CN112564579A - Master-slave architecture electromechanical controller and health management method - Google Patents
Master-slave architecture electromechanical controller and health management method Download PDFInfo
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- H—ELECTRICITY
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- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/0004—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
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- H—ELECTRICITY
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- H02P23/0077—Characterised by the use of a particular software algorithm
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P23/00—Arrangements or methods for the control of AC motors characterised by a control method other than vector control
- H02P23/14—Estimation or adaptation of motor parameters, e.g. rotor time constant, flux, speed, current or voltage
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Abstract
The application provides a master-slave architecture electromechanical controller and a health management method, wherein the master-slave architecture electromechanical controller comprises a master control chip, a slave control chip, a CPLD and a data storage circuit, wherein: the master control chip, the slave control chip and the CPLD are all connected with the data storage circuit; the master control chip and the slave control chip are debugged through RS232 serial ports; the CPLD is connected with the motor through a conditioning circuit and is used for receiving a control monitoring signal sent by the motor; the CPLD is connected with the motor through the RS422 and is used for communicating with the motor; the slave control chip is connected with the on-board temperature monitoring circuit; and a main control core. The chip and the slave control chip are both connected with the CPLD; the master control chip, the slave control chip, the CPLD and the data storage circuit are connected with the power circuit.
Description
Technical Field
The application belongs to the technical field of motor control, and particularly relates to a master-slave architecture electromechanical controller and a health management method.
Background
The motor control system is widely applied to a motor position servo system and a transmission system and plays an important role in the industrial production process. With the development and application of control chips with higher performance and higher operation speed in the field of industrial control systems, the computing power and the working efficiency of the controller system are greatly improved. However, during the operation of the precision motor system, many objective environmental factors (such as external temperature, humidity, electrostatic field, etc.) may have a certain influence on the stability and safety of the motor controller. For example, in the case of a motor controller operating at a high speed, local temperature may be exceeded due to uneven heat dissipation, and the internal components may have an uncertain fault in the process, which affects the safety of the whole system. The influence of the external electrostatic field can also cause electrostatic overload carried by the device, and the phenomenon of instability of current, voltage, signals and the like easily occurs in the system. The esd process may damage many core chips in the controller, causing its function to fail.
Health management is a concept proposed by NASA in the United states in the 70 s and applied to system maintenance of complex aircrafts, and is developed into a PHM (cosmetics and health management) technology with a relatively mature system at present, the technology is deeply researched, popularized and applied in military and strong countries such as America and English, and is gradually becoming an important component part in development and use stages of new-generation weapons such as airplanes and ships. PHM is still a field to be deeply researched at present when the PHM is started later in China. The diagnosis and prediction technical research based on fault physics, data driving, models and expert knowledge is carried out in France above the research. However, the technical maturity and the application range are advanced abroad and domestically. Especially, the PHM has obvious advantages in foreign countries compared with domestic in the research and development of novel intelligent sensor technology and devices applied to PHM.
A paper published by scholars of Harbin university of Engineers in 2018, namely 'brushless direct current motor sensor fault detection and fault-tolerant system based on a neural network', provides a fault-tolerant control system of a Hall sensor fault detection method based on the neural network, diagnoses various fault types such as phase change delay, commutation advance, single-phase fault and other fault types by utilizing a neural network classification function, and through simulation and test platform experimental verification, the proposed sensor fault detection and fault-tolerant control system based on the neural network can remarkably reduce the influence of Hall sensor fault on the rotating speed of a motor and ensure that the motor can stably run when the Hall sensor fails.
The article is mainly introduced and explained from a software level, a neural network architecture is also used for carrying out fault diagnosis on the motor, but the input parameter of the hall sensor signal is a hall sensor signal, so that the hall sensor signal is greatly different from the hall sensor signal which is introduced in the patent and carries out position-free algorithm analysis through input phase voltage, phase current, bus voltage and motor temperature signals, and meanwhile, the hall sensor does not have the advantages of improving the system integration level and the safety by the master-slave architecture, which is similar to the hardware level analysis of a controller module in the patent.
A patent "fault prediction and health management method applied to a circuit and a system", which is published by a scholars of electronic science and technology university in 2016, 11 months introduces a fault prediction and health management method based on deep learning, which is applied to a circuit and a system and is used for monitoring operating circuit and system equipment in real time; firstly, preparing a plurality of circuits to be tested, respectively carrying out aging tests to obtain training data and storing the training data in a database; carrying out PCA analysis to obtain a training sample; then training a neural network by adopting a deep learning model and putting the neural network into a test chip; and finally, monitoring the health state of the circuit to be tested in the working state in real time by adopting the test chip, and calculating the residual service life of the circuit to be tested.
It can be seen that, the current health management method based on deep learning mainly focuses on the analysis introduction of the software level, there is no specific research applied to the master-slave hardware architecture, and the idea of integrating the health management algorithm based on no position signal analysis into the motor controller has never been proposed yet.
Disclosure of Invention
In order to solve the technical problems, the application provides a master-slave architecture electromechanical controller and a health management method, and the working safety and the integration level of a motor control system are improved.
In a first aspect, the present application provides a master-slave architecture electromechanical controller, which includes a master control chip, a slave control chip, a CPLD, and a data storage circuit, wherein: the master control chip, the slave control chip and the CPLD are all connected with the data storage circuit; the master control chip and the slave control chip are debugged through RS232 serial ports; the CPLD is connected with the motor through a conditioning circuit and is used for receiving a control monitoring signal sent by the motor; the CPLD is connected with the motor through the RS422 and is used for communicating with the motor; the slave control chip is connected with the on-board temperature monitoring circuit; the master control chip and the slave control chip are both connected with the CPLD; the master control chip, the slave control chip, the CPLD and the data storage circuit are connected with the power circuit.
Specifically, the master control chip and the slave control chip are connected with the data storage circuit through an XINTF bus.
Specifically, the data storage circuit includes an NVRAM and a dual-port RAM.
Specifically, the master control chip and the slave control chip comprise a DSP, a Power PC and an ARM processor.
In a second aspect, the present application provides a health management method, which is applied to the master-slave architecture electromechanical controller as described above, and includes:
collecting phase voltages (Va, Vb and Vc), phase currents (Ia, Ib and Ic), bus voltage (Vm) and a motor temperature signal (Tp) from a control chip, and collecting a temperature signal (Tc) by an on-board temperature monitoring circuit;
establishing a feedforward neural network model by the slave control chip according to the Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc;
mapping input signals (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc) for n times, and outputting corresponding state models, wherein the state models comprise normal states and k fault modes;
and repeatedly training the feedforward neural network model to realize the functions of recognizing and diagnosing the failure mode.
Specifically, when the slave control chip judges that the input parameters (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp, Tc) are all within a reasonable range according to the feedforward neural network model, the state model is in a normal state.
Specifically, when the slave control chip judges that some of the input parameters (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp, Tc) are not within the expected reasonable range according to the feedforward neural network model, the state model is the corresponding failure mode.
Specifically, the main control chip collects phase voltages (Va, Vb and Vc) and phase currents (Ia, Ib and Ic) from the motor, a motor control algorithm is operated to judge the current motor operation condition, PWM waves are calculated, and the PWM waves are output to the motor to complete control.
Has the advantages that: the master control chip and the slave control chip are integrated in one module, and the two functions of motor control and health management are managed, so that the motor control is realized, the real-time monitoring and diagnosis of the health state of a motor system are ensured, and the safety and the integration level of the system work are improved.
The health management function of the slave control chip is realized through a software algorithm, and the acquired parameters are trained by utilizing the deep learning function of the feedforward neural network, so that the system can automatically identify the corresponding failure mode and display and report in time.
Drawings
FIG. 1 is a schematic diagram of an electromechanical controller based on a master-slave architecture according to the present application;
fig. 2 is a schematic diagram of a neural network algorithm architecture provided in the present application.
Detailed Description
The invention belongs to the field of motor control, and relates to a health management method applied to a master-slave architecture system. With the continuous development of a motor control system, a motor controller is required to complete real-time control and has motor health monitoring and fault diagnosis capabilities, and faults faced by a motor controller health management system mainly result from the following aspects:
1. the motor is overloaded, single-phase operation is carried out, the use is excessive, parts are damaged, and the like, and faults are caused by manual misoperation or self mechanical problems.
2. Environmental factors such as temperature, humidity, dust, electromagnetic fields, etc., may increase the rate of various types of failures, including mechanical and electronic circuit failures.
3. In the real-time calculation process of software algorithms and the like of the controller, the motor controller is in an abnormal state due to non-deterministic errors.
In order to reduce the influence of the factors on the motor as much as possible and improve the safety and the integration level of the whole motor control system, the invention particularly discloses a health management system based on a master-slave architecture electromechanical controller, which is characterized in that in a double-control chip architecture, a master control chip is responsible for the electrical control core functions of PWM wave output, analog signal acquisition and processing and the like; the slave control chip realizes the functions of real-time monitoring, fault diagnosis and the like of the state of a key system in the operation of the motor by calculating a self deep learning related algorithm program, and the specific contents are acquisition and analysis of the temperature of a controller module, the working temperature of the motor and the working voltage and current of the motor.
Example of the implementation
A health management method based on master-slave architecture electromechanical controller, in a double-control chip architecture, a control chip is responsible for PWM wave output, control signal (including three-phase current and three-phase voltage) acquisition and processing and other electromechanical control core functions; the slave control chip collects, analyzes and monitors signals (including three-phase current, three-phase voltage, motor temperature signals, bus voltage and plate temperature monitoring), and synchronously realizes the health management of the motor system through the calculation of self feedforward neural network model related algorithm programs. The specific method comprises the following steps:
step one, a master-slave architecture controller collects key state parameters of a controlled motor and respectively processes and analyzes the key state parameters of the controlled motor to a master control chip, a slave control chip and a CPLD;
secondly, the upper computer signals collected by the main control chip mainly comprise phase voltage and phase current; collecting phase voltage, phase current, bus voltage, motor temperature signals and controller module temperature signals from a control chip; the CPLD collects Hall signals and communicates with an upper computer through a 422 bus.
And thirdly, the main control chip performs motor control algorithm analysis on the acquired signals and outputs PWM waves to guide the motor to work in the next step.
And fourthly, the slave control chip carries out monitoring and diagnosis algorithm analysis on the signals of the upper computer and the signals inside the controller, judges whether the running states of the motor and the controller module are good or not, and identifies a fault mode when the parameters are abnormal.
Specifically, as shown in fig. 2, the slave controller chip collects phase voltage, phase current, bus voltage (Vm), and motor temperature signal (Tp) from the upper computer, collects module temperature signal (Tc) from the temperature sensor chip (HWD7461) of the controller module, establishes the following neural network algorithm framework, maps input signals (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp, Tc) n times, classifies output results, and represents the output results as different state models: (0) and (4) normal, (1) fault mode 1, (2) fault modes 2, … and (k) fault mode k, and the recognition and diagnosis functions of the fault modes can be realized by applying the Feedforward Neural Network (FNN) model to train repeatedly.
The FNN architecture comprises an input layer, a hidden layer and an output layer, wherein a convolutional neural network is not used in the FNN architecture, the input sample parameters and complex picture identification parameters are lower in dimensionality, simple and easy to read, a pooling layer is not needed for feature dimensionality reduction and data compression, meanwhile, the input parameters are subjected to nonlinear mapping through the hidden layer to directly obtain output fault modes, and output results are not needed to be returned to an input end for circular feedback.
When the slave control chip judges that the input parameters (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc) are all in a reasonable range according to the algorithm, the state model (0) is normal) is fed back to the human-computer interface. If some of the input parameters (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc) are not in the expected reasonable range, the human-computer interface feeds back 'failure' and feeds back a corresponding 'failure mode' according to the calculation result of the deep learning algorithm. For example, when Tp is higher than a normal value and three-phase currents (Ia, Ib and Ic) of the motor are unbalanced in the input parameters, a fault mode which can be output and judged through a deep learning algorithm is 'stator winding fault'; when Vm, Ia, Ib, Ic and Tp are generally increased and are higher than normal values, the fault mode output by software analysis is overvoltage.
Specifically, a master control chip and a slave control chip are integrated in one module, and the two functions of motor control and health management are managed, so that the real-time monitoring and diagnosis of the health state of a motor system are ensured while the motor control is realized, and the safety and the integration level of the system work are improved.
Specifically, the health management function of the slave control chip is realized through a software algorithm, and the acquired parameters are trained by utilizing the deep learning function of the feedforward neural network, so that the system can automatically identify the corresponding failure mode and timely display and report.
Specifically, when the slave control chip judges that the motor works normally, but the controller module has a fault, the CPLD enters an open-loop control state, and a PWM signal with a fixed duty ratio is generated inside the CPLD to maintain the basic control of the motor.
The health management method based on the master-slave architecture electromechanical controller is successfully applied to a certain type of module, and practice shows that the architecture can realize the work control and the health management of the motor.
In summary, the present invention specifically discloses a motor health management method for a master-slave controller, in which the controller respectively collects the operation condition and health status information of an external controlled object by using master-slave control chips, and respectively completes the functions of real-time motor control and monitoring diagnosis after respective software algorithm calculation. According to the technical scheme, the controller module integrates the real-time health management function of the motor system on the premise of meeting the requirement of commanding the motor to work, a circuit system is simplified, the real-time performance of state monitoring is improved, and the simultaneous implementation of motor control and protection is ensured. In addition, the slave control chip analyzes and processes parameters by using a feedforward neural network architecture, and can simply and efficiently realize motor fault mode diagnosis. The invention has wide application prospect in the field of motor control requiring high safety and high integration. This technical scheme has played control motor system, carries out health management's effect simultaneously, has improved motor controller's security and integrated level from many aspects:
1. the multi-layer processing capability of a master-slave architecture system is fully exerted, the functions of the controller can be effectively expanded by reasonably distributing resources, and meanwhile, the real-time control and monitoring diagnosis of the motor are realized.
2. The master control chip is used for analyzing key parameters of the health state of the motor system while calculating the running state of the motor system, if the motor has mechanical faults and the mechanical faults are reflected in the parameters collected by the controller, the slave control chip can find abnormal conditions at the first time, and meanwhile, a fault mode is diagnosed according to a deep learning algorithm, so that motor protection is provided.
3. The slave control chip also collects the relevant parameters of the internal health of the controller module, and analyzes and diagnoses the state of the controller module through a software algorithm, namely, the controller module is protected at the same time.
The invention has wide application prospect in the field of motor control requiring high efficiency, high safety and high integration.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A master-slave architecture electromechanical controller is characterized in that the master-slave architecture electromechanical controller comprises a master control chip, a slave control chip, a CPLD and a data storage circuit, wherein:
the master control chip, the slave control chip and the CPLD are all connected with the data storage circuit; the master control chip and the slave control chip are debugged through RS232 serial ports; the CPLD is connected with the motor through a conditioning circuit and is used for receiving a control monitoring signal sent by the motor; the CPLD is connected with the motor through the RS422 and is used for communicating with the motor; the slave control chip is connected with the on-board temperature monitoring circuit; the master control chip and the slave control chip are both connected with the CPLD; the master control chip, the slave control chip, the CPLD and the data storage circuit are connected with the power circuit.
2. The master-slave architecture electromechanical controller of claim 1, wherein the master control chip and the slave control chip are connected to the data storage circuit through an XINTF bus.
3. The master-slave architecture electromechanical controller of claim 1, wherein the data storage circuitry comprises NVRAM, dual port RAM.
4. The master-slave architecture electromechanical controller of claim 1, wherein the master control chip and the slave control chip comprise DSP, Power PC, ARM processors.
5. A health management method applied to the master-slave architecture electromechanical controller according to claim 1, the method comprising:
collecting phase voltages (Va, Vb and Vc), phase currents (Ia, Ib and Ic), bus voltage (Vm) and a motor temperature signal (Tp) from a control chip, and collecting a temperature signal (Tc) by an on-board temperature monitoring circuit;
establishing a feedforward neural network model by the slave control chip according to the Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc;
mapping input signals (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc) for n times, and outputting corresponding state models, wherein the state models comprise normal states and k fault modes;
and repeatedly training the feedforward neural network model to realize the functions of recognizing and diagnosing the failure mode.
6. The method of claim 5,
when the slave control chip judges that the input parameters (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc) are all in a reasonable range according to the feedforward neural network model, the state model is in a normal state.
7. The method of claim 5,
and when the slave control chip judges that the input parameters (Va, Vb, Vc, Ia, Ib, Ic, Vm, Tp and Tc) are partially out of the expected reasonable range according to the feedforward neural network model, the state model is a corresponding fault mode.
8. The method of claim 5,
the main control chip collects phase voltage (Va, Vb, Vc) and phase current (Ia, Ib, Ic) from the motor, a motor control algorithm is operated to judge the current motor operation condition, PWM waves are calculated, and the PWM waves are output to the motor to complete control.
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