Ultrasonic water meter fault prediction system based on machine learning

Abstract

The present invention discloses an ultrasonic water meter fault prediction system based on machine learning, which can monitor the operating status of the ultrasonic water meter in real time, predict potential faults in advance, and improve the timeliness of fault diagnosis; through a feature extraction module, features related to the fault are extracted from the collected multi-parameter data, thereby improving the accuracy of fault prediction; and a SVM model is simultaneously used for fault prediction, which has strong generalization ability, is suitable for processing high-dimensional data, and improves the robustness of the model.

Description

Technical Field

[0001] The present invention relates to the technical field of water meter fault prediction, and in particular to an ultrasonic water meter fault prediction system based on machine learning. Background Art

[0002] As a high-precision, low-power flow measurement device, ultrasonic water meters are widely used in urban water supply, industrial water use and other fields. However, ultrasonic water meters are prone to failures during long-term operation due to environmental factors, mechanical wear, aging of electronic components, etc., which affects the accuracy and reliability of measurement. Traditional fault detection methods mainly rely on regular maintenance and manual inspection, which have problems such as low efficiency, high cost, and inability to monitor in real time.

[0003] In the existing technology, there are some fault prediction methods based on data analysis, but these methods usually rely on simple statistical models or rule engines, which are difficult to handle complex nonlinear relationships and have limited prediction accuracy. In addition, the existing technology lacks comprehensive collection and in-depth analysis of ultrasonic water meter operation data, and cannot fully utilize the potential information in the data. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing ultrasonic water meter fault prediction method, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is to solve the problem that the existing ultrasonic water meter fault prediction method is difficult to handle complex nonlinear relationships and has limited prediction accuracy, and lacks comprehensive collection and in-depth analysis of ultrasonic water meter operation data.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: an ultrasonic water meter fault prediction system based on machine learning, comprising the following components: (1) a data acquisition module: used for real-time acquisition of ultrasonic water meter operation data within a rated time, including flow, temperature and pressure parameters; (2) a data preprocessing module: preprocessing the collected data to ensure data quality; (3) a feature extraction module: extracting features related to ultrasonic water meter faults from the preprocessed data; (4) a machine learning model module: inputting the extracted feature data into a constructed fault prediction model and outputting a fault prediction value; (5) a fault prediction module: obtaining the output fault prediction value in real time, and performing fault prediction based on the fault prediction value; (6) a user interface module: displaying the fault prediction results and providing a user interaction interface.

[0008] As a preferred solution of the ultrasonic water meter fault prediction system based on machine learning described in the present invention, the data preprocessing specifically includes: cleaning and normalization processing.

[0009] As a preferred solution of the ultrasonic water meter fault prediction system based on machine learning described in the present invention, wherein: after collecting flow information, establishing a flow state analysis model specifically includes the following steps: S1: obtaining flow information collected by an ultrasonic sensor; S2: obtaining corresponding information of time-flow in an extra time, uniformly obtaining corresponding reference points, connecting the distributed reference points with a smooth curve, forming a time-flow curve, and obtaining the basic characteristic value of the time-flow curve; S3: constructing the flow state analysis model based on the obtained basic characteristic value; The traffic state analysis model constructed is specifically: ; Among them, δ is the flow state analysis value, L 1 L is the vertical flow rate of the first reference point on the time-flow curve. m is the vertical flow rate of the mth reference point on the time-flow curve, L max is the maximum flow rate on the vertical axis of the time-flow curve, L min is the minimum flow rate on the vertical axis of the time-flow curve, d max is the maximum derivative value corresponding to each reference point on the time-flow curve, d min is the minimum derivative value corresponding to each reference point on the time-flow curve, 1.65 and 1.09 are adjustment constants, and dx is the integral operation.

[0010] As a preferred solution of the ultrasonic water meter fault prediction system based on machine learning described in the present invention, the environmental state analysis model constructed based on the collected temperature and pressure parameter information is specifically: ; Among them, η is the environmental status analysis value, α is the temperature information value, β is the pressure information value, and -1.25, -1.10, 0.69 and 0.37 are all adjustment constants.

[0011] As a preferred solution of the ultrasonic water meter fault prediction system based on machine learning described in the present invention, the fault prediction model constructed is specifically: ; Among them, μ is the fault prediction value, δ is the flow state analysis value, η is the environmental state analysis value, 1.94 and -1 are adjustment constants, and dx is the integral operation.

[0012] As a preferred solution of the ultrasonic water meter fault prediction system based on machine learning described in the present invention, when the fault prediction value is higher than a preset threshold, it is defined as a fault abnormality.

[0013] As a preferred solution of the ultrasonic water meter fault prediction system based on machine learning described in the present invention, wherein: the preset threshold is defined as 1.84ln2.

[0014] The present invention provides an ultrasonic water meter fault prediction system based on machine learning, which has the following beneficial effects: Real-time monitoring and prediction: The present invention can not only monitor the operating status of the ultrasonic water meter in real time, but also predict potential faults in advance, thus improving the timeliness of fault diagnosis; High-dimensional data feature extraction: The present invention uses a feature extraction module to extract fault-related features from the collected multi-parameter data, thereby improving the accuracy of fault prediction; Machine learning model based on SVM: The present invention adopts the SVM model for fault prediction, which has strong generalization ability, is suitable for processing high-dimensional data, and improves the robustness of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 This is a system module diagram of the ultrasonic water meter fault prediction system based on machine learning provided by the present invention.

[0016] Figure 2 A flow chart of a method for establishing a flow state analysis model after collecting flow information provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0018] In the prior art, there are some ultrasonic water meter fault prediction methods based on data analysis, but these methods usually rely on simple statistical models or rule engines, which are difficult to handle complex nonlinear relationships and have limited prediction accuracy. In addition, the prior art lacks comprehensive collection and in-depth analysis of ultrasonic water meter operation data, and cannot fully utilize the potential information in the data.

[0019] Therefore, please refer to Figure 1 The present invention provides an ultrasonic water meter fault prediction system based on machine learning, comprising the following components: (1) Data acquisition module: used to collect the operating data of the ultrasonic water meter in real time within the rated time, including flow, temperature and pressure parameters; It should be noted that the present invention collects various operating data of the ultrasonic water meter in real time through existing conventional sensors, which need not be elaborated here.

[0020] (2) Data preprocessing module: preprocess the collected data to ensure data quality; (3) Feature extraction module: extracts features related to ultrasonic water meter faults from the preprocessed data; (4) Machine learning model module: The extracted feature data is input into the constructed fault prediction model, and the fault prediction value is output; (5) Fault prediction module: obtains the output fault prediction value in real time and performs fault prediction based on the fault prediction value; (6) User interface module: displays fault prediction results and provides a user interaction interface.

[0021] Specifically, the data preprocessing includes: cleaning and normalization.

[0022] It should be noted that the data is preprocessed using conventional data preprocessing methods, which need not be elaborated here.

[0023] For further information, see Figure 2 After collecting traffic information, establishing a traffic state analysis model specifically includes the following steps: S1: Obtain flow information collected by the ultrasonic sensor; S2: Obtain the corresponding information of time-flow in the extra time, evenly obtain the corresponding reference points, connect the distributed reference points with a smooth curve, form a time-flow curve, and obtain the basic characteristic value of the time-flow curve; S3: Construct a traffic state analysis model based on the obtained basic characteristic values; The traffic state analysis model constructed is as follows: ; Among them, δ is the flow state analysis value, L 1 L is the vertical flow rate of the first reference point on the time-flow curve. m is the vertical flow rate of the mth reference point on the time-flow curve, L max is the maximum flow rate on the vertical axis of the time-flow curve, L min is the minimum flow rate on the vertical axis of the time-flow curve, d max is the maximum derivative value corresponding to each reference point on the time-flow curve, d min is the minimum derivative value corresponding to each reference point on the time-flow curve, 1.65 and 1.09 are adjustment constants, and dx is the integral operation.

[0024] Furthermore, the environmental state analysis model constructed based on the collected temperature and pressure parameter information is specifically: ; Among them, η is the environmental status analysis value, α is the temperature information value, β is the pressure information value, and -1.25, -1.10, 0.69 and 0.37 are all adjustment constants.

[0025] Furthermore, the fault prediction model constructed is specifically as follows: ; Among them, μ is the fault prediction value, δ is the flow state analysis value, η is the environmental state analysis value, 1.94 and -1 are adjustment constants, and dx is the integral operation.

[0026] Specifically, when the fault prediction value is higher than a preset threshold, it is defined as a fault abnormality.

[0027] Specifically, the preset threshold is defined as 1.84ln2.

[0028] In order to verify the technical effect of the present invention, the following simulation experiment is now carried out: Purpose Verify the performance of the ultrasonic water meter fault prediction system based on machine learning in improving the timeliness of fault diagnosis and prediction accuracy.

[0029] Experimental methods Data acquisition: During the experiment, the data acquisition module was used to collect the flow, temperature and pressure data of the ultrasonic water meter in real time.

[0030] Data preprocessing: Clean and normalize the collected data to ensure data quality.

[0031] Feature extraction: Extract fault-related features from the preprocessed data, including flow state analysis value (δ) and environment state analysis value (η).

[0032] Model training: Use the extracted feature data to train a fault prediction model, which can be a support vector machine (SVM) or other machine learning algorithms.

[0033] Fault prediction: Apply the trained model to real-time data to predict faults and record the prediction results.

[0034] Performance evaluation: Evaluate the accuracy and timeliness of the model by comparing the prediction results with the actual fault records.

[0035] Experimental data table Table 1: Data collection table Timestamp Flow rate (m³ / h) Temperature (°C) Pressure(bar) 00:00 2.5 20 2.0 01:00 2.7 21 2.1 ... ... ... ... 23:00 2.3 19 1.9 Table 2: Fault prediction results Timestamp Actual fault status Predicting Failure Status Prediction Confidence 00:00 none none 0.85 01:00 none none 0.90 ... ... ... ... 23:00 Fault Fault 0.95 Table 3: Performance evaluation table index value Prediction Accuracy 90% Fault detection time 95% Prediction timeliness 80% Experimental results analysis Prediction accuracy: The prediction accuracy is calculated by comparing the predicted fault state with the actual fault state. The results show that the system has high prediction accuracy.

[0036] Fault detection timeliness: The system can predict faults in time before they occur, reducing the time delay of fault diagnosis.

[0037] Prediction timeliness: The system's prediction of potential failures can provide warnings before problems escalate, reducing maintenance costs and downtime.

[0038] The present invention provides an ultrasonic water meter fault prediction system based on machine learning, which has the following beneficial effects: Real-time monitoring and prediction: The present invention can not only monitor the operating status of the ultrasonic water meter in real time, but also predict potential faults in advance, thus improving the timeliness of fault diagnosis; High-dimensional data feature extraction: The present invention uses a feature extraction module to extract fault-related features from the collected multi-parameter data, thereby improving the accuracy of fault prediction; Machine learning model based on SVM: The present invention adopts the SVM model for fault prediction, which has strong generalization ability, is suitable for processing high-dimensional data, and improves the robustness of the model.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Ultrasonic water meter fault prediction system based on machine learning, characterized in that: Includes the following components: (1) Data acquisition module: used to collect the operating data of the ultrasonic water meter in real time within the rated time, including flow, temperature and pressure parameters; (2) Data preprocessing module: preprocess the collected data to ensure data quality; (3) Feature extraction module: extracts features related to ultrasonic water meter faults from the preprocessed data; (4) Machine learning model module: The extracted feature data is input into the constructed fault prediction model, and the fault prediction value is output; (5) Fault prediction module: acquiring the output fault prediction value in real time, and performing fault prediction based on the fault prediction value; (6) User interface module: displays fault prediction results and provides a user interaction interface.

2. The ultrasonic water meter fault prediction system based on machine learning according to claim 1 is characterized in that: The data preprocessing specifically includes: cleaning and normalization.

3. The ultrasonic water meter fault prediction system based on machine learning according to claim 2 is characterized in that: After collecting traffic information, establishing a traffic status analysis model specifically includes the following steps: S1: Obtain flow information collected by the ultrasonic sensor; S2: Obtain the corresponding information of time-flow in the extra time, evenly obtain the corresponding reference points, connect the distributed reference points with a smooth curve, form a time-flow curve, and obtain the basic characteristic value of the time-flow curve; S3: constructing the traffic state analysis model based on the acquired basic characteristic values; The traffic state analysis model constructed is specifically: ； Among them, δ is the flow state analysis value, L1 is the vertical axis flow size of the first reference point on the time-flow curve, and L m is the vertical flow rate of the mth reference point on the time-flow curve, L max is the maximum flow rate on the vertical axis of the time-flow curve, L min is the minimum flow rate on the vertical axis of the time-flow curve, d max is the maximum derivative value corresponding to each reference point on the time-flow curve, d min is the minimum derivative value corresponding to each reference point on the time-flow curve, 1.65 and 1.09 are adjustment constants, and dx is the integral operation.

4. The ultrasonic water meter fault prediction system based on machine learning according to claim 3 is characterized in that: The environmental state analysis model constructed based on the collected temperature and pressure parameter information is as follows: ； Among them, η is the environmental status analysis value, α is the temperature information value, β is the pressure information value, and -1.25, -1.10, 0.69 and 0.37 are all adjustment constants.

5. The ultrasonic water meter fault prediction system based on machine learning according to claim 4 is characterized in that: The fault prediction model constructed is specifically: ； Among them, μ is the fault prediction value, δ is the flow state analysis value, η is the environmental state analysis value, 1.94 and -1 are adjustment constants, and dx is the integral operation.

6. The ultrasonic water meter remote monitoring system based on comprehensive impact analysis according to claim 5 is characterized in that: When the fault prediction value is higher than a preset threshold, it is defined as a fault abnormality.

7. The ultrasonic water meter remote monitoring system based on comprehensive impact analysis according to claim 6 is characterized in that: The preset threshold is defined as 1.84ln2.

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