CN113561786B - Suspension redundancy control system and method based on vehicle rail state monitoring - Google Patents
Suspension redundancy control system and method based on vehicle rail state monitoring Download PDFInfo
- Publication number
- CN113561786B CN113561786B CN202110987666.0A CN202110987666A CN113561786B CN 113561786 B CN113561786 B CN 113561786B CN 202110987666 A CN202110987666 A CN 202110987666A CN 113561786 B CN113561786 B CN 113561786B
- Authority
- CN
- China
- Prior art keywords
- module
- control
- suspension
- control board
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000000725 suspension Substances 0.000 title claims abstract description 74
- 238000012544 monitoring process Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000003745 diagnosis Methods 0.000 claims description 16
- 210000002569 neuron Anatomy 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 12
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 10
- 238000004891 communication Methods 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 230000000087 stabilizing effect Effects 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 238000002405 diagnostic procedure Methods 0.000 claims description 2
- 238000001914 filtration Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000005339 levitation Methods 0.000 description 17
- 238000005457 optimization Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L13/00—Electric propulsion for monorail vehicles, suspension vehicles or rack railways; Magnetic suspension or levitation for vehicles
- B60L13/04—Magnetic suspension or levitation for vehicles
- B60L13/06—Means to sense or control vehicle position or attitude with respect to railway
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0421—Multiprocessor system
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2200/00—Type of vehicles
- B60L2200/26—Rail vehicles
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24182—Redundancy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
Abstract
The invention relates to a suspension redundancy control system and a suspension redundancy control method based on rail state monitoring, wherein the system is used for detecting the rail state while controlling a train to stably suspend and determining the state type through classifying and learning suspension clearance and electromagnet vibration conditions; the electromagnet driving main circuit module comprises a third FPGA module used for controlling and switching the first control board and the second control board and a driving module used for suspension control, and the third FPGA module and the driving module are connected with each other. Compared with the prior art, the method has the advantages of good real-time performance, high fault tolerance and the like.
Description
Technical Field
The invention relates to the field of levitation control of maglev trains, in particular to a levitation redundancy control system and method based on track state monitoring.
Background
The magnetic levitation train is used as a novel rail transportation tool, and can realize non-contact operation on the premise of effectively achieving similar passenger carrying requirements of high-capacity transportation tools such as high-speed rails and subways, so that the wheel rail abrasion problem and the noise problem faced by the conventional rail transportation are avoided, the operation and maintenance cost is further effectively reduced, and the upper limit of the operation speed is improved. In addition, the magnetic levitation trains developed and operated at present all adopt a rail holding mode, so that the possibility of heavy accidents such as derailment and the like is effectively avoided, and the safety and the reliability are further improved. The electromagnet is an actuating mechanism for ensuring the stable levitation of the maglev train, and the levitation control system combines signals output by the levitation sensor and the acceleration sensor to change the internal current of the electromagnet, so that the maglev train can stably levitate at an expected levitation gap (8-10 mm). In the development and research process of the past decades, the technology of the maglev train is basically mature, and the maglev train gradually goes to commercial production and operation.
At present, all commercial operations are electromagnetic levitation type magnetic levitation trains (EMS type magnetic levitation trains). Because the adjustable range of the suspension gap of the train in the dynamic suspension running process is small, the rail state has a larger influence on the suspension stability of the train.
The suspension redundancy control system comprises four suspension electromagnet poles arranged on an electromagnet at one side of a suspension frame, two suspension sensors arranged at two sides of the electromagnet, four suspension choppers connected with the four suspension electromagnet poles in one-to-one correspondence, and suspension controllers respectively connected with the suspension choppers, wherein the suspension controllers are respectively connected with the two suspension sensors; however, based on the current technical means, parameters of a suspension control algorithm are relatively fixed, and the rail state cannot be analyzed in real time, and the rail state can be determined only by adopting a method of acquisition before analysis, so that the analysis efficiency is too low and the real-time performance for train suspension clearance adjustment in a dynamic suspension process is not realized.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a suspension redundancy control system and method based on rail state monitoring, which have good real-time performance and high fault tolerance.
The aim of the invention can be achieved by the following technical scheme:
according to a first aspect of the present invention, there is provided a levitation redundancy control system based on track state monitoring for detecting a track state while controlling stable levitation of a train and determining a state type by classifying and learning levitation gap and vibration conditions of electromagnets, the system comprising a main electromagnet driving circuit module, a first control board for normal levitation control, and a second control board for normal track state monitoring diagnosis and abnormal levitation redundancy control instead of the first control board, which are connected to each other;
the electromagnet driving main circuit module comprises a third FPGA module used for controlling and switching the first control board and the second control board and a driving module used for suspension control, and the third FPGA module and the driving module are connected with each other.
Preferably, the first control board comprises a first FPGA module and a first DSP module which are connected with each other; the second control board comprises a second FPGA module, a second DSP module and a storage module which are connected with each other; the first DSP module is connected with the second DSP module; the output end of the driving module is respectively connected with the first DSP module and the second DSP module;
the first FPGA module and the second FPGA module are used for collecting and processing sensor signals;
the first DSP module is used for calculating the control quantity and carrying out Ethernet or CAN communication;
and the second DSP module is used for monitoring and diagnosing the state of the vehicle track.
Preferably, the storage module comprises a Flash memory for storing the sensor signal and the monitoring diagnosis signal sent by the second DSP module.
Preferably, the sensor signal comprises 4 gap signals and 2 acceleration signals.
Preferably, the first control board and the second control board are respectively connected with the electromagnet driving main circuit module through connectors.
Preferably, the driving module comprises a driving plate and an IGBT module which are sequentially connected; and the IGBT module is used for receiving the PWM waves output by the driving plate and outputting control current to the electromagnet for suspension control.
According to a second aspect of the present invention, there is provided a method of suspending redundant control system based on rail status monitoring as described above, the method comprising the steps of:
step S1: the sensor signals are transmitted to the first control board and the second control board at the same time, and the first FPGA module and the second FPGA module receive the sensor signals at the same time;
step S2: under normal conditions, the first control panel performs suspension control, and the second control panel performs monitoring diagnosis of the running rail state;
step S3: under abnormal conditions, when the first control board fails, the second DSP module cannot receive signals sent by the first DSP module, at the moment, the second control board is controlled by the third FPGA module to replace the first control board for suspension control, the track state monitoring and diagnosis work of the original second control board is suspended, and the classification and analysis results are stored in the Flash memory by taking the suspension time as a termination time point; after the first control board resumes operation, the second control board continues to assume the corresponding monitoring function.
Preferably, the step S2 specifically includes:
suspension control process: the first FPGA module is used for filtering the acquired sensor signals, transmitting the sensor signals to the first DSP module to calculate control quantity and realizing Ethernet or CAN communication, feeding back the control signals to the first FPGA module, transmitting the control signals to the driving board through the third FPGA module to generate PWM waves, and driving the IGBT module to generate control current to control the electromagnet to float;
monitoring a diagnostic process: the second FPGA module processes the sensor signals and stores the processed sensor signals in a Flash memory; the second DSP module establishes a probabilistic neural network, analyzes the suspension situation according to different fault types, classifies the suspension situation according to the states of the vehicle rails, feeds the classification result back to the Flash memory for storage, transmits the analysis result to the first DSP module, and adjusts the output quantity for controlling suspension in real time, so that the control output can ensure the control current for stabilizing the suspension gap.
Preferably, the monitoring and diagnosing process specifically includes the following steps:
step S21: acquiring electromagnet vibration signals and suspension gap signals in different track states in the suspension running process, carrying out Fourier transform on the electromagnet vibration signals, and extracting time domain information and frequency domain information; and calculating to obtain vibration mean value, variance, mean square value, peak value and frequency information;
step S22: establishing a probability neural network, and classifying the rail states according to a Bayesian minimum risk criterion;
input and output relationships Φ determined by the jth neuron defining the ith class of rail states ij (x) The method comprises the following steps:
wherein i=1, 2..6, d is the dimension of the sample, specifically represents the electromagnet vibration peak value, average value, frequency and suspension gap peak value, average value key feature, x ij For the j center of the i-th sample, sigma is a smoothing factor, and is generally 0.1;
weighted averaging of the outputs of implicit neurons belonging to the same class in the implicit layer is performed in the summation layer:
wherein v is i The output of the state of the ith class of track is the number of the ith class of neurons; the number of neurons of the summation layer is the same as the number of the rail state categories;
the output layer takes the largest one of the sum layers as the output class:
y＝argmax(v i )
in actual calculation, the vector of the input layer is multiplied by the weighting coefficient, and then is input into the radial basis function of the hidden layer for calculation:
Z i ＝xω i
wherein Z is i Computing a vector for the ith neuron radial basis layer, x is the input vector for the input layer, ω i The corresponding weight of the ith neuron which is delivered to the hidden layer for the input layer;
assuming x and ω i Are normalized to unit length, and radial basis calculation is performed on the resultThereby obtaining a monitoring diagnosis result;
step S23: and the classification and analysis results are transmitted to a first DSP module of the first control board to dynamically adjust the control quantity output, so that the adaptive upgrading of the suspension system is realized.
Preferably, the track state in step S21 includes: the device comprises a stable suspension working state, a state that the track height difference exceeds +/-3 mm, a state that a large arc angle is generated at the end part of a single span beam, an electromagnet is unstable and breaks the track, a controller no-current output state and an electromagnet suction state.
Compared with the prior art, the invention has the following advantages:
1) The invention realizes monitoring and diagnosing of the state of the vehicle track while carrying out suspension control; the method has the advantages that the suspension gap data and the electromagnet vibration data are obtained in real time in the suspension operation process, fourier transformation is carried out on the vibration data, time domain information and frequency domain information are extracted, peak values, average values, inherent frequencies and other key characteristics are calculated according to the time domain information and the frequency domain information, the state of the vehicle track can be diagnosed in real time, the type of the vehicle track state is monitored and diagnosed by combining a probability neural network, the problems that the prior acquisition is low in analysis efficiency and poor in real time are effectively avoided, and convenience is brought to system overhaul and maintenance;
2) In the invention, the electromagnet vibration signal and the suspension gap data acquired by the second control board are transmitted to the first DSP module of the first control board in real time, and the control quantity output of the first control board is adjusted to ensure that the train can reach the optimal stable state, thereby realizing the real-time adaptability optimization of the suspension control system;
3) The third FPGA in the electromagnet driving main circuit module for redundancy control is used for controlling switching; when the system is in a normal working state, the first control board is used for calculating electromagnet control current and CAN/Ethernet communication; the second control board stores the received and processed sensor signals in a Flash memory, and the sensor signals are used as a data base for constructing and training the probabilistic neural network after performing Fourier transform and other calculations on the data. And classifying the states of the vehicle rails based on the probabilistic neural network. And after further processing, the analysis result is transmitted to the first control board for optimization adjustment. When the first control panel fails, the second control panel takes over the first control panel to carry out suspension control and carries out Ethernet or CAN communication; at this time, the track state monitoring and diagnosing work is suspended, and the classification and analysis results are stored in the Flash memory by taking the suspension as a termination time point. After the first control board resumes operation, the second control board continues to assume the corresponding monitoring function.
4) The invention integrates the original signal board and the control board, and can increase the track diagnosis function on the basis of not changing the original chassis structure; the functions of the control board are expanded, so that on one hand, the real-time optimization adjustment of suspension control can be realized, and on the other hand, the electrical redundancy of the control board is realized.
Drawings
FIG. 1 is a block diagram of a redundant levitation system of a maglev train with rail condition monitoring diagnostics;
fig. 2 is a structure of a fault diagnosis algorithm for monitoring the state of a vehicle rail according to the present patent.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The following is a system example of the present invention. The suspension redundancy control system based on the rail state monitoring mainly comprises an electromagnet driving main circuit module, a first control board for normal suspension control and a second control board for suspension redundancy control by replacing the first control board under normal rail state monitoring diagnosis and abnormal conditions, wherein the electromagnet driving main circuit module is connected with the first control board; the electromagnet driving main circuit module comprises a third FPGA module and a driving module, wherein the third FPGA module is used for controlling and switching the first control board and the second control board, and the driving module is used for outputting control current for suspension control; the first control board comprises a first FPGA module and a first DSP module which are connected with each other; the second control board comprises a second FPGA module, a second DSP module and a storage module which are connected with each other; the first control board is connected with the second control board through a first DSP module and a second DSP module; the output end of the driving module is respectively connected with the first DSP module and the second DSP module; and the second control board is additionally provided with a Flash memory for storing basic data learned by the probabilistic neural network. The first control board and the second control board are respectively connected with the electromagnet driving main circuit module through connectors; the driving module comprises a driving plate and an IGBT module which are sequentially connected; and the IGBT module is used for receiving the PWM waves output by the driving plate and outputting control current to the electromagnet for suspension control. The system also comprises supporting circuit devices such as a shared supporting capacitor, a charging and discharging circuit and the like.
The controller comprises an external power interface for receiving the DC-440V power supply and the DC-110V power supply, wherein the DC-110V power supply is connected with the power supply module, is used for signal receiving, processing and transmitting modules, a control module, a monitoring and diagnosing module and a sensor unit after being subjected to voltage reduction. The DC-440V power supply is used for driving the main circuit electromagnet to drive the main circuit module.
During normal operation, the electromagnet outputs 4 paths of gap signals and 2 paths of acceleration signals to the first control board and the second control board respectively, and the signals are received, processed and transmitted by adopting the FPGA. The first DSP module in the first control board receives the data processed by the first FPGA to perform control quantity calculation and CAN/Ethernet communication, the data is transmitted to the first FPGA after the calculation result is obtained, the signal is transmitted to the driving board through the third FPGA module, and the PWM wave is output to drive the IGBT module to transmit control current.
Meanwhile, data sent by the electromagnet are simultaneously transmitted to a second FPGA module in a second control panel, and the sent data are stored in a Flash memory and used as a data base for constructing and training the probabilistic neural network.
And carrying out Fourier transform on the data in a second DSP module in the second control panel based on the data stored in the Flash memory, and extracting the vibration frequency. By time domain information and frequency domain information of the acquired vibration data. And defining 6 track state types: 1) Stabilizing the suspension working state; 2) The track height difference exceeds +/-3 mm; 3) The end part of the single span beam generates a larger arch angle state; 4) An electromagnet is unstable and breaks a rail; 5) A controller no-current output state; 6) And in the electromagnet suction state, a large amount of data of each rail state is used as a network input layer and transmitted to a network hidden layer for training the probabilistic neural network.
The input/output relationship determined by the j-th neuron of the i-th class rail state can be defined as:
i=1, 2..6, d is the dimension of the sample, and is the key characteristics of the electromagnet vibration peak value, average value, frequency, suspension gap peak value, average value and the like, and x ij Is the j center of the i-th sample. Weighted averaging of the outputs of implicit neurons belonging to the same class in the implicit layer is performed in the summation layer:
v i and L is the number of the ith nerve cells for outputting the ith track state. The number of neurons of the summation layer is the same as the number of rail state categories.
The output layer takes the largest one of the sum layers as the output class:
y＝argmax(v i )
in actual calculation, the vector of the input layer is multiplied by the weighting coefficient, and then is input into the radial basis function of the hidden layer for calculation:
Z i ＝xω i
assuming x and ω i Are normalized to unit length, and radial basis calculation is performed on the resultThereby obtaining the monitoring diagnosis result.
After the monitoring and diagnosis result is obtained, the monitoring and diagnosis result is stored in the Flash memory for further analysis, and the first DSP module transmitted to the first control panel adjusts the suspension output control quantity, so that the suspension system is adaptively upgraded in real time.
When the first control board breaks down, the life signal between the first DSP module and the second DSP module is interrupted, and the second control board takes over the first control board to carry out suspension control calculation and CAN/Ethernet communication work. And simultaneously suspending the monitoring and diagnosing functions of the rail state of the second control panel. The data cut-off point is the current moment when the second control board takes over the control board work. In addition, a third FPGA module on the motherboard is switched, and signals sent by the second FPGA module are output to the driving board to replace the first control board to work completely.
In summary, the invention adopts the integrated design of redundancy and monitoring diagnosis, the two control boards are mutually independent and have the functions of signal receiving, processing and transmitting, the DSP is used as the main control chip, and the normal operation of the suspension controller is not affected when any control board fails, so the reliability is high. And the second control panel can monitor and diagnose the track state during normal operation, and the diagnosis result can be used for further optimizing analysis on one hand, and is transmitted to the first control panel for real-time adjustment of control quantity on the other hand, thereby further improving the stability and control precision of the suspension controller.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (5)
1. The method is characterized in that the system is used for detecting the state of the train rail while controlling the train to stably suspend and determining the state type through classifying and learning the suspension clearance and the vibration condition of the electromagnets, and comprises a main electromagnet driving circuit module, a first control board for suspending control under normal conditions and a second control board for suspending redundancy control under abnormal conditions instead of the first control board, wherein the first control board is used for suspending control under normal conditions;
the electromagnet driving main circuit module comprises a third FPGA module for controlling and switching the first control board and the second control board and a driving module for suspension control, and the third FPGA module and the driving module are connected with each other;
the first control board comprises a first FPGA module and a first DSP module which are connected with each other; the second control board comprises a second FPGA module, a second DSP module and a storage module which are connected with each other; the first DSP module is connected with the second DSP module; the output end of the driving module is respectively connected with the first DSP module and the second DSP module;
the first FPGA module and the second FPGA module are used for collecting and processing sensor signals;
the first DSP module is used for calculating the control quantity and carrying out Ethernet or CAN communication;
the second DSP module is used for monitoring and diagnosing the state of the rail;
the sensor signals comprise 4 paths of gap signals and 2 paths of acceleration signals;
the method comprises the following steps:
step S1: the sensor signals are transmitted to the first control board and the second control board at the same time, and the first FPGA module and the second FPGA module receive the sensor signals at the same time;
step S2: under normal conditions, the first control panel performs suspension control, and the second control panel performs monitoring diagnosis of the running rail state;
step S3: under abnormal conditions, when the first control board fails, the second DSP module cannot receive signals sent by the first DSP module, at the moment, the second control board is controlled by the third FPGA module to replace the first control board for suspension control, the track state monitoring and diagnosis work of the original second control board is suspended, and the classification and analysis results are stored in the Flash memory by taking the suspension time as a termination time point; after the first control board resumes work, the second control board continues to bear the corresponding monitoring function;
the step S2 specifically comprises the following steps:
suspension control process: the first FPGA module is used for filtering the acquired sensor signals, transmitting the sensor signals to the first DSP module to calculate control quantity and realizing Ethernet or CAN communication, feeding back the control signals to the first FPGA module, transmitting the control signals to the driving board through the third FPGA module to generate PWM waves, and driving the IGBT module to generate control current to control the electromagnet to float;
monitoring a diagnostic process: the second FPGA module processes the sensor signals and stores the processed sensor signals in a Flash memory; the second DSP module establishes a probabilistic neural network, analyzes the suspension situation according to different fault types, classifies the suspension situation according to the states of the vehicle rails, feeds the classification result back to the Flash memory for storage, transmits the analysis result to the first DSP module, and adjusts the output quantity for controlling suspension in real time so that the control output can ensure the control current for stabilizing the suspension gap;
the monitoring and diagnosing process specifically comprises the following steps:
step S21: acquiring electromagnet vibration signals and suspension gap signals in different track states in the suspension running process, carrying out Fourier transform on the electromagnet vibration signals, and extracting time domain information and frequency domain information; and calculating to obtain vibration mean value, variance, mean square value, peak value and frequency information;
step S22: establishing a probability neural network, and classifying the rail states according to a Bayesian minimum risk criterion;
input and output relationships Φ determined by the jth neuron defining the ith class of rail states ij (x) The method comprises the following steps:
wherein i=1, 2..6, d is the dimension of the sample, specifically represents the electromagnet vibration peak value, average value, frequency and suspension gap peak value, average value key feature, x ij The j center of the i-th sample, sigma is a smoothing factor;
weighted averaging of the outputs of implicit neurons belonging to the same class in the implicit layer is performed in the summation layer:
wherein v is i The output of the state of the ith class of track is the number of the ith class of neurons; the number of neurons of the summation layer is the same as the number of the rail state categories;
the output layer takes the largest one of the sum layers as the output class:
y＝argmax(v i )
in actual calculation, the vector of the input layer is multiplied by the weighting coefficient, and then is input into the radial basis function of the hidden layer for calculation:
Z i ＝xω i
wherein Z is i Computing a vector for the ith neuron radial basis layer, x is the input vector for the input layer, ω i The corresponding weight of the ith neuron which is delivered to the hidden layer for the input layer;
assuming x and ω i Are normalized to unit length, and radial basis calculation is performed on the resultThereby obtaining a monitoring diagnosis result;
step S23: and the classification and analysis results are transmitted to a first DSP module of the first control board to dynamically adjust the control quantity output, so that the adaptive upgrading of the suspension system is realized.
2. The method of claim 1, wherein the memory module comprises a Flash memory for storing the sensor signal and the monitoring diagnostic signal sent by the second DSP module.
3. The method of claim 1, wherein the first control board and the second control board are each connected to the electromagnet drive main circuit module by a connector.
4. The method of claim 1, wherein the drive module comprises a drive board and an IGBT module connected in sequence; and the IGBT module is used for receiving the PWM waves output by the driving plate and outputting control current to the electromagnet for suspension control.
5. The method according to claim 1, wherein the track state in step S21 includes: the device comprises a stable suspension working state, a state that the track height difference exceeds +/-3 mm, a state that a large arc angle is generated at the end part of a single span beam, an electromagnet is unstable and breaks the track, a controller no-current output state and an electromagnet suction state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110987666.0A CN113561786B (en) | 2021-08-26 | 2021-08-26 | Suspension redundancy control system and method based on vehicle rail state monitoring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110987666.0A CN113561786B (en) | 2021-08-26 | 2021-08-26 | Suspension redundancy control system and method based on vehicle rail state monitoring |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113561786A CN113561786A (en) | 2021-10-29 |
| CN113561786B true CN113561786B (en) | 2024-01-12 |
Family
ID=78172793
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110987666.0A Active CN113561786B (en) | 2021-08-26 | 2021-08-26 | Suspension redundancy control system and method based on vehicle rail state monitoring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113561786B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114264908A (en) * | 2021-12-23 | 2022-04-01 | 同济大学 | Automatic-triggering magnetic suspension track online monitoring system and method |
| CN116039393A (en) * | 2023-01-09 | 2023-05-02 | 西南交通大学 | Superconducting magnetic material suspension state monitoring system of superconducting magnetic suspension train |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101134440A (en) * | 2007-10-17 | 2008-03-05 | 中国人民解放军国防科学技术大学 | Orbit tightly sucking method and system of the protective maglev train |
| KR20080056988A (en) * | 2006-12-19 | 2008-06-24 | 한국전기연구원 | Duplex levitation controller for magnetic levitation vehicle |
| CN105599634A (en) * | 2016-02-29 | 2016-05-25 | 同济大学 | Magnetic-levitation train levitation control device and method |
| KR20180094211A (en) * | 2017-02-14 | 2018-08-23 | 주식회사 팩테크 | Redundant Scheme of Levitation and Guidance Control System for Magnetic Levitation Train |
| CN109664772A (en) * | 2019-01-16 | 2019-04-23 | 肖力 | A kind of dual redundant suspension controller |
| CN111267628A (en) * | 2020-02-12 | 2020-06-12 | 中车株洲电力机车有限公司 | Suspension control method for magnetic-levitation train |
| CN113263921A (en) * | 2021-05-28 | 2021-08-17 | 同济大学 | Dynamic suspension control method of magnetic-levitation train combined with vibration identification |
-
2021
- 2021-08-26 CN CN202110987666.0A patent/CN113561786B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080056988A (en) * | 2006-12-19 | 2008-06-24 | 한국전기연구원 | Duplex levitation controller for magnetic levitation vehicle |
| CN101134440A (en) * | 2007-10-17 | 2008-03-05 | 中国人民解放军国防科学技术大学 | Orbit tightly sucking method and system of the protective maglev train |
| CN105599634A (en) * | 2016-02-29 | 2016-05-25 | 同济大学 | Magnetic-levitation train levitation control device and method |
| KR20180094211A (en) * | 2017-02-14 | 2018-08-23 | 주식회사 팩테크 | Redundant Scheme of Levitation and Guidance Control System for Magnetic Levitation Train |
| CN109664772A (en) * | 2019-01-16 | 2019-04-23 | 肖力 | A kind of dual redundant suspension controller |
| CN111267628A (en) * | 2020-02-12 | 2020-06-12 | 中车株洲电力机车有限公司 | Suspension control method for magnetic-levitation train |
| CN113263921A (en) * | 2021-05-28 | 2021-08-17 | 同济大学 | Dynamic suspension control method of magnetic-levitation train combined with vibration identification |
Non-Patent Citations (1)
| Title |
|---|
| 磁悬浮列车运行平稳性研究;李万磊;陈天星;胡瑞霞;李响;;机械科学与技术(第05期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113561786A (en) | 2021-10-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113561786B (en) | Suspension redundancy control system and method based on vehicle rail state monitoring | |
| CN106302064B (en) | The data transfer optimization method and system of binary-channel redundancy CAN bus for electric vehicle | |
| CN104842818B (en) | The torque monitoring method and its system of electric automobile | |
| CN104360679A (en) | Train suspension system fault diagnosis and fault-tolerant control method based on dynamic actuator | |
| CN104442435B (en) | For monitoring the system and method for contactor reliability | |
| CN108422956A (en) | The active control noise-reduction method and system of Vehicle Interior Noise | |
| CN109591613A (en) | A kind of municipal rail train traction control method and system | |
| CN109239485B (en) | Energy storage tramcar super capacitor fault identification method and system based on BP neural network | |
| CN110435476A (en) | The control of the power battery pack of automatic replacement vehicle | |
| CN107852021A (en) | For running the method and apparatus and electric system of electric system | |
| CN111695201B (en) | Data-based monitoring method for running state of maglev train | |
| CN115326437A (en) | Embedded system device and method for monitoring and diagnosing bogie faults | |
| CN112026524A (en) | Fault diagnosis and processing method and system for fuel cell vehicle | |
| CN115610356A (en) | Battery management controller and communication method for battery management controller | |
| CN112172534A (en) | Suspension frame and centralized suspension controller thereof | |
| CN110562047A (en) | Redundancy torque monitoring system based on master-slave hardware framework | |
| CN115158367A (en) | Empty rail rubber wheel vehicle and active safety grounding device and method thereof | |
| Ma et al. | Research on operational state monitoring of Maglev train based on machine learning | |
| CN112118049B (en) | Optical fiber ring network communication method for tramcar hybrid energy storage system | |
| CA2387969A1 (en) | Computerized method and system for determining degradation of dc link capacitors | |
| CN113335336B (en) | Train bogie axle temperature monitoring system | |
| CN113002582B (en) | Bogie equipment hanging device and vibration adjusting method thereof | |
| CN114655270B (en) | Variable architecture control system for air brake of motor train unit | |
| CN115452429A (en) | High-speed rail vehicle detection device | |
| CN114720154A (en) | Component failure detection system of vehicle electric drive system and power exchange station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |