Fault diagnosis system and method

Abstract

The application relates to the technical field of fault diagnosis, and discloses a fault diagnosis system and method, which comprises a data acquisition module, a data transmission module, a data processing and analysis module, a fault diagnosis and positioning module, an alarm and display module, the data acquisition module is used for periodically collecting operation data of a valve of a gas compression station, the data transmission module is used for transmitting the operation data to a central processing unit through a wireless network, the data processing and analysis module is used for inputting the operation data transmitted to the central processing unit into a valve operation model to obtain a data analysis result, the fault diagnosis and positioning module is used for generating a fault diagnosis report based on the data analysis result and a preset fault set, and the alarm and display module is used for outputting the fault diagnosis report. Through cooperation of the modules, the accuracy and efficiency of valve fault identification are improved.

Description

Technical Field

[0001] This invention relates to the field of fault diagnosis technology, and more specifically, to a fault diagnosis system and method. Background Technology

[0002] Gas compressor stations are critical facilities in natural gas transmission systems, their primary function being to pressurize natural gas for efficient delivery to users or other pipeline networks. Valves play an indispensable role in gas compressor stations, mainly used to control gas flow, regulate flow rate, and ensure the safe operation of the system. The reliability of valves directly affects the safety and economy of the entire gas compressor station. Therefore, how to quickly and accurately identify valve malfunctions in gas compressor stations is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] In view of this, the present invention proposes a fault diagnosis system and method that can quickly and accurately identify faults in compressor stations.

[0004] In a first aspect, the present invention proposes a fault diagnosis system, comprising: a data acquisition module, a data transmission module, a data processing and analysis module, a fault diagnosis and location module, and an alarm and display module;

[0005] The data acquisition module is used to periodically collect the operating data of the valves in the compressor station;

[0006] The data transmission module is used to transmit operational data to the central processing unit via a wireless network;

[0007] The data processing and analysis module is used to input the operational data transmitted to the central processing unit into the valve operation model to obtain data analysis results; the valve operation model is a model built based on the valve's historical operational data;

[0008] The fault diagnosis and location module is used to generate a fault diagnosis report based on data analysis results and a preset fault set; the fault diagnosis report includes the fault type and / or fault location.

[0009] The alarm and display module is used to output fault diagnosis reports.

[0010] In one possible implementation, the data acquisition module uses multiple sensors to monitor the valve's operating data; the operating data includes at least one of the following: the valve's on / off status, the valve's operating pressure, the valve's flow rate, the valve's temperature, and the valve's vibration information.

[0011] In one possible implementation, the operational data includes the valve's internal pressure gradient and the valve's internal wear information; the data acquisition module is specifically used to acquire the valve's internal pressure gradient and the valve's internal wear information.

[0012] In one possible implementation, the data acquisition module is specifically used to acquire internal pressure gradients through pressure sensors installed at different parts of the valve, and to acquire internal wear information through ultrasonic sensors or coating thickness monitoring devices installed at different parts of the valve.

[0013] In one possible implementation, the data processing and analysis module is also used to establish a valve operation model based on historical operating data and a clustering analysis algorithm; the clustering analysis algorithm is a weighted clustering algorithm based on distance and density.

[0014] In one possible implementation, the data processing and analysis module is also used to periodically update the valve operation model based on the valve's operating data.

[0015] In one possible implementation, the data processing and analysis module is specifically used to compare the running data with the running data of historical periods to obtain data analysis results.

[0016] In one possible implementation, the preset fault set includes at least one of the following: valve jamming, poor sealing, drive system failure, valve component deformation, and valve sealing surface damage.

[0017] In one possible implementation, the fault diagnosis and location module is specifically used to analyze the data analysis results based on time series analysis and fault thresholds to obtain a fault diagnosis report.

[0018] Secondly, the present invention proposes a fault diagnosis method, comprising:

[0019] Periodically acquire the operating data of the valves in the compressor station;

[0020] The running data is transmitted to the central processing unit via a wireless network;

[0021] The operational data transmitted to the central processing unit is input into the valve operation model to obtain data analysis results; the valve operation model is a model built based on the valve's historical operational data;

[0022] A fault diagnosis report is generated based on the data analysis results and a preset fault set; the fault diagnosis report includes the fault type and / or fault location.

[0023] Output a fault diagnosis report.

[0024] Thirdly, the present invention proposes a fault diagnosis device, comprising: an acquisition unit, a transmission unit, a processing unit, and an output unit;

[0025] The acquisition unit is used to periodically acquire the operating data of the valves in the compressor station.

[0026] The transmission unit is used to transmit operational data to the central processing unit via a wireless network.

[0027] The processing unit is used to input the operating data transmitted to the central processing unit into the valve operation model to obtain data analysis results; the valve operation model is a model built based on the valve's historical operating data.

[0028] The processing unit is also used to generate a fault diagnosis report based on the data analysis results and a preset fault set; the fault diagnosis report includes the fault type and / or fault location.

[0029] Output unit, used to output fault diagnosis report.

[0030] Fourthly, a fault diagnosis device is provided, including a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory via a bus; when the fault diagnosis device is running, the processor executes the computer execution instructions stored in the memory, so that the fault diagnosis device performs the fault diagnosis method described in the first aspect.

[0031] The fault diagnosis device can be a network device or a component of a network device, such as a chip system within the network device. This chip system supports the network device in implementing the functions involved in the first aspect and any of its possible implementations, such as acquiring, determining, and transmitting the data and / or information involved in the aforementioned fault diagnosis method. The chip system includes a chip, but may also include other discrete devices or circuit structures.

[0032] Fifthly, a computer-readable storage medium is provided, comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the fault diagnosis method described in the first aspect.

[0033] In a sixth aspect, a computer program product is also provided, which includes computer instructions that, when executed on a fault diagnosis device, cause the fault diagnosis device to perform the fault diagnosis method as described in the first aspect above.

[0034] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the fault diagnosis device, or it may be packaged separately from the processor of the fault diagnosis device; this application does not limit this.

[0035] The descriptions of the second, third, fourth, fifth, and sixth aspects of this application can be referenced to the detailed description of the first aspect.

[0036] In the embodiments of this application, the names of the aforementioned fault diagnosis devices do not limit the devices or functional modules themselves. In actual implementation, these devices or functional modules may appear under other names. For example, the receiving unit may also be called a receiving module, receiver, etc. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalents.

[0037] Compared with existing technologies, the advantages of this invention are as follows: The data acquisition module periodically collects valve operating data, and the data is transmitted to the central processing unit via a wireless network through the data transmission module. The data processing and analysis module then inputs the operating data into the valve operating model to obtain data analysis results. The fault diagnosis and location module generates a fault diagnosis report based on the data analysis results, which can be output by the alarm and display module.

[0038] Since the valve operation model is established based on the valve's operating data under normal operating conditions, by inputting the valve's operating data into the valve operation model, it is possible to determine whether there are any abnormalities in the current operating data, and to determine the fault type and fault location based on the different types of abnormalities in the operating data, thereby quickly and accurately identifying the valve's fault. Attached Figure Description

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0040] Figure 1 This is a structural block diagram of a fault diagnosis system provided in an embodiment of the present invention;

[0041] Figure 2 This is a hardware schematic diagram of a fault diagnosis device provided in an embodiment of the present invention;

[0042] Figure 3 This is a flowchart illustrating a fault diagnosis method provided in an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of the structure of a fault diagnosis device provided in an embodiment of the present invention. Detailed Implementation

[0044] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0045] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0046] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0047] Gas compressor stations are critical facilities in natural gas transmission systems, their primary function being to pressurize natural gas for efficient delivery to users or other pipeline networks. Valves play an indispensable role in gas compressor stations, mainly used to control gas flow, regulate flow rate, and ensure the safe operation of the system. The reliability of valves directly affects the safety and economy of the entire gas compressor station. Therefore, how to quickly and accurately identify valve malfunctions in gas compressor stations is a pressing technical problem that needs to be solved.

[0048] Traditional monitoring equipment typically collects only limited parameters, such as pressure and temperature, and cannot comprehensively reflect the operating status of valves. Furthermore, these devices have limited data processing capabilities, making it impossible to analyze and identify fault modes accurately and in a timely manner. Although some systems have begun to incorporate intelligent analysis technologies, most systems remain inadequate in data acquisition, processing, and fault diagnosis. They cannot effectively integrate various operational data for in-depth analysis, resulting in fault detection accuracy and timeliness failing to meet the requirements of modern compressor stations.

[0049] To address this issue, this application provides a fault diagnosis system. The system periodically collects valve operating data via a data acquisition module and transmits this data to a central processing unit via a wireless network. A data processing and analysis module then inputs this data into a valve operating model to obtain data analysis results. Based on these results, a fault diagnosis and location module generates a fault diagnosis report, which can be output by the alarm and display module.

[0050] Since the valve operation model is established based on the valve's operating data under normal operating conditions, by inputting the valve's operating data into the valve operation model, it is possible to determine whether there are any abnormalities in the current operating data, and to determine the fault type and fault location based on the different types of abnormalities in the operating data, thereby quickly and accurately identifying the valve's fault.

[0051] like Figure 1 As shown in some embodiments of this application, this embodiment provides a fault diagnosis system, including: a data acquisition module, a data transmission module, a data processing and analysis module, a fault diagnosis and location module, and an alarm and display module.

[0052] The data acquisition module is used to periodically collect operating data from the valves of the compressor station. Optionally, the data acquisition module can process and verify the collected data to eliminate obviously abnormal data and obtain the operating data.

[0053] Specifically, the data acquisition module can monitor the valve's real-time data using various sensors installed on the valve (such as pressure sensors, temperature sensors, flow sensors, and vibration sensors). It then processes the real-time data acquired by the sensors using stream processing technologies (such as Apache Kafka and Apache Flow), thereby ensuring the real-time performance and consistency of the data. The operational data includes at least one of the following: valve on / off status, valve operating pressure, valve flow rate, valve temperature, and valve vibration information.

[0054] For different types of operational data, the collection frequency can be:

[0055] Pressure and temperature data are collected at a frequency of at least once per minute. For example, for critical equipment requiring real-time monitoring, the collection frequency can be once per second.

[0056] Traffic can be collected at a frequency of once per minute or more, while for some scenarios with large dynamic changes, the collection frequency can be once per second.

[0057] Vibration data can be acquired at a frequency of once per minute or higher. Higher acquisition frequencies are typically required to capture subtle changes and anomalies. For example, acquiring the number of vibrations or the vibration frequency over a given time period.

[0058] In one possible implementation, the operational data also includes the valve's internal pressure gradient and internal wear information. The data acquisition module can collect pressure distribution data in real time using high-precision pressure sensors at different locations on the valve to more accurately determine if there are localized pressure anomalies. Furthermore, considering the potential for cavitation caused by high-speed gas flow in the compressor station, the monitoring data on the erosion degree of the valve's inner wall material is increased. This can be achieved by using ultrasonic sensors or specialized coating thickness monitoring devices to periodically check the wear condition of the inner wall, thus providing more comprehensive data support for fault diagnosis.

[0059] The data transmission module transmits operational data to the central processing unit (CPU) via a wireless network. This allows the CPU to perform complex processing operations without requiring high-performance processing capabilities from valve-side devices (e.g., sensors), thus reducing the workload on valve-side equipment. Optionally, secure transmission can be achieved using data encryption technology.

[0060] The data transmission module can transmit runtime data to the central processing unit, i.e., to the server, via a network interface (such as Hypertext Transfer Protocol (HTTP) or Message Queuing Telemetry Transport (MQTT)). After receiving the runtime data, the server can store the runtime data in a database (such as SQLite, MySQL, MongoDB, etc.), thereby enabling long-term storage and management of the data for subsequent use.

[0061] The data processing and analysis module is used to input the operational data transmitted to the central processing unit into the valve operation model and obtain data analysis results. The valve operation model is a model built based on the valve's historical operational data. The data processing and analysis module can use machine learning algorithms or expert system algorithms to build the valve operation model using historical operational data from normal valve operation. Because this valve operation model is built using historical operational data, it can identify abnormal data. For example, the valve operation model can output predicted data, and this actual operational data can be compared with the predicted data. If the difference between the two is outside the preset range, the operational data can be determined to be abnormal.

[0062] Machine learning algorithms, including but not limited to classification algorithms, regression analysis, and cluster analysis, are used to model the normal operating status of valves and detect anomalies in operating data. Optionally, considering the characteristics of nonlinear variations in pressure and flow rates at compressor stations, cluster analysis algorithms can be optimized by employing density- and distance-weighted clustering methods. This allows the valve operating model to better adapt to the complex operating conditions of valves, improving the accuracy of the valve operating model.

[0063] Specifically, the data processing and analysis module uses machine learning algorithms to analyze the operating data. The valve operation model, which is an application type of machine learning model, performs anomaly detection on the operating data. Then, it learns the operating data under normal operating conditions based on historical operating data and detects whether the current operating data deviates from the predicted operating data under normal operating conditions obtained by the valve operation model. In this way, abnormal data can be identified and data analysis results can be obtained. That is, the data analysis results include abnormal data in the operating data.

[0064] In one possible implementation, since the flow of gas or liquid is through the valves in the compressor station, the data processing and analysis module, when determining the data analysis results based on the valve operation model, can use data analysis methods based on physical models, combined with the fluid dynamics and thermodynamics principles of the compressor station valves, to perform real-time verification and analysis of the collected operating data. For example, based on the gas state equation and fluid dynamics formulas, the theoretical operating data of the valve under different operating conditions can be calculated and compared with the actual collected operating data to obtain abnormal operating data, i.e., the data analysis results.

[0065] In another possible implementation, when the data processing and analysis module determines the data analysis results based on the valve operation model, it can use a real-time anomaly detection algorithm to detect abnormal data in the operational data, thereby forming the data analysis results. Real-time anomaly detection algorithms can include real-time Isolation Forest, real-time One-Class SVM, and real-time Autoencoders, among others. In this way, these real-time anomaly detection algorithms can detect abnormal data in the operational data caused by valve malfunctions.

[0066] In another possible implementation, when the data processing and analysis module determines the data analysis results based on the valve operation model, it can compare the operating data with the operating data of historical periods. If they are inconsistent (or the difference exceeds a preset difference range), the operating data can be identified as abnormal data. For example, by comparing the operating data of different periods, abnormal data in the current period can be identified, thereby further improving the accuracy of fault detection and enabling the prediction of fault trends.

[0067] In another possible implementation, the data processing and analysis module is also used to periodically update the valve operation model based on the valve's operating data. The data processing and analysis module can acquire continuously increasing operating data over time and periodically update the valve operation model based on the acquired operating data, thereby making the valve operation model more accurate. It should be understood that the periods for acquiring operating data and updating the valve operation model can be the same or different. If both periods are the same, the valve operation model can be updated after each fault diagnosis report is output, thus ensuring the real-time performance and accuracy of the valve operation model. Optionally, the model update algorithm can be an online learning algorithm (e.g., online support vector machine (SVM), online long short-term memory network (LSTM), etc.).

[0068] In another possible implementation, the data processing and analysis module is also used to preprocess the collected operational data, removing noise and outliers to determine the quality of the data input to the valve operation model and improve the reliability of the collected data. Data preprocessing may include: data cleaning, normalization, feature selection, filtering, and data smoothing. Data cleaning refers to removing noise and outliers to ensure data quality.

[0069] Normalization refers to scaling data to a uniform scale to facilitate model processing.

[0070] Feature selection refers to selecting features that have a significant impact on the valve's operating status, such as pressure, flow rate, and vibration information.

[0071] The fault diagnosis and location module is used to generate a fault diagnosis report based on data analysis results and a preset fault set; the fault diagnosis report includes the fault type and / or fault location.

[0072] Specifically, since the data analysis results include abnormal data identified by the data processing and analysis module, the fault diagnosis and location module can determine whether the abnormal data is caused by a fault based on the abnormal data in the data analysis results. After determining the fault, it can further determine the fault type and / or fault location corresponding to the abnormal data based on the type of abnormal data and the preset fault set, thereby generating a fault diagnosis report.

[0073] It should be noted that since the preset fault set includes different types of valve faults, and the types of abnormal data caused by different types or different locations of faults are also different, the type or location of the fault is determined by the type of abnormal data and the type of abnormal data corresponding to the fault type in the preset fault set.

[0074] The preset fault set includes at least one of the following: valve jamming, poor sealing, drive system failure, valve component deformation, and valve sealing surface damage. This preset fault set, also known as a fault knowledge database, includes not only common faults such as valve jamming, poor sealing, and drive system failure, but also special fault modes such as valve deformation caused by high-pressure impact and sealing surface damage caused by cavitation. By collecting a large number of compressor station valve fault cases and experimental data, the knowledge base is continuously improved, thereby enhancing the comprehensiveness and professionalism of fault diagnosis.

[0075] In one possible implementation, the fault diagnosis and location module can analyze the data analysis results based on time series analysis and fault thresholds to obtain a fault diagnosis report. Specifically, if abnormal data in the data analysis results is greater than or less than the fault threshold, the abnormal data is determined to be caused by a fault, indicating a valve malfunction. For example, by analyzing the data variation patterns during different pressurization stages and flow regulation processes, and combining this with a dynamically set fault threshold based on the valve operation model, the fault type and location can be accurately identified. The valve operation model can predict the fault threshold; when abnormal data is greater than or less than the fault threshold, the abnormal data can be determined to be caused by a fault. Optionally, the fault threshold can also be determined based on expert experience rules.

[0076] For example, the fault diagnosis and location module can determine the fault diagnosis report in any of the following ways:

[0077] Classification algorithm: used to classify data into normal and abnormal categories.

[0078] Support Vector Machine (SVM): Suitable for binary classification problems, capable of handling high-dimensional data and non-linear relationships.

[0079] Decision trees and random forests: provide easily interpretable models suitable for handling complex decision rules and feature combinations.

[0080] k-Nearest Neighbors (k-NN): Classifies data points based on their similarity and is suitable for scenarios with simple data patterns.

[0081] Regression algorithms: used to predict data trends and detect anomalies.

[0082] Linear regression: used to predict continuous variables and detect outliers where data deviates from predicted values.

[0083] Support Vector Regression (SVR): Handles nonlinear regression problems and is suitable for predicting complex data trends.

[0084] Clustering algorithms: used to discover natural groupings and patterns in data.

[0085] K-means clustering: used to divide data into k clusters, suitable for discovering outlier clusters in the data.

[0086] Density-based spatial clustering of applications with noise (DBSCAN): A density-based clustering algorithm suitable for handling noisy data and detecting outliers.

[0087] Anomaly detection algorithms: specifically designed to detect anomalous patterns in data.

[0088] Isolation Forest: Suitable for processing large-scale data, it detects outliers based on a tree structure.

[0089] One-Class SVM: Specifically designed for anomaly detection, it learns the boundaries of normal data and detects data points that exceed those boundaries.

[0090] Autoencoders: Deep learning models used for data reconstruction, detecting reconstruction errors to identify anomalies.

[0091] The alarm and display module is used to output fault diagnosis reports.

[0092] Specifically, after receiving a fault diagnosis report, the alarm and display module can alert operators through visual display or audible alarm, and display the fault diagnosis report, i.e., fault information, on the display interface, thereby quickly prompting staff to make decisions and carry out maintenance. The output methods can include sound, email, and SMS, thus improving the safety of valve operation.

[0093] In one possible implementation, the fault diagnosis and location module can also pre-store the corresponding handling measures for each fault type. Therefore, the fault diagnosis report can also include the corresponding handling measures for each fault, and the corresponding handling measures can be displayed through the alarm and display model.

[0094] The basic hardware structure of multiple modules in a fault diagnosis system includes Figure 2 The fault diagnosis device shown includes the following components. Figure 2 Taking the fault diagnosis device shown as an example, the hardware structure of multiple modules in the fault diagnosis system is introduced.

[0095] like Figure 2 The diagram shown is a hardware structure schematic of a fault diagnosis device provided in an embodiment of this application. The fault diagnosis device includes a processor 21, a memory 22, a communication interface 23, and a bus 24. The processor 21, memory 22, and communication interface 23 are connected via the bus 24.

[0096] Processor 21 is the control center of the fault diagnosis device. It can be a single processor or a collective term for multiple processing elements. For example, processor 21 can be a general-purpose central processing unit (CPU) or other general-purpose processors. Among them, the general-purpose processor can be a microprocessor or any conventional processor.

[0097] As one embodiment, processor 21 may include one or more CPUs, for example Figure 2 CPU 0 and CPU 1 are shown in the diagram.

[0098] The memory 22 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0099] In one possible implementation, the memory 22 can exist independently of the processor 21. The memory 22 can be connected to the processor 21 via a bus 24 and is used to store instructions or program code. When the processor 21 calls and executes the instructions or program code stored in the memory 22, it can implement the quantum entanglement-based laser data synchronization transmission method provided in the following embodiments of this application.

[0100] In this embodiment of the application, the software programs stored in the memory 22 are different for the multiple modules in the fault diagnosis system, so the multiple modules in the fault diagnosis system implement different functions.

[0101] In another possible implementation, the memory 22 can also be integrated with the processor 21.

[0102] Communication interface 23 is used for connecting the fault diagnosis device to other devices via a communication network, such as Ethernet, wireless access network, or wireless local area network (WLAN). Communication interface 23 may include a receiving unit for receiving data and a transmitting unit for sending data.

[0103] Bus 24 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0104] It should be pointed out that, Figure 2 The structure shown does not constitute a limitation on the fault diagnosis device, except Figure 2 In addition to the components shown, the fault diagnosis device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0105] The foregoing mainly describes the solutions provided by the embodiments of this application from a system perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] The fault diagnosis method provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0107] The fault diagnosis method provided in this application embodiment is applied to Figure 1 The data processing and analysis module in the fault diagnosis system shown. For example... Figure 3 As shown, the fault diagnosis method includes:

[0108] S301. Periodically acquire the operating data of the valves in the compressor station.

[0109] S302, Transmit the running data to the central processing unit via wireless network.

[0110] S303. Input the operating data transmitted to the central processing unit into the valve operation model to obtain the data analysis results.

[0111] Among them, the valve operation model is a model built based on the historical operation data of the valve.

[0112] S304. Generate a fault diagnosis report based on data analysis results and a preset fault set.

[0113] The fault diagnosis report includes the fault type and / or fault location.

[0114] S305, Output fault diagnosis report.

[0115] Since the valve operation model is established based on the valve's operating data under normal operating conditions, by inputting the valve's operating data into the valve operation model, it is possible to determine whether there are any abnormalities in the current operating data, and to determine the fault type and fault location based on different types of abnormalities in the operating data, thereby quickly and accurately identifying valve faults.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as systems, apparatus, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This application embodiment can divide the fault diagnosis device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0118] like Figure 4 The diagram shown is a structural schematic of a fault diagnosis device provided in an embodiment of this application. Figure 4 The fault diagnosis device shown includes: an acquisition unit 401, a transmission unit 402, a processing unit 403, and an output unit 404.

[0119] Acquisition unit 401 is used to periodically acquire the operating data of the valves in the compressor station.

[0120] The transmission unit 402 is used to transmit the operating data to the central processing unit via a wireless network.

[0121] The processing unit 403 is used to input the operating data transmitted to the central processing unit into the valve operating model to obtain data analysis results; the valve operating model is a model established based on the historical operating data of the valve.

[0122] The processing unit 403 is also configured to generate a fault diagnosis report based on the data analysis results and a preset fault set; the fault diagnosis report includes the fault type and / or fault location.

[0123] Output unit 404 is used to output the fault diagnosis report.

[0124] In one possible implementation, the acquisition unit 401 is specifically used for:

[0125] Multiple sensors are used to monitor the valve's operating data; the operating data includes at least one of the following: the valve's on / off status, the valve's operating pressure, the valve's flow rate, the valve's temperature, and the valve's vibration information.

[0126] In one possible implementation, the operating data includes the valve's internal pressure gradient and the valve's internal wear information; the data acquisition module is specifically used to acquire the valve's internal pressure gradient and the valve's internal wear information.

[0127] In one possible implementation, the acquisition unit 401 is specifically used for:

[0128] The internal pressure gradient is collected by pressure sensors installed at different parts of the valve, and the internal wear information is collected by ultrasonic sensors or coating thickness monitoring devices installed at different parts of the valve.

[0129] In one possible implementation, the processing unit 403 is further configured to establish the valve operation model based on the historical operating data and a clustering analysis algorithm; the clustering analysis algorithm is a clustering algorithm based on distance and density weighting.

[0130] In one possible implementation, the processing unit 403 is further configured to periodically update the valve operation model based on the valve's operating data.

[0131] In one possible implementation, the processing unit 403 is specifically used to: compare the running data with the running data of historical periods to obtain the data analysis results.

[0132] In one possible implementation, the preset fault set includes at least one of the following: valve jamming, poor sealing, drive system failure, valve component deformation, and valve sealing surface damage.

[0133] In one possible implementation, the processing unit 403 is specifically used to: analyze the data analysis results according to the time series analysis method and the fault threshold to obtain the fault diagnosis report.

[0134] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0136] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A fault diagnosis system, characterized in that, include: Data acquisition module, data transmission module, data processing and analysis module, fault diagnosis and location module, alarm and display module; The data acquisition module is used to periodically collect the operating data of the valves in the compressor station; The data transmission module is used to transmit the running data to the central processing unit via a wireless network; The data processing and analysis module is used to input the operating data transmitted to the central processing unit into the valve operation model to obtain data analysis results; the valve operation model is a model built based on the historical operating data of the valve. The fault diagnosis and location module is used to generate a fault diagnosis report based on the data analysis results and a preset fault set; the fault diagnosis report includes the fault type and / or fault location. The alarm and display module is used to output the fault diagnosis report.

2. The system according to claim 1, characterized in that, The data acquisition module uses multiple sensors to monitor the valve's operating data; the operating data includes at least one of the following: the valve's on / off status, the valve's operating pressure, the valve's flow rate, the valve's temperature, and the valve's vibration information.

3. The system according to claim 1, characterized in that, The operational data includes the valve's internal pressure gradient and the valve's internal wear information; the data acquisition module is specifically used to acquire the valve's internal pressure gradient and the valve's internal wear information.

4. The method according to claim 3, characterized in that, The data acquisition module is specifically used to acquire the internal pressure gradient through pressure sensors installed at different parts of the valve, and to acquire the internal wear information through ultrasonic sensors or coating thickness monitoring devices installed at different parts of the valve.

5. The system according to claim 1, characterized in that, The data processing and analysis module is also used to establish the valve operation model based on the historical operation data and the clustering analysis algorithm; the clustering analysis algorithm is a weighted clustering algorithm based on distance and density.

6. The system according to claim 1, characterized in that, The data processing and analysis module is also used to periodically update the valve operation model based on the valve's operating data.

7. The system according to claim 1, characterized in that, The data processing and analysis module is specifically used to compare the running data with the running data of historical periods to obtain the data analysis results.

8. The system according to claim 1, characterized in that, The preset fault set includes at least one of the following: valve jamming, poor sealing, drive system failure, valve component deformation, and valve sealing surface damage.

9. The system according to claims 1-8, characterized in that, The fault diagnosis and location module is specifically used to analyze the data analysis results according to the time series analysis method and the fault threshold to obtain the fault diagnosis report.

10. A fault diagnosis method, applied to the fault diagnosis system as described in any one of claims 1-9, characterized in that, include: Periodically acquire the operating data of the valves in the compressor station; The operational data is transmitted to the central processing unit via a wireless network; The operational data transmitted to the central processing unit is input into the valve operation model to obtain data analysis results; the valve operation model is a model established based on the historical operational data of the valve. A fault diagnosis report is generated based on the data analysis results and the preset fault set. The fault diagnosis report includes the fault type and / or fault location; Output the fault diagnosis report.

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