Fault prediction system for motor and its transmission mechanism based on motion control system architecture

Abstract

The present invention discloses a motor and its transmission mechanism fault prediction system based on a motion control system architecture, including a main station, at least two substations and a cloud server, wherein the main station is a motion controller, one of the substations is an edge computing module, and the other substations are motor controllers, the motor controller controls the movement of the motor and its transmission mechanism and receives the feedback signal of the position encoder on the motor and detects the current signal on the motor. The main station and the substations are connected via a high-speed motion control bus, the edge computing module is connected to the main station as a substation, and is connected to the cloud server through an information technology channel. The present invention is implemented using a high-speed real-time bus architecture in a motion control system, based on the original motor controller and motion control bus network architecture, without increasing the storage cost of the motor controller, and realizing real-time synchronous transmission of characteristic data and prediction results of each motor shaft, and realizing fault prediction of the motor and its transmission mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of motor fault prediction, and in particular to a motor and transmission mechanism fault prediction system based on a motion control system architecture. Background Art

[0002] The fault state of the motor and its transmission mechanism can be predicted by analyzing the current, acceleration and other data during the operation of the motor. In a network-based multi-axis motor control system, data can be collected and analyzed through the network. Patent CN112434879A proposes a method that uses an advanced server to collect data during the operation of the motor through the network and stores historical data, and then obtains the motor fault state after fault model analysis. The bus used in the motion control system is usually a real-time high-speed bus such as EtherCAT and Profinet. Two types of data are transmitted on this bus, one is time-critical data and the other is non-time-critical data. Time-critical data requires data transmission within a certain time, and non-time-critical data is a type of data transmission with an uncertain time. The sampling time of data such as current and position in motor control is usually less than 125us. During the operation, a large amount of data will continue to be generated in the time domain. The usual method is to store these data and then transmit the data to the server for analysis through the communication method of non-time-critical data. Storing these data requires additional storage space, which increases the cost of the motor controller. In addition, due to the use of the communication method of non-time-critical data, it is impossible to achieve time-synchronous high-speed real-time transmission of multi-axis data. Summary of the invention

[0003] The present invention provides a motor and transmission mechanism fault prediction system based on a motion control system architecture. The system has the advantage of being implemented using a high-speed real-time bus architecture in a motion control system. Based on the original motor controller and motion control bus network architecture, the system does not need to increase the storage cost of the motor controller, and can achieve real-time synchronous transmission of characteristic data and prediction results of each motor shaft, thereby achieving motor and transmission mechanism fault prediction.

[0004] The above-mentioned object of the present invention is achieved through the following technical solutions: a motor and its transmission mechanism fault prediction system based on a motion control system architecture, characterized in that it includes a main station, at least two substations and a cloud server, the main station is a motion controller, one of the substations is an edge computing module, and the other substations are motor controllers. The motor controller controls the movement of the motor and its transmission mechanism and receives the feedback signal of the position encoder on the motor. The motor controller detects the current signal on the motor. The main station and the substation are connected through a high-speed motion control bus. The edge computing module is connected to the main station as a substation, and is connected to the cloud server through an information technology channel;

[0005] The fault prediction method of the motor fault prediction system is:

[0006] Step 1: During the operation of the motor controller, the current and position data are sampled through the current sensor and position encoder detection of the motor controller itself;

[0007] Step 2: The motor controller pre-processes and analyzes the sampled current and position data in real time, extracts features in the frequency domain, and reduces the data dimension and data volume;

[0008] Step 3: The motion controller master station reads the feature data preprocessed by the motor controller substation and transmits the feature data to the edge computing module substation;

[0009] Step 4: The edge computing module substation performs fault analysis and result prediction on the characteristic data of each axis;

[0010] Step 5: The motion controller master station reads the fault prediction results of the edge module substation in real time and transmits them to the motor controller substation;

[0011] Step 6: The motor controller protects the motor and transmission structure according to the fault prediction result;

[0012] Step 7: The edge computing module substation transmits the fault prediction results to the cloud server. The analysis results and other control parameters of the cloud server are transmitted to the motor controller through the edge computing substation and the motion controller main station.

[0013] Furthermore, the remaining sensors in the motor and its transmission system are also connected to the edge computing module substation.

[0014] Furthermore, the edge computing module substation receives real-time data transmitted by the motion control system, and also receives data from other sensors in the motor and its transmission system.

[0015] Furthermore, the information technology channel is a TCP / IP communication network or a 4G / 5G communication network.

[0016] In summary, the beneficial effects of the present invention are: it adopts the high-speed real-time bus architecture in the motion control system, based on the original motor controller and network architecture, and does not need to increase the storage cost of the motor controller to realize the fault prediction of the motor and its transmission mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0018] The specific implementation of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Example: Reference Figure 1 A motor and transmission mechanism fault prediction system based on a motion control system architecture, characterized in that it includes a main station, at least two substations and a cloud server, wherein the main station is a motion controller, one of the substations is an edge computing module, and the other substations are motor controllers, the motor controller controls the movement of the motor and its transmission mechanism and receives the feedback signal of the position encoder on the motor, and the motor controller detects the current signal on the motor, the main station and the substation are connected through a high-speed motion control bus, the edge computing module is connected to the main station as a substation, and is connected to the cloud server through an information technology channel. The information technology channel is a TCP / IP communication network or a 4G / 5G communication network. The edge computing module substation receives real-time data transmitted by the motion control system and receives data from other sensors in the motor and its transmission system.

[0020] The remaining sensors in the motor and its transmission system are also connected to the edge computing module substation.

[0021] The fault prediction method of the motor fault prediction system is:

[0022] Step 1: During the operation of the motor controller, the current and position data are sampled through the current sensor and position encoder detection of the motor controller itself;

[0023] Step 2: The motor controller pre-processes and analyzes the sampled current and position data in real time, extracts features in the frequency domain, and reduces the data dimension and data volume;

[0024] Step 3: The motion controller master station reads the feature data preprocessed by the motor controller substation and transmits the feature data to the edge computing module substation;

[0025] Step 4: The edge computing module substation performs fault analysis and result prediction on the characteristic data of each axis;

[0026] Step 5: The motion controller master station reads the fault prediction results of the edge module substation in real time and transmits them to the motor controller substation;

[0027] Step 6: The motor controller protects the motor and transmission structure according to the fault prediction result;

[0028] In step seven, the edge computing module substation transmits the fault prediction results to the cloud server, where big data analysis can be further performed. The analysis results and other control parameters of the cloud server are transmitted to the motor controller through the edge computing substation and the motion controller main station; the edge computing module substation transmits the data of the remaining sensors in the motor and its transmission system to the cloud server through the information technology channel, where big data analysis can be further performed.

[0029] The present invention is based on the high-speed bus network architecture of the motion control system, and adds an edge computing unit that supports the substation of the motion control system. The unit is connected to the motion control system as a substation and is connected to the cloud server through an information technology channel. In order not to increase the storage space, the present invention first pre-processes the sampled current and position data in the motor controller, converts from the time domain to the frequency domain, performs feature screening on the data, reduces the number and dimension of the data, and then transmits the pre-processed data to the edge computing unit through the motion control bus. In order to transmit data synchronously in real time at high speed and perform fault analysis and prediction, the present invention adds an edge computing unit that supports the substation of the motion control system to the system, and the edge unit and the motor controller receive time-critical data as a substation in the same bus network. The motor controller of each axis pre-processes the collected data and transmits it to the edge computing unit synchronously and in real time. After the edge computing unit analyzes the received features and predicts the fault, it transmits the predicted fault state synchronously and in real time to each motor controller. While the edge computing unit predicts the motor fault, it collects other sensor information in the system and transmits it to the cloud server through the information technology channel. The cloud server interacts with the motion control system through the edge computing unit. The present invention is implemented using a high-speed real-time bus architecture in a motion control system. Based on the original motor controller and motion control bus network architecture, the present invention does not need to increase the storage cost of the motor controller and achieves real-time synchronous transmission of characteristic data and prediction results of each motor shaft.

[0030] The above is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A motor and transmission mechanism fault prediction system based on a motion control system architecture, characterized in that: It includes a main station, at least two substations and a cloud server, wherein the main station is a motion controller, one of the substations is an edge computing module, and the other substations are motor controllers. The motor controller controls the motion of the motor and its transmission mechanism and receives feedback signals from the position encoder on the motor. The motor controller detects current signals on the motor. The main station and the substations are connected via a high-speed motion control bus. The edge computing module is connected to the main station as a substation and is connected to the cloud server through an information technology channel. The fault prediction method of the motor and its transmission mechanism fault prediction system is as follows: Step 1: During the operation of the motor controller, the current and position data are sampled through the current sensor and position encoder detection of the motor controller itself; Step 2: The motor controller pre-processes and analyzes the sampled current and position data in real time, extracts features in the frequency domain, and reduces the data dimension and data volume; Step 3: The motion controller master station reads the feature data preprocessed by the motor controller substation and transmits the feature data to the edge computing module substation; Step 4: The edge computing module substation performs fault analysis and result prediction on the characteristic data of each axis; Step 5: The motion controller master station reads the fault prediction results of the edge module substation in real time and transmits them to the motor controller substation; Step 6: The motor controller protects the motor and transmission structure according to the fault prediction result; Step 7: The edge computing module substation transmits the fault prediction results to the cloud server. The analysis results and other control parameters of the cloud server are transmitted to the motor controller through the edge computing substation and the motion controller main station.

2. The motor and transmission mechanism fault prediction system based on motion control system architecture according to claim 1 is characterized in that: The remaining sensors in the motor and its transmission system are also connected to the edge computing module substation.

3. The motor and transmission mechanism fault prediction system based on motion control system architecture according to claim 2 is characterized in that: The edge computing module substation receives real-time data transmitted by the motion control system, and also receives data from other sensors in the motor and its transmission system.

4. The motor and transmission mechanism fault prediction system based on motion control system architecture according to claim 1, characterized in that: The information technology channel is a TCP / IP communication network or a 4G / 5G communication network.

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