CN-122020471-A - Water pump running state big data analysis and prediction method and system

CN122020471ACN 122020471 ACN122020471 ACN 122020471ACN-122020471-A

Abstract

The application relates to the technical field of water pump running state big data analysis and prediction, and particularly discloses a water pump running state big data analysis and prediction method and a water pump running state big data analysis and prediction system. The method can effectively solve the problems that in the prior art, transient data are difficult to accurately identify under a water pump rapid mode switching scene, so that data analysis is difficult and abnormal running states of the water pump cannot be effectively predicted. By analyzing the difference data, the method can judge whether the water pump has abnormal parts, thereby realizing early warning of the potential risk of the water pump, avoiding unplanned shutdown caused by sudden abnormality, improving the stability and reliability of the operation of the water pump and providing powerful guarantee for the production flow of enterprises.

Inventors

SUN XIANYOU
CHEN PING
ZHENG XIAOPING

Assignees

浙江绿美泵业科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260129

Claims (10)

1. A large data analysis and prediction method for a water pump running state is applied to a water pump control system and is characterized by comprising the steps of acquiring reference data of changes of various running parameters of a water pump along with time when a first water pump running mode is switched to a second water pump running mode in a healthy running state in a water pump running application occasion, acquiring real-time running parameter data of the water pump when the water pump actually executes the first water pump running mode to switch to the second water pump running mode in the same water pump running application occasion, matching and comparing the real-time running parameter data of the water pump with the reference data to obtain difference data reflecting deviation degree between the actual running state of the water pump and the healthy running state of the water pump, and analyzing according to the difference data to judge whether abnormal parts appear in the water pump.
2. The method for analyzing and predicting the operation state of a water pump according to claim 1, wherein the reference data includes one or more of vibration data, temperature data and pressure data.
3. The method for analyzing and predicting the running state big data of the water pump according to claim 1, wherein the step of acquiring the reference data of the time-varying operation parameters of the water pump when the first water pump running mode is switched to the second water pump running mode in the healthy running state in the water pump running application occasion comprises the steps of acquiring the reference data of the time-varying operation parameters of the water pump when the first water pump running mode is switched to the second water pump running mode in the healthy running scene, repeatedly executing the operations for a preset number of times to obtain a data set containing the reference data of the time-varying operation parameters of a plurality of groups of water pumps, accumulating and averaging the reference data of the time-varying operation parameters of the plurality of groups of water pumps in the data set to obtain the reference data of the time-varying operation parameters of the processed water pump.
4. The method for analyzing and predicting the running state big data of the water pump according to claim 1 is characterized by comprising the steps of analyzing according to the difference data, judging whether the water pump has abnormal parts or not, and specifically comprising the steps of fitting the difference data by a linear interpolation method to obtain a difference signal curve graph, identifying the accumulated time length corresponding to the curve segment exceeding a set difference threshold value from the difference signal curve graph, and judging whether the water pump has abnormal parts or not according to the accumulated time length, wherein when the accumulated time length exceeds the set time length threshold value, the water pump has abnormal parts.
5. The method for analyzing and predicting the running state of the water pump according to claim 4, wherein the step of judging whether the water pump has the abnormal parts according to the accumulated time length, wherein when the accumulated time length exceeds a set time length threshold value, the step of acquiring basic characteristic distribution of a difference signal graph after the abnormal parts in the water pump are determined; Acquiring sensitivity parameters of the fluid in the current water pump from a priori database of sensitivity parameters related to the fluid properties, and obtaining a reference signal graph by adopting a water flow dynamics equation and combining iterative algorithm reconstruction according to the sensitivity parameters of the fluid in the current water pump and basic characteristic distribution; The difference signal graph is compared with the reference signal graph to verify whether an abnormality occurs in the actual running state of the water flow due to the characteristic shift caused by the change of the fluid property.
6. The method for analyzing and predicting the running state of the water pump according to claim 1, wherein after the step of analyzing according to the difference data and judging whether the abnormality occurs in the parts in the water pump, the method comprises the steps of calculating an entropy value of the difference data by adopting an entropy value method and giving a timestamp to the entropy value when the abnormality occurs in the parts in the water pump, forming structural data, storing the structural data in a historical entropy value database, extracting the structural data stored in the historical entropy value database according to a preset period after each time the water pump actually executes the first water pump running mode to the second water pump running mode, analyzing the plurality of extracted structural data, and confirming that the abnormality occurs in the property of the material conveyed in the water pump when the entropy value in the extracted structural data is recognized to be in a continuous increasing trend along with time and the slope of the continuous increasing trend exceeds a set slope threshold value.
7. The method for analyzing and predicting the running state of the water pump according to the invention as set forth in claim 1, wherein the step of analyzing according to the difference data to determine whether the water pump has an abnormality comprises extracting priori knowledge of the flow rate of the water pump and the water pump abrasion-prone components from a database of the water pump running of an enterprise and constructing a water pump running knowledge base, collecting water pump flow rate information of the current water pump in a second water pump running mode when the running state of the water pump is confirmed to be abnormal, and matching the water pump flow rate information with the water pump running knowledge base to obtain the associated water pump abrasion-prone components, and analyzing according to the water pump abrasion-prone components and the difference signal graph to identify the components with the abnormality in the water pump.
8. The method for analyzing and predicting the running state of the water pump according to claim 7 is characterized in that the step of analyzing according to the parts easy to wear of the water pump and the difference signal graph to identify the parts with abnormality in the water pump specifically comprises the steps of identifying the difference points exceeding the set difference amplitude from the difference signal graph, performing back extraction based on the difference points to obtain the running parameter data of the water pump corresponding to the difference points, identifying the abnormal components in the running parameter data of the water pump corresponding to the difference points by using a time-frequency analysis method to obtain the running parameter data of the water pump corresponding to the difference points by using the back extraction, obtaining the suspected wear parts causing the abnormal components to appear according to the abnormal components, matching the suspected wear parts from the parts easy to wear of the water pump, and identifying the parts with abnormality based on the intersection between the suspected wear parts and the parts easy to wear of the water pump.
9. The method according to claim 8, wherein the step of obtaining a suspected wear part causing the abnormal component according to the abnormal component, matching the suspected wear part from the water pump easy-wear parts, and confirming the abnormal part based on the intersection between the suspected wear part and the water pump easy-wear part comprises: the method comprises the steps of obtaining first operation parameter data of a water pump in a first operation mode and second operation parameter data of the water pump in a second operation mode, extracting first abnormality information and second abnormality information of the abnormal parts from the first operation parameter data and the second operation parameter data according to the confirmed abnormal parts, and associating the first abnormality information, the second abnormality information and difference data to form development path instructions of the abnormal parts.
10. A big data analysis and prediction system for the running state of a water pump is characterized by comprising a health parameter acquisition module, a real-time parameter acquisition and difference signal generation module and a data analysis module, wherein the health parameter acquisition module is used for acquiring reference data of various running parameters of the water pump along with time when the water pump runs healthily and is switched to a second water pump running mode in a water pump running application occasion, the real-time parameter acquisition and difference signal generation module is used for acquiring real-time running parameter data of the water pump when the water pump actually executes the first water pump running mode to be switched to the second water pump running mode in the same water pump running application occasion, the real-time running parameter data of the water pump is matched with the reference data and is compared to obtain difference data reflecting the deviation degree between the actual running state of the water pump and the healthy running state of the water pump, and the data analysis module is used for analyzing according to the difference data and judging whether the water pump has abnormal parts.

Description

Water pump running state big data analysis and prediction method and system Technical Field The application relates to the technical field of water pump running state big data analysis and prediction, in particular to a water pump running state big data analysis and prediction method and a water pump running state big data analysis and prediction system. Background In large industrial production facilities, water pumps are the core equipment for fluid delivery, and their stable operation is directly related to the continuity and efficiency of the entire production process. To ensure the reliability of these critical devices and to enable a transition from passive maintenance to active preventative maintenance, many businesses have introduced advanced water pump operation state analysis and prediction systems. These systems typically utilize data analysis techniques to identify potential equipment problems in advance by continuously collecting various operational data of the water pump, such as vibration signals, bearing temperatures, pump body pressures, motor currents, etc., thereby effectively avoiding unplanned outages due to sudden anomalies. However, in practical applications, existing methods and systems for analyzing and predicting the operational status of water pumps pose significant challenges, particularly when the water pump needs to be frequently and quickly switched between two distinct modes of operation. For example, switching from a high flow, high lift bulk delivery mode to a low flow, high precision controlled fine mix mode may last only tens of seconds to minutes throughout the switching process. During this time, the data stream acquired by the system is often contaminated with a large number of transient signals transitioning from one steady state to another. These transient data contain a hybrid of the two modes, for example, during deceleration, the vibration spectrum may contain both the residual frequency component at high speeds and the new excitation frequency component at low speeds, and the pressure sensor may register the instantaneous pressure shock generated when the valve is rapidly closed or opened. Because the input data contains a large amount of mixed working conditions and transient change information which are not clearly distinguished, the existing water pump running state big data analysis and prediction method and system face a great challenge in processing. This approach does not adequately learn the complex dynamic behavior of these fast switching processes while learning, and therefore, it tends to treat these complex signals as normal systematic noise or environmental disturbances rather than component anomalies that may occur under certain conditions. For example, when switching from a high flow mode to a low flow mode, abrupt changes in fluid velocity inside the pump body may cause transient cavitation, creating broadband vibration and noise. The original system may misjudge such normal physical fluctuations as early signs of bearing wear or impeller failure, thereby frequently giving false alarms, i.e., false alarms. These false positives may not be true device anomalies, but rather are a manifestation of insufficient understanding of transient conditions by the system. In view of the above, there is a need in the art for improvements. Disclosure of Invention The application provides a method and a system for analyzing and predicting big data of a water pump running state, and aims to solve the problems that in the water pump running mode switching process, transient data are difficult to accurately identify, so that data analysis is difficult and water pump running state abnormality cannot be effectively predicted. In order to solve the problems, the scheme of the application is as follows: As one aspect of the present application, there is provided a method for analyzing and predicting a water pump operation state big data, the method being applied to a water pump control system, comprising: In a water pump operation application occasion, acquiring reference data of time variation of various operation parameters of the water pump when the first water pump operation mode is switched to the second water pump operation mode in a healthy operation state; In the same water pump operation application occasion, when the water pump actually executes the first water pump operation mode to switch to the second water pump operation mode, acquiring real-time operation parameter data of the water pump, matching and comparing the real-time operation parameter data of the water pump with reference data to obtain difference data reflecting the deviation degree between the actual operation state of the water pump and the healthy operation state of the water pump; And analyzing according to the difference data to judge whether the water pump has abnormal parts. Further, the reference data includes one or more of vibration data, temperature data, or pressure data. Further, i