CN112562277A - Equipment fault early warning method and system - Google Patents
Equipment fault early warning method and system Download PDFInfo
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- CN112562277A CN112562277A CN202011343564.7A CN202011343564A CN112562277A CN 112562277 A CN112562277 A CN 112562277A CN 202011343564 A CN202011343564 A CN 202011343564A CN 112562277 A CN112562277 A CN 112562277A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/187—Machine fault alarms
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
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Abstract
The invention discloses a method and a system for early warning equipment faults, wherein the method comprises the following steps: 1) acquiring current operating parameters of equipment by using an instrument control system; 2) generating a digital model of the equipment by using a parameter analysis system according to the current operating parameters of the equipment; 3) predicting the operation parameters of the next stage of the equipment by using the digital model of the equipment, and performing early warning analysis on the operation parameters of the next stage of the equipment to generate an early warning analysis result; 4) and generating an early warning solution according to the early warning analysis result by using the decision-making system, and sending the early warning solution to the early warning system for early warning. The system of the present invention employs the method described above. The invention can carry out preventive active monitoring on the equipment, effectively avoids the loss caused by alarming after the fault, obviously improves the safety level of the equipment and the production safety level, greatly reduces the human intervention in the fault monitoring process of the equipment, and obviously improves the efficiency and the reliability of fault prediction and processing.
Description
Technical Field
The invention relates to the technical field of mechanical equipment fault early warning processing, in particular to an equipment fault early warning method and an equipment fault early warning system.
Background
Modern mechanical equipment is more and more complicated in structure, and the equipment is required to be ensured to normally operate by polling the mechanical equipment. However, due to uncertainty of human factors, inspection personnel cannot perform inspection completely according to regulations, inspection points are omitted, conditions such as all hidden dangers cannot be reported in time, inspection efficiency is low, and potential safety hazards are caused.
Therefore, there is a need for a method and system for early warning of equipment failure with high reliability and high safety.
Disclosure of Invention
It is a primary object of the present invention to overcome at least one of the above-mentioned drawbacks of the prior art and to provide a method and system for early warning of equipment failure with high safety and reliability.
In order to achieve the purpose, the invention adopts the following technical scheme:
according to one aspect of the invention, an equipment fault early warning method is provided, which comprises the following steps:
1) acquiring current operating parameters of equipment by using an instrument control system;
2) generating a digital model of the equipment by using a parameter analysis system according to the current operating parameters of the equipment;
3) predicting the operation parameters of the next stage of the equipment by using the digital model of the equipment, and performing early warning analysis on the operation parameters of the next stage of the equipment to generate an early warning analysis result;
4) and generating an early warning solution according to the early warning analysis result by using the decision-making system, and sending the early warning solution to the early warning system for early warning.
According to an embodiment of the present invention, step 1) further includes:
and acquiring the current running image of the equipment by using a video monitoring system.
According to an embodiment of the present invention, step 2) includes:
splitting the current operating parameters of the equipment by using a parameter analysis system to generate a splitting result;
and generating a digital model of the equipment by using a parameter analysis system according to the splitting result and the current running image of the equipment.
According to an embodiment of the present invention, after the generating the splitting result, the method further includes:
and carrying out health analysis on the split result by using a health analysis system to generate a current health value of the equipment.
According to an embodiment of the present invention, step 3) includes:
utilizing the equipment digital model to perform fitting modeling processing on a split result formed by the current operation parameters of the equipment and a split result formed by the historical operation parameters of the equipment to generate a fitting modeling result;
and generating the operating parameters of the next stage of the equipment according to the fitting modeling result by utilizing the equipment digital model.
According to an embodiment of the present invention, step 3) further includes:
and judging whether the operating parameters of the next stage of the equipment exceed the limits or not by using the equipment digital model according to the preset values of the operating parameters of the next stage of the equipment, and if so, generating an early warning analysis result.
According to an embodiment of the present invention, step 4) includes:
and matching the early warning solution to the early warning analysis result by using the decision system according to the preset value of the early warning analysis result, and sending the early warning solution to the early warning system.
According to an embodiment of the present invention, step 4) further includes:
and generating a work order by using the early warning system according to the early warning solution, and sending the work order to the inspection terminal.
According to another aspect of the present invention, an equipment fault early warning system is provided, where the equipment fault early warning method is adopted, the equipment fault early warning system includes an instrument control system, a parameter analysis system, a decision system, and an early warning system, the instrument control system is configured to obtain a current operation parameter of equipment measured by an instrument, the parameter analysis system is configured to generate an equipment digitization model according to the current operation parameter of the equipment, the equipment digitization model is configured to predict an operation parameter of a next stage of the equipment and generate an early warning analysis result, the decision system is configured to generate an early warning solution according to the early warning analysis result, and the early warning system is configured to send the early warning solution to an inspection terminal.
According to an embodiment of the present invention, the equipment failure early warning system further includes a health analysis system, and the health analysis system is configured to generate a current health value of the equipment.
According to the technical scheme, the equipment fault early warning method has the advantages and positive effects that:
in the invention, 1) the current operation parameters of the equipment are obtained by using an instrument control system; 2) generating a digital model of the equipment by using a parameter analysis system according to the current operating parameters of the equipment; 3) predicting the operation parameters of the next stage of the equipment by using the digital model of the equipment, and performing early warning analysis on the operation parameters of the next stage of the equipment to generate an early warning analysis result; 4) utilize decision-making system to generate the early warning solution according to early warning analysis result to send the early warning solution to the early warning system and carry out the early warning, can carry out preventive initiative monitoring to equipment, effectively avoid the loss that reports to the police and bring after the trouble again, equipment safety and production safety level are showing and are improving, greatly reduce the manpower intervention in the equipment trouble monitoring process, the efficiency and the reliability of failure prediction and processing can be showing and promote, have very high economic nature, especially adapted uses widely in the industry.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of an apparatus fault early warning method according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their detailed description will be omitted.
In the following description of various examples of the invention, reference is made to the accompanying drawings, which form a part hereof, and in which are shown by way of illustration various example structures, systems, and steps in which aspects of the invention may be practiced. It is to be understood that other specific arrangements of parts, structures, example devices, systems, and steps may be utilized and structural and functional modifications may be made without departing from the scope of the present invention. Moreover, although the terms "top," "bottom," "front," "back," "side," and the like may be used in this specification to describe various example features and elements of the invention, these terms are used herein for convenience only, e.g., as to the orientation of the examples described in the figures. Nothing in this specification should be construed as requiring a specific three dimensional orientation of structures in order to fall within the scope of the invention.
As shown in fig. 1, the method for warning the equipment fault according to the embodiment includes the following steps:
1) acquiring current operating parameters of equipment by using an instrument control system;
2) generating a digital model of the equipment by using a parameter analysis system according to the current operating parameters of the equipment;
3) predicting the operation parameters of the next stage of the equipment by using the digital model of the equipment, and performing early warning analysis on the operation parameters of the next stage of the equipment to generate an early warning analysis result;
4) and generating an early warning solution according to the early warning analysis result by using the decision-making system, and sending the early warning solution to the early warning system for early warning.
In this embodiment, step 1) includes:
and acquiring the current operating parameters of the equipment by using the instrument control system. Specifically, a plurality of equipment operating parameters to be acquired, warning values of the operating parameters and the interval time between two adjacent acquired operating parameters are preset by the instrument control system, then the instrument control system is used for acquiring the current operating parameters of the equipment according to the preset values, and further the instrument control system is used for sending the current operating parameters of the equipment to the parameter analysis system.
Further, step 1) also includes:
and acquiring the current running image of the equipment by using a video monitoring system. Specifically, a plurality of cameras of the video monitoring system are used for shooting a plurality of directions and parts of the equipment, current running images of the equipment are generated, and then the video monitoring system is used for sending the current running images of the equipment to the parameter analysis system.
Further, step 2) comprises: and splitting the current operating parameters of the equipment by using a parameter analysis system to generate a splitting result. Specifically, a parameter analysis system is utilized to split the current operating parameters of the device according to the communication protocol of the device, so as to generate a split result, where the split result may include a plurality of specific operating parameters of the device, such as a first rectifier operating parameter, a second rectifier operating parameter, a third rectifier operating parameter, and the like, furthermore, a parameter analysis system is used for identifying the presetting according to the identification of each specific operation parameter in the splitting result, and performing operation parameter type matching on the split result, wherein the operation parameter type can comprise a system fault code, a system alarm code instruction, an M1 mode instruction, an M1 alarm code instruction, an M1 fault code instruction, an M2 mode instruction, an M2 alarm code instruction, an M2 fault code instruction, a hydraulic fault code instruction, an M1 operation code instruction, an M2 operation code instruction and the like, and the matching result can be stored after the operation parameter type matching.
Further, step 2) also includes:
and splitting the current operating parameters of the equipment by using a parameter analysis system, generating a splitting result, matching the operating parameters, and performing health analysis on the splitting result by using a health analysis system to generate a current health value of the equipment. Specifically, the health analysis system is used for analyzing the current indexes of the equipment, such as energy consumption, fault rate and equipment comprehensive efficiency OEE, and generating the current health value of the equipment, the health value can provide technical basis and reference for daily maintenance and management of the equipment, and the following program codes can be referred to in part of the health analysis process:
further, step 2) also includes:
and generating a digital model of the equipment by using a parameter analysis system according to the splitting result and the current running image of the equipment. Specifically, the machine learning model can be trained by using a machine learning algorithm, taking the structure parameters and the external load of the equipment as input, taking the operation parameters corresponding to the equipment under the conditions of different production environments such as temperature, different process logics, different operation logics such as a single operation mode and a cycle operation mode and the like of finite element calculation as output, storing the trained machine learning model, and further correspondingly importing the splitting result formed after the splitting processing of the current operation parameters of the equipment and the operation images of all parts of each current direction of the equipment into the trained machine learning model, namely generating the digital model of the equipment.
Further, step 3) comprises:
utilizing the equipment digital model to perform fitting modeling processing on a split result formed by the current operation parameters of the equipment and a split result formed by the historical operation parameters of the equipment to generate a fitting modeling result; and generating the operating parameters of the next stage of the equipment according to the fitting modeling result by utilizing the equipment digital model. Specifically, the splitting result may include a plurality of categories, such as a motor temperature, a motor current, a motor voltage, and the like, and the fitting modeling process may process the plurality of categories in the splitting result, in this embodiment, the fitting modeling process takes the motor temperature in the splitting result as an example, and performs the fitting modeling process on the splitting result formed by the current operation parameter of the device, such as the current motor temperature and the splitting result formed by the historical operation parameter of the device, such as the historical motor temperature, by using the device digital model to generate the fitting modeling result, which may be a linear model, a triangular model, a pulse model, or another nonlinear model of time and an operation parameter, such as the motor temperature, and further obtains the motor temperature at the next time in the fitting modeling result, that is, the operation parameter at the next stage of the device is generated.
Further, step 3) also includes:
and judging whether the operating parameters of the next stage of the equipment exceed the limits or not by using the equipment digital model according to the preset values of the operating parameters of the next stage of the equipment, and if so, generating an early warning analysis result. Specifically, preset values of operation parameters are preset in an equipment digital model, whether the operation parameters of the next stage of the equipment exceed the preset values or not is judged by using the equipment digital model, if not, the step 1) is returned to obtain the current operation parameters of the new equipment, new fitting modeling processing is carried out, and the like, if yes, the predicted fault type and the predicted fault level are generated according to the weight presetting of various parameters in the operation parameters and the ladder presetting of the predicted fault type and the level corresponding to the multiple weight presets respectively, the operation parameters, the predicted fault type and the predicted fault level of the next stage of the equipment form an early warning analysis result together, and then the early warning analysis result is transmitted to a decision-making system by using a parameter analysis system.
Further, step 4) comprises:
and matching the early warning solution to the early warning analysis result by using the decision system according to the preset value of the early warning analysis result, and sending the early warning solution to the early warning system. Specifically, the decision system is used for transmitting an early warning analysis result containing a predicted fault category and a predicted fault level to an expert base of the decision system, the expert base is used for matching an early warning solution to the early warning analysis result according to the preset values of the predicted fault category and the predicted fault level in the early warning analysis result, when the predicted fault category and/or the predicted fault level contained in the early warning analysis result received by the expert base does not find a corresponding preset value in the expert base, the expert base matches the early warning solution with a slightly low matching degree for the early warning analysis result through a fuzzy matching algorithm, and then the expert base feeds the early warning solution generated by matching back to the decision system, and the decision system sends the early warning analysis result and the early warning solution to the early warning system.
Further, step 4) also includes:
and generating a work order by using the early warning system according to the early warning solution, and sending the work order to the inspection terminal. Specifically, a work order is generated by the early warning system according to an early warning analysis result and an early warning solution, the work order can comprise work order codes, equipment types, equipment positions, equipment states, starting time, duration, completion date, operation reasons, operation results, names of responsible persons, mobile phone numbers of the responsible persons, operation states, operation notes and other information, then the work order is sent to a patrol inspection terminal and/or a related display screen in a plant area by the early warning system through APP information, mail notification, short message notification and other modes according to preset contact information of related persons, and the patrol inspection terminal can be a mobile intelligent terminal of a patrol inspection person.
The equipment fault early warning system of the embodiment adopts the equipment fault early warning method, and comprises an instrument control system, a parameter analysis system, a decision system and an early warning system, wherein the instrument control system is used for acquiring current operation parameters of equipment measured by an instrument, the parameter analysis system is used for generating an equipment digital model according to the current operation parameters of the equipment, the equipment digital model is used for predicting the operation parameters of the next stage of the equipment and generating an early warning analysis result, the decision system is used for generating an early warning solution according to the early warning analysis result, and the early warning system is used for sending the early warning solution to the inspection terminal.
In this embodiment, the equipment failure early warning system further includes a health analysis system, and the health analysis system is used to generate a current health value of the equipment, and the health value may be used to provide technical basis and reference for daily maintenance, repair, upgrade, and the like of the equipment. The above instrument control system, parameter analysis system, decision system and early warning system can be used for the above device fault early warning, and can also be used for current device fault warning, for example, the instrument control system is used for obtaining current operation parameters of the device measured by the instrument, the parameter analysis system is used for splitting the current operation parameters of the processing device, and judging whether the device is in fault or not according to the preset condition, if so, the decision system is used for matching a solution for the current fault of the device, and the early warning system is used for displaying and sending current fault information and a fault solution of the device to a mobile intelligent terminal of a relevant person.
In the invention, 1) the current operation parameters of the equipment are obtained by using an instrument control system; 2) generating a digital model of the equipment by using a parameter analysis system according to the current operating parameters of the equipment; 3) predicting the operation parameters of the next stage of the equipment by using the digital model of the equipment, and performing early warning analysis on the operation parameters of the next stage of the equipment to generate an early warning analysis result; 4) utilize decision-making system to generate the early warning solution according to early warning analysis result to send the early warning solution to the early warning system and carry out the early warning, can carry out preventive initiative monitoring to equipment, effectively avoid the loss that reports to the police and bring after the trouble again, equipment safety and production safety level are showing and are improving, greatly reduce the manpower intervention in the equipment trouble monitoring process, the efficiency and the reliability of failure prediction and processing can be showing and promote, have very high economic nature, especially adapted uses widely in the industry.
It should be understood by those of ordinary skill in the art that the specific constructions and processes illustrated in the foregoing detailed description are exemplary only, and are not limiting. Furthermore, the various features shown above can be combined in various possible ways to form new solutions, or other modifications, by a person skilled in the art, all falling within the scope of the present invention.
Claims (10)
1. An equipment fault early warning method is characterized by comprising the following steps:
1) acquiring current operating parameters of equipment by using an instrument control system;
2) generating a digital model of the equipment by using a parameter analysis system according to the current operating parameters of the equipment;
3) predicting the operation parameters of the next stage of the equipment by using the digital model of the equipment, and performing early warning analysis on the operation parameters of the next stage of the equipment to generate an early warning analysis result;
4) and generating an early warning solution according to the early warning analysis result by using the decision-making system, and sending the early warning solution to the early warning system for early warning.
2. The equipment fault early warning method according to claim 1, wherein the step 1) further comprises:
and acquiring the current running image of the equipment by using a video monitoring system.
3. The equipment fault early warning method according to claim 2, wherein the step 2) comprises:
splitting the current operating parameters of the equipment by using a parameter analysis system to generate a splitting result;
and generating a digital model of the equipment by using a parameter analysis system according to the splitting result and the current running image of the equipment.
4. The device fault pre-warning method according to claim 3, wherein the step of generating the splitting result further comprises:
and carrying out health analysis on the split result by using a health analysis system to generate a current health value of the equipment.
5. The equipment fault early warning method according to claim 1, wherein the step 3) comprises:
utilizing the equipment digital model to perform fitting modeling processing on a split result formed by the current operation parameters of the equipment and a split result formed by the historical operation parameters of the equipment to generate a fitting modeling result;
and generating the operating parameters of the next stage of the equipment according to the fitting modeling result by utilizing the equipment digital model.
6. The equipment fault early warning method according to claim 5, wherein the step 3) further comprises:
and judging whether the operating parameters of the next stage of the equipment exceed the limits or not by using the equipment digital model according to the preset values of the operating parameters of the next stage of the equipment, and if so, generating an early warning analysis result.
7. The equipment fault early warning method according to claim 1, wherein the step 4) comprises:
and matching the early warning solution to the early warning analysis result by using the decision system according to the preset value of the early warning analysis result, and sending the early warning solution to the early warning system.
8. The equipment fault early warning method according to claim 7, wherein the step 4) further comprises:
and generating a work order by using the early warning system according to the early warning solution, and sending the work order to the inspection terminal.
9. An equipment fault early warning system is characterized in that the equipment fault early warning method according to any one of claims 1 to 8 is adopted, the equipment fault early warning system comprises an instrument control system, a parameter analysis system, a decision system and an early warning system, the instrument control system is used for obtaining current operation parameters of equipment measured by an instrument, the parameter analysis system is used for generating an equipment digital model according to the current operation parameters of the equipment, the equipment digital model is used for predicting the operation parameters of the next stage of the equipment and generating an early warning analysis result, the decision system is used for generating an early warning solution according to the early warning analysis result, and the early warning system is used for sending the early warning solution to an inspection terminal.
10. The equipment fault early warning system of claim 9, further comprising a health analysis system to generate a current health value for the equipment.
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| CN202011343564.7A CN112562277A (en) | 2020-11-25 | 2020-11-25 | Equipment fault early warning method and system |
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| CN202011343564.7A CN112562277A (en) | 2020-11-25 | 2020-11-25 | Equipment fault early warning method and system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115027634A (en) * | 2022-06-24 | 2022-09-09 | 中海油田服务股份有限公司 | Power maintenance supply ship fault early warning method and system |
| CN115499370A (en) * | 2021-06-17 | 2022-12-20 | 中国移动通信集团浙江有限公司 | Signaling network link fault processing method and device and computer readable storage medium |
| CN116758719A (en) * | 2023-08-23 | 2023-09-15 | 深圳市磐锋精密技术有限公司 | Online monitoring system for production workshop equipment environment |
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| CN111371180A (en) * | 2020-03-23 | 2020-07-03 | 国网黑龙江省电力有限公司鹤岗供电公司 | Substation patrol supervision and data analysis system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115499370A (en) * | 2021-06-17 | 2022-12-20 | 中国移动通信集团浙江有限公司 | Signaling network link fault processing method and device and computer readable storage medium |
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| CN116758719B (en) * | 2023-08-23 | 2023-11-10 | 深圳市磐锋精密技术有限公司 | Online monitoring system for production workshop equipment environment |
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