Screw drive structure fault prediction method, device, storage medium and electronic equipment

Abstract

The present application proposes a method, device, storage medium and electronic device for predicting the fault of a screw transmission structure, which divides the operation data of the screw transmission structure within the target time period into continuous window data, and obtains the corresponding time series set based on the window data, wherein the time series set includes any one or more of the mean time series, the variance time series and the coefficient of variation time series; annotates the abnormal points in the sequence of the time series set, and uses the prediction model to process the annotated time series set to obtain estimated fault information, wherein the estimated fault information includes the estimated time point of the fault occurrence and the estimated probability of the fault occurrence. By estimating the possibility of the fault in advance, early warning of the screw transmission fault can be achieved to avoid sudden downtime. It is beneficial for the staff to understand the overall health status of the screw transmission structure, carry out planned downtime maintenance, and increase the service life of the screw transmission structure.

Description

Technical Field

[0001] The present application relates to the field of processing machinery, and in particular, to a method, device, storage medium and electronic device for predicting a screw transmission structure fault. Background Art

[0002] As automation becomes more and more popular, automation control technology is increasingly used in life and industry. As a component with high frequency of use and wide application scenarios in automation control technology, the screw transmission structure is also fully used in various fields.

[0003] The screw transmission structure includes a driving device and a screw, wherein the screw is connected to the driving device in a transmission manner, and the driving device is used to drive the screw to reciprocate or move in one direction, so that the screw drives the load to move and completes the corresponding action.

[0004] When the screw transmission structure fails or breaks down, it may cause the corresponding product to be damaged or production to stop, resulting in a decline in production capacity. Therefore, how to predict the possible failure of the screw transmission structure in advance has become a difficult problem that technicians in this field need to solve urgently. Summary of the invention

[0005] The purpose of the present application is to provide a screw transmission structure fault prediction method, device, storage medium and electronic device to at least partially improve the above-mentioned problems.

[0006] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for predicting a fault of a screw transmission structure, wherein the screw transmission structure includes a driving device and a screw, wherein the screw is connected to the driving device in a transmission manner, and wherein the driving device is used to drive the screw to reciprocate or move in one direction, and wherein the method includes:

[0008] The operation data of the screw transmission structure within the target time period is divided into continuous window data, wherein the operation data includes the corresponding collection time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, speed data of the driving device at the collection time point, and the position data of the screw;

[0009] Acquire a corresponding time series set based on the window data, wherein the time series set includes any one or more of a mean time series, a variance time series, and a coefficient of variation time series;

[0010] Annotating abnormal points in the sequence of the time series set, wherein the abnormal points are points in the sequence whose values ​​exceed corresponding preset thresholds;

[0011] The prediction model is used to process the annotated time series set to obtain estimated fault information, wherein the estimated fault information includes an estimated fault occurrence time point and an estimated fault occurrence probability.

[0012] In a second aspect, an embodiment of the present application provides a screw transmission structure fault prediction device, wherein the screw transmission structure includes a driving device and a screw, wherein the screw is connected to the driving device in a transmission manner, and the driving device is used to drive the screw to reciprocate or move in one direction, and the device includes:

[0013] A processing unit, used for dividing the operation data of the screw transmission structure within the target time period into continuous window data, wherein the operation data includes the corresponding collection time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, rotation speed data of the driving device at the collection time point and the position data of the screw;

[0014] The processing unit is further used to obtain a corresponding time series set based on the window data, wherein the time series set includes any one or more of a mean time series, a variance time series, and a coefficient of variation time series;

[0015] The processing unit is further used to mark abnormal points in the sequence of the time series set, wherein the abnormal point is a point in the sequence whose value exceeds a corresponding preset threshold;

[0016] The model unit is used to process the annotated time series set using a prediction model to obtain estimated fault information, wherein the estimated fault information includes an estimated fault occurrence time point and an estimated fault occurrence probability.

[0017] In a third aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, and the computer program implements the above method when executed by a processor.

[0018] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store one or more programs; when the one or more programs are executed by the processor, the above method is implemented.

[0019] Compared with the prior art, the present application provides a method, device, storage medium and electronic device for predicting the fault of a screw transmission structure, wherein the screw transmission structure includes a driving device and a screw, the screw is connected to the driving device in a transmission manner, and the driving device is used to drive the screw to reciprocate or move in one direction. The method includes: dividing the operation data of the screw transmission structure within the target time period into continuous window data, wherein the operation data includes the corresponding acquisition time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, speed data and screw position data of the driving device at the acquisition time point; obtaining the corresponding time series set based on the window data, wherein the time series set includes any one or more of the mean time series, variance time series and coefficient of variation time series; marking the abnormal points in the sequence of the time series set, wherein the abnormal points are points in the sequence whose values ​​exceed the corresponding preset threshold; using the prediction model to process the time series set after the marking to obtain the estimated fault information, wherein the estimated fault information includes the estimated fault occurrence time point and the estimated fault occurrence probability. By predicting the possibility of the fault in advance, early warning of the screw transmission fault can be achieved to avoid sudden downtime. It is beneficial for the staff to understand the overall health status of the screw drive structure, carry out planned shutdown maintenance, and increase the service life of the screw drive structure.

[0020] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of a process flow of a screw transmission structure fault prediction method provided in an embodiment of the present application;

[0024] Figure 3 One of the flow charts of the screw transmission structure fault prediction method provided in the embodiment of the present application;

[0025] Figure 4 A schematic diagram of the sub-steps of S101 provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of sub-steps of S103 provided in an embodiment of the present application;

[0027] Figure 6 One of the flow charts of the screw transmission structure fault prediction method provided in the embodiment of the present application;

[0028] Figure 7 A unit schematic diagram of a screw transmission structure fault prediction device provided in an embodiment of the present application.

[0029] In the figure: 10 - processor; 11 - memory; 12 - bus; 13 - communication interface; 201 - processing unit; 202 - model unit. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0031] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0032] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0033] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0034] In the description of the present application, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the product of the application is usually placed when in use. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0035] In the description of this application, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "disposed" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0036] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0037] It should be understood that the screw and the drive device can be connected through a transmission structure, which may include a coupling, a bearing, a slider, etc. If the screw transmission structure fails during normal production of the equipment, such as loose couplings, worn bearings, insufficient lubrication of the slider, etc., the resulting equipment failure will cause production line downtime and production capacity decline, so it is necessary to monitor the failure.

[0038] In one possible implementation, when the screw transmission structure has become stuck, the drive device cannot drive the screw structure to move and an alarm is sounded. This indicates that a fault has occurred, causing production downtime and product damage. No risk prediction is performed, and the fault problem cannot be discovered in advance and an early warning cannot be issued.

[0039] An embodiment of the present application provides an electronic device, which may be a server capable of acquiring the operating data of a screw transmission structure; it may also be an operating equipment equipped with a screw transmission structure, such as an engraving machine, a blister forming machine, and a feeder, etc.; it may also be an Internet of Things gateway for connecting the screw transmission structure to a network (Internet, local area network, or corporate intranet).

[0040] Please refer to Figure 1 , a schematic diagram of the structure of an electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected via the bus 12, and the processor 10 is used to execute an executable module stored in the memory 11, such as a computer program.

[0041] The processor 10 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the screw transmission structure fault prediction method can be completed by the hardware integrated logic circuit in the processor 10 or the instructions in the form of software. The above-mentioned processor 10 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0042] The memory 11 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0043] The bus 12 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. Figure 1 Although only one bidirectional arrow is used in the figure, it does not mean that there is only one bus 12 or only one type of bus 12 .

[0044] The memory 11 is used to store programs, such as programs corresponding to the screw transmission structure fault prediction device. The screw transmission structure fault prediction device includes at least one software function module that can be stored in the memory 11 in the form of software or firmware or solidified in the operating system (OS) of the electronic device. After receiving the execution instruction, the processor 10 executes the program to implement the screw transmission structure fault prediction method.

[0045] Possibly, the electronic device provided in the embodiment of the present application further includes a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus.

[0046] Optionally, the electronic device can, but is not limited to, obtain the operation data of the screw transmission structure through the communication interface 13. The screw transmission structure includes a driving device and a screw, the screw is connected to the driving device in a transmission manner, and the driving device is used to drive the screw to reciprocate or move in one direction. The operation data of the screw transmission structure includes a corresponding collection time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, speed data, and screw position data of the driving device at the collection time point.

[0047] It should be understood that Figure 1 The structure shown is only a schematic diagram of a portion of the electronic device. The electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0048] The present invention provides a method for predicting a screw transmission structure fault, which can be applied to but is not limited to Figure 1 For detailed procedures, please refer to the electronic equipment shown in Figure 2 ,The screw transmission structure fault prediction method includes: S101, S103, S104 and S105, which are specifically described as follows.

[0049] S101, dividing the operation data of the screw transmission structure within the target time period into continuous window data.

[0050] Among them, the operating data of the screw transmission structure includes the corresponding collection time point, and the operating data also includes any one or more of the motor load rate, current data, torque data, speed data and screw position data of the driving device at the collection time point.

[0051] Assuming that Ai represents the i-th operating data, Ai may specifically include the collection time point Ti of Ai, and the motor load rate, current data, torque data, speed data and screw position data of the driving device at the collection time point at time Ti.

[0052] Optionally, the window data in the embodiment of the present application may be a sliding window, for example, (A1, A2, A3), (A2, A3, A4) and (A3, A4, A5) are 3 consecutive window data, and the same applies to the others. The window data in the embodiment of the present application may also be non-overlapping windows, for example, (A1, A2, A3), (A4, A5, A6) and (A7, A8, A9) are 3 consecutive window data, and the same applies to the others. It should be noted that the number of running data in each window data may be the same or different, and is not limited here.

[0053] S103, acquiring a corresponding time series set based on the window data.

[0054] The time series set includes any one or more of a mean time series, a variance time series, and a coefficient of variation time series.

[0055] It should be understood that a window includes multiple operating data. Possibly, one operating data includes multiple sampling information. For example, the operating data also includes any one or more of the motor load rate, current data, torque data, speed data and screw position data of the driving device at the collection time point.

[0056] In a possible implementation, the mean, variance, and coefficient of variation corresponding to each type of sampling information in the window data may be obtained. The mean of the sampling information in the window data is then aggregated to obtain the mean information of the window data, the variance of the sampling information in the window data is then aggregated to obtain the variance information of the window data, and the coefficient of variation of the sampling information in the window data is then aggregated to obtain the coefficient of variation information of the window data.

[0057] Based on the mean information, variance information and coefficient of variation information corresponding to the window data, the mean time series, variance time series and coefficient of variation time series can be obtained.

[0058] Taking the mean time series as an example, it is assumed that it is represented by a two-dimensional mean time series graph. In the mean time series graph, the horizontal axis represents the collection time point or collection time period corresponding to the window data, and the vertical axis represents the mean information corresponding to the window data.

[0059] The same is true for the variance time series and the coefficient of variation time series, so I will not go into details here.

[0060] S104, marking abnormal points in the sequence of the time series set.

[0061] Among them, an outlier point is a point in the sequence whose value exceeds the corresponding preset threshold.

[0062] Optionally, different types of sequences are set with different preset thresholds, such as a mean threshold, a variance threshold, and a coefficient of variation threshold.

[0063] By marking the abnormal points in the sequence, it is easier for subsequent models to predict faults based on the abnormal points, thereby improving the accuracy and efficiency of fault prediction.

[0064] S105: Process the annotated time series set using a prediction model to obtain estimated fault information.

[0065] The estimated fault information includes the estimated fault occurrence time point and the estimated fault occurrence probability.

[0066] By predicting the possibility of failure in advance, early warning of screw drive failure can be achieved to avoid sudden downtime. It is helpful for staff to understand the overall health of the screw drive structure, carry out planned downtime maintenance, and extend the service life of the screw drive structure.

[0067] In summary, the present application provides a method for predicting a screw transmission structure fault, wherein the screw transmission structure includes a drive device and a screw, the screw is connected to the drive device in a transmission manner, and the drive device is used to drive the screw to reciprocate or move in one direction. The method includes: dividing the operation data of the screw transmission structure within the target time period into continuous window data, wherein the operation data includes the corresponding acquisition time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, speed data and screw position data of the drive device at the acquisition time point; obtaining the corresponding time series set based on the window data, wherein the time series set includes any one or more of the mean time series, variance time series and coefficient of variation time series; marking the abnormal points in the sequence of the time series set, wherein the abnormal points are points in the sequence whose values ​​exceed the corresponding preset thresholds; using the prediction model to process the time series set after the marking to obtain the estimated fault information, wherein the estimated fault information includes the estimated fault occurrence time point and the estimated fault occurrence probability. By predicting the possibility of the fault in advance, early warning of the screw transmission fault can be achieved to avoid sudden downtime. It is beneficial for the staff to understand the overall health status of the screw drive structure, carry out planned shutdown maintenance, and increase the service life of the screw drive structure.

[0068] exist Figure 2 On this basis, regarding how to obtain operation data, the embodiment of the present application also provides a possible implementation method, please refer to the following.

[0069] In a possible implementation, the driver of the driving device can collect corresponding operation data. The electronic device can read the data in the driver of the driving device in real time through the CIP communication protocol, such as reading any one or more of the motor load rate, current data, torque data, speed data and screw position data of the driving device at the collection time point. Optionally, the driver can also obtain the data collection period N ms (for example, N≤20), and based on the collection period, the collection time point of each operation data can be determined. The CIP communication protocol has the advantage of stable communication quality.

[0070] In a possible implementation, a programmable logic controller (PLC) for controlling the drive device is configured in the screw transmission structure. The standard speed V1, standard acceleration A1, standard deceleration B1, standard acceleration and deceleration C1, target position S1 and load mass M1 can be preset in the PLC. The electronic device can obtain the data preset in the PLC and store the obtained data in a corresponding database.

[0071] exist Figure 2 On the basis of, in order to ensure the accuracy of the prediction results, the embodiment of the present application also provides a possible implementation method, please refer to Figure 3 , after S101, the screw transmission structure fault prediction method further includes: S102, which is specifically described as follows.

[0072] S102, eliminating the fluctuating data in the window data.

[0073] The fluctuation data is operating data that satisfies any one or more of the first preset condition, the second preset condition, the third preset condition and the fourth preset condition.

[0074] The first preset condition is that the speed data does not belong to the preset speed range, the second preset condition is that the acceleration data does not belong to the preset acceleration range, the third preset condition is that the deceleration data does not belong to the preset deceleration range, and the fourth preset condition is that the acceleration / deceleration change degree data does not belong to the preset change degree range. The acceleration / deceleration change degree data is the change rate of acceleration or deceleration.

[0075] It should be understood that, based on knowing the speed data and the acquisition time point in each operation data, the corresponding acceleration, deceleration and acceleration / deceleration change degree can be calculated.

[0076] Optionally, the preset speed range can be but is not limited to (V1-1, V1+1); the preset acceleration range can be but is not limited to (A1-1, A1+1); the preset deceleration range can be but is not limited to (B1-1, B1+1); the preset degree of variation range can be but is not limited to (C1-1, C1+1).

[0077] It should be understood that when the screw drive structure is in an unstable motion state, the corresponding operating data will fluctuate greatly and be unstable. If this part of the data is used for fault prediction, it is likely to lead to errors in the prediction results. In order to avoid this problem, the fluctuating data in the window data is eliminated, and the operating data collected in the scene of uniform acceleration or uniform motion is retained. The fluctuation should be stable, which is conducive to improving the accuracy of fault prediction.

[0078] exist Figure 2 Based on the content in S101, the present application embodiment also provides a possible implementation method, please refer to Figure 4 , S101 includes: S101-1 and S101-2, which are described in detail as follows.

[0079] S101-1, determining a preset target length as the window length.

[0080] The window length represents the length that the lead screw travels from the position corresponding to the first operating data in the window to the position corresponding to the last operating data in the window.

[0081] The target length can be the maximum distance of the screw rod's unilateral movement, that is, the distance between the target position S1 and the initial point. Dividing the window data according to the target length can ensure the consistency of the window data, thereby improving the accuracy of the prediction results.

[0082] S101 - 2 , dividing the operation data within the target time period according to the window length to obtain continuous window data.

[0083] It should be understood that the operation data includes the position data of the screw rod. Based on the position data of the screw rod, the movement distance of the screw rod can be calculated between any two acquisition time points, thereby completing the window division.

[0084] exist Figure 2 Based on the content in S103, the present application embodiment also provides a possible implementation method, please refer to Figure 5 , S103 includes: S103-1, S103-2 and S103-3, which are specifically described as follows.

[0085] S103 - 1 , dividing the window data into at least one target interval based on a collection time point corresponding to the window data.

[0086] The time width of the target interval is equal to the first preset time length, and the first preset time length is, for example, 10 minutes.

[0087] It should be understood that the running data includes the corresponding collection time point, so the collection time point of the window data can be determined. In order to avoid the sequence length being too long and reduce the difficulty of running the prediction model, the window data can be divided into at least one target interval to achieve the purpose of reducing the sequence length.

[0088] S103-2, acquiring a corresponding subsequence based on each target interval.

[0089] The subsequence is any one of a mean time subsequence, a variance time subsequence, and a coefficient of variation time subsequence.

[0090] Optionally, based on mean information, variance information and coefficient of variation information corresponding to the window data in the target interval, corresponding subsequences may be obtained, such as a mean time subsequence, a variance time subsequence and a coefficient of variation time subsequence.

[0091] S103-3, sorting the subsequences based on the acquisition time points to obtain a corresponding time series set.

[0092] It should be understood that in order to facilitate the prediction of the correlation and time sequence between models or sequences, the subsequences need to be sorted on the timeline to obtain a corresponding time series set, for example, numbered in the order of acquisition time.

[0093] It should be understood that before different types of faults occur, abnormal conditions or abnormal manifestations may be different, and the predicted fault type can also be obtained based on the prediction model. Optionally, the predicted fault information also includes the predicted fault type.

[0094] exist Figure 2 or Figure 3 On the basis of, when the estimated fault information also includes the estimated fault type, as to how to further facilitate the management and maintenance of the staff, the embodiment of the present application also provides a possible implementation method, such as Figure 6 As shown, after S105, the screw transmission structure fault prediction method further includes: S106, S107 and S108, which are described in detail as follows.

[0095] S106, determining an alarm level based on the estimated fault type, the estimated fault occurrence time point, and the estimated fault occurrence probability.

[0096] S107, determine whether the alarm level is greater than the preset alarm threshold. If yes, execute S108; if no, skip.

[0097] S108: Issue an alarm.

[0098] It should be understood that if it is a minor fault that does not affect production, or the probability of a fault is very small, or the time of the fault is a long time away from the current time, it is not necessary to temporarily perform maintenance, and choose to perform maintenance during normal downtime. The alarm threshold can be set according to the level of sophistication required for the work.

[0099] In an embodiment of the present application, the prediction model is a model set that is trained to convergence using a first type of time series set, wherein the first type of time series set is a time series set corresponding to the first type of label data, the first type of label data is the operating data of the faulty screw transmission structure within a preset second time period before the fault, and the first type of time series set is marked with the corresponding time point of the fault occurrence.

[0100] The second preset time length may be, but is not limited to, 30 minutes, and the first type of time series set may also be marked with a corresponding fault type.

[0101] Alternatively, the prediction model is a model set that is trained to convergence using a first type of time series set and a second type of time series set, wherein the first type of time series set is a time series set corresponding to the first type of label data, the first type of label data is the operating data of the screw transmission structure with a fault within a preset second time period before the fault, and the first type of time series set is marked with the corresponding time point of the fault occurrence; the second type of time series set is a time series set corresponding to the second type of label data, the second type of label data is the operating data of the screw transmission structure without a fault within the preset second time period, and the second type of time series set is marked with no fault occurring.

[0102] Optionally, how to convert the first type of label data and the second type of label data into corresponding sequence sets may be implemented by referring to the steps in S101 to S104 above, which will not be described in detail here.

[0103] See also Figure 7 , Figure 7 A screw transmission structure fault prediction device is provided in an embodiment of the present application. Optionally, the screw transmission structure fault prediction device is applied to the electronic device described above.

[0104] The screw transmission structure fault prediction device includes: a processing unit 201 and a model unit 202 .

[0105] The processing unit 201 is used to divide the operation data of the screw transmission structure within the target time period into continuous window data, wherein the operation data includes the corresponding collection time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, speed data and screw position data of the driving device at the collection time point;

[0106] The processing unit 201 is further used to obtain a corresponding time series set based on the window data, wherein the time series set includes any one or more of a mean time series, a variance time series, and a coefficient of variation time series;

[0107] The processing unit 201 is also used to mark abnormal points in the sequence of the time series set, wherein the abnormal point is a point in the sequence whose value exceeds the corresponding preset threshold;

[0108] The model unit 202 is used to process the annotated time series set using a prediction model to obtain estimated fault information, wherein the estimated fault information includes an estimated fault occurrence time point and an estimated fault occurrence probability.

[0109] Optionally, the processing unit 201 may execute the above-mentioned S101 - S104 and S106 - S108 , and the model unit 202 may execute the above-mentioned S105 .

[0110] It should be noted that the screw transmission structure fault prediction device provided in this embodiment can execute the method flow shown in the above method flow embodiment to achieve the corresponding technical effect. For the sake of brief description, for parts not mentioned in this embodiment, reference can be made to the corresponding contents in the above embodiment.

[0111] The present application also provides a storage medium storing computer instructions and programs, which, when read and executed, execute the screw transmission structure fault prediction method of the above embodiment. The storage medium may include memory, flash memory, register, or a combination thereof.

[0112] The following provides an electronic device, which can be a server that can obtain the operating data of the screw drive structure; it can also be an operating device equipped with a screw drive structure, such as an engraving machine, a blister forming machine, a feeder, etc.; it can also be an Internet of Things gateway for connecting the screw drive structure to a network (Internet, local area network or enterprise intranet). Figure 1 As shown, the above-mentioned screw transmission structure fault prediction method can be implemented; specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 can be a CPU. The memory 11 is used to store one or more programs. When the one or more programs are executed by the processor 10, the screw transmission structure fault prediction method of the above-mentioned embodiment is executed.

[0113] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing 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 from the order marked in the accompanying drawings. For example, two consecutive boxes 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 the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0115] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0116] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0117] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for predicting a screw transmission structure fault, characterized in that: The screw transmission structure comprises a driving device and a screw, wherein the screw is in driving connection with the driving device, and the driving device is used to drive the screw to reciprocate or move in one direction. The method comprises: The operation data of the screw transmission structure within the target time period is divided into continuous window data, wherein the operation data includes the corresponding collection time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, speed data of the driving device at the collection time point and the position data of the screw; Acquire a corresponding time series set based on the window data, wherein the time series set includes any one or more of a mean time series, a variance time series, and a coefficient of variation time series; Annotating abnormal points in the sequence of the time series set, wherein the abnormal points are points in the sequence whose values ​​exceed corresponding preset thresholds; The predicted model is used to process the annotated time series set to obtain estimated fault information, wherein the estimated fault information includes an estimated fault occurrence time point and an estimated fault occurrence probability; The step of acquiring the corresponding time series set based on the window data includes: dividing the window data into at least one target interval based on the collection time point corresponding to the window data, wherein the time width of the target interval is equal to a first preset time length; acquiring a corresponding subsequence based on each target interval, wherein the subsequence is any one of a mean time subsequence, a variance time subsequence, and a coefficient of variation time subsequence; and sorting the subsequences based on the collection time point to obtain the corresponding time series set.

2. The screw transmission structure fault prediction method according to claim 1, characterized in that: After dividing the operation data of the screw transmission structure within the target time period into continuous window data, the method further includes: Eliminating fluctuation data in the window data, wherein the fluctuation data is operating data that satisfies any one or more of a first preset condition, a second preset condition, a third preset condition, and a fourth preset condition; The first preset condition is that the rotational speed data does not belong to the preset speed range, the second preset condition is that the acceleration data does not belong to the preset acceleration range, the third preset condition is that the deceleration data does not belong to the preset deceleration range, and the fourth preset condition is that the acceleration / deceleration variation data does not belong to the preset variation range.

3. The screw transmission structure fault prediction method according to claim 1, characterized in that: The step of dividing the operation data of the screw transmission structure within the target time period into continuous window data includes: Determine a preset target length as the window length, wherein the window length represents the length that the lead screw travels from a position corresponding to the first operating data in the window to a position corresponding to the last operating data in the window; The operation data within the target time period is segmented according to the window length to obtain continuous window data.

4. The screw transmission structure fault prediction method according to claim 1, characterized in that: The predicted fault information also includes the predicted fault type. After obtaining the predicted fault information, the method further includes: Determine an alarm level based on the estimated fault type, the estimated fault occurrence time point, and the estimated fault occurrence probability; When the alarm level is greater than a preset alarm threshold, an alarm is issued.

5. The screw transmission structure fault prediction method according to claim 1, characterized in that: The prediction model is a model set that is trained to convergence using a first type of time series set, wherein the first type of time series set is a time series set corresponding to a first type of label data, the first type of label data is the operating data of the faulty screw transmission structure within a preset second time period before the fault, and the first type of time series set is marked with the corresponding time point of the fault occurrence.

6. The screw transmission structure fault prediction method according to claim 5, characterized in that: The prediction model is a model set that is trained to convergence using a first type of time series set and a second type of time series set, wherein the first type of time series set is a time series set corresponding to the first type of label data, the first type of label data is the operating data of the screw transmission structure with a fault within a preset second time length before the fault, and the first type of time series set is marked with the corresponding fault occurrence time point; the second type of time series set is a time series set corresponding to the second type of label data, the second type of label data is the operating data of the screw transmission structure without a fault within a preset second time length, and the second type of time series set is marked with no fault occurring.

7. A screw transmission structure fault prediction device, characterized in that: The screw transmission structure includes a driving device and a screw, the screw is in driving connection with the driving device, the driving device is used to drive the screw to reciprocate or move in one direction, and the device includes: A processing unit, used for dividing the operation data of the screw transmission structure within the target time period into continuous window data, wherein the operation data includes the corresponding collection time point, and the operation data also includes any one or more of the motor load rate, current data, torque data, rotation speed data of the driving device at the collection time point and the position data of the screw; The processing unit is further used to obtain a corresponding time series set based on the window data, wherein the time series set includes any one or more of a mean time series, a variance time series, and a coefficient of variation time series; The processing unit is further used to mark abnormal points in the sequence of the time series set, wherein the abnormal point is a point in the sequence whose value exceeds a corresponding preset threshold; A model unit, used to process the annotated time series set using a prediction model to obtain estimated fault information, wherein the estimated fault information includes an estimated fault occurrence time point and an estimated fault occurrence probability; The acquiring of the corresponding time series set based on the window data includes: dividing the window data into at least one target interval based on the collection time point corresponding to the window data, wherein the time width of the target interval is equal to a first preset time length; acquiring a corresponding subsequence based on each target interval, wherein the subsequence is any one of a mean time subsequence, a variance time subsequence, and a coefficient of variation time subsequence; and sorting the subsequences based on the collection time point to obtain the corresponding time series set.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store one or more programs; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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