Integrated power generation equipment remote monitoring platform and method

Abstract

The invention relates to the technical field of power generation equipment remote monitoring, and discloses an integrated power generation equipment remote monitoring platform and method.The integrated power generation equipment remote monitoring platform comprises a sensing layer module, a transmission layer module and an application layer module.The sensing layer module collects and analyzes equipment operation data in real time; the transmission layer module can transmit analyzed operation data from the sensing layer to the application layer in real time, and in the application layer module, the fault diagnosis and prediction maintenance module plays a core role and utilizes advanced data processing and analysis technologies to accurately predict and diagnose potential faults of the equipment, so that the fault diagnosis and prediction maintenance of the equipment is realized. The equipment fault occurrence probability is effectively reduced; meanwhile, the prediction maintenance sub-module can reasonably arrange maintenance work according to the actual operation condition of the equipment, unnecessary downtime and maintenance cost are avoided, and the intelligent maintenance management mode not only prolongs the service life of the equipment, but also improves the overall operation efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote monitoring of power generation equipment, and in particular relates to an integrated remote monitoring platform and method for power generation equipment. Background Art

[0002] With the continuous improvement of industrial automation and intelligence, power generation equipment is becoming increasingly important in modern society. Power generation equipment usually undertakes long-term and high-intensity operation tasks. During the continuous operation, the mechanical parts, electrical components and control systems inside the equipment will inevitably be affected by wear, aging or external environmental factors, which will cause various faults. These faults will not only cause a significant decline in equipment performance, affect power generation efficiency and energy quality, but in severe cases may also cause sudden shutdowns, resulting in chain reactions such as production line interruptions and power supply shortages, causing huge economic losses to enterprises and even threatening social production safety and public safety.

[0003] Traditional power generation equipment maintenance strategies mostly rely on manual regular inspections and post-maintenance models. Although this maintenance method can guarantee the basic operation of the equipment to a certain extent, it has obvious limitations. On the one hand, the regular inspection cycle is often set based on experience, which makes it difficult to accurately capture the early signs of equipment failure, resulting in delayed preventive maintenance measures; on the other hand, post-maintenance is often carried out only after the equipment has obviously failed or performance has deteriorated. At this time, not only is the maintenance cost high, but the economic losses may also be further aggravated due to excessive downtime.

[0004] In order to solve this problem, remote monitoring systems for power generation equipment came into being. Such systems provide managers with more timely and accurate information feedback by monitoring the operating status of the equipment in real time, collecting and analyzing equipment data, thereby improving the management efficiency and response speed of the equipment to a certain extent. However, most of the existing remote monitoring systems for power generation equipment are still at the level of data collection and display, lacking in-depth analysis of equipment failures and predictive maintenance functions. This means that even if the system can detect abnormal data of the equipment in real time, it is often unable to immediately determine the type, severity and possible development trend of the failure, let alone provide managers with scientific and reasonable maintenance suggestions. Summary of the invention

[0005] The purpose of the present invention is to overcome the above problems and provide an integrated power generation equipment remote monitoring platform and method, which can improve the management efficiency of the equipment, discover potential faults in advance, optimize maintenance resource allocation, improve production safety and extend the service life of the equipment.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides an integrated power generation equipment remote monitoring platform, including a perception layer module, a transmission layer module and an application layer module; The perception layer module is used to collect the operation data of the equipment and analyze the operation data; The transport layer module is used to transmit the analyzed operation data from the perception layer module to the application layer module; The application layer module is used to receive the analyzed operation data, perform fault diagnosis and predictive maintenance on the equipment according to the analysis results of the operation data, and arrange maintenance work; The application layer modules include a data storage module, a data processing and analysis module, a monitoring and display module, a user management module, a remote control module, and a fault diagnosis and predictive maintenance module; The fault diagnosis and predictive maintenance module includes a data preprocessing submodule, a fault feature extraction submodule, a fault diagnosis submodule, a predictive maintenance submodule, a model training and optimization submodule and a knowledge base management submodule; The fault diagnosis and predictive maintenance module is used to predict and diagnose potential faults of equipment and arrange maintenance work according to the actual operating status of the equipment.

[0007] A further improvement of the present invention is that the perception layer module includes a sensor module, a data acquisition module and a controller module; The sensor module is used to collect the operating data of the device and transmit the operating data to the data acquisition module, wherein the operating data includes temperature, humidity and pressure; The data acquisition module is used to receive the operating data output by the sensor module and convert it into a digital signal; The controller module is used to analyze the digital signal output by the data acquisition module, execute the control command according to the preset program and algorithm, and then send the analyzed data to the transmission layer module.

[0008] A further improvement of the present invention is that the data storage module is used to store and manage the device operation data received from the perception layer module and the transport layer module in the application layer module; The data processing and analysis module is used to process and analyze the stored equipment operation data and extract the processed data information; The monitoring and display module is used to display the extracted data information to the user in the form of a graphical interface, including real-time data monitoring, historical data query and trend chart drawing; The user management module is responsible for user identity authentication, authority allocation and operation log recording of the system, wherein the user management module is also responsible for maintaining user account information, monitoring system usage and ensuring system security; The remote control module is used to allow authorized users to remotely control power generation equipment through the network. It can execute commands to start, stop and adjust parameters to achieve real-time monitoring and control of the equipment.

[0009] A further improvement of the present invention is that the data preprocessing submodule is used to clean, standardize and reduce the data dimension of the collected equipment operation data; The standardized formula of the data preprocessing submodule is:

[0010] in, is the original data, is the mean of the data, is the standard deviation of the data.

[0011] A further improvement of the present invention is that the fault feature extraction submodule is used to extract feature parameters that can characterize the device status from the data after data preprocessing, and the feature parameters are time domain features, frequency domain features, or time-frequency domain features; The frequency domain features are extracted by Fourier transform, and the calculation formula is:

[0012] in, is the frequency domain signal, is the time domain signal, is the number of sample points, is the frequency index.

[0013] A further improvement of the present invention is that the fault diagnosis submodule is used to identify whether a device has a fault based on the extracted characteristic parameters, including a training phase and a classification phase; The formula for maximizing the objective function is:

[0014] in, is the normal vector of the hyperplane, is the Lagrange multiplier, is the category label, is the kernel function.

[0015] A further improvement of the present invention is that the predictive maintenance submodule is used to predict the time and probability of future failures by analyzing the operating data of the equipment and arrange maintenance plans in advance; The prediction is made by linear regression method, and the calculation formula is:

[0016] in, is the predicted value, is the eigenvalue, is the intercept, is the slope, is the error term.

[0017] A further improvement of the present invention is that the model training and optimization submodule is used to optimize the model parameters, and the calculation formula is:

[0018] in, are model parameters, is the learning rate, is the loss function, about gradient.

[0019] A further improvement of the present invention is that the knowledge base management submodule is used to store and manage fault cases, expert knowledge and rules, and includes a database and an inference engine.

[0020] In a second aspect, the present invention further provides a monitoring method for an integrated power generation equipment remote monitoring platform, comprising the following steps: Collect the operation data of the equipment and analyze the operation data; Transmit the analyzed operation data from the perception layer module to the application layer module; Receive the analyzed operation data, perform fault diagnosis and predictive maintenance on the equipment based on the operation data analysis results, and arrange maintenance work.

[0021] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an integrated remote monitoring platform for power generation equipment. The platform collects and analyzes equipment operation data in real time through a perception layer module, ensuring that operators can quickly grasp the latest operation status of the equipment. The efficient data transmission mechanism of the transmission layer module enables the analyzed operation data to be instantly transmitted from the perception layer to the application layer, providing strong support for rapid response. In the application layer module, the fault diagnosis and predictive maintenance module plays a core role. It uses advanced data processing and analysis technology to accurately predict and diagnose potential equipment failures, effectively reducing the probability of equipment failure. At the same time, the predictive maintenance submodule can reasonably arrange maintenance work according to the actual operation status of the equipment, avoiding unnecessary downtime and maintenance costs. This intelligent maintenance management method not only extends the service life of the equipment, but also improves the overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings described herein are only for explanation purposes and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the various components in the drawings are only schematic, used to help understand the present invention, and are not intended to specifically limit the shapes and proportional dimensions of the various components of the present invention.

[0023] Figure 1 It is an overall block diagram of the integrated power generation equipment remote monitoring platform of the present invention; Figure 2 This is a system block diagram of the fault diagnosis and predictive maintenance module of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0027] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0028] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0029] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings: One or more embodiments provide an integrated power generation equipment remote monitoring platform, see Figure 1 and Figure 2 , the platform includes perception layer module, transport layer module and application layer module, among which: The perception layer module is used to collect the operation data of the equipment and analyze the operation data, wherein the perception layer module is the bottom layer of the system and directly interacts with the power generation equipment; The transport layer module is used to transmit the analyzed operation data from the perception layer module to the application layer module, wherein the transport layer module is internally provided with a communication interface, a communication protocol converter and a network device. As an intermediate layer, the transport layer module is responsible for reliably transmitting the data collected by the perception layer to the application layer. The communication interface receives the data from the controller module and converts the data into a format suitable for transmission on the network device through the communication protocol converter. The network device is responsible for the long-distance transmission of the data to ensure that the data can reach the application layer module safely and efficiently. In this process, the edge computing module can perform preprocessing before transmitting the data to the cloud to reduce the computing pressure on the cloud. The application layer module is used to receive the analyzed operation data, perform fault diagnosis and predictive maintenance on the equipment according to the analysis results of the operation data, and arrange maintenance work.

[0031] Specifically, the perception layer module includes a sensor module, a data acquisition module and a controller module, wherein: The sensor module is used to collect the operating data of the equipment and transmit the operating data to the data acquisition module, wherein the operating data includes temperature, humidity and pressure; the sensor module is a key component in the perception layer, which is composed of various types of sensors and is responsible for real-time monitoring of the physical parameters of the power generation equipment, such as temperature, pressure, vibration, current and voltage; the sensor converts the collected analog signals into electrical signals, which are then transmitted to the data acquisition module for processing. The design of the sensor needs to consider its accuracy, response time and reliability to ensure the accuracy of the monitoring data and the stable operation of the system; The data acquisition module is used to receive the operating data output by the sensor module and convert it into a digital signal. The data acquisition module usually includes an analog-to-digital converter (ADC) and a microprocessor, and can perform preliminary data processing such as filtering, sampling and encoding. The performance of the data acquisition module directly affects the accuracy of the data and the real-time performance of the system.

[0032] The controller module is used to analyze the digital signal output by the data acquisition module, execute control commands according to preset programs and algorithms, including starting and stopping the equipment and adjusting parameters, and then send the analyzed data to the transmission layer module through the communication interface; the controller module plays a core control role in the perception layer. It analyzes the data provided by the data acquisition module according to preset programs and algorithms, and executes corresponding control commands. The controller module can adjust the operating status of the equipment, respond to faults, or communicate with the host computer system to achieve higher-level monitoring and control functions.

[0033] Specifically, the application layer module includes a data storage module, a data processing and analysis module, a monitoring and display module, a user management module, a remote control module and a fault diagnosis and predictive maintenance module, wherein: The data storage module is used to store and manage the equipment operation data received from the perception layer module and the transport layer module in the application layer module; it usually includes a database management system (DBMS) for organizing, querying and maintaining data; the data storage module needs to have high capacity, high reliability and fast reading and writing capabilities to ensure persistent storage and efficient access to a large amount of monitoring data; The data processing and analysis module is used to process and analyze the stored equipment operation data and extract the processed data information to extract valuable information and insights; it includes various data analysis techniques, such as statistical analysis, signal processing, pattern recognition and machine learning algorithms. The goal of this module is to extract features from raw data to provide data support for monitoring, diagnosis and maintenance; The monitoring and display module is used to display the extracted data information to the user in the form of a graphical interface, including real-time data monitoring, historical data query and trend chart drawing, providing intuitive and real-time information feedback for operators. The beneficial effects of the monitoring and display module are mainly reflected in the following aspects: operators can observe the operation of the equipment in real time through the monitoring interface, and once an abnormality occurs, they can immediately discover and take corresponding measures, thereby reducing the risk of accidents; displaying data in the form of charts, curves, etc. makes complex data analysis intuitive and easy to understand, and improves the operator's cognitive speed and accuracy of the equipment status; the monitoring and display module provides decision support for the management, and through comparative analysis of historical data and real-time data, it helps to formulate more reasonable maintenance plans and operation strategies and optimize resource allocation; The user management module is responsible for user identity authentication, authority allocation and operation log recording of the system, wherein the user management module is also responsible for maintaining user account information, monitoring system usage and ensuring system security; The remote control module is used to allow authorized users to remotely control power generation equipment through the network. It can execute commands to start, stop and adjust parameters to achieve real-time monitoring and control of the equipment. The design of the remote control module needs to consider the real-time nature of communication and the security of command execution to ensure the accuracy of remote operation and the stable operation of the system; The fault diagnosis and predictive maintenance module is used to predict and diagnose potential equipment failures and arrange maintenance work according to the actual operating status of the equipment; the fault diagnosis and predictive maintenance module can issue an early warning before a serious equipment failure occurs, so that maintenance personnel can perform preventive maintenance in a targeted manner, thereby avoiding the high maintenance costs and production interruptions caused by sudden failures, and arranging maintenance work according to the actual operating status of the equipment, effectively reducing unnecessary maintenance activities and reducing maintenance costs.

[0034] Specifically, the fault diagnosis and predictive maintenance module includes a data preprocessing submodule, a fault feature extraction submodule, a fault diagnosis submodule, a predictive maintenance submodule, a model training and optimization submodule and a knowledge base management submodule, wherein: The data preprocessing submodule is used to clean, standardize and reduce the dimension of the collected equipment operation data to provide high-quality data for subsequent analysis; the algorithm process includes data cleaning (removing outliers and missing values), data standardization (such as Z-score standardization, converting data into a distribution with a mean of 0 and a standard deviation of 1) and data dimension reduction (such as principal component analysis PCA); The standardized formula of the data preprocessing submodule is:

[0035] in, is the original data, is the mean of the data, is the standard deviation of the data.

[0036] The fault feature extraction submodule is used to extract feature parameters that can characterize the device status from the data after data preprocessing, and the feature parameters are time domain features (such as average value, root mean square value), frequency domain features (such as spectrum energy, frequency center) or time-frequency domain features (such as wavelet transform coefficients); The frequency domain features are extracted by Fourier transform (FFT), and the calculation formula is:

[0037] in, is the frequency domain signal, is the time domain signal, is the number of sample points, is the frequency index.

[0038] The fault diagnosis submodule is used to identify whether the device has a fault based on the extracted characteristic parameters, including a training phase (finding the optimal hyperplane) and a classification phase (determining the category of new samples); The formula for maximizing the objective function is:

[0039] in, is the normal vector of the hyperplane, is the Lagrange multiplier, is the category label, is the kernel function.

[0040] The predictive maintenance submodule is used to predict the time and probability of future failures by analyzing the operating data of the equipment, and arrange maintenance plans in advance, that is, to issue early warnings before serious failures occur in the equipment, so that maintenance personnel can carry out preventive maintenance in a targeted manner, avoiding high maintenance costs and production interruptions caused by sudden failures; Common algorithms include regression analysis, time series analysis, and machine learning models. The linear regression method is used for prediction, and the calculation formula is:

[0041] in, is the predicted value, is the eigenvalue, is the intercept, is the slope, is the error term.

[0042] The model training and optimization submodule is used to optimize model parameters, mainly through the selection of machine learning algorithms, parameter tuning and model verification; The model training and optimization submodule uses a gradient descent algorithm to optimize model parameters, and the calculation formula is:

[0043] in, are model parameters, is the learning rate, is the loss function, about The gradient of The knowledge base management submodule is used to store and manage fault cases, expert knowledge and rules, and includes a database and an inference engine.

[0044] A remote monitoring method for integrated power generation equipment comprises the following steps: Collect the operation data of the equipment and analyze the operation data; Transmit the analyzed operation data from the perception layer module to the application layer module; Receive the analyzed operation data, perform fault diagnosis and predictive maintenance on the equipment based on the operation data analysis results, and arrange maintenance work.

[0045] Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of the present teachings should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a disclaimer of such subject matter, nor should it be considered that the applicants did not consider such subject matter to be part of the disclosed inventive subject matter.

[0046] The above content is a further detailed description of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, they can also make several simple deductions or substitutions, which should be regarded as belonging to the protection scope of the present invention determined by the submitted claims.

Claims

1. An integrated power generation equipment remote monitoring platform, characterized in that: It includes perception layer module, transport layer module and application layer module; The perception layer module is used to collect the operation data of the equipment and analyze the operation data; The transport layer module is used to transmit the analyzed operation data from the perception layer module to the application layer module; The application layer module is used to receive the analyzed operation data, perform fault diagnosis and predictive maintenance on the equipment according to the analysis results of the operation data, and arrange maintenance work; The application layer modules include a data storage module, a data processing and analysis module, a monitoring and display module, a user management module, a remote control module, and a fault diagnosis and predictive maintenance module; The fault diagnosis and predictive maintenance module includes a data preprocessing submodule, a fault feature extraction submodule, a fault diagnosis submodule, a predictive maintenance submodule, a model training and optimization submodule and a knowledge base management submodule; The fault diagnosis and predictive maintenance module is used to predict and diagnose potential faults of equipment and arrange maintenance work according to the actual operating status of the equipment.

2. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The perception layer module includes a sensor module, a data acquisition module and a controller module; The sensor module is used to collect the operating data of the device and transmit the operating data to the data acquisition module, wherein the operating data includes temperature, humidity and pressure; The data acquisition module is used to receive the operating data output by the sensor module and convert it into a digital signal; The controller module is used to analyze the digital signal output by the data acquisition module, execute the control command according to the preset program and algorithm, and then send the analyzed data to the transmission layer module.

3. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The data storage module is used to store and manage the device operation data received from the perception layer module and the transport layer module in the application layer module; The data processing and analysis module is used to process and analyze the stored equipment operation data and extract the processed data information; The monitoring and display module is used to display the extracted data information to the user in the form of a graphical interface, including real-time data monitoring, historical data query and trend chart drawing; The user management module is responsible for user identity authentication, authority allocation and operation log recording of the system, wherein the user management module is also responsible for maintaining user account information, monitoring system usage and ensuring system security; The remote control module is used to allow authorized users to remotely control power generation equipment through the network. It can execute commands to start, stop and adjust parameters to achieve real-time monitoring and control of the equipment.

4. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The data preprocessing submodule is used to clean, standardize and reduce the dimension of the collected equipment operation data; The standardized formula of the data preprocessing submodule is: in, is the original data, is the mean of the data, is the standard deviation of the data.

5. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The fault feature extraction submodule is used to extract feature parameters that can characterize the device status from the data after data preprocessing, and the feature parameters are time domain features, frequency domain features or time-frequency domain features; The frequency domain features are extracted by Fourier transform, and the calculation formula is: in, is the frequency domain signal, is the time domain signal, is the number of sample points, is the frequency index.

6. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The fault diagnosis submodule is used to identify whether a device has a fault based on the extracted characteristic parameters, including a training phase and a classification phase; The formula for maximizing the objective function is: in, is the normal vector of the hyperplane, is the Lagrange multiplier, is the category label, is the kernel function.

7. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The predictive maintenance submodule is used to predict the time and probability of future failures by analyzing the operating data of the equipment and arrange maintenance plans in advance; The prediction is made by linear regression method, and the calculation formula is: in, is the predicted value, is the eigenvalue, is the intercept, is the slope, is the error term.

8. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The model training and optimization submodule is used to optimize model parameters, and the calculation formula is: in, are model parameters, is the learning rate, is the loss function, about gradient.

9. The integrated power generation equipment remote monitoring platform according to claim 1, characterized in that: The knowledge base management submodule is used to store and manage fault cases, expert knowledge and rules, and includes a database and an inference engine.

10. A monitoring method for an integrated power generation equipment remote monitoring platform according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect the operation data of the equipment and analyze the operation data; Transmit the analyzed operation data from the perception layer module to the application layer module; Receive the analyzed operation data, perform fault diagnosis and predictive maintenance on the equipment based on the operation data analysis results, and arrange maintenance work.

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