Water feed pump fault early warning diagnosis processing method and device

Abstract

The invention provides a water feed pump fault early warning and diagnosis processing method and device. The technical problem that effective fault early warning and diagnosis cannot be formed through diversified field monitoring data in the existing monitoring process is solved. The method comprises the following steps: forming a potential fault list and a fault function module list associated with each potential fault according to prior data; extracting presentation monitoring data related to potential fault performance from the field monitoring data, and binding the presentation monitoring data with the potential faults; extracting entity monitoring data related to the entity of the associated function module from the field monitoring data, and binding the entity monitoring data with the potential fault; and forming a fault diagnosis process according to the presentation monitoring data and the entity monitoring data, and forming a fault removal process according to a fault diagnosis result. Faults are divided into potential faults reflecting overall abnormity and entity faults needing to be clearly positioned, and then effective clustering of mass field monitoring data to fault types and optimization of a fault diagnosis process are formed. And the accuracy of early warning and evaluation of the fault is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a method and device for diagnosing and processing fault early warning of a water pump. Background Art

[0002] The feedwater pump group is an important auxiliary machine of the steam-water circulation system of a thermal power plant. It changes the feedwater flow and pressure supplied to the boiler by adjusting the speed of the feedwater pump, and provides cooling water or emergency water spraying to the reheater and superheater to adapt to the start and stop, load change, voltage conversion and sliding pressure operation of the unit, meet the needs of the power grid for unit reduction and frequency regulation, and improve the economy, safety and reliability of the unit's variable operating conditions.

[0003] In the prior art, the main components of a feedwater pump usually include a balancing plate, a pump shaft, a shaft seal, bearings supporting the pump shaft, a lubricating oil system matching the bearings, an oil cooler, and couplings for torque transmission between the pump shaft and the motor or turbine. The working performance of each functional module during the operation of the feedwater pump group can be realized by using mature sensor technology to collect the state of targeted signals of the physical components in the module, and the feedback of on-site monitoring data can be formed by using wired or wireless communication technology, and data comparison and alarm can be formed by using computer technology.

[0004] However, existing monitoring technology solutions can only make direct judgments based on the set thresholds of each data to form alarm signals for massive on-site monitoring data. They are unable to identify the underlying logical meaning of the alarm status, cannot form a judgment on the overall operating stability and failure rate of the water supply pump group, and cannot make effective diagnosis and evaluation of potential failures during operation. Summary of the invention

[0005] In view of the above problems, an embodiment of the present invention provides a water pump fault warning diagnosis processing method and device to solve the technical problem that the existing monitoring process cannot use diverse on-site monitoring data to form effective fault warning and diagnosis.

[0006] The water supply pump fault early warning diagnosis and processing method according to the embodiment of the present invention comprises:

[0007] Forming a potential fault list and a fault function module list associated with each potential fault according to prior data;

[0008] Extract the surface monitoring data related to the potential fault manifestation from the on-site monitoring data and bind it to the potential fault;

[0009] Extracting entity monitoring data related to entities of associated functional modules from the field monitoring data and binding them with potential faults;

[0010] A fault diagnosis process is formed based on the surface monitoring data and the entity monitoring data, and a fault elimination process is formed based on the fault diagnosis results.

[0011] In one embodiment of the present invention, the priori data is statistically quantified from past failure data of the water supply pump equipment or failure data of the same configuration equipment under the same use environment.

[0012] In one embodiment of the present invention, the normal operating conditions of the water supply pump include 0.5 times the frequency, 1 times the frequency, and 2 times the frequency of the rated speed.

[0013] In one embodiment of the present invention, the potential fault list includes abnormal oil temperature alarm in the lubricating oil tank, low oil level alarm in the lubricating oil tank, low lubricating oil pressure alarm, high bearing temperature alarm, large bearing vibration alarm of the water pump group, excessive shaft seal leakage, and wear of the balancing disc device.

[0014] In one embodiment of the present invention, in the list of fault function modules associated with each potential fault, the mapping relationship between the potential fault and the fault entity includes:

[0015] Abnormal oil temperature in the lubricating oil tank:

[0016] Lubricating oil system [electric heater, oil cooler, lubricating oil pipeline, lubricating oil];

[0017] Cooling water system [cold oil pump, closed cold water pipeline];

[0018] Lubricating oil tank low oil level alarm:

[0019] Lubricating oil system [lubricating oil pump, lubricating oil flow];

[0020] Low lubricating oil pressure alarm:

[0021] Lubricating oil system [lubricating oil pump, lubricating oil flow];

[0022] High bearing temperature alarm:

[0023] Lubricating oil system [lubricating oil pump, lubricating oil flow, lubricating oil quality, lubricating oil station temperature control door];

[0024] Cooling water system [closed cold water supply, closed cold water pipeline];

[0025] bearings;

[0026] Large vibration alarm of water pump group bearing:

[0027] Dynamic balancing [rotor, rotor blades, local high temperature of the main shaft, balancing blocks];

[0028] Bearing seat [base plate, bearing seat];

[0029] Coupling [concentricity, coupling opening, pin hole, rubber ring];

[0030] Dynamic and static friction [spindle, steam-driven water pump];

[0031] specific state [hydraulic system];

[0032] Excessive water leakage from shaft seal:

[0033] shaft seal [shaft seal];

[0034] Balance disc device wear:

[0035] Balance plate [Balance plate].

[0036] In one embodiment of the present invention, the fault diagnosis process is formed according to the appearance monitoring data and the entity monitoring data, and the fault elimination process is formed according to the fault diagnosis result, including:

[0037] Predict potential fault types through real-time phenomenon monitoring data;

[0038] Determine the faulty entity in the faulty functional module causing the potential fault through real-time entity monitoring data;

[0039] Determine the troubleshooting process based on the fault entity.

[0040] In one embodiment of the present invention, predicting potential fault types through real-time appearance monitoring data includes:

[0041] Identify outliers in surface monitoring data;

[0042] Predict potential failures based on the frequency or duration of abnormal values;

[0043] Determine the potential fault type based on the abnormal value type and generate corresponding alarm information.

[0044] The water supply pump fault early warning diagnosis and processing device according to the embodiment of the present invention comprises:

[0045] A memory, used to store program codes in the process of the water supply pump fault early warning diagnosis and processing method as described above;

[0046] A processor is used to execute the program code.

[0047] The water supply pump fault early warning diagnosis and processing device according to the embodiment of the present invention comprises:

[0048] A fault factor classification module, used for forming a potential fault list and a fault function module list associated with each potential fault according to prior data;

[0049] Prediction data binding module, used to extract the appearance monitoring data related to the potential fault manifestation from the field monitoring data and bind it with the potential fault;

[0050] A diagnostic data binding module, used to extract entity monitoring data related to entities of associated functional modules from field monitoring data and bind them to potential faults;

[0051] The fault prediction and diagnosis module is used to form a fault diagnosis process based on the appearance monitoring data and the entity monitoring data, and to form a fault elimination process based on the fault diagnosis results.

[0052] In one embodiment of the present invention, the fault prediction and diagnosis module includes:

[0053] Fault prediction module, used to predict potential fault types through real-time appearance monitoring data;

[0054] A fault diagnosis module, used to determine a fault entity in a faulty functional module causing a potential fault through real-time entity monitoring data;

[0055] The troubleshooting module is used to determine the troubleshooting process according to the fault entity.

[0056] The water supply pump fault early warning diagnosis processing method and device of the embodiment of the present invention effectively integrates discrete massive field monitoring data and isolated data judgment and diagnosis processes. Faults are divided into potential faults that reflect overall abnormalities and physical faults that need to be clearly located, thereby forming effective clustering of massive field monitoring data into fault types and optimizing the fault diagnosis process. The existing discrete data judgment and diagnosis process can be fully utilized and updated to construct a new fault early warning diagnosis processing process, thereby improving the accuracy of early fault warning and evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The figure is a schematic diagram of the flow chart of a method for diagnosing and processing a water pump fault early warning according to an embodiment of the present invention.

[0058] Figure 2 The figure shows a schematic diagram of a fault diagnosis process formed in the actual application of a water supply pump fault early warning diagnosis and processing method according to an embodiment of the present invention. Figure 1 .

[0059] Figure 3 The figure shows a schematic diagram of a fault diagnosis process formed in the actual application of a water supply pump fault early warning diagnosis and processing method according to an embodiment of the present invention. Figure 2 .

[0060] Figure 4 Shown is a schematic diagram of the architecture of a water pump fault early warning diagnosis and processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention clearer and more understandable, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] A method for diagnosing and processing a water pump fault early warning according to an embodiment of the present invention is as follows Figure 1 As shown. Figure 1 In this embodiment, the present invention comprises:

[0063] Step 100: Form a potential fault list and a faulty functional module list associated with each potential fault according to prior data.

[0064] Those skilled in the art can understand that the performance and type of water supply pump failure can be obtained from the statistical quantitative analysis of the previous failure data of the water supply pump equipment or the failure data of the same configuration equipment under the same use environment (i.e., using prior data). The field monitoring data is also formed by collecting physical signals after multiple types of sensors are deployed for the working conditions of the hardware of each functional module according to the feedback requirements under the actual working condition of the water supply pump. The same type of sensor deployment can feedback the local or overall signal state of this type for different positions and different hardware entities. The water supply pump as a whole includes the operating state of multiple working conditions. The abnormal state in the overall operating state performance can often be quantified as the existence of related potential faults. The basic distinction of each potential fault is formed according to the difference in the abnormal performance of the overall operating state. For example, the preliminary abnormal manifestations such as local overheating, liquid level reduction, abnormal pressure, vibration and leakage shown by the overall signal feedback of the water supply pump can determine the potential fault list. According to the statistical quantitative analysis of the prior data, the main and clear functional modules that cause the preliminary abnormal manifestations in the water supply pump can be determined and a list of faulty functional modules corresponding to the potential faults can be formed. The functional modules include but are not limited to the equipment component hardware, functional subsystems, assembly fixed structures and the working condition coupling state between functional hardware. The field monitoring data of the functional module is usually collected by sensors deployed on site and fitted or converted according to the relevant collected data.

[0065] Step 200: extracting the appearance monitoring data related to the potential fault manifestation from the on-site monitoring data and binding it with the potential fault.

[0066] Potential faults are reflected in relatively direct surface monitoring data under the overall working condition of the water supply pump. Binding these types of surface monitoring data as data sources with potential fault types can form a clear prediction data basis for potential faults. The probability of potential faults can be predicted based on the valuation and trend value of the prediction data.

[0067] Step 300: extracting entity monitoring data related to entities of associated functional modules from the field monitoring data and binding them with potential faults.

[0068] Those skilled in the art can understand that the field monitoring data of the functional module contains monitoring data of many module constituent entities, which are used to express different attributes or parameters in the working state of the functional module. Different types of entity monitoring data of the same functional module will form correlations with different potential faults. The entity monitoring data of the most relevant part of the functional module that has fault correlation in the determined potential fault can be used.

[0069] Step 400: forming a fault diagnosis process according to the appearance monitoring data and the entity monitoring data, and forming a fault elimination process according to the fault diagnosis result.

[0070] Those skilled in the art can understand that evaluation logic can be preset for different fault types and fault location diagnosis, and prediction or diagnosis evaluation conclusions can be formed through threshold comparison, data logic comparison and type data fitting. And according to the diagnosis results, targeted elimination means are determined to form the construction of the troubleshooting process. The appearance monitoring data is used to form the qualitative prediction basis of the potential fault diagnosis process, and the entity monitoring data is used to form the quantitative diagnosis basis of the potential fault diagnosis process.

[0071] The water supply pump fault early warning diagnosis processing method of the embodiment of the present invention effectively integrates discrete massive field monitoring data and isolated data judgment and diagnosis processes. Faults are divided into potential faults that reflect overall abnormalities and physical faults that need to be clearly located, thereby forming effective clustering of massive field monitoring data into fault types and optimizing the fault diagnosis process. The existing discrete data judgment and diagnosis process can be fully utilized and updated to construct a new fault early warning diagnosis processing process, thereby improving the accuracy of early fault warning and evaluation.

[0072] A fault diagnosis process formed by the water supply pump fault early warning diagnosis processing method according to the embodiment of the present invention is as follows: Figure 2 and Figure 3 As shown. Figure 2 and Figure 3 In the process, the field monitoring data and the water supply pump working conditions are bound to different levels of data sources to form a fault warning diagnosis process that can be mapped using the relational database data storage structure. Figure 2 and Figure 3In the data, the normal working conditions of the water pump include three rated speeds: 0.5 times the frequency, 1 times the frequency, and 2 times the frequency. The potential fault list formed includes abnormal oil temperature alarm in the lubricating oil tank, low oil level alarm in the lubricating oil tank, low lubricating oil pressure alarm, high bearing temperature alarm, large bearing vibration alarm of the water pump group, excessive shaft seal leakage, and wear of the balance disk device. Each potential fault corresponds to a list of fault function modules that may cause potential faults. Possible fault entities (lists) are mapped to the corresponding fault function modules. At the same time, according to the potential fault type and function module type, the fault location logic using field monitoring data at different levels such as the overall level, module level, and entity level is given. At the same time, alternative troubleshooting procedures are given after the corresponding fault location.

[0073] The fault entities for different potential faults are often only part of the functional module. The fault entity list has a strong correlation with the potential fault type. By effectively diagnosing and processing the on-site monitoring data of the fault entity through preset logic, accurate fault judgment can be formed, forming an effective support for fault prediction. Figure 2 and Figure 3 In one embodiment of the present invention, the mapping relationship between potential faults and fault entities includes:

[0074] Abnormal oil temperature in the lubricating oil tank:

[0075] Lubricating oil system [electric heater, oil cooler, lubricating oil pipeline, lubricating oil];

[0076] Cooling water system [cold oil pump, closed cold water pipeline];

[0077] Lubricating oil tank low oil level alarm:

[0078] Lubricating oil system [lubricating oil pump, lubricating oil flow];

[0079] Low lubricating oil pressure alarm:

[0080] Lubricating oil system [lubricating oil pump, lubricating oil flow];

[0081] High bearing temperature alarm:

[0082] Lubricating oil system [lubricating oil pump, lubricating oil flow, lubricating oil quality, lubricating oil station temperature control door];

[0083] Cooling water system [closed cold water supply, closed cold water pipeline];

[0084] bearings;

[0085] Large vibration alarm of water pump group bearing:

[0086] Dynamic balancing [rotor, rotor blades, local high temperature of the main shaft, balancing blocks];

[0087] Bearing seat [base plate, bearing seat];

[0088] Coupling [concentricity, coupling opening, pin hole, rubber ring];

[0089] Dynamic and static friction [spindle, steam-driven water pump];

[0090] specific state [hydraulic system];

[0091] Excessive water leakage from shaft seal:

[0092] shaft seal [shaft seal];

[0093] Balance disc device wear:

[0094] Balance plate [Balance plate].

[0095] By using the data results generated during the processing of the water supply pump fault early warning diagnosis processing method of the above embodiment as the data basis, a corresponding specific processing method for water supply pump fault early warning diagnosis can be formed.

[0096] like Figure 1 As shown, in one embodiment of the present invention, step 400 includes:

[0097] Step 410: Predict potential fault types through real-time symptom monitoring data.

[0098] Potential fault prediction is performed based on the real-time operating status of the water supply pump at the overall level fed back by the surface monitoring data. In one embodiment of the present invention, the prediction process includes:

[0099] Determine the abnormal values ​​of the surface monitoring data. The abnormal value determination process adopts the preset fault location logic using field monitoring data at different levels such as overall level, module level and entity level.

[0100] Predict potential faults based on the frequency or duration of abnormal values. Generate prediction results through preset abnormal value analysis in the time domain or frequency domain.

[0101] Determine the type of potential fault based on the type of abnormal value and generate corresponding alarm information. Determine the type of isolated or associated potential fault and generate corresponding alarm information.

[0102] Step 420: Determine the faulty entity in the faulty functional module causing the potential fault through real-time entity monitoring data.

[0103] According to the determination of the potential fault type, a list of faulty functional modules related thereto is obtained, and real-time entity monitoring data corresponding to the faulty functional modules is obtained. In one embodiment of the present invention, a diagnosis process formed according to the real-time entity monitoring data includes:

[0104] Determine outliers in entity monitoring data. The outlier determination process uses preset fault location logic using field monitoring data at different levels, such as overall level, module level, and entity level.

[0105] Diagnose faulty entities and faulty functional modules based on the frequency or duration of abnormal values. Generate diagnostic results through preset abnormal value analysis in the time domain or frequency domain.

[0106] Generate alarm information based on the diagnosis results. Determine isolated or associated fault entities and faulty functional modules and generate corresponding alarm information.

[0107] Step 430: Determine the troubleshooting process according to the fault entity.

[0108] The scheduling order of the troubleshooting process is formed according to the correlation of the fault entities.

[0109] The water supply pump fault early warning diagnosis and processing method of the embodiment of the present invention uses the mapping of data levels in the field monitoring data to form a prediction judgment of potential faults and a diagnosis judgment of the fault entity. The preset logic processing of the appearance monitoring data focuses on the potential fault range, and the fault function module is determined through the entity monitoring data to obtain the recommended means and process arrangement for troubleshooting. It effectively improves the efficiency of fault identification and evaluation when facing complex fault scenarios and massive field monitoring data.

[0110] A water pump fault early warning diagnosis and processing device according to an embodiment of the present invention comprises:

[0111] A memory, used to store program codes in the process of the water supply pump fault early warning diagnosis and processing method of the above embodiment;

[0112] The processor is used to execute the program code in the process of the water pump fault early warning diagnosis and processing method of the above embodiment.

[0113] The processor can be a DSP (Digital Signal Processor) digital signal processor, an FPGA (Field-Programmable Gate Array) field programmable gate array, an MCU (Microcontroller Unit) system board, a SoC (system on a chip) system board, a PLC (Programmable Logic Controller) minimum system including I / O, or cloud computing power.

[0114] A water pump fault early warning diagnosis and processing device according to an embodiment of the present invention is as follows Figure 4 As shown. Figure 4 In this embodiment, the present invention comprises:

[0115] A fault factor classification module 10 is used to form a potential fault list and a fault function module list associated with each potential fault according to prior data;

[0116] Prediction data binding module 20, used to extract appearance monitoring data related to potential fault manifestation from on-site monitoring data and bind it to the potential fault;

[0117] The diagnostic data binding module 30 is used to extract entity monitoring data related to entities of the associated functional modules from the field monitoring data and bind them to potential faults;

[0118] The fault prediction and diagnosis module 40 is used to form a fault diagnosis process according to the appearance monitoring data and the entity monitoring data, and to form a fault elimination process according to the fault diagnosis result.

[0119] like Figure 4 As shown, in one embodiment of the present invention, the fault prediction and diagnosis module 40 includes:

[0120] A fault prediction module 41 is used to predict potential fault types through real-time appearance monitoring data;

[0121] A fault diagnosis module 42, used to determine a faulty entity in a faulty functional module causing a potential fault through real-time entity monitoring data;

[0122] The fault elimination module 43 is used to determine the fault elimination process according to the fault entity.

[0123] The above description of the water pump fault early warning diagnosis and processing device of one embodiment of the present invention is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A water pump fault early warning diagnosis and processing method, characterized in that: include: Forming a potential fault list and a fault function module list associated with each potential fault according to prior data; Extract the surface monitoring data related to the potential fault manifestation from the on-site monitoring data and bind it to the potential fault; Extracting entity monitoring data related to entities of associated functional modules from the field monitoring data and binding them with potential faults; A fault diagnosis process is formed based on the surface monitoring data and the entity monitoring data, and a fault elimination process is formed based on the fault diagnosis results.

2. The water supply pump fault early warning diagnosis and processing method according to claim 1, characterized in that: The priori data is statistically quantified from past failure data of the water supply pump equipment or failure data of the equipment with the same configuration under the same use environment.

3. The water supply pump fault early warning diagnosis and processing method according to claim 1, characterized in that: The normal operating conditions of the water supply pump include 0.5 times, 1 times, and 2 times the rated speed.

4. The water supply pump fault early warning diagnosis and processing method according to claim 1, characterized in that: The potential fault list includes alarms for abnormal oil temperature in the lubricating oil tank, low oil level in the lubricating oil tank, low lubricating oil pressure, high bearing temperature, large bearing vibration alarm for the water pump group, excessive shaft seal leakage, and wear of the balancing disc device.

5. The water supply pump fault early warning diagnosis and processing method according to claim 1, characterized in that: In the list of fault function modules associated with each potential fault, the mapping relationship between the potential fault and the fault entity includes: Abnormal oil temperature in the lubricating oil tank: Lubricating oil system [electric heater, oil cooler, lubricating oil pipeline, lubricating oil]; Cooling water system [cold oil pump, closed cold water pipeline]; Lubricating oil tank low oil level alarm: Lubricating oil system [lubricating oil pump, lubricating oil flow]; Lubricating oil low pressure alarm: Lubricating oil system [lubricating oil pump, lubricating oil flow]; High bearing temperature alarm: Lubricating oil system [lubricating oil pump, lubricating oil flow, lubricating oil quality, lubricating oil station temperature control door]; Cooling water system [closed cold water supply, closed cold water pipeline]; bearings; Large vibration alarm of water pump group bearing: Dynamic balancing [rotor, rotor blades, local high temperature of the main shaft, balancing blocks]; Bearing seat [base plate, bearing seat]; Coupling [concentricity, coupling opening, pin hole, rubber ring]; Dynamic and static friction [spindle, steam-driven water pump]; specific state [hydraulic system]; Excessive water leakage from shaft seal: shaft seal [shaft seal]; Balance disc device wear: Balance plate [Balance plate].

6. The water supply pump fault early warning diagnosis and processing method according to claim 1, characterized in that: The fault diagnosis process is formed according to the appearance monitoring data and the entity monitoring data, and the fault elimination process is formed according to the fault diagnosis result, including: Predict potential fault types through real-time phenomenon monitoring data; Determine the faulty entity in the faulty functional module causing the potential fault through real-time entity monitoring data; Determine the troubleshooting process based on the fault entity.

7. The water supply pump fault early warning diagnosis and processing method according to claim 6, characterized in that: The prediction of potential fault types through real-time appearance monitoring data includes: Identify outliers in surface monitoring data; Predict potential failures based on the frequency or duration of abnormal values; Determine the potential fault type based on the abnormal value type and generate corresponding alarm information.

8. A water supply pump fault early warning diagnosis and processing device, characterized in that: include: A memory, used to store program codes in the process of the water supply pump fault early warning diagnosis and processing method according to any one of claims 1 to 7; A processor is used to execute the program code.

9. A water pump fault early warning diagnosis and processing device, characterized in that: include: A fault factor classification module, used for forming a potential fault list and a fault function module list associated with each potential fault according to prior data; Prediction data binding module, used to extract the appearance monitoring data related to the potential fault manifestation from the field monitoring data and bind it with the potential fault; A diagnostic data binding module, used to extract entity monitoring data related to entities of associated functional modules from field monitoring data and bind them to potential faults; The fault prediction and diagnosis module is used to form a fault diagnosis process based on the appearance monitoring data and the entity monitoring data, and to form a fault elimination process based on the fault diagnosis results.

10. The water supply pump fault early warning diagnosis and processing device according to claim 9, characterized in that: The fault prediction and diagnosis module comprises: Fault prediction module, used to predict potential fault types through real-time appearance monitoring data; A fault diagnosis module, used to determine a fault entity in a faulty functional module causing a potential fault through real-time entity monitoring data; The troubleshooting module is used to determine the troubleshooting process according to the fault entity.

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