Servo motor fault detection system based on cloud network end architecture

Abstract

The utility model discloses a servo motor fault detection system based on a cloud network end architecture. The servo motor fault detection system comprises a servo motor, a driver, an edge computing device, a data acquisition module, a cloud server and a mobile terminal. The servo motor is connected with the driver, and the driver is connected with the edge computing device; the data acquisition module acquires operation data of the servo motor in real time, performs preliminary analysis on the data through the edge calculation module, and then uploads the data to the cloud server through a communication network; the cloud server analyzes the uploaded data and returns a fault diagnosis result and a maintenance suggestion to the edge computing device and the mobile terminal through a communication network; a user can access the cloud server through the mobile terminal and inquire a fault diagnosis result and historical data, so that real-time monitoring, fault detection and remote maintenance of the servo motor are realized, and the reliability and the maintenance efficiency of the servo motor are effectively improved.

Description

Technical Field

[0001] The utility model relates to the technical field of industrial automation, in particular to a servo motor fault detection system based on a cloud-network-edge architecture. Background Art

[0002] With the improvement of industrial automation level, as an important part of precision motion control, servo motors are widely used in fields such as robots, numerically controlled machine tools, and automated production lines. The operating state of servo motors directly affects the performance and reliability of the entire automation system. However, during operation, servo motors may malfunction due to various reasons, such as mechanical wear, overheating, and overload. These faults not only cause equipment downtime and affect production efficiency but may also lead to more serious safety accidents. Therefore, how to effectively detect and predict servo motor faults has become an urgent problem to be solved.

[0003] Traditional servo motor fault detection methods mainly rely on the experience of on-site engineers and regular manual inspections. This method is not only time-consuming and laborious but also unable to monitor the operating state of equipment in real time, resulting in faults not being detected and processed in a timely manner. Regarding the problem of motor fault detection and protection, in the generator fault detection system (CN2015210487930), the collected current and voltage data are transmitted to the background server through a data transceiver antenna by DTU for fault detection, but it lacks edge computing support and cannot achieve real-time fault protection at the local end; a motor fault detection device and system (CN2016211115925) mainly dynamically monitors the motor power supply phase loss and open circuit faults, but the detection range is relatively limited and cannot comprehensively cover various fault types of the motor; a motor fault detection system based on the total harmonic distortion coefficient of current (CN2017216415494) can accurately determine the type of motor fault based on the total harmonic distortion coefficient of current and take timely fault protection measures, but it mainly relies on local processing of the DSP processor and lacks the ability of cloud real-time monitoring and remote diagnosis; a stepping motor fault detection system and air conditioner (CN202123148764X) realizes local end fault detection and alarm of the motor by setting current and voltage thresholds with a single-chip microcomputer, but does not have the ability of big data analysis and fault diagnosis.

[0004] With the development of emerging technologies such as cloud computing, the Internet of Things, and edge computing, an intelligent fault detection system based on a cloud-network-edge architecture has emerged. Through advanced sensing technology, data acquisition and transmission technology, and big data analysis and deep learning algorithms in the cloud, it can realize real-time monitoring and remote fault diagnosis of servo motors. It can not only achieve local end fault detection and protection but also has the capabilities of cloud monitoring, big data analysis, and fault diagnosis, improving the reliability and maintenance efficiency of the servo system. Summary of the Utility Model

[0005] The purpose of the present utility model is to provide a servo motor fault detection system based on a cloud-network-edge architecture, which can monitor the operation state data of the servo motor in real time through a data acquisition module, realize real-time acquisition and detection of faults, and improve the operation reliability and maintenance efficiency of the equipment.

[0006] To achieve the above object, the present utility model provides a servo motor fault detection system based on a cloud-network-edge architecture, including a servo motor, a driver, an edge computing device, a data acquisition module, a cloud server and a mobile terminal. The servo motor is connected to the driver, and the driver is connected to the edge computing device. The edge computing device uses STM32, which is not only used to control the driver of the servo motor, but also used to collect and monitor data in real time. The data acquisition module collects the operation data of the servo motor in real time, analyzes and processes the data through the edge computing module, and then uploads the data to the cloud server through a communication network. The cloud server deeply analyzes the uploaded data, and returns the fault diagnosis results and maintenance suggestions to the edge computing device and the mobile terminal through a communication network. Users can access the cloud server through the mobile terminal to query the fault diagnosis results and historical data, realizing real-time monitoring, fault detection and remote maintenance of the servo motor, and effectively improving the reliability and maintenance efficiency of the servo motor.

[0007] The present utility model is achieved by at least one of the following technical solutions.

[0008] A servo motor fault detection system based on a cloud-network-edge architecture, including a servo motor, a driver, an edge computing device, a data acquisition module, a cloud server and a mobile terminal;

[0009] The servo motor is connected to the driver, and the driver is connected to the edge computing device;

[0010] The data acquisition module is connected to the servo motor, collects the operation state data of the servo motor in real time, and transmits the data to the edge computing device;

[0011] The edge computing device uploads the data to the cloud server through a communication network;

[0012] The cloud server analyzes the uploaded data, and returns the fault diagnosis results and maintenance suggestions to the edge computing device and the mobile terminal through a communication network;

[0013] Users access the cloud server through the mobile terminal to query the fault diagnosis results and historical data.

[0014] Further, the data acquisition module includes multiple sensors installed on the servo motor. The sensors include a vibration sensor, a temperature sensor, and a current sensor, and are used to collect the vibration, temperature, and current signals of the servo motor in real time.

[0015] Further, the vibration sensor is installed on the bearing seat of the servo motor, the temperature sensor is installed on the surface of the servo motor housing, and the current sensor is connected in series in the power supply line of the servo motor.

[0016] Further, the vibration sensor, the temperature sensor, and the current sensor are connected to the interface of the edge computing device through data lines, and are used to transmit the collected vibration, temperature, and current data.

[0017] Further, the edge computing device includes an embedded single-chip microcomputer STM32. The vibration sensor, the temperature sensor, and the current sensor of the data acquisition module are connected to the I2C interface of the STM32 through data lines, and are used to transmit the collected vibration, temperature, and current data.

[0018] Further, the edge computing device includes a data storage unit.

[0019] Further, the data storage unit uses an SPI flash chip and is connected to the SPI interface of the STM32 through the SPI bus, and is used to store local historical data.

[0020] Further, the communication network is connected to the local area network using a WiFi module.

[0021] Further, the WiFi module uses ESP8266 and is connected to the UART interface of the STM32 through a data line to realize the wireless transmission of processed data.

[0022] Further, the edge computing device further includes an audible and visual alarm. The audible and visual alarm is connected to the edge computing device through a signal line and is used for on-site fault alarm.

[0023] Further, the system further includes a power module, which is connected to the driver, the edge computing device, and the data acquisition module, and is used to ensure the stable operation of the system.

[0024] Compared with the existing technology, the beneficial effects of the present utility model are:

[0025] 1. Real-time data monitoring: The utility model realizes the real-time monitoring of the operating state data such as the vibration, temperature and current of the servo motor through the data acquisition module and the edge computing device. The traditional servo motor fault detection method mainly relies on manual regular inspection and cannot obtain the motor operating state in real time. This system can monitor various operating parameters of the servo motor in real time through advanced sensors and data acquisition technologies, timely detect potential faults, and improve the accuracy and timeliness of monitoring.

[0026] 2. Remote maintenance warning: The utility model transmits the fault results and maintenance suggestions to the edge computing device and the mobile terminal through the wireless network, realizing the remote maintenance and warning of the servo motor device. Compared with the traditional method, users can obtain the operating state and fault information of the motor without going to the site, so that they can take maintenance measures in time to avoid the expansion of faults. In addition, the system can also provide accurate maintenance suggestions according to historical data and fault modes, effectively improving the scientificity and predictability of maintenance work.

[0027] 3. Improve maintenance efficiency: Users can access the cloud server through the mobile terminal to query the fault diagnosis results and historical data, reducing the frequency and workload of manual inspection and improving the maintenance efficiency. The traditional maintenance method requires frequent manual inspections, which is not only time-consuming and laborious, but also prone to omissions or misjudgments. This system can automatically perform fault diagnosis and historical data analysis through cloud big data analysis and intelligent algorithms. Users only need to obtain relevant information conveniently through the mobile terminal, thus greatly reducing the workload and cost of maintenance.

[0028] 4. Intelligent fault diagnosis: The utility model utilizes the powerful computing power of the cloud server to comprehensively analyze and diagnose the collected data through big data analysis and deep learning algorithms. Traditional fault detection systems mostly rely on simple threshold judgment or local processing and are difficult to accurately identify complex faults. This system can identify more types of fault types through cloud big data analysis and provide detailed fault cause analysis and processing suggestions, greatly improving the intelligent level of fault diagnosis. Description of the Drawings

[0029] Figure 1 is the overall system architecture schematic diagram of the utility model;

[0030] Figure 2 is the composition schematic diagram of the data acquisition module and the edge computing module of the utility model;

[0031] Figure 3 is the system connection relationship schematic diagram of the utility model;

[0032] Description of the attached drawing reference numerals: 1 - servo motor; 2 - driver; 3 - edge computing device; 4 - data acquisition module; 5 - cloud server; 6 - mobile terminal; 7 - STM32; 8 - vibration sensor; 9 - temperature sensor; 10 - current sensor; 11 - data storage unit; 12 - audible and visual alarm; 13 - WiFi module. Detailed implementation manners

[0033] The present utility model will be further described below in conjunction with embodiments and the attached drawings.

[0034] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations of the present utility model. Unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present utility model belongs.

[0035] Embodiment 1

[0036] As Figures 1-3 shown, this embodiment provides a servo motor fault detection system based on a cloud-network-edge architecture, including: a servo motor 1, a driver 2, an edge computing device 3, a data acquisition module 4, a cloud server 5, and a mobile terminal 6.

[0037] Among them, the servo motor 1 is connected to the driver 2, and the driver 2 is connected to the edge computing device 3; the edge computing device 3 includes an embedded single-chip microcomputer STM32 for controlling the driver of the servo motor and for real-time collecting and processing data.

[0038] In the actual application of this embodiment, the specific implementation manners are as follows:

[0039] The data acquisition module 4 includes a plurality of sensors installed on the servo motor. The edge computing device 3 is connected to the sensors through an I2C interface. These sensors include a vibration sensor 8, a temperature sensor 9, and a current sensor 10, which are respectively installed on the bearing seat of the servo motor, the outer surface of the housing, and the power line for collecting the vibration, temperature, and current signals of the servo motor.

[0040] The edge computing device 3 uploads the collected data to the cloud server 5 through the WiFi module 13. The WiFi module 13 uses ESP8266 and is connected to the STM32 through a UART interface to ensure wireless transmission of data.

[0041] The cloud server 5 analyzes the uploaded data, generates a fault diagnosis result and a maintenance suggestion, and returns the result to the edge computing device 3 and the mobile terminal 6 through a communication network. The user can access the cloud server 5 through a mobile terminal 6 such as a smart phone or a PC to query the fault diagnosis result and historical data.

[0042] As a preferred embodiment, the vibration sensor 8 is a triaxial acceleration sensor of model ADXL355; the temperature sensor 9 is a temperature sensor of model TMP36; the current sensor 10 is a Hall current sensor of model ACS712.

[0043] As a preferred embodiment, the driver 2 is a servo driver of model SD-500, supporting the PWM control mode and capable of achieving precise speed control of the servo motor 1.

[0044] As a preferred embodiment, the STM32 is a development board of model STM32F407VGT6, equipped with 512KB RAM and 2MB Flash memory, capable of processing various sensor data in real time and controlling the driver 2 of the servo motor 1.

[0045] As a preferred embodiment, the cloud server 5 can adopt the AWS cloud platform of model AWS-EC2, configured with a 4-core CPU, 16GB RAM and 500GB storage space.

[0046] Embodiment 2

[0047] In this embodiment, the edge computing device 3 further includes a data storage unit 11 for storing and subsequent analysis of local data. The data storage unit uses an SPI flash chip and is connected to the SPI interface of the STM32 through the SPI bus. The data acquisition module transmits the acquired data to the STM32 through the data line for preliminary processing, and the processed data is uploaded to the cloud server 5 through the WiFi module. At the same time, the STM32 transmits the processed data to the SPI flash chip through the SPI bus, and the SPI flash chip stores the received data.

[0048] When there are problems with the communication network, the edge computing device 3 can continue to monitor and store data, and upload the data to the cloud server after the network is restored. In addition, users can access the cloud server 5 through the mobile terminal 6 to query the historical data stored in the edge computing device 3 for fault backtracking and analysis, improving the system's fault diagnosis and maintenance capabilities.

[0049] As a preferred embodiment, the SPI flash chip used is of model W25Q64, configured with 64MB of storage space, supporting fast read and write operations, and capable of meeting the application requirements of high capacity and high performance.

[0050] Embodiment 3

[0051] In this embodiment, a servo motor fault detection system based on a cloud-network-edge architecture includes a field fault alarm function and a remote fault alarm function. The edge computing device 3 further includes an audible and visual alarm 12, which is connected to the STM32 through a signal line and is used for on-site fault alarm.

[0052] Specifically, this embodiment provides a servo motor fault detection and processing flow based on a cloud-network-edge architecture, including the following steps:

[0053] (1) The data acquisition module 4 collects the vibration, temperature, and current data of the servo motor 1 in real time through the vibration sensor 8, temperature sensor 9, and current sensor 10.

[0054] (2) The edge computing device 3 analyzes the vibration, temperature, or current data of the servo motor detected by the data acquisition module 4. If the data exceeds the set normal threshold range, it directly activates the audible and visual alarm 12 for local alarm, controls the driver 2 to immediately stop the operation of the servo motor 1, and sends an emergency stop notice to the cloud server 5 through the communication network to improve the fault response speed.

[0055] (3) The cloud server 5 deeply analyzes the uploaded data, predicts and diagnoses the occurrence of the fault, and generates a fault diagnosis result and maintenance suggestion, which are returned to the edge computing device 3. The edge computing device 3 controls the driver 2 of the servo motor 1 according to the diagnosis result to adjust the operating state of the servo motor 1. Then, the cloud server 5 sends the fault diagnosis report and maintenance suggestion to the mobile terminal 6 via text message for remote fault alarm.

[0056] (4) After receiving the fault report and maintenance suggestion through the mobile terminal 6, the user notifies the maintenance personnel to perform fault repair and maintenance operations. After the fault is eliminated, the user sends a resume operation instruction to the edge computing device 3 and the cloud server 4 through the mobile terminal 6, and the edge computing device 3 restarts the operation of the servo motor 1.

[0057] In addition, for the servo motor fault detection system based on a cloud-network-edge architecture provided in this embodiment, in addition to using the WiFi module 13 to implement the communication network, a Bluetooth module or a 4G / 5G module can also be used to achieve wireless data transmission; in addition to using the embedded single-chip microcomputer STM32 as the edge computing device 3, microcontrollers such as Arduino and ESP32 can also be used.

[0058] Finally, it should be noted that in this specification, terms such as "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0059] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present utility model. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined in this application can be implemented in other embodiments without departing from the spirit or scope of the present utility model. Therefore, the present utility model will not be limited to these embodiments shown in this application, but rather will conform to the widest scope consistent with the principles and novel features disclosed in this application.

Claims

1. A servo motor fault detection system based on cloud network architecture, characterized in that: Includes servo motors, drivers, edge computing devices, data acquisition modules, cloud servers and mobile terminals; The servo motor is connected to a driver, and the driver is connected to an edge computing device; The data acquisition module is connected to the servo motor to collect the running status data of the servo motor in real time and transmit it to the edge computing device; The edge computing device uploads the data to the cloud server via the communication network; The cloud server analyzes the uploaded data and returns the fault diagnosis results and maintenance suggestions to the edge computing device and the mobile terminal via the communication network; The user accesses the cloud server through the mobile terminal to query fault diagnosis results and historical data.

2. A servo motor fault detection system based on cloud network terminal architecture according to claim 1, characterized in that: The data acquisition module includes a plurality of sensors installed on the servo motor, wherein the sensors include a vibration sensor, a temperature sensor and a current sensor, and are used for real-time acquisition of vibration, temperature and current signals of the servo motor.

3. A servo motor fault detection system based on cloud network terminal architecture according to claim 2, characterized in that: The vibration sensor is installed on the bearing seat of the servo motor, the temperature sensor is installed on the surface of the servo motor housing, and the current sensor is connected in series in the power supply circuit of the servo motor.

4. The servo motor fault detection system based on cloud network terminal architecture according to claim 2 is characterized in that: The vibration sensor, temperature sensor and current sensor are connected to the interface of the edge computing device via a data line to transmit the collected vibration, temperature and current data.

5. The servo motor fault detection system based on cloud network terminal architecture according to claim 1 is characterized in that: The edge computing device includes an embedded single-chip microcomputer STM32. The vibration sensor, temperature sensor and current sensor of the data acquisition module are connected to the I2C interface of the STM32 through a data line for transmitting the collected vibration, temperature and current data.

6. A servo motor fault detection system based on cloud network terminal architecture according to claim 5, characterized in that: The edge computing device includes a data storage unit.

7. A servo motor fault detection system based on cloud network terminal architecture according to claim 6, characterized in that: The data storage unit adopts an SPI flash memory chip, which is connected to the SPI interface of the STM32 via an SPI bus and is used to store local historical data.

8. The servo motor fault detection system based on cloud network terminal architecture according to claim 5 is characterized in that: The communication network is connected to the local area network using a WiFi module.

9. A servo motor fault detection system based on cloud network terminal architecture according to claim 8, characterized in that: The WiFi module adopts ESP8266, which is connected to the UART interface of the STM32 through a data line to realize wireless transmission of processed data.

10. A servo motor fault detection system based on cloud network terminal architecture according to any one of claims 1 to 9, characterized in that: The edge computing device also includes an audible and visual alarm, which is connected to the edge computing device via a signal line and is used for on-site fault alarm.

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