Fault early warning method and device and electronic equipment

Abstract

The invention provides a fault early warning method and device and electronic equipment. The method comprises the steps that first operation data of the industrial equipment in a first time period are acquired, second operation data of the industrial equipment in a second time period are predicted based on the first operation data, and the end moment of the first time period is earlier than the start moment of the second time period; calculating the data number of the second operation data in a fault range corresponding to each fault level in a plurality of fault levels of the industrial equipment; if the data number of the second operation data in the fault range corresponding to the target fault level is maximum, predicting that the industrial equipment triggers the target fault level in a second time period; and in response to the target fault level triggered by the industrial equipment, executing a fault early warning operation corresponding to the target fault level on the industrial equipment. Therefore, fault early warning can be carried out based on the fault level, so that the continuity of the production line is kept.

Description

Technical Field

[0001] The present application relates to the technical field of fault warning, and in particular to a fault warning method, device and electronic equipment. Background Art

[0002] In the related art, it is usually necessary to maintain industrial equipment to ensure the reliable operation of the industrial equipment and thus maintain the continuity of the production line. At present, the maintenance of industrial equipment mainly includes preventive maintenance and corrective maintenance. Among them, preventive maintenance is usually based on a fixed maintenance plan, which cannot accurately predict whether the industrial equipment will fail, and the maintenance cost is high, and there is a risk of production line interruption; corrective maintenance is usually repaired after the industrial equipment fails, resulting in production losses and high maintenance costs.

[0003] Therefore, how to timely warn of potential failures of industrial equipment to ensure the reliable operation of industrial equipment is an urgent problem to be solved. Summary of the invention

[0004] In view of this, the present application provides a fault warning method, device and electronic equipment, which can perform fault warning based on the fault level, thereby maintaining the continuity of the production line.

[0005] In a first aspect, the present application provides a fault warning method, comprising: obtaining first operating data of an industrial equipment within a first time period, and based on the first operating data, predicting second operating data of the industrial equipment within a second time period, wherein the end time of the first time period is earlier than the start time of the second time period; calculating the number of data in a fault range corresponding to each fault level of the second operating data in multiple fault levels of the industrial equipment; if the number of data in the fault range corresponding to the target fault level of the second operating data is the largest, predicting that the industrial equipment will trigger the target fault level within the second time period; in response to the industrial equipment triggering the target fault level, performing a fault warning operation corresponding to the target fault level on the industrial equipment.

[0006] Through this method, the operation data of the industrial equipment in the future time period can be predicted based on the operation data of the industrial equipment in the current time period or the historical time period, and the fault level triggered by the industrial equipment in the future time period can be predicted based on the operation data of the future time period, so as to perform a fault warning operation corresponding to the fault level. In this way, the fault level of the industrial equipment in the future time period can be accurately predicted, and a fault warning can be performed based on the fault level, thereby maintaining the continuity of the production line.

[0007] In another implementation of the present application, based on the first operating data, predicting the second operating data of the industrial equipment in the second time period includes: obtaining the third operating data of the industrial equipment in the third time period, and the fourth operating data in the fourth time period, the end time of the third time period is earlier than the start time of the fourth time period, and the end time of the fourth time period is earlier than the start time of the second time period; fitting the third operating data and the fourth operating data respectively to obtain a first fitting function and a second fitting function; calculating the difference function between the first fitting function and the second fitting function; and predicting the second operating data based on the difference function and the first operating data.

[0008] Through this method, the difference function of the operating data in the two time periods before and after can be calculated based on the fitting function, and the second operating data can be predicted based on the difference function and the first operating data, so that the potential risk of failure of industrial equipment can be predicted based on the second operating data. This helps to take preventive maintenance measures in advance and avoid production losses and increased maintenance costs caused by industrial equipment failure.

[0009] In another implementation of the present application, based on the difference function and the first operating data, predicting the second operating data includes: based on the difference function and the first operating data, calculating the difference data between the first operating data and the second operating data; accumulating the difference data and the first operating data to obtain the second operating data.

[0010] Through this method, the second operating data can be predicted based on the difference function and the first operating data, so that the failure of industrial equipment can be discovered earlier based on the second operating data, so that maintenance measures can be taken in time to reduce the risk of production interruption.

[0011] In another implementation of the present application, based on the first operating data, second operating data of the industrial equipment in a second time period is predicted, including: inputting the first operating data into a target artificial intelligence model to obtain the second operating data.

[0012] Through this method, based on the trained artificial intelligence model, the first operating data in the current time period or the historical time period can be analyzed to predict the second operating data of the industrial equipment in the future period, so as to identify potential faults of the industrial equipment based on the second operating data, thereby improving the accuracy of fault prediction and facilitating maintenance personnel to formulate maintenance plans more accurately.

[0013] In another implementation of the present application, the method also includes: obtaining fifth operating data of the industrial equipment in a fifth time period, and sixth operating data in a sixth time period, the end time of the fifth time period is earlier than the start time of the sixth time period, and the end time of the sixth time period is earlier than the start time of the second time period; inputting the fifth operating data into the artificial intelligence model to be trained to obtain seventh operating data of the industrial equipment in the sixth time period; determining the first loss based on the sixth operating data and the seventh operating data; based on the first loss, adjusting the model parameters of the artificial intelligence model to be trained so that the loss obtained based on the adjusted artificial intelligence model meets the convergence conditions to obtain the target artificial intelligence model.

[0014] Through this method, by using the operating data of industrial equipment in the current time period or the historical time period to train the artificial intelligence model to be trained, the target artificial intelligence model obtained after training can have a more accurate prediction ability for the second operating data, so that the subsequent prediction of whether the industrial equipment will fail based on the second operating data will also be more accurate, which will help reduce production losses and maintenance costs, and avoid additional costs caused by long-term production interruptions and emergency repairs.

[0015] In another implementation of the present application, the method also includes: obtaining eighth operating data of the industrial equipment within a seventh time period, and multiple fault types under the eighth operating data, wherein the end time of the seventh time period is earlier than the start time of the second time period; dividing the eighth operating data based on multiple fault types to obtain operating data corresponding to each fault type; determining that each fault type corresponds to a fault level, and based on the operating data corresponding to each fault type, determining the fault range corresponding to each fault level.

[0016] Through this method, each fault level and the fault range corresponding to each fault level can be determined based on the operating data of the industrial equipment in the current time period or the historical time period, which is conducive to predicting the fault level triggered by the industrial equipment in the second time period, and facilitating timely maintenance measures corresponding to the fault level for the industrial equipment, so as to improve the reliability and availability of the industrial equipment and reduce production losses caused by industrial equipment failures.

[0017] In another implementation of the present application, multiple fault levels include a first fault level, a second fault level, and a third fault level; in response to the industrial equipment triggering a target fault level, a fault warning operation corresponding to the target fault level is performed on the industrial equipment, including: in response to the industrial equipment triggering the first fault level, no processing is performed on the industrial equipment; or, in response to the industrial equipment triggering the second fault level, the speed and / or load of the industrial equipment is reduced; or, in response to the industrial equipment triggering the third fault level, the industrial equipment is controlled to stop operating.

[0018] Through this method, based on the target fault level triggered by the industrial equipment in the second time period, a fault warning operation corresponding to the target fault level can be performed on the industrial equipment, thereby reducing production losses and increased maintenance costs caused by industrial equipment failures.

[0019] In another implementation of the present application, the method further includes: in response to the industrial equipment triggering a target fault level, sending an early warning message to a monitoring person; wherein the early warning message is used to prompt the monitoring person to maintain the industrial equipment based on the target fault level.

[0020] Through this method, when industrial equipment triggers the target fault level, it can promptly warn the monitoring personnel so that the monitoring personnel can maintain the industrial equipment based on the target fault level. In this way, the need for manual monitoring and manual warning is eliminated, the response speed and efficiency are improved, and unnecessary maintenance and repair costs are reduced, and the risk of production line interruption is reduced.

[0021] In a second aspect, the present application provides a fault warning device, comprising:

[0022] An acquisition module, used for acquiring first operation data of the industrial equipment within a first time period;

[0023] A prediction module, configured to predict second operation data of the industrial equipment in a second time period based on the first operation data, wherein an end time of the first time period is earlier than a start time of the second time period;

[0024] A calculation module, used for calculating the number of data in a fault range corresponding to each fault level of the second operation data in multiple fault levels of the industrial equipment;

[0025] The prediction module is further configured to predict that the industrial equipment will trigger the target fault level within a second time period if the number of data of the second operation data in the fault range corresponding to the target fault level is the largest;

[0026] The execution module is used for executing a fault warning operation corresponding to the target fault level on the industrial equipment in response to the industrial equipment triggering the target fault level.

[0027] In a third aspect, the present application provides an electronic device comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store one or more executable instructions, and the executable instructions enable the processor to execute the method described in the first aspect above.

[0028] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by one or more processors, it implements the method described in the first aspect.

[0029] In a fifth aspect, the present application provides a computer program product, comprising computer program instructions, which enable a computer to execute the method described in the first aspect above.

[0030] In a sixth aspect, the present application provides a computer program, which, when executed on a computer, enables the computer to execute the method described in the first aspect above.

[0031] The embodiment of the present application provides a fault warning method, which can predict the operation data of industrial equipment in a future time period based on the operation data of industrial equipment in the current time period or the historical time period, and predict the fault level triggered by the industrial equipment in the future time period based on the operation data of the future time period, so as to perform a fault warning operation corresponding to the fault level. In this way, the fault level of the industrial equipment in the future time period can be accurately predicted, and a fault warning can be performed based on the fault level, thereby maintaining the continuity of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of a fault warning method provided in an embodiment of the present application;

[0034] Figure 2 is a schematic block diagram of an execution fault warning provided by an embodiment of the present application;

[0035] Figure 3 It is a schematic diagram of the structure of a fault warning device provided in an embodiment of the present application;

[0036] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0037] List of reference numerals:

[0038] S110: Acquire first operating data of the industrial equipment in a first time period, and predict second operating data of the industrial equipment in a second time period based on the first operating data, where the end time of the first time period is earlier than the start time of the second time period;

[0039] S120: Calculating the number of data in a fault range corresponding to each fault level among multiple fault levels of the industrial equipment where the second operation data is located;

[0040] S130: If the number of data of the second operation data in the fault range corresponding to the target fault level is the largest, predicting that the industrial equipment will trigger the target fault level within the second time period;

[0041] S140: In response to the industrial equipment triggering a target fault level, performing a fault warning operation corresponding to the target fault level on the industrial equipment;

[0042] 210: database; 220: server; 230: industrial equipment; 240: artificial intelligence model to be trained; 250: target artificial intelligence model; 260: early warning module;

[0043] 310: acquisition module; 320: prediction module; 330: calculation module; 340: execution module; 350: fitting module; 360: determination module; 370: division module; 380: communication module;

[0044] 410: processor; 420: communication interface; 430: memory; 440: communication bus; 450: program. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0047] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0048] It should also be pointed out that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0049] In addition, the term "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0050] To facilitate understanding of the technical solutions of the embodiments of the present application, the relevant technologies of the embodiments of the present application are described below. The following related technologies can be arbitrarily combined with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application.

[0051] In the related art, it is usually necessary to maintain industrial equipment to ensure the reliable operation of the industrial equipment and thus maintain the continuity of the production line. At present, the maintenance of industrial equipment mainly includes preventive maintenance and corrective maintenance. Among them, preventive maintenance is usually based on a fixed maintenance plan, which cannot accurately predict whether the industrial equipment will fail, and the maintenance cost is high, and there is a risk of production line interruption; corrective maintenance is usually repaired after the industrial equipment fails, resulting in production losses and high maintenance costs.

[0052] For example, a fixed time can be set to perform maintenance on industrial equipment by manual inspection. However, this solution may not be able to monitor whether the industrial equipment fails in real time, and the maintenance cost is high.

[0053] For example, a first threshold can be set based on experience to determine whether the operating data of the industrial equipment in the current time period is greater than the first threshold. If it is greater than the first threshold, it means that the industrial equipment is currently faulty. However, since the first threshold is set based on experience, and the data used to detect whether the industrial equipment has failed is the operating data of the current time period, this solution cannot accurately predict whether the industrial equipment has failed, and there is a risk of interrupting the production line.

[0054] For example, a statistical analysis method can be used to analyze whether the industrial equipment will fail in the future time period based on the operating data of the industrial equipment in the current time period. However, this solution focuses on data analysis and pattern recognition and may not provide accurate failure prediction.

[0055] For example, monitoring systems (such as sensors and monitoring devices) can be used to collect the operating data of industrial equipment in the current time period. However, these monitoring systems have real-time monitoring capabilities, but may lack accurate fault prediction capabilities.

[0056] Therefore, how to timely warn of potential failures of industrial equipment to ensure the reliable operation of industrial equipment is an urgent problem to be solved.

[0057] Based on this, the embodiment of the present application provides a fault warning method, which can predict the operation data of industrial equipment in the future time period based on the operation data of industrial equipment in the current time period or the historical time period, and predict the fault level triggered by the industrial equipment in the future time period based on the operation data of the future time period, so as to perform the fault warning operation corresponding to the fault level. In this way, the fault level of industrial equipment in the future time period can be accurately predicted, and fault warning can be performed based on the fault level, thereby maintaining the continuity of the production line.

[0058] To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The above related technologies can be combined arbitrarily with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0059] It should be understood that the following embodiments mention operating data (such as first operating data and second operating data) many times. It can be understood that the operating data of industrial equipment in the current time period or the historical time period (such as first operating data) can be collected by sensors and other collection devices, and sent to electronic devices; industrial equipment can also be directly connected to electronic devices, so that the electronic devices can directly obtain the operating data of the industrial equipment in the current time period or the historical time period; the operating data of industrial equipment in the future time period (such as second operating data) can be predicted by the operating data of the current time period or the historical time period.

[0060] Exemplarily, the operating data may include operating parameters, vibration data, temperature, pressure, etc. of the industrial equipment, which is not limited in the embodiments of the present application.

[0061] It should also be understood that the operating data (such as the first to eighth operating data) mentioned in the following embodiments can be continuously updated as time changes.

[0062] Figure 1 is a flow chart of a fault warning method provided by an embodiment of the present application, such as Figure 1 As shown, the method may include the following steps.

[0063] S110, obtaining first operating data of the industrial equipment in a first time period, and predicting second operating data of the industrial equipment in a second time period based on the first operating data, wherein an end time of the first time period is earlier than a start time of the second time period.

[0064] It should be noted that in the embodiments of the present application, the fault warning method can be applied to a fault warning device, or an electronic device integrated with the device. The electronic device can be implemented in various forms, for example, the electronic device can include such as a smart phone, a tablet computer, a laptop computer, a PDA, a portable media player (PMP), etc., which is not limited here.

[0065] Exemplarily, the first time period may be a current time period or a historical time period.

[0066] Exemplarily, the second time period may be a future time period.

[0067] It can be understood that in the embodiment of the present application, based on the first operating data, predicting the second operating data of the industrial equipment in the second time period may be implemented in the following two possible ways.

[0068] In a possible implementation, a difference function between the first operating data and the second operating data may be determined, and the difference function may be called based on the first operating data to predict the second operating data.

[0069] In some embodiments, based on the first operating data, predicting the second operating data of the industrial equipment in the second time period may include: obtaining the third operating data of the industrial equipment in the third time period, and the fourth operating data in the fourth time period, the end time of the third time period is earlier than the start time of the fourth time period, and the end time of the fourth time period is earlier than the start time of the second time period; fitting the third operating data and the fourth operating data respectively to obtain a first fitting function and a second fitting function; calculating the difference function between the first fitting function and the second fitting function; and predicting the second operating data based on the difference function and the first operating data.

[0070] It should be noted that the method for fitting data in the embodiments of the present application may include least squares method, Newton's method, bisection method, linear fitting, polynomial fitting, etc., and the embodiments of the present application are not limited to this.

[0071] Exemplarily, the third time period may be a current time period or a historical time period.

[0072] Exemplarily, the fourth time period may be a current time period or a historical time period.

[0073] It should be noted that the third time period may be the same time period as the first time period, or may not be the same time period as the first time period, and this embodiment of the present application does not limit this.

[0074] It should also be noted that the fourth time period may be the same time period as the first time period, or may not be the same time period as the first time period, and this is not limited in the embodiments of the present application.

[0075] It should also be noted that, since the end time of the third time period is earlier than the start time of the fourth time period, and the end time of the fourth time period is earlier than the start time of the second time period, the end time of the third time period is also earlier than the start time of the second time period.

[0076] It should be understood that the fitting function obtained by fitting the operation data (such as the first fitting function obtained by fitting the third operation data) can characterize the change trend of the operation data within a period of time, so as to facilitate the subsequent calculation of the difference function of the operation data in the two time periods before and after through the fitting function.

[0077] Furthermore, calculating the difference function between the first fitting function and the second fitting function may include: performing difference processing on the second fitting function and the first fitting function to obtain the difference function.

[0078] It should be noted that the difference function can be used to characterize the difference between the operating data in two time periods. Therefore, based on the difference function and the first operating data in the first time period, the second operating data in the second time period after the first time period can be obtained.

[0079] Through this method, the difference function of the operating data in the two time periods before and after can be calculated based on the fitting function, and the second operating data can be predicted based on the difference function and the first operating data, so that the potential risk of failure of industrial equipment can be predicted based on the second operating data. This helps to take preventive maintenance measures in advance and avoid production losses and increased maintenance costs caused by industrial equipment failure.

[0080] In some embodiments, predicting the second operating data based on the difference function and the first operating data may include: calculating the difference data between the first operating data and the second operating data based on the difference function and the first operating data; and accumulating the difference data and the first operating data to obtain the second operating data.

[0081] Through this method, the second operating data can be predicted based on the difference function and the first operating data, so that the failure of industrial equipment can be discovered earlier based on the second operating data, so that maintenance measures can be taken in time to reduce the risk of production interruption.

[0082] Another possible implementation method is to use a trained artificial intelligence model to use the operating data of industrial equipment in the previous time period to predict the operating data of industrial equipment in the next time period.

[0083] In some embodiments, predicting second operating data of industrial equipment within a second time period based on first operating data may include: inputting the first operating data into a target artificial intelligence model to obtain second operating data.

[0084] Exemplarily, the target artificial intelligence model may be a trained artificial intelligence model.

[0085] Exemplarily, the target artificial intelligence model can be a long short-term memory network (Long Short-Term Memory, LSTM) model.

[0086] Through this method, based on the trained artificial intelligence model, the first operating data in the current time period or the historical time period can be analyzed to predict the second operating data of the industrial equipment in the future period, so as to identify potential faults of the industrial equipment based on the second operating data, thereby improving the accuracy of fault prediction and facilitating maintenance personnel to formulate maintenance plans more accurately.

[0087] In some embodiments, the method may also include: obtaining fifth operating data of the industrial equipment in a fifth time period, and sixth operating data in a sixth time period, the end time of the fifth time period is earlier than the start time of the sixth time period, and the end time of the sixth time period is earlier than the start time of the second time period; inputting the fifth operating data into the artificial intelligence model to be trained to obtain the seventh operating data of the industrial equipment in the sixth time period; determining the first loss based on the sixth operating data and the seventh operating data; based on the first loss, adjusting the model parameters of the artificial intelligence model to be trained so that the loss obtained based on the adjusted artificial intelligence model meets the convergence conditions to obtain the target artificial intelligence model.

[0088] Exemplarily, the fifth time period may be a current time period or a historical time period.

[0089] Exemplarily, the sixth time period may be a current time period or a historical time period.

[0090] It should be noted that the fifth time period may be the same time period as one or more of the first time period, the third time period, and the fourth time period, or may not be the same time period as the first time period, the third time period, and the fourth time period. This embodiment of the present application does not limit this.

[0091] It should also be noted that the sixth time period may be the same time period as one or more of the first time period, the third time period, and the fourth time period, or may not be the same time period as one or more of the first time period, the third time period, and the fourth time period, and the embodiments of the present application are not limited to this.

[0092] It should also be noted that, since the end time of the fifth time period is earlier than the start time of the sixth time period, and the end time of the sixth time period is earlier than the start time of the second time period, the end time of the fifth time period is also earlier than the start time of the second time period.

[0093] Further, determining the first loss based on the sixth operating data and the seventh operating data may include: performing a difference process on the seventh operating data and the sixth operating data to obtain the first loss.

[0094] It should be understood that the loss obtained by the adjusted artificial intelligence model meets the convergence condition, which can be understood as that the loss obtained by the adjusted artificial intelligence model is less than or equal to the second threshold.

[0095] The second threshold may be set manually or in other ways, and this is not limited in the embodiments of the present application.

[0096] Through this method, by using the operating data of industrial equipment in the current time period or the historical time period to train the artificial intelligence model to be trained, the target artificial intelligence model obtained after training can have a more accurate prediction ability for the second operating data, so that the subsequent prediction of whether the industrial equipment will fail based on the second operating data will also be more accurate, which will help reduce production losses and maintenance costs, and avoid additional costs caused by long-term production interruptions and emergency repairs.

[0097] S120: Calculate the number of data in a fault range corresponding to each fault level of the second operating data in multiple fault levels of the industrial equipment.

[0098] S130: If the second operating data has the largest number of data in the fault range corresponding to the target fault level, predict that the industrial equipment will trigger the target fault level within the second time period.

[0099] It should be noted that the second operation data is a collection of multiple data, and the fault level triggered by the industrial equipment in the second time period can be determined by the number of data of the second operation data located in the fault range corresponding to each fault level.

[0100] It should be understood that the fault range corresponding to each fault level can be understood as the threshold value corresponding to each fault level, that is, the fault range is used to divide the fault levels.

[0101] In the embodiment of the present application, there are two possible implementation methods for determining each fault level and the fault range corresponding to each fault level.

[0102] A possible implementation method is to set each fault level and the fault range corresponding to each fault level based on experience.

[0103] Through this method, each fault level and the fault range corresponding to each fault level can be quickly obtained, so that the fault level triggered by the industrial equipment in the second time period can be quickly predicted, and then it is convenient to take maintenance measures corresponding to the fault level for the industrial equipment in time, thereby reducing the maintenance cost of the industrial equipment, avoiding production line interruptions, and improving the production efficiency of the industrial equipment.

[0104] Another possible implementation manner is to determine each fault level and a fault range corresponding to each fault level based on the operation data of the industrial equipment in the current time period or the historical time period.

[0105] In some embodiments, determining each fault level and the fault range corresponding to each fault level may include: obtaining eighth operating data of the industrial equipment within a seventh time period, and multiple fault types under the eighth operating data, wherein the end time of the seventh time period is earlier than the start time of the second time period; dividing the eighth operating data based on multiple fault types to obtain operating data corresponding to each fault type; determining that each fault type corresponds to a fault level, and based on the operating data corresponding to each fault type, determining the fault range corresponding to each fault level.

[0106] Exemplarily, the seventh time period may be a current time period or a historical time period.

[0107] It should be noted that the seventh time period may be the same time period as one or more of the first time period, the third time period, the fourth time period, the fifth time period, and the sixth time period, or may not be the same time period as the first time period, the third time period, the fourth time period, the fifth time period, and the sixth time period. This is not limited in the embodiments of the present application.

[0108] It should be understood that, since it is necessary to obtain various fault types of industrial equipment through the eighth operation data, in order to make the fault types more comprehensive, the seventh time period is usually required to be longer to make the eighth operation data richer.

[0109] Furthermore, the various fault types of the industrial equipment under the eighth operation data may include: temporary fault, long-term fault and permanent fault.

[0110] For example, temporary faults are generally caused by external factors such as voltage fluctuations and temperature changes. Although such faults will affect the normal operation of industrial equipment, they usually do not have long-term impacts on industrial equipment. Once the external factors return to normal, the industrial equipment can resume normal operation.

[0111] For example, a long-term failure refers to a gradual degradation in the performance of industrial equipment due to fatigue, corrosion, wear and tear of some parts during operation of the industrial equipment.

[0112] For example, permanent faults are usually caused by irreversible changes in components of industrial equipment. Such faults permanently change the original logic of components, and after the fault occurs, it may often endanger industrial equipment and personal safety.

[0113] Further, determining that each fault type corresponds to a fault level may include: determining the fault level corresponding to each fault type based on the fault degree corresponding to each fault type.

[0114] Exemplarily, for a temporary fault, the fault degree corresponding to the temporary fault is relatively light, and thus the fault level corresponding to the temporary fault may be determined as the first fault level.

[0115] Exemplarily, for a long-term fault, the fault degree corresponding to the long-term fault is medium, and thus the fault level corresponding to the long-term fault can be determined as the second fault level.

[0116] Exemplarily, for a permanent fault, the fault degree corresponding to the permanent fault is relatively serious, and thus the fault level corresponding to the permanent fault may be determined as the third fault level.

[0117] It should be noted that, since each fault type corresponds to a fault level, the operating data corresponding to each fault type can be understood as the operating data corresponding to each fault level.

[0118] It should also be noted that determining the fault range corresponding to each fault level based on the operating data corresponding to each fault type may include: determining the fault range corresponding to each fault level based on the maximum and minimum values ​​in the operating data corresponding to each fault level.

[0119] Exemplarily, taking the first fault level as an example, the maximum and minimum values ​​in the operating data corresponding to the first fault level can be used as boundary values ​​of the fault range corresponding to the first fault level, so that the fault range corresponding to the first fault level can be determined based on the boundary values.

[0120] Through this method, each fault level and the fault range corresponding to each fault level can be determined based on the operating data of the industrial equipment in the current time period or the historical time period, which is conducive to predicting the fault level triggered by the industrial equipment in the second time period, and facilitating timely maintenance measures corresponding to the fault level for the industrial equipment, so as to improve the reliability and availability of the industrial equipment and reduce production losses caused by industrial equipment failures.

[0121] S140 . In response to the industrial equipment triggering a target fault level, performing a fault warning operation corresponding to the target fault level on the industrial equipment.

[0122] It should be noted that the target fault level is one of the first fault level, the second fault level and the third fault level.

[0123] In some embodiments, the multiple fault levels include a first fault level, a second fault level, and a third fault level; in response to the industrial device triggering a target fault level, performing a fault warning operation corresponding to the target fault level on the industrial device may include:

[0124] In response to the industrial device triggering the first fault level, no processing is performed on the industrial device; or,

[0125] In response to the industrial equipment triggering the second fault level, reducing the speed and / or load of the industrial equipment; or,

[0126] In response to the industrial equipment triggering the third fault level, the industrial equipment is controlled to stop operating.

[0127] It should be noted that, when the industrial equipment triggers the first fault level, it means that the industrial equipment has a temporary fault in the second time period. This type of fault usually does not cause long-term impact on the industrial equipment, so it can be chosen not to process the industrial equipment.

[0128] It should also be noted that when the industrial equipment triggers the second fault level, it means that the industrial equipment has a long-term fault in the second time period. This type of fault can be delayed by reducing the speed and / or load of the industrial equipment and reducing the load pressure of the components, thereby delaying the further development of the fault and buying time for the repair and maintenance of the industrial equipment. In addition, reducing the speed and / or load can also reduce the wear of the industrial equipment, extend the service life of the industrial equipment, and avoid production interruptions or safety accidents caused by industrial equipment failures.

[0129] It should also be noted that when industrial equipment triggers the third fault level, it means that the industrial equipment has a permanent fault in the second time period. Since this type of fault causes irreversible changes in the components of the industrial equipment, it may often endanger the industrial equipment and personal safety, so it is necessary to control the industrial equipment to stop operating.

[0130] Through this method, based on the target fault level triggered by the industrial equipment in the second time period, a fault warning operation corresponding to the target fault level can be performed on the industrial equipment, thereby reducing production losses and increased maintenance costs caused by industrial equipment failures.

[0131] In some embodiments, the method may further include: in response to the industrial equipment triggering the target fault level, sending warning information to a monitoring person; wherein the warning information is used to prompt the monitoring person to maintain the industrial equipment based on the target fault level.

[0132] Through this method, when industrial equipment triggers the target fault level, it can promptly warn the monitoring personnel so that the monitoring personnel can maintain the industrial equipment based on the target fault level. In this way, the need for manual monitoring and manual warning is eliminated, the response speed and efficiency are improved, and unnecessary maintenance and repair costs are reduced, and the risk of production line interruption is reduced.

[0133] The embodiment of the present application provides a fault warning method, which can predict the operation data of industrial equipment in a future time period based on the operation data of industrial equipment in the current time period or the historical time period, and predict the fault level triggered by the industrial equipment in the future time period based on the operation data of the future time period, so as to perform a fault warning operation corresponding to the fault level. In this way, the fault level of the industrial equipment in the future time period can be accurately predicted, and a fault warning can be performed based on the fault level, thereby maintaining the continuity of the production line.

[0134] The fault warning method provided in the above embodiment is described in detail below in conjunction with specific application scenarios.

[0135] Figure 2 is a schematic block diagram of an execution fault warning provided by an embodiment of the present application. Figure 2 As shown, the block diagram may include a database 210, a server 220, an industrial device 230, an artificial intelligence model to be trained 240, a target artificial intelligence model 250, and an early warning module 260; wherein:

[0136] The database 210, the server 220, the artificial intelligence model to be trained 240, the target artificial intelligence model 250, and the early warning module 260 can be located in the electronic device, and the industrial device 230 can be connected to the server 220 to achieve interaction between the industrial device 230 and the electronic device.

[0137] Exemplarily, the server 220 may collect first operating data of the industrial equipment 230 within a first time period; the database 210 may pre-process (such as cleaning, sorting and storing) the first operating data in the server 220 to filter out redundant data in the first operating data; the server 220 may periodically read the first operating data stored in the database 210 through a script (it can be understood that the first operating data will be continuously updated over time), and send the read first operating data to the target artificial intelligence model 250, so as to output the second operating data of the industrial equipment within a second time period through the target artificial intelligence model 250; the target artificial intelligence model 250 may output the second operating data of the industrial equipment within a second time period ... first operating data of the industrial equipment within a first time period; the target artificial intelligence model 250 may output the first operating data of the industrial equipment within a first time period; the target artificial intelligence model 250 may output the first operating data of the industrial equipment within a first time period; the 50 can send the second operating data to the server 220; after receiving the second operating data, the server 220 can calculate the number of data in the fault range corresponding to each fault level of the second operating data in the multiple fault levels of the industrial equipment 230, and predict that the industrial equipment 230 will trigger the target fault level within the second time period when the number of data in the fault range corresponding to the target fault level of the second operating data is the largest; the server can notify the early warning module 260: the target fault level triggered by the industrial equipment 230 within the second time period; the early warning module 260 can perform a fault early warning operation corresponding to the target fault level on the industrial equipment 230.

[0138] In addition, the server 220 can collect the fifth operating data of the industrial equipment 230 within the fifth time period, and the sixth operating data within the sixth time period; the database 210 can pre-process the fifth operating data and the sixth operating data in the server 220 (such as cleaning, sorting and storing) to filter out redundant data in the fifth operating data and the sixth operating data; the server 220 can read the fifth operating data and the sixth operating data stored in the database 210 through a script at regular intervals (it can be understood that the fifth operating data and the sixth operating data will be continuously updated over time), and send the read fifth operating data and the sixth operating data to the artificial intelligence model 240 to be trained; the artificial intelligence model 240 to be trained can adjust the model parameters based on the fifth operating data and the sixth operating data so that the loss obtained based on the adjusted artificial intelligence model meets the convergence conditions to obtain the target artificial intelligence model 250.

[0139] Through this method, on the one hand, the server can realize real-time monitoring and collection of the operating data of industrial equipment; the database can pre-process the operating data collected by the server so that the server can obtain more comprehensive and accurate operating data, providing a basis for the subsequent target artificial intelligence model to predict the operating data of industrial equipment in future time periods; on the other hand, by combining the operating data obtained by the server with the trained target artificial intelligence model, it is possible to monitor the health status and potential failure risks of industrial equipment in real time, thereby ensuring the continuity of the production line.

[0140] The preferred embodiments of the present application are described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the specific details in the above embodiments. Within the technical concept of the present application, the technical solution of the present application can be subjected to a variety of simple modifications, and these simple modifications all belong to the protection scope of the present application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present application will not further explain various possible combinations. For another example, the various different embodiments of the present application can also be arbitrarily combined, as long as they do not violate the idea of ​​the present application, they should also be regarded as the contents disclosed in the present application. For another example, the various embodiments and / or the technical features in the various embodiments described in the present application can be arbitrarily combined with the prior art without conflict, and the technical solution obtained after the combination should also fall within the protection scope of the present application.

[0141] It should also be understood that in the various method embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0142] Based on the same inventive concept as the above embodiments, Figure 3 is a schematic diagram of the structure of a fault warning device provided in an embodiment of the present application, such as Figure 3 As shown, the fault warning device may include:

[0143] An acquisition module 310 is used to acquire first operation data of an industrial device in a first time period;

[0144] A prediction module 320, configured to predict second operation data of the industrial equipment in a second time period based on the first operation data, wherein the end time of the first time period is earlier than the start time of the second time period;

[0145] A calculation module 330, configured to calculate the number of data in a fault range corresponding to each fault level of the second operation data in multiple fault levels of the industrial equipment;

[0146] The prediction module 320 is further configured to predict that the industrial device will trigger the target fault level within the second time period if the number of data of the second operation data located in the fault range corresponding to the target fault level is the largest;

[0147] The execution module 340 is used to execute a fault warning operation corresponding to the target fault level on the industrial equipment in response to the industrial equipment triggering the target fault level.

[0148] In some embodiments, the fault warning device may further include a fitting module 350, wherein:

[0149] The acquisition module 310 is further configured to acquire third operation data of the industrial equipment in a third time period, and fourth operation data in a fourth time period, wherein the end time of the third time period is earlier than the start time of the fourth time period, and the end time of the fourth time period is earlier than the start time of the second time period;

[0150] A fitting module 350, used to fit the third operation data and the fourth operation data respectively to obtain a first fitting function and a second fitting function;

[0151] The calculation module 330 is further used to calculate the difference function between the first fitting function and the second fitting function;

[0152] The prediction module 320 is further configured to predict the second operation data based on the difference function and the first operation data.

[0153] In some embodiments, the calculation module 330 is further used to calculate the difference data between the first operation data and the second operation data based on the difference function and the first operation data; and accumulate the difference data and the first operation data to obtain the second operation data.

[0154] In some embodiments, the execution module 340 is also used to input the first operating data into the target artificial intelligence model to obtain second operating data.

[0155] In some embodiments, the fault warning device may further include a determination module 360, wherein:

[0156] The acquisition module 310 is further configured to acquire fifth operation data of the industrial equipment in a fifth time period, and sixth operation data in a sixth time period, wherein the end time of the fifth time period is earlier than the start time of the sixth time period, and the end time of the sixth time period is earlier than the start time of the second time period;

[0157] The execution module 340 is further used to input the fifth operation data into the artificial intelligence model to be trained to obtain seventh operation data of the industrial equipment in a sixth time period;

[0158] A determination module 360, configured to determine a first loss based on the sixth operation data and the seventh operation data;

[0159] The execution module 340 is also used to adjust the model parameters of the artificial intelligence model to be trained based on the first loss, so that the loss obtained based on the adjusted artificial intelligence model meets the convergence condition to obtain the target artificial intelligence model.

[0160] In some embodiments, the fault warning device may further include a division module 370, wherein:

[0161] The acquisition module 310 is further used to acquire eighth operation data of the industrial equipment in a seventh time period, and multiple fault types under the eighth operation data, wherein the end time of the seventh time period is earlier than the start time of the second time period;

[0162] A division module 370, configured to divide the eighth operation data based on multiple fault types to obtain operation data corresponding to each fault type;

[0163] The determination module 360 ​​is further used to determine that each fault type corresponds to a fault level, and based on the operation data corresponding to each fault type, determine the fault range corresponding to each fault level.

[0164] In some embodiments, the multiple fault levels include a first fault level, a second fault level, and a third fault level; the execution module 340 is also used to, in response to the industrial equipment triggering the first fault level, not process the industrial equipment; or, in response to the industrial equipment triggering the second fault level, reduce the speed and / or load of the industrial equipment; or, in response to the industrial equipment triggering the third fault level, control the industrial equipment to stop operating.

[0165] In some embodiments, the fault warning device may further include a communication module 380, wherein:

[0166] The communication module 380 is used to send warning information to the monitoring personnel in response to the industrial equipment triggering the target fault level; wherein the warning information is used to prompt the monitoring personnel to maintain the industrial equipment based on the target fault level.

[0167] In the solution of the embodiment of the present application, the operation data of the industrial equipment in the future time period can be predicted based on the operation data of the industrial equipment in the current time period or the historical time period, and the fault level triggered by the industrial equipment in the future time period can be predicted based on the operation data of the future time period, so as to perform the fault warning operation corresponding to the fault level. In this way, the fault level of the industrial equipment in the future time period can be accurately predicted, and the fault warning can be performed based on the fault level, thereby maintaining the continuity of the production line.

[0168] Those skilled in the art should understand that the relevant description of the above-mentioned fault warning device in the embodiment of the present application can be understood by referring to the relevant description of the fault warning method in the embodiment of the present application.

[0169] Figure 4It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: a processor (Processor) 410, a communication interface (Communications Interface) 420, a memory (Memory) 430 storing a program 450, and a communication bus 440.

[0170] The processor 410 , the communication interface 420 , and the memory 430 communicate with each other via the communication bus 440 .

[0171] The communication interface 420 is used to communicate with other electronic devices or servers.

[0172] The processor 410 is used to execute the program 450, and specifically can execute the relevant steps in the above method embodiment.

[0173] Specifically, the program 450 may include a program 450 code, which includes one or more executable computer operation instructions.

[0174] The processor 410 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0175] The memory 430 is used to store one or more executable instructions. The memory 430 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (Non-Volatile Memory), such as one or more disk memories.

[0176] One or more executable instructions may be specifically used to enable the processor 410 to execute the method provided in the embodiment of the present application.

[0177] In addition, the specific implementation of each step in one or more executable instructions can refer to the corresponding description of the corresponding steps and units in the above method embodiments, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above method embodiments, which will not be repeated here.

[0178] An embodiment of the present application also provides a computer-readable storage medium for storing a computer program.

[0179] In some embodiments, the computer-readable storage medium can be applied to the electronic device in the embodiments of the present application, and when the computer program is executed by one or more processors, it implements the corresponding processes implemented by the electronic device in the various methods of the embodiments of the present application. For the sake of brevity, they are not repeated here.

[0180] An embodiment of the present application also provides a computer program product, including computer program instructions.

[0181] In some embodiments, the computer program product can be applied to the electronic device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the electronic device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0182] The embodiment of the present application also provides a computer program.

[0183] In some embodiments, the computer program can be applied to the electronic devices in the embodiments of the present application. When the computer program runs on a computer, the computer executes the corresponding processes implemented by the electronic devices in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0184] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of this application.

[0185] It should be noted that, in this application, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0186] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0187] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0188] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0189] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0190] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. A fault warning method, characterized in that: include: Acquire first operating data of the industrial equipment in a first time period, and predict second operating data of the industrial equipment in a second time period based on the first operating data, wherein the end time of the first time period is earlier than the start time of the second time period (S110); Calculating the number of data in a fault range corresponding to each fault level of the industrial equipment where the second operating data is located (S120); If the second operating data has the largest number of data in the fault range corresponding to the target fault level, predicting that the industrial equipment will trigger the target fault level within the second time period (S130); In response to the industrial device triggering the target fault level, a fault warning operation corresponding to the target fault level is performed on the industrial device ( S140 ).

2. The method according to claim 1, characterized in that The predicting, based on the first operating data, second operating data of the industrial equipment in a second time period includes: Acquire third operating data of the industrial equipment in a third time period, and fourth operating data in a fourth time period, wherein the end time of the third time period is earlier than the start time of the fourth time period, and the end time of the fourth time period is earlier than the start time of the second time period; Fitting the third operating data and the fourth operating data respectively to obtain a first fitting function and a second fitting function; Calculating a difference function between the first fitting function and the second fitting function; The second operating data is predicted based on the difference function and the first operating data.

3. The method according to claim 2, characterized in that The predicting the second operating data based on the difference function and the first operating data includes: Calculating difference data between the first operating data and the second operating data based on the difference function and the first operating data; The difference data and the first operation data are accumulated to obtain the second operation data.

4. The method according to claim 1, characterized in that The predicting, based on the first operating data, second operating data of the industrial equipment in a second time period includes: The first operating data is input into a target artificial intelligence model to obtain the second operating data.

5. The method according to claim 4, characterized in that The method further comprises: Acquire fifth operating data of the industrial equipment in a fifth time period and sixth operating data in a sixth time period, wherein the end time of the fifth time period is earlier than the start time of the sixth time period, and the end time of the sixth time period is earlier than the start time of the second time period; Inputting the fifth operating data into the artificial intelligence model to be trained to obtain seventh operating data of the industrial equipment within the sixth time period; determining a first loss based on the sixth operating data and the seventh operating data; Based on the first loss, the model parameters of the artificial intelligence model to be trained are adjusted so that the loss obtained based on the adjusted artificial intelligence model meets the convergence condition to obtain the target artificial intelligence model.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Acquire eighth operating data of the industrial equipment in a seventh time period, and multiple fault types under the eighth operating data, wherein the end time of the seventh time period is earlier than the start time of the second time period; Dividing the eighth operation data based on the multiple fault types to obtain operation data corresponding to each fault type; It is determined that each fault type corresponds to a fault level, and based on the operation data corresponding to each fault type, a fault range corresponding to each fault level is determined.

7. The method according to any one of claims 1 to 5, characterized in that The plurality of fault levels include a first fault level, a second fault level, and a third fault level; In response to the industrial equipment triggering the target fault level, performing a fault warning operation corresponding to the target fault level on the industrial equipment, including: In response to the industrial device triggering the first fault level, no processing is performed on the industrial device; or, In response to the industrial equipment triggering the second fault level, reducing the rotation speed and / or load of the industrial equipment; or, In response to the industrial equipment triggering the third fault level, the industrial equipment is controlled to stop operating.

8. The method according to any one of claims 1 to 5, characterized in that The method further comprises: In response to the industrial equipment triggering the target fault level, sending an early warning message to a monitoring person; The warning information is used to prompt the monitoring personnel to maintain the industrial equipment based on the target fault level.

9. A fault warning device, characterized in that: include: An acquisition module (310), configured to acquire first operating data of the industrial equipment within a first time period; A prediction module (320), configured to predict second operation data of the industrial equipment within a second time period based on the first operation data, wherein the end time of the first time period is earlier than the start time of the second time period; A calculation module (330) is used to calculate the number of data in a fault range corresponding to each fault level of the second operation data in a plurality of fault levels of the industrial equipment; The prediction module (320) is further configured to predict that the industrial equipment will trigger the target fault level within the second time period if the number of data of the second operation data in the fault range corresponding to the target fault level is the largest; An execution module (340) is used for executing a fault warning operation corresponding to the target fault level on the industrial equipment in response to the industrial equipment triggering the target fault level.

10. An electronic device comprising: A processor (410), a memory (430), a communication interface (420) and a communication bus (440), wherein the processor (410), the memory (430) and the communication interface (420) communicate with each other via the communication bus (440); the memory (430) is used to store one or more executable instructions, wherein the executable instructions enable the processor to execute the method according to any one of claims 1 to 8.

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