CN114493204A - Industrial equipment monitoring method and equipment based on industrial Internet - Google Patents
Industrial equipment monitoring method and equipment based on industrial Internet Download PDFInfo
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Abstract
The application discloses an industrial equipment monitoring method and equipment based on an industrial internet, wherein the method comprises the following steps: acquiring a plurality of operating parameters of the industrial equipment; if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance; calling a fault verification mode corresponding to the industrial equipment according to the fault probability information; judging whether the industrial equipment has faults or not according to the fault verification mode; if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a recent period of time; according to reference operation parameter sets of the industrial equipment in different working states, correlation analysis is carried out on historical operation parameters and corresponding reference operation parameters within a recent period of time, the type of faults to be generated by the industrial equipment is predicted, and the fault time corresponding to the type of faults generated by the industrial equipment is predicted. The industrial equipment monitoring efficiency of the industrial internet can be improved.
Description
Technical Field
The application relates to the technical field of industrial internet, in particular to an industrial equipment monitoring method and equipment based on the industrial internet.
Background
Along with the popularization of big data technology, traditional equipment maintenance, maintenance mode will gradually be replaced by intelligent maintenance, in time discover industrial equipment's trouble, can promote functional industrial equipment's life.
Currently, in the process of monitoring the industrial equipment, the operation parameters of the industrial equipment are generally analyzed through a fault detection model, so as to find fault information of the industrial equipment. When a fault occurs, the industrial equipment has a fault to cause shutdown or loss, and early warning cannot be performed.
Furthermore, because the performance attenuation and the service life reduction of the electromechanical equipment are dynamic processes from quantitative change to qualitative change, the industrial equipment also needs to predict the development trend of the equipment, but because the field equipment is complex, the trend prediction is directly carried out on the operation parameters of the industrial equipment through a fault trend prediction model, the difficulty is high, and the monitoring efficiency of the industrial equipment is low.
Disclosure of Invention
The embodiment of the application provides an industrial equipment monitoring method, equipment and a medium based on an industrial internet, which are used for solving the problem of low monitoring efficiency of industrial equipment.
The embodiment of the application adopts the following technical scheme:
in one aspect, an embodiment of the present application provides an industrial device monitoring method based on an industrial internet, including: acquiring a plurality of operating parameters of the industrial equipment; if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance; calling a fault verification mode corresponding to the industrial equipment according to the fault probability information; judging whether the industrial equipment has faults or not according to the fault verification mode; if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time; according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types, determining the corresponding operation parameters of the fault types in the multiple operation parameters, and taking the corresponding operation parameters as the fault operation parameters; acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time; and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
In one example, the predicting a type of a fault that the industrial device is about to generate includes: determining the association degree between the historical operating parameters and the reference operating parameters in the latest period of time through a Euclidean distance function according to the historical operating parameter values and the reference operating parameter values in the latest period of time; taking the historical operating parameter with the maximum degree of association and the reference operating parameter as an associated historical operating parameter and an associated reference operating parameter; and predicting the type of the fault to be generated by the industrial equipment according to the associated historical operating parameters and the associated reference operating parameters.
In one example, the determining, according to the historical operating parameter value and the reference operating parameter value in the latest period of time, the degree of association between the historical operating parameter and the reference operating parameter in the latest period of time through a euclidean distance function specifically includes: dividing the historical duration according to a preset time interval, and dividing the historical operating parameters into a plurality of sample data fragments; traversing the reference operation parameter by taking the sample data fragment as a unit, and calculating the Euclidean distance between the historical characteristic quantity and the reference characteristic quantity; wherein the Euclidean distance is used as the correlation degree between the reference operation parameter and the historical operation parameter.
In one example, the predicting a type of a fault that the industrial equipment is about to generate according to the associated historical operating parameter and the associated reference operating parameter specifically includes: determining a reference fault type corresponding to the associated reference operating parameter; and under the reference fault type, the industrial equipment runs from normal operation to a first parameter change trend graph corresponding to the fault type; acquiring a second parameter change trend graph of the associated historical operating parameters of the industrial equipment within a latest period of time according to a time sequence; and comparing the first parameter change trend graph with the second parameter change trend graph, and if the matching error of the first parameter change trend graph and the second parameter change trend graph is smaller than a preset matching threshold, taking the reference fault type as the fault type to be generated by the industrial equipment.
In one example, after comparing the first parameter trend graph with the second parameter trend graph, the method further comprises: taking each industrial device in an industrial internet as a node, taking the association between each industrial device as an edge, and establishing a knowledge graph corresponding to each industrial device; if the matching error of the first parameter variation trend graph and the second parameter variation trend graph is larger than or equal to the preset matching threshold value, determining associated equipment corresponding to the industrial equipment in the knowledge graph; if the associated equipment is fault equipment, predicting the associated fault type of the industrial equipment according to the fault type of the associated equipment; and taking the associated fault type as a fault type to be generated by the industrial equipment.
In one example, the generating of the fault probability information of the industrial equipment through the pre-constructed mechanism model and the operation parameter value specifically includes: obtaining a plurality of sample operating parameters of the industrial equipment; training an initial neural network model according to the sample operation parameter values, and determining the mechanism model; and inputting the operation parameter values into the mechanism model, determining an operation result value, and generating the fault probability of the industrial equipment according to the operation result value.
In one example, the invoking of the fault verification mode corresponding to the industrial device through the fault probability information specifically includes: if the probability of the industrial equipment failing in the failure probability information is a first failure probability, calling a first failure verification mode of the industrial equipment; the first fault probability represents that the operation result value is greater than a preset threshold value; the determining whether the industrial equipment has a fault according to the fault verification mode specifically includes: and based on the first fault verification mode, carrying out online identification on the industrial equipment through an expert system, and judging whether the industrial equipment has faults or not.
In one example, the invoking of the fault verification mode corresponding to the industrial device through the fault probability information specifically includes: if the probability of the industrial equipment failing in the failure probability information is a second failure probability, calling a second failure verification mode of the industrial equipment; the second fault probability represents that the operation result value is less than or equal to a preset threshold value; the determining whether the industrial equipment has a fault according to the fault verification mode specifically includes: linking the camera equipment corresponding to the industrial equipment based on the second fault verification mode to obtain a video image of the industrial equipment; and identifying the video image and judging whether the industrial equipment has faults or not.
In one example, the predicting, according to the output power curve, a fault time corresponding to the fault type of the industrial equipment specifically includes: acquiring a first fault operation parameter value of the industrial equipment at the current time; determining a second fault operation parameter value corresponding to the industrial equipment when the fault type occurs; calculating a difference between the first faulty operating parameter value and the second faulty operating parameter value; and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve and the difference value and the current time.
On the other hand, the embodiment of the application provides an industrial equipment monitoring facilities based on industry internet, includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: acquiring a plurality of operating parameters of the industrial equipment; if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance; calling a fault verification mode corresponding to the industrial equipment according to the fault probability information; judging whether the industrial equipment has faults or not according to the fault verification mode; if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time; according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types; determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter; acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time; and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
In another aspect, an embodiment of the present application provides a non-volatile computer storage medium for monitoring industrial equipment based on an industrial internet, where the non-volatile computer storage medium stores computer-executable instructions, and the computer-executable instructions are configured to: acquiring a plurality of operating parameters of the industrial equipment; if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance; calling a fault verification mode corresponding to the industrial equipment according to the fault probability information; judging whether the industrial equipment has faults or not according to the fault verification mode; if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time; according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types; determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter; acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time; and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the operation parameter corresponding value is compared with the corresponding preset range, the industrial equipment which is likely to break down can be screened out, the fault probability information of the industrial equipment is generated, the industrial equipment which is likely to break down can be further screened out through the fault probability of the industrial equipment, early warning is timely carried out on the industrial equipment, relevance analysis is carried out on historical operation parameters within a period of time recently and corresponding reference operation parameters, the dynamic process of industrial equipment development can be combined, which operation parameters are likely to break down is reported, the fact that the operation parameters are likely to break down is predicted to break down after a long time, the industrial equipment which is likely to break down is further screened out, early warning is timely carried out on the industrial equipment, and the monitoring efficiency and accuracy of the industrial equipment are improved.
Drawings
In order to more clearly explain the technical solutions of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which:
fig. 1 is a schematic flow chart of an industrial equipment monitoring method based on an industrial internet according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an industrial device monitoring device based on an industrial internet according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following embodiments and accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. 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 application.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an industrial equipment monitoring method based on an industrial internet according to an embodiment of the present application. Certain input parameters or intermediate results in the procedure allow for manual intervention adjustments to help improve accuracy.
The analysis method according to the embodiment of the present application may be implemented by a terminal device or a server, and the present application is not limited to this. For convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.
It should be noted that the server may be a single device, or may be a system composed of multiple devices, that is, a distributed server, which is not specifically limited in this application.
The process in fig. 1 may include the following steps:
s101: a plurality of operating parameters of an industrial device are obtained.
In some embodiments of the present application, industrial equipment includes various production equipment in a factory such as machine tools, bearings, etc., each containing different operating parameters, such as operating state, operating time, hardware information. The operation state comprises temperature and humidity, voltage and current, vibration signals and the like, and the hardware information comprises the size, the angle and the like of industrial equipment parts.
For example, based on the industrial internet, after the industrial equipment is networked, the server can obtain hardware information and running time of the industrial equipment, and can obtain running states of the industrial equipment through various sensors, for example, the server obtains a temperature and humidity environment of the industrial equipment through a temperature and humidity sensor, and obtains a vibration signal of the industrial equipment through a vibration sensor.
S102: and if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance.
In some embodiments of the present application, if the corresponding values of the operating parameters are all within the preset range, it indicates that the operating parameters are within the normal range, and if the corresponding values of the operating parameters are not within the preset range, it indicates that the operating parameters are abnormal, and at the same time, it indicates that the industrial equipment may have failed currently. Therefore, if the abnormal operation parameters exist, the fault probability information of the industrial equipment is not generated through the mechanism model and the operation parameter values which are constructed in advance, and the abnormal operation parameters are analyzed to determine the fault type which may occur to the industrial equipment.
Further, if the corresponding values of the operation parameters are within the preset range, the server inputs the operation parameter values into the mechanism model, the operation result outputs a result value, and the value corresponds to different fault probabilities of the industrial equipment. For example, if the operation result is 0, it indicates that the industrial equipment is normal, and if the operation result is between 1 and 2, it indicates that the industrial equipment has a small probability of failure, and if the operation result is greater than 2, it indicates that the industrial equipment has a large probability of failure, so as to take corresponding preventive measures.
When the mechanism model is constructed, the server firstly obtains a plurality of sample operation parameters of the industrial equipment, combines the sample operation parameters to obtain a plurality of groups of sample operation parameters, wherein different groups of sample operation parameters correspond to different operation result values, and trains the initial neural network model through the plurality of groups of sample operation parameters and the corresponding operation result values to obtain the mechanism model.
S103: and calling a fault verification mode corresponding to the industrial equipment according to the fault probability information.
In some implementations of the present application, different failure probabilities of the industrial device correspond to different failure verification modes.
Specifically, if the probability of the industrial equipment failing is the first failure probability, the first failure verification mode of the industrial equipment is called. The first fault probability means that the operation result value is larger than a preset threshold value, the industrial equipment has a high probability of fault occurrence, and the first fault verification mode means that an expert diagnoses on line to verify whether the industrial equipment has a fault or not.
And if the probability of the industrial equipment failing is the second failure probability, calling a second failure verification mode of the industrial equipment. The second failure probability means that the operation result value is smaller than or equal to a preset threshold value, the industrial equipment fails with a small probability, and the second failure verification mode means that a video image of the industrial equipment is identified to verify whether the industrial equipment fails.
S104: and judging whether the industrial equipment has faults or not according to the fault verification mode.
In some embodiments of the application, a high-definition camera is pre-installed in a workshop, so that when a first fault verification mode is executed and online identification is performed on industrial equipment through an expert system, video information and audio information of the industrial equipment can be shared to an expert side in real time, so that the expert can analyze the video information and the audio information and judge whether the industrial equipment has a fault.
And when the second fault verification mode is executed, the server acquires the video image of the industrial equipment by linking the camera equipment corresponding to the industrial equipment. And then, identifying the video image and judging whether the industrial equipment has faults or not.
Further, if the industrial equipment has a fault, notification information is sent to a corresponding management user, so that the management user can timely process the fault of the industrial equipment.
If the industrial equipment has no fault, step S105 is executed.
S105: and if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time.
After the industrial equipment is verified, the industrial equipment can be considered to be free of faults at present, however, the performance attenuation and the service life reduction of the electromechanical equipment are dynamic processes from quantity to quality, so that the development trend of the industrial equipment is also required to be predicted, and therefore, in order to acquire the development trend of the industrial equipment, historical operating parameters of the industrial equipment within a period of time in the recent past are acquired. The last period of time may be set according to actual needs, and is not limited herein, for example, a week, a month, and the like. It should be noted that historical operating parameters long ago have not been referenced, such as one year, two years, etc.
S106: according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; wherein the reference operation parameter is used for describing the working state of the industrial equipment under different fault types.
In some embodiments of the present application, the set of reference operating parameters includes a plurality of reference operating parameters, wherein the industrial equipment has different reference operating parameter values under different fault type operating conditions. That is, one type of fault corresponds to a set of reference operating parameter values.
For example, the types of failure include misalignment, rolling bearing defects, and the like. The misalignment of two rotating shafts in the industrial equipment can occur in the parallel direction and the angular direction, or in the combination of the two rotating shafts, the misalignment error can force the component to work under the stress or the load higher than the original design capacity, so that a larger system is affected, and finally premature failure can be caused.
Rolling element bearing defects are often an artifact of mechanically induced stress or lubrication problems that create small cracks or defects in the mechanical components of the bearing, resulting in increased vibration, where the industrial equipment produces excessive vibration, and therefore the vibration signal uploaded by the vibration sensor is abnormal, i.e., the vibration signal of the industrial equipment is abnormal, while the remaining operating parameters change adaptively according to changes in the vibration signal.
Further, when the server performs relevance analysis on the historical operating parameters in the latest period of time and the corresponding reference operating parameters, the relevance between the historical operating parameters and the reference operating parameters in the latest period of time is determined through an Euclidean distance function according to the historical operating parameter values and the reference operating parameter values in the latest period of time.
The smaller the Euclidean distance is, the greater the degree of association between the historical operating parameter and the reference operating parameter is, and the greater the Euclidean distance is, the smaller the degree of association between the historical operating parameter and the reference operating parameter is.
And then, taking the historical operating parameter with the maximum relevance degree and the reference operating parameter as the relevant historical operating parameter and the relevant reference operating parameter. And finally, predicting the fault type to be generated by the industrial equipment according to the associated historical operating parameters and the associated reference operating parameters, namely, taking the reference fault type corresponding to the associated reference operating parameters as the fault type to be generated by the industrial equipment by the server.
For example, if the associated historical operating parameter and the associated reference operating parameter are the associated historical vibration signal and the associated reference vibration signal, respectively, the type of the fault to be generated by the industrial equipment is a rolling element bearing defect.
Further, when determining the correlation between the historical operating parameters and the reference operating parameters, the server divides the historical duration according to a preset time interval, divides the historical operating parameters into a plurality of sample data fragments, then traverses the reference operating parameters by taking the sample data fragments as a unit, and calculates the Euclidean distance between the historical characteristic quantity and the reference characteristic quantity. Wherein, the Euclidean distance is used as the correlation degree between the reference operation parameter and the historical operation parameter.
Furthermore, since it is possible that the reference fault type corresponding to the associated reference operation parameter is directly used as the fault type to be generated by the industrial equipment, the fault type to be generated by the industrial equipment is determined by combining the dynamic process of the operation parameter in order to more accurately predict the fault type to be generated by the industrial equipment.
Specifically, the server obtains a first parameter change trend graph corresponding to the type of the fault occurring from normal operation of the industrial equipment under the reference fault type. And then, acquiring a second parameter change trend graph of the associated historical operating parameters of the industrial equipment in a recent period of time according to the time sequence. And finally, comparing the first parameter change trend graph with the second parameter change trend graph, and if the matching error of the first parameter change trend graph and the second parameter change trend graph is smaller than a preset matching threshold, taking the reference fault type as the fault type to be generated by the industrial equipment.
Due to the mutual cooperation between the industrial devices, when other industrial devices fail, the failure of the industrial device may be caused.
Based on the method, the server takes all industrial equipment in the industrial internet as nodes, takes the association among the industrial equipment as edges, and establishes the knowledge graph corresponding to the industrial equipment in advance.
If the matching error of the first parameter change trend graph and the second parameter change trend graph is smaller than or equal to a preset matching threshold value, determining associated equipment corresponding to the industrial equipment in the knowledge graph, if the associated equipment is fault equipment, predicting the associated fault type of the industrial equipment according to the fault type of the associated equipment, and taking the associated fault type as the fault type to be generated by the industrial equipment.
On the contrary, if the associated device is a non-failure device, it is determined that the industrial device will not fail within the preset future time period, and S107 is not executed again.
S107: and determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter.
When the reference fault type is used as a fault type to be generated by the industrial equipment, the fault operation parameter is actually a correlation historical operation parameter, and when the correlation fault type is used as a fault model to be generated by the industrial equipment, the fault operation parameter is actually an operation parameter corresponding to the correlation fault type.
S108: and acquiring the fault operation parameter from the historical operation parameters in the latest period of time so as to determine an output power curve of the fault operation parameter in the latest period of time.
For a certain operation parameter, when different industrial devices execute the operation parameter, the corresponding output powers are different, and meanwhile, the same industrial device also has different operation parameters for different operation parameters. Thus, to obtain the time elapsed between the value of the faulty operating parameter of the industrial equipment reaching the value at which the fault type occurred, the server will determine the output power curve of the faulty operating parameter over the last period of time.
S109: and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
In some embodiments of the present application, when predicting a failure time corresponding to a failure type of an industrial device, a server first obtains a first failure operation parameter value of the industrial device at a current time. And then, determining a corresponding second fault operation parameter value when the industrial equipment has a fault type, and calculating a difference value between the first fault operation parameter value and the second fault operation parameter value. And finally, according to the output power curve, predicting the fault time corresponding to the fault type of the industrial equipment according to the difference value and the current time.
It should be noted that, although the embodiment of the present application describes steps S101 to S109 sequentially with reference to fig. 1, this does not mean that steps S101 to S109 must be executed in strict sequence. The embodiment of the present application is described by sequentially describing step S101 to step S109 according to the sequence shown in fig. 1, so as to facilitate a person skilled in the art to understand the technical solutions of the embodiments of the present application. In other words, in the embodiment of the present application, the sequence between step S101 and step S109 may be appropriately adjusted according to actual needs.
By the method of FIG. 1, the industrial equipment which is likely to have faults can be screened out by comparing the corresponding value of the operation parameter with the preset range, the industrial equipment which is likely to have faults can be further screened out by generating the fault probability information of the industrial equipment according to the fault probability of the industrial equipment, early warning is carried out on the industrial equipment in time, historical operation parameters in a recent period of time and corresponding reference operation parameters are subjected to correlation analysis, which operation parameters are likely to be abnormal can be reported in combination with the dynamic process of the development of the industrial equipment, the industrial equipment which is likely to have faults is further screened out, and the abnormality of the industrial equipment which is likely to have faults is predicted after a long time, so that the early warning of the trend in advance is realized, the early warning is carried out on the industrial equipment in time, the monitoring efficiency and the accuracy of the industrial equipment are improved, and enough time is provided for adjusting the equipment and solving the faults, the equipment operation process is more controllable, and the maintenance efficiency is improved.
Based on the same idea, some embodiments of the present application further provide a device and a non-volatile computer storage medium corresponding to the above method.
Fig. 2 is a schematic structural diagram of an industrial device monitoring device based on an industrial internet according to an embodiment of the present application, where the device includes:
at least one processor; and (c) a second step of,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a plurality of operating parameters of the industrial equipment;
if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance;
calling a fault verification mode corresponding to the industrial equipment according to the fault probability information;
judging whether the industrial equipment has faults or not according to the fault verification mode;
if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time;
according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types;
determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter;
acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time;
and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
Some embodiments of the present application provide a non-volatile computer storage medium for industrial internet-based monitoring of industrial equipment, storing computer-executable instructions configured to:
acquiring a plurality of operating parameters of the industrial equipment;
if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance;
calling a fault verification mode corresponding to the industrial equipment according to the fault probability information;
judging whether the industrial equipment has faults or not according to the fault verification mode;
if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time;
according to the reference operation parameter set of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types;
determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter;
acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time;
and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and media embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for relevant points.
The device and the medium provided by the embodiment of the application correspond to the method one to one, so the device and the medium also have the similar beneficial technical effects as the corresponding method, and the beneficial technical effects of the method are explained in detail above, so the beneficial technical effects of the device and the medium are not repeated herein.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the technical principle of the present application shall fall within the protection scope of the present application.
Claims (10)
1. An industrial equipment monitoring method based on industrial internet is characterized by comprising the following steps:
acquiring a plurality of operating parameters of the industrial equipment;
if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance;
calling a fault verification mode corresponding to the industrial equipment according to the fault probability information;
judging whether the industrial equipment has faults or not according to the fault verification mode;
if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time;
according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types;
determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter;
acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time;
and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
2. The method according to claim 1, wherein the performing correlation analysis on the historical operating parameters and the corresponding reference operating parameters within the recent period of time according to the reference operating parameter set of the industrial equipment in different working states to predict the type of fault that will occur to the industrial equipment specifically includes:
determining the association degree between the historical operating parameters and the reference operating parameters in the latest period of time through a Euclidean distance function according to the historical operating parameter values and the reference operating parameter values in the latest period of time;
taking the historical operating parameter with the maximum relevance degree and the reference operating parameter as a relevant historical operating parameter and a relevant reference operating parameter;
and predicting the type of the fault to be generated by the industrial equipment according to the associated historical operating parameters and the associated reference operating parameters.
3. The method according to claim 2, wherein the determining the correlation between the historical operating parameter and the reference operating parameter in the recent period of time by a euclidean distance function according to the historical operating parameter value and the reference operating parameter value in the recent period of time specifically comprises:
dividing the historical duration according to a preset time interval, and dividing the historical operating parameters into a plurality of sample data fragments;
traversing the reference operation parameter by taking the sample data fragment as a unit, and calculating the Euclidean distance between the historical characteristic quantity and the reference characteristic quantity;
wherein the Euclidean distance is used as the correlation degree between the reference operation parameter and the historical operation parameter.
4. The method according to claim 2, wherein predicting the type of fault that the industrial equipment will experience based on the correlated historical operating parameters and the correlated reference operating parameters comprises:
determining a reference fault type corresponding to the associated reference operating parameter;
acquiring a first parameter change trend chart corresponding to the fault type from normal operation to occurrence of the industrial equipment under the reference fault type;
acquiring a second parameter change trend graph of the associated historical operating parameters of the industrial equipment within a latest period of time according to a time sequence;
and comparing the first parameter change trend graph with the second parameter change trend graph, and if the matching error of the first parameter change trend graph and the second parameter change trend graph is smaller than a preset matching threshold, taking the reference fault type as the fault type to be generated by the industrial equipment.
5. The method of claim 4, wherein after comparing the first parameter trend graph to the second parameter trend graph, the method further comprises:
taking industrial equipment in an industrial internet as nodes, taking the association between the industrial equipment as edges, and establishing a knowledge graph corresponding to the industrial equipment;
if the matching error of the first parameter variation trend graph and the second parameter variation trend graph is larger than or equal to the preset matching threshold value, determining associated equipment corresponding to the industrial equipment in the knowledge graph;
if the associated equipment is fault equipment, predicting the associated fault type of the industrial equipment according to the fault type of the associated equipment;
and taking the associated fault type as a fault type to be generated by the industrial equipment.
6. The method according to claim 1, wherein the generating of the fault probability information of the industrial equipment through the pre-constructed mechanism model and the operation parameter values specifically comprises:
obtaining a plurality of sample operating parameters of the industrial equipment;
training an initial neural network model according to the sample operation parameter values, and determining the mechanism model;
and inputting the operation parameter values into the mechanism model, determining an operation result value, and generating the fault probability of the industrial equipment according to the operation result value.
7. The method according to claim 1, wherein the invoking of the fault verification mode corresponding to the industrial device through the fault probability information specifically includes:
if the probability of the industrial equipment failing in the failure probability information is a first failure probability, calling a first failure verification mode of the industrial equipment; the first fault probability represents that the operation result value is greater than a preset threshold value;
the determining whether the industrial equipment has a fault according to the fault verification mode specifically includes:
and based on the first fault verification mode, carrying out online identification on the industrial equipment through an expert system, and judging whether the industrial equipment has faults or not.
8. The method according to claim 1, wherein the invoking of the fault verification mode corresponding to the industrial device through the fault probability information specifically includes:
if the probability of the industrial equipment failing in the failure probability information is a second failure probability, calling a second failure verification mode of the industrial equipment; the second fault probability represents that the operation result value is less than or equal to a preset threshold value;
the determining whether the industrial equipment has a fault according to the fault verification mode specifically includes:
linking the camera equipment corresponding to the industrial equipment based on the second fault verification mode to obtain a video image of the industrial equipment;
and identifying the video image and judging whether the industrial equipment has faults or not.
9. The method according to claim 1, wherein the predicting a fault time corresponding to the fault type of the industrial equipment according to the output power curve specifically comprises:
acquiring a first fault operation parameter value of the industrial equipment at the current time;
determining a second fault operation parameter value corresponding to the industrial equipment when the fault type occurs;
calculating a difference between the first faulty operating parameter value and the second faulty operating parameter value;
and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve and the difference value and the current time.
10. An industrial internet-based industrial equipment monitoring device, the device comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a plurality of operating parameters of the industrial equipment;
if the corresponding values of the operation parameters are all in the corresponding preset ranges, generating fault probability information of the industrial equipment through a mechanism model and operation parameter values which are constructed in advance;
calling a fault verification mode corresponding to the industrial equipment according to the fault probability information;
judging whether the industrial equipment has faults or not according to the fault verification mode;
if the industrial equipment has no fault, acquiring historical operating parameters of the industrial equipment within a latest period of time;
according to the reference operation parameter sets of the industrial equipment in different working states, performing relevance analysis on the historical operation parameters in the recent period of time and the corresponding reference operation parameters, and predicting the type of the fault to be generated by the industrial equipment; the reference operation parameters are used for describing the working states of the industrial equipment under different fault types;
determining the corresponding operation parameter of the fault type in the plurality of operation parameters, and taking the corresponding operation parameter as a fault operation parameter;
acquiring the fault operation parameter from historical operation parameters in the latest period of time to determine an output power curve of the fault operation parameter in the latest period of time;
and predicting the fault time corresponding to the fault type of the industrial equipment according to the output power curve.
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| CN202210034719.1A CN114493204A (en) | 2022-01-13 | 2022-01-13 | Industrial equipment monitoring method and equipment based on industrial Internet |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115345485A (en) * | 2022-08-17 | 2022-11-15 | 珠海爱浦京软件股份有限公司 | Intelligent factory equipment data analysis management system and method based on big data |
| CN116612552A (en) * | 2023-07-17 | 2023-08-18 | 北京经纬物联科技有限公司 | Intelligent monitoring method and system for agricultural machinery production based on Internet of things |
| CN117009789A (en) * | 2023-09-27 | 2023-11-07 | 通用技术集团机床工程研究院有限公司 | Machine tool fault prediction method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115345485A (en) * | 2022-08-17 | 2022-11-15 | 珠海爱浦京软件股份有限公司 | Intelligent factory equipment data analysis management system and method based on big data |
| CN116612552A (en) * | 2023-07-17 | 2023-08-18 | 北京经纬物联科技有限公司 | Intelligent monitoring method and system for agricultural machinery production based on Internet of things |
| CN116612552B (en) * | 2023-07-17 | 2023-09-29 | 北京经纬物联科技有限公司 | Intelligent monitoring method and system for agricultural machinery production based on Internet of Things |
| CN117009789A (en) * | 2023-09-27 | 2023-11-07 | 通用技术集团机床工程研究院有限公司 | Machine tool fault prediction method and device, electronic equipment and storage medium |
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| CN117217740B (en) * | 2023-11-09 | 2024-02-06 | 江苏德琛常工新能源科技创新有限公司 | Hydrogen energy equipment fault data transmission system and method based on artificial intelligence |
| CN117348491A (en) * | 2023-11-16 | 2024-01-05 | 江苏凯立达数据科技有限公司 | Networking equipment data acquisition system and method based on industrial Internet |
| CN117348491B (en) * | 2023-11-16 | 2024-05-03 | 安徽睿新智造科技有限公司 | Networking equipment data acquisition system and method based on industrial Internet |
| CN117391644A (en) * | 2023-12-12 | 2024-01-12 | 国网物资有限公司 | Parameter adjustment method, device, equipment and storage medium |
| CN117391644B (en) * | 2023-12-12 | 2024-02-06 | 国网物资有限公司 | Parameter adjustment method, device, equipment and medium in contract management process |
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