Fault reason determination method and device, electronic equipment and storage medium

Abstract

The invention belongs to the field of Internet of Things, and provides a fault cause determination method and device, electronic equipment and a storage medium. The method comprises the following steps: determining operation indexes of production equipment according to production data of the production equipment on products; based on the operation indexes, detecting whether the production equipment operates abnormally; and if the production equipment runs abnormally, determining a fault reason of the production equipment based on the log file of the production equipment. The method can quickly determine the fault reason of the equipment.

Description

Technical Field

[0001] This application relates to the field of the Internet of Things, and particularly to a method, apparatus, electronic device, and storage medium for determining the cause of a failure. Background Art

[0002] Industrial assembly processes usually involve multiple links and multiple factors. When a problem occurs in a certain link, if it is not solved in time, it will lead to the stagnation or delay of the entire process. In the related art, it is usually necessary for staff to analyze information such as the operating status and production data of equipment to determine the cause of equipment failure, resulting in the inability to quickly determine the cause of equipment failure, thereby affecting production efficiency. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for determining the cause of a failure to solve the technical problem of being unable to quickly determine the cause of equipment failure.

[0004] In a first aspect of an embodiment of this application, a method for determining the cause of a failure is provided. The method includes: determining an operating index of the production equipment according to production data of the product by the production equipment; detecting whether the production equipment is operating abnormally based on the operating index; if the production equipment is operating abnormally, determining the cause of the failure of the production equipment based on the log file of the production equipment.

[0005] In some embodiments, the method further includes: extracting data corresponding to a preset keyword from the monitoring data of the production equipment as the production data based on a preset time.

[0006] In some embodiments, the production data includes: the actual processing duration of the production equipment for processing the product, the theoretical processing duration of the production equipment for processing the product, the number of defective products produced by the production equipment within a preset time, the total number of products processed by the production equipment within the preset time, the first downtime duration of the production equipment within the preset time, and the second downtime duration of the production equipment within the preset time. The operation metrics include the overall equipment effectiveness (OEE) of the production equipment, the total productive maintenance (TPM) of the production equipment, and the time utilization rate of the production equipment. Determining the operation metrics of the production equipment based on the production data of the product by the production equipment includes: determining the actual working duration of the production equipment based on the first downtime duration, the second downtime duration, and the preset time; calculating the planned working duration of the production equipment based on the first downtime duration and the preset time; calculating the product yield rate of the production equipment based on the number of defective products and the total number of products; calculating the overall equipment effectiveness (OEE) based on the actual working duration, the planned working duration, the actual processing duration, the theoretical processing duration, and the product yield rate; determining the time utilization rate based on the planned working duration and the actual working duration; and calculating the total productive maintenance (TPM) based on the actual working duration, the product yield rate, and the preset time.

[0007] In some embodiments, after calculating the product yield rate of the production equipment based on the number of defective products and the total number of products, the method further includes: if the product yield rate is less than a first preset threshold, determining that the production equipment is operating abnormally.

[0008] In some embodiments, the method further includes: calculating the production rate of the production equipment for the product based on the theoretical processing duration, the actual working duration, and the total number of products; if the difference between the time utilization rate and the production rate is greater than a second preset threshold, determining that the production equipment is operating abnormally.

[0009] In some embodiments, the operation metrics include the overall equipment effectiveness (OEE) of the production equipment, the total productive maintenance (TPM) of the production equipment, and the time utilization rate of the production equipment. Detecting whether the production equipment is operating abnormally based on the operation metrics includes: if the overall equipment effectiveness (OEE) is greater than the time utilization rate, or the total productive maintenance (TPM) is greater than the time utilization rate, determining that the production equipment is operating abnormally.

[0010] In some embodiments, determining the cause of the failure of the production equipment based on the log file of the production equipment includes: extracting the log keywords in the log file based on a preset rule; and determining the information corresponding to the log keywords as the cause of the failure.

[0011] In the second aspect of the embodiments of the present application, a device for determining the cause of a fault is provided. The device includes: a determining unit, configured to determine an operation index of the production equipment according to production data of products by the production equipment; a detecting unit, configured to detect whether the production equipment is operating abnormally based on the operation index; and the determining unit is further configured to, if the production equipment is operating abnormally, determine the cause of the fault of the production equipment based on a log file of the production equipment.

[0012] In the third aspect of the embodiments of the present application, an electronic device is provided. The electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps in the method provided in the first aspect above are implemented.

[0013] In the fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in the method provided in the first aspect above are implemented.

[0014] In multiple embodiments of the present application, by using production data to determine the operation index of production equipment, the operation status and performance data of the production equipment can be quantitatively obtained. By analyzing the operation index, the operation condition of the production equipment can be detected. When the production equipment is operating abnormally, by analyzing the log file, the root cause of the problem of the production equipment can be quickly located, thereby improving the efficiency of determining the cause of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for describing the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is an application scenario diagram of a method for determining the cause of a fault provided by an embodiment of the present application.

[0017] Figure 2 It is a flowchart of a method for determining the cause of a fault provided by an embodiment of the present application.

[0018] Figure 3 It is a schematic diagram of the time utilization rate and the total effective productivity provided by an embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of the time utilization rate provided by another embodiment of the present application.

[0020] Figure 5 It is a schematic diagram of the time utilization rate provided by an embodiment of the present application.

[0021] Figure 6 It is a schematic diagram of the time utilization rate provided by another embodiment of the present application.

[0022] Figure 7 It is a schematic diagram of the actual processing duration and the theoretical processing duration provided by an embodiment of the present application.

[0023] Figure 8 It is a flowchart of a method for determining the cause of a fault provided by another embodiment of the present application.

[0024] Figure 9 It is a functional module diagram of a device for determining the cause of a fault provided by an embodiment of the present application.

[0025] Figure 10 It is a schematic structural diagram of an electronic device for a method for determining the cause of a fault provided by an embodiment of the present application. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0027] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used in the description of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that unless otherwise stated in this application, " / " means "or". For example, A / B may mean A or B. The "and / or" in this application is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone, these three situations. "At least one" means one or more. "Multiple" means two or more than two. For example, at least one of a, b, or c may mean: a, b, c, a and b, a and c, b and c, a, b, and c, these seven situations.

[0029] As Figure 1 shown, it is an application scenario diagram of a fault cause determination method provided by an embodiment of this application.

[0030] In the embodiment of this application, the fault cause determination method is applied to one or more electronic devices 10. The electronic device 10 can communicate with the production device 20. The electronic device 10 can obtain the production data of the product by the production device 20. The production data may include but is not limited to: the number of defective products produced by the production device within a preset time, the total number of products processed by the production device within a preset time, etc. The electronic device can determine the operation index of the production device according to the production data of the production device 20. The operation index may include the overall equipment effectiveness of the production device, the total productive maintenance of the production device, etc. The electronic device 10 can determine the fault cause of the production device 20 according to the operation index, so as to assist the maintenance personnel in troubleshooting the production device 20.

[0031] The electronic device 10 is a device that can execute computer-readable instructions to automatically perform numerical calculations and / or information processing. Its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0032] The electronic device 10 may include a network device and / or a user device. Among them, the network device includes but is not limited to a single network electronic device, a group of electronic devices composed of multiple network electronic devices, or a cloud composed of a large number of hosts or network electronic devices based on cloud computing (Cloud Computing).

[0033] The network where the electronic device 10 is located includes, but is not limited to: the Internet, wide area network, metropolitan area network, local area network, Virtual Private Network (VPN), etc.

[0034] The production device 20 can be any production device that can perform human-machine interaction with the user. For example, industrial robots, laser cutting machines, numerical control bending machines, numerical control punching machines, etc.

[0035] As Figure 2 shown, it is a flowchart of the fault cause determination method provided by the embodiment of the present application. The fault cause determination method can be applied to an electronic device. As Figure 2 shown, the fault cause determination method in the embodiment of the present application includes steps S201-S203. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0036] S201, determine the operation index of the production device according to the production data of the product by the production device.

[0037] In at least one embodiment of the present application, the production data includes: the actual processing duration of the production device for processing the product, the theoretical processing duration of the production device for processing the product, the number of defective products produced by the production device within a preset time, the total number of products processed by the production device within a preset time, the first downtime duration of the production device within a preset time, and the second downtime duration of the production device within a preset time. The first downtime duration can represent the downtime duration of the production device in a normal state, and the second downtime duration can represent the downtime duration of the production device in an abnormal state. The operation index includes the overall efficiency of the production device, the total effective productivity of the production device, and the time utilization rate of the production device.

[0038] In at least one embodiment of the present application, the electronic device calculates the product yield rate corresponding to the production device according to the number of defective products and the total number of products. Among them, the determination formula of the product yield rate can be: .

[0039] The electronic device calculates the actual working duration of the production device based on the first downtime duration, the second downtime duration, and the preset time. Among them, the determination formula of the actual working duration of the production device can be: actual working duration = preset time first downtime duration second downtime duration.

[0040] The electronic device calculates the planned working duration of the production device according to the first downtime duration and the preset time. Among them, the determination formula of the planned working duration of the production device can be: planned working duration = preset time first downtime duration.

[0041] The electronic device calculates the overall equipment effectiveness (OEE) of the production equipment based on the actual working hours, planned working hours, actual processing time, theoretical processing time, and product yield. Among them, the formula for determining the overall equipment effectiveness can be: .

[0042] The electronic device determines the time utilization rate of the production equipment according to the actual working hours and the planned working hours. Among them, the formula for determining the time utilization rate can be: .

[0043] The electronic device calculates the net production rate of the production equipment based on the actual working hours, product yield, and preset time. The way to determine the net production rate can be: .

[0044] In some other embodiments, the electronic device calculates the theoretical production volume of the production equipment according to the actual working hours and the theoretical processing time. The way to determine the theoretical production volume can be: Theoretical production volume = actual working hours ÷ theoretical processing time. The electronic device calculates the production rate of the production equipment for the product based on the theoretical production volume and the total number of products. Among them, the way to determine the production rate of the production equipment for the product can be: .

[0045] For example, assume that the preset time of a production equipment in one day is 8 hours (the preset duration is 480 minutes), the first downtime is 20 minutes, the second downtime is 40 minutes, the theoretical processing time of the product is 0.5 minutes per piece, the actual processing time is 0.8 minutes per piece, the total number of products processed in one day is 400 pieces, and the number of defective products is 8 pieces.

[0046] The electronic device obtains the planned working hours of the production equipment as 480 - 20 = 460 minutes according to the first downtime and the preset time; the electronic device obtains the actual working hours of the production equipment as 480 - 20 - 40 = 420 minutes according to the first downtime, the second downtime, and the preset time.

[0047] The electronic device calculates the product yield = (400 - 8) ÷ 400 = 98% according to the number of defective products and the total number of products, and obtains the product yield corresponding to the production equipment as 98%. The electronic device calculates the overall equipment effectiveness = 420 ÷ 460 ÷ (0.5 ÷ 8) 98% = 56%, obtaining the overall equipment effectiveness of the production equipment as 56%. The electronic device calculates the time utilization rate = (480 60) 460 = 91%, obtaining the time utilization rate of the production equipment as 91%. The electronic device calculates the total productive maintenance rate = 420 98% 480 = 86%, obtaining the total productive maintenance rate of the production equipment as 86%. The electronic device calculates the theoretical production volume = 420 / 0.5 = 840, and the total number of products is 400. The production rate of the production equipment for the products = 400 / 840 = 48%, obtaining the production rate of the production equipment for the products as 48%.

[0048] In this embodiment, by using production data to calculate the operation indicators of the production equipment, the performance data of the production equipment can be quantitatively obtained, and then the operation status of the production equipment can be quickly determined.

[0049] S202. Based on the operation indicators, detect whether the equipment is operating abnormally.

[0050] In at least one embodiment of the present application, if the total productive maintenance rate is greater than the time utilization rate, the electronic device determines that the production equipment is operating abnormally. For example, if the time utilization rate is 86% and the total productive maintenance rate is 90%, and the total productive maintenance rate is greater than the time utilization rate, it is determined that the production equipment is operating abnormally. Refer to Figure 3 shown Figure 3 is a schematic diagram of the time utilization rate and the total productive maintenance rate provided by the embodiment of the present application. As Figure 3 shown, if the time utilization rate at each moment is greater than the total productive maintenance rate at the corresponding moment, it is determined that the production equipment is operating normally. Refer to Figure 4 shown Figure 4 is a schematic diagram of the time utilization rate and the total productive maintenance rate provided by another embodiment of the present application. As Figure 4 shown, on August 3, 2024, August 9, 2024, and August 10, 2024, the time utilization rate is greater than the total productive maintenance rate; from August 1 to August 2, 2024 and from August 4 to August 8, 2024, the time utilization rate is less than the total productive maintenance rate. Since the total productive maintenance rate is greater than the time utilization rate from August 1 to August 2, 2024 and from August 4 to August 8, 2024, it is determined that the production equipment is operating abnormally from August 1 to August 2, 2024 and from August 4 to August 8, 2024.

[0051] In at least one embodiment of the present application, if the overall efficiency is greater than the time operation rate, the electronic device determines that the production equipment is operating abnormally. For example, if the overall equipment efficiency is 89% and the time operation rate is 85%, and the overall equipment efficiency of 89% is greater than the time operation rate of 85%, it is determined that the production equipment is operating abnormally.

[0052] In some other embodiments, the electronic device may preset a third preset threshold, which can be adjusted according to actual requirements. If the time operation rate is less than the third preset threshold and the difference between the time operation rate and the third preset threshold is greater than the difference threshold, the electronic device determines that the production equipment is abnormal. For example, the third preset threshold can be set to 80% and the difference threshold is 5%. Refer to Figure 7 as shown Figure 7 is a schematic diagram of the time operation rate provided by an embodiment of the present application. As Figure 7 shown, on August 2, 2024 and from August 9 to August 12, 2024, the time operation rate is less than 80% and the difference between the time operation rate and the third preset threshold is greater than the difference threshold of 5%, it is determined that the production equipment is abnormal. Refer to Figure 6 as shown Figure 6 is a schematic diagram of the time operation rate provided by another embodiment. As Figure 6 shown, although the time operation rates on August 1, August 11, and August 12, 2024 in the figure are less than 80%, the difference between the time operation rate and the third preset threshold is less than 5%, and it is determined that the production equipment is in a normal state.

[0053] In some other embodiments, if the product yield is less than the first set threshold, the electronic device determines that the production equipment is operating abnormally. The first preset threshold can be set and adjusted according to actual requirements. Continuing with the above example, the product yield corresponding to the production equipment is 98%. If the first preset threshold is set to 99%, since 98% is less than 99%, the product yield is less than the first preset threshold, and it is determined that the production equipment is operating abnormally.

[0054] In some other embodiments, if the difference between the time operation rate and the production rate is greater than the second preset threshold, the electronic device determines that the production equipment is operating abnormally. Continuing with the above example, the time operation rate is 91% and the production rate is 48%, and the difference between the time operation rate and the production rate is 43%. If the second preset threshold is set to 5%, since 43% is greater than 5%, the difference between the time operation rate and the production rate is greater than the second preset threshold, and it is determined that the production equipment is operating abnormally.

[0055] In some other embodiments, the electronic device obtains the actual processing duration and the theoretical processing duration of the production equipment in multiple time periods. If the difference between the actual processing duration and the theoretical processing duration is greater than the fourth preset threshold, the electronic device determines that the production equipment is abnormal. For example, the fourth preset threshold is set to 20. Refer to Figure 5 as shownFigure 5 This is a schematic diagram of the actual processing duration and the theoretical processing duration provided by the embodiments of the present application. As Figure 5 shown, in the figure, the theoretical processing time remains unchanged, while the actual processing time changes. The actual processing duration corresponding to 2024 / 8 / 12 in the figure is zero, the theoretical processing duration is 30, the difference between the actual processing duration and the theoretical processing duration is 30, and the difference of 30 between the actual processing duration and the theoretical processing duration is greater than the fourth preset threshold of 20. The electronic device determines that the production equipment is operating abnormally. As Figure 5 shown, the actual processing duration being zero indicates that a power outage in the workshop may have caused a failure.

[0056] Through the relationship between the operation indicators in this embodiment, the health status of the production equipment can be quickly judged.

[0057] S203. If the production equipment is operating abnormally, based on the log file of the production equipment, determine the cause of the failure of the production equipment.

[0058] In at least one embodiment of the present application, the log file records the operation event information of the production equipment. For example, the log file can record the event time of the production equipment, the equipment identifier, the specific operation type of the event (such as the start of the production equipment, the stop of the production equipment, etc.), and the parameters of the production equipment during the operation.

[0059] In at least one embodiment of the present application, the electronic device determines the cause of the failure of the production equipment based on the log file of the production equipment, including: the electronic device extracts the log keywords in the log file based on the preset rules, and determines the message corresponding to the log keywords as the cause of the failure.

[0060] Among them, the preset rules can be set as: the information that appears in the log file more than or equal to the configured value, where the configured value can be set and adjusted according to actual needs. For example, a log file of the production equipment is collected as: "{When the assembly robot STU is performing the assembly task, the sensor fails to locate the part W. It has tried to re-locate three times, all of which have failed. It is initially judged that the sensor is faulty. The maintenance team has been notified}". The electronic device extracts the keywords in the log file based on the preset rules. If the configured value is set to 2, the information that appears in the log file more than or equal to 2 times is used as the keyword. Since "assembly" appears 2 times in the log file, "failure" appears 2 times in the log file, "location" appears 2 times in the log file, and "sensor" appears 2 times in the log file, therefore, the log keywords in the log file include: "assembly", "failure", "location", "sensor".

[0061] In this embodiment, through preset rules, keywords related to abnormal events can be extracted from the log file, so that important information can be quickly located and the time for processing irrelevant information can be reduced.

[0062] In some embodiments, the electronic device determines the message corresponding to the log keyword as the cause of the fault, including: the electronic device traverses the preset logs in the database. Based on the log keyword, the log matching degree between the log file and the preset log is calculated, and the information corresponding to the preset log with the log matching degree greater than the fifth preset threshold is used as the cause of the fault. Among them, the fifth preset threshold can be set and adjusted according to actual needs.

[0063] For example, there are 3 preset logs in the database, {Log 1: "During the assembly of the assembly robot A, the positioning of part B fails"; Log 2: "When the welding equipment C is welding, cracks appear in the weld"; Log 3: "When the cutting machine D cuts the material, the blade suddenly breaks."}, if the log file is: "When the assembly robot A assembles part B, the positioning fails, resulting in the failure of part B assembly. It has been tried many times again, and the positioning fails." The keywords extracted from the log file may include: assembly, part B, positioning, assembly, a total of 4 keywords. Since Log 1 includes 3 keywords of "assembly robot A", "part B", and "positioning failure", Log 2 includes 1 keyword of "welding", and Log 3 includes 2 keywords of "cutting" and "breaking", and the preset threshold is set to 70%. Therefore, the log matching degree between Log 1 and the log file is 3 / 4 = 75%, the log matching degree between Log 2 and the log file is 1 / 4 = 25%, and the log matching degree between Log 3 and the log file is 2 / 4 = 50%. Therefore, the information corresponding to Log 1 with the log matching degree greater than 70% is used as the cause of the fault.

[0064] In this embodiment, by traversing the preset logs in the database through keywords, the log that matches the log file can be quickly found among a large number of preset logs, and the information corresponding to the log is obtained as the cause of the fault, improving the determination efficiency of the cause of the fault.

[0065] In multiple embodiments of the present application, by using production data to determine the operation indicators of production equipment, the operation status and performance data of production equipment can be quantitatively obtained. By analyzing the operation indicators, the operation status of production equipment can be detected. When the production equipment runs abnormally, by analyzing the log file, the root cause of the problem of the production equipment can be quickly located, thereby improving the determination efficiency of the cause of the fault.

[0066] As Figure 8 shown, Figure 8 is a flowchart of a method for determining the cause of a fault provided in another embodiment of the present application. The method for determining the cause of a fault can be applied to an electronic device, such as Figure 8As shown in the figure, the fault cause determination method in the embodiments of the present application includes steps S801 - S804. According to different requirements, the order of steps in this flowchart can be changed, and some steps can be omitted.

[0067] S801, based on a preset time, extract data corresponding to a preset keyword from the monitoring data of the production equipment as production data.

[0068] In at least one embodiment of the present application, the preset time can be the frequency of data acquisition set by the electronic device. For example, it is set to extract production data every half hour, or to extract production data once after the production equipment has produced a unit of products. The monitoring data can include the work station name of the produced product, the address for storing data, the time of reading data, the total output quantity, the air pressure status, etc. The preset keyword can be set and adjusted according to the actual situation. For example, the preset keyword can be the total output quantity, the air pressure status, etc.

[0069] In at least one embodiment of the present application, the preset communication route is one or more data transmission paths preset according to the network topology structure, communication protocol, and device capabilities before data transmission. The preset communication route generally includes: the optimal path from the source point to the target point, the communication protocol used for data transmission, the production equipment configuration parameters, etc.

[0070] The electronic device obtains the monitoring data in the following manner: The production equipment can be connected to a computer device. In a Programmable Logic Controller (PLC) network system, the electronic device is used as the PLC master station, and the computer device connected to the production equipment is used as the PLC slave station. The PLC master station issues a data acquisition request, and the data acquisition request can include: the address of the PLC slave station, the address corresponding to the target data, etc. The PLC master station forwards the data acquisition request to the PLC slave station based on the address of the PLC slave station according to the preset communication route; the PLC slave station responds to the data acquisition instruction and collects the monitoring data from the production equipment corresponding to the address of the PLC slave station based on the address corresponding to the target data; the PLC slave station transmits the monitoring data to the PLC master station (electronic device) through the preset communication route.

[0071] In this embodiment, by using the electronic device as the master station and the computer device connected to the production equipment as the slave station, in the PLC network, the acquisition and transmission of data are completed through the preset communication route. Since no manual intervention is required, the efficiency of data acquisition is improved.

[0072] In some embodiments, the electronic device preprocesses the monitoring data to obtain preprocessed data. Information matching a preset keyword is extracted from the preprocessed data to obtain production data. The production data is classified into corresponding categories according to the categories of the preset keywords. For example, data related to "temperature" is classified into one category, data related to "pressure" is classified into another category, data related to "total number of products" is classified into one category, and so on.

[0073] In this embodiment, by preprocessing the monitoring data, the quality of the monitoring data can be improved. Through the preset keyword, key information related to production can be extracted from the preprocessed data, improving the accuracy of the production data.

[0074] S802. Determine the operating indicators of the production equipment according to the production data of the product by the production equipment.

[0075] S803. Detect whether the production equipment is operating abnormally based on the operating indicators.

[0076] S804. If the production equipment is operating abnormally, determine the cause of the failure of the production equipment based on the log file of the production equipment.

[0077] For the detailed content of steps S802 - S804, reference can be made to the above Figure 2 detailed description of steps S201 - S203, and it will not be repeated here.

[0078] As Figure 9 shown, it is a functional module diagram of a device for determining the cause of a failure provided by an embodiment of the present application. The device 11 for determining the cause of a failure includes a determination unit 110, a detection unit 111, an extraction unit 112, and a calculation unit 113. As used herein, a module / unit refers to a series of computer-readable instruction segments that can be obtained by a processor (such as Figure 10 the processor 102 shown) and can perform fixed functions, and is stored in a memory (such as Figure 10 the memory 101 shown).

[0079] The determination unit 110 is configured to determine the operating indicators of the production equipment according to the production data of the product by the production equipment. The detection unit 111 is configured to detect whether the production equipment is operating abnormally based on the operating indicators. The determination unit is further configured to, if the production equipment is operating abnormally, determine the cause of the failure of the production equipment based on the log file of the production equipment.

[0080] In some embodiments, the extraction unit 112 is configured to: extract data corresponding to a preset keyword from the monitoring data of the production equipment based on a preset time as production data.

[0081] In some embodiments, the production data includes: the actual processing duration of the production equipment for processing products, the theoretical processing duration of the production equipment for processing products, the number of defective products produced by the production equipment within a preset time, the total number of products processed by the production equipment within a preset time, the first downtime duration of the production equipment within a preset time, and the second downtime duration of the production equipment within a preset time. The operation indicators include the overall equipment effectiveness (OEE) of the production equipment, the total productive maintenance (TPM) rate of the production equipment, and the time utilization rate of the production equipment. Based on the production data of the production equipment for products, the operation indicators of the production equipment are determined. The determination unit 113 is specifically configured to: determine the actual working duration of the production equipment based on the first downtime duration, the second downtime duration, and the preset time; calculate the planned working duration of the production equipment according to the first downtime duration and the preset time; calculate the product yield rate of the production equipment according to the number of defective products and the total number of products; calculate the overall equipment effectiveness based on the actual working duration, the planned working duration, the actual processing duration, the theoretical processing duration, and the product yield rate; determine the time utilization rate according to the planned working duration and the actual working duration; calculate the total productive maintenance based on the actual working duration, the product yield rate, and the preset time.

[0082] In some embodiments, the determination unit 110 is further configured to: if the product yield rate is less than the first preset threshold, determine that the production equipment is operating abnormally.

[0083] In some embodiments, the calculation unit 113 is configured to calculate the production rate of the production equipment for products according to the theoretical processing duration, the actual working duration, and the total number of products. The determination unit 110 is further configured to: if the difference between the time utilization rate and the production rate is greater than the second preset threshold, determine that the production equipment is operating abnormally.

[0084] In some embodiments, the operation indicators include the overall equipment effectiveness (OEE) of the production equipment, the total productive maintenance (TPM) rate of the production equipment, and the time utilization rate of the production equipment. The detection unit 111 is specifically configured to: if the overall equipment effectiveness is greater than the time utilization rate, or the total productive maintenance is greater than the time utilization rate, determine that the production equipment is operating abnormally.

[0085] In some embodiments, the determination unit 110 is specifically further configured to: extract log keywords from the log file based on a preset rule; determine the information corresponding to the log keywords as the cause of the fault.

[0086] In multiple embodiments of the present application, by determining the operation indicators of the production equipment through production data, the operation status and performance data of the production equipment can be quantitatively obtained. By analyzing the operation indicators, the operation status of the production equipment can be detected. When the production equipment is operating abnormally, by analyzing the log file, the root cause of the problem of the production equipment can be quickly located, thereby improving the determination efficiency of the cause of the fault.

[0087] Such as Figure 10As shown, it is a schematic structural diagram of an electronic device for a fault cause determination method provided by an embodiment of the present application.

[0088] In an embodiment of the present application, the electronic device 10 includes, but is not limited to, a memory 101, a processor 102, and computer-readable instructions stored in the memory 101 and executable on the processor 102, such as a fault cause determination program.

[0089] Those skilled in the art can understand that the schematic diagram is only an example of the electronic device 10, and does not constitute a limitation on the electronic device 10. It may include more or fewer components than shown, or combine some components, or different components. For example, the electronic device 10 may further include input / output devices, network access devices, buses, etc.

[0090] The processor 102 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or a processor, or any conventional processor, etc. The processor 102 is the operation core and control center of the electronic device 10, connecting various parts of the entire electronic device 10 through various interfaces and lines, and executing the operating system of the electronic device 10 and various installed application programs, program codes, etc.

[0091] The processor 102 obtains the operating system of the electronic device 10 and various installed application programs. The processor 103 obtains the application programs to implement the steps in the above various display method embodiments, such as Figure 2 、 Figure 8 the steps shown.

[0092] Exemplarily, the computer program may be divided into one or more modules / units. One or more modules / units are stored in the memory 101 and obtained by the processor 102 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the obtaining process of the computer program in the electronic device 10.

[0093] The memory 101 can be used to store computer programs and / or modules. By running or obtaining the computer programs and / or modules stored in the memory 101, and invoking the data stored in the memory 101, the processor 102 realizes various functions of the electronic device 10. The memory 101 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device. In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0094] The memory 101 can be an external memory and / or an internal memory of the electronic device 10. Further, the memory 101 can be a memory in physical form, such as a memory stick, a TF card (Trans-flash Card), etc.

[0095] Combined with Figure 2 、 Figure 8 , the memory 101 in the electronic device 10 stores computer-readable instructions, and the processor 102 can execute the computer-readable instructions stored in the memory 101 to implement multiple processes as shown in Figure 2 、 Figure 8 to implement the fault cause determination method.

[0096] If the modules / units integrated in the electronic device 10 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by instructing relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium. When the computer-readable instructions are executed by the processor, the steps of the above-mentioned various method embodiments can be implemented.

[0097] Among them, the computer-readable instructions include computer-readable instruction codes, and the computer-readable instruction codes can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium can include: any entity or device capable of carrying the computer-readable instruction code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory).

[0098] Exemplarily, the computer-readable instructions may be divided into one or more modules / units, and one or more modules / units are stored in the memory 101 and executed by the processor 102 to complete the present application. One or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and the computer-readable instruction segments are used to describe the execution process of the computer-readable instructions in the electronic device 10. For example, the computer-readable instructions may be divided into a determination unit 110, a detection unit 111, an extraction unit 112, and a calculation unit 113.

[0099] For the detailed content of the functions of each module / unit, reference may be made to the above Figure 2 、 Figure 8 detailed description, and no repeated description will be given here. In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0100] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, in each embodiment of the present application, the various functional modules may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a hardware plus a software functional module.

[0102] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0103] In addition, obviously, the term "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices may also be implemented by one unit or device through software or hardware. The terms such as first and second are used to represent names and do not represent any specific order.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for determining the cause of a fault, characterized in that, The method includes: Determining an operation index of the production equipment according to production data of a product by the production equipment; Detecting whether the production equipment operates abnormally based on the operation index; If the production equipment operates abnormally, determining a fault cause of the production equipment based on a log file of the production equipment.

2. The method for determining the cause of a fault according to claim 1, wherein The method further includes: Extracting data corresponding to a preset keyword from monitoring data of the production equipment as the production data based on a preset time.

3. The method for determining the cause of a fault according to claim 1, characterized in that, The production data includes: an actual processing duration for the production equipment to process the product, a theoretical processing duration for the production equipment to process the product, a quantity of defective products produced by the production equipment within a preset time, a total quantity of products processed by the production equipment within the preset time, a first downtime duration of the production equipment within the preset time, and a second downtime duration of the production equipment within the preset time. The operation index includes an overall equipment effectiveness of the production equipment, a total productive maintenance of the production equipment, and a time utilization rate of the production equipment. Determining the operation index of the production equipment according to the production data of the product by the production equipment includes: Determining an actual working duration of the production equipment based on the first downtime duration, the second downtime duration, and the preset time; Calculating a planned working duration of the production equipment according to the first downtime duration and the preset time; Calculating a product yield rate corresponding to the production equipment according to the quantity of defective products and the total quantity of products; Calculating the overall equipment effectiveness based on the actual working duration, the planned working duration, the actual processing duration, the theoretical processing duration, and the product yield rate; Determining the time utilization rate according to the planned working duration and the actual working duration; Calculating the total productive maintenance based on the actual working duration, the product yield rate, and the preset time.

4. The method for determining the cause of a fault according to claim 3, characterized in that, After calculating the product yield rate corresponding to the production equipment according to the quantity of defective products and the total quantity of products, the method further includes: If the product yield rate is less than a first preset threshold, determining that the production equipment operates abnormally.

5. The method for determining the cause of a fault according to claim 3, characterized in that, The method further includes: Calculating a production rate of the production equipment for the product according to the theoretical processing duration, the actual working duration, and the total quantity of products; If a difference between the time utilization rate and the production rate is greater than a second preset threshold, determining that the production equipment operates abnormally.

6. The method for determining the cause of a fault according to claim 1, characterized in that, The operation index includes an overall equipment effectiveness of the production equipment, a total productive maintenance of the production equipment, and a time utilization rate of the production equipment. Detecting whether the production equipment operates abnormally based on the operation index includes: If the overall equipment effectiveness is greater than the time utilization rate, or the total productive maintenance is greater than the time utilization rate, determining that the production equipment operates abnormally.

7. The method for determining the cause of a fault according to claim 1, characterized in that Determining the fault cause of the production equipment based on the log file of the production equipment includes: Extracting log keywords in the log file based on a preset rule; Determining information corresponding to the log keywords as the fault cause.

8. A fault cause determination device, characterized in that, Includes: A determination unit, configured to determine an operation index of the production equipment according to production data of a product by the production equipment; A detection unit, configured to detect whether the production equipment operates abnormally based on the operation index; The determination unit is further configured to, if the production equipment operates abnormally, determine a fault cause of the production equipment based on a log file of the production equipment.

9. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the method for determining a fault cause according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for determining a fault cause according to any one of claims 1 to 7 is implemented.

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