Self-diagnosis method and non-negative pressure stacked water supply equipment

Abstract

The present application provides a self-diagnosis method and non-negative pressure laminated water supply equipment. The method includes: acquiring measurement data of multiple operating parameters of the non-negative pressure laminated water supply equipment; when at least one of the operating parameters is detected When the measured data of the parameter is not in its corresponding preset range, the measured data of multiple operating parameters is used as the first input of the fault detection model, and the predicted fault type and predicted similarity corresponding to the first input are output; when It is determined that no fault occurs when the predicted similarity corresponding to the first input is less than a preset similarity threshold, and it is determined that a fault occurs when the predicted similarity corresponding to the first input is not less than the preset similarity threshold. A two-stage detection process is used to judge whether there is a fault. When a possible fault is detected in the first stage, it is not directly judged as a fault, but enters the second stage of fault detection to avoid misjudgment.

Description

technical field [0001] The present application relates to the technical field of water supply equipment, in particular to a self-diagnosis method and a non-negative pressure stacked water supply equipment. Background technique [0002] With the development of information technology, big data analysis applied to the water supply field has become the development trend of today's water supply industry. The traditional non-negative pressure stacked water supply equipment is in the stage of data collection or data "backward analysis". The safety of secondary water supply has gradually become a hot spot of public concern. Therefore, in order to improve the safety factor of secondary water supply, a non-negative pressure stacked water supply equipment with self-diagnosis function is proposed, which adopts the method of "forward analysis" of data to make equipment failures predictable. [0003] At present, some non-negative pressure stacking water supply equipment already has data...

AI Technical Summary

Problems solved by technology

[0003] At present, some non-negative pressure superimposed water supply equipment already has data collection function and fault alarm function, but the collected data requires engineers with considerable experience to judge the type of fault

In addition, the fault alarms in the prior art are usually judged when the numerical fluctuation of a certain operating parameter exceeds the preset range, but the occasional numerical fluctuation of a single operating parameter may be caused by an error in data collection or uploading. Fault alarm, there is a high probability of misjudgment

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