A system for processing data acquired by a portable zero-frequency micro-vibration accelerometer.

Abstract

This invention discloses a system for processing data acquired by a portable zero-frequency micro-vibration accelerometer sensor, relating to the field of data processing technology. The system includes a monitoring center connected to a data input module, a sensor module, a signal analysis module, a display module, and a robotic arm anomaly detection module. The data input module acquires data information and generates data samples. The displacement sensor and accelerometer in the sensor module acquire signals from the product. The signal analysis module analyzes the product signals, performs filtering and frequency domain analysis to obtain frequency domain characteristic curves. The display module displays the frequency domain characteristic curves and compares them with the data samples. The robotic arm anomaly detection module detects abnormal parts of the robotic arm.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically a system for processing data acquired by a portable zero-frequency micro-vibration accelerometer. Background Technology

[0002] Currently, robotic arms are widely used in industrial environments. However, the installation and fixation of robotic arms are subject to many limitations imposed by the industrial environment, often resulting in abnormal displacements such as internal displacement or loosening. Current sensors monitor the corresponding equipment or objects but cannot monitor abnormal displacement changes such as loosening or vibration in the robotic arm. Furthermore, the presence of foreign objects in the working environment can cause minute fluctuations that cannot be detected or may generate false alarms. Therefore, this paper presents a system for processing data acquired by a portable zero-frequency micro-vibration acceleration sensor. Summary of the Invention

[0003] The purpose of this invention is to provide a system for processing data acquired by a portable zero-frequency micro-vibration accelerometer.

[0004] The objective of this invention can be achieved through the following technical solution: a system for processing data acquired by a portable zero-frequency micro-vibration acceleration sensor, comprising a monitoring center, wherein the monitoring center is connected to a data input module, a sensor module, a signal analysis module, a display module, and a robotic arm anomaly detection module;

[0005] The data entry module is used to enter data information and generate data samples;

[0006] The sensor module is used to collect acceleration and displacement signals generated by the robotic arm in manufacturing products;

[0007] The signal analysis module is used to perform frequency domain analysis on the signals collected by the sensor module to obtain the frequency domain characteristic curve of the acceleration sequence sample and the compaction mass cloud map;

[0008] The display module is used to analyze the state of the robotic arm based on the frequency domain characteristic curve of the obtained acceleration sequence sample and the compaction mass cloud map, and to determine whether the robotic arm is abnormal.

[0009] The robotic arm anomaly detection module is used to detect abnormal parts of the robotic arm.

[0010] Furthermore, the process by which the data entry module enters data information and generates data samples includes:

[0011] Pre-enter standard acceleration and displacement signals and product information generated during the manufacturing of different products to generate acceleration sequence samples;

[0012] Pre-enter standard acceleration and displacement signals of each part of the robotic arm when it is operating normally on its own to generate samples of the robotic arm parts;

[0013] Pre-enter production nodes and robotic arm component information for each product manufacturing process using the robotic arm;

[0014] Associate the robotic arm component information with the product's production nodes to generate a node library;

[0015] The robotic arm component samples, acceleration sequence samples, and node library are transmitted to the monitoring center.

[0016] Furthermore, the process by which the sensor module acquires acceleration and displacement signals includes:

[0017] When the robotic arm manufactures a product, displacement sensors and acceleration sensors collect displacement signals and a first acceleration signal from the product.

[0018] The sensor module is equipped with a recording unit that records the time of data acquisition.

[0019] The collected displacement signal and first acceleration signal are sent to the monitoring center.

[0020] Furthermore, the process by which the signal analysis module performs frequency domain analysis on the signal acquired by the sensor module includes:

[0021] The signal analysis module includes filters and an image acquisition unit;

[0022] The image capturing unit captures images of the working environment and the position of the robotic arm, obtaining the posture data of the robotic arm;

[0023] The first acceleration signal from the monitoring center is retrieved, filtered to obtain the second acceleration signal, and frequency domain analysis is performed to obtain the frequency domain characteristic curve of the second acceleration signal.

[0024] The displacement signal in the monitoring center is retrieved, the displacement signal is differentiated once and marked as the first displacement signal, and the frequency domain analysis is performed to obtain the frequency domain characteristic curve of the first displacement.

[0025] The position and attitude data of the robotic arm are integrated with the compaction quality data to obtain a data stream, and a compaction quality cloud map is drawn based on the data stream;

[0026] The collected images of the robotic arm working site are fused with the compaction quality cloud map in real time, and the processed compaction quality cloud map is sent to the display module for display.

[0027] The acceleration sequence samples are called up, frequency domain analysis is performed to obtain the frequency domain characteristic curve of the acceleration sequence samples, and then the curve is input to the display module for display.

[0028] Furthermore, the process by which the display module analyzes and determines whether the robotic arm is malfunctioning includes:

[0029] Receive the frequency domain characteristic curve of the acceleration sequence sample and the compaction mass cloud map, set the three-level curve error threshold, and establish a two-dimensional coordinate system with the velocity value on the Y-axis and the time on the X-axis.

[0030] When the Y-axis of two frequency domain characteristic curves bifurcates, the bifurcation point is marked as a warning point.

[0031] Record the time of the warning point as t1, the time of the end point as t2, the time period from t1 to t2 as t3, and set the judgment period T;

[0032] The frequency domain characteristic curves within the compaction mass cloud map are compared with the corresponding frequency domain characteristic curves of the acceleration sequence samples using a two-dimensional coordinate system.

[0033] Generate abnormal warning information from the three levels of curve error information;

[0034] The generated abnormal warning information and time period t3 are transmitted to the abnormal detection unit of the robotic arm.

[0035] Furthermore, the process by which the robotic arm anomaly detection module detects abnormal robotic arm parts includes:

[0036] The robotic arm anomaly detection module is equipped with a fault log unit;

[0037] Receive abnormal warning information and time period t3, and retrieve node database, robotic arm part samples and working environment images in the monitoring center;

[0038] Based on the curve error level in the abnormal warning information and the sample of the robotic arm part, the robotic arm is inspected. If it is a level one curve error, the working environment image is transmitted to the staff in the monitoring center.

[0039] If it is a level 2 curve error, obtain the nodes used in time period t3 from the node library, and based on the parts used by the nodes, operate the robotic arm part of the node separately for detection, input the abnormal mechanical parts into the fault log unit and send it to the staff in the monitoring center.

[0040] If it is a level three curve error, each part of the robotic arm will be run completely and individually. The abnormal part of the robotic arm will be entered into the fault log unit, and the fault log unit and the working environment image of the robotic arm will be transmitted to the staff in the monitoring center.

[0041] Compared with the prior art, the beneficial effects of the present invention are: the frequency domain characteristic curve of the manufactured product can be used to see whether the robotic arm is in an abnormal state; the shooting of the working environment can determine whether it is the vibration of foreign objects in the working environment, reducing the possibility of false alarms; and the abnormal robotic arm part can be well located through the node. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0043] like Figure 1 As shown, a system for processing data acquired by a portable zero-frequency micro-vibration accelerometer includes a monitoring center, which is connected to a data input module, a sensor module, a signal analysis module, a display module, and a robotic arm anomaly detection module.

[0044] The data entry module is used to enter data information and generate data samples. The specific process includes:

[0045] The data entry module pre-enters standard acceleration and displacement signals generated by the robotic arm manufacturing different products;

[0046] Pre-enter the node information and robot arm part information for each product manufacturing process of the robotic arm;

[0047] The robotic arm component information includes the gripper, wrist, arm, and column;

[0048] The information on the robotic arm parts used at each product node is associated one-to-one with the product's production node to generate a node library;

[0049] Product information is pre-entered, including the corresponding manufacturing robotic arm, product name, and product number.

[0050] Pre-enter standard acceleration and displacement signals generated when each part of the robotic arm operates normally on its own to generate robotic arm part samples;

[0051] The standard acceleration and displacement signals are matched one-to-one with the product information. An acceleration sequence sample is generated based on the product information. The acceleration signals and displacement information are input into the corresponding acceleration sequence sample. The acceleration signals and displacement information in the acceleration sequence sample are input into the corresponding machine, so that the machine runs according to the sample to manufacture the product.

[0052] The data entry module transmits samples of robotic arm parts, acceleration sequence samples, and node libraries to the monitoring center.

[0053] The sensor module is used to collect acceleration and displacement signals generated by the robotic arm during product manufacturing. The specific process includes:

[0054] The sensor module is equipped with a recording unit;

[0055] The displacement sensor and the acceleration sensor are arranged adjacent to each other on the product, and the product is used to make the displacement sensor and the acceleration sensor move synchronously.

[0056] When the robotic arm moves during product manufacturing, displacement sensors and acceleration sensors will collect the displacement and acceleration signals generated by the product in real time.

[0057] The displacement signal and the first acceleration signal are obtained, and the recording unit will record the displacement signal and the first acceleration signal collected at each sampling time according to the sampling time.

[0058] It should be further explained that, during the specific implementation process, displacement sensors and acceleration sensors are installed inside various parts of the robotic arm;

[0059] The sensor module sends the collected displacement signal and first acceleration signal to the monitoring center.

[0060] The signal analysis module is used to perform frequency domain analysis on the signals collected by the sensor module. The specific process includes:

[0061] The signal analysis module includes filters and an image acquisition unit;

[0062] The image capturing unit is used to capture images of the working environment of the robotic arm and the position of the robotic arm, and to obtain attitude data through the position of the robotic arm. The image capturing unit transmits the working environment images to the monitoring center.

[0063] The signal analysis module retrieves the first acceleration signal from the monitoring center and inputs it into the filter unit for filtering to obtain the second acceleration signal, denoted as a.

[0064] The displacement signal from the monitoring center is retrieved. The information analysis module will perform a first derivative on the displacement signal, denoted by x. The derivative formula is expressed as x = Asin(ωt + φ), where ω is the frequency, A is the amplitude of the displacement signal, and t is the time. The differentiated displacement signal is marked as the first displacement signal.

[0065] Frequency domain analysis of the first displacement signal yields the frequency domain characteristic curve of the first displacement signal.

[0066] The second acceleration signal is analyzed in the frequency domain to obtain the frequency domain characteristic curve of the second acceleration signal;

[0067] The frequency domain characteristic curves of the first displacement signal and the second acceleration signal are processed in real time to obtain compaction quality data. The formula for the compaction quality data is as follows:

[0068] The position and attitude data of the robotic arm are integrated with the compaction quality data to form an integrated data stream, and a compaction quality cloud map is drawn in real time based on the integrated data stream;

[0069] The collected images of the robotic arm working site are fused with the compaction quality cloud map in real time, and the processed compaction quality cloud map is sent to the display module for display.

[0070] The signal analysis module calls the acceleration sequence samples, performs frequency domain analysis to obtain the frequency domain characteristic curve of the acceleration sequence samples, and inputs it to the display module for display.

[0071] The display module is used to analyze the state of the robotic arm based on the obtained acceleration sequence sample frequency domain characteristic curve and compaction mass cloud map, and to determine whether the robotic arm is abnormal. The specific process includes:

[0072] The display module receives the frequency domain characteristic curves of the acceleration sequence samples and the compaction mass cloud map;

[0073] Set a three-level curve error threshold, labeled P1, P2, and P3 respectively;

[0074] A two-dimensional coordinate system is established, and the velocity values ​​of the frequency domain curves in the compaction mass cloud map and the velocity values ​​of the frequency domain characteristic curves of the acceleration sequence samples are mapped to the Y-axis of the two-dimensional coordinate system, and the recording time in the recording unit is mapped to the X-axis of the two-dimensional coordinate system.

[0075] Based on the established two-dimensional coordinate system, the frequency domain characteristic curves within the compaction mass cloud map are compared with the corresponding frequency domain characteristic curves of the acceleration sequence samples;

[0076] When the velocity value corresponding to the Y-axis on the frequency domain characteristic curve within the compaction quality cloud map is lower or higher than the velocity value on the frequency domain characteristic curve of the acceleration sequence sample, it is marked as a warning point.

[0077] Based on the recorded time in the recording unit, obtain the time corresponding to the warning point and the time of the end point of the curve error duration. Record the time corresponding to the warning point as t1 and the time corresponding to the end point as t2.

[0078] Starting at time t1 and ending at time t2, this time interval is set as t3, and the judgment period T is set.

[0079] The area of ​​the region formed by the Y-axis of the two-dimensional coordinate system is obtained by the difference between the frequency domain characteristic curve of the compaction mass cloud map in the two-dimensional coordinate system and the frequency domain characteristic curve of the acceleration sequence sample. The area of ​​the region is denoted as Q.

[0080] When Q = P1 and t3 = T, the display module shows that the first-level curve error is foreign object vibration.

[0081] When Q = P1, t3 > T or Q = P2, t3 = T, it indicates that the second-order curve error is a robot arm anomaly.

[0082] If the robotic arm exhibits multiple Q segments during operation, it is represented as a third-level curve error P3.

[0083] The display module generates abnormal warning information from three levels of curve error information;

[0084] The generated abnormal warning information and time period t3 are transmitted to the abnormal detection unit of the robotic arm.

[0085] The robotic arm anomaly detection module is used to detect abnormal robotic arm parts, and the specific process includes:

[0086] The robotic arm anomaly detection module is equipped with a fault log unit;

[0087] Receive abnormal warning information and time period t3, and retrieve node database, robotic arm part samples and working environment images in the monitoring center;

[0088] When the robotic arm anomaly detection module receives an anomaly warning message, the robotic arm anomaly detection module will stop the operation of the abnormal robotic arm;

[0089] The robotic arm is inspected based on the curve error level in the abnormal warning information. If it is a level one curve error, the robotic arm inspection module will transmit the captured working environment image to the staff in the monitoring center and notify the staff to clean up the foreign object.

[0090] If it is a second-level curve error, the robotic arm detection module will obtain the nodes used by the abnormal robotic arm in time period t3 according to the node library, obtain the part of the robotic arm used according to the node, operate the part of the robotic arm at that node separately, and obtain the acceleration and displacement signals of the separately operated part by displacement sensor and acceleration sensor. The obtained acceleration and displacement signals will be analyzed in the frequency domain to obtain the frequency domain characteristic curve and marked as J1. This segment of operation is recorded as the first running cycle.

[0091] The frequency domain characteristic curve obtained by performing the first operating cycle operation on the sample of the robotic arm is denoted as J2.

[0092] Compare J1 and J2. If they are inconsistent, it is determined that there is an abnormality in the robotic arm. The abnormal mechanical part is entered into the fault log unit and sent to the staff in the monitoring center.

[0093] If the error is a level three curve, the robotic arm anomaly detection module will run independently to detect each part of the entire robotic arm. The abnormal robotic arm part will be input into the fault log unit. The robotic arm anomaly detection module will then transmit the fault log unit and the image of the robotic arm's working environment to the staff in the monitoring center for repair.

[0094] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A system for processing data acquired by a portable zero-frequency micro-vibration accelerometer, comprising a monitoring center, characterized in that, The monitoring center is connected to a data input module, a sensor module, a signal analysis module, a display module, and a robotic arm anomaly detection module. The data entry module is used to enter data information and generate data samples; The sensor module is used to collect acceleration and displacement signals generated by the robotic arm in manufacturing products; The signal analysis module is used to perform frequency domain analysis on the signals collected by the sensor module to obtain the frequency domain characteristic curve of the acceleration sequence sample and the compaction mass cloud map; The display module is used to analyze the state of the robotic arm based on the frequency domain characteristic curve of the obtained acceleration sequence sample and the compaction mass cloud map, and to determine whether the robotic arm is abnormal. The robotic arm anomaly detection module is used to detect abnormal robotic arm parts; The process by which the sensor module acquires acceleration and displacement signals includes: When the robotic arm manufactures a product, displacement sensors and acceleration sensors collect displacement signals and a first acceleration signal from the product. The sensor module is equipped with a recording unit that records the time of data acquisition. The collected displacement signal and first acceleration signal are sent to the monitoring center; The process of the data entry module entering data information and generating data samples includes: Pre-enter standard acceleration and displacement signals and product information generated during the manufacturing of different products to generate acceleration sequence samples; Pre-enter standard acceleration and displacement signals of each part of the robotic arm when it is operating normally on its own to generate samples of the robotic arm parts; Pre-enter production nodes and robotic arm component information for each product manufacturing process using the robotic arm; Associate the robotic arm component information with the product's production nodes to generate a node library; The robotic arm component samples, acceleration sequence samples, and node library are transmitted to the monitoring center. The signal analysis module is equipped with a filter and an image capturing unit; The image capturing unit captures images of the working environment and the position of the robotic arm, obtaining the posture data of the robotic arm; The first acceleration signal from the monitoring center is retrieved, filtered to obtain the second acceleration signal, and frequency domain analysis is performed to obtain the frequency domain characteristic curve of the second acceleration signal. The displacement signal in the monitoring center is retrieved, the displacement signal is differentiated once and marked as the first displacement signal, and the frequency domain analysis is performed to obtain the frequency domain characteristic curve of the first displacement. The frequency domain characteristic curves of the first displacement signal and the second acceleration signal are processed in real time to obtain compaction quality data. The position and attitude data of the robotic arm are integrated with the compaction quality data to obtain a data stream, and a compaction quality cloud map is drawn based on the data stream; The collected images of the robotic arm working site are fused with the compaction quality cloud map in real time, and the processed compaction quality cloud map is sent to the display module for display. The acceleration sequence samples are called up, frequency domain analysis is performed to obtain the frequency domain characteristic curve of the acceleration sequence samples, and then the curve is input to the display module for display.

2. The system for processing data acquired by a portable zero-frequency micro-vibration accelerometer according to claim 1, characterized in that, The process by which the display module analyzes and determines whether the robotic arm is abnormal includes: Receive the frequency domain characteristic curve of the acceleration sequence sample and the compaction mass cloud map, set the three-level curve error threshold, and establish a two-dimensional coordinate system with the velocity value on the Y-axis and the time on the X-axis. When the Y-axis of two frequency domain characteristic curves bifurcates, the bifurcation point is marked as a warning point. Record the time of the warning point as t1, the time of the end point as t2, the time period from t1 to t2 as t3, and set the judgment period T; The frequency domain characteristic curves within the compaction mass cloud map are compared with the corresponding frequency domain characteristic curves of the acceleration sequence samples using a two-dimensional coordinate system. Generate abnormal warning information from the three levels of curve error information; The generated abnormal warning information and time period t3 are transmitted to the abnormal detection unit of the robotic arm.

3. The system for processing data acquired by a portable zero-frequency micro-vibration accelerometer according to claim 2, characterized in that, The process by which the robotic arm anomaly detection module detects abnormal robotic arm parts includes: The robotic arm anomaly detection module is equipped with a fault log unit; Receive abnormal warning information and time period t3, and retrieve node database, robotic arm part samples and working environment images in the monitoring center; Based on the curve error level in the abnormal warning information and the sample of the robotic arm part, the robotic arm is inspected. If it is a level one curve error, the working environment image is transmitted to the staff in the monitoring center. If it is a level 2 curve error, obtain the nodes used in time period t3 from the node library, obtain the parts used from the nodes, operate the robotic arm part of the node separately for detection, input the abnormal mechanical parts into the fault log unit and send it to the staff in the monitoring center. If it is a level three curve error, each part of the robotic arm will be run completely and individually. The abnormal part of the robotic arm will be entered into the fault log unit, and the fault log unit and the working environment image of the robotic arm will be transmitted to the staff in the monitoring center.

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