CN115990629A - Abnormality detection method, device, equipment and medium for stamping equipment - Google Patents
Abnormality detection method, device, equipment and medium for stamping equipment Download PDFInfo
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- CN115990629A CN115990629A CN202111210348.XA CN202111210348A CN115990629A CN 115990629 A CN115990629 A CN 115990629A CN 202111210348 A CN202111210348 A CN 202111210348A CN 115990629 A CN115990629 A CN 115990629A
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Abstract
The application discloses an abnormality detection method, device, equipment and medium of stamping equipment, wherein the abnormality detection method of the stamping equipment comprises the following steps: acquiring first measurement data of the stamping equipment in real time through a sensor; the tonnage measuring device acquires second measurement data of the stamping equipment as reference data; the first measurement data is subjected to data feature extraction operation to obtain effective data corresponding to the first measurement data; inputting the reference data as a training set into a convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model; and detecting whether the punching equipment is abnormal or not according to error data between the effective data and the reference data based on the equipment detection model. The method and the device can rapidly judge the faults of the stamping equipment or the die, so that the repair cost of the stamping equipment or the production cost of the die is reduced, and the operation efficiency of the stamping equipment is improved.
Description
Technical Field
The application belongs to the field of equipment detection, and particularly relates to an abnormality detection method, device, equipment and medium of stamping equipment.
Background
The existing pain points in the current stamping field are faults of stamping equipment or dies which cannot be distinguished rapidly in the stamping production process, so that the fault detection efficiency is lower in the stamping production process, the current requirement of the industry on the abnormal detection accuracy of the stamping equipment is higher, and therefore the long-term working state of the stamping equipment is required to be monitored in the stamping production process, and the loss caused by production interruption due to the abnormal condition of the stamping equipment in the production process is avoided.
Disclosure of Invention
The application provides an abnormality detection method, an abnormality detection device and an abnormality detection medium for punching equipment, and aims to solve the technical problems that the abnormality detection method for punching equipment is not fast enough in detection failure and historical detection data cannot be fed back in real time as a reference, so that the detection efficiency is low.
A first aspect of the present application provides an abnormality detection method of a press apparatus, the abnormality detection method of the press apparatus including:
acquiring first measurement data of the stamping equipment in real time through a sensor;
acquiring second measurement data of the stamping equipment by using a tonnage measuring device as reference data;
performing data feature extraction operation on the first measurement data to obtain effective data corresponding to the first measurement data;
inputting the reference data as a training set into a convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model;
and detecting whether the punching equipment is abnormal or not according to error data between the effective data and the reference data based on the equipment detection model.
In one possible embodiment, the acquiring, by the sensor, the first measurement data of the stamping device in real time includes:
and acquiring pressure data of at least one position on the stamping equipment in real time through the sensor, wherein the at least one position comprises a connecting rod position, two side positions and a sliding block position as the first measurement data.
In one possible implementation manner, the performing a data feature extraction operation on the first measurement data to obtain valid data corresponding to the first measurement data includes:
performing feature transformation on the first measurement data acquired by the sensor in real time to obtain frequency domain data;
filtering high frequency signals in the frequency domain data;
the windowing operation is carried out on the filtered frequency domain data, so that effective frequency domain data are obtained;
and executing inverse characteristic transformation on the effective frequency domain data to obtain effective data.
In one possible embodiment, the method further comprises:
after obtaining the effective frequency domain data, judging whether a curve corresponding to the effective frequency domain data accords with a Gaussian distribution curve, including:
if the curve corresponding to the effective frequency domain data is judged to be in accordance with a Gaussian distribution curve, performing inverse characteristic transformation on the effective frequency domain data to obtain the effective data; or (b)
And if the curve corresponding to the effective frequency domain data is judged not to accord with the Gaussian distribution curve, continuously acquiring the updated first measurement data until the curve corresponding to the effective frequency domain data acquired based on the updated first measurement data accords with the Gaussian distribution curve.
In one possible implementation manner, the training the reference data as a training set input into a convolutional neural network model to obtain a device detection model, and calibrating the effective data based on the device detection model includes:
obtaining a mapping relation between effective data and reference data of the stamping equipment;
and inputting the effective data and the mapping relation into the convolutional neural network model for training, and establishing the equipment detection model.
In one possible embodiment, after establishing the device detection model of the stamping device, the method further comprises:
acquiring historical error data between effective data and reference data of the stamping equipment based on the equipment detection model; and adjusting parameters of the equipment detection model according to the equipment detection model, the effective data, the reference data, the mapping relation and the historical error data, and updating the equipment detection model.
In one possible embodiment, the detecting whether the punching apparatus has an abnormality based on the apparatus detection model according to error data between the effective data and the reference data includes:
inputting the real-time acquired effective data of the stamping equipment into the equipment detection model to obtain actual error data;
and if the actual error data is larger than the calibrated error data, judging that the stamping equipment is abnormal, or if the actual error data is smaller than or equal to the calibrated error data, judging that the stamping equipment is not abnormal.
The application also provides an abnormality detection device of a stamping apparatus, the abnormality detection device including:
a first acquisition unit for acquiring first measurement data of the stamping equipment in real time through the sensor;
a second acquisition unit for acquiring second measurement data of the stamping equipment by a tonnage measuring device as reference data;
an extraction unit, configured to perform a data feature extraction operation on the first measurement data, to obtain valid data corresponding to the first measurement data;
the calibration unit is used for inputting the reference data as a training set into a convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model;
and the detection unit is used for detecting whether the punching equipment is abnormal or not according to the error data between the effective data and the reference data based on the equipment detection model.
The application also provides electronic equipment which comprises a processor and a memory, wherein the processor is used for executing a computer program stored in the memory to realize the abnormality detection method of the stamping equipment.
The present application also provides a computer-readable storage medium storing at least one instruction that when executed by a processor implements the method of anomaly detection for a stamping device.
The method and the device can rapidly judge the faults of the stamping equipment or the die, and are convenient for finding out the faults of the stamping equipment or the die more rapidly and accurately in time, so that the repair cost of the stamping equipment or the production cost of the die is reduced, and the operation efficiency of the stamping equipment is improved. Meanwhile, the monitoring data can be fed back timely, the monitoring data can be checked conveniently in real time, faults of the stamping equipment or the die are judged and found in real time on line according to the analysis and calibration curve of the monitoring data, and the problem that the die production of the stamping equipment does not meet the standard in the batch production operation process due to improper operation or other faults is avoided, so that a large amount of labor and material costs are wasted in a factory and huge production cost is lost, the loss in the factory production operation process is reduced, and the operation efficiency of the stamping equipment is greatly improved.
Drawings
Fig. 1 is a flowchart of an abnormality detection method of a press apparatus provided in an embodiment of the present application.
Fig. 2 is a diagram of data collected by a sensor according to an embodiment of the present application.
Fig. 3 is a signal waveform diagram of a sensor according to an embodiment of the present application.
Fig. 4 is a frame diagram of an abnormality detection device of a press apparatus provided in an embodiment of the present application.
Fig. 5 is a schematic structural diagram of an electronic device for implementing an abnormality detection method of a stamping device according to an embodiment of the present application.
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The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. The method for detecting the abnormality of the stamping equipment generally has two technical problems, namely, the first is that the stamping equipment is not fast enough in detecting faults, mainly because the sensor on the stamping equipment is not sensitive enough in collecting data, and the second is that the stamping equipment cannot feed back historical detection data in real time as a reference, so that the detection efficiency is low. The method for detecting the abnormality of the stamping equipment solves the technical problems that detection faults are not fast enough and historical detection data cannot be fed back timely as a reference, so that detection efficiency is low.
Based on the technical problems, the embodiment of the application provides an abnormality detection method for stamping equipment, which can rapidly judge the faults of the stamping equipment or a die, and is convenient for finding the faults of the stamping equipment or the die more rapidly and accurately in time, so that the repair cost of the stamping equipment or the production cost of the die is reduced, and the operation efficiency of the stamping equipment is improved. Meanwhile, the monitoring data can be fed back timely, the monitoring data can be checked conveniently in real time, faults of the stamping equipment or the die are judged and found in real time on line according to the analysis and calibration curve of the monitoring data, and the problem that the die production of the stamping equipment does not meet the standard in the batch production operation process due to improper operation or other faults is avoided, so that a large amount of labor and material costs are wasted in a factory and huge production cost is lost, the loss in the factory production operation process is reduced, and the operation efficiency of the stamping equipment is greatly improved.
Fig. 1 shows a flowchart of an implementation of an abnormality detection method of a stamping device according to an embodiment of the present application, where the order of steps in the flowchart may be changed and some steps may be omitted according to different requirements.
S101: first measurement data of the stamping device are acquired in real time through the sensor.
In one embodiment of the present application, the stamping device is mainly applied to a production die workshop, and is mainly used for blanking, punching, forming, deep drawing, trimming, fine punching, shaping, riveting, extruding and the like on a plate through a die, and one or more stamping devices can be disposed in the production die workshop, and the stamping devices can be connected with an industrial personal computer, an upper computer and the like, and the stamping devices include, but are not limited to, a punch press, and are particularly not limited to.
It should be noted that, this application acquires the first measurement data of stamping equipment in real time through the sensor, and this first measurement data mainly detects and records the numerical value of the impact force of product stamping forming in-process in real time through the sensor, and product stamping forming process includes decline process and ascending process, and decline process impact force gradually increases, and ascending process impact force gradually reduces, and correspondingly, first measurement data of product stamping forming in-process gradually increases earlier and then gradually reduces. The sensor can be a novel high-sensitivity ultrasonic sensor, and has the advantages of small size, quick response, wide measuring frequency range, high linearity, no need of an external power supply and the like, and meanwhile, the sensor is convenient to paste on structural members such as a punch press, a stamping die and a motor, and is beneficial to accurately detecting and recording specific values of impact force in the stamping forming process of a product.
Specifically, in the whole stamping process, pressure data of at least one position on the stamping equipment is obtained in real time through a sensor and is used as the first measurement data; the at least one position includes a link position, a side position, and a slider position.
The sensor may be attached to at least one of the link position, the both side positions, and the slider position of the pressing device, so that the sensor may collect and record pressure data of the at least one of the link position, the both side positions, and the slider position of the pressing device. For example, as shown in fig. 2, the first sensor is attached to the connecting rod of the stamping device, the second sensor is attached to two sides of the stamping device, the third sensor is attached to the slide of the stamping device, and at each moment, the first sensor, the second sensor and the third sensor record the pressure data at the positions of the first sensor, the second sensor and the third sensor, so that the accuracy of collecting the pressure data is improved.
S102: and acquiring second measurement data of the stamping equipment by using a tonnage measuring device as reference data.
In the embodiment of the application, the tonnage measuring device is arranged under the driving component of the stamping equipment, the driving component of the stamping equipment generates impact force on the tonnage measuring device in the descending and ascending processes, so that the driving component of the tonnage measuring device senses and records pressure data generated by the impact force in real time in the descending and ascending processes, the pressure data generated by the impact force of the driving component of the stamping equipment in each moment in the descending and ascending processes is obtained through the tonnage measuring device to truly reflect the stress of a product in the forming process, and therefore the pressure data collected through the tonnage measuring device can be used as reference data to be compared with the pressure data collected by the sensor. Thereby being convenient for reminding factory operators of abnormal punching machine or die.
It should be noted that the tonnage measuring equipment includes, but is not limited to, tonnage meters, and is not limited in particular.
S103: and executing data characteristic extraction operation on the first measurement data to obtain effective data corresponding to the first measurement data.
In the embodiment of the application, the plurality of sensors collect and record pressure data at a plurality of positions of the stamping device at each moment, as shown in fig. 3, the pressure data at three positions of the stamping device at each moment collected and recorded by the three sensors are presented in a signal waveform mode, and the pressure data at three positions of the stamping device are presented in Normal under the condition that no abnormality occurs in the pressure data at three positions of the stamping device, and in AbNormal under the condition that the pressure data at three positions of the stamping device is AbNormal.
It should be noted that, in practical applications, the pressure data acquired by the sensor often includes a large amount of redundant data types, and in order to improve the efficiency and accuracy of the subsequent data analysis, it is necessary to extract effective pressure data from a large amount of pressure data as effective data.
Specifically, performing feature transformation on the first measurement data acquired by the sensor in real time to obtain frequency domain data;
filtering high frequency signals in the frequency domain data;
the windowing operation is carried out on the filtered frequency domain data, so that effective frequency domain data are obtained;
and executing inverse characteristic transformation on the effective frequency domain data to obtain effective data.
The pressure signal obtained by the sensor is filtered by a filter to remove the high-frequency interference time domain signal, then the time domain signal is converted into a frequency domain signal by fourier transformation, the high-frequency interference signal in the frequency domain signal is further removed, and finally the inverse fourier transformation is performed to transform the inverse characteristic of the frequency domain signal filtered by the high-frequency interference signal into the time domain signal, wherein the time domain signal is an effective signal.
Further specifically, the method further comprises:
after obtaining the effective frequency domain data, judging whether a curve corresponding to the effective frequency domain data accords with a Gaussian distribution curve, including:
if the curve corresponding to the effective frequency domain data is judged to be in accordance with a Gaussian distribution curve, performing inverse characteristic transformation on the effective frequency domain data to obtain the effective data; or (b)
And if the curve corresponding to the effective frequency domain data is judged not to accord with the Gaussian distribution curve, continuously acquiring the updated first measurement data until the curve corresponding to the effective frequency domain data acquired based on the updated first measurement data accords with the Gaussian distribution curve.
The curve corresponding to the effective frequency domain data accords with the gaussian distribution curve, if the frequency domain data is abnormal-free data, the effective frequency domain data is further subjected to inverse characteristic transformation to obtain effective data.
And if the curve corresponding to the effective frequency domain data does not accord with the Gaussian distribution curve, continuously collecting the pressure data of the stamping equipment, and repeating the data processing process until the curve corresponding to the frequency domain data accords with the Gaussian distribution curve.
S104: and inputting the reference data as a training set into a convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model.
In this embodiment of the present application, the convolutional neural network model includes an input layer, a first convolutional layer, a first downsampling layer, a second convolutional layer, a second downsampling layer, a full connection layer, and an output layer. The input of the first convolution layer is connected with the input layer, and the output of the first convolution layer is connected with the input of the first downsampling layer; the output of the first downsampling layer is connected with the input of the second convolution layer; the output of the second convolution layer is connected with the input of the second downsampling; the output of the second downsampling layer is connected with the input of the output layer through the full-connection layer. And the training set taking the reference data as a training sample is input into an input layer of a convolutional neural network model, automatic iteration and convergence are carried out through the convolutional neural network model until the error between the output value and the target value meets the expectation, so that an equipment detection model is established, and the effective data is calibrated according to the equipment detection model.
Specifically, a mapping relation between effective data and reference data of the stamping equipment is obtained;
and inputting the effective data and the mapping relation into the convolutional neural network model for training, and establishing the equipment detection model.
Optionally, after establishing the device detection model of the stamping device, the method further comprises:
acquiring historical error data between effective data and reference data of the stamping equipment based on the equipment detection model;
and adjusting parameters of the equipment detection model according to the equipment detection model, the effective data, the reference data, the mapping relation and the historical error data, and updating the equipment detection model.
S105: and detecting whether the punching equipment is abnormal or not according to error data between the effective data and the reference data based on the equipment detection model.
Specifically, inputting the effective data of the stamping equipment obtained in real time into the equipment detection model to obtain actual error data;
and if the actual error data is larger than the calibrated error data, judging that the stamping equipment is abnormal, or if the actual error data is smaller than or equal to the calibrated error data, judging that the stamping equipment is not abnormal.
Fig. 4 shows a frame diagram of an abnormality detection device of a press apparatus provided in an embodiment of the present application, and for convenience of explanation, only parts related to the embodiment of the present application are shown, and detailed below:
a first acquisition unit 41 for acquiring first measurement data of the punching apparatus in real time by the sensor.
In one embodiment of the present application, the stamping device is mainly applied to a production die workshop, and is mainly used for blanking, punching, forming, deep drawing, trimming, fine punching, shaping, riveting, extruding and the like on a plate through a die, and one or more stamping devices can be disposed in the production die workshop, and the stamping devices can be connected with an industrial personal computer, an upper computer and the like, and the stamping devices include, but are not limited to, a punch press, and are particularly not limited to.
It should be noted that, this application acquires the first measurement data of stamping equipment in real time through the sensor, and this first measurement data mainly detects and records the numerical value of the impact force of product stamping forming in-process in real time through the sensor, and product stamping forming process includes decline process and ascending process, and decline process impact force gradually increases, and ascending process impact force gradually reduces, and correspondingly, first measurement data of product stamping forming in-process gradually increases earlier and then gradually reduces. The sensor can be a novel high-sensitivity ultrasonic sensor, and has the advantages of small size, quick response, wide measuring frequency range, high linearity, no need of an external power supply and the like, and meanwhile, the sensor is convenient to paste on structural members such as a punch press, a stamping die and a motor, and is beneficial to accurately detecting and recording specific values of impact force in the stamping forming process of a product.
A second acquisition unit 42 for acquiring second measurement data of the press apparatus as reference data by the tonnage measuring equipment.
In the embodiment of the application, the tonnage measuring device is arranged under the driving component of the stamping equipment, the driving component of the stamping equipment generates impact force on the tonnage measuring device in the descending and ascending processes, so that the driving component of the tonnage measuring device senses and records pressure data generated by the impact force in real time in the descending and ascending processes, the pressure data generated by the impact force of the driving component of the stamping equipment in each moment in the descending and ascending processes is obtained through the tonnage measuring device to truly reflect the stress of a product in the forming process, and therefore the pressure data collected through the tonnage measuring device can be used as reference data to be compared with the pressure data collected by the sensor. Thereby being convenient for reminding factory operators of abnormal punching machine or die.
It should be noted that the tonnage measuring equipment includes, but is not limited to, tonnage meters, and is not limited in particular.
And an extracting unit 43, configured to perform a data feature extracting operation on the first measurement data, so as to obtain valid data corresponding to the first measurement data.
In the embodiment of the application, the plurality of sensors collect and record pressure data at a plurality of positions of the stamping device at each moment, as shown in fig. 3, the pressure data at three positions of the stamping device at each moment collected and recorded by the three sensors are presented in a signal waveform mode, and the pressure data at three positions of the stamping device are presented in Normal under the condition that no abnormality occurs in the pressure data at three positions of the stamping device, and in AbNormal under the condition that the pressure data at three positions of the stamping device is AbNormal.
It should be noted that, in practical applications, the pressure data acquired by the sensor often includes a large amount of redundant data types, and in order to improve the efficiency and accuracy of the subsequent data analysis, it is necessary to extract effective pressure data from a large amount of pressure data as effective data.
And the calibration unit 44 is used for inputting the reference data as a training set into the convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model.
In this embodiment of the present application, the convolutional neural network model includes an input layer, a first convolutional layer, a first downsampling layer, a second convolutional layer, a second downsampling layer, a full connection layer, and an output layer. The input of the first convolution layer is connected with the input layer, and the output of the first convolution layer is connected with the input of the first downsampling layer; the output of the first downsampling layer is connected with the input of the second convolution layer; the output of the second convolution layer is connected with the input of the second downsampling; the output of the second downsampling layer is connected with the input of the output layer through the full-connection layer. And the training set taking the reference data as a training sample is input into an input layer of a convolutional neural network model, automatic iteration and convergence are carried out through the convolutional neural network model until the error between the output value and the target value meets the expectation, so that an equipment detection model is established, and the effective data is calibrated according to the equipment detection model.
A detecting unit 45 for detecting whether or not the punching apparatus is abnormal based on the apparatus detection model from error data between the effective data and the reference data.
Specifically, inputting the effective data of the stamping equipment obtained in real time into the equipment detection model to obtain actual error data;
and if the actual error data is larger than the calibrated error data, judging that the stamping equipment is abnormal, or if the actual error data is smaller than or equal to the calibrated error data, judging that the stamping equipment is not abnormal.
Fig. 5 is a schematic structural diagram of an electronic device 1 according to a preferred embodiment of the present invention, which implements an abnormality detection method for a stamping device. As shown in fig. 5, the electronic device 1 includes a memory 11, a processor 12, and an input-output device 13.
The electronic device 1 is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable gate array (Field-Programmable Gate Array, FPGA), a digital processor (Digital Signal Processor, DSP), an embedded device, and the like.
The electronic device 1 may be any electronic product that can interact with a user in a human-computer manner, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (Personal Digital Assistant, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc. The electronic device 1 may be a server including, but not limited to, a single web server, a server group of multiple web servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or web servers, wherein Cloud Computing is one of distributed Computing, and is a super virtual computer consisting of a group of loosely coupled computer sets. The network in which the electronic device 1 is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), etc.
The memory 11 is used to store a program and various data of an abnormality detection method of the press apparatus, and to realize high-speed, automatic access of the program or data during operation of the electronic apparatus 1. The memory 11 may be an external storage device and/or an internal storage device of the electronic device 1. Further, the Memory 11 may be a circuit with a Memory function, such as a RAM (Random-Access Memory), a FIFO (First In First Out), etc., without a physical form in the integrated circuit, or the Memory 11 may be a Memory device with a physical form, such as a Memory bank, a TF Card (Trans-flash Card), etc.
The processor 12 may be a central processing unit (CPU, central Processing Unit). The CPU is a very large-scale integrated circuit, and is an operation Core (Core) and a Control Core (Control Unit) of the electronic apparatus 1. The processor 12 may execute an operating system of the electronic device 1 and various kinds of applications, program codes, etc. installed, for example, an operating system in each module or unit in an abnormality detection apparatus of a punching device and various kinds of applications, program codes installed, etc. to realize an abnormality detection method of a punching device.
The input-output device 13 is mainly used to realize input-output functions of the electronic device 1, such as transceiving input digital or character information, or displaying information input by a user or information provided to the user, and various menus of the electronic device 1.
The integrated modules/units of the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of modules or means recited in the system claims can also be implemented by means of one module or means in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above embodiments are merely for illustrating the technical solution of the present application and not for limiting, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. An abnormality detection method of a press apparatus, characterized by comprising:
acquiring first measurement data of the stamping equipment in real time through a sensor;
acquiring second measurement data of the stamping equipment by using a tonnage measuring device as reference data;
performing data feature extraction operation on the first measurement data to obtain effective data corresponding to the first measurement data;
inputting the reference data as a training set into a convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model;
and detecting whether the punching equipment is abnormal or not according to error data between the effective data and the reference data based on the equipment detection model.
2. The abnormality detection method of a press apparatus according to claim 1, wherein the acquiring first measurement data of the press apparatus in real time by the sensor includes:
and acquiring pressure data of at least one position on the stamping equipment in real time through the sensor, wherein the at least one position comprises a connecting rod position, two side positions and a sliding block position as the first measurement data.
3. The abnormality detection method of a press apparatus according to claim 2, wherein the performing a data feature extraction operation on the first measurement data to obtain valid data corresponding to the first measurement data includes:
performing feature transformation on the first measurement data acquired by the sensor in real time to obtain frequency domain data;
filtering high frequency signals in the frequency domain data;
the windowing operation is carried out on the filtered frequency domain data, so that effective frequency domain data are obtained;
and executing inverse characteristic transformation on the effective frequency domain data to obtain effective data.
4. The abnormality detection method of a press apparatus according to claim 3, characterized in that the method further comprises:
after obtaining the effective frequency domain data, judging whether a curve corresponding to the effective frequency domain data accords with a Gaussian distribution curve, including:
if the curve corresponding to the effective frequency domain data is judged to be in accordance with a Gaussian distribution curve, performing inverse characteristic transformation on the effective frequency domain data to obtain the effective data; or (b)
And if the curve corresponding to the effective frequency domain data is judged not to accord with the Gaussian distribution curve, continuously acquiring the updated first measurement data until the curve corresponding to the effective frequency domain data acquired based on the updated first measurement data accords with the Gaussian distribution curve.
5. The method for detecting anomalies in a press machine according to claim 4, wherein said inputting said reference data as a training set into a convolutional neural network model for training to obtain a machine detection model, and calibrating said effective data based on said machine detection model comprises:
obtaining a mapping relation between effective data and reference data of the stamping equipment;
and inputting the effective data and the mapping relation into the convolutional neural network model for training, and establishing the equipment detection model.
6. The abnormality detection method of a press apparatus according to claim 5, wherein after establishing an apparatus detection model of the press apparatus, the method further comprises:
acquiring historical error data between effective data and reference data of the stamping equipment based on the equipment detection model;
and adjusting parameters of the equipment detection model according to the equipment detection model, the effective data, the reference data, the mapping relation and the historical error data, and updating the equipment detection model.
7. The abnormality detection method of a press apparatus according to claim 6, wherein the detecting whether there is an abnormality of the press apparatus based on the apparatus detection model from error data between the effective data and the reference data includes:
inputting the real-time acquired effective data of the stamping equipment into the equipment detection model to obtain actual error data;
and if the actual error data is larger than the calibrated error data, judging that the stamping equipment is abnormal, or if the actual error data is smaller than or equal to the calibrated error data, judging that the stamping equipment is not abnormal.
8. An abnormality detection device of a press machine, characterized by comprising:
a first acquisition unit for acquiring first measurement data of the stamping equipment in real time through the sensor;
a second acquisition unit for acquiring second measurement data of the stamping equipment by a tonnage measuring device as reference data;
an extraction unit, configured to perform a data feature extraction operation on the first measurement data, to obtain valid data corresponding to the first measurement data;
the calibration unit is used for inputting the reference data as a training set into a convolutional neural network model for training to obtain an equipment detection model, and calibrating the effective data based on the equipment detection model;
and the detection unit is used for detecting whether the punching equipment is abnormal or not according to the error data between the effective data and the reference data based on the equipment detection model.
9. An electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the abnormality detection method of the press device according to any one of claims 1 to 7.
10. A computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the abnormality detection method of the press apparatus according to any one of claims 1 to 7.
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| CN202111210348.XA CN115990629A (en) | 2021-10-18 | 2021-10-18 | Abnormality detection method, device, equipment and medium for stamping equipment |
| TW111106892A TWI820612B (en) | 2021-10-18 | 2022-02-24 | Method for detecting abnormities of stamping equipment,device,equipment and storage medium using the same |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US5910894A (en) * | 1994-01-11 | 1999-06-08 | Sensor Adaptive Machines, Inc. | Sensor based assembly tooling improvements |
| JP6823910B2 (en) * | 2015-02-24 | 2021-02-03 | 蛇の目ミシン工業株式会社 | Servo press, control method and program |
| JP6624100B2 (en) * | 2017-02-03 | 2019-12-25 | 新東工業株式会社 | A system that detects the cause of abnormalities in multiple devices that make up a casting facility |
| US11226615B2 (en) * | 2017-05-02 | 2022-01-18 | Lateral Solutions, Inc. | Control system for machine with a plurality of components and methods of operation |
| JP7195729B2 (en) * | 2017-07-04 | 2022-12-26 | コマツ産機株式会社 | Mechanical system and control method |
| CA3072045A1 (en) * | 2017-08-02 | 2019-02-07 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for detection in an industrial internet of things data collection environment with large data sets |
| CN207397075U (en) * | 2017-11-15 | 2018-05-22 | 天津宏宇坤泰模具有限公司 | A kind of automatic checkout equipment for numerically-controlled machine tool |
| CN110026507A (en) * | 2018-01-12 | 2019-07-19 | 宁波会德丰铜业有限公司 | Automated installation of system and its method |
| JP6861893B2 (en) * | 2018-04-26 | 2021-04-21 | 三菱電機株式会社 | Condition monitoring device and asynchronous data adjustment method |
| CN109213122A (en) * | 2018-08-10 | 2019-01-15 | 合肥工业大学 | Method for diagnosing faults and computer storage medium for stamping equipment |
| TWM610487U (en) * | 2020-02-10 | 2021-04-11 | 金豐機器工業股份有限公司 | Stamping press with monitoring system |
| TWM604905U (en) * | 2020-08-05 | 2020-12-01 | 固德科技股份有限公司 | Intelligent monitoring system applied to stamping process |
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