LED lamp residual life prediction method

Abstract

The invention provides an LED lamp residual life prediction method, and the method comprises the steps: a control module records the use time R of an LED lamp through a timing module, employs an environment sensor array to detect the environment data of the LED lamp in the use process, and calculates a comprehensive influence index E; the actual environment where the LED lamp is located is reflected, meanwhile, the control module calculates the actual service life R1 of the LED lamp in the environment according to the comprehensive influence index E and the rated service life R0 of the LED lamp, and therefore the remaining service life R2 of the LED lamp is equal to R1-R; therefore, according to the method, the actual service life R1 of the LED lamp is determined firstly, and then the residual service life of the LED lamp is judged, so that the problem of inaccurate service life prediction caused by neglecting factors of environment change when a conventional light attenuation detection method is adopted is solved.

Description

Technical Field

[0001] This invention relates to the field of lighting testing technology, and specifically to a method for predicting the remaining lifespan of LED lamps. Background Technology

[0002] In recent years, LED lights have been widely used in various lighting scenarios due to their advantages of high efficiency, energy saving, and long lifespan. Whether in home lighting, commercial lighting, or industrial lighting, LED lights have gradually replaced traditional incandescent and fluorescent lamps, becoming the mainstream light source. With technological advancements and reduced production costs, the market demand for LED lights continues to grow.

[0003] Among relevant technical methods, such as the patent document with publication number "CN117848682B", this patent document mainly predicts the remaining lifespan of LED lamps by monitoring their operating time and light output attenuation rate. The light decay detection method is typically used, which predicts the remaining lifespan by measuring the change in LED light output over time. This method can accurately reflect the aging degree of LED lamps, thereby estimating their lifespan.

[0004] Although the above-mentioned technical solutions can predict the remaining lifespan of LED lamps relatively accurately through light decay detection methods, in practical applications, especially under conditions of significant environmental changes, the lifespan of LED lamps will be accelerated. Existing light decay detection methods ignore the factors of environmental changes, resulting in inaccurate lifespan predictions. For example, in coastal areas with high temperature and humidity, the synergistic effect of temperature and humidity on the aging of LED lamp chips is significant. Therefore, there is a need for a method to accurately quantify the remaining lifespan of LED lamps by integrating multiple environmental data. Summary of the Invention

[0005] The purpose of this invention is to design a method for predicting the remaining lifespan of LED lamps, in order to solve the problems raised in the background art. To achieve the above objective, this invention provides the following technical solution, comprising the following steps: Step 1: The timing module uploads the LED light usage time R to the control module, and the environmental sensor array uploads the environmental data of the LED light during use to the control module. Step 2: The control module calculates the comprehensive impact index E based on the environmental data; Step 3: The rated lifespan R0 of the LED lamp under standard conditions is pre-input into the control module. The control module calculates the actual lifespan of the LED lamp as R1 based on E and R0. Step 4: The control module calculates the remaining lifespan of the LED lamp, R2 = R1 - R, based on the actual lifespan R1 and the usage time R. In step two, E= Where fi is the influence function corresponding to different environmental data, wi is the weighting coefficient corresponding to different environmental data, and n is the number of environmental data. In this embodiment, n=3.

[0006] Furthermore, in step one, a temperature sensor is used to monitor the temperature T of the LED light's operating environment in real time, a humidity sensor is used to obtain the ambient humidity H, and a wind speed sensor is used to monitor the ambient wind speed V; at this point, in step two, E=w1 f1+w2 f2+w3 f3; f1 is the temperature influence function, w1 is the temperature weighting coefficient, f2 is the humidity influence function, w2 is the humidity weighting coefficient, f3 is the ventilation influence function, w3 is the ventilation weighting coefficient; where w1+w2+w3=1.

[0007] Furthermore, in step three, the control module predicts the actual lifespan of the LED lamp, R1, based on E and R0. ; where k and a are pre-input coefficients.

[0008] Furthermore, the temperature effect function is f1= The humidity effect function is f2= The ventilation influence function is f3= .

[0009] Furthermore, in step three, the lifespan data of no less than 200 sets of LED lights under different environments are collected and a dataset is established. Then, the least squares method is used, with the objective function being to minimize the sum of squared errors between the actual lifespan data and the model-predicted lifespan data. By taking the partial derivative of the objective function and setting it to zero, a system of equations is established and solved to obtain k=0.78 and a=1.42.

[0010] Furthermore, in step one, the operator pre-sets experimental environmental data values: temperature T1, wind speed V1, and humidity H1, while simultaneously setting standard temperature T0, standard wind speed V0, and standard humidity H0, and obtaining actual lifespans R1, R2, and R3. Here, R1 is the actual lifespan of the LED light under environmental data of T1, V0, and H0; R2 is the actual lifespan of the LED light under environmental data of T0, V1, and H0; and R3 is the actual lifespan of the LED light under environmental data of T0, V0, and H1. Step two also includes: w1 = A1 / (A1+A2+A3), w2 = A2 / (A1+A2+A3), w3 = A3 / (A1+A2+A3), where A1 = |R1-R0|, A2 = |R2-R0|, and A3 = |R3-R0|.

[0011] Further, in the first step: the relationship between the standard temperature T0, the standard wind speed V0, the standard humidity H0, the temperature T1, the wind speed V1, and the humidity H1 is: |T1 - T0| / T0 = |V1 - V0| / V0 = |H1 - H0| / H0, where T1 > T0, V1 < V0, and H1 > H0; or T1 < T0, V1 > V0, and H1 < H0.

[0012] Further, during the use of the LED lamp, the data sets measured by the temperature sensor, the humidity sensor, and the wind speed sensor are processed through optimization to obtain the values of T, V, and H in the theoretical environment data where the LED lamp is located during use. The optimization process is as follows: input each data in the data set into the number axis, and then take a dynamic circle with a diameter of ɑ unit lengths as the radius and the center moving on the number axis. When the number of data in the data set falling into the dynamic circle reaches the maximum, the position of the center of the dynamic circle is the value of T, V, or H in the theoretical environment data where the LED lamp is located during use.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: while the control module records the usage duration R of the LED lamp through the timing module, it uses the environmental sensor array to detect the environmental data where the LED lamp is located during use and calculates the comprehensive influence index E, which reflects the actual environment where the LED lamp is located. At the same time, the control module calculates the actual life R1 of the LED lamp in this environment according to the comprehensive influence index E and the rated life R0 of the LED lamp. Therefore, the remaining life R2 of the LED lamp at this time is R2 = R1 - R; thus, this method determines the actual life R1 of the LED lamp first and then judges the remaining life of the LED lamp, eliminating the problem of inaccurate life prediction caused by ignoring environmental changes in the previous light decay detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will describe in detail the specific embodiments, structures, features, and effects of the present invention in combination with the accompanying drawings and preferred embodiments.

[0017] Embodiment, please refer to Figure 1 This invention proposes a method for predicting the remaining lifespan of LED lamps, comprising the following steps: Step 1: The timing module uploads the LED lamp's usage time (R) to the control module. The environmental sensor array uploads environmental data during LED lamp use to the control module, including temperature, humidity, and wind speed data. High ambient temperatures (such as in enclosed lamp shades during summer, high-temperature workshops, or near heat sources) can cause the LED chip junction temperature to rise (exceeding its rated junction temperature, typically 85-125℃). High temperatures also accelerate the degradation of internal semiconductor materials, phosphor decay (the phosphor in white LEDs is temperature-sensitive), and aging and cracking of the encapsulating adhesive (silicone / epoxy resin), ultimately leading to accelerated light decay and a significantly shortened lifespan, thus affecting the LED lamp's lifespan. Furthermore, humid air can cause oxidation and corrosion of metal components such as the LED lamp's driver circuit PCB board, solder joints, and electrodes, increasing contact voltage. Obstruction can affect the lifespan of LED lights. Furthermore, enclosed spaces (such as recessed ceilings or sealed lampshades) or poorly ventilated environments (such as wardrobes or drawers) can cause heat to accumulate and not dissipate in time, keeping the LED junction temperature high for extended periods and shortening its lifespan. Conversely, well-ventilated environments (such as open outdoor areas or lamps with ventilation holes) can effectively reduce ambient temperature and extend lifespan. Therefore, based on the above analysis, it can be preliminarily determined that temperature, humidity, and ventilation are the three major factors affecting the lifespan of LED lights. Thus, in this embodiment, the environmental sensor array includes a temperature sensor, a humidity sensor, and a wind speed sensor. In other embodiments, this is not limited to these; vibration and mechanical shock, dust and pollutants, or wind speed can also be added as influencing factors, thereby making the actual lifespan prediction of LED lights more accurate. Specifically, a temperature sensor is used to monitor the temperature T of the LED light's working environment in real time, a humidity sensor is used to obtain the humidity H of the environment, and a wind speed sensor is used to monitor the wind speed V of the environment. Since the sensors usually use intermittent measurement, the temperature, humidity and wind speed are all discrete values. Therefore, in order to fully reflect the actual environmental conditions during the use of the LED light, this embodiment describes the optimization process of the three sets of data: temperature, humidity and wind speed. Taking temperature as an example, since the measured temperature is the ambient temperature, the heat generated during the use of the LED light is not considered. The change in ambient temperature is not a function of time, but a discrete value fluctuating within a certain range. Therefore, the optimization of the dataset measured by the temperature sensor is as follows: input each data point in the dataset into a number axis, and then take a dynamic circle with a radius of α units and a moving center on the number axis. α can be selected according to the actual environment; in this embodiment, α is 1℃. Therefore, when the number of data points falling into the dynamic circle with a radius of 1℃ reaches its maximum, the position of the center of the dynamic circle is the theoretical T value of the LED light during use. At this point, the ambient temperature of the LED light can be considered to fluctuate at the theoretical T value. Similarly, wind speed V and humidity H can be confirmed using the same method, the difference being the value of α. Refer to the unit length of each number axis; for example, α can be 5% in humidity H and 0.25 m / s in wind speed V.

[0018] Step 2: The control module calculates the comprehensive impact index E based on the environmental data; where E = w1 f1+w2 f2+w3 f3; f1 is the temperature effect function, f2 is the humidity effect function, and f3 is the ventilation effect function. Since each environmental data has a different impact on the lifespan of the LED lamp, weighting coefficients are introduced based on the influence of temperature, humidity, and wind speed on the lifespan of the LED lamp. That is, w1 is the temperature weighting coefficient, w2 is the humidity weighting coefficient, and w3 is the ventilation weighting coefficient; and w1+w2+w3=1. In step one, the operator pre-sets the environmental data experimental values: temperature T1, wind speed V1 and humidity H1, and at the same time sets the standard temperature T0, standard wind speed V0 and standard humidity H0, and obtains the actual lifespan R1, R2 and R3. Among them, the relationships between the standard temperature T0, the standard wind speed V0, the standard humidity H0, the temperature T1, the wind speed V1, and the humidity H1 are such that |T1 - T0| / T0 = |V1 - V0| / V0 = |H1 - H0| / H0, and T1 > T0, V1 < V0, and H1 > H0; or T1 < T0, V1 > V0, and H1 < H0 (the greater the wind speed, the stronger the heat dissipation, which is beneficial to improving the actual life of the LED lamp; the smaller the wind speed, the weaker the heat dissipation, which reduces the actual life of the LED lamp); and R1 is the actual life of the LED lamp when the environmental data is T1, V0, and H0, R2 is the actual life of the LED lamp when the environmental data is T0, V1, and H0, and R3 is the actual life of the LED lamp when the environmental data is T0, V0, and H1; the second step further includes: w1 = A1 / (A1 + A2 + A3), w2 = A2 / (A1 + A2 + A3), w3 = A3 / (A1 + A2 + A3), where A1 = |R1 - R0|, A2 = |R2 - R0|, A3 = |R3 - R0|; therefore, by controlling variables, the influence of each environmental data on the actual life of the LED lamp is determined, so as to obtain a comprehensive influence index E that is more in line with the actual situation, and further improve the prediction accuracy of the LED lamp life.

[0019] In addition, since the influence of temperature on the life of the LED lamp is not linearly correlated, and as the environmental temperature increases, the life of the LED lamp decreases significantly. Therefore, in this embodiment, an exponential function form is used to reflect the significant influence of temperature on the life of the LED lamp, that is, let ; where b and c are obtained by performing non-linear regression analysis on the accelerated aging experimental data of the LED lamp in different temperature environments; where b = 0.015 represents the incremental coefficient of the internal light decay rate of the LED lamp for every 1°C increase in the environmental temperature, and this coefficient reflects the linear influence of temperature on the attenuation of luminous flux; c = 0.042 is the quadratic coefficient of temperature influence, which reflects the non-linear exacerbation effect of temperature on the life loss of the LED lamp in a high-temperature environment; therefore, in this embodiment, the temperature influence function is f1 = ; Similar to the temperature influence function, since it is considered that humidity will have an adverse impact on the LED lamp when it is too high or too low. When the environmental humidity is too low, the insulating materials inside the LED lamp are prone to crack due to dryness, accelerating the aging of electronic components; when the humidity is too high, water vapor will penetrate into the lamp body, resulting in problems such as corrosion of metal parts and short circuits of circuit boards. Therefore, after fitting a large amount of environmental humidity and LED lamp performance degradation data, this method uses a quadratic function form to accurately describe the influence of humidity on the life of the LED lamp, so the humidity influence function is taken as f2 = ; When the ambient wind speed reaches or exceeds 1.5 m / s, the ventilation conditions are sufficient to maintain the ideal heat dissipation state of the LED light, and the function value f3 = 1. When the wind speed is below this threshold, the heat dissipation efficiency is linearly positively correlated with the wind speed. A proportional conversion using V / 1.5 directly reflects the attenuation of heat dissipation efficiency caused by insufficient wind speed, i.e., f3 = .

[0020] Step 3: The rated lifespan R0 of the LED lamp under standard conditions is pre-input into the control module. After 500 sets of accelerated aging test data (temperature -20℃ to 60℃, humidity 20%-90%, ventilation 0-3m / s), the model R1 is obtained by fitting the data using the least squares method combined with a genetic algorithm. Wherein, R0 is the standard environmental rated lifespan, and k and a are pre-input coefficients. The method for determining the values ​​of k and a is as follows: collect lifespan data of 200 sets of LED lights under different environments and establish a dataset; then use the least squares method, with the objective function being to minimize the sum of squared errors between the actual lifespan data and the lifespan data predicted by the above model, and by taking the partial derivative of the objective function and setting it to zero, a system of equations is established and solved to obtain k=0.78 and a=1.42. Those skilled in the art can replicate the above process, and k and a will not be elaborated on in this paper. For model R1= Five sets of experimental data are shown in the table below: Therefore, as shown in the table above, through model R1= The error between the predicted and actual lifespan is low, with an average error rate of 1.16% across 5 experimental groups, and a maximum of 2.50%, all below the ±5% threshold. Furthermore, the error was 2.50% in high-temperature, high-humidity, and low-ventilation environments (group 5) and 0.38% in low-temperature, low-humidity environments (group 4), demonstrating good model adaptability. In addition, monitoring over a year in an additional 100 samples showed an average prediction error rate of 2.3%, with 95% of samples having an error ≤3.8%, indicating good long-term model adaptability. Moreover, because this model focuses on the most significant factors of temperature, humidity, and ventilation, it balances prediction accuracy with model simplicity. It quantifies the coupling effect of the three factors through weighted coefficients, thus addressing the shortcomings of traditional methods that lack reference environmental factors (temperature, humidity, and wind speed) when determining lifespan. The prediction method in this invention is also lower in cost, easier to deploy, and simpler in structure than traditional methods that typically use light decay detection, allowing for direct integration into intelligent lighting systems for online prediction.

[0021] Step 4: The control module calculates the remaining lifespan of the LED lamp, R2 = R1 - R, based on the actual lifespan R1 and the usage time R. In this embodiment, multiple online predictions can be made at different times, and the predictions between the detections are independent and do not affect each other. Furthermore, the closer to the later stage, the more data measured by the environmental sensor array, and the more accurate the theoretical values ​​of temperature T, wind speed V, and humidity H, the more accurate the lifespan prediction will be.

[0022] It should be noted that when a component is said to be "fixed to" another component, it can be directly attached to the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "upper," "lower," "left," "right," "front," "back," and similar expressions used in this document are for illustrative purposes only.

[0023] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for predicting the remaining lifespan of an LED lamp, characterized in that, It includes the following steps: Step 1: The timing module is used to upload the usage duration R of the LED lamp to the control module, and the environmental sensor array uploads the environmental data during the use of the LED lamp to the control module. The environmental data includes temperature data, humidity data, and wind speed data; Step 2: The control module calculates the comprehensive influence index E based on the environmental data; Step 3: The rated life R0 of the LED lamp under standard environment is pre-input in the control module. The control module calculates the actual life of the LED lamp as R1 according to E and R0; Step 4: The control module calculates the remaining life R2 = R1 - R of the LED lamp according to the actual life R1 and the usage duration R.

2. The method for predicting the remaining lifespan of an LED lamp according to claim 1, characterized in that, In step one, a temperature sensor is used to monitor the temperature T of the LED light's operating environment in real time, a humidity sensor is used to obtain the ambient humidity H, and a wind speed sensor is used to monitor the ambient wind speed V; at this point, in step two, E=w1 f1+w2 f2+w3 f3; f1 is the temperature influence function, w1 is the temperature weighting coefficient, f2 is the humidity influence function, w2 is the humidity weighting coefficient, f3 is the ventilation influence function, w3 is the ventilation weighting coefficient; where w1+w2+w3=1.

3. The method for predicting the remaining lifespan of an LED lamp according to claim 2, characterized in that, In step three, the control module predicts the actual lifespan of the LED lamp, R1, based on E and R0. ; where k and a are pre-input coefficients.

4. The method for predicting the remaining lifespan of an LED lamp according to claim 3, characterized in that, The temperature effect function is f1= The humidity effect function is f2= The ventilation influence function is f3= .

5. The method for predicting the remaining lifespan of an LED lamp according to claim 3, characterized in that, In Step 3, it also includes collecting no less than 200 groups of life data of the LED lamp in different environments and establishing a data set; then using the least squares method, with the sum of the squared errors between the actual life data and the model-predicted life data minimized as the objective function, establishing a system of equations by taking the partial derivatives of the objective function and setting them to zero, and solving to obtain k = 0.78 and a = 1.

42.

6. The method for predicting the remaining lifespan of an LED lamp according to claim 2, characterized in that, In Step 1: The operator pre-sets the experimental values of environmental data: temperature T1, wind speed V1, and humidity H1, and at the same time sets the standard temperature T0, standard wind speed V0, and standard humidity H0, and obtains the actual lives R1, R2, and R3. R1 is the actual life of the LED lamp when the environmental data is T1, V0, and H0; R2 is the actual life of the LED lamp when the environmental data is T0, V1, and H0; R3 is the actual life of the LED lamp when the environmental data is T0, V0, and H1. Step 2 also includes: w1 = A1 / (A1 + A2 + A3), w2 = A2 / (A1 + A2 + A3), w3 = A3 / (A1 + A2 + A3), where A1 = |R1 - R0|, A2 = |R2 - R0|, A3 = |R3 - R0|.

7. The method for predicting the remaining lifespan of an LED lamp according to claim 6, characterized in that, In Step 1: The relationship between the standard temperature T0, standard wind speed V0, standard humidity H0, temperature T1, wind speed V1, and humidity H1 is: |T1 - T0| / T0 = |V1 - V0| / V0 = |H1 - H0| / H0, where T1 > T0, V1 < V0, and H1 > H0; or T1 < T0, V1 > V0, and H1 < H0.

8. The method for predicting the remaining lifespan of an LED lamp according to claim 6, characterized in that, During the use of the LED lamp, the data sets measured by the temperature sensor, the humidity sensor, and the wind speed sensor are optimized to obtain the values of T, V, and H in the theoretical environmental data where the LED lamp is located during use. The optimization process is: input each data in the data set into the number axis, and then take a dynamic circle with a diameter of ɑ unit lengths and the center moving on the number axis. When the number of data in the data set falling into the dynamic circle reaches the maximum, the position of the center of the dynamic circle is the value of T, V, or H in the theoretical environmental data where the LED lamp is located during use.

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