CN114444536A - Equipment production condition monitoring method and system based on vibration detection and storage medium - Google Patents
Equipment production condition monitoring method and system based on vibration detection and storage medium Download PDFInfo
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Abstract
The application provides a method, a system and a computer medium for monitoring equipment conditions based on vibration detection, in particular to a method, a system and a computer medium for acquiring vibration measurement data in the production process of equipment; predicting the production state of the equipment according to the vibration measurement data; predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model; and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result. According to the method, a vibration detection device is installed on the paper packaging and cutting equipment, and the production and operation conditions of the equipment are monitored in real time through deep learning model analysis; meanwhile, the production efficiency of an enterprise is promoted through capacity estimation, and the production and labor cost of the enterprise is reduced.
Description
Technical Field
The application belongs to the technical field of equipment detection, and particularly relates to an equipment condition monitoring method and system based on vibration detection and a storage medium.
Background
At present, the paper packaging industry has undergone a high-speed development stage, and now forms a considerable production scale, which becomes an important component in the manufacturing field. With the development of informatization, the introduction of informatization and intellectualization into the paper packaging industry is the key of competition of the market industry in the future. For example, it is an industry problem to be solved urgently to improve the production efficiency in the paper packaging production process by making the mechanical equipment intelligent.
However, the production equipment and the production mode in the paper packaging industry are still mainly based on the traditional mode at present. The production conditions of most paper package cutting equipment are still monitored manually, and the efficiency and the reliability are low. Therefore, in the production process, under the condition that the paper packaging and cutting equipment is not intelligently monitored, serious consequences such as production halt and the like are caused due to difficulty in timely finding when abnormality or fault occurs. Other problems also include that the data interfaces between different devices are not uniform and opaque, all devices are monitored by lacking of a universal technical means, and a decision layer cannot timely acquire the production data of the whole factory, so that the overall arrangement of production tasks is influenced.
Disclosure of Invention
The invention provides a device condition monitoring method system and a storage medium based on vibration detection, and aims to solve the problems that the production condition of equipment cannot be intelligently and effectively monitored and manpower and material resources are consumed in the existing paper packaging industry.
According to a first aspect of the embodiments of the present application, there is provided a device condition monitoring method based on vibration detection, specifically including the following steps:
obtaining vibration measurement data in the production process of equipment;
predicting the production state of the equipment according to the vibration measurement data;
predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model;
and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result.
In some embodiments of the present application, predicting the equipment production status from the vibration measurement data comprises:
fitting the vibration measurement data to obtain a vibration fitting curve;
according to the vibration fitting curve, counting vibration wave peaks to calculate the cutting times of the equipment;
and predicting the equipment capacity according to the cutting times of the equipment.
In some embodiments of the present application, fitting the vibration measurement data to obtain a vibration fitting curve specifically includes:
carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data;
subtracting a current value from a vertical component of the vibration frequency spectrum data, taking an absolute value, selecting a maximum value of the left edge of the vibration frequency spectrum data after Hilbert transform, Fourier transform and normalization, and obtaining frequency data to be selected;
and carrying out low-pass filtering according to the frequency data to be selected to obtain a vibration fitting curve.
In some embodiments of the present application, predicting the production state of the equipment according to the vibration measurement data specifically includes:
obtaining vibration sample data in the production process of equipment;
clustering the vibration sample data through a k-means algorithm to obtain data characteristic samples in different equipment production states;
and comparing and analyzing the vibration measurement data with the data characteristic sample, and predicting to obtain the current equipment production state.
In some embodiments of the present application, the device production state includes a start-up state, a device idle state, and a cutting state.
In some embodiments of the present application, before predicting, by using the trained device state model, device state data corresponding to a device production state, the method further includes:
obtaining vibration sample data in the production process of equipment;
carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data;
and inputting the vibration frequency spectrum data into the LSTM model for training to obtain a trained equipment state model.
In some embodiments of the present application, performing spectrum conversion and denoising on vibration sample data to obtain vibration spectrum data specifically includes:
carrying out frequency spectrum conversion on the vibration sample data to obtain a vibration frequency spectrum, and judging whether the vibration frequency spectrum has a unique maximum value or not;
if the only maximum value exists, directly carrying out frequency spectrum fitting in a simple mode to obtain fitted vibration frequency spectrum data;
if the unique maximum value does not exist, judging that the vibration frequency spectrum does not contain time parameters, and directly carrying out frequency spectrum fitting in a simple mode to obtain fitted vibration frequency spectrum data when the unique maximum value exists after the time parameters are input; otherwise, automatically estimating the detection frequency spectrum parameters by using a genetic algorithm, and obtaining vibration frequency spectrum data under the minimum loss function through repeated estimation.
According to a second aspect of the embodiments of the present application, there is provided a device condition monitoring system based on vibration detection, specifically including:
a data acquisition module: the device is used for acquiring vibration measurement data in the production process of equipment;
a production state prediction module: for predicting the production state of the equipment according to the vibration measurement data;
a device state model module: the device state data corresponding to the production state of the device is predicted through the trained device state model;
an equipment condition monitoring unit: and the device state data and the vibration measurement data are compared to obtain a data comparison result, and whether the device is abnormal or not is judged according to the data comparison result.
According to a third aspect of embodiments of the present application, there is provided a device condition monitoring device based on vibration detection, comprising:
a memory: for storing executable instructions; and
and the processor is connected with the memory to execute the executable instructions so as to complete the device condition monitoring method based on vibration detection.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement a method of monitoring a condition of a device based on vibration detection.
By adopting the equipment condition monitoring method and system based on vibration detection and the computer medium in the embodiment of the application, specifically, vibration measurement data in the production process of equipment is obtained; predicting the production state of the equipment according to the vibration measurement data; predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model; and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result. According to the method, a vibration detection device is installed on the paper packaging and cutting equipment, and the production and operation conditions of the equipment are monitored in real time through deep learning model analysis; meanwhile, the production efficiency of an enterprise is promoted through capacity estimation, and the production and labor cost of the enterprise is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram illustrating steps of a method for monitoring the condition of a device based on vibration detection according to an embodiment of the present application;
FIG. 2 is a schematic diagram showing a fitting curve obtained by fitting according to vibration measurement data according to an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating a process of performing spectrum conversion and denoising on vibration sample data according to an embodiment of the present application;
a schematic structural diagram of a device condition monitoring system based on vibration detection according to an embodiment of the present application is shown in fig. 4;
a schematic structural diagram of a device condition monitoring device based on vibration detection according to an embodiment of the present application is shown in fig. 5.
Detailed Description
In the process of implementing the application, the inventor finds that the production equipment and the production mode in the paper packaging industry are still mainly the traditional mode. The production conditions of most paper package cutting equipment are still monitored manually, and the efficiency and the reliability are low. In the production process, under the condition that the paper packaging and cutting equipment is not intelligently monitored, serious consequences such as production halt and the like are difficult to find in time when abnormity or fault occurs.
In order to solve the problem, the method for monitoring the production condition and estimating the productivity of the equipment in the paper packaging industry based on vibration detection is designed, and has the following beneficial effects:
1. the detection precision is high: the cutting precision of the paper packaging switching equipment can reach over 90% through the vibration detection device; 2. real-time monitoring: the vibration detection device can be used for monitoring the production condition of equipment in real time, so that the labor is saved, and the monitoring efficiency is improved; 3. and (4) abnormal alarming: the deep learning algorithm is utilized to train vibration data in normal operation, and an alarm is automatically given after abnormality is found, so that the failure probability of equipment is reduced; 4. the universality is strong: based on an unsupervised learning algorithm, the device can automatically adapt to different production equipment and environments without any configuration aiming at scenes; 5. the deployment is simple: the device only needs to scan a code to distribute the network and install the power supply, and has no influence on production equipment; 6. the equipment is light: the device has the advantages of light weight, small volume, no space occupation and easy transportation and installation.
Specifically, the present application is a method, a system and a computer medium for monitoring equipment conditions based on vibration detection, which obtains vibration measurement data during the production process of the equipment; predicting the production state of the equipment according to the vibration measurement data; predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model; and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result.
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1
A schematic step diagram of a device condition monitoring method based on vibration detection according to an embodiment of the present application is shown in fig. 1. As shown in fig. 1, the method for monitoring the condition of the device based on vibration detection in the embodiment of the present application specifically includes the following steps:
s101: and acquiring vibration measurement data in the production process of the equipment.
The vibration detection device is firstly required to be installed, and can be installed at the position where the vibration of the paper packaging and cutting equipment is obvious in a magnetic adsorption mode. Specifically, a wired power supply is installed or a self-contained rechargeable battery is used; and scanning the two-dimensional code by using the mobile phone to set the wifi network to be connected with the server detection center.
When the vibration detection device is used for collecting data, the vibration detection device utilizes an acceleration sensor built in embedded hardware to collect vibration data of various production states of the paper packaging and cutting equipment, such as acceleration data generated by equipment vibration. Then, converting the analog signal into a digital signal and sending the digital signal to a server detection center through wifi.
And then, obtaining vibration measurement data in the equipment production process.
The detection method and a series of algorithms can be completed in a server detection center.
S102: and predicting the production state of the equipment according to the vibration measurement data.
Specifically, firstly, obtaining vibration sample data in the production process of equipment; clustering the vibration sample data through a k-means algorithm to obtain data characteristic samples in different equipment production states;
and then, comparing and analyzing the vibration measurement data with the data characteristic sample, and predicting to obtain the current equipment production state.
Specifically, the production state of the device includes a startup state, an idle state of the device, a cutting state and the like.
S103: and predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model.
Specifically, before predicting the device state data corresponding to the device production state through the trained device state model, the method further includes a process of training the device state model.
Firstly, obtaining vibration sample data in the production process of equipment; then, carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data; and finally, inputting the vibration frequency spectrum data into an LSTM model for training to obtain a trained equipment state model.
S104: and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result.
Finally, when the production condition of the paper packaging and cutting equipment is monitored, when the difference between the equipment state data predicted by the model and the real vibration measurement data is too large and exceeds a threshold value, the system judges that the production equipment is abnormal; meanwhile, the system can send a message to the detection device to alarm in real time.
In a preferred embodiment, the server receives the vibration data, counts the cutting times of the paper package cutting equipment in a cutting state within a period of time, calculates the production capacity of the equipment according to the cutting times, and performs capacity estimation.
After the vibration measurement data in the equipment production process is obtained, the equipment production state prediction method based on the vibration measurement data further comprises the equipment productivity prediction process, and specifically comprises the following steps:
first, as shown in fig. 2, the vibration measurement data is fitted to obtain a vibration fitting curve C.
Specifically, the vibration measurement data is fitted to obtain a vibration fitting curve. The specific fitting process comprises the following steps:
carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data; subtracting a current value from a vertical component of the vibration spectrum data, taking an absolute value, selecting a maximum value for removing the left edge of the vibration spectrum data after Hilbert transform, Fourier transform and normalization, and obtaining frequency data to be selected; and carrying out low-pass filtering according to the frequency data to be selected to obtain a vibration fitting curve.
Secondly, according to the vibration fitting curve, the vibration wave crest A is counted, and the cutting times of the equipment are calculated.
And finally, predicting the equipment capacity according to the cutting times of the equipment.
Fig. 3 is a schematic flow chart illustrating the process of performing spectrum conversion and denoising on vibration sample data according to the embodiment of the present application.
The process of training the equipment state model and the process of predicting the equipment capacity comprise the following steps: and carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data.
The method specifically comprises the following steps: 1) carrying out frequency spectrum conversion on the vibration sample data to obtain a vibration frequency spectrum, and judging whether the vibration frequency spectrum has a unique maximum value or not; 2) if the only maximum value exists, directly performing frequency spectrum fitting in a simple mode to obtain fitted vibration frequency spectrum data; 3) if the unique maximum value does not exist, judging that the vibration frequency spectrum does not contain time parameters, and directly carrying out frequency spectrum fitting in a simple mode to obtain fitted vibration frequency spectrum data when the unique maximum value exists after the time parameters are input; otherwise, automatically estimating the detection frequency spectrum parameters by using a genetic algorithm, and obtaining vibration frequency spectrum data under the minimum loss function through repeated estimation.
As shown in fig. 3, specifically, firstly, performing spectrum conversion on input device vibration data, and determining whether a unique maximum exists in a spectrum, if so, directly performing simple mode calculation on model parameters; if not, judging whether the input vibration data contains time parameters or not, if not, inputting the time parameters, and continuously judging whether a unique maximum value exists in the frequency spectrum interval or not. If the unique maximum value does not exist, the parameters of the complex mode configuration are carried out; if so, simple mode configuration parameters are performed.
In the simple mode, the maximum value of the left edge of the removed frequency spectrum is selected as the frequency to be selected, and a wavelet threshold transformation threshold, a band-pass filter parameter, the length of a smoothing filter window and the order of a polynomial for fitting a sample are calculated.
In a complex mode, a genetic algorithm is used for automatically estimating detection parameters, and a loss function is repeatedly calculated for multiple times to obtain a wavelet threshold transformation threshold value, a band-pass filter parameter, the length of a smoothing filter window and the order of a polynomial for fitting a sample under the minimum loss function.
The equipment condition monitoring method based on vibration detection in the embodiment of the application specifically comprises the steps of obtaining vibration measurement data in the production process of equipment; predicting the production state of the equipment according to the vibration measurement data; predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model; and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result. According to the method, a vibration detection device is installed on the paper packaging and cutting equipment, and the production and operation conditions of the equipment are monitored in real time through deep learning model analysis; meanwhile, the production efficiency of an enterprise is promoted through capacity estimation, and the production and labor cost of the enterprise is reduced.
Example 2
For details not disclosed in the system for monitoring the condition of the device based on vibration detection in this embodiment, please refer to specific implementation contents of the method for monitoring the condition of the device based on vibration detection in other embodiments.
A schematic structural diagram of a device condition monitoring system based on vibration detection according to an embodiment of the present application is shown in fig. 4.
As shown in fig. 4, the system for monitoring the condition of the equipment based on vibration detection according to the embodiment of the present application specifically includes a data acquisition module 10, a production state prediction module 20, an equipment state model module 30, and an equipment condition monitoring unit 40.
In particular, the method comprises the following steps of,
the data acquisition module 10: the method is used for acquiring vibration measurement data in the equipment production process.
The vibration detection device is firstly required to be installed, and can be installed at the position where the vibration of the paper packaging and cutting equipment is obvious in a magnetic adsorption mode. Specifically, a wired power supply is installed or a self-contained rechargeable battery is used; and scanning the two-dimensional code by using the mobile phone to set the wifi network to be connected with the server detection center.
When the vibration detection device is used for collecting data, the vibration detection device utilizes an acceleration sensor built in embedded hardware to collect vibration data of various production states of the paper packaging and cutting equipment, such as acceleration data generated by equipment vibration. Then, converting the analog signal into a digital signal and sending the digital signal to a server detection center through wifi.
And then, obtaining vibration measurement data in the equipment production process.
The detection method and a series of algorithms can be completed in a server detection center.
The production state prediction module 20: for predicting the production state of the device based on the vibration measurement data.
Specifically, firstly, obtaining vibration sample data in the production process of equipment; clustering the vibration sample data through a k-means algorithm to obtain data characteristic samples in different equipment production states;
and then, comparing and analyzing the vibration measurement data with the data characteristic sample, and predicting to obtain the current equipment production state.
Specifically, the production state of the device includes a startup state, an idle state of the device, a cutting state and the like.
Device state model module 30: and predicting equipment state data corresponding to the equipment production state through the trained equipment state model.
Specifically, before predicting the device state data corresponding to the device production state through the trained device state model, the method further includes a process of training the device state model.
Firstly, obtaining vibration sample data in the production process of equipment; then, carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data; and finally, inputting the vibration frequency spectrum data into an LSTM model for training to obtain a trained equipment state model.
The device condition monitoring unit 40: and the device state data and the vibration measurement data are compared to obtain a data comparison result, and whether the device is abnormal or not is judged according to the data comparison result.
Finally, when the production condition of the paper packaging and cutting equipment is monitored, when the difference between the equipment state data predicted by the model and the real vibration measurement data is too large and exceeds a threshold value, the system judges that the production equipment is abnormal; meanwhile, the system can send a message to the detection device to alarm in real time.
In a preferred embodiment, the server receives the vibration data, counts the cutting times of the paper package cutting equipment in a cutting state within a period of time, calculates the production capacity of the equipment according to the cutting times, and performs capacity estimation.
After the vibration measurement data in the equipment production process is obtained, the method further comprises the process of predicting the equipment capacity, and specifically comprises the following steps:
firstly, fitting the vibration measurement data to obtain a vibration fitting curve C.
Specifically, the vibration measurement data is fitted to obtain a vibration fitting curve. The specific fitting process comprises the following steps:
carrying out spectrum conversion and denoising processing on the vibration sample data to obtain vibration spectrum data; subtracting a current value from a vertical component of the vibration spectrum data, taking an absolute value, selecting a maximum value for removing the left edge of the vibration spectrum data after Hilbert transform, Fourier transform and normalization, and obtaining frequency data to be selected; and carrying out low-pass filtering according to the frequency data to be selected to obtain a vibration fitting curve.
Secondly, according to the vibration fitting curve, the vibration wave crest A is counted, and the cutting times of the equipment are calculated.
And finally, predicting the equipment capacity according to the cutting times of the equipment.
Through the equipment condition monitoring system based on vibration detection in the embodiment of the application, the data acquisition module 10 acquires vibration measurement data in the equipment production process; the production state prediction module 20 predicts the production state of the equipment according to the vibration measurement data; the equipment state model module 30 predicts equipment state data corresponding to the equipment production state through the trained equipment state model; the equipment condition monitoring unit 40 compares the equipment state data and the vibration measurement data to obtain a data comparison result, and determines whether the equipment is abnormal or not according to the data comparison result. According to the method, a vibration detection device is installed on the paper packaging and cutting equipment, and the production and operation conditions of the equipment are monitored in real time through deep learning model analysis; meanwhile, the production efficiency of an enterprise is promoted through capacity estimation, and the production and labor cost of the enterprise is reduced.
Example 3
For details not disclosed in the apparatus condition monitoring apparatus based on vibration detection of this embodiment, please refer to specific implementation contents of the apparatus condition monitoring method or system based on vibration detection in other embodiments.
A schematic structural diagram of a device condition monitoring device 400 based on vibration detection according to an embodiment of the present application is shown in fig. 5.
As shown in fig. 5, the device condition monitoring device 400 includes:
the memory 402: for storing executable instructions; and
a processor 401 is coupled to the memory 402 to execute executable instructions to perform the motion vector prediction method.
Those skilled in the art will appreciate that the schematic diagram 5 is merely an example of the device condition monitoring device 400 and does not constitute a limitation of the device condition monitoring device 400 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the device condition monitoring device 400 may also include input output devices, network access devices, buses, etc.
The Processor 401 (CPU) may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor 401 may be any conventional processor or the like, and the processor 401 is the control center of the device condition monitoring device 400 and is connected to the various parts of the overall device condition monitoring device 400 by various interfaces and lines.
The memory 402 may be used to store computer readable instructions and the processor 401 may implement the various functions of the device condition monitoring device 400 by executing or executing computer readable instructions or modules stored in the memory 402 and by invoking data stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the device condition monitoring device 400, and the like. In addition, the Memory 402 may include a hard disk, a Memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Memory Card (Flash Card), at least one disk storage device, a Flash Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), or other non-volatile/volatile storage devices.
The modules integrated by the device condition monitoring device 400, if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer-readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by hardware related to computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program; the computer program is executed by a processor to implement the vibration detection based device condition monitoring method in other embodiments.
The equipment condition monitoring equipment based on vibration detection and the computer storage medium in the embodiment of the application acquire vibration measurement data in the production process of the equipment; predicting the production state of the equipment according to the vibration measurement data; predicting equipment state data corresponding to the production state of the equipment through the trained equipment state model; and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result. According to the method, a vibration detection device is installed on the paper packaging and cutting equipment, and the production and operation conditions of the equipment are monitored in real time through deep learning model analysis; meanwhile, the production efficiency of an enterprise is promoted through capacity estimation, and the production and labor cost of the enterprise is reduced.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such 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 intended to include such modifications and variations as well.
Claims (10)
1. A device condition monitoring method based on vibration detection is characterized by comprising the following steps:
obtaining vibration measurement data in the production process of equipment;
predicting the production state of the equipment according to the vibration measurement data;
predicting equipment state data corresponding to the equipment production state through the trained equipment state model;
and comparing the equipment state data with the vibration measurement data to obtain a data comparison result, and judging whether the equipment is abnormal or not according to the data comparison result.
2. The equipment condition monitoring method of claim 1, wherein said predicting an equipment production state from said vibration measurement data comprises:
fitting the vibration measurement data to obtain a vibration fitting curve;
according to the vibration fitting curve, counting vibration wave crests to calculate the cutting times of the equipment;
and estimating the productivity of the equipment according to the cutting times of the equipment.
3. The device condition monitoring method according to claim 2, wherein fitting the vibration measurement data to obtain a vibration fitting curve specifically comprises:
carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data;
subtracting a current value from the vertical component of the vibration spectrum data, taking an absolute value, then selecting a maximum value from which the left edge of the vibration spectrum data is removed after Hilbert transform, Fourier transform and normalization, and obtaining frequency data to be selected;
and carrying out low-pass filtering according to the frequency data to be selected to obtain a vibration fitting curve.
4. The equipment condition monitoring method according to claim 1, wherein predicting an equipment production state from the vibration measurement data specifically comprises:
obtaining vibration sample data in the production process of equipment;
clustering the vibration sample data through a k-means algorithm to obtain data characteristic samples in different equipment production states;
and comparing and analyzing the vibration measurement data and the data characteristic sample, and predicting to obtain the current equipment production state.
5. The equipment condition monitoring method of claim 4, wherein the equipment production state comprises a power-on state, an equipment idle state, and a cutting state.
6. The method for monitoring equipment conditions according to claim 1, wherein before predicting the equipment state data corresponding to the equipment production state through the trained equipment state model, the method further comprises:
obtaining vibration sample data in the production process of equipment;
carrying out frequency spectrum conversion and denoising processing on the vibration sample data to obtain vibration frequency spectrum data;
and inputting the vibration frequency spectrum data into an LSTM model for training to obtain a trained equipment state model.
7. The device condition monitoring method according to claim 3 or 6, wherein the performing spectrum conversion and denoising processing on the vibration sample data to obtain vibration spectrum data specifically comprises:
carrying out frequency spectrum conversion on the vibration sample data to obtain a vibration frequency spectrum, and judging whether the vibration frequency spectrum has a unique maximum value or not;
if the only maximum value exists, directly carrying out frequency spectrum fitting in a simple mode to obtain fitted vibration frequency spectrum data;
if the unique maximum value does not exist, judging that the vibration frequency spectrum does not contain the time parameter, and directly carrying out frequency spectrum fitting in a simple mode to obtain fitted vibration frequency spectrum data when the unique maximum value exists after the time parameter is input; otherwise, automatically estimating the detection frequency spectrum parameters by using a genetic algorithm, and obtaining vibration frequency spectrum data under the minimum loss function through repeated estimation.
8. The method for device condition monitoring based on vibration detection according to any of claims 1-7, the system comprising:
a data acquisition module: the vibration measuring device is used for acquiring vibration measuring data in the production process of equipment;
a production state prediction module: for predicting the equipment production state from the vibration measurement data;
a device state model module: the device state data corresponding to the production state of the device is predicted through the trained device state model;
an equipment condition monitoring unit: and the device state data and the vibration measurement data are compared to obtain a data comparison result, and whether the device is abnormal or not is judged according to the data comparison result.
9. An apparatus condition monitoring apparatus based on vibration detection, comprising:
a memory: for storing executable instructions; and
a processor for interfacing with the memory to execute the executable instructions to perform the method of vibration detection based device condition monitoring of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program; a computer program to be executed by a processor for implementing a method for device condition monitoring based on vibration detection as claimed in any one of claims 1-7.
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