Equipment failure warning method, device, equipment and storage medium

Abstract

The present application discloses a method, apparatus, equipment and storage medium for warning of equipment failure, which relates to the technical field of equipment data processing, and discloses: obtaining operating data of target industrial equipment and associated data of each operating data; performing anomaly analysis based on the correlation between the operating data and the associated data to obtain abnormal data; performing graph correlation analysis on the abnormal data based on a preset fault map to obtain the correlation of the fault type; determining the warning level and predicting the fault type based on the correlation of the fault type; performing fault warning on the target industrial equipment based on the warning level and the predicted fault type; collecting various sensor data of industrial equipment, performing abnormal data map analysis based on the correlation of each data at different faults in the fault map, identifying the root cause of the fault, and then quantifying the correlation degree of different fault types, determining the fault type and determining the warning level, thereby improving the accuracy and efficiency of fault identification and performing equipment maintenance in advance based on potential fault problems.

Description

Technical Field

[0001] The present application relates to the field of equipment data processing technology, and in particular to equipment failure early warning methods, devices, equipment and storage media. Background Art

[0002] With the widespread adoption of IoT technology, modern industrial production is increasingly reliant on highly automated and intelligent equipment. These devices generate vast amounts of operational data, including but not limited to temperature, pressure, vibration, current, and other parameters. Through IoT technology, this data can be collected and transmitted to central processing systems in real time, providing a rich source of data for analysis.

[0003] With the advancement of big data and artificial intelligence technologies, companies are increasingly adopting a data-driven approach to decision-making. Modern industrial production requires equipment to operate stably and for extended periods of time, and any unexpected downtime can result in significant economic losses. Traditional preventive maintenance typically involves inspecting and maintaining equipment at fixed intervals, which is often inflexible and prone to wasting resources. In contrast, predictive maintenance focuses on predicting potential failures through real-time monitoring of equipment status, allowing for proactive action before they occur. For industrial equipment, this requires extracting valuable information from massive amounts of operational data to support maintenance decisions. However, during factory equipment maintenance, due to the large volume of data generated by the large number of devices and the complex nature of the failure types, it is difficult to quickly and accurately identify potential equipment failures.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide an equipment fault warning method, device, equipment and storage medium, aiming to solve the technical problem that during the equipment maintenance process of the factory, due to the large amount of equipment data and the complexity of fault types, it is impossible to quickly and accurately identify possible faults in the equipment.

[0006] To achieve the above objectives, the present application proposes a device failure early warning method, which includes:

[0007] Obtaining the operating data of target industrial equipment and the associated data of each operating data;

[0008] Performing anomaly analysis based on the correlation between the operating data and the correlation data to obtain abnormal data;

[0009] Performing graph correlation analysis on the abnormal data according to a preset fault map to obtain a fault type correlation degree;

[0010] Determine the warning level and predicted fault type according to the fault type correlation degree;

[0011] A fault warning is performed on the target industrial equipment based on the warning level and the predicted fault type.

[0012] In one embodiment, performing graph correlation analysis on the abnormal data according to a preset fault map to obtain a fault type correlation degree includes:

[0013] identifying abnormal points among the abnormal data;

[0014] Obtaining anomaly weights between each abnormal data based on the abnormal point;

[0015] Determining preset fault data in a preset fault map according to the abnormality weight;

[0016] The correlation degree of each fault type is determined according to the preset fault data.

[0017] In one embodiment, determining the preset fault data in the preset fault map according to the abnormality weight includes:

[0018] Extracting abnormal features of the abnormal data, matching the abnormal features with preset fault data, and obtaining initial fault data;

[0019] sorting the initial fault data according to the abnormal weight of the fault data to obtain reference fault data;

[0020] The reference fault data with a weight greater than a preset weight threshold in the reference fault data is used as the preset fault data.

[0021] In one embodiment, determining the correlation between each fault type according to the preset fault data includes:

[0022] Obtaining preset fault data associated with each fault type in the preset fault map;

[0023] Obtaining the failure rate of each failure type according to the weight of the preset failure data;

[0024] The correlation degree of each fault type is obtained according to the abnormal weights between the abnormal data.

[0025] In one embodiment, obtaining the correlation between each fault type according to the abnormal weights between each abnormal data includes:

[0026] Get weight threshold;

[0027] Comparing the weight threshold with the abnormal weights of the abnormal data to obtain a comparison result;

[0028] Obtaining the number of connected nodes of each abnormal point according to the comparison result;

[0029] The correlation degree of each fault type is obtained according to the number of connected nodes and the fault rate.

[0030] In one embodiment, after obtaining the correlation degree of each fault type according to the number of connected nodes and the fault rate, the method further includes:

[0031] When the correlation degree is greater than or equal to the correlation degree threshold, the steps of determining the warning level and predicting the fault type according to the fault type correlation degree are performed;

[0032] When the correlation degree is less than a correlation degree threshold, generating an abnormal signal according to the abnormal data, and performing an abnormal alarm based on the abnormal signal;

[0033] Obtaining a reference abnormality type based on the abnormal alarm feedback from the user and generating an abnormality release signal;

[0034] The preset fault map is updated according to the reference abnormality type and the abnormality relief signal.

[0035] In one embodiment, determining the warning level and predicting the fault type according to the fault type correlation includes:

[0036] The fault type with the greatest fault type correlation is taken as the predicted fault type;

[0037] Obtaining a data abnormality degree according to the abnormal data and a preset data standard value;

[0038] A preset fault level table is matched with the data abnormality to obtain a warning level, wherein the preset fault level table includes one-to-one correspondence between the data abnormality and the fault level.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes an equipment failure early warning device, which includes:

[0040] A data acquisition module is used to acquire the operating data of the target industrial equipment and the associated data of each operating data;

[0041] a fault analysis module, configured to perform an anomaly analysis based on the correlation between the operating data and the correlation data to obtain abnormal data;

[0042] The fault analysis module is further configured to perform a graph correlation analysis on the abnormal data according to a preset fault graph to obtain a fault type correlation degree;

[0043] A fault warning module is used to determine the warning level and predict the fault type according to the correlation degree of the fault type;

[0044] The fault warning module is used to provide a fault warning to the target industrial equipment based on the warning level and the predicted fault type.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes an equipment failure warning device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the equipment failure warning method as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the equipment failure warning method described above are implemented.

[0047] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the equipment failure early warning method as described above.

[0048] One or more technical solutions proposed in this application have at least the following technical effects:

[0049] By collecting various sensor data of industrial equipment in real time and analyzing abnormal data maps based on the correlation between various data of the equipment according to the correlation between various data at different faults through fault maps, the root cause of the problem can be identified, thereby improving the accuracy and efficiency of diagnosis; and then quantifying the degree of correlation between different fault types, determining the fault type and the warning level, quickly and accurately identifying potential fault problems of the equipment and taking preventive measures, and being able to issue timely and effective alarms to operators to avoid serious equipment damage or production interruptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1 A flowchart of the first embodiment of the device failure warning method of this application is provided;

[0053] Figure 2A flow chart illustrating a second embodiment of the device failure warning method of this application;

[0054] Figure 3 A schematic diagram of a preset fault spectrum provided in Example 2 of the device fault warning method of this application;

[0055] Figure 4 This is a schematic diagram of the module structure of the equipment failure warning device according to an embodiment of the present application;

[0056] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the device failure warning method in the embodiment of the present application. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0058] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of the embodiment of the present application is: obtaining the operating data of the target industrial equipment and the associated data of each operating data; performing anomaly analysis based on the correlation between the operating data and the associated data to obtain abnormal data; performing graph correlation analysis on the abnormal data based on a preset fault map to obtain the fault type correlation; determining the warning level and predicted fault type based on the fault type correlation; and performing fault warning on the target industrial equipment based on the warning level and predicted fault type.

[0060] In this embodiment, for ease of description, the following description is made with the device for identifying device failures and providing early warning as the execution subject.

[0061] With the widespread adoption of existing technologies and the Internet of Things (IoT), modern industrial production is increasingly reliant on highly automated and intelligent equipment. These devices generate vast amounts of operational data, including but not limited to temperature, pressure, vibration, and current. Through IoT technology, this data can be collected and transmitted to central processing systems in real time, providing a rich source of data for analysis.

[0062] With the advancement of big data and artificial intelligence technologies, companies are increasingly adopting a data-driven approach to decision-making. Modern industrial production requires equipment to operate stably and for extended periods of time, and any unexpected downtime can result in significant economic losses. Traditional preventive maintenance typically involves inspecting and maintaining equipment at fixed intervals, which is often inflexible and prone to wasting resources. In contrast, predictive maintenance focuses on predicting potential failures through real-time monitoring of equipment status, allowing for proactive action before they occur. For industrial equipment, this requires extracting valuable information from massive amounts of operational data to support maintenance decisions. However, during factory equipment maintenance, due to the large volume of data generated by the large number of devices and the complex nature of the failure types, it is difficult to quickly and accurately identify potential equipment failures.

[0063] This application provides a solution that collects various sensor data from industrial equipment, performs abnormal data map analysis based on the correlation of various data at different fault times in the fault map, identifies the root cause of the fault, and then quantifies the degree of correlation between different fault types, determines the fault type and the warning level, improves the accuracy and efficiency of fault identification, and performs equipment maintenance in advance based on potential fault problems.

[0064] It can be seen from the above embodiments that the present application discloses an equipment fault warning method, device, equipment and storage medium, which relates to the field of equipment data processing technology, and discloses: obtaining the operating data of the target industrial equipment and the associated data of each operating data; performing anomaly analysis based on the correlation between the operating data and the associated data to obtain abnormal data; performing graph correlation analysis on the abnormal data according to a preset fault map to obtain the fault type correlation; determining the warning level and predicted fault type based on the fault type correlation; performing fault warning on the target industrial equipment based on the warning level and predicted fault type; by collecting various sensor data of industrial equipment, performing abnormal data map analysis based on the correlation of each data at different faults in the fault map, identifying the root cause of the fault, and then quantifying the correlation degree of different fault types, determining the fault type and determining the warning level, improving the accuracy and efficiency of fault identification and performing equipment maintenance in advance based on potential fault problems.

[0065] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, such as a device failure warning device. This embodiment and the following embodiments will be described below using a device failure warning device as an example.

[0066] Based on this, the embodiment of the present application provides a device failure warning method, referring to Figure 1 , Figure 1This is a flow chart of the first embodiment of the device failure early warning method of the present application.

[0067] In this embodiment, the device failure early warning method includes steps S10 to S40:

[0068] Step S10: Acquire the operating data of the target industrial equipment and the associated data of each operating data.

[0069] Understandably, comprehensive monitoring and analysis of target industrial equipment requires an effective method for acquiring operational data and related context. Specifically, various sensors and monitoring devices are used to collect real-time data generated by the target industrial equipment during operation, such as temperature, pressure, speed, and vibration. This data reflects the equipment's operating status and performance, and is crucial for subsequent equipment maintenance and management.

[0070] It should be understood that contextual data can include equipment operating environments, production tasks, maintenance records, and more. By studying and analyzing this contextual data, we can gain a deeper understanding of equipment operating conditions, identify potential problems and hidden dangers, and take proactive measures to ensure normal equipment operation and production efficiency.

[0071] Step S20: performing an abnormality analysis based on the correlation between the operating data and the correlation data to obtain abnormal data.

[0072] It is understandable that operating data may be affected by related data and change. For example, the temperature of industrial equipment will be higher when the ambient temperature is high. When the temperature is high, short circuits are more likely to occur, and electronic components may be damaged by high temperature, which may lead to abnormal operating data.

[0073] It is understandable that, similarly, not all operating data will be affected by associated data. When collecting operating data of industrial equipment, associated data with a high degree of correlation with the operating data of each industrial equipment is also collected.

[0074] It should be emphasized that there can be multiple or only one associated data for the operating data. The correlation between the operating data and the associated data is analyzed to determine whether there is abnormal data or whether the data is abnormal due to the influence of the associated data.

[0075] It should be understood that before performing the abnormality analysis, the associated data of each operating data may be determined through a test experiment, and a weight, ie, a correlation degree, may be set according to the correlation between each operating data and each associated data.

[0076] In a specific implementation, the abnormal data obtained by performing an anomaly analysis based on the correlation between the operating data and the associated data can be obtained by first comparing the real-time operating data of the industrial equipment with the normal data when the industrial equipment is running; and setting a data deviation ratio for each operating data. When the deviation between the real-time operating data and the normal data exceeds the deviation ratio, it is considered that the real-time operating data may be abnormal.

[0077] Furthermore, when it is considered that the real-time operation data may be abnormal, based on the pre-set correlation data of each operation data, it is further determined whether the abnormality of the current operation data is caused by the current correlation data. For example, if the operating temperature of the equipment is too high, which is related to the ambient temperature and the operating time, and the correlation between the operating time and the ambient temperature is different, then the temperature deviation between the current ambient temperature and the normal temperature; the temperature rise correlation between the operating time and the temperature increase; and the weight of the temperature deviation and the temperature rise correlation are used to calculate whether the current operation data is abnormal. If abnormal, the data is regarded as abnormal data. The calculation method for further determining whether the abnormality of the current operation data is caused by the current correlation data can refer to the following formula:

[0078]

[0079] Among them, P represents the degree of abnormality, θ represents the associated data, Indicates the standard data of θ1.

[0080] Furthermore, an abnormality threshold is set based on the abnormality degree P, and when the abnormality degree is greater than or equal to the abnormality threshold, the operating data is used as an abnormality parameter.

[0081] Step S30 : performing graph correlation analysis on the abnormal data according to a preset fault graph to obtain a fault type correlation degree.

[0082] It is understood that the preset fault map can be a pre-established fault data structure diagram for various equipment fault types, for example, abnormal data types a, b, and c corresponding to fault A. The data range under the structure a specifically includes, the data range under the structure b specifically includes, the data range under the structure c specifically includes, and other data types may also be included under the structures ac, thereby forming the preset fault map.

[0083] It should be understood that performing graph association analysis on the abnormal data according to the preset fault map may be locating the abnormal data from the preset fault map, and then calculating the abnormal correlation degree between the abnormal data and each abnormal category based on the fault type associated with the abnormal data in the preset fault map.

[0084] It should be noted that multiple fault categories are predetermined in the preset fault map, and the fault data generated when the fault type occurs is obtained through testing, and the various related data associated with the fault data are further associated, and the relationship between each fault data and each fault-related data is set in advance with an associated weight or correlation degree based on expert opinions, and a correlation degree or association weight is also set between each fault type and fault data as a calculation parameter for whether the data will cause a failure of this type of equipment.

[0085] It should be emphasized that this matching degree can be quantified by various statistical methods or machine learning algorithms, such as similarity calculation, pattern recognition technology or fault diagnosis model.

[0086] Step S40: determining the warning level and the predicted fault type according to the fault type correlation.

[0087] It is understandable that whether the fault occurrence threshold is exceeded is determined based on the correlation degree, and if exceeded, it is determined that a corresponding device fault may occur.

[0088] It should be understood that, when it is determined that a fault may occur, the type of the fault that may occur in the preset fault map is used as the predicted fault type.

[0089] It should be noted that a normal value can be set in advance for each abnormal data. The warning level is evaluated based on the percentage of the abnormal data exceeding the normal value or the size of the abnormal data exceeding the normal value. Different warning levels can give different warning feedback, such as voice reminders, data abnormality reporting, emergency reminders and other different types of warnings.

[0090] In a feasible implementation, step S40 may include steps A41 to A44:

[0091] Step A41: The fault type with the highest fault type correlation is used as the predicted fault type.

[0092] It is understandable that a higher correlation degree indicates a higher matching degree between the fault type and the current symptom, thereby making it possible to determine which fault type is most likely to cause the current problem.

[0093] It should be noted that based on the preset fault map, we can obtain the correlation value between each data point and each fault type. This value reflects the degree of match between the abnormal data and the possible equipment fault. The fault type with the highest correlation can be selected as the predicted fault type.

[0094] It is understandable that the predicted fault type can be the most likely cause based on the current performance of the equipment. Selecting this fault type as the basis for prediction can greatly improve the efficiency and accuracy of subsequent maintenance work, avoid unnecessary disassembly and inspection, reduce maintenance costs, and speed up the equipment's return to operation.

[0095] It should be emphasized that it is also possible to provide feedback on multiple possible predicted fault types with relatively high probability, which can narrow the range of equipment fault types, and at the same time as providing early warning of equipment faults, it can also speed up the efficiency of equipment fault location.

[0096] Step A42: Obtain the data abnormality degree according to the abnormal data and a preset data standard value.

[0097] It is understandable that the preset data standard value may be a preset value that each operating data has under normal circumstances.

[0098] It should be understood that if the real-time collected operating data is close to the preset data standard value, the data can be considered normal, and the more it deviates from the preset data standard value, the more abnormal the operating data can be considered.

[0099] It should be noted that the calculation of the preset data standard value can be based on the pre-collected operating data of the industrial equipment during normal operation, and multiple data of each operating data are calculated to obtain an average value, and the average value is used as the preset data standard value.

[0100] It should be emphasized that the calculation of data anomaly can refer to the following formula:

[0101]

[0102] Among them, w represents the data abnormality, x i Indicates abnormal data, Indicates the preset data standard value of the abnormal data.

[0103] Step A43 : Matching a preset fault level table with the data abnormality to obtain a warning level, wherein the preset fault level table includes a one-to-one correspondence between the data abnormality and the fault level.

[0104] It is understood that the fault levels include normal, level 1, level 2, and level 3.

[0105] Furthermore, the preset fault level table is matched with the predicted fault type to obtain the initial warning level, which can refer to the following formula:

[0106]

[0107] Among them, the abnormal data is compared with the preset data standard value. If the abnormal data is greater than or less than the preset data standard value by 8%, it can be considered that the abnormal data is a normal deviation; if the abnormal data is greater than or less than the preset data standard value by 20%, the abnormality of the abnormal data can be considered as a first-level abnormality; if the abnormal data is greater than or less than the preset data standard value by 40%, the abnormality of the abnormal data can be considered as a second-level abnormality; if the abnormal data is greater than or less than the preset data standard value by more than 60%, the abnormality of the abnormal data can be considered as a third-level abnormality.

[0108] It is understandable that the classification of data anomaly levels and the data anomaly levels corresponding to the degree of deviation from the preset standard value can be adjusted. This embodiment does not limit this and can be adjusted according to actual conditions.

[0109] In this implementation, by collecting equipment operating data, monitoring its performance indicators, and analyzing any abnormal signals or symptoms to accurately and quickly identify problems, the maintenance team will use historical failure data and known failure modes to compare and determine the type of equipment abnormality.

[0110] The above is merely a feasible implementation of step S40 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S40.

[0111] Step S50 : performing a fault warning on the target industrial equipment based on the warning level and the predicted fault type.

[0112] It is understandable that performing fault warning according to the warning level and the predicted fault type may be determining a warning strategy according to the warning level.

[0113] It should be noted that the warning strategy does not issue a warning when the warning level is normal; when the warning level is level one, abnormal data is recorded and abnormal marks are made; when the warning level is level two, a text fault reminder is made based on the predicted fault type; when the warning level is level three, a voice alarm and text voice reminder are made and abnormal data is fed back for user viewing.

[0114] This embodiment provides an equipment fault early warning method, which collects various sensor data from industrial equipment, performs abnormal data map analysis based on the correlation of various data at different fault times in the fault map, identifies the root cause of the fault, and then quantifies the degree of correlation between different fault types. The fault type and warning level are determined, thereby improving the accuracy and efficiency of fault identification and performing equipment maintenance in advance based on potential fault problems.

[0115] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , step S30, the equipment failure early warning method further includes steps S31 to S34:

[0116] In this embodiment, the device failure early warning method includes steps S10 to S40:

[0117] Step S31: identifying abnormal points among the abnormal data.

[0118] It is understandable that the abnormal data may be a plurality of data, and the abnormal point may be the location of the abnormal data with an abnormal degree higher than the abnormal threshold among the various abnormal data in the preset fault map.

[0119] It should be understood that identifying abnormal points facilitates locating the data that mainly generates abnormalities among a plurality of abnormal data, thereby facilitating analyzing the causes of data abnormalities based on the data that generates the main abnormalities.

[0120] It should be noted that there can be one or more abnormal points. Any abnormal data point with an abnormality level higher than an abnormality threshold in a preset fault map can be considered an abnormal point. The abnormality threshold can be 160% of the standard data value of the abnormal data, in which case the abnormal data point can be considered an abnormal point.

[0121] Step S32: obtaining anomaly weights between each abnormal data based on the abnormal point.

[0122] It is understandable that after determining the abnormal point, the abnormal data can be located in the preset fault map, and the same abnormal data may have different weights in different fault types.

[0123] It should be understood that after the abnormal point is determined, the preset correlation between each abnormal data and the abnormal point under the graph of each abnormal type is used as the abnormal weight between the abnormal data and the abnormal point.

[0124] It should be emphasized that the abnormal weights between the abnormal point and each abnormal data under the preset map of each possible abnormal type are analyzed respectively.

[0125] Step S33: determining preset fault data in a preset fault map according to the abnormality weight.

[0126] It should be noted that in the preset map, the correlation between various fault types and various operating data when abnormalities occur can be set. For example, the correlation between fault A and the abnormality of data a is 50%. It can be understood that the probability of fault A occurring when data a is abnormal is 50%. Furthermore, if the abnormality of data a is 50%, the correlation of the current data a with the occurrence of fault A is 25%.

[0127] It should be further explained that if there are multiple abnormalities in the associated data of fault A, the abnormal correlation degrees of the multiple associated data are added together as the weight of the occurrence of fault A.

[0128] In a feasible implementation, step S33 may include steps A331 to A333:

[0129] Step A331 : extracting abnormal features of the abnormal data, matching the abnormal features with preset fault data, and obtaining initial fault data.

[0130] It is understandable that abnormal features can be numerical features and time features, and feature extraction is performed from two directions: time and numerical size.

[0131] It should be understood that the initial fault data may be analyzed based on the weights between the various abnormal data to determine which abnormal data is the most important among the various abnormal data, so that the fault type can be better located based on the analysis of the main abnormal data.

[0132] It should be noted that matching the abnormal characteristics with the preset fault data can be matching the abnormal characteristics with the numerical characteristics in each fault type. Different fault types have different standards for the same operating data. For example, the normal standard for data a in fault type A is 5, and the normal standard for data a in fault type B is 10.

[0133] It should be emphasized that matching the abnormal characteristics with the preset fault data may be matching the data associated with each abnormal point to obtain the initial fault data associated with the abnormal point.

[0134] Step A332: sort the initial fault data according to the abnormal weight of the fault data to obtain reference fault data.

[0135] It is understandable that the weights of various initial fault data and abnormal points in the fault type are not the same, and the initial fault data needs to be reduced in redundancy according to the weights to avoid excessive data volume.

[0136] It should be understood that the initial fault data may be sorted according to the correlation between the initial fault data and the abnormal points.

[0137] Step A333: Using the reference fault data whose weight is greater than a preset weight threshold as the preset fault data.

[0138] It is understandable that the preset weight threshold may be a pre-set evaluation value of the degree of association between two data, and may be adjusted according to actual conditions, which is not limited in this embodiment.

[0139] In this embodiment, the weights between the various fault data in the preset fault map are used to reduce the redundancy of the abnormal data, reduce the amount of data calculation, and evaluate each fault type through data that is more representative of the fault type.

[0140] The above is only a feasible implementation of step S33 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S33.

[0141] Step S34: determining the correlation degree of each fault type according to the preset fault data.

[0142] In a feasible implementation, step S34 may include steps A341 to A343:

[0143] Step A341: Obtain preset fault data associated with each fault type in the preset fault map.

[0144] It is understandable that the preset fault data may be one or more.

[0145] Step A342: Obtain the failure rate of each failure type according to the weight of the preset failure data.

[0146] It is understandable that there is a preset weight between each preset fault data and each fault type, and the failure rate of the preset fault data and the fault type is obtained by adding the weights of each preset fault data and the fault type.

[0147] In the specific implementation, you can refer to Figure 3 , Figure 3 This is a schematic diagram of a preset fault spectrum, which includes fault type A and fault type B. The preset fault data of fault type A includes fault data a and fault data c, where the weight of fault data a and fault type A is wa1, and the weight of fault data c and fault type A is wc1. The failure rate of fault type A is a*wa1+c*wc1.

[0148] Step A343: Obtain the correlation degree of each fault type according to the abnormal weights between the abnormal data.

[0149] It should be noted that the correlation between each fault type is obtained based on the abnormal weights between the various abnormal data, including: obtaining a weight threshold; comparing the weight threshold with the abnormal weights between the various abnormal data to obtain a comparison result; obtaining the number of connection nodes of each abnormal point based on the comparison result; and obtaining the correlation between each fault type based on the number of connection nodes and the failure rate.

[0150] Among them, it is understandable that reference Figure 3After calculation, the abnormal points are fault data a and fault data c. Among them, fault data a and fault data c have other related data, which are not marked one by one in the figure. The abnormal weights and weight thresholds between the abnormal data and fault data a are compared. The abnormal data with weights greater than the weight threshold are used as connecting nodes, such as fault data 1 and fault data 2.

[0151] It can be known that the number of connection nodes of fault type A in fault data a is 2. Therefore, when calculating the correlation degree, the number of connection nodes can be used to correct the failure rate.

[0152] The correction method may be to set the correction parameter closer to 1 as the number of connected nodes increases, and to perform correction by multiplying the number of connected nodes by the failure rate. Other correction strategies may also be set, which are not limited in this embodiment.

[0153] Among them, after obtaining the correlation of each fault type according to the number of connected nodes and the failure rate, it also includes: when the correlation is greater than or equal to the correlation threshold, executing the steps of determining the warning level and predicting the fault type according to the correlation of the fault type; when the correlation is less than the correlation threshold, generating an abnormal signal according to the abnormal data, and performing an abnormal alarm based on the abnormal signal; obtaining a reference abnormal type based on the abnormal alarm feedback from the user and generating an abnormal release signal; updating the preset fault map according to the reference abnormal type and the abnormal release signal.

[0154] Among them, it is understandable that when an unrecognizable fault type occurs, it can be fed back to the user for manual identification, and after the user identifies it, the preset fault map is updated according to the detection data and the fault cause fed back by the user. It can adaptively learn and continuously optimize and update the fault map to ensure the accuracy of fault identification.

[0155] In this embodiment, the failure rate of each fault type is calculated through preset fault map analysis, thereby improving the accuracy and efficiency of fault prediction and diagnosis, reducing downtime and maintenance costs caused by equipment failure, supporting predictive maintenance strategies, taking measures in advance to prevent failures, enhancing system stability and reliability, and improving overall performance.

[0156] The above is only a feasible implementation of step S34 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S34.

[0157] This embodiment provides a method for early warning of equipment failures. By detecting outliers in data, it quickly and accurately screens out abnormal situations worthy of attention from a large amount of data. The correlation or importance between different abnormal data is calculated, thereby assigning a weight to each abnormality and distinguishing which abnormalities are more critical or more likely to indicate the existence of a specific problem. By comparing the abnormal data and its weights with pre-defined fault maps, the most matching fault type is found, thereby improving the accuracy of fault diagnosis and reducing false positives and missed negatives. The mutual influence or connection between different fault types is evaluated to provide a more comprehensive fault view, helping maintenance personnel understand potential chain reactions or common causes.

[0158] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the equipment failure warning method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0159] This application also provides an equipment failure warning device, please refer to Figure 4 , the equipment failure early warning device includes:

[0160] The data acquisition module 10 is used to acquire the operating data of the target industrial equipment and the associated data of each operating data;

[0161] A fault analysis module 20 is configured to perform an abnormality analysis based on the correlation between the operating data and the correlation data to obtain abnormal data;

[0162] The fault analysis module 20 is further configured to perform a graph correlation analysis on the abnormal data according to a preset fault map to obtain a fault type correlation degree;

[0163] A fault warning module 30 is configured to determine a warning level and predict a fault type based on the fault type correlation;

[0164] The fault warning module 30 is configured to provide a fault warning to the target industrial equipment based on the warning level and the predicted fault type.

[0165] The equipment failure warning device provided in this application, which utilizes the equipment failure warning method of the aforementioned embodiment, can resolve the technical problem of being unable to quickly and accurately identify potential equipment failures during factory equipment maintenance due to the large amount of equipment data and complex fault types. Compared to the prior art, the beneficial effects of the equipment failure warning device provided in this application are the same as those of the equipment failure warning method provided in the aforementioned embodiment, and the other technical features of the equipment failure warning device are the same as those disclosed in the aforementioned embodiment method, and are not further described here.

[0166] The present application provides an equipment failure warning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the equipment failure warning method in the above-mentioned embodiment one.

[0167] Reference below Figure 5 , which shows a schematic diagram of the structure of a device failure warning device suitable for implementing an embodiment of the present application. The device failure warning device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The device failure warning device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0168] like Figure 5As shown, the equipment failure warning device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the equipment failure warning device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007, such as a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication device 1009. Communication device 1009 can allow the equipment failure warning device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an equipment failure warning device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0169] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0170] The equipment failure warning device provided in this application, which utilizes the equipment failure warning method described in the aforementioned embodiment, can address the technical issue of being unable to quickly and accurately identify potential equipment failures during factory equipment maintenance due to the large volume of equipment data and complex fault types. Compared to the prior art, the beneficial effects of the equipment failure warning device provided in this application are the same as those of the equipment failure warning method described in the aforementioned embodiment. Other technical features of this equipment failure warning device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0171] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0172] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0173] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the device failure early warning method in the above-mentioned embodiment.

[0174] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0175] The computer-readable storage medium may be included in the equipment failure warning device; or may exist independently without being assembled into the equipment failure warning device.

[0176] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the equipment fault warning device, the equipment fault warning device enables the following: to obtain the operating data of the target industrial equipment and the associated data of each operating data; to perform an anomaly analysis based on the correlation between the operating data and the associated data to obtain abnormal data; to perform a graph correlation analysis on the abnormal data based on a preset fault map to obtain the fault type correlation; to determine the warning level and the predicted fault type based on the fault type correlation; and to perform a fault warning on the target industrial equipment based on the warning level and the predicted fault type.

[0177] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0178] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0179] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0180] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned equipment failure early warning method. This computer-readable storage medium can address the technical issue of being unable to quickly and accurately identify potential equipment failures during factory equipment maintenance due to the large volume of equipment, large amounts of data, and complex fault types. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the equipment failure early warning method provided in the aforementioned embodiment, and are not further elaborated here.

[0181] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned device failure early warning method when executed by a processor.

[0182] The computer program product provided in this application can address the technical problem of being unable to quickly and accurately identify potential equipment faults during factory equipment maintenance due to the large volume of equipment, large amounts of data, and complex fault types. Compared to the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the equipment fault early warning method provided in the above-mentioned embodiment, and will not be elaborated here.

[0183] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A device failure early warning method, characterized in that: The equipment failure early warning method includes: Obtain the operating data of the target industrial equipment and the associated data of each operating data. The operating data includes temperature, pressure, speed, and vibration. The associated data includes the equipment's working environment, production tasks, and maintenance records. Performing anomaly analysis based on the correlation between the operating data and the correlation data to obtain abnormal data; Performing a graph association analysis on the abnormal data according to a preset fault map to obtain a fault type association degree, wherein a plurality of fault categories are predetermined in the preset fault map, and fault data generated when the fault type occurs is obtained through testing, and each related data associated with the fault data is further associated, and an association weight or association degree is pre-set for the relationship between each fault data and each fault-related data, and a correlation degree or association weight is set between each fault type and the fault data as a calculation parameter for whether the data will cause a device fault of that type; Determine the warning level and predicted fault type according to the fault type correlation degree; Providing a fault warning to the target industrial equipment based on the warning level and the predicted fault type; The performing of graph correlation analysis on the abnormal data according to the preset fault graph to obtain the fault type correlation degree includes: Identify abnormal points between the abnormal data, and obtain abnormal weights between the abnormal data based on the abnormal points, wherein the abnormal points are locations in a preset fault map where the abnormal data with abnormal degrees higher than an abnormal threshold value are located among the abnormal data; Determine preset fault data in a preset fault map according to the abnormality weight, and determine the correlation degree of each fault type according to the preset fault data; The determining of the preset fault data in the preset fault map according to the abnormality weight includes: Extracting abnormal features of the abnormal data, matching the abnormal features with preset fault data, and obtaining initial fault data; sorting the initial fault data according to the abnormal weight of the fault data to obtain reference fault data; Using reference fault data with a weight greater than a preset weight threshold in the reference fault data as preset fault data; Determining the correlation between various fault types according to the preset fault data includes: Obtaining preset fault data associated with each fault type in the preset fault map; Obtaining the failure rate of each failure type according to the weight of the preset failure data; Obtaining the correlation degree of each fault type according to the abnormal weights between the various abnormal data, including: obtaining a weight threshold; comparing the weight threshold with the abnormal weights between the various abnormal data to obtain a comparison result; obtaining the number of connection nodes of the various abnormal data according to the comparison result; and obtaining the correlation degree of each fault type according to the number of connection nodes and the failure rate.

2. The equipment failure early warning method according to claim 1, characterized in that: The performing anomaly analysis based on the correlation between the operating data and the correlation data to obtain the abnormal data includes: Obtaining the abnormality degree of the operating data according to the preset standard associated data, the associated data, and the weight of each associated data; When the abnormality degree is greater than or equal to a preset abnormality threshold, the operating data is regarded as abnormal data.

3. The equipment failure early warning method according to claim 1, characterized in that: After obtaining the correlation degree of each fault type according to the number of connected nodes and the fault rate, the method further includes: When the correlation degree is greater than or equal to the correlation degree threshold, the steps of determining the warning level and predicting the fault type according to the fault type correlation degree are performed; When the correlation degree is less than a correlation degree threshold, generating an abnormal signal according to the abnormal data, and performing an abnormal alarm based on the abnormal signal; Obtaining a reference abnormality type based on the abnormal alarm feedback from the user and generating an abnormality release signal; The preset fault map is updated according to the reference abnormality type and the abnormality relief signal.

4. The equipment failure early warning method according to any one of claims 1 to 3, characterized in that: The determining of the warning level and the predicted fault type according to the fault type correlation degree includes: The fault type with the greatest fault type correlation is taken as the predicted fault type; Obtaining a data abnormality degree according to the abnormal data and a preset data standard value; A preset fault level table is matched with the data abnormality to obtain a warning level, wherein the preset fault level table includes one-to-one correspondence between the data abnormality and the fault level.

5. An equipment failure early warning device, characterized in that: The equipment failure early warning device comprises: The data acquisition module is used to obtain the operating data of the target industrial equipment and the associated data of each operating data. The operating data includes temperature, pressure, speed, and vibration. The associated data includes the working environment, production tasks, and maintenance records of the equipment. a fault analysis module, configured to perform an anomaly analysis based on the correlation between the operating data and the correlation data to obtain abnormal data; The fault analysis module is further configured to perform a graph association analysis on the abnormal data according to a preset fault map to obtain a fault type association degree, wherein a plurality of fault categories are predetermined in the preset fault map, and fault data generated when the fault type occurs is obtained through testing, and each related data associated with the fault data is further associated, and an association weight or association degree is pre-set for the relationship between each fault data and each fault-related data, and a correlation degree or association weight is set between each fault type and the fault data as a calculation parameter for whether the data will cause a device fault of that type; A fault warning module is used to determine the warning level and predict the fault type according to the correlation degree of the fault type; The fault warning module is configured to provide a fault warning to the target industrial equipment based on the warning level and the predicted fault type; The fault analysis module is further configured to identify abnormal points between the abnormal data; obtain abnormal weights between the abnormal data based on the abnormal points, wherein the abnormal points are locations in the preset fault map where the abnormal data with abnormal degrees exceeding the abnormal threshold are located; determine preset fault data in the preset fault map based on the abnormal weights; and determine the correlation between the various fault types based on the preset fault data. The fault analysis module is further configured to extract abnormal features of the abnormal data, match the abnormal features with preset fault data, and obtain initial fault data; sort the initial fault data according to the abnormal weight of the fault data to obtain reference fault data; use the reference fault data with a weight greater than a preset weight threshold in the reference fault data as preset fault data; obtain preset fault data associated with each fault type in the preset fault map; obtain the failure rate of each fault type according to the weight of the preset fault data; and obtain the correlation degree of each fault type according to the abnormal weight between each abnormal data, including: obtaining a weight threshold; comparing the weight threshold with the abnormal weight between each abnormal data to obtain a comparison result; obtaining the number of connection nodes of each abnormal data according to the comparison result; and obtaining the correlation degree of each fault type according to the number of connection nodes and the failure rate.

6. An equipment failure warning device, characterized in that: The device includes: a memory, a processor, and a device failure warning program stored in the memory and executable on the processor, wherein the device failure warning program is configured to implement the device failure warning method according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium stores an equipment failure early warning program, which, when executed by a processor, implements the equipment failure early warning method according to any one of claims 1 to 4.

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