CN111650898A - Distributed control system and method with high fault tolerance performance - Google Patents
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Abstract
The invention belongs to the technical field of automatic control, and particularly relates to a distributed control system and a distributed control method with high fault tolerance. The system comprises: a fail-safe controller, a server, a process knowledge system, and an operator station; the fault safety controller is connected with the server through signals; the server is in signal connection with the process knowledge system; the process knowledge system is signally connected to an operator station; the fail-safe controller is also in signal connection with the process knowledge system via the communication module. The invention acquires the operation parameters of the system by monitoring the operation state of the system in real time, analyzes the operation parameters, predicts the fault and solves the system problem before the system has problems. The safety of the system is further improved.
Description
Technical Field
The invention belongs to the technical field of automatic control, and particularly relates to a distributed control system and a distributed control method with high fault tolerance.
Background
The dcs (distributed Control system) system is also called a distributed Control system. The instrument control system is a new generation of instrument control system based on a microprocessor and adopting a design principle of decentralized control function, centralized display operation, and consideration of division, autonomy and comprehensive coordination. The distributed control system is called DCS for short, and can also be translated into a distributed control system or a distributed computer control system.
The method adopts the basic design idea of controlling dispersion, operation and management centralization and adopts a structural form of multilayer grading, cooperation and autonomy. Its main features are its centralized management and decentralized control. DCS is widely applied to various industries such as electric power, metallurgy, petrochemical industry and the like at present.
DCS generally employs a hierarchical structure, each level consisting of several subsystems, each subsystem achieving several specific finite goals to form a pyramid structure.
The reliability is the life of DCS development, and three measures are mainly adopted to ensure the high reliability of DCS: firstly, hardware equipment with high reliability and a production process are widely applied; secondly, a redundancy technology is widely adopted; and thirdly, the fault-tolerant technology, the fault self-diagnosis technology, the automatic processing technology and the like of the system are widely realized on the aspect of software design. The MTBF of most distributed control systems today can reach tens of thousands or even hundreds of thousands of hours.
The development of the DCS system mainly focuses on the following aspects:
the system function develops towards an open direction: the structure of the conventional DCS is closed, and it is difficult to be compatible between DCS of different manufacturers. The open DCS can endow a user with a larger system integration autonomy, and the user can select equipment of different manufacturers and software resources to be connected into the control system according to actual needs to achieve the optimal system integration. The integration of DCS and DCS is included, and the broad integration of DCS and PLC, FCS and various control devices and software resources is included.
The intelligent and networked development of industrial control equipment is developed in the direction of digitalization, intellectualization and networking by the instrument technology, the function of process control can be further dispersed and moved down, and the full-digital and full-decentralized control is realized in the true sense. In addition, the intelligent instruments have the characteristics of high precision, good repeatability, high reliability, bidirectional communication, self-diagnosis function and the like, so that the system is more convenient to install, use and maintain.
The development of the industrial control software in the forward advanced control direction and the wide application of various advanced control and optimization technologies are the development directions which are most effective, most direct and most valuable for excavating and improving the comprehensive performance of the DCS, and mainly comprise the development and industrial application of advanced control, process optimization, information integration, system integration and other software. In the future, industrial control software will continue to move towards standardization, networking, intelligence, and openness.
From the technical point of the FCS development of the system architecture, the field bus integrated in the DCS at the present stage can be realized in three ways: integration of the fieldbus on the DCS system I/O bus-suspended from the DCS I/O bus by a fieldbus interface card-allows the information from the fieldbus to be seen by the DCS controller as if it were from a conventional DCS device card. Such an integration scheme is used, for example, by the DeltaV system introduced by Fisher-Rosemount Inc. Secondly, the field bus is integrated on a DCS network layer, namely, a field bus system is integrated on a higher layer of a DCS, the integration mode does not need to change a DCS control station, and the influence on the original system is small. Such as the 302 series fieldbus products by Smar corporation, may be implemented to integrate their fieldbus functions at the DCS network layer. And thirdly, the field bus is integrated with the DCS system in parallel through the gateway, and the field bus and the DCS can also be integrated in parallel through gateway bridging. A fieldbus system, such as SUPCON, utilizes a HART protocol network bridge to connect a system operator station and a field instrument, thereby implementing a communication function between the fieldbus device management system operator station and the HART protocol field instrument.
The emphasis of DCS has been on control, which has been keyed to "scatter". However, modern development focuses more on comprehensive management of whole system information, and the 'comprehensive' will become a keyword in the future, and the system develops towards the realization of comprehensive automation of a control system, an operation system, a plan system and a management system, and implements the real-time control and optimization control from the bottommost layer to the production scheduling and operation management, so as to form a flexible and highly automated control integrated system.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide a distributed control system and method with high fault tolerance, which increases the safety and robustness of the system by adding a communication line connecting the fail-safe controller and the process knowledge system; meanwhile, the invention acquires the operation parameters of the system by monitoring the operation state of the system in real time, analyzes the operation parameters, predicts the failure and solves the system problem before the system has problems. The safety of the system is further improved.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a distributed control system with high fault tolerance, said system comprising: a fail-safe controller, a server, a process knowledge system, and an operator station; the fault safety controller is connected with the server through signals; the server is in signal connection with the process knowledge system; the process knowledge system is signally connected to an operator station; the fail-safe controller is also in signal connection with the process knowledge system via the communication module.
Further, the system further comprises: a fault prediction system; the failure prediction system includes: the system comprises a temperature sensor, an equipment operation monitoring subsystem and a fault analysis and prediction subsystem; the system comprises a plurality of temperature sensors, a fault safety controller, a server, a communication module and a process knowledge system, wherein the plurality of temperature sensors are respectively arranged on the fault safety controller, the server, the communication module and the process knowledge system to acquire temperature data of the fault safety controller, the server, the communication module and the process knowledge system; the equipment operation monitoring subsystem is a computer readable storage medium stored in a non-transitory state, is respectively arranged on the fault safety controller, the server, the communication module and the process knowledge system, and is used for acquiring the operation parameters of the fault safety controller, the server, the communication module and the process knowledge system in real time and sending the acquired operation parameters to the fault analysis and prediction subsystem; the operating parameters include at least: processor occupancy, data throughput, storage hard disk rotational speed, and processor processing speed.
Further, the failure analysis and prediction subsystem comprises: the system comprises a data preprocessing unit, a data specification unit, a data standardization unit, an algorithm prediction unit and a modeling analysis unit; the data preprocessing unit sequentially performs unique attribute removal, missing value processing and abnormal value detection processing on the operation parameters and the temperature data; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information is kept; the data standardization unit scales the data processed by the protocol according to a proportion to enable the data to fall into a set interval; the algorithm prediction unit carries out data modeling according to the data processed by the data standardization unit; and the modeling analysis unit is used for calculating the precision of the fault rate generated by the calculation model and the original fault rate.
Further, the method for performing specification processing by the data specification unit includes: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept.
Further, the method for the data standardization unit to carry out data standardization processing comprises the following steps: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a specific interval; wherein the data is linearized using a transfer function as followsChange over so that the result falls to [0.1,1 ]]Interval, the transfer function is as follows:
wherein x is*The result is the result after data standardization processing; x is data to be processed; min is the minimum value in the data; max is the maximum value in the data.
A distributed control method with high fault tolerance, the method performing the steps of:
step 1: the process knowledge system adjusts the field analog quantity and optimally adjusts and controls the pressure, the temperature and the liquid level; the process knowledge system is in signal connection with the server and sends data information to the server; meanwhile, the process knowledge system is in signal connection with the fault safety controller through a communication module;
step 2: the server sends the received data information to the operator station;
and step 3: the fault safety controller is connected with the server, and when the server fails, data information is acquired from the server; when the server fails, data information is directly acquired from the process knowledge system through the communication module.
Further, the method further comprises: and acquiring the operating parameters and temperature data of other equipment and systems in the system, and performing data analysis on the operating parameters and the temperature data to perform fault prediction.
Further, the method for acquiring the operating parameters and the temperature data of other devices and systems in the system, analyzing the data according to the operating parameters and the temperature data, and predicting the fault includes the following steps:
step S1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step S2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept.
Step S3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a specific interval; in which the data is linearly transformed using a transfer function such that the result falls to [0.1,1]Interval, the transfer function is as follows:
wherein x is*The result is the result after data standardization processing; x is data to be processed; min is the minimum value in the data; max is the maximum value in the data;
step S4: carrying out data modeling;
step S5: performing an effect analysis comprising: after model training is finished, calculating the fault rate generated by the model and the original fault rate by adopting the following formula to perform precision calculation, namely obtaining R2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents the model-generated power equipment failure rate (predicted value);
nsamplesrepresenting the size of the sample size entering the model.
Further, the operating parameters include at least: processor occupancy, data throughput, storage hard disk rotational speed, and processor processing speed.
Further, the step S4: the method for modeling data performs the following steps:
step S4.1: obtaining data for modeling as input variables, using xiRepresenting, wherein i represents the ith variable in the data; said xiAt least comprises the following steps: voltage value of power equipment during operationCurrent value, arc value, temperature value and humidity value;
step S4.2: setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result;
step S4.3: setting an excitation function, wherein the excitation function is as follows:
setting the neuron threshold of the neural network as follows: theta; and operating the first intermediate result, the excitation function and the neuron threshold value to obtain a result of the forward neural network, wherein the result is as follows:
step S4.4: calculating a training error of the forward neural network; because the output variable E of the training is 'failure rate of power equipment', but a predicted value generated after model training is O, the obtained error function is as follows:
where m represents the number of samples input into the modeling this time and i represents the ith variable.
Step S4.5: backpropagating update weights w
In order to reduce the error and improve the accuracy of model prediction, the neural network reversely transmits data from the output layer to the input layer, and the value of the weight w is readjusted until the model error reaches the minimum, and then the training is stopped, so that the model creation is completed.
The distributed control system and the distributed control method with high fault tolerance performance have the following beneficial effects: in the system, the communication between the failure safety controller and the process knowledge system can not pass through the server under the condition that the server fails, so that the failure of the server is avoided, the communication between the failure safety controller and the process knowledge system is interrupted, the field equipment is not monitored, and the communication mode has the advantages of high stability, strong anti-interference capability and the like. Meanwhile, the acquired operation data of each device and each system and the temperature data of each device and each system are analyzed, and before the device fails, the device and each system can be subjected to failure prediction analysis through data analysis, so that the occurrence of device failures is greatly reduced. Besides the reduction of the failure rate of the equipment, the production efficiency can be ensured not to be reduced due to the failure of the equipment, and the problem can be avoided before the problem occurs.
Drawings
Fig. 1 is a schematic system structure diagram of a distributed control system with high fault tolerance according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method of a distributed control method with high fault tolerance according to an embodiment of the present invention.
Detailed Description
The method of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 1, a distributed control system with high fault tolerance includes: a fail-safe controller, a server, a process knowledge system, and an operator station; the fault safety controller is connected with the server through signals; the server is in signal connection with the process knowledge system; the process knowledge system is signally connected to an operator station; the fail-safe controller is also in signal connection with the process knowledge system via the communication module.
By adopting the technical scheme, the communication between the fault safety controller and the process knowledge system does not pass through the server, so that the communication between the fault safety controller and the process knowledge system is realized, the server fault is avoided, the communication between the two parties is interrupted, the field device is not monitored, and the communication mode has the advantages of high stability, strong anti-interference capability and the like.
Example 2
On the basis of the above embodiment, the system further includes: a fault prediction system; the failure prediction system includes: the system comprises a temperature sensor, an equipment operation monitoring subsystem and a fault analysis and prediction subsystem; the system comprises a plurality of temperature sensors, a fault safety controller, a server, a communication module and a process knowledge system, wherein the plurality of temperature sensors are respectively arranged on the fault safety controller, the server, the communication module and the process knowledge system to acquire temperature data of the fault safety controller, the server, the communication module and the process knowledge system; the equipment operation monitoring subsystem is a computer readable storage medium stored in a non-transitory state, is respectively arranged on the fault safety controller, the server, the communication module and the process knowledge system, and is used for acquiring the operation parameters of the fault safety controller, the server, the communication module and the process knowledge system in real time and sending the acquired operation parameters to the fault analysis and prediction subsystem; the operating parameters include at least: processor occupancy, data throughput, storage hard disk rotational speed, and processor processing speed.
Specifically, the fault prediction means that the equipment fault, service and accessory demand is predicted through equipment use data, working condition data, host and accessory performance data, accessory replacement data and other equipment and service data based on data stored in a big data storage and analysis platform, so that technical support is provided for active service, the service life of the equipment is prolonged, and the fault rate is reduced.
Example 3
On the basis of the above embodiment, the failure analysis prediction subsystem includes: the system comprises a data preprocessing unit, a data specification unit, a data standardization unit, an algorithm prediction unit and a modeling analysis unit; the data preprocessing unit sequentially performs unique attribute removal, missing value processing and abnormal value detection processing on the operation parameters and the temperature data; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information is kept; the data standardization unit scales the data processed by the protocol according to a proportion to enable the data to fall into a set interval; the algorithm prediction unit carries out data modeling according to the data processed by the data standardization unit; and the modeling analysis unit is used for calculating the precision of the fault rate generated by the calculation model and the original fault rate.
Specifically, data reduction means to reduce the data volume to the maximum extent on the premise of keeping the original appearance of the data as much as possible (the necessary premise for completing the task is to understand the mining task and to be familiar with the content of the data). There are two main approaches to data reduction: attribute selection and data sampling, for attributes and records in the original dataset, respectively. Assume that data is selected for analysis at the company's data warehouse. So that the data set will be very large. Complex data analysis buckle mining on massive data would take a long time, making such analysis impractical or infeasible. Data reduction techniques may be used to obtain a reduced representation of a data set that, while small, substantially maintains the integrity of the original data. In this way, mining on the reduced data set will be more efficient and produce the same (or nearly the same) analysis results.
Before data analysis, we usually need to normalize the data (normalization) and perform data analysis by using the normalized data. Data normalization is the indexing of statistical data. The data standardization processing mainly comprises two aspects of data chemotaxis processing and dimensionless processing. The data homochemotaxis processing mainly solves the problem of data with different properties, directly sums indexes with different properties and cannot correctly reflect the comprehensive results of different acting forces, and firstly considers changing the data properties of inverse indexes to ensure that all the indexes are homochemotactic for the acting forces of the evaluation scheme and then sum to obtain correct results. The data dimensionless process mainly addresses the comparability of data. There are many methods for data normalization, and the methods are commonly used, such as "min-max normalization", "Z-score normalization", and "normalization on a decimal scale". Through the standardization processing, the original data are all converted into non-dimensionalized index mapping evaluation values, namely, all index values are in the same quantity level, and comprehensive evaluation analysis can be carried out.
Example 4
On the basis of the previous embodiment, the method for performing specification processing by the data specification unit includes: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept.
Example 5
On the basis of the above embodiment, the method for the data normalization unit to perform data normalization processing includes: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a specific interval; in which the data is linearly transformed using a transfer function such that the result falls to [0.1,1]Interval, the transfer function is as follows:
wherein x is*The result is the result after data standardization processing; x is data to be processed; min is the minimum value in the data; max is the maximum value in the data.
Example 6
A distributed control method with high fault tolerance, the method performing the steps of:
step 1: the process knowledge system adjusts the field analog quantity and optimally adjusts and controls the pressure, the temperature and the liquid level; the process knowledge system is in signal connection with the server and sends data information to the server; meanwhile, the process knowledge system is in signal connection with the fault safety controller through a communication module;
step 2: the server sends the received data information to the operator station;
and step 3: the fault safety controller is connected with the server, and when the server fails, data information is acquired from the server; when the server fails, data information is directly acquired from the process knowledge system through the communication module.
Specifically, a Honeywell control system is used for maintaining a DCS (distributed control system), the system consists of a PKS (public key system) and an FSC (self-service data center) system of the Honeywell, wherein the PKS mainly is used for adjusting field analog quantity equipment and mainly is used for optimally adjusting and controlling pressure, temperature and liquid level, namely a process knowledge system; the FSC system is mainly used for controlling the safety of field equipment, namely a failure safety controller, the system is upgraded and modified in 2016, the communication mode among the systems is SCADA communication, all communication points are stored on a server, and the server adopts redundancy configuration and is responsible for reading data from the controller and then sending the data to an operator station picture. If the server fails (the server is halted, the server is powered off, and the like), the communication of the FSC system is interrupted, and the field device is uncontrollable, another communication mode (without the server) needs to be designed at present to realize communication redundancy control, and real-time data transmission is carried out between the PKS controller and the FSC controller, so that the safety and controllability of the field device are ensured.
Specifically, the PKS system is physically connected to the PCDI device: there are three main types of physical connections between the FSC system and the PKS system: serial connection, Ethernet connection and terminal server connection; in the invention, the adopted communication mode is that the FSC system adopts a serial port to Ethernet connection mode, and the middle part adopts PCDI communication module conversion.
Specifically, the invention applies Quick Builder software, utilizes PCDI series modules of a function block library to establish peer-to-peer connection between a PKS system C300 controller and FSC system controller equipment, and carries out data exchange between the PKS system controller C300 and the FSC system, wherein a data communication path comprises an FCS system, the PKS system C300 controller, a server and an operator station. The Experion PKS system communicates with FSC system equipment, generally, the PKS system is a master station, the FSC system is a slave station, all slave station addresses must be unique for the master station, otherwise, communication failure can be caused, and communication parameter settings of the master station and the slave station must be consistent.
Specifically, the distributed control system of the present invention, in terms of configuration: and opening Quick Builder software, wherein the PCDI module is used for defining the type and the IP address of the FSC system equipment. Dragging the PCDI module in the Library to the C300 control execution configuration logic, selecting the device type: device Type, fills in the IP address of the FSC Device. Configuring the communication point, writing the configuration of all communication points of the FSC system equipment into a PCDI configuration database, after the configuration is completed, the C300 controller can flexibly apply the data of the FSC system equipment, wherein the data correspond to PCDIFLAARRCH.PVFL [ N ], PCDINUMARRCHA.PV [ N ]. pins and the internal logic control of the PKS system. The scheme has the characteristics that: the PCDI serial communication module is equivalent to an I/O channel connecting the PKS system controller C300 and the FSC system device, and the C300 controller can conveniently and quickly apply PCDI communication data to process control inside the C300 controller, for example: logic comparison, picture display and chain protection.
Example 7
On the basis of the above embodiment, the method further includes: and acquiring the operating parameters and temperature data of other equipment and systems in the system, and performing data analysis on the operating parameters and the temperature data to perform fault prediction.
Example 8
On the basis of the previous embodiment, the method for acquiring the operating parameters and the temperature data of other devices and systems in the system, analyzing the operating parameters and the temperature data, and predicting the fault comprises the following steps:
step S1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step S2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept.
Step S3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a specific interval; in which the data is linearly transformed using a transfer function such that the result falls to [0.1,1]Interval, the transfer function is as follows:
wherein x is*The result is the result after data standardization processing; x is data to be processed; min is the minimum value in the data; max is the maximum value in the data;
step S4: carrying out data modeling;
step S5: performing an effect analysis comprising: after the model training is finished, calculating the fault generated by the model by adopting the following formulaThe precision of the rate and the original fault rate is calculated, namely R is obtained2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents the model-generated power equipment failure rate (predicted value);
nsamplesrepresenting the size of the sample size entering the model.
Example 9
On the basis of the above embodiment, the operating parameters at least include: processor occupancy, data throughput, storage hard disk rotational speed, and processor processing speed.
Example 10
On the basis of the above embodiment, the step S4: the method for modeling data performs the following steps:
step S4.1: obtaining data for modeling as input variables, using xiRepresenting, wherein i represents the ith variable in the data; said xiAt least comprises the following steps: the voltage value, the current value, the arc value, the temperature value and the humidity value when the power equipment operates;
step S4.2: setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result;
step S4.3: setting an excitation function, wherein the excitation function is as follows:
setting the neuron threshold of the neural network as follows: theta; and operating the first intermediate result, the excitation function and the neuron threshold value to obtain a result of the forward neural network, wherein the result is as follows:
step S4.4: calculating a training error of the forward neural network; because the output variable E of the training is 'failure rate of power equipment', but a predicted value generated after model training is O, the obtained error function is as follows:
where m represents the number of samples input into the modeling this time and i represents the ith variable.
Step S4.5: backpropagating update weights w
In order to reduce the error and improve the accuracy of model prediction, the neural network reversely transmits data from the output layer to the input layer, and the value of the weight w is readjusted until the model error reaches the minimum, and then the training is stopped, so that the model creation is completed.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. A distributed control system with high fault tolerance, said system comprising: a fail-safe controller, a server, a process knowledge system, and an operator station; the fault safety controller is connected with the server through signals; the server is in signal connection with the process knowledge system; the process knowledge system is signally connected to an operator station; the safety fault controller is characterized in that the safety fault controller is further in signal connection with a process knowledge system through a communication module.
2. The system of claim 1, wherein the system further comprises: a fault prediction system; the failure prediction system includes: the system comprises a temperature sensor, an equipment operation monitoring subsystem and a fault analysis and prediction subsystem; the system comprises a plurality of temperature sensors, a fault safety controller, a server, a communication module and a process knowledge system, wherein the plurality of temperature sensors are respectively arranged on the fault safety controller, the server, the communication module and the process knowledge system to acquire temperature data of the fault safety controller, the server, the communication module and the process knowledge system; the equipment operation monitoring subsystem is a computer readable storage medium stored in a non-transitory state, is respectively arranged on the fault safety controller, the server, the communication module and the process knowledge system, and is used for acquiring the operation parameters of the fault safety controller, the server, the communication module and the process knowledge system in real time and sending the acquired operation parameters to the fault analysis and prediction subsystem; the operating parameters include at least: processor occupancy, data throughput, storage hard disk rotational speed, and processor processing speed.
3. The system of claim 2, wherein the fault analysis prediction subsystem comprises: the system comprises a data preprocessing unit, a data specification unit, a data standardization unit, an algorithm prediction unit and a modeling analysis unit; the data preprocessing unit sequentially performs unique attribute removal, missing value processing and abnormal value detection processing on the operation parameters and the temperature data; the data protocol unit is used for carrying out protocol processing on the data after data preprocessing, so that the data after protocol processing are irrelevant pairwise, but original information is kept; the data standardization unit scales the data processed by the protocol according to a proportion to enable the data to fall into a set interval; the algorithm prediction unit carries out data modeling according to the data processed by the data standardization unit; and the modeling analysis unit is used for calculating the precision of the fault rate generated by the calculation model and the original fault rate.
4. The system of claim 3, wherein said data reduction unit performs reduction processing by: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept.
5. The system of claim 4, wherein the data normalization unit performs a data normalization process by a method comprising: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a specific interval; in which the data is linearly transformed using a transfer function such that the result falls to [0.1,1]Interval, the transfer function is as follows:
wherein x is*The result is the result after data standardization processing; x is data to be processed; min is the minimum value in the data; max is the maximum value in the data.
6. A distributed control method with high fault tolerance based on the system of any one of claims 1 to 5, characterized in that the method performs the following steps:
step 1: the process knowledge system adjusts the field analog quantity and optimally adjusts and controls the pressure, the temperature and the liquid level; the process knowledge system is in signal connection with the server and sends data information to the server; meanwhile, the process knowledge system is in signal connection with the fault safety controller through a communication module;
step 2: the server sends the received data information to the operator station;
and step 3: the fault safety controller is connected with the server, and when the server fails, data information is acquired from the server; when the server fails, data information is directly acquired from the process knowledge system through the communication module.
7. The method of claim 6, wherein the method further comprises: and acquiring the operating parameters and temperature data of other equipment and systems in the system, and performing data analysis on the operating parameters and the temperature data to perform fault prediction.
8. The method of claim 7, wherein the step of obtaining operating parameters and temperature data of other devices and systems in the system, performing data analysis on the operating parameters and temperature data, and performing fault prediction comprises the steps of:
step S1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step S2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but the original information is kept.
Step S3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a specific interval; in which the data is linearly transformed using a transfer function such that the result falls to [0.1,1]Interval, the transfer function is as follows:
wherein x is*For data standardization processingThe latter result; x is data to be processed; min is the minimum value in the data; max is the maximum value in the data;
step S4: carrying out data modeling;
step S5: performing an effect analysis comprising: after model training is finished, calculating the fault rate generated by the model and the original fault rate by adopting the following formula to perform precision calculation, namely obtaining R2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents the model-generated power equipment failure rate (predicted value);
nsamplesrepresenting the size of the sample size entering the model.
9. The method of claim 8, wherein the operating parameters include at least: processor occupancy, data throughput, storage hard disk rotational speed, and processor processing speed.
10. The method of claim 9, wherein the step S4: the method for modeling data performs the following steps:
step S4.1: obtaining data for modeling as input variables, using xiRepresenting, wherein i represents the ith variable in the data; said xiAt least comprises the following steps: the voltage value, the current value, the arc value, the temperature value and the humidity value when the power equipment operates;
step S4.2: setting a weight function of wiExpressing, performing convolution operation on each input variable and the corresponding weight function to obtain a first intermediate result;
step S4.3: setting an excitation function, said excitationThe function is:
setting the neuron threshold of the neural network as follows: theta; and operating the first intermediate result, the excitation function and the neuron threshold value to obtain a result of the forward neural network, wherein the result is as follows:
step S4.4: calculating a training error of the forward neural network; because the output variable E of the training is 'failure rate of power equipment', but a predicted value generated after model training is O, the obtained error function is as follows:
where m represents the number of samples input into the modeling this time and i represents the ith variable.
Step S4.5: backpropagating update weights w
In order to reduce the error and improve the accuracy of model prediction, the neural network reversely transmits data from the output layer to the input layer, and the value of the weight w is readjusted until the model error reaches the minimum, and then the training is stopped, so that the model creation is completed.
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