Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Street lamp intelligent fault diagnosis method

A technology for fault diagnosis and street lamps, applied in energy-saving control technology, lamp circuit layout, program control, etc., can solve the problem of failure to identify the attenuation of luminous flux of lamps but not completely damaged, no illuminance data given, fault diagnosis of street lamps, and fault diagnosis of lamp groups and other issues, to achieve the effect of model update, simple structure, enhanced robustness and intelligence

Active Publication Date: 2017-12-08
SHANDONG JIANZHU UNIV
View PDF6 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] (1) The existing non-informationized street lamp fault diagnosis method is mainly based on current or voltage, which needs to be broken and reconstructed on existing lines, and cannot identify the situation where the luminous flux of the lamp is attenuated but not completely damaged
In addition, the diagnosis is mainly for a single street light, and it cannot diagnose the failure of the light group caused by the local route.
[0005] (2) Existing information-based street lamp management systems, such as "A Smart Street Light System for the Internet of Things (Application Number: 201610041523.X)" need to use a single-lamp control box, which still needs to be modified on the basis of the original circuit. Moreover, many devices are used and the structure is complex, which makes the cost relatively high
In addition, it can only realize street lamp information collection based on the Internet of Things, and does not give a specific method to use illuminance data to diagnose street lamp faults

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Street lamp intelligent fault diagnosis method
  • Street lamp intelligent fault diagnosis method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] Such as figure 1 As shown, a street lamp intelligent fault diagnosis method includes the following steps:

[0036] (1) Install a narrow-band Internet of Things (NB-IoT) module embedded with an illuminance sensor at the light source of each street lamp, and number each NB-IoT module, and each illuminance sensor measures the corresponding NB-IoT module. illuminance, and upload the collected illuminance data to the server to build an illuminance database, where the illuminance data includes the daily illuminance sequence of each street lamp;

[0037] The acquisition frequency of the illumination sequence is once every five minutes. For any street lamp i, i=1,2,...,N, the illuminance sequence on day t is Among them, N is an integer greater than 2, indicating the number of street lamps, and 288 is the number of illuminance samples per day.

[0038] (2) Select the normal illuminance data set from the illuminance database as the training data set, use the machine learning ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a street lamp intelligent fault diagnosis method. A narrow band Internet of Things (IoT) module embedded with an illuminance sensor is installed at a light source of each street lamp, and each narrow band IoT module is numbered. Each illuminance sensor measures the illuminance at the corresponding narrow band IoT module, and uploads the collected illuminance data to a server to construct an illuminance database. A normal illuminance data set is selected from the illuminance database as a training data set, and a machine learning method is used for pattern learning of an illuminance sequence to construct a street lamp turn-on model. On the basis of the street lamp turn-on model, the real-time illuminance sampling data sequence is processed to realize the fault diagnosis and fault type diagnosis of a single street lamp and a lamp group. Also, the machine learning method is used for updating the street lamp turn-on model. According to the invention, the faults of the single lamp and the lamp group can be automatically detected in time without breaking the line, the fault types can be determined and fed back to working staff, and the intelligent diagnosis of the street lamp faults is realized.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to an intelligent fault diagnosis method for street lamps. It belongs to the field of intelligent lighting technology. Background technique [0002] With the development of the economy and the continuous improvement of public infrastructure, street lights, as an important public infrastructure, are increasingly playing an indispensable role in people's lives. However, due to natural or man-made factors, urban street lamps often have failures such as lamp damage and line damage. If these failures cannot be found and repaired in time, it will bring great inconvenience to people's lives, and will also increase the frequency of traffic accidents. [0003] The existing street lamp fault diagnosis method has the following problems: [0004] (1) The existing non-informationized street lamp fault diagnosis method is mainly based on current or voltage, which needs to break the existing line and...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G05B19/048G06N99/00H05B37/03
CPCG05B19/048G06N20/00H05B47/20Y02B20/40
Inventor 李成栋许沥文谢秀颖颜秉洋张桂青
Owner SHANDONG JIANZHU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products