Fault detection method for sewage pipeline

Abstract

The invention discloses a fault detection method for a sewage pipeline, and the method comprises the following steps: 1, constructing a sewage pipeline sensing network, and installing a sensor at a key node of the sewage pipeline for monitoring; 2, processing and storing known data of the water plant pipeline, and comparing the known data with prediction evaluation data so as to establish a sewage pipeline operation data model; 3, constructing a mechanism and data dual-drive diagnosis model; 4, inputting the preprocessed data into the diagnosis model; and 5, determining a fault point location by combining the sewage pipeline position and the sensor position information, and judging a fault reason through a fault correlation analysis module. Each node of a sewage pipeline network is monitored in real time through sensor acquisition equipment, and the operation condition and the fault condition of the sewage pipeline can be effectively recorded, so that operation or fault data is transmitted and then compared with fault data generated by a prediction and evaluation system; the data processing and analysis capability can be improved, so that the efficiency of the sewage pipeline network during troubleshooting is improved.

Description

Technical Field

[0001] This invention relates to the field of sewage pipeline fault monitoring technology, specifically a fault detection method for sewage pipelines. Background Technology

[0002] Fault detection methods for sewage pipelines are mainly divided into trenchless detection methods and trenchless detection methods. Among them, trenchless detection methods have become the core technology for troubleshooting urban sewage pipelines due to their high efficiency and minimal environmental impact. They can accurately identify problems such as blockages, ruptures, leaks, and corrosion. However, existing sewage pipeline fault detection methods have some shortcomings, such as:

[0003] Application No.: CN202111055715.3 discloses a method for detecting and classifying structural defects in drainage pipelines. This method identifies structural defects in drainage pipelines based on image or video detection and classifies the degree of their defects. It is effectively applied to the detection and identification of structural defects in drainage pipelines. This method has significant practical engineering application value in reducing workload and improving the detection efficiency and accuracy of drainage pipelines. However, when using this method, it is difficult to predict the location of the fault in the sewage pipeline, which may lead to the problem of difficulty in determining the location of the fault during maintenance.

[0004] The method for fault detection in sewage pipe networks, application number CN202311220740.1, calculates the flow retention coefficient and velocity retention coefficient from the sewage level change curve, which can determine the fault in the pipe to be tested and reduce the cost of fault detection in sewage pipe networks. However, when using this method, it is difficult to predict the fault condition and location of the sewage pipe, which may lead to a decrease in maintenance efficiency.

[0005] Therefore, we propose a fault detection method for sewage pipelines to address the problems mentioned above. Summary of the Invention

[0006] The purpose of this invention is to provide a fault detection method for sewage pipelines to solve the problems currently existing in the market as described in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a fault detection method for sewage pipelines, comprising the following steps:

[0008] Step 1: Construct a sewage pipeline sensing network, install sensors at key nodes of the sewage pipeline for real-time monitoring, and generate known data on the water plant pipeline. The key nodes are the inlet, bends, pipe diameter change connections, and outlet.

[0009] Step 2: Process and store the known data of the water plant pipeline, and compare it with the predicted and evaluated data to establish a sewage pipeline operation data model;

[0010] Step 3: Record the sewage pipeline operation data model, and then construct a mechanism- and data-driven diagnostic model. Based on the core process mechanism model of the water plant, a deep learning model is integrated. The mechanism model calculates the water flow dynamic parameters in the pipeline to achieve fault identification.

[0011] Step 4: Input the preprocessed data into the diagnostic model. The model outputs the fault type and confidence level in real time. The fault types are divided into three categories: blockage, internal wall corrosion and interface leakage.

[0012] Step 5: Combine the location of the sewage pipe and sensor location information to determine the fault location, call the historical maintenance records and pipe material parameters in the water plant operation data service, and use the fault correlation analysis module to determine the cause of the fault, such as the specific cause of the blockage caused by grease buildup or the corrosion caused by abnormal water pH, and then generate a fault report.

[0013] By using sensor data acquisition devices to monitor each node of the sewage pipeline network in real time, the operational status and fault conditions of the sewage pipelines can be effectively recorded. This transmits operational or fault data, which is then compared with fault data generated by the predictive assessment system. This improves data processing and analysis capabilities, enhances system stability and security, and enables the system to reach a state where it can be practically deployed and operated. This provides strong support for the intelligent management of the sewage pipeline network, while ensuring that the system functions are deeply integrated with the overall architecture, meeting the intelligent needs of the entire sewage treatment process, and thus improving the efficiency of the sewage pipeline network during fault repair.

[0014] As a preferred technical solution of the present invention, the sewage pipeline sensing network is constructed through an underlying system. The underlying system includes sensor acquisition devices, which connect and manage all sensor components at the sewage pipeline. The types of sensors include water quality sensors, flow sensors, pressure sensors, and ultrasonic sensors. The sensors are transmitted via Ethernet to an edge gateway deployed in Docker containers. The sensor acquisition devices are associated with a database, which is associated with a process simulation system. The process simulation system is associated with a deep learning prediction system and a prediction evaluation system. The process simulation system and the prediction evaluation system are associated with a correction system, which is associated with the correction system and the actual equipment. The underlying system has an API interface, and the underlying system is associated with a backend server through the API interface.

[0015] The above technical solution enables sensor acquisition devices to manage multiple types of sensors in a unified manner. Water quality sensors, flow sensors, pressure sensors, and ultrasonic sensors can collaboratively acquire multiple parameters. Docker containerized edge gateways enable local data preprocessing, which improves the response speed of the sensors. Furthermore, through database settings, the data acquired by the sensor acquisition devices can be effectively recorded.

[0016] As a preferred technical solution of the present invention, the process simulation system can transform the core processes of sewage pipelines and water plants into calculable and interactive digital models, providing a process logic benchmark for fault detection, namely by simulating pipeline water flow, water quality, and pressure parameters under normal or abnormal operating conditions; while the deep learning prediction system, based on historical data and real-time sensing data, uses deep learning algorithms, such as the time series analysis model implicit in the project or past sewage pipeline fault data, to predict sewage pipeline fault risks in advance; the prediction evaluation system evaluates the accuracy of the prediction model by comparing and analyzing the prediction results with the actual situation, ensuring that the fault prediction does not deviate from the actual operating conditions; the correction system adjusts the deep learning prediction system, the process simulation system, and the fault detection process to solve problems such as prediction deviation and insufficient detection accuracy, ensuring data accuracy, and can use past sewage pipeline fault data or data modified by the administrator as a benchmark.

[0017] The above technical solutions enable the process simulation system to build a digital model of the pipeline, which can simulate abnormal operating conditions such as sudden increase in flow during rainy days and pipeline blockage, providing a scientific process benchmark for fault detection. Furthermore, by integrating historical fault data with real-time sensing data through a deep learning prediction system, the risk of blockage, corrosion, and leakage can be predicted in advance, giving time for preventive maintenance of the pipeline. The prediction and evaluation system can accurately establish the differences by comparing the prediction results with the actual operating conditions every hour, avoiding detection failure caused by model drift.

[0018] As a preferred technical solution of the present invention, the backend server is equipped with a water plant operation data module, a simulation control module, an equipment control module, a model prediction module, a model evaluation module, and a database module. The water plant operation data module is used to collect, integrate, and transmit full-dimensional operation data of the physical water plant and sewage pipelines. The simulation control module constructs a dynamic simulation environment of the process flow through a mechanistic model, which can provide a normal operating condition benchmark for fault detection. The equipment control module is used to convert the control commands of the digital system into the operation actions of the physical equipment, realizing remote control of sewage pipelines and water plant equipment. The model prediction module mines the patterns in historical and real-time data based on deep learning algorithms to realize the early prediction of sewage pipeline faults, water plant process parameters, and equipment operating status. The model evaluation module is used to compare the prediction results with the actual situation, evaluate the accuracy and applicability of the prediction model, and provide data basis for model iteration and optimization. The database module is used to store prediction data and actual data.

[0019] The above technical solution enables the water plant operation data module to collect pipeline flow, water quality and equipment status data. Through data cleaning and integration, it provides a high-quality data source for subsequent modules. The simulation control module builds a dynamic simulation environment for the pipeline and can output the pressure reference range under normal operating conditions. During fault detection, it can quickly identify anomalies by comparing actual parameters with the reference.

[0020] As a preferred technical solution of the present invention, the backend server is associated with a frontend data display unit, and the frontend data display unit includes a homepage module, an operation status instrument module, a simulation display module, a model prediction comparison module, an equipment status display module, and an equipment control system; wherein the operation status instrument module can record the status of various instruments in the sewage pipeline, the simulation display module is used to display the simulation operation data, and the frontend data display is connected to a human-computer interaction interface.

[0021] The above technical solution enables the front-end data display unit to visualize fault detection data. The homepage module centrally displays the overall operating status of the pipeline, and key indicators are alerted with red, yellow and green colors. Managers can quickly grasp the overall situation. The operating status instrument module records instrument data such as flow, pressure and COD, supports historical data backtracking, and can accurately locate abnormal periods during fault analysis.

[0022] As a preferred technical solution of the present invention, the fault judgment criteria can be customized and adjusted through the front-end equipment control interface. Managers can modify the judgment thresholds such as flow rate, pressure, and wall thickness according to the actual process of the water plant, such as the three-stage AAO process and the pipe material. The system automatically updates the model judgment logic.

[0023] The above technical solution enables the front-end data display unit to visualize fault detection data. The homepage module centrally displays the overall operating status of the pipeline, with key indicators using red, yellow, and green warnings, allowing managers to quickly grasp the overall situation. The operating status instrument module records instrument data such as flow, pressure, and COD, which can accurately locate abnormal periods during fault analysis, thus enabling managers to better judge the fault situation.

[0024] As a preferred technical solution of the present invention, the training and iteration of the mechanism- and data-driven diagnostic model need to rely on more than 3 years of multi-dimensional historical data. The data covers different operating conditions of sewage pipelines, such as the operating parameters, fault records and pipeline basic information under the impact of industrial wastewater, such as the sudden increase in flow on rainy days, low temperature operation in winter, and industrial wastewater impact.

[0025] The above technical solution enables the fault judgment standard to be adapted to different scenarios. Managers can modify the judgment parameters according to the pipe material and process type through the front-end interface, thereby increasing the efficiency and accuracy of the method in judging sewage pipe faults.

[0026] As a preferred technical solution of the present invention, the operating parameters are flow rate, pressure, and water quality, while the fault records are the occurrence time, treatment plan, and repair effect of blockage, corrosion, and leakage, and the pipeline basic information is material, pipe diameter, and service life.

[0027] The above technical solution makes it more convenient for managers to adjust operating parameters or retrieve fault records, thereby increasing the accuracy of the method during adjustment.

[0028] As a preferred technical solution of the present invention, the fault report should include four categories: basic fault information, cause tracing, impact assessment, and handling suggestions. The basic fault information should specify the fault location type, occurrence time, and real-time parameters. The cause tracing should combine historical pipeline maintenance records, material parameters, and water quality data to analyze the root cause of the fault. The impact assessment should use a process simulation system to simulate the impact of the fault on the downstream water plant process. The handling suggestions should include standard operating procedures, required tools, and estimated working hours, while also linking historical similar cases in the database module.

[0029] The above technical solution enables fault reporting to support decision-making, and the basic fault information provides maintenance personnel with accurate location information, avoiding the time-consuming problem of on-site location due to vague descriptions. Furthermore, by tracing the cause, the root cause of the fault can be deeply investigated, providing a basis for pipeline modification.

[0030] Compared with existing technologies, the beneficial effects of this invention are: by using sensor acquisition devices to monitor each node of the sewage pipeline network in real time, the operating status and fault status of the sewage pipeline can be effectively recorded, thereby transmitting operating or fault data. Subsequently, the data is compared with the fault data generated by the predictive evaluation system, which can improve data processing and analysis capabilities, enhance system stability and security, and enable the system to reach a state that can be practically deployed and operated. This provides strong support for the intelligent management of the sewage pipeline network, while ensuring that the system functions and overall architecture are deeply integrated to meet the intelligent needs of the entire sewage treatment business process, thereby improving the efficiency of the sewage pipeline network during fault repair.

[0031] Furthermore, by configuring the sensor acquisition device, it is possible to achieve unified management of multiple types of sensors. Water quality sensors, flow sensors, pressure sensors, and ultrasonic sensors can collaboratively collect multiple parameters. The Docker containerized edge gateway enables local data preprocessing, which improves the response speed of the sensors. Moreover, by configuring the database, the data collected by the sensor acquisition device can be effectively recorded.

[0032] Furthermore, through the configuration of the backend server, the water plant operation data module can collect pipeline flow, water quality, and equipment status data. Through data cleaning and integration, it provides a high-quality data source for subsequent modules. The simulation control module constructs a dynamic simulation environment for the pipeline and can output the pressure reference range under normal operating conditions. During fault detection, it can quickly identify anomalies by comparing actual parameters with the reference. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the sewage pipeline monitoring architecture system of the present invention;

[0034] Figure 2 This is a schematic diagram of the sewage pipeline fault monitoring process of the present invention. Detailed Implementation

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0036] Example 1: Please refer to Figures 1-2 The present invention provides a technical solution: a fault detection method for sewage pipelines, comprising the following steps:

[0037] Step 1: Construct a sewage pipeline sensing network, install sensors at key nodes of the sewage pipeline for real-time monitoring, and generate known data on the water plant pipeline. The key nodes are the inlet, bends, pipe diameter change connections, and outlet.

[0038] Step 2: Process and store the known data of the water plant pipeline, and compare it with the predicted and evaluated data to establish a sewage pipeline operation data model;

[0039] Step 3: Record the sewage pipeline operation data model, and then construct a mechanism- and data-driven diagnostic model. Based on the core process mechanism model of the water plant, a deep learning model is integrated. The mechanism model calculates the water flow dynamic parameters in the pipeline to achieve fault identification.

[0040] Step 4: Input the preprocessed data into the diagnostic model. The model outputs the fault type and confidence level in real time. The fault types are divided into three categories: blockage, internal wall corrosion and interface leakage.

[0041] Step 5: Combine the location of the sewage pipeline and sensor location information to determine the fault location, call up the historical maintenance records and pipeline material parameters in the water plant operation data service, and use the fault correlation analysis module to determine the cause of the fault, thereby generating a fault report;

[0042] The wastewater pipeline sensing network is constructed through an underlying system, which includes sensor acquisition devices that connect and manage all sensor components along the wastewater pipeline. These sensors include water quality sensors, flow sensors, pressure sensors, and ultrasonic sensors. The sensors transmit data via Ethernet to an edge gateway deployed in Docker containers. The sensor acquisition devices are associated with a database, which in turn is associated with a process simulation system. This process simulation system is associated with a deep learning prediction system and a prediction evaluation system. The process simulation system and prediction evaluation system are associated with a correction system, which in turn is associated with the actual equipment. Furthermore, the underlying system has an API interface that connects to a backend server.

[0043] The process simulation system can transform the core processes of sewage pipelines and water plants into calculable and interactive digital models, providing a process logic benchmark for fault detection. This is achieved by simulating pipeline flow, water quality, and pressure parameters under normal or abnormal operating conditions. The deep learning prediction system, based on historical and real-time sensing data, uses deep learning algorithms, such as the project's implicit time-series analysis model or past sewage pipeline fault data, to predict sewage pipeline fault risks in advance. The prediction and evaluation system assesses the accuracy of the prediction model by comparing the prediction results with the actual situation, ensuring that fault predictions do not deviate from actual operating conditions. The correction system adjusts the deep learning prediction system, process simulation system, and fault detection process to solve problems such as prediction bias and insufficient detection accuracy, ensuring data accuracy. It can use past sewage pipeline fault data or data modified by the administrator as a benchmark.

[0044] The backend server internally includes a water plant operation data module, a simulation control module, an equipment control module, a model prediction module, a model evaluation module, and a database module. The water plant operation data module is used to collect, integrate, and transmit full-dimensional operation data of the physical water plant and sewage pipelines. The simulation control module constructs a dynamic simulation environment of the process flow through a mechanistic model, which can provide a normal operating condition benchmark for fault detection. The equipment control module is used to convert the control commands of the digital system into the operation actions of the physical equipment, realizing remote control of sewage pipelines and water plant equipment. The model prediction module uses deep learning algorithms to mine the patterns in historical and real-time data, realizing the early prediction of sewage pipeline faults, water plant process parameters, and equipment operating status. The model evaluation module is used to compare the prediction results with the actual situation, evaluate the accuracy and applicability of the prediction model, and provide data basis for model iteration and optimization. The database module is used to store prediction data and actual data.

[0045] The backend server is associated with a frontend data display unit, which includes a homepage module, an operation status instrument module, a simulation display module, a model prediction and comparison module, an equipment status display module, and an equipment control system. The operation status instrument module can record the status of various instruments in the sewage pipeline, the simulation display module is used to display the simulation operation data, and the frontend data display is connected to a human-computer interaction interface.

[0046] The fault judgment criteria can be customized and adjusted through the front-end equipment control interface. Managers can modify the judgment thresholds such as flow rate, pressure, and wall thickness according to the actual process of the water plant, such as the tertiary AAO process and pipeline material. The system will automatically update the model judgment logic.

[0047] The training and iteration of the mechanism- and data-driven diagnostic model need to rely on more than 3 years of multi-dimensional historical data, which covers different operating conditions of sewage pipelines, such as the sudden increase in flow during rainy days, low temperature operation in winter, operating parameters, fault records and basic pipeline information under the impact of industrial wastewater.

[0048] The operating parameters are flow rate, pressure, and water quality, while the fault records include the time of occurrence of blockage, corrosion, and leakage, the handling plan, and the repair effect. The pipeline basic information includes material, pipe diameter, and service life.

[0049] The fault report should include four categories: basic fault information, cause tracing, impact assessment, and handling recommendations. The basic fault information should clearly state the fault location type, occurrence time, and real-time parameters. The cause tracing should combine historical pipeline maintenance records, material parameters, and water quality data to analyze the root cause of the fault. The impact assessment should use a process simulation system to simulate the impact of the fault on the downstream water plant process. The handling recommendations should include standard operating procedures, required tools, and estimated man-hours, while also linking historical similar cases in the database module.

[0050] Example 2: This example differs from Example 1 in that it provides a fault detection method for sewage pipelines, comprising the following steps:

[0051] Step 1: Construct a sewage pipeline sensing network and install sensors at key nodes of the sewage pipeline for real-time monitoring;

[0052] Step 2: Process and store the known data of the water plant pipeline, and compare it with the predicted and evaluated data to establish a sewage pipeline operation data model;

[0053] Step 3: Record the sewage pipeline operation data model, and then construct a mechanism- and data-driven diagnostic model. Based on the core process mechanism model of the water plant, a deep learning model is integrated. The mechanism model calculates the water flow dynamic parameters in the pipeline to achieve fault identification.

[0054] Step 4: Input the preprocessed data into the diagnostic model. The model outputs the fault type and confidence level in real time. The fault types are divided into three categories: blockage, internal wall corrosion and interface leakage.

[0055] Step 5: Combine the location of the sewage pipe and sensor location information to determine the fault location, call the historical maintenance records and pipe material parameters in the water plant operation data service, and use the fault correlation analysis module to determine the cause of the fault, such as the specific cause of the blockage caused by grease buildup or the corrosion caused by abnormal water pH, and then generate a fault report.

[0056] When using this fault detection method for sewage pipelines, pipeline data is first collected through multi-dimensional sensors, preprocessed by a containerized edge gateway, and then stored in a database. The mechanism and data-driven model diagnoses faults in real time, and the source is traced by combining GIS maps and historical data. Early warning and disposal plans are pushed out, and the water plant equipment is linked to adjust operating parameters. The model is iterated and optimized monthly through actual cases to achieve regular real-time monitoring of the entire pipeline fault process.

[0057] This completes a series of tasks. The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0058] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A fault detection method for sewage pipelines, characterized in that, Includes the following steps: Step 1: Construct a sewage pipeline sensing network, install sensors at key nodes of the sewage pipeline for real-time monitoring, and generate known data on the water plant pipeline. Step 2: Process and store the known data of the water plant pipeline, and compare it with the predicted and evaluated data to establish a sewage pipeline operation data model; Step 3: Record the sewage pipeline operation data model, and then construct a mechanism- and data-driven diagnostic model. Based on the core process mechanism model of the water plant, a deep learning model is integrated. The mechanism model calculates the water flow dynamic parameters in the pipeline to achieve fault identification. Step 4: Input the preprocessed data into the diagnostic model. The model outputs the fault type and confidence level in real time. The fault types are divided into three categories: blockage, internal wall corrosion and interface leakage. Step 5: Combine the location of the sewage pipe and sensor location information to determine the fault location, call up the historical maintenance records and pipe material parameters in the water plant operation data service, and use the fault correlation analysis module to determine the cause of the fault, thereby generating a fault report.

2. The fault detection method for sewage pipelines according to claim 1, characterized in that, The wastewater pipeline sensing network is constructed through an underlying system, which includes sensor acquisition devices that connect and manage all sensor components along the wastewater pipeline. These sensors include water quality sensors, flow sensors, pressure sensors, and ultrasonic sensors. The sensors transmit data via Ethernet to an edge gateway deployed in Docker containers. The sensor acquisition devices are associated with a database, which in turn is associated with a process simulation system. This process simulation system is associated with a deep learning prediction system and a prediction evaluation system. The process simulation system and prediction evaluation system are associated with a correction system, which in turn is associated with the actual equipment. The underlying system also has an API interface, through which it connects to a backend server.

3. The fault detection method for sewage pipelines according to claim 2, characterized in that, The process simulation system can transform the core processes of sewage pipelines and water plants into calculable and interactive digital models, providing a process logic benchmark for fault detection. This is achieved by simulating pipeline flow, water quality, and pressure parameters under normal or abnormal operating conditions. The deep learning prediction system, based on historical and real-time sensing data, uses deep learning algorithms, such as the project's implicit time-series analysis model or past sewage pipeline fault data, to predict sewage pipeline fault risks in advance. The prediction and evaluation system assesses the accuracy of the prediction model by comparing the prediction results with the actual situation, ensuring that fault predictions do not deviate from actual operating conditions. The correction system adjusts the deep learning prediction system, process simulation system, and fault detection process to solve problems such as prediction bias and insufficient detection accuracy, ensuring data accuracy. Past sewage pipeline fault data or data modified by the administrator can be used as a benchmark.

4. The fault detection method for sewage pipelines according to claim 2, characterized in that, The backend server internally includes a water plant operation data module, a simulation control module, an equipment control module, a model prediction module, a model evaluation module, and a database module. The water plant operation data module collects, integrates, and transmits comprehensive operational data of the physical water plant and sewage pipelines. The simulation control module constructs a dynamic simulation environment of the process flow through a mechanistic model, providing a normal operating condition benchmark for fault detection. The equipment control module translates control commands from the digital system into operational actions of physical equipment, enabling remote control of sewage pipelines and water plant equipment. The model prediction module uses deep learning algorithms to mine patterns in historical and real-time data, enabling advance prediction of sewage pipeline faults, water plant process parameters, and equipment operating status. The model evaluation module compares the prediction results with actual conditions to evaluate the accuracy and applicability of the prediction model, providing data for model iteration and optimization. The database module stores both predicted and actual data.

5. The fault detection method for sewage pipelines according to claim 4, characterized in that, The backend server is associated with a frontend data display unit, which includes a homepage module, an operation status meter module, a simulation display module, a model prediction and comparison module, an equipment status display module, and an equipment control system. The operation status instrument module can record the status of various instruments in the sewage pipeline, while the simulation display module is used to display the simulation operation data.

6. The fault detection method for sewage pipelines according to claim 1, characterized in that, The fault judgment criteria can be customized and adjusted through the front-end equipment control interface. Managers can modify the judgment thresholds such as flow rate, pressure, and wall thickness according to the actual process of the water plant, such as the three-stage AAO process and pipeline material. The system will automatically update the model judgment logic.

7. The fault detection method for sewage pipelines according to claim 1, characterized in that, The training and iteration of the mechanism- and data-driven diagnostic model requires more than 3 years of multi-dimensional historical data, which covers different operating conditions of sewage pipelines, such as sudden increase in flow during rainy days, low temperature operation in winter, operating parameters, fault records and basic pipeline information under the impact of industrial wastewater.

8. The fault detection method for sewage pipelines according to claim 7, characterized in that, The operating parameters are flow rate, pressure, and water quality, while the fault records include the occurrence time, handling plan, and repair effect of blockage, corrosion, and leakage. The basic pipeline information includes material, pipe diameter, and service life.

9. The fault detection method for sewage pipelines according to claim 1, characterized in that, The fault report must include four categories: basic fault information, cause tracing, impact assessment, and handling recommendations. The basic fault information should specify the fault location type, occurrence time, and real-time parameters. The cause tracing should combine historical pipeline maintenance records, material parameters, and water quality data to analyze the root cause of the fault. The impact assessment should use a process simulation system to simulate the impact of the fault on the downstream water plant process. The handling recommendations should include standard operating procedures, required tools, and estimated man-hours, while also linking historical similar cases in the database module.

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