Equipment production status monitoring method, system and storage medium based on vibration detection

Abstract

The present application provides a method, system and computer medium for monitoring equipment status based on vibration detection. Specifically, the vibration measurement data in the equipment production process is obtained; the equipment production status is predicted based on the vibration measurement data; the equipment status data corresponding to the equipment production status is predicted through the trained equipment status model; the equipment status data and the vibration measurement data are compared to obtain data comparison results, and whether the equipment is abnormal is determined based on the data comparison results. The present application installs a vibration detection device on the paper packaging cutting equipment, and monitors the production and operation of the equipment in real time through deep learning model analysis; at the same time, the production efficiency of the enterprise is promoted through capacity estimation, and the production and labor costs of the enterprise are reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of equipment detection, and specifically, relates to a method, system and storage medium for monitoring equipment status based on vibration detection. Background Art

[0002] At present, the paper packaging industry has experienced a period of rapid development and has now formed a considerable production scale, becoming an important part of the manufacturing field. With the development of informatization, introducing informatization and intelligence into the paper packaging industry is the key to future market competition. For example, improving production efficiency in the paper packaging production process by making mechanical equipment intelligent is an industry problem that needs to be solved urgently.

[0003] However, the production equipment and production mode in the paper packaging industry are still mainly traditional. The production status of most paper packaging cutting equipment is still monitored manually, which is inefficient and unreliable. Therefore, in the absence of intelligent monitoring of paper packaging cutting equipment during the production process, it is difficult to detect abnormalities or failures in time, which will cause serious consequences such as production suspension. Other problems include the lack of unified and transparent data interfaces between different devices and the lack of common technical means to monitor all equipment. The decision-making level cannot obtain the overall production data of the factory in a timely manner, which affects the overall arrangement of production tasks. Summary of the invention

[0004] The present invention proposes a vibration detection-based equipment condition monitoring method system and storage medium, aiming to solve the problem that the current paper packaging industry cannot intelligently and effectively monitor the production status of equipment, which wastes manpower and material resources.

[0005] According to a first aspect of an embodiment of the present application, a method for monitoring equipment condition based on vibration detection is provided, which specifically includes the following steps:

[0006] Obtain vibration measurement data during equipment production;

[0007] Predict equipment production status based on vibration measurement data;

[0008] The trained equipment status model is used to predict the equipment status data corresponding to the equipment production status;

[0009] The equipment status data and the vibration measurement data are compared to obtain a data comparison result, and whether an abnormality occurs in the equipment is determined based on the data comparison result.

[0010] In some embodiments of the present application, predicting the equipment production status according to the vibration measurement data includes:

[0011] Fitting the vibration measurement data to obtain a vibration fitting curve;

[0012] According to the vibration fitting curve, the number of equipment cutting times is calculated by counting the vibration peaks;

[0013] Predict equipment capacity based on the number of equipment cuts.

[0014] In some implementations of the present application, the vibration measurement data is fitted to obtain a vibration fitting curve, specifically including:

[0015] Perform spectrum conversion and denoising on the vibration sample data to obtain vibration spectrum data;

[0016] Subtract the current value from the vertical component of the vibration spectrum data and take the absolute value. Then, after Hilbert transform, Fourier transform and normalization, select the maximum value of the left edge of the vibration spectrum data to obtain the selected frequency data.

[0017] The vibration fitting curve is obtained by low-pass filtering the selected frequency data.

[0018] In some implementations of the present application, predicting the equipment production status based on vibration measurement data specifically includes:

[0019] Obtain vibration sample data during equipment production;

[0020] The vibration sample data is clustered by the k-means algorithm to obtain data feature samples under different equipment production states;

[0021] The vibration measurement data is compared and analyzed with the data feature samples to predict the current equipment production status.

[0022] In some embodiments of the present application, the equipment production status includes a startup status, an equipment idle status, and a cutting status.

[0023] In some embodiments of the present application, before predicting the device status data corresponding to the device production status through the trained device status model, the method further includes:

[0024] Obtain vibration sample data during equipment production;

[0025] Perform spectrum conversion and denoising on the vibration sample data to obtain vibration spectrum data;

[0026] The vibration spectrum data is input into the LSTM model for training to obtain the trained equipment state model.

[0027] In some embodiments of the present application, spectrum conversion and denoising are performed on the vibration sample data to obtain vibration spectrum data, which specifically includes:

[0028] Perform spectrum conversion on the vibration sample data to obtain a vibration spectrum, and determine whether there is a unique maximum value in the vibration spectrum;

[0029] If there is a unique maximum value, the spectrum fitting in the simple mode is directly performed to obtain the fitted vibration spectrum data;

[0030] If there is no unique maximum value, and when it is judged that the vibration spectrum does not contain time parameters, and there is a unique maximum value after the time parameters are entered, the spectrum fitting in the simple mode is directly performed to obtain the fitted vibration spectrum data; otherwise, the genetic algorithm is used to automatically estimate the detection spectrum parameters, and the vibration spectrum data under the minimum loss function is obtained through repeated estimation.

[0031] According to a second aspect of an embodiment of the present application, a vibration detection-based equipment condition monitoring system is provided, specifically comprising:

[0032] Data acquisition module: used to obtain vibration measurement data during the equipment production process;

[0033] Production status prediction module: used to predict the equipment production status based on vibration measurement data;

[0034] Equipment status model module: used to predict the equipment status data corresponding to the equipment production status through the trained equipment status model;

[0035] Equipment condition monitoring unit: used to compare equipment condition data and vibration measurement data to obtain data comparison results, and determine whether the equipment is abnormal based on the data comparison results.

[0036] According to a third aspect of an embodiment of the present application, there is provided a device for monitoring device condition based on vibration detection, comprising:

[0037] Memory: used to store executable instructions; and

[0038] Processor: used to connect with the memory to execute executable instructions to complete the equipment condition monitoring method based on vibration detection.

[0039] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement a method for monitoring equipment condition based on vibration detection.

[0040] The vibration detection-based equipment condition monitoring method, system and computer medium in the embodiments of the present application are used. Specifically, vibration measurement data in the equipment production process is obtained; the equipment production status is predicted based on the vibration measurement data; the equipment status data corresponding to the equipment production status is predicted through the trained equipment status model; the equipment status data and the vibration measurement data are compared to obtain data comparison results, and whether the equipment is abnormal is determined based on the data comparison results. The present application installs a vibration detection device on the paper packaging cutting equipment, and monitors the production and operation of the equipment in real time through deep learning model analysis; at the same time, the production efficiency of the enterprise is promoted through capacity estimation, and the production and labor costs of the enterprise are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0042] Figure 1 A schematic diagram of the steps of the equipment condition monitoring method based on vibration detection according to an embodiment of the present application is shown in FIG.

[0043] Figure 2 : shows a schematic diagram of a fitting curve obtained by fitting according to vibration measurement data in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the process of spectrum conversion and denoising of vibration sample data according to an embodiment of the present application is shown in FIG.

[0045] Figure 4 A schematic diagram of the structure of the equipment condition monitoring system based on vibration detection according to an embodiment of the present application is shown in FIG.

[0046] Figure 5 Schematic diagram of the structure of the equipment condition monitoring device based on vibration detection according to an embodiment of the present application is shown in FIG. DETAILED DESCRIPTION

[0047] In the process of realizing this application, the inventor found that the production equipment and production mode in the current paper packaging industry are still mainly traditional. The production status of most paper packaging cutting equipment is still monitored manually, which is inefficient and unreliable. In the absence of intelligent monitoring of paper packaging cutting equipment during the production process, it is difficult to detect abnormalities or failures in time, which will cause serious consequences such as suspension of production.

[0048] In order to solve this problem, this application designs a method for monitoring the production status and estimating the production capacity of paper packaging industry equipment based on vibration detection, which has the following beneficial effects:

[0049] 1. High detection accuracy: The cutting accuracy of paper packaging switching equipment can reach more than 90% through the vibration detection device; 2. Real-time monitoring: The vibration detection device can be used to monitor the production status of the equipment in real time, saving manpower and improving monitoring efficiency; 3. Abnormal alarm: The deep learning algorithm is used to train the vibration data during normal operation, and automatically alarm after the abnormality is found, reducing the probability of equipment failure; 4. Strong versatility: Based on the unsupervised learning algorithm, the device of this application can automatically adapt to different production equipment and environments without any configuration for the scene; 5. Simple deployment: The device of this application only needs to scan the code for network configuration and install the power supply, which has no effect on the production equipment; 6. Light equipment: The device of this application is light in weight, small in size, does not take up space, and is easy to transport and install.

[0050] Specifically, the present application is a method, system and computer medium for monitoring equipment condition based on vibration detection, which obtains vibration measurement data during the equipment production process; predicts the equipment production status based on the vibration measurement data; predicts the equipment status data corresponding to the equipment production status through a trained equipment status model; compares the equipment status data and the vibration measurement data to obtain a data comparison result, and determines whether the equipment has an abnormality based on the data comparison result.

[0051] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0052] Example 1

[0053] Figure 1 FIG. 4 is a schematic diagram showing the steps of the device condition monitoring method based on vibration detection according to an embodiment of the present application. Figure 1 As shown, the device condition monitoring method based on vibration detection in the embodiment of the present application specifically includes the following steps:

[0054] S101: Acquire vibration measurement data during the equipment production process.

[0055] First, you need to install a vibration detection device. You can install the vibration detection device at the place where the paper packaging cutting equipment vibrates more obviously by magnetic adsorption. Specifically, install a wired power supply or use a self-contained rechargeable battery; use a mobile phone to scan the QR code to set up a wifi network and connect to the server detection center.

[0056] When collecting data through the vibration detection device, the vibration detection device uses the built-in acceleration sensor of the embedded hardware to collect vibration data of various production states of the paper packaging cutting equipment, such as the acceleration data generated by the vibration of the equipment. Then the analog signal is converted into a digital signal and sent to the server detection center via wifi.

[0057] Furthermore, vibration measurement data during the equipment production process is obtained.

[0058] The detection method and a series of algorithms of this application can be completed in the server-side detection center.

[0059] S102: Predicting equipment production status according to vibration measurement data.

[0060] Specifically, first, obtain vibration sample data during the equipment production process; cluster the vibration sample data using the k-means algorithm to obtain data feature samples under different equipment production states;

[0061] Then, the vibration measurement data is compared and analyzed with the data feature samples to predict the current equipment production status.

[0062] Specifically, the equipment production status includes the startup status, equipment idling status and cutting status, etc.

[0063] S103: Predicting device status data corresponding to the device production status through the trained device status model.

[0064] Specifically, before predicting the equipment status data corresponding to the equipment production status through the trained equipment status model, a process of training the equipment status model is also included.

[0065] First, the vibration sample data in the equipment production process is obtained; then, the vibration sample data is spectrally converted and denoised to obtain vibration spectrum data; finally, the vibration spectrum data is input into the LSTM model for training to obtain the trained equipment state model.

[0066] S104: Compare the device status data and the vibration measurement data to obtain a data comparison result, and determine whether the device is abnormal based on the data comparison result.

[0067] Finally, when monitoring the production status of paper packaging cutting equipment, when the difference between the equipment status data predicted by the model and the actual vibration measurement data exceeds the threshold, the system determines that an abnormality has occurred in the production equipment; at the same time, the system will send a message to the detection device for real-time alarm.

[0068] In a preferred embodiment, the server receives vibration data, calculates the production capacity of the equipment according to the number of cuts by the paper packaging cutting equipment in a cutting state within a period of time, and estimates the production capacity.

[0069] After obtaining the vibration measurement data during the equipment production process, the present application predicts the equipment production status according to the vibration measurement data and also includes predicting the equipment production capacity process, specifically:

[0070] First, if Figure 2 As shown, the vibration measurement data is fitted to obtain a vibration fitting curve C.

[0071] Specifically, the vibration measurement data is fitted to obtain a vibration fitting curve. The specific fitting process includes:

[0072] The vibration sample data is subjected to spectrum conversion and denoising to obtain vibration spectrum data; the vertical component of the vibration spectrum data is subtracted from the current value and the absolute value is taken; then, after Hilbert transform and Fourier transform and normalization, the maximum value of the left edge of the vibration spectrum data is removed to obtain the selected frequency data; low-pass filtering is performed on the selected frequency data to obtain the vibration fitting curve.

[0073] Secondly, according to the vibration fitting curve, the number of equipment cutting times is calculated by counting the vibration peaks A.

[0074] Finally, the equipment capacity is predicted based on the number of equipment cutting times.

[0075] Figure 3 FIG. 4 is a schematic diagram of a process for performing spectrum conversion and denoising on vibration sample data according to an embodiment of the present application.

[0076] The process of training the equipment status model and predicting the equipment capacity includes: performing spectrum conversion and denoising on the vibration sample data to obtain vibration spectrum data.

[0077] Specifically, it includes: 1) performing spectrum conversion on the vibration sample data to obtain the vibration spectrum, and judging whether there is a unique maximum value in the vibration spectrum; 2) if there is a unique maximum value, directly performing spectrum fitting in simple mode to obtain the fitted vibration spectrum data; 3) if there is no unique maximum value, and when it is judged that the vibration spectrum does not contain a time parameter, and there is a unique maximum value after inputting the time parameter, directly performing spectrum fitting in simple mode to obtain the fitted vibration spectrum data; otherwise, using a genetic algorithm to automatically estimate the detection spectrum parameters, and obtaining the vibration spectrum data under the minimum loss function through repeated estimation.

[0078] like Figure 3As shown, specifically, first, the input equipment vibration data is converted into a spectrum, and it is determined whether there is a unique maximum value in the spectrum. If there is, the model parameters are calculated directly in a simple mode; if not, it is first determined whether the input vibration data contains a time parameter. If it does not contain a time parameter, the time parameter needs to be input, and then it is determined whether there is a unique maximum value in the spectrum interval. If there is no unique maximum value, the complex mode configuration parameters are performed; if there is, the simple mode configuration parameters are performed.

[0079] Among them, in the simple mode, the maximum value without the left edge of the spectrum is selected as the candidate frequency, and the wavelet threshold transform threshold, bandpass filter parameters, the length of the smoothing filter window, and the order of the polynomial used to fit the sample are calculated.

[0080] Among them, in the complex mode, a genetic algorithm is used to automatically estimate the detection parameters. By repeatedly calculating the loss function, the wavelet threshold transform threshold, bandpass filter parameters, smoothing filter window length, and the order of the polynomial used to fit the sample under the minimum loss function are obtained.

[0081] The equipment condition monitoring method based on vibration detection in the embodiment of the present application specifically obtains vibration measurement data during the equipment production process; predicts the equipment production status based on the vibration measurement data; predicts the equipment status data corresponding to the equipment production status through the trained equipment status model; compares the equipment status data and the vibration measurement data to obtain a data comparison result, and determines whether the equipment is abnormal based on the data comparison result. The present application installs a vibration detection device on the paper packaging cutting equipment, monitors the production and operation of the equipment in real time through deep learning model analysis; and at the same time promotes the production efficiency of the enterprise through capacity estimation, and reduces the production and labor costs of the enterprise.

[0082] Example 2

[0083] This embodiment provides an equipment condition monitoring system based on vibration detection. For details not disclosed in the equipment condition monitoring system based on vibration detection in this embodiment, please refer to the specific implementation content of the equipment condition monitoring method based on vibration detection in other embodiments.

[0084] Figure 4 Schematic diagram of the structure of the equipment condition monitoring system based on vibration detection according to an embodiment of the present application is shown in FIG.

[0085] like Figure 4 As shown, the equipment condition monitoring system based on vibration detection according to the embodiment of the present application specifically includes a data acquisition module 10 , a production status prediction module 20 , an equipment status model module 30 and an equipment condition monitoring unit 40 .

[0086] Specifically,

[0087] Data acquisition module 10: used to acquire vibration measurement data during the equipment production process.

[0088] First, you need to install a vibration detection device. You can install the vibration detection device at the place where the paper packaging cutting equipment vibrates more obviously by magnetic adsorption. Specifically, install a wired power supply or use a self-contained rechargeable battery; use a mobile phone to scan the QR code to set up a wifi network and connect to the server detection center.

[0089] When collecting data through the vibration detection device, the vibration detection device uses the built-in acceleration sensor of the embedded hardware to collect vibration data of various production states of the paper packaging cutting equipment, such as the acceleration data generated by the vibration of the equipment. Then the analog signal is converted into a digital signal and sent to the server detection center via wifi.

[0090] Furthermore, vibration measurement data during the equipment production process is obtained.

[0091] The detection method and a series of algorithms of this application can be completed in the server-side detection center.

[0092] The production status prediction module 20 is used to predict the equipment production status according to the vibration measurement data.

[0093] Specifically, first, obtain vibration sample data during the equipment production process; cluster the vibration sample data using the k-means algorithm to obtain data feature samples under different equipment production states;

[0094] Then, the vibration measurement data is compared and analyzed with the data feature samples to predict the current equipment production status.

[0095] Specifically, the equipment production status includes the startup status, equipment idling status and cutting status, etc.

[0096] Equipment status model module 30: used to predict equipment status data corresponding to the equipment production status through the trained equipment status model.

[0097] Specifically, before predicting the equipment status data corresponding to the equipment production status through the trained equipment status model, a process of training the equipment status model is also included.

[0098] First, the vibration sample data in the equipment production process is obtained; then, the vibration sample data is spectrally converted and denoised to obtain vibration spectrum data; finally, the vibration spectrum data is input into the LSTM model for training to obtain the trained equipment state model.

[0099] The equipment status monitoring unit 40 is used to compare the equipment status data and the vibration measurement data to obtain a data comparison result, and determine whether an abnormality occurs in the equipment according to the data comparison result.

[0100] Finally, when monitoring the production status of paper packaging cutting equipment, when the difference between the equipment status data predicted by the model and the actual vibration measurement data exceeds the threshold, the system determines that an abnormality has occurred in the production equipment; at the same time, the system will send a message to the detection device for real-time alarm.

[0101] In a preferred embodiment, the server receives vibration data, calculates the production capacity of the equipment according to the number of cuts by the paper packaging cutting equipment in a cutting state within a period of time, and estimates the production capacity.

[0102] After obtaining the vibration measurement data during the equipment production process, this application also includes the process of predicting the equipment production capacity, specifically:

[0103] First, the vibration measurement data is fitted to obtain the vibration fitting curve C.

[0104] Specifically, the vibration measurement data is fitted to obtain a vibration fitting curve. The specific fitting process includes:

[0105] The vibration sample data is subjected to spectrum conversion and denoising to obtain vibration spectrum data; the vertical component of the vibration spectrum data is subtracted from the current value and the absolute value is taken; then, after Hilbert transform and Fourier transform and normalization, the maximum value of the left edge of the vibration spectrum data is removed to obtain the selected frequency data; low-pass filtering is performed on the selected frequency data to obtain the vibration fitting curve.

[0106] Secondly, according to the vibration fitting curve, the number of equipment cutting times is calculated by counting the vibration peaks A.

[0107] Finally, the equipment capacity is predicted based on the number of equipment cutting times.

[0108] Through the equipment condition monitoring system based on vibration detection in the embodiment of the present application, the data acquisition module 10 acquires the vibration measurement data during the equipment production process; the production status prediction module 20 predicts the equipment production status based on the vibration measurement data; the equipment status model module 30 predicts the equipment status data corresponding to the equipment production status through the trained equipment status model; the equipment condition monitoring unit 40 compares the equipment status data and the vibration measurement data to obtain a data comparison result, and determines whether the equipment is abnormal based on the data comparison result. The present application installs a vibration detection device on the paper packaging cutting equipment, and monitors the production and operation of the equipment in real time through deep learning model analysis; at the same time, it promotes the production efficiency of the enterprise through capacity estimation and reduces the production and labor costs of the enterprise.

[0109] Example 3

[0110] This embodiment provides a device condition monitoring device based on vibration detection. For details not disclosed in the device condition monitoring device based on vibration detection in this embodiment, please refer to the specific implementation content of the device condition monitoring method or system based on vibration detection in other embodiments.

[0111] Figure 5 Schematic diagram of the structure of the equipment condition monitoring device 400 based on vibration detection according to an embodiment of the present application is shown in FIG.

[0112] like Figure 5 As shown, the equipment condition monitoring device 400 includes:

[0113] Memory 402: used to store executable instructions; and

[0114] Processor 401: used to connect with memory 402 to execute executable instructions to complete the motion vector prediction method.

[0115] Those skilled in the art will understand that Figure 5 It is only an example of the equipment condition monitoring device 400 and does not constitute a limitation of the equipment condition monitoring device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the equipment condition monitoring device 400 may also include input and output devices, network access devices, buses, etc.

[0116] The processor 401 (Central Processing Unit, CPU) may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor 401 may also be any conventional processor, etc. The processor 401 is the control center of the equipment condition monitoring device 400, and uses various interfaces and lines to connect various parts of the entire equipment condition monitoring device 400.

[0117] The memory 402 can be used to store computer-readable instructions. The processor 401 implements various functions of the equipment condition monitoring device 400 by running or executing the computer-readable instructions or modules stored in the memory 402 and calling the data stored in the memory 402. The memory 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application 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 equipment condition monitoring device 400. In addition, the memory 402 can include a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM) or other non-volatile / volatile storage devices.

[0118] If the module integrated in the equipment condition monitoring device 400 is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware to complete it 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 each of the above-mentioned method embodiments can be implemented.

[0119] Example 4

[0120] This embodiment provides a computer-readable storage medium on which a computer program is stored; the computer program is executed by a processor to implement the equipment condition monitoring method based on vibration detection in other embodiments.

[0121] The equipment condition monitoring device and computer storage medium based on vibration detection in the embodiments of the present application obtain vibration measurement data during the equipment production process; predict the equipment production status based on the vibration measurement data; predict the equipment status data corresponding to the equipment production status through the trained equipment status model; compare the equipment status data and the vibration measurement data to obtain a data comparison result, and determine whether the equipment is abnormal based on the data comparison result. The present application installs a vibration detection device on the paper packaging cutting equipment, monitors the production and operation of the equipment in real time through deep learning model analysis; and promotes the production efficiency of the enterprise through capacity estimation, and reduces the production and labor costs of the enterprise.

[0122] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0126] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0127] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0128] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0129] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for monitoring equipment condition based on vibration detection, characterized in that: The following steps are involved: Obtain vibration measurement data during equipment production; predicting a production status of the equipment based on the vibration measurement data; Predicting the equipment status data corresponding to the equipment production status through the trained equipment status model; Comparing the device status data and the vibration measurement data to obtain a data comparison result, and determining whether an abnormality occurs in the device according to the data comparison result; Predicting the equipment production status based on the vibration measurement data includes: Fitting the vibration measurement data to obtain a vibration fitting curve; According to the vibration fitting curve, the number of equipment cutting times is calculated by counting the vibration peaks; Estimate the equipment capacity based on the number of cutting times of the equipment.

2. The equipment condition monitoring method according to claim 1, characterized in that: The step of fitting the vibration measurement data to obtain a vibration fitting curve specifically includes: Perform spectrum conversion and denoising on the vibration sample data to obtain vibration spectrum data; Subtract the current value from the vertical component of the vibration spectrum data and take the absolute value, then perform Hilbert transform, Fourier transform and normalize, select and remove the maximum value of the left edge of the vibration spectrum data to obtain the frequency data to be selected; A vibration fitting curve is obtained by performing low-pass filtering on the selected frequency data.

3. The equipment condition monitoring method according to claim 1, characterized in that: The predicting of the equipment production status according to the vibration measurement data specifically includes: Obtain vibration sample data during equipment production; Clustering the vibration sample data by using a k-means algorithm to obtain data feature samples under different equipment production states; The vibration measurement data is compared and analyzed with the data feature samples to predict the current equipment production status.

4. The equipment condition monitoring method according to claim 3, characterized in that: The equipment production status includes a startup status, an equipment idling status and a cutting status.

5. The equipment condition monitoring method according to claim 1, characterized in that: Before predicting the equipment status data corresponding to the equipment production status by the trained equipment status model, the method further includes: Obtain vibration sample data during equipment production; Performing spectrum conversion and denoising on the vibration sample data to obtain vibration spectrum data; The vibration spectrum data is input into the LSTM model for training to obtain a trained equipment state model.

6. The equipment condition monitoring method according to claim 2 or 5, characterized in that: The performing spectrum conversion and denoising on the vibration sample data to obtain vibration spectrum data specifically includes: Performing spectrum conversion on the vibration sample data to obtain a vibration spectrum, and determining whether the vibration spectrum has a unique maximum value; If there is a unique maximum value, the spectrum fitting in the simple mode is directly performed to obtain the fitted vibration spectrum data; If there is no unique maximum value, and when it is determined that the vibration spectrum does not contain a time parameter, and there is a unique maximum value after the time parameter is input, the spectrum fitting in the simple mode is directly performed to obtain the fitted vibration spectrum data; otherwise, a genetic algorithm is used to automatically estimate the detection spectrum parameters, and the vibration spectrum data under the minimum loss function is obtained through repeated estimation.

7. The equipment condition monitoring system based on vibration detection according to any one of claims 1 to 5, the system comprising: Data acquisition module: used to obtain vibration measurement data during the equipment production process; Production status prediction module: used to predict the equipment production status according to the vibration measurement data; Equipment status model module: used to predict the equipment status data corresponding to the production status of the equipment through the trained equipment status model; Equipment status monitoring unit: used for comparing the equipment status data and the vibration measurement data to obtain a data comparison result, and determining whether an abnormality occurs in the equipment according to the data comparison result.

8. A device for monitoring equipment condition based on vibration detection, characterized in that: include: Memory: used to store executable instructions; as well as Processor: used to connect to the memory to execute the executable instructions to complete the equipment condition monitoring method based on vibration detection as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the equipment condition monitoring method based on vibration detection as described in any one of claims 1-6.

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