidity, and a turbidity estimation equation for estimating turbidity using a coefficient of variation as a variable is obtained in advance based on the luminance distribution curve for each turbidity. The turbidity estimation value is calculated by applying a coefficient of variation obtained from the captured image of the degradation prediction target illumination lamp to the turbidity estimation calculation formula.

The third invention is the first invention or the second invention,
In the illumination output estimating step, a brightness distribution curve is obtained for each turbidity, and an illumination output for estimating and calculating the illumination output using the maximum brightness value as a variable based on the brightness distribution curve and the estimated turbidity value for each turbidity An estimation calculation formula is obtained in advance, and the illumination output estimation value is calculated by applying the luminance maximum value and the turbidity estimation value obtained from the photographed image of the deterioration prediction target illumination lamp to the lighting output estimation calculation formula. It is characterized by doing.

A fourth invention is any one of the first invention to the third invention,
The degradation prediction target illumination lamp installed on the tunnel lining surface is photographed by photographing means mounted on a vehicle traveling in the tunnel.

The fifth invention
A deterioration prediction system for an illumination lamp, wherein a deterioration prediction target illumination lamp installed on a tunnel lining surface is photographed by a photographing means mounted on a vehicle traveling in a tunnel, and the deterioration of the deterioration prediction target illumination lamp is predicted. ,
Preset the correspondence between the turbidity of the illuminating lamp, the illumination output of the illuminating lamp, and the illuminance ratio as a ratio to the reference value of the illuminance of the illuminating lamp,
Photograph the illumination lamp subject to deterioration prediction by the photographing means,
Obtaining a luminance distribution curve from the photographed image, obtaining a variation coefficient as a slope when shifting to the maximum luminance value on the luminance distribution curve, and estimating the turbidity of the deterioration prediction target illumination lamp based on the variation coefficient Calculate the value
Based on the luminance maximum value on the luminance distribution curve and the turbidity estimated value, an estimated value of the illumination output of the deterioration prediction target illumination lamp is calculated,
Applying the turbidity estimated value and the illumination output estimated value to the set correspondence, to obtain an estimated value of the illuminance ratio of the deterioration prediction target illumination lamp,
Based on the turbidity estimated value, the illumination output estimated value, and the illuminance ratio estimated value of the deterioration prediction target illumination lamp, the degree of necessity of cleaning of the deterioration prediction target illumination lamp and the degree of deterioration of the light source of the deterioration prediction target illumination lamp It is characterized by predicting.

A sixth invention is the fifth invention,
For each turbidity, a luminance distribution curve is obtained. Based on the luminance distribution curve for each turbidity, a turbidity estimation formula for estimating and calculating turbidity using a coefficient of variation as a variable is obtained in advance. A turbidity estimated value is calculated by applying a coefficient of variation obtained from a photographed image of the deterioration prediction target illumination lamp to an arithmetic expression.

The seventh invention is the fifth invention or the sixth invention,
For each turbidity, a luminance distribution curve is obtained. Based on the luminance distribution curve for each turbidity and the estimated turbidity value, an illumination output estimation formula for estimating and calculating the illumination output using the maximum luminance value as a variable is obtained in advance. The illumination output estimation value is calculated by applying the maximum luminance value and the turbidity estimation value obtained from the captured image of the deterioration prediction target illumination lamp to the illumination output estimation calculation formula.

According to an eighth invention, in any one of the fifth invention to the seventh invention,
The deterioration prediction target lighting fixture includes a plurality of deterioration prediction target lighting fixtures, and predicts the deterioration of the deterioration prediction target lighting fixtures by predicting the deterioration of the individual deterioration prediction target lighting fixtures. .

  According to the present invention, it is possible to evaluate the degree of deterioration of individual lighting lamps, that is, the reduction in illumination output and turbidity of the glass surface, for example, by running a tunnel lining surface photographing vehicle and analyzing the photographed image, As a result, it becomes possible to accurately predict the cleaning time of each lamp and the replacement time of each lamp.

FIG. 1 is a diagram showing a configuration of an illumination lamp deterioration prediction system according to the present invention. FIG. 2 is a flowchart showing the processing procedure of the illumination lamp deterioration prediction method according to the present invention. Fig.3 (a) is a figure which shows a lighting fixture, FIG.3 (b) is a figure which shows the experimental fixture used for the pre-processing for preparation of a lamp deterioration prediction processing program. FIG. 4 is a diagram illustrating a correspondence relationship between the turbidity of the illumination lamp, the illumination output of the illumination lamp, and the illuminance ratio of the illumination lamp. FIG. 5A is a diagram illustrating an example of a captured image of a lighting fixture. FIGS. 5B, 5C, 5D, and 5E each have an illumination output of 100%, and turbidity. It is a figure which shows the image of the analysis area when each is a level of 0, 1, 3, and 5. FIG. FIG. 6 is a graph showing a luminance distribution curve in an image in the analysis area, and shows the pixel position in the transverse direction of the analysis area on the horizontal axis and the luminance value (grayscale) on the vertical axis. FIG. 7 is a graph showing the relationship between the variation coefficient obtained from the analysis result of the luminance distribution curve shown in FIG. 6 and turbidity (turbidity level). The variation coefficient is plotted on the horizontal axis, and turbidity is logarithmic. It is the figure taken on the vertical axis. FIG. 8 shows the maximum luminance value (peak value) and the illumination output obtained from the analysis result of the luminance distribution curve for each level of illumination output and turbidity (luminance output: 0 to 100%, turbidity: 0 to 5 level). It is the graph which showed the relationship with (%), and is the figure which took the luminance maximum value (peak value) on the horizontal axis, and took the illumination output (%) on the vertical axis. FIG. 9A is a graph showing the correspondence between the turbidity estimation calculation value and the slope, and FIG. 9B is a graph showing the correspondence between the turbidity estimation calculation value and the intercept. FIG. 10 is a diagram illustrating a luminance distribution curve obtained from the measurement result of the example. FIG. 11 is a diagram illustrating an example of diagnosis of a lighting fixture.

  Hereinafter, embodiments of a method and system for predicting deterioration of an illumination lamp according to the present invention will be described with reference to the drawings.

FIG. 1 shows a configuration of an illumination lamp deterioration prediction system according to the present invention.

The vehicle 1 is a work vehicle based on a work truck used for road maintenance work, for example. The vehicle 1 is equipped with a photographing means 10 and an image data storage unit 20.

The personal computer 2 outside the vehicle 1 is installed with a program for illuminating lamp deterioration prediction processing.

That is, the luminaire 50 installed on the tunnel lining surface 90 is photographed by the photographing means 10 mounted on the vehicle 1 while the vehicle 1 travels in the tunnel. As shown in FIG. 3A, the luminaire 50 includes individual illuminating lamps 51, 51. Each illuminating lamp 51 is composed of, for example, an LED element.

The image data storage unit 20 of the vehicle 1 stores image data captured by the imaging unit 10. The image data stored in the image data storage unit 20 is read out for the deterioration prediction process of the illuminating lamp 51 and is taken into the external personal computer 2 via a storage medium or a data communication network 95 such as the Internet.

FIG. 2 is a flowchart showing the processing procedure of the illumination lamp deterioration prediction method according to the present invention. The processing contents of the illuminating lamp deterioration prediction method include pre-processing (steps 101, 102, and 103) performed in advance for creating the above-described illuminating lamp deterioration prediction processing program and a personal in which the illuminating lamp deterioration prediction processing program is installed. It consists of image analysis processing, deterioration factor calculation processing, and lighting fixture deterioration prediction processing (steps 104, 105, 106, 107) executed by the computer 2.

FIG.3 (b) shows the experimental instrument used for the pre-processing for preparation of a lamp deterioration prediction processing program.

That is, as shown in FIG. 3A, the same lighting device 50 that is installed on the tunnel lining surface 90 is prepared and installed on the stand 61. An illuminometer 70 and a single-lens reflex camera 80 are disposed in front of the luminaire 50.

Hereinafter, the process of the deterioration prediction method of the illuminating lamp shown in FIG. 2 will be described.

(Preprocessing)
In the pre-processing, first, a process of presetting the correspondence M between the turbidity of the illumination lamp 51, the illumination output of the illumination lamp 51, and the illuminance ratio as a ratio of the illuminance of the illumination lamp 51 to the reference value is performed.

  For this purpose, first, each illuminating lamp 51, 51... Of the luminaire 50 is photographed by the single-lens reflex camera 80 shown in FIG. 3B, and the illuminance of the luminaire 50 is measured by the illuminometer 70. .

  The factor of the illuminance reduction of the lighting fixture 50 in the tunnel is a reduction in lighting output, which is the deterioration of the individual lighting lamps 51 (LED elements), and turbidity, which is mainly the degree of contamination of the glass surface of the lighting fixture 50.

Therefore, in FIG. 3B, in order to reproduce the degree of dirt on the glass surface which is the front surface of the lighting fixture 50, the number of vinyls coated on the front surface of the lighting fixture 50 is changed, and the turbidity is changed in six stages. I let you. In addition, in order to reproduce the illumination output, which is the deterioration of the individual illumination lamps 51 (LED elements), the power applied to the illumination lamps 51, that is, the illumination output was changed in nine stages. The illuminance was measured with an illuminometer 70 for each level of turbidity and illumination output.

  In this way, the illuminance meter 70 measures the illuminance for each of the 6 levels of turbidity and the 9 levels of illumination output, measures the turbidity and the level of illumination output, and the measured illuminance (lux) value is the predicted value. (True value).

  FIG. 4 shows the correspondence M between the turbidity of the illuminating lamp 51, the illumination output of the illuminating lamp 51, and the illuminance ratio of the illuminating lamp 51. In FIG. 4, the degree of deterioration of the lighting fixture 50 is appended with a photograph.

That is, the turbidity is represented by each level of 0, 1, 2, 3, 4, 5 and the degree of contamination increases sequentially as the level value increases, and the illumination output is 100, 90, 80, 70. , 60, 50, 40, 30, and 20 are expressed as percentages (%), and the illumination output decreases sequentially as the percentage (%) of the illumination output decreases.

The illuminance ratio is a ratio of the measured illuminance to a reference value. The measured illuminance when the illumination output of the illuminating lamp 51 is the maximum (100%) and the turbidity is the minimum (0 level) as the reference value (illuminance ratio 1.00), the reference value (1 .00) is shown as the illuminance ratio. For example, the illuminance ratio when the turbidity is 3 levels and the illumination output is 60% is 0.59 (step 101).

In the preprocessing, next, a turbidity estimation calculation formula for estimating and calculating turbidity using a variation coefficient as a variable is obtained based on the luminance distribution curve for each turbidity.

That is, the single-lens reflex camera 80 shown in FIG. 3B photographed the illuminating lamps 51, 51... Of the luminaire 50 for each level of turbidity and illumination output.

In this way, a photographed image for analysis was acquired by the single lens reflex camera 80 for each of the 6 levels of turbidity and 9 levels of illumination output.

FIG. 5A shows an example of a captured image of the lighting fixture 50. The surrounding area 52 of the specific illuminating lamp 51 among the illuminating lamps 51 constituting the luminaire 50 was set as an analysis area.

5 (b), (c), (d), and (e), images of the analysis area 52 when the illumination output is 100% and the turbidity is 0, 1, 3, and 5 levels, respectively. Indicates. The center of the analysis area 52 corresponds to the center position of the specific illumination lamp 51. When the image in the analysis area 52 is represented by a gray scale of luminance gradation, the luminance increases sequentially from black to white in the figure. In photographing, an ND filter was used so that the luminance value peak of the illumination lamp 51 could be captured.

FIG. 6 is a graph showing a luminance distribution curve in the image of the analysis area 52. The pixel position in the transverse direction T (see FIGS. 5B, 5C, 5D, and 5E) of the analysis area 52 is shown. The horizontal axis is the luminance value (grayscale). When the illumination output is 100% and the turbidity is 0 level, the luminance distribution curve is L0, and when the illumination output is 100% and the turbidity is 1 level, the luminance distribution curve is L1 and the illumination output. Is a luminance distribution curve when L is 100% and the turbidity is 3 levels, and L5 is a luminance distribution curve when the illumination output is 100% and the turbidity is 5 levels.

As can be seen from FIG. 6, a uniform tendency was observed in the luminance distribution shape, such as the peak value of the luminance value decreased as the turbidity increased (becomes dirty) at levels 0, 1, 3, and 5. Therefore, five values of an average value, a standard deviation, a maximum value, a minimum value, and a contrast having a difference in distribution shape of the luminance distribution curve as a feature amount are calculated as analysis values. As a result, the coefficient of variation calculated from the average value and standard deviation of the analysis values was adopted as a factor that affects turbidity prediction. This is because the difference due to the turbidity level is regarded as a slope when shifting to the maximum luminance value. The variation coefficient is represented as a slope when shifting to the maximum luminance value on the luminance distribution curve.

FIG. 7 shows a relationship L11 between the coefficient of variation and the turbidity (turbidity level) obtained from the analysis result of the luminance distribution curve shown in FIG. FIG. 7 is a graph in which the coefficient of variation is plotted on the horizontal axis and the turbidity is taken as a logarithm on the vertical axis.

From the relationship L11 between the variation coefficient and turbidity (turbidity level) shown in FIG. 7, as shown in the following equation (1), a turbidity estimation equation for estimating turbidity y using the variation coefficient as a variable x is can get.
y = -3.303 × ln (x) +0.0491 (1)
y: Turbidity level (estimated calculation value)
x: coefficient of variation (step 102)

In the preprocessing, next, an illumination output estimation arithmetic expression for estimating and calculating the illumination output using the maximum luminance value as a variable is obtained based on the luminance distribution curve and the turbidity estimated value for each turbidity.

FIG. 8 shows a luminance distribution curve for each level of illumination output and turbidity (luminance output: 0 to 100%, turbidity: 0 to 5 level) (in FIG. 6, the luminance distribution curve when the illumination output is 100%). The relationship L20, L21, L22, L23, L24, and L25 between the luminance maximum value (peak value) and the illumination output (%) obtained from the analysis result is shown. FIG. 8 is a graph with the luminance maximum value (peak value) on the horizontal axis and the illumination output (%) on the vertical axis.

The correspondence (linear) when the turbidity is 0 level is L20, the correspondence (linear) when the turbidity is 1 level is L21, and the correspondence (linear) when the turbidity is 2 level is L22. The correspondence (linear) when the turbidity is 3 levels is L23, the correspondence (linear) when the turbidity is 4 levels is L24, and the correspondence (linear) when the turbidity is 5 levels is L25. Respectively.

From the correspondence relationship L20, L21, L22, L23, L24, L25 between the maximum luminance value (peak value) and the illumination output (%) for each turbidity level shown in FIG. An illumination output estimation calculation formula for estimating and calculating the illumination output v using the maximum value as a variable u is obtained.

However, the slope a and the intercept b of the linear function in the following formula (2) are obtained from the turbidity estimation calculation value y obtained from the turbidity estimation calculation formula of the above formula (1).

The correspondence L31 between the turbidity estimation calculation value y and the slope a is shown in FIG. Further, the correspondence L32 between the turbidity estimation calculation value y and the intercept b is shown in FIG.

Therefore, the gradient a is obtained by applying the turbidity estimation calculation value y to the correspondence L31. Further, the intercept b is obtained by applying the turbidity estimation calculation value y to the correspondence L32.
v = a × u + b (2)
v: Illumination output (estimated calculation value)
u: Maximum luminance value (peak value)
a: Inclination (applying turbidity estimation calculation value y to correspondence L31)
b: intercept (applying turbidity estimation calculation value y to correspondence L32)
(Step 103)

(Image analysis processing and deterioration factor calculation processing and deterioration prediction processing executed by the personal computer 2)
Correspondence M between the turbidity of the illuminating lamp 51 shown in FIG. 4, the illumination output of the illuminating lamp 51, and the illuminance ratio of the illuminating lamp 51 obtained as described above, as shown in the above equation (1). The turbidity estimation calculation formula, the illumination output estimation calculation formula shown in the above equation (2), the correspondence L31 for obtaining the slope a from the turbidity estimation calculation value y shown in FIG. 9A, and shown in FIG. 9B. On the basis of the correspondence L32 for obtaining the intercept b from the calculated turbidity estimated value y, an illumination lamp deterioration prediction processing program incorporating these is created and installed in the personal computer 2.

Then, the vehicle 1 is actually traveled, and as shown in FIG. 1, the lighting fixture 50 (illumination lamp 51) to be predicted for deterioration is photographed.

That is, the lighting fixture 50 installed on the tunnel lining surface 90 is photographed by the photographing means 10 mounted on the vehicle 1 while the vehicle 1 travels in the tunnel. The image data storage unit 20 of the vehicle 1 stores image data captured by the imaging unit 10. The image data stored in the image data storage unit 20 is read out and taken into the external personal computer 2 for the deterioration prediction process of the illuminating lamp 51. In the personal computer 2, an illumination lamp deterioration prediction processing program is executed.

(Turbidity estimation calculation processing)
In the same manner as described with reference to FIGS. 5 and 6, a process for obtaining a luminance distribution curve is executed for a specific illuminating lamp 51 in the luminaire 50 to be predicted for deterioration from image data.

Here, for example, it is assumed that a luminance distribution curve L41 corresponding to L3 (turbidity level 3) is obtained as shown in FIG.

Then, the process for obtaining the value of the variation coefficient x from the luminance distribution curve L41 is executed next. For example, assume that a value of 0.4 corresponding to turbidity level 3 is obtained (see arrow A in FIG. 7).

Then, the obtained variation coefficient x (for example, 0.4) is applied to the turbidity estimation formula (y = -3.303 × ln (x) +0.0491) of the above formula (1), and the turbidity is calculated. A degree estimation calculation value y (for example, about three levels of turbidity estimation calculation value) is obtained (step 104).

(Lighting output calculation processing)
Next, processing for obtaining the maximum luminance value u as the peak value from the luminance distribution curve L41 is executed.

Here, for example, it is assumed that a value 40 corresponding to 60% of the illumination output is obtained as the maximum luminance value u (see arrow B in FIG. 10 and arrow C in FIG. 8).

Therefore, the obtained maximum luminance value u (for example, 40) is applied to the illumination output calculation formula (v = a × u + b) of the above formula (2) to calculate the illumination output estimation calculation value v (for example, the illumination output estimation calculation). Value approximately 60%). However, the slope a in the above equation (2) is obtained by applying the turbidity estimation calculation value y (about 3 levels) obtained in step 104 to the correspondence L31 shown in FIG. (See arrow D in FIG. 9A). Further, it is obtained by applying the intercept b in the above equation (2) and the turbidity estimation calculation value y (about 3 levels) obtained in step 104 to the correspondence L32 shown in FIG. 9 (b) arrow E).

As described above, the turbidity estimation calculation value y (for example, about 3 levels) and the illumination output estimation calculation value v (for example, about 60%) are calculated (step 105).

(Illuminance ratio calculation processing)
Next, the estimated turbidity value y (eg, about 3 levels) obtained in step 104 and the estimated illumination output value v (eg, about 60%) obtained in step 105 are applied to the correspondence M shown in FIG. Thus, a process for obtaining an estimated value of the illuminance ratio of the degradation prediction target illumination lamp 51 is executed.

For example, the illuminance ratio when the turbidity estimation calculation value y is about 3 and the illumination output estimation calculation value is about 60% is about 0.59 from the correspondence M shown in FIG. 4 (step 106).

(Lighting equipment deterioration prediction process)
As described above, based on the obtained turbidity estimated value y, illumination output estimated value v, and illuminance ratio estimated value of the deterioration prediction target illumination lamp 51, the diagnosis for predicting the deterioration of each deterioration prediction target illumination lamp 51 is performed. Processing is executed. Further, by predicting the deterioration of each deterioration prediction target illumination lamp 51, a diagnosis process for predicting the deterioration of the deterioration prediction target lighting fixture 50 is executed. In this diagnosis processing, the soundness of the entire lighting fixture 50 is diagnosed from the turbidity estimated value y, the illumination output estimated value v, and the illuminance ratio estimated value for the individual lighting lamps 51 constituting the lighting fixture 50, and cleaning is necessary. It predicts the degree of progress and the degree of deterioration.

FIG. 11 shows a diagnosis example of the lighting fixture 50. In this case, a predetermined threshold value is provided for the turbidity estimated value y, the illumination output estimated value v, and the illuminance ratio estimated value for each of the illuminating lamps 51. ), “Possible” (enclosed by a broken line), and failure (enclosed by an alternate long and short dash line).

For example, a certain illuminating lamp 51a is judged as “bad” with “turbidity estimated value y is 3.21, illumination output estimated value v is 62%, degree ratio estimated value is about 0.59”, and there is another The illuminating lamp 51b is judged as “good” with “turbidity estimated value y is 1.85, illumination output estimated value v is 78%, degree ratio estimated value is about 0.79”.

Appropriate measures such as replacement can be taken for the illuminating lamps 51a and 51a ′ determined to be “defective”.

In addition, it is possible to predict the overall deterioration of the luminaire 50 from the results of obtaining the turbidity estimated value y, the illumination output estimated value v, and the illuminance ratio estimated value of the plurality of illuminating lamps 51 constituting the luminaire 50.

For example, the number of illuminating lamps 51 that are “defective” among the plurality of illuminating lamps 51 constituting the luminaire 50, the average value of the turbidity estimated value y of all the illuminating lamps 51, and the average of the illumination output estimated value v The average value of the value and the illuminance ratio estimated value is obtained, and the deterioration of the entire lighting fixture 50 can be predicted by comparing these with a predetermined threshold value.

In the example of FIG. 11, the number of illuminating lamps 51 that are “defective” is “2”, the average value of the turbidity estimated value y of all the illuminating lamps 51 is 2.2, and the average value of the estimated illumination output value v. Is 78% and the average value of the illuminance ratio is 0.77. From a comparison between these values and the threshold value, for example, although there is an illuminating lamp 51 that is “defective”, the overall lighting fixture 50 is “possible. ", It is possible to evaluate and predict the degree of deterioration of the lighting fixture 50 as a whole.

According to the present embodiment, three factors such as the turbidity estimated value y, the illumination output estimated value v, and the illuminance ratio estimated value can be obtained. Make appropriate judgments and take appropriate measures.

For example, when the illumination output estimated value v is high (no abnormality of the LED element) and the turbidity estimated value y is high (dirt on the glass surface or the like is severe), the illumination lamp 51 or a lighting fixture including the illumination lamp 51 is used. This can be dealt with by cleaning 50. Further, when the estimated illumination output value v is low (the LED element is abnormal), it can be dealt with by replacing the illumination lamp 51 or the luminaire 50 including the illumination lamp 51 without cleaning. (Step 107).

As described above, according to the present embodiment, the turbidity estimated value y, the illumination output estimated value v, and the illuminance ratio estimated value are obtained for each of the illuminating lamps 51. In particular, the equipment maintenance of the luminaires in the tunnel is performed. It is possible to optimally maintain and manage road facilities, such as optimally performing cleaning work on concrete lining surfaces and interior boards.

In addition, although the Example demonstrated the illuminating lamp and lighting fixture in a tunnel, this invention is not limited to this, It can apply to the deterioration prediction of arbitrary illuminating lamps and lighting fixtures.

DESCRIPTION OF SYMBOLS 1 Vehicle 2 Personal computer 10 Image | photographing means 20 Image data memory | storage part 50 Lighting fixture 51 Illumination lamp 90 Tunnel lining surface

Claims (8)

Correspondence setting step for presetting the correspondence between the turbidity of the illuminating lamp, the illumination output of the illuminating lamp, and the illuminance ratio as a ratio to the reference value of the illuminance of the illuminating lamp;
A shooting step for shooting the illumination light subject to deterioration prediction;
Obtaining a luminance distribution curve from the photographed image, obtaining a variation coefficient as a slope when shifting to the maximum luminance value on the luminance distribution curve, and estimating the turbidity of the deterioration prediction target illumination lamp based on the variation coefficient A turbidity estimation step for calculating a value;
Illumination output estimation step for calculating an estimated value of the illumination output of the deterioration prediction target illumination lamp based on the luminance maximum value on the luminance distribution curve and the turbidity estimated value;
Illuminance ratio estimation step of applying the turbidity estimated value and the illumination output estimated value to the set correspondence and obtaining an estimated value of the illuminance ratio of the deterioration prediction target illumination lamp. Method. In the turbidity estimation step, a luminance distribution curve is obtained for each turbidity, and a turbidity estimation equation for estimating turbidity using a coefficient of variation as a variable is obtained in advance based on the luminance distribution curve for each turbidity. The illuminance lamp according to claim 1, wherein a turbidity estimation value is calculated by applying a coefficient of variation obtained from a photographed image of the deterioration prediction target illumination lamp to the turbidity estimation calculation formula. Degradation prediction method. In the illumination output estimation step, a brightness distribution curve is obtained for each turbidity, and the illumination output is estimated and calculated based on the brightness distribution curve for each turbidity and the estimated turbidity, with the maximum brightness value as a variable. An output estimation calculation formula is obtained in advance, and the illumination output estimation value is obtained by applying the maximum luminance value and the turbidity estimation value obtained from the captured image of the deterioration prediction target illumination lamp to the illumination output estimation calculation formula. The method for predicting deterioration of an illuminating lamp according to claim 1, wherein calculation is performed. 4. The deterioration of the illumination lamp according to claim 1, wherein the degradation prediction target illumination lamp installed on the tunnel lining surface is photographed by a photographing means mounted on a vehicle traveling in the tunnel. 5. Prediction method. A deterioration prediction system for an illumination lamp, wherein a deterioration prediction target illumination lamp installed on a tunnel lining surface is photographed by a photographing means mounted on a vehicle traveling in a tunnel, and the deterioration of the deterioration prediction target illumination lamp is predicted. ,
Preset the correspondence between the turbidity of the illuminating lamp, the illumination output of the illuminating lamp, and the illuminance ratio as a ratio to the reference value of the illuminance of the illuminating lamp,
Photograph the illumination lamp subject to deterioration prediction by the photographing means,
Obtaining a luminance distribution curve from the photographed image, obtaining a variation coefficient as a slope when shifting to the maximum luminance value on the luminance distribution curve, and estimating the turbidity of the deterioration prediction target illumination lamp based on the variation coefficient Calculate the value
Based on the luminance maximum value on the luminance distribution curve and the turbidity estimated value, an estimated value of the illumination output of the deterioration prediction target illumination lamp is calculated,
Applying the turbidity estimated value and the illumination output estimated value to the set correspondence, to obtain an estimated value of the illuminance ratio of the deterioration prediction target illumination lamp,
A deterioration prediction system for an illumination lamp, wherein deterioration of the deterioration prediction target illumination lamp is predicted based on a turbidity estimation value, the illumination output estimation value, and an illuminance ratio estimation value of the deterioration prediction target illumination lamp. For each turbidity, a luminance distribution curve is obtained. Based on the luminance distribution curve for each turbidity, a turbidity estimation formula for estimating and calculating turbidity using a coefficient of variation as a variable is obtained in advance. 6. The deterioration prediction system for an illumination lamp according to claim 5, wherein a turbidity estimated value is calculated by applying a coefficient of variation obtained from a photographed image of the deterioration prediction target illumination lamp to an arithmetic expression. A luminance distribution curve is obtained for each turbidity, and an illumination output estimation calculation formula for estimating and calculating the illumination output using the maximum luminance value as a variable is obtained in advance based on the luminance distribution curve for each turbidity and the estimated turbidity value. The illumination output estimation value is calculated by applying the luminance maximum value obtained from the photographed image of the deterioration prediction target illumination lamp and the turbidity estimation value to the illumination output estimation calculation formula. The illumination lamp deterioration prediction system according to claim 5 or 6.   The deterioration prediction target lighting fixture includes a plurality of deterioration prediction target lighting fixtures, and predicts the deterioration of the deterioration prediction target lighting fixtures by predicting the deterioration of the individual deterioration prediction target lighting fixtures. The deterioration prediction system according to any one of claims 5 to 7.