TWI820612B - Method for detecting abnormities of stamping equipment,device,equipment and storage medium using the same - Google Patents
Method for detecting abnormities of stamping equipment,device,equipment and storage medium using the same Download PDFInfo
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Abstract
Description
本申請涉及設備檢測領域、尤指一種衝壓設備的異常檢測方法、裝置、設備及存儲媒體。 This application relates to the field of equipment detection, and in particular, to an abnormality detection method, device, equipment and storage medium for stamping equipment.
目前衝壓領域存在的痛點是無法在衝壓生產過程中快速區分衝壓設備或模具的故障，導致在衝壓生產過程中故障檢測效率比較低下，行業目前對衝壓設備的異常檢測準確率要求比較高，因此在衝壓生產過程中常常需要對衝壓設備的長期工作狀態進行監測，從而避免在生產過程中因衝壓設備的異常而造成生產中斷帶來的損失。 The current pain point in the stamping field is that it is impossible to quickly distinguish the faults of stamping equipment or molds during the stamping production process, resulting in relatively low fault detection efficiency during the stamping production process. The industry currently has relatively high requirements for the accuracy of abnormal detection of stamping equipment, so In the stamping production process, it is often necessary to monitor the long-term working status of the stamping equipment to avoid losses caused by production interruption due to abnormalities in the stamping equipment during the production process.
本申請提供一種衝壓設備的異常檢測方法、裝置、設備及存儲媒體，旨在解決衝壓設備的異常檢測方法檢測故障不夠快以及不能即時反饋歷史檢測數據作為參照導致檢測效率低下的技術問題。 This application provides an abnormality detection method, device, equipment and storage medium for stamping equipment, aiming to solve the technical problem that the abnormality detection method of stamping equipment does not detect faults quickly enough and cannot immediately feed back historical detection data as a reference, resulting in low detection efficiency.
本申請提供一種衝壓設備的異常檢測方法，所述衝壓設備的異常檢測方法包括：藉由感測器實時獲取衝壓設備的第一測量數據；藉由噸位測量裝置獲取所述衝壓設備的第二測量數據，作為參照數據；對所述第一測量數據執行數據特徵提取操作，得到與所述第一測量數據對應的有效數據；將所述參照數據作為訓練集輸入卷積神經網路模型進行訓練，得到設備檢測模型，基於所述設備檢測模型對所述有效數據進行標定；基於所述設備檢測模型，根據所 述有效數據與所述參照數據之間的誤差數據檢測所述衝壓設備是否存在異常。 This application provides an abnormality detection method for stamping equipment. The abnormality detection method for stamping equipment includes: obtaining the first measurement data of the stamping equipment in real time through a sensor; obtaining the second measurement data of the stamping equipment through a tonnage measuring device. data as reference data; perform a data feature extraction operation on the first measurement data to obtain valid data corresponding to the first measurement data; input the reference data as a training set into a convolutional neural network model for training, Obtain an equipment detection model, calibrate the effective data based on the equipment detection model; Based on the equipment detection model, according to the The error data between the valid data and the reference data is used to detect whether there is an abnormality in the stamping equipment.
在一種可能的實施方式中，所述藉由感測器實時獲取衝壓設備的第一測量數據包括：藉由所述感測器實時獲取所述衝壓設備上至少一個位置的壓力數據，作為所述第一測量數據，所述至少一個位置包括連桿位置、兩側位置和滑塊位置。 In a possible implementation, obtaining the first measurement data of the stamping equipment in real time through the sensor includes: obtaining the pressure data of at least one position on the stamping equipment in real time through the sensor, as the First measurement data, the at least one position includes the position of the connecting rod, the position of both sides and the position of the slider.
在一種可能的實施方式中，所述對所述第一測量數據執行數據特徵提取操作，得到與所述第一測量數據對應的有效數據包括：對所述感測器實時獲取的所述第一測量數據執行特徵變換，得到頻域數據；過濾所述頻域數據中的高頻信號；藉由對過濾後的頻域數據執行加窗操作，得到有效的頻域數據；對有效的頻域數據執行逆特徵變換，得到有效數據。 In a possible implementation, performing a data feature extraction operation on the first measurement data to obtain valid data corresponding to the first measurement data includes: performing a data feature extraction operation on the first measurement data obtained by the sensor in real time. Perform feature transformation on the measured data to obtain frequency domain data; filter high-frequency signals in the frequency domain data; obtain effective frequency domain data by performing a windowing operation on the filtered frequency domain data; perform a windowing operation on the filtered frequency domain data; Perform inverse feature transformation to obtain valid data.
在一種可能的實施方式中，所述方法還包括：在得到所述有效的頻域數據之後，判斷所述有效的頻域數據對應的曲線是否符合高斯分布曲線，包括：若判斷所述有效的頻域數據對應的曲線符合高斯分布曲線，對所述有效的頻域數據執行逆特徵變換，得到所述有效數據；或若判斷所述有效的頻域數據對應的曲線不符合高斯分布曲線，繼續獲取更新的第一測量數據，直至基於所述更新的第一測量數據而獲取的有效的頻域數據對應的曲線符合高斯分布曲線。 In a possible implementation, the method further includes: after obtaining the effective frequency domain data, determining whether the curve corresponding to the effective frequency domain data conforms to a Gaussian distribution curve, including: if determining that the effective frequency domain data The curve corresponding to the frequency domain data conforms to the Gaussian distribution curve, and the inverse feature transformation is performed on the effective frequency domain data to obtain the effective data; or if it is judged that the curve corresponding to the effective frequency domain data does not conform to the Gaussian distribution curve, continue The updated first measurement data is obtained until the curve corresponding to the effective frequency domain data obtained based on the updated first measurement data conforms to a Gaussian distribution curve.
在一種可能的實施方式中，所述將所述參照數據作為訓練集輸入卷積神經網路模型進行訓練，得到設備檢測模型，基於所述設備檢測模型對所述有效數據進行標定包括：獲取所述衝壓設備的有效數據與參照數據之間的映射關係；將所述有效數據與所述映射關係，輸入所述卷積神經網路模型進行訓練，建立所述設備檢測模型。 In a possible implementation, the reference data is input into a convolutional neural network model as a training set for training to obtain a device detection model, and calibrating the effective data based on the device detection model includes: obtaining all The mapping relationship between the effective data and the reference data of the stamping equipment is described; the effective data and the mapping relationship are input into the convolutional neural network model for training, and the equipment detection model is established.
在一種可能的實施方式中，建立所述衝壓設備的設備檢測模型之後，所述方法還包括：基於所述設備檢測模型，獲取所述衝壓設備的有效數據 與參照數據之間的歷史誤差數據；根據所述設備檢測模型、所述有效數據、所述參照數據、所述映射關係以及所述歷史誤差數據，對所述設備檢測模型的參數進行調整，並對所述設備檢測模型進行更新。 In a possible implementation, after establishing an equipment detection model of the stamping equipment, the method further includes: obtaining effective data of the stamping equipment based on the equipment detection model. historical error data between the equipment detection model and the reference data; adjusting the parameters of the equipment detection model according to the equipment detection model, the valid data, the reference data, the mapping relationship and the historical error data, and The device detection model is updated.
在一種可能的實施方式中，所述基於所述設備檢測模型，根據所述有效數據與所述參照數據之間的誤差數據檢測所述衝壓設備是否存在異常包括：將實時獲取的所述衝壓設備的有效數據輸入所述設備檢測模型，得到實際誤差數據；若所述實際誤差數據大於標定的誤差數據，判定所述衝壓設備異常，或者若所述實際誤差數據小於或等於所述標定的誤差數據，判定所述衝壓設備無異常。 In a possible implementation, the step of detecting whether there is an abnormality in the stamping equipment based on the error data between the valid data and the reference data based on the equipment detection model includes: using the stamping equipment acquired in real time. Valid data is input into the equipment detection model to obtain actual error data; if the actual error data is greater than the calibrated error data, it is determined that the stamping equipment is abnormal, or if the actual error data is less than or equal to the calibrated error data , it is determined that there is no abnormality in the stamping equipment.
本申請還提供一種衝壓設備的異常檢測裝置，所述異常檢測裝置包括：第一獲取單元，用於藉由感測器實時獲取衝壓設備的第一測量數據；第二獲取單元，用於藉由噸位測量裝置獲取所述衝壓設備的第二測量數據，作為參照數據；提取單元，用於對所述第一測量數據執行數據特徵提取操作，得到與所述第一測量數據對應的有效數據；標定單元，用於將所述參照數據作為訓練集輸入卷積神經網路模型進行訓練，得到設備檢測模型，基於所述設備檢測模型對所述有效數據進行標定；檢測單元，用於基於所述設備檢測模型，根據所述有效數據與所述參照數據之間的誤差數據檢測所述衝壓設備是否存在異常。 The application also provides an abnormality detection device for stamping equipment. The abnormality detection device includes: a first acquisition unit for acquiring the first measurement data of the stamping equipment in real time through a sensor; and a second acquisition unit for acquiring the first measurement data of the stamping equipment through a sensor. The tonnage measuring device obtains the second measurement data of the stamping equipment as reference data; an extraction unit is used to perform a data feature extraction operation on the first measurement data to obtain valid data corresponding to the first measurement data; calibration A unit configured to input the reference data as a training set into a convolutional neural network model for training, to obtain a device detection model, and to calibrate the effective data based on the device detection model; a detection unit configured to perform calibration based on the device detection model A detection model is used to detect whether there is an abnormality in the stamping equipment based on the error data between the valid data and the reference data.
本申請還提供一種電子設備，所述電子設備包括處理器和記憶體，所述處理器用於執行記憶體中存儲的計算機程式以實現所述的衝壓設備的異常檢測方法。 This application also provides an electronic device. The electronic device includes a processor and a memory. The processor is used to execute a computer program stored in the memory to implement the abnormality detection method of stamping equipment.
本申請還提供一種存儲媒體，所述存儲媒體存儲有至少一個指令，所述至少一個指令被處理器執行時實現所述的衝壓設備的異常檢測方法。 This application also provides a storage medium that stores at least one instruction. When the at least one instruction is executed by a processor, the abnormality detection method of stamping equipment is implemented.
本申請能夠快速判斷衝壓設備或者模具的故障，便於更快速更準 確地及時發現衝壓設備或模具的故障，從而減少衝壓設備的修復成本或者模具的生產成本，進而提高衝壓設備作業的效率。同時能夠即時反饋監測數據，便於實時查看監測數據，根據監測數據分析標定曲線在線實時判斷發現衝壓設備或模具的故障，避免因為操作不當或者其他故障導致衝壓設備在批量化生產作業過程中模具生產不符合標準，進而造成工廠浪費大量的人力物力成本以及損失巨大的生產成本，從而減少了工廠生產作業過程中的損失，大大地提高了衝壓設備的作業效率。 This application can quickly determine the fault of stamping equipment or molds, making it faster and more accurate. It can accurately and timely detect the faults of stamping equipment or molds, thereby reducing the repair costs of stamping equipment or the production costs of molds, thereby improving the efficiency of stamping equipment operations. At the same time, monitoring data can be fed back immediately, which facilitates real-time viewing of monitoring data. Based on the monitoring data analysis and calibration curve, online real-time judgment can be made to find faults in stamping equipment or molds, so as to avoid improper operation or other faults that may cause stamping equipment to fail in mold production during mass production operations. Complying with the standards will cause the factory to waste a lot of manpower and material costs and lose huge production costs, thereby reducing losses during the factory's production operations and greatly improving the operating efficiency of stamping equipment.
41:第一獲取單元 41: First acquisition unit
42:第二獲取單元 42: Second acquisition unit
43:提取單元 43: Extraction unit
44:標定單元 44:Calibration unit
45:檢測單元 45:Detection unit
1:電子設備 1: Electronic equipment
11:記憶體 11:Memory
12:處理器 12: Processor
13:輸入輸出設備 13: Input and output devices
S101-S105:步驟 S101-S105: Steps
圖1是本申請實施例提供的一種衝壓設備的異常檢測方法的流程圖。 Figure 1 is a flow chart of an abnormality detection method for stamping equipment provided by an embodiment of the present application.
圖2是本申請實施例提供的感測器採集的數據資料圖。 Figure 2 is a diagram of data collected by a sensor provided by an embodiment of the present application.
圖3是本申請實施例提供的感測器的信號波形圖。 Figure 3 is a signal waveform diagram of a sensor provided by an embodiment of the present application.
圖4是本申請實施例提供的一種衝壓設備的異常檢測裝置的框架圖。 Figure 4 is a framework diagram of an abnormality detection device for stamping equipment provided by an embodiment of the present application.
圖5是本申請實施例提供的實現衝壓設備的異常檢測方法的電子設備的結構示意圖。 FIG. 5 is a schematic structural diagram of an electronic device that implements an abnormality detection method for stamping equipment provided by an embodiment of the present application.
為了使本申請的目的、技術方案及優點更加清楚明白，以下結合附圖及實施例，對本申請進行進一步詳細說明。應當理解，此處所描述的具體實施例僅僅用以解釋本申請，並不用於限定本申請。 In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.
為了更清楚地說明本發明實施例中的技術方案，下面將對實施例描述中所需要使用的附圖作簡單地介紹，顯而易見地，下面描述中的附圖僅僅 是本發明的一些實施例，對於本領域技術人員來講，在不付出創造性勞動的前提下，還可以根據這些附圖獲得其他的附圖。 In order to explain the technical solutions in the embodiments of the present invention more clearly, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only These are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without exerting creative efforts.
此外，術語“第一”、“第二”僅用於描述目的，而不能理解為指示或暗示相對重要性或者隱含指明所指示的技術特徵的數量。由此，限定有“第一”、“第二”的特徵可以明示或者隱含地包括一個或者更多個所述特徵。在本申請的描述中，“多個”的含義是兩個或兩個以上，除非另有明確具體的限定。 In addition, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Thus, features defined as “first” and “second” may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more than two, unless otherwise explicitly and specifically limited.
除非另有定義，本文所使用的所有的技術和科學術語與術語本申請的技術領域的技術人員通常理解的含義相同。本文中在本申請的說明書中所使用的術語只是為了描述具體的實施例的目的，不是旨在於限制本申請。本文所使用的術語“和/或”包括一個或多個相關的所列項目的任意的和所有的組合。對衝壓設備的異常檢測方法通常存在兩個技術問題，第一是衝壓設備檢測故障不夠快，主要是由於衝壓設備上的感測器採集數據不夠靈敏，第二是衝壓設備不能即時反饋歷史檢測數據作為參照導致檢測效率低下。本申請實施例對衝壓設備的異常檢測方法解決了檢測故障不夠快以及不能即時反饋歷史檢測數據作為參照導致檢測效率低下的技術問題。 Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the term is applied. The terminology used herein in the description of the application is for the purpose of describing specific embodiments only and is not intended to limit the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. There are usually two technical problems in abnormal detection methods for stamping equipment. The first is that the stamping equipment cannot detect faults quickly enough, mainly because the sensors on the stamping equipment are not sensitive enough to collect data. The second is that the stamping equipment cannot instantaneously feedback historical detection data. As a reference, detection efficiency is low. The anomaly detection method for stamping equipment in the embodiment of the present application solves the technical problems of low detection efficiency due to insufficient detection of faults quickly and inability to instantly feed back historical detection data as a reference.
基於上述技術問題，本申請實施例提出一種衝壓設備的異常檢測方法，能夠快速判斷衝壓設備或者模具的故障，便於更快速更準確地及時發現衝壓設備或模具的故障，從而減少衝壓設備的修復成本或者模具的生產成本，進而提高衝壓設備作業的效率。同時能夠即時反饋監測數據，便於實時查看監測數據，根據監測數據分析標定曲線在線實時判斷發現衝壓設備或模具的故障，避免因為操作不當或者其他故障導致衝壓設備在批量化生產作業過程中模具生產不符合標準，進而造成工廠浪費大量的人力物力成本以及損失巨大的生產成 本，從而減少了工廠生產作業過程中的損失，大大地提高了衝壓設備的作業效率。 Based on the above technical problems, the embodiment of the present application proposes an anomaly detection method for stamping equipment, which can quickly determine the fault of stamping equipment or molds, facilitate timely discovery of faults of stamping equipment or molds more quickly and accurately, and thereby reduce the repair cost of stamping equipment. Or the production cost of the mold, thereby improving the efficiency of stamping equipment operations. At the same time, monitoring data can be fed back immediately, which facilitates real-time viewing of monitoring data. Based on the monitoring data analysis and calibration curve, online real-time judgment can be made to find faults in stamping equipment or molds, so as to avoid improper operation or other faults that may cause stamping equipment to fail in mold production during mass production operations. meet the standards, causing the factory to waste a lot of manpower and material costs and lose huge production costs. cost, thereby reducing losses during factory production operations and greatly improving the operating efficiency of stamping equipment.
圖1示出了本申請實施例提供的一種衝壓設備的異常檢測方法的實現流程圖，根據不同的需求，該流程圖中步驟的順序可以改變，某些步驟可以省略。 Figure 1 shows a flow chart for implementing an abnormality detection method for stamping equipment provided by an embodiment of the present application. According to different requirements, the order of steps in the flow chart can be changed, and some steps can be omitted.
S101：藉由感測器實時獲取衝壓設備的第一測量數據。 S101: Obtain the first measurement data of the stamping equipment in real time through the sensor.
在本申請的一個實施例中，衝壓設備主要應用於生產模具車間，主要用於對板材藉由模具做出落料、沖孔、成型、拉深、修整、精沖、整形、鉚接及擠壓件等等，在生產模具車間可以部署一個或者多個衝壓設備，這些衝壓設備可以連接工控機、上位機等設備，該衝壓設備包括但不限於衝床，具體不作限定。 In one embodiment of the present application, the stamping equipment is mainly used in the production mold workshop, mainly used for blanking, punching, forming, deep drawing, trimming, fine blanking, shaping, riveting and extrusion of the plate through the mold. Parts, etc., one or more stamping equipment can be deployed in the production mold workshop. These stamping equipment can be connected to industrial computers, host computers and other equipment. The stamping equipment includes but is not limited to punch machines, and the specifics are not limited.
需要說明的是，本申請藉由感測器實時獲取衝壓設備的第一測量數據，該第一測量數據主要是藉由感測器實時檢測和記錄產品衝壓成型過程中的衝擊力的數值，產品衝壓成型過程包括下降過程和上升過程，下降過程衝擊力逐漸增大，上升過程衝擊力逐漸減小，相應地，產品衝壓成型過程中第一測量數據先逐漸增大後逐漸減小。感測器可以為新型高敏感度的超聲感測器，該超聲感測器具有尺寸小巧，回應快，測量頻率範圍寬，線性度高，無需外接電源等優點，同時便於黏貼在衝床、衝壓模具、電機等結構件上，從而有利於感測器準確檢測和記錄產品衝壓成型過程中的衝擊力的具體數值。 It should be noted that this application uses a sensor to obtain the first measurement data of the stamping equipment in real time. The first measurement data mainly uses the sensor to detect and record the value of the impact force during the stamping and molding process of the product in real time. The product The stamping forming process includes a descending process and an ascending process. The impact force gradually increases during the descending process, and the impact force gradually decreases during the ascending process. Correspondingly, the first measurement data during the stamping forming process of the product first gradually increases and then decreases. The sensor can be a new type of highly sensitive ultrasonic sensor. This ultrasonic sensor has the advantages of small size, fast response, wide measurement frequency range, high linearity, and no need for external power supply. It is also easy to stick on punch machines and stamping dies. , motors and other structural parts, which facilitates the sensor to accurately detect and record the specific value of the impact force during the stamping and forming process of the product.
具體地，在整個衝壓過程中，藉由感測器實時獲取該衝壓設備上至少一個位置的壓力數據，作為上述第一測量數據；上述至少一個位置包括連桿位置、兩側位置和滑塊位置。 Specifically, during the entire stamping process, the pressure data of at least one position on the stamping equipment is obtained in real time through a sensor as the above-mentioned first measurement data; the above-mentioned at least one position includes the connecting rod position, both sides position and the slider position. .
需要說明的是，感測器可以黏貼在衝壓設備的連桿位置、兩側位置和滑塊位置的至少一個位置上，這樣感測器可以採集和記錄衝壓設備的連桿 位置、兩側位置和滑塊位置的至少一個位置上的壓力數據。例如，如圖2所示，第一感測器黏貼在衝壓設備的連桿位置上，第二感測器黏貼在衝壓設備的兩側位置上，第三感測器黏貼在衝壓設備的滑塊位置上，每一個時刻，第一感測器、第二感測器和第三感測器均記錄所在位置上的壓力數據，提高採集壓力數據的準確性。 It should be noted that the sensor can be attached to at least one of the connecting rod position, both sides position and the slider position of the stamping equipment, so that the sensor can collect and record the connecting rod of the stamping equipment. Pressure data at at least one of the position, side position and slider position. For example, as shown in Figure 2, the first sensor is attached to the connecting rod of the stamping equipment, the second sensor is attached to both sides of the stamping equipment, and the third sensor is attached to the slider of the stamping equipment. At each position, the first sensor, the second sensor and the third sensor record the pressure data at the position, thereby improving the accuracy of collecting pressure data.
S102：藉由噸位測量裝置獲取所述衝壓設備的第二測量數據，作為參照數據。 S102: Obtain the second measurement data of the stamping equipment through the tonnage measuring device as reference data.
在本申請實施例中，噸位測量裝置置於衝壓設備驅動組件的正下方，衝壓設備驅動組件在下降和上升過程中對噸位測量裝置產生衝擊力，於是該噸位測量裝置驅動組件在下降和上升過程中實時感應並記錄衝擊力所產生的壓力數據，藉由噸位測量裝置獲取衝壓設備驅動組件在下降和上升過程中每一刻衝擊力所產生的壓力數據真實地反映產品在成型過程中的受力大小，因此藉由噸位測量裝置採集的壓力數據可以作為參照數據，與感測器採集到的壓力數據進行比對。從而便於提醒工廠操作人員衝床或者模具出現異常。 In the embodiment of the present application, the tonnage measuring device is placed directly below the driving assembly of the stamping equipment. The driving assembly of the stamping equipment generates an impact force on the tonnage measuring device during the descending and rising processes. Therefore, the tonnage measuring device driving assembly The pressure data generated by the impact force is sensed and recorded in real time. The tonnage measuring device is used to obtain the pressure data generated by the impact force at each moment during the descent and rise of the driving component of the stamping equipment, which truly reflects the force exerted on the product during the molding process. , so the pressure data collected by the tonnage measuring device can be used as reference data to compare with the pressure data collected by the sensor. This makes it easy to alert factory operators that there is an abnormality in the punch or mold.
需要說明的是，噸位測量裝置包括但不限於噸位儀，具體不作限定。 It should be noted that tonnage measuring devices include but are not limited to tonnage meters, and are not specifically limited.
S103：對所述第一測量數據執行數據特徵提取操作，得到與所述第一測量數據對應的有效數據。 S103: Perform a data feature extraction operation on the first measurement data to obtain valid data corresponding to the first measurement data.
在本申請實施例中，多個感測器採集和記錄每一時刻衝壓設備多個位置上的壓力數據，如圖3所示，三個感測器採集和記錄的每一時刻衝壓設備三個位置上的壓力數據以信號波形的方式呈現出來，衝壓設備三個位置上的壓力數據沒有出現異常的情況下，以Normal呈現，衝壓設備三個位置上的壓力數據出現異常的情況下，以AbNormal呈現。 In the embodiment of this application, multiple sensors collect and record pressure data at multiple locations of the stamping equipment at each moment. As shown in Figure 3, three sensors collect and record three pressure data on the stamping equipment at each moment. The pressure data at the position is presented in the form of signal waveform. If there is no abnormality in the pressure data at the three positions of the stamping equipment, it will be presented as Normal. If there is an abnormality in the pressure data at the three positions of the stamping equipment, it will be presented as AbNormal. Present.
需要說明的是，在實際應用中，感測器獲取的壓力數據往往包含大量冗餘的數據類型，為了提高後續數據分析的效率和準確率，需要從大量的壓力數據中提取出有效的壓力數據作為有效數據。 It should be noted that in practical applications, the pressure data obtained by sensors often contain a large number of redundant data types. In order to improve the efficiency and accuracy of subsequent data analysis, effective pressure data needs to be extracted from a large amount of pressure data. as valid data.
具體地，對所述感測器實時獲取的所述第一測量數據執行特徵變換，得到頻域數據；過濾所述頻域數據中的高頻信號；藉由對過濾後的頻域數據執行加窗操作，得到有效的頻域數據；對有效的頻域數據執行逆特徵變換，得到有效數據。 Specifically, performing feature transformation on the first measurement data obtained by the sensor in real time to obtain frequency domain data; filtering high-frequency signals in the frequency domain data; and performing processing on the filtered frequency domain data. Window operation is performed to obtain effective frequency domain data; inverse feature transformation is performed on the effective frequency domain data to obtain effective data.
需要說明的是，藉由對感測器獲取的壓力信號首先藉由濾波器過濾掉高頻干擾時域信號，再進行傅里葉變換將時域信號轉換成頻域信號，進一步去除頻域信號中的高頻干擾信號，最後傅里葉變換逆變換將過濾掉高頻干擾信號的頻域信號逆特徵變換成時域信號，此時的時域信號為有效信號。 It should be noted that the pressure signal obtained by the sensor first filters out the high-frequency interference time domain signal through a filter, and then performs Fourier transform to convert the time domain signal into a frequency domain signal, and further removes the frequency domain signal. The high-frequency interference signal in the filter, and finally the inverse Fourier transform transforms the inverse characteristics of the frequency domain signal that filters out the high-frequency interference signal into a time domain signal. At this time, the time domain signal is an effective signal.
進一步具體地，所述方法還包括：在得到所述有效的頻域數據之後，判斷所述有效的頻域數據對應的曲線是否符合高斯分布曲線，包括：若判斷所述有效的頻域數據對應的曲線符合高斯分布曲線，對所述有效的頻域數據執行逆特徵變換，得到所述有效數據；或若判斷所述有效的頻域數據對應的曲線不符合高斯分布曲線，繼續獲取更新的第一測量數據，直至基於所述更新的第一測量數據而獲取的有效的頻域數據對應的曲線符合高斯分布曲線。 More specifically, the method further includes: after obtaining the valid frequency domain data, determining whether the curve corresponding to the valid frequency domain data conforms to a Gaussian distribution curve, including: if determining whether the valid frequency domain data corresponds to The curve conforms to the Gaussian distribution curve, perform inverse feature transformation on the effective frequency domain data, and obtain the effective data; or if it is judged that the curve corresponding to the effective frequency domain data does not conform to the Gaussian distribution curve, continue to obtain the updated third A measurement data until the curve corresponding to the effective frequency domain data obtained based on the updated first measurement data conforms to a Gaussian distribution curve.
需要說明的是，當有效的頻域數據對應的曲線符合高斯分布曲線，則該頻域數據是無異常的數據，則進一步對有效的頻域數據經過逆特徵變換，得到有效數據。 It should be noted that when the curve corresponding to the effective frequency domain data conforms to the Gaussian distribution curve, then the frequency domain data is data without abnormality, and then the effective frequency domain data is further subjected to inverse feature transformation to obtain the effective data.
當有效的頻域數據對應的曲線不符合高斯分布曲線，則繼續採集衝壓設備的壓力數據，重複以上數據處理過程，直至頻域數據對應的曲線符合高斯分布曲線。 When the curve corresponding to the effective frequency domain data does not conform to the Gaussian distribution curve, continue to collect the pressure data of the stamping equipment, and repeat the above data processing process until the curve corresponding to the frequency domain data conforms to the Gaussian distribution curve.
S104：將所述參照數據作為訓練集輸入卷積神經網路模型進行訓練，得到設備檢測模型，基於所述設備檢測模型對所述有效數據進行標定。 S104: Input the reference data as a training set into the convolutional neural network model for training, obtain a device detection model, and calibrate the effective data based on the device detection model.
在本申請實施例中，所述卷積神經網路模型包括輸入層、第一卷積層、第一降採樣層、第二卷積層、第二降採樣層、全連接層及輸出層。所述第一卷積層的輸入與輸入層連接，第一卷積層的輸出與所述第一降採樣層的輸入連接；所述第一降採樣層的輸出與所述第二卷積層的輸入連接；所述第二卷積層的輸出與所述第二降採樣的輸入連接；所述第二降採樣層的輸出藉由所述全連接層與所述輸出層的輸入連接。以參照數據作為訓練樣本的訓練集輸入至卷積神經網路模型的輸入層，藉由所述卷積神經網路模型進行自動迭代及收斂，直至輸出值與目標值之間的誤差符合期望，從而建立設備檢測模型，根據所述設備檢測模型對所述有效數據進行標定。 In this embodiment of the present application, the convolutional neural network model includes an input layer, a first convolution layer, a first downsampling layer, a second convolution layer, a second downsampling layer, a fully connected layer and an output layer. The input of the first convolution layer is connected to the input layer, the output of the first convolution layer is connected to the input of the first down-sampling layer; the output of the first down-sampling layer is connected to the input of the second convolution layer ; The output of the second convolutional layer is connected to the input of the second downsampling layer; the output of the second downsampling layer is connected to the input of the output layer through the fully connected layer. The training set using the reference data as the training sample is input to the input layer of the convolutional neural network model, and the convolutional neural network model automatically iterates and converges until the error between the output value and the target value meets expectations. Thereby, a device detection model is established, and the effective data is calibrated according to the device detection model.
具體地，獲取所述衝壓設備的有效數據與參照數據之間的映射關係；將所述有效數據與所述映射關係，輸入所述卷積神經網路模型進行訓練，建立所述設備檢測模型。 Specifically, the mapping relationship between the effective data and the reference data of the stamping equipment is obtained; the effective data and the mapping relationship are input into the convolutional neural network model for training, and the equipment detection model is established.
可選地，建立所述衝壓設備的設備檢測模型之後，所述方法還包括：基於所述設備檢測模型，獲取所述衝壓設備的有效數據與參照數據之間的歷史誤差數據； 根據所述設備檢測模型、所述有效數據、所述參照數據、所述映射關係以及所述歷史誤差數據，對所述設備檢測模型的參數進行調整，並對所述設備檢測模型進行更新。 Optionally, after establishing the equipment detection model of the stamping equipment, the method further includes: based on the equipment detection model, obtaining historical error data between the valid data and reference data of the stamping equipment; According to the equipment detection model, the valid data, the reference data, the mapping relationship and the historical error data, the parameters of the equipment detection model are adjusted, and the equipment detection model is updated.
S105：基於所述設備檢測模型，根據所述有效數據與所述參照數據之間的誤差數據檢測所述衝壓設備是否存在異常。 S105: Based on the equipment detection model, detect whether there is an abnormality in the stamping equipment according to the error data between the valid data and the reference data.
具體地，將實時獲取的所述衝壓設備的有效數據輸入所述設備檢測模型，得到實際誤差數據；若所述實際誤差數據大於標定的誤差數據，判定所述衝壓設備異常，或者若所述實際誤差數據小於或等於所述標定的誤差數據，判定所述衝壓設備無異常。 Specifically, the valid data of the stamping equipment obtained in real time is input into the equipment detection model to obtain actual error data; if the actual error data is greater than the calibrated error data, it is determined that the stamping equipment is abnormal, or if the actual error data is If the error data is less than or equal to the calibrated error data, it is determined that there is no abnormality in the stamping equipment.
圖4示出了本申請實施例提供的一種衝壓設備的異常檢測裝置的框架圖，為了便於說明，僅示出了與本申請實施例相關的部分，詳述如下：第一獲取單元41，用於藉由感測器實時獲取衝壓設備的第一測量數據。
Figure 4 shows a frame diagram of an anomaly detection device for stamping equipment provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. The details are as follows: the
在本申請的一個實施例中，衝壓設備主要應用於生產模具車間，主要用於對板材藉由模具做出落料、沖孔、成型、拉深、修整、精沖、整形、鉚接及擠壓件等等，在生產模具車間可以部署一個或者多個衝壓設備，這些衝壓設備可以連接工控機、上位機等設備，該衝壓設備包括但不限於衝床，具體不作限定。 In one embodiment of the present application, the stamping equipment is mainly used in the production mold workshop, mainly used for blanking, punching, forming, deep drawing, trimming, fine blanking, shaping, riveting and extrusion of the plate through the mold. Parts, etc., one or more stamping equipment can be deployed in the production mold workshop. These stamping equipment can be connected to industrial computers, host computers and other equipment. The stamping equipment includes but is not limited to punch machines, and the specifics are not limited.
需要說明的是，本申請藉由感測器實時獲取衝壓設備的第一測量數據，該第一測量數據主要是藉由感測器實時檢測和記錄產品衝壓成型過程中的衝擊力的數值，產品衝壓成型過程包括下降過程和上升過程，下降過程衝擊力逐漸增大，上升過程衝擊力逐漸減小，相應地，產品衝壓成型過程中第一測量數據先逐漸增大後逐漸減小。感測器可以為新型高敏感度的超聲感測器，該 超聲感測器具有尺寸小巧，回應快，測量頻率範圍寬，線性度高，無需外接電源等優點，同時便於黏貼在衝床、衝壓模具、電機等結構件上，從而有利於感測器準確檢測和記錄產品衝壓成型過程中的衝擊力的具體數值。 It should be noted that this application uses a sensor to obtain the first measurement data of the stamping equipment in real time. The first measurement data mainly uses the sensor to detect and record the value of the impact force during the stamping and molding process of the product in real time. The product The stamping forming process includes a descending process and an ascending process. The impact force gradually increases during the descending process, and the impact force gradually decreases during the ascending process. Correspondingly, the first measurement data during the stamping forming process of the product first gradually increases and then decreases. The sensor can be a new type of highly sensitive ultrasonic sensor, which Ultrasonic sensors have the advantages of small size, fast response, wide measurement frequency range, high linearity, and no need for external power supply. They are also easy to stick on punch machines, stamping dies, motors and other structural parts, which is conducive to accurate detection and detection of sensors. Record the specific value of the impact force during the stamping and forming process of the product.
第二獲取單元42，用於噸位測量裝置獲取所述衝壓設備的第二測量數據，作為參照數據。
The
在本申請實施例中，噸位測量裝置置於衝壓設備驅動組件的正下方，衝壓設備驅動組件在下降和上升過程中對噸位測量裝置產生衝擊力，於是該噸位測量裝置驅動組件在下降和上升過程中實時感應並記錄衝擊力所產生的壓力數據，藉由噸位測量裝置獲取衝壓設備驅動組件在下降和上升過程中每一刻衝擊力所產生的壓力數據真實地反映產品在成型過程中的受力大小，因此藉由噸位測量裝置採集的壓力數據可以作為參照數據，與感測器採集到的壓力數據進行比對。從而便於提醒工廠操作人員衝床或者模具出現異常。 In the embodiment of the present application, the tonnage measuring device is placed directly below the driving assembly of the stamping equipment. The driving assembly of the stamping equipment generates an impact force on the tonnage measuring device during the descending and rising processes. Therefore, the tonnage measuring device driving assembly The pressure data generated by the impact force is sensed and recorded in real time. The tonnage measuring device is used to obtain the pressure data generated by the impact force at each moment during the descent and rise of the driving component of the stamping equipment, which truly reflects the force exerted on the product during the molding process. , so the pressure data collected by the tonnage measuring device can be used as reference data to compare with the pressure data collected by the sensor. This makes it easy to alert factory operators that there is an abnormality in the punch or mold.
需要說明的是，噸位測量裝置包括但不限於噸位儀，具體不作限定。 It should be noted that tonnage measuring devices include but are not limited to tonnage meters, and are not specifically limited.
提取單元43，用於對所述第一測量數據執行數據特徵提取操作，得到所述第一測量數據對應的有效數據。
The
在本申請實施例中，多個感測器採集和記錄每一時刻衝壓設備多個位置上的壓力數據，如圖3所示，三個感測器採集和記錄的每一時刻衝壓設備三個位置上的壓力數據以信號波形的方式呈現出來，衝壓設備三個位置上的壓力數據沒有出現異常的情況下，以Normal呈現，衝壓設備三個位置上的壓力數據出現異常的情況下，以AbNormal呈現。 In the embodiment of this application, multiple sensors collect and record pressure data at multiple locations of the stamping equipment at each moment. As shown in Figure 3, three sensors collect and record three pressure data on the stamping equipment at each moment. The pressure data at the position is presented in the form of signal waveform. If there is no abnormality in the pressure data at the three positions of the stamping equipment, it will be presented as Normal. If there is an abnormality in the pressure data at the three positions of the stamping equipment, it will be presented as AbNormal. Present.
需要說明的是，在實際應用中，感測器獲取的壓力數據往往包含大量冗餘的數據類型，為了提高後續數據分析的效率和準確率，需要從大量的壓力數據中提取出有效的壓力數據作為有效數據。 It should be noted that in practical applications, the pressure data obtained by sensors often contain a large number of redundant data types. In order to improve the efficiency and accuracy of subsequent data analysis, effective pressure data needs to be extracted from a large amount of pressure data. as valid data.
標定單元44，用於將所述參照數據作為訓練集輸入卷積神經網路模型進行訓練，得到設備檢測模型，基於所述設備檢測模型對所述有效數據進行標定。
The
在本申請實施例中，所述卷積神經網路模型包括輸入層、第一卷積層、第一降採樣層、第二卷積層、第二降採樣層、全連接層及輸出層。所述第一卷積層的輸入與輸入層連接，第一卷積層的輸出與所述第一降採樣層的輸入連接；所述第一降採樣層的輸出與所述第二卷積層的輸入連接；所述第二卷積層的輸出與所述第二降採樣的輸入連接；所述第二降採樣層的輸出藉由所述全連接層與所述輸出層的輸入連接。以參照數據作為訓練樣本的訓練集輸入至卷積神經網路模型的輸入層，藉由所述卷積神經網路模型進行自動迭代及收斂，直至輸出值與目標值之間的誤差符合期望，從而建立設備檢測模型，根據所述設備檢測模型對所述有效數據進行標定。 In this embodiment of the present application, the convolutional neural network model includes an input layer, a first convolution layer, a first downsampling layer, a second convolution layer, a second downsampling layer, a fully connected layer and an output layer. The input of the first convolution layer is connected to the input layer, the output of the first convolution layer is connected to the input of the first down-sampling layer; the output of the first down-sampling layer is connected to the input of the second convolution layer ; The output of the second convolutional layer is connected to the input of the second downsampling layer; the output of the second downsampling layer is connected to the input of the output layer through the fully connected layer. The training set using the reference data as the training sample is input to the input layer of the convolutional neural network model, and the convolutional neural network model automatically iterates and converges until the error between the output value and the target value meets expectations. Thereby, a device detection model is established, and the effective data is calibrated according to the device detection model.
檢測單元45，用於基於所述設備檢測模型，根據所述有效數據與所述參照數據之間的誤差數據檢測所述衝壓設備是否存在異常。
The
具體地，將實時獲取的所述衝壓設備的有效數據輸入所述設備檢測模型，得到實際誤差數據；若所述實際誤差數據大於標定的誤差數據，判定所述衝壓設備異常，或者若所述實際誤差數據小於或等於所述標定的誤差數據，判定所述衝壓設備無異常。 Specifically, the valid data of the stamping equipment obtained in real time is input into the equipment detection model to obtain actual error data; if the actual error data is greater than the calibrated error data, it is determined that the stamping equipment is abnormal, or if the actual error data is If the error data is less than or equal to the calibrated error data, it is determined that there is no abnormality in the stamping equipment.
圖5是本申請實施例提供的實現一種衝壓設備的異常檢測方法的較佳實施例的電子設備1的結構示意圖。如圖5所示，電子設備1包括記憶體11、處理器12及輸入輸出設備13。
FIG. 5 is a schematic structural diagram of an electronic device 1 according to a preferred embodiment of an abnormality detection method for stamping equipment provided by the embodiment of the present application. As shown in FIG. 5 , the electronic device 1 includes a
所述電子設備1是一種能夠按照事先設定或存儲的指令，自動進行數值計算和/或信息處理的設備，其硬體包括但不限於微處理器、專用集成電 路(Application Specific Integrated Circuit，ASIC)、可編程門陣列(Field-Programmable Gate Array，FPGA)、數位處理器(Digital Signal Processor，DSP)、嵌入式設備等。 The electronic device 1 is a device that can automatically perform numerical calculations and/or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to microprocessors, special integrated circuits. Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), Digital Signal Processor (DSP), embedded devices, etc.
所述電子設備1可以是任何一種可與用戶進行人機交互的電子產品，例如，個人計算機、平板電腦、智能手機、個人數位助理(Personal Digital Assistant，PDA)、游戲機、交互式網路電視(Internet Protocol Television，IPTV)、智能式穿戴式設備等。所述電子設備1可以是伺服器，所述伺服器包括但不限於單個網路伺服器、多個網路伺服器組成的伺服器組或基於雲計算(Cloud Computing)的由大量主機或網路伺服器構成的雲，其中，雲計算是分布式計算的一種，由一群鬆散耦合的計算機集組成的一個超級虛擬計算機。所述電子設備1所處的網路包括但不限於區域網路、虛擬專用網路(Virtual Private Network，VPN)等。 The electronic device 1 can be any electronic product that can perform human-computer interaction with the user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (Personal Digital Assistant, PDA), a game console, and an interactive Internet TV. (Internet Protocol Television, IPTV), smart wearable devices, etc. The electronic device 1 may be a server. The server includes but is not limited to a single network server, a server group composed of multiple network servers, or a large number of hosts or networks based on cloud computing. A cloud composed of servers. Cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computer sets. The network where the electronic device 1 is located includes but is not limited to a local area network, a virtual private network (Virtual Private Network, VPN), etc.
記憶體11用於存儲一種衝壓設備的異常檢測方法的程式和各種數據，並在電子設備1運行過程中實現高速、自動地完成程式或數據的存取。記憶體11可以是電子設備1的外部存儲設備和/或內部存儲設備。進一步地，記憶體11可以是集成電路中沒有實物形式的具有存儲功能的電路，如RAM(Random-Access Memory，隨機存取存儲設備)、FIFO(First In First Out，)等，或者，記憶體11也可以是具有實物形式的存儲設備，如內存條、TF卡(Trans-flash Card)等等。
The
處理器12可以是中央處理器(CPU，Central Processing Unit)。CPU是一塊超大規模的集成電路，是電子設備1的運算核心(Core)和控制核心(Control Unit)。處理器12可執行電子設備1的操作系統以及安裝的各類應用程式、程式代碼等，例如執行一種衝壓設備的異常檢測裝置中的各個單元中
的操作系統以及安裝的各類應用程式、程式代碼，以實現一種衝壓設備的異常檢測方法。
The
輸入輸出設備13主要用於實現電子設備1的輸入輸出功能，比如收發輸入的數字或字符信息，或顯示由用戶輸入的信息或提供給用戶的信息以及電子設備1的各種菜單。
The input and
所述電子設備1集成的模塊/單元如果以軟件功能單元的形式實現並作為獨立的產品銷售或使用時，可以存儲在一個計算機可讀取存儲存儲媒體中。基於這樣的理解，本申請實現上述實施例方法中的全部或部分流程，也可以藉由計算機程式來指令相關的硬體來完成，所述的計算機程式可存儲於一存儲媒體中，該計算機程式在被處理器執行時，可實現上述各個方法實施例的步驟。其中，所述計算機程式包括計算機程式代碼，所述計算機程式代碼可以為源代碼形式、對象代碼形式、可執行文件或某些中間形式等。所述存儲媒體可以包括：能夠攜帶所述計算機程式代碼的任何實體或裝置、記錄存儲媒體、隨身碟、移動硬盤、磁碟、光盤、計算機記憶體、唯讀記憶體(ROM，Read-Only Memory)、隨機存取記憶體(RAM，Random Access Memory)、電載波信號、電信信號以及軟件分發存儲媒體等。 If the integrated modules/units of the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the above embodiment methods by instructing relevant hardware through a computer program. The computer program can be stored in a storage medium. The computer program can be stored in a storage medium. When executed by a processor, the steps of each of the above method embodiments may be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The storage medium may include: any entity or device capable of carrying the computer program code, a recording storage medium, a flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM). ), random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution storage media, etc.
在本申請所提供的幾個實施例中，應該理解到，所揭露的系統，裝置和方法，可以藉由其它的方式實現。例如，以上所描述的裝置實施例僅僅是示意性的，例如，所述單元的劃分，僅僅為一種邏輯功能劃分，實際實現時可以有另外的劃分方式。 In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods during actual implementation.
所述作為分離部件說明的單元可以是或者也可以不是物理上分開的，作為單元顯示的部件可以是或者也可以不是物理單元，即可以位於一個地方，或者也可以分布到多個網路單元上。可以根據實際的需要選擇其中的部分或者全部單元來實現本實施例方案的目的。 The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. . Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外，在本申請各個實施例中的各功能單元可以集成在一個處理單元中，也可以是各個單元單獨物理存在，也可以兩個或兩個以上單元集成在一個單元中。上述集成的單元既可以採用硬體的形式實現，也可以採用硬體加軟件功能單元的形式實現。 In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.
對於本領域技術人員而言，顯然本申請不限於上述示範性實施例的細節，而且在不背離本申請的精神或基本特徵的情況下，能夠以其他的具體形式實現本申請。因此，無論從哪一點來看，均應將實施例看作是示範性的，而且是非限制性的，本申請的範圍由所附請求項而不是上述說明限定，因此旨在將落在請求項的等同要件的含義和範圍內的所有變化涵括在本申請內。不應將請求項中的任何附關聯圖標記視為限制所涉及的請求項。此外，顯然“包括”一詞不排除其他單元或步驟，單數不排除複數。第一，第二等詞語用來表示名稱，而並不表示任何特定的順序。 It is obvious to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as illustrative and non-restrictive from any point of view, and the scope of the present application is defined by the appended claims rather than the above description, and it is therefore intended that those falling within the claims All changes within the meaning and scope of the equivalent elements are included in this application. Any associated association markup in a request item should not be considered to limit the request item in question. Furthermore, it is clear that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. Words such as first and second are used to indicate names and do not indicate any specific order.
最後應說明的是，以上實施例僅用以說明本申請的技術方案而非限制，儘管參照較佳實施例對本申請進行了詳細說明，本領域的普通技術人員應當理解，可以對本申請的技術方案進行修改或等同替換，而不脫離本申請技術方案的精神和範圍。 Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified. Modifications or equivalent substitutions may be made without departing from the spirit and scope of the technical solution of the present application.
S101-S105:步驟 S101-S105: Steps
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Also Published As
| Publication number | Publication date |
|---|---|
| TW202318124A (en) | 2023-05-01 |
| CN115990629A (en) | 2023-04-21 |
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