CN216561497U - Early warning system based on EH oil pump adjusting module is invalid under big data failure diagnosis - Google Patents
Early warning system based on EH oil pump adjusting module is invalid under big data failure diagnosis Download PDFInfo
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Abstract
The utility model discloses an early warning system for failure of an EH oil pump adjusting module under big data fault diagnosis, which comprises a first subtraction module, a first absolute value module, a first small selection module, a second subtraction module, a second absolute value module, a second small selection module, a third subtraction module, a third absolute value module, a first big selection module, a first AND module, a fourth subtraction module, a fourth absolute value module, a second big selection module, a second AND module, a delay opening module and a final early warning output module.
Description
Technical Field
The utility model belongs to the technical field of thermal control of an EH oil system of a steam turbine, and relates to an early warning system for failure of an EH oil pump adjusting module based on big data fault diagnosis.
Background
In the current thermal power generating unit development process, unit operation personnel only discover actual problems on site and process the problems in time through the early warning prompt of the photon board, and the early warning mode of the photon board is too single. The photon board of the common monitoring picture has the defects of misinformation and missing report, the workload of monitoring the picture of the unit by operating personnel is increased, the operating personnel often have misjudgment and wrong operation, and the stable operation of the unit is seriously threatened. The method comprises the steps of establishing an early warning of an EH (hydraulic control system with Chinese name and electro hydraulic oil system with English name) oil system based on a big data platform, detecting all real-time data of a unit by means of the big data platform, extracting data according to the requirements of each system, screening the extracted data, and finally processing the screened data. Real-time data of the unit and data finally processed by a big data platform are used as objects judged by the control logic, accurate early warning prompts are sent out according to the judgment basis of various control logics, and the reasons of the faults of the EH oil system are more comprehensively evaluated and analyzed. The false alarm and the missing alarm of the photon board of the common monitoring picture are avoided, so that the operation personnel can more efficiently and safely maintain the operation of the unit.
When the EH oil system has the problems of oil leakage and the like, the oil temperature of the EH oil system is rapidly changed, the output of an oil pump frequently fluctuates and the like, and the stable operation of a unit is greatly threatened. When the oil level and the oil temperature of the EH system are both in a normal range, the current for the EH oil pump to operate and the oil pressure of the EH oil system main pipe are fluctuated. This is often the case because the pump regulation module fails, resulting in insufficient pump output. Generally, in the stable operation of a unit, the photon plate cannot give an early warning of oil leakage of an EH oil system in advance, so that an operator cannot find the current for operating the EH oil pump and the change of the oil pressure of a main pipe of the EH oil system in time to process the fault of an oil pump adjusting module on site.
SUMMERY OF THE UTILITY MODEL
The utility model aims to overcome the defects of the prior art and provides a warning system for failure of an EH oil pump regulating module based on big data fault diagnosis, which can give a warning when the EH oil pump regulating module fails.
In order to achieve the above object, the early warning system for failure of the EH oil pump adjusting module based on big data fault diagnosis according to the present invention comprises a first subtraction module, a first absolute value module, a first small-selection module, a second subtraction module, a second absolute value module, a second small-selection module, a third subtraction module, a third absolute value module, a first big-selection module, a first sum module, a fourth subtraction module, and a fourth absolute value module, the second large selection module, the second AND module, the delay opening module, the final early warning output module, an AI block of the oil temperature of the EH oil system, an AI block of the average oil temperature of the EH oil system of the large data platform, an AI block of the liquid level of the EH oil tank of the large data platform, an AI block of the oil pressure of the EH oil main, an AI block of the average oil pressure of the EH oil main of the large data platform, a DI block of the running signal of the EH oil pump, an AI block of the current of the EH oil pump and an AI block of the average value of the current of the EH oil pump of the large data platform;
the AI block of the oil temperature of the EH oil system and the AI block of the average oil temperature of the EH oil system of the big data platform are connected with the input end of the first subtraction module, the output end of the first subtraction module is connected with the input end of the first absolute value module, and the output end of the first absolute value module is connected with the input end of the first small selection module;
the AI block of the EH oil tank liquid level and the AI block of the EH oil tank average liquid level of the big data platform are connected with the input end of a second subtraction module, the output end of the second subtraction module is connected with the input end of a second absolute value module, and the output end of the second absolute value module is connected with the input end of a second small selection module;
the AI block of the EH oil header oil pressure and the AI block of the EH oil header average oil pressure of the big data platform are connected with the input end of a third subtraction module, the output end of the third subtraction module is connected with the input end of a third absolute value module, the output end of the third absolute value module is connected with the input end of a first big selection module, and the output end of the first big selection module and the DI block of an operation signal of the EH oil pump are connected with the input end of the first big selection module;
the AI block of the EH oil pump current and the AI block of the EH oil pump current average value of the big data platform are connected with the input end of a fourth subtraction module, the output end of the fourth subtraction module is connected with the input end of a fourth absolute value module, and the output end of the fourth absolute value module is connected with the input end of a second big selection module;
the output end of the second large-selection module, the output end of the first small-selection module, the output end of the second small-selection module and the output end of the first small-selection module are connected with the input end of the second large-selection module, the output end of the second small-selection module is connected with the input end of the delay opening module, and the output end of the delay opening module is connected with the final early warning output module.
The oil temperature of the EH oil system and the average oil temperature of the EH oil system of the big data platform are subtracted by the first subtraction module, an absolute value is obtained by the first absolute value module, the output of the first absolute value module is used as one input of the first small selection module, a fixed value of 2 ℃ is used as the other input of the first small selection module, when the oil temperature variation is less than or equal to 2 ℃, the output of the first small selection module is 1, otherwise, the output is 0.
The EH oil tank liquid level subtracts through the second subtraction module with the EH oil tank average liquid level of big data platform, and the absolute value is got to rethread second absolute value module, then input to the second and select little module, and another input of the little module of second selection is 5mm, and when the variable quantity less than or equal to 5mm of EH oil tank liquid level, then the output of the little module of second selection is 1, and when the variable quantity of EH oil tank liquid level is greater than 5mm, then the output of the little module of second selection is 0.
The method comprises the steps that the difference value between the oil pressure of an EH oil main pipe and the average oil pressure of the EH oil main pipe of a large data platform is calculated through a third subtraction module, an absolute value is obtained through a third absolute value module, the absolute value is obtained as the first input of a first big selection module, a constant 2MPa is used as the other input of the first big selection module, the output of the first big selection module and the operation signal of an EH oil pump are subjected to AND operation through the first AND module, when the change range of the oil pressure of the EH oil main pipe is not within 2MPa and the EH oil pump operates, the output of the first AND module is 1, and when the change range of the oil pressure of the EH oil main pipe is within 2MPa or the EH oil pump does not operate, the output of the first AND module is 0.
And the EH oil pump current average value of the large data platform are subjected to subtraction operation through a fourth subtraction module, an absolute value is obtained through a fourth absolute value module, then the absolute value is used as one input of a second large selection module, a fixed value 2 is used as the other input of the second large selection module, when the EH oil pump current variation range is not 2A, the output is 1, and when the EH oil pump current variation range is 2A, the output is 0.
The utility model has the following beneficial effects:
when the early warning system for the failure of the EH oil pump adjusting module based on big data fault diagnosis is in specific operation, the set early warning judgment conditions comprise that: the difference value of the oil temperature of the EH oil system and the EH oil average temperature of the big data platform is within a preset normal temperature range, the difference value of the liquid level of the EH oil tank and the EH oil average level of the big data platform is within a preset normal liquid level range, the difference value of the oil pressure of the EH oil main pipe and the oil pressure of the EH oil average main pipe of the big data platform is not within a preset normal oil pressure range, the difference value of the current of the EH oil pump and the EH oil pump average current of the big data platform is not within a preset normal current range and the operation signal of the EH oil pump, all conditions in the control logic need to be met, the failure of the EH oil pump adjusting module based on big data fault diagnosis can be predicted, and therefore early warning is carried out when the EH oil pump adjusting module fails.
Drawings
FIG. 1 is a schematic structural diagram of the present invention.
Wherein, 1 is a first subtraction module, 2 is a first absolute value module, 3 is a first small selection module, 4 is a second subtraction module, 5 is a second absolute value module, 6 is a second small selection module, 7 is a third subtraction module, 8 is a third absolute value module, 9 is a first large selection module, 10 is a first and module, 11 is a fourth subtraction module, 12 is a fourth absolute value module, 13 is a second large selection module, 14 is a second and module, 15 is a time-delay opening module, 16 is a final warning output module, 001 is an AI block of the oil temperature of the EH oil system, 002 is an AI block of the average oil temperature of the EH oil system of the large data platform, 003 is an AI block of the oil tank liquid level, 004 is an AI block of the average liquid level of the EH oil tank of the large data platform, 005 is an AI block of the oil pressure of the EH oil header, 006 is an AI block of the average oil pressure of the EH oil header of the large data platform, 007 is an AI block of the operation signal of the EH oil pump, 008 is the AI block for EH oil pump current, 009 is the AI block for EH oil pump current average for the large data platform.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments, and are not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
There is shown in the drawings a schematic block diagram of a disclosed embodiment in accordance with the utility model. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
Referring to fig. 1, the early warning system for EH oil pump regulation module failure based on big data fault diagnosis according to the present invention includes a first subtraction module 1, a first absolute value module 2, a first small-selection module 3, a second subtraction module 4, a second absolute value module 5, a second small-selection module 6, a third subtraction module 7, a third absolute value module 8, a first large-selection module 9, a first and module 10, a fourth subtraction module 11, a fourth absolute value module 12, a second large-selection module 13, a second and module 14, a time-delay opening module 15, a final early warning output module 16, an AI block 011 of the oil temperature of the EH oil system, an AI block 002 of the average oil temperature of the EH oil system of the big data platform, an AI block 003 of the oil tank liquid level, an AI block 004 of the EH oil tank average liquid level of the big data platform, an AI block 005 of the oil mother pipe oil pressure, an AI block 006 of the EH oil mother pipe average oil pressure of the big data platform, an AI block 006 of the oil pipe average oil pressure of the big data platform, DI block 007 for the running signal of the EH oil pump, AI block 008 for the EH oil pump current, and AI block 009 for the EH oil pump current average for the big data platform;
the AI block 011 of the oil temperature of the EH oil system and the AI block 002 of the average oil temperature of the EH oil system of the big data platform are connected with the input end of the first subtraction module 1, the output end of the first subtraction module 1 is connected with the input end of the first absolute value module 2, and the output end of the first absolute value module 2 is connected with the input end of the first small selection module 3;
the AI block 003 of the EH oil tank liquid level and the AI block 004 of the EH oil tank average liquid level of the big data platform are connected with the input end of the second subtraction module 4, the output end of the second subtraction module 4 is connected with the input end of the second absolute value module 5, and the output end of the second absolute value module 5 is connected with the input end of the second small selection module 6;
an AI block 005 of the EH oil pressure of the oil header and an AI block 006 of the EH average oil pressure of the big data platform are connected with the input end of a third subtraction module 7, the output end of the third subtraction module 7 is connected with the input end of a third absolute value module 8, the output end of the third absolute value module 8 is connected with the input end of a first big selection module 9, and the output end of the first big selection module 9 and a DI block 007 of an operation signal of the EH oil pump are connected with the input end of a first and module 10;
the AI block 008 of the EH oil pump current and the AI block 009 of the EH oil pump current average value of the big data platform are connected with the input end of the fourth subtraction module 11, the output end of the fourth subtraction module 11 is connected with the input end of the fourth absolute value module 12, and the output end of the fourth absolute value module 12 is connected with the input end of the second big selection module 13;
the output end of the second large selection module 13, the output end of the first and module 10, the output end of the second small selection module 6 and the output end of the first small selection module 3 are connected with the input end of the second and module 14, the output end of the second and module 14 is connected with the input end of the delay opening module 15, and the output end of the delay opening module 15 is connected with the final early warning output module 16.
When the utility model works, the early warning judgment conditions of the utility model comprise: the method comprises the steps that the difference value between the oil temperature of an EH oil system and the EH oil average temperature of a big data platform is within a preset normal temperature range, the difference value between the liquid level of the EH oil tank and the EH oil average level of the big data platform is within a preset normal liquid level range, the difference value between the oil pressure of an EH oil main pipe and the oil pressure of the EH oil main pipe of the big data platform is not within a preset normal oil pressure range, the difference value between the current of the EH oil pump and the EH oil pump average current of the big data platform is not within a preset normal current range, and the operation signals of the EH oil pump are required to meet all conditions in control logic, so that the failure of an EH oil pump adjusting module based on big data fault diagnosis can be predicted.
Subtracting the oil temperature of the EH oil system from the average oil temperature of the EH oil system of the big data platform by a first subtraction module 1, taking an absolute value by a first absolute value module 2, taking the output of the first absolute value module 2 as one input of a first small selection module 3, taking a fixed value of 2 ℃ as the other input of the first small selection module 3, when the variation of the oil temperature is less than or equal to 2 ℃, the output of the first small selection module 3 is 1, otherwise, the output is 0, and thus, judging whether the internal leakage of the EH oil system is caused by the variation of the EH oil temperature;
the liquid level of the EH oil tank and the average liquid level of the EH oil tank of the big data platform are subjected to subtraction operation through a second subtraction module 4, an absolute value of the subtracted value is obtained through a second absolute value module 5 and then input into a second small selection module 6, the other input of the second small selection module 6 is 5mm, when the variation of the liquid level of the EH oil tank is less than or equal to 5mm, the output of the second small selection module 6 is 1, and when the variation of the liquid level of the EH oil tank is greater than 5mm, the output of the second small selection module 6 is 0, so that whether the internal leakage of the EH oil system is caused by the variation of the liquid level of the EH oil tank is described;
calculating a difference value between the EH oil main pipe oil pressure and the EH oil main pipe average oil pressure of the large data platform through a third subtraction module 7, taking an absolute value through a third absolute value module 8, taking the result of taking the absolute value as a first input of a first large selection module 9, taking a constant 2MPa as the other input of the first large selection module 9, and performing AND operation on the output of the first large selection module 9 and an operation signal of the EH oil pump through a first AND module 10, wherein when the change range of the EH oil main pipe oil pressure is not within 2MPa and the EH oil pump operates, the output of the first AND module 10 is 1, and when the change range of the EH oil main pipe oil pressure is within 2MPa or the EH oil pump does not operate, the output of the first AND module 10 is 0, so as to indicate whether the EH oil system internal leakage is caused by the fluctuation of the EH oil main pipe oil pressure;
the EH oil pump current and the EH oil pump current average value of the large data platform are subjected to subtraction operation through a fourth subtraction module 11, an absolute value is obtained through a fourth absolute value module 12, then the absolute value is used as one input of a second large selection module 13, a fixed value 2 is used as the other input of the second large selection module 13, when the EH oil pump current variation range is not 2A, the output is 1, when the EH oil pump current variation range is 2A, the output is 0, and therefore whether the current fluctuation of the EH oil pump causes internal leakage of an oil system or not is indicated;
when the output of the second and module 14 is 1 and within 2min, the output of the delay opening module 15 is 1, when the output of the second and module 14 is not 1 or is greater than 2min, the output of the delay opening module 15 is 0, when the output of the delay opening module 15 is 1, the early warning information is pushed, the EH oil pump adjusting module is pushed to be out of order through the logic judgment, and an operator is asked to switch to the standby EH oil pump to operate in time.
Claims (5)
1. The early warning system for failure of the EH oil pump adjusting module based on big data fault diagnosis is characterized by comprising a first subtraction module (1), a first absolute value module (2), a first small selection module (3), a second subtraction module (4), a second absolute value module (5), a second small selection module (6), a third subtraction module (7), a third absolute value module (8), a first large selection module (9), a first and module (10), a fourth subtraction module (11), a fourth absolute value module (12), a second large selection module (13), a second and module (14), a time delay opening module (15), a final early warning output module (16), an AI block (011) of the oil temperature of the EH oil system, an AI block (002) of the average oil temperature of the EH oil system of a big data platform, an AI block (003) of the liquid level of the EH oil tank of the big data platform, an AI block (004) of the average liquid level of the EH oil tank of the big data platform, a first absolute value module (2) and a second absolute value module (6), An AI block (005) of EH oil header oil pressure, an AI block (006) of EH oil header average oil pressure of a big data platform, a DI block (007) of an operation signal of the EH oil pump, an AI block (008) of EH oil pump current and an AI block (009) of EH oil pump current average value of the big data platform;
an AI block (011) of the oil temperature of the EH oil system and an AI block (002) of the average oil temperature of the EH oil system of the big data platform are connected with the input end of the first subtraction module (1), the output end of the first subtraction module (1) is connected with the input end of the first absolute value module (2), and the output end of the first absolute value module (2) is connected with the input end of the first small selection module (3);
the AI block (003) of the liquid level of the EH oil tank and the AI block (004) of the average liquid level of the EH oil tank of the big data platform are connected with the input end of a second subtraction module (4), the output end of the second subtraction module (4) is connected with the input end of a second absolute value module (5), and the output end of the second absolute value module (5) is connected with the input end of a second small selection module (6);
an AI block (005) of the oil pressure of the EH oil header and an AI block (006) of the average oil pressure of the EH oil header of the big data platform are connected with the input end of a third subtraction module (7), the output end of the third subtraction module (7) is connected with the input end of a third absolute value module (8), the output end of the third absolute value module (8) is connected with the input end of a first big selection module (9), and the output end of the first big selection module (9) and a DI block (007) of an operation signal of the EH oil pump are connected with the input end of a first and module (10);
an AI block (008) of the EH oil pump current and an AI block (009) of the EH oil pump current average value of the big data platform are connected with the input end of a fourth subtraction module (11), the output end of the fourth subtraction module (11) is connected with the input end of a fourth absolute value module (12), and the output end of the fourth absolute value module (12) is connected with the input end of a second big selection module (13);
the output end of the second large selection module (13), the output end of the first small selection module (10), the output end of the second small selection module (6) and the output end of the first small selection module (3) are connected with the input end of the second small selection module (14), the output end of the second small selection module (14) is connected with the input end of the delay opening module (15), and the output end of the delay opening module (15) is connected with the final early warning output module (16).
2. The early warning system for the failure of the EH oil pump adjusting module under the big data fault diagnosis according to claim 1, wherein the oil temperature of the EH oil system and the average oil temperature of the EH oil system of the big data platform are subtracted by the first subtraction module (1), an absolute value is obtained by the first absolute value module (2), the output of the first absolute value module (2) is used as one input of the first small-selection module (3), a fixed value of 2 ℃ is used as the other input of the first small-selection module (3), when the oil temperature variation is less than or equal to 2 ℃, the output of the first small-selection module (3) is 1, and otherwise, the output is 0.
3. The early warning system for the failure of the EH oil pump regulating module under the big data fault diagnosis according to claim 1, wherein the EH oil tank liquid level and the EH oil tank average liquid level of the big data platform are subtracted by a second subtraction module (4), an absolute value is obtained by a second absolute value module (5), the absolute value is input into a second small selection module (6), another input of the second small selection module (6) is 5mm, when the variation of the EH oil tank liquid level is less than or equal to 5mm, the output of the second small selection module (6) is 1, and when the variation of the EH oil tank liquid level is greater than 5mm, the output of the second small selection module (6) is 0.
4. The early warning system for failure of the EH oil pump regulation module under big data fault diagnosis according to claim 1, it is characterized in that the difference value between the oil pressure of the EH oil header and the average oil pressure of the EH oil header of the big data platform is calculated by a third subtraction module (7), and then the absolute value is obtained by a third absolute value module (8), then the result of the absolute value is taken as the first input of the first big selection module (9), the constant 2MPa is taken as the other input of the first big selection module (9), the output of the first big selection module (9) and the running signal of the EH oil pump are AND-operated through the first AND module (10), when the variation range of the oil pressure of the EH oil header is not within 2MPa and the EH oil pump is operated, the output of the first and module (10) is 1, and when the variation range of the EH oil header oil pressure is within 2MPa or the EH oil pump is not operated, the output of the first and module (10) is 0.
5. The early warning system for the failure of the EH oil pump regulating module under the big data fault diagnosis according to claim 1, wherein the EH oil pump current and the average value of the EH oil pump current of the big data platform are subtracted by a fourth subtraction module (11), an absolute value is obtained by a fourth absolute value module (12), the absolute value is used as one input of a second big selection module (13), a fixed value 2 is used as the other input of the second big selection module (13), when the variation range of the EH oil pump current is not 2A, the output is 1, and when the variation range of the EH oil pump current is 2A, the output is 0.
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| CN114743356A (en) * | 2022-04-12 | 2022-07-12 | 西安热工研究院有限公司 | Intelligent monitoring and early warning system for whole operation process of steam-driven water supply pump system |
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| CN114743356A (en) * | 2022-04-12 | 2022-07-12 | 西安热工研究院有限公司 | Intelligent monitoring and early warning system for whole operation process of steam-driven water supply pump system |
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