Fault diagnosis system and method for sewage treatment equipment based on lasso regression

Abstract

The invention belongs to the field of equipment fault diagnosis, and particularly relates to a sewage treatment equipment fault diagnosis system and method based on Lasso regression. The fault diagnosis system comprises a sewage treatment subsystem, a plurality of supervision clients and a plurality of data acquisition devices; the sewage treatment subsystem comprises a central console, a communication server, a total data storage server and a data acquisition server; the central console comprises a control module, a data interface module, a data classification module, a machine learning module and a data communication module; and by acquiring data on the data acquisition devices, the machine learning model carries out learning training on the data so as to construct a plurality of sub regression prediction models for predicting fault types of various types of sewage treatment equipment. The sewage treatment equipment fault diagnosis system and method disclosed by the invention providea certain basic diagnosis help for engineering supervision and treatment decisions of sewage treatment, and effectively meet requirements for real-time monitoring and diagnosis on the sewage treatment environment fault problem in the sewage treatment process.

Description

Technical field [0001] The invention relates to the field of equipment fault diagnosis, in particular to a sewage treatment equipment fault diagnosis system and method based on Lasso regression. Background technique [0002] Water environmental governance has always been a major project to improve people's livelihood and build my country's ecological civilization, of which sewage treatment is the top priority. At this stage, with the gradual improvement of my country’s social level and the deepening of industrialization, the social demand for water is large, and the discharge of industrial sewage has increased. This has caused damage to my country’s water resources environment to a certain extent, water shortages, water ecological damage, and water Environmental pollution and other issues are becoming more and more prominent, and water security is facing severe challenges. In the government's water treatment plan, the treatment of black and odorous water bodies is currently the t...

AI Technical Summary

Problems solved by technology

[0004] In the existing sewage treatment field, the treatment of faults still relies heavily on manual processing methods, and there are deficiencies in efficiency and quality, for example: (1) The faulty equipment is not handled in time

In order to check whether the equipment is in normal operation, the current method of traditional water companies relies on manual recording of equipment failure data one by one, or after the equipment transmits data, the staff then organizes the obtained reports, regularly inspects and maintains the equipment in stages according to the processing process, and the workload is heavy. and low efficiency

However, due to the continuous improvement of machinery and equipment and the continuous expansion of the number of water enterprises, this makes manual processing very difficult. Often equipment failures can only be detected after a certain period of time, which affects the processing process to a certain extent.

(2) Faulty equipment is not properly handled

In the current sewage treatment plant with many equipment and instruments, large amount of data, and complicated equipment structure, the existing equipment detection methods still mostly rely on manual experience, mainly for online monitoring and judgment, and inspection personnel's sense of vision, smell, and touch Experience, equipment failure indication, etc., lack of precise positioning means for the faulty equipment, so negligence and mistakes are very prone to occur. If the equipment or type of the fault occurs is misjudged, it may lead to prolonged maintenance time or waste of spare parts, and even cause new repairs. Faults, increasing the difficulty of maintenance

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