Oil and gas field system-oriented wireless transmitter fault analysis method and system

Abstract

The invention discloses a wireless transmitter fault analysis method and system oriented to an oil and gas field system. The method comprises the following steps: S1, acquiring historical operation data of a wireless transmitter in the oil and gas field system; s2, determining a fault attribute node of the wireless transmitter; s3, determining a fault response coefficient of the wireless transmitter under the steady-state working condition, determining fault matching information of the wireless transmitter under the steady-state working condition, and further determining a fault offset domain when the working state of the wireless transmitter is transferred; s4, acquiring a fault identification feature of the wireless transmitter under the variable working condition, determining an unbalanced data segment of the wireless transmitter under the variable working condition through the fault identification feature and a compatible adjustment parameter of the wireless transmitter during operation instability, and further obtaining a dynamic compensation amount of the wireless transmitter during fault operation; s5, determining a response trend value when the wireless transmitter breaks down according to the fault offset domain and the dynamic compensation amount; and grading and classifying the faults of the wireless transmitter according to the response trend value.

Description

Technical Field

[0001] This application belongs to the field of sensor technology, and in particular relates to a method and system for fault analysis of wireless transmitters for oil and gas field systems. Background Technology

[0002] Sensors are devices used to sense, measure, and convert physical, chemical, or biological quantities into electrical signals or other readable data. This field encompasses the core technologies of sensors, including sensing principles (such as resistance, capacitance, optics, and magnetism), sensor material and structural design, signal processing techniques, and data conversion and transmission technologies. The key objective of sensor technology is to improve the accuracy, sensitivity, and reliability of measurements, while ensuring that sensors can operate stably under various environmental conditions. Sensors have a wide range of applications, covering multiple industries such as industrial automation, environmental monitoring, medical devices, automotive electronics, and consumer electronics.

[0003] In existing fault analysis methods for wireless transmitters in oil and gas field systems, the process involves several steps. First, operational data from the wireless transmitter is collected using conventional monitoring methods, and preliminary statistical analysis is performed to identify potential anomalies. Second, fault diagnosis algorithms, such as model-based or data-driven methods, are used to analyze anomaly patterns in the data and determine the fault type. Next, rules or empirical rules are applied to locate the fault and analyze its causes. Finally, maintenance and repair are carried out based on the fault diagnosis results. However, existing wireless transmitter analysis methods rely on static data analysis and rules, resulting in a limited range of fault classification and grading methods. This leads to a lack of dynamic compensation for fault analysis under complex operating conditions, reducing the reliability of wireless transmitter fault analysis. Therefore, how to achieve adaptive grading for wireless transmitter fault analysis under complex operating conditions to improve the reliability of wireless transmitter fault analysis is a challenge facing the industry. Summary of the Invention

[0004] The purpose of this application is to overcome the problems of the prior art by disclosing a method and system for wireless transmitter fault analysis in oil and gas field systems, so as to realize adaptive classification of wireless transmitter fault analysis under complex operating conditions and improve the reliability of wireless transmitter fault analysis.

[0005] The objective of this application is achieved through the following technical solution:

[0006] A method for fault analysis of wireless transmitters in oil and gas field systems, the method comprising:

[0007] S1: Acquire historical operating data of wireless transmitters in oil and gas field systems;

[0008] S2: Perform outlier detection on the historical operating data to obtain multiple fault outliers, and then determine the fault attribute node of the wireless transmitter based on all fault outliers.

[0009] S3: Determine the fault response coefficient of the wireless transmitter under steady-state conditions, determine the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node, and then determine the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information.

[0010] S4: Obtain the fault identification features of the wireless transmitter under varying operating conditions. Determine the unbalanced data segment of the wireless transmitter under varying operating conditions by using the fault identification features and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation. Then, perform proximity compensation on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation.

[0011] S5: Determine the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount; classify the faults of the wireless transmitter according to the response trend value.

[0012] According to a preferred embodiment, in step S2, outlier detection is performed on the historical operating data to obtain multiple fault outliers, specifically including:

[0013] Identify outlier characteristics of wireless transmitters during faulty operation.

[0014] The historical operating data is analyzed and verified using the outlier characteristic factors to obtain multiple fault outliers.

[0015] According to a preferred embodiment, in step S2, determining the fault attribute node of the wireless transmitter based on all fault outliers specifically includes:

[0016] Determine the abnormal characteristic identifier when the wireless transmitter malfunctions based on all fault outliers.

[0017] The fault attribute structure of the wireless transmitter is determined by the abnormal feature identifier;

[0018] The fault attribute nodes of the wireless transmitter are extracted from the fault attribute structure.

[0019] According to a preferred embodiment, in step S3, determining the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node specifically includes:

[0020] The fault distribution dispersion is determined based on the fault response coefficient and the fault attribute nodes;

[0021] Determine the fault matching confidence of the wireless transmitter under steady-state conditions;

[0022] The fault matching information of the wireless transmitter under steady-state conditions is determined based on the fault distribution dispersion and the fault matching confidence.

[0023] According to a preferred embodiment, in step S3, determining the fault offset domain when the wireless transmitter undergoes a state transition based on the fault matching information specifically includes:

[0024] The steady-state operating range when the wireless converter undergoes a working state transition is determined based on the fault matching information.

[0025] Obtain the buffer deviation coefficient when the wireless transmitter undergoes a state transition;

[0026] The fault offset domain when the wireless transmitter undergoes a working state transition is determined based on the steady-state operating range and the buffer deviation coefficient.

[0027] According to a preferred embodiment, step S4, determining the unbalanced data segment of the wireless transmitter under varying operating conditions through the fault identification features and the compatibility adjustment parameters of the wireless transmitter during operational instability, specifically includes:

[0028] Extract the associated difference sequence of the wireless transmitter operating under varying conditions from the fault identification features;

[0029] The compatibility correction factor for fault identification of the wireless transmitter during operational instability is determined based on the compatibility adjustment parameters of the wireless transmitter during operational instability.

[0030] The unbalanced data segment of the wireless transmitter under varying operating conditions is determined based on the associated difference sequence and the compatibility correction factor.

[0031] According to a preferred embodiment, in step S4, performing proximity compensation on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation specifically includes:

[0032] Obtain the nearest missing index among the data in the unbalanced data segment;

[0033] Determine the fault update parameters for the wireless transmitter during fault operation;

[0034] The dynamic compensation amount of the wireless transmitter during fault operation is obtained by fitting the nearest missing index and the fault update parameters.

[0035] According to a preferred embodiment, in step S5, determining the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount specifically includes:

[0036] The fault judgment level when the wireless transmitter malfunctions is determined based on the fault offset domain.

[0037] The response trend characteristics of the wireless transmitter when a fault occurs are generated based on the dynamic compensation amount.

[0038] The response trend value when the wireless transmitter malfunctions is determined by the fault judgment level and the response trend characteristics.

[0039] According to a preferred embodiment, in step S1, the historical operating data of the wireless transmitter in the oil and gas field system is obtained by reading the database of the oil and gas field system.

[0040] On the other hand, this application also discloses:

[0041] A wireless transmitter fault analysis system for oil and gas field systems, the wireless transmitter fault analysis system being used to execute the aforementioned wireless transmitter fault analysis method, the wireless transmitter fault analysis system comprising:

[0042] The initialization module is used to acquire historical operating data of wireless transmitters in oil and gas field systems;

[0043] The fault attribute identification module is used to perform outlier detection on the historical operating data to obtain multiple fault outliers, and then determine the fault attribute node of the wireless transmitter based on all fault outliers.

[0044] The fault matching module is used to determine the fault response coefficient of the wireless transmitter under steady-state conditions, determine the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node, and then determine the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information.

[0045] The dynamic compensation module is used to acquire the fault identification characteristics of the wireless transmitter under varying operating conditions. Based on the fault identification characteristics and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation, the unbalanced data segment of the wireless transmitter under varying operating conditions is determined. Then, the unbalanced data segment is compensated for by proximity to obtain the dynamic compensation amount of the wireless transmitter during fault operation.

[0046] The fault classification module is used to determine the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount; and to classify the faults of the wireless transmitter according to the response trend value.

[0047] The aforementioned main solution and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application. Those skilled in the art, after understanding the solution of this application, will realize that there are many combinations based on the prior art and common general knowledge, all of which are technical solutions to be protected in this application, and will not be exhaustively listed here.

[0048] The beneficial effects of this application are:

[0049] This application acquires historical operating data of wireless transmitters in an oil and gas field system; performs outlier detection on the historical operating data to obtain multiple fault outliers, and then determines the fault attribute nodes of the wireless transmitter based on all fault outliers; determines the fault response coefficient of the wireless transmitter under steady-state conditions, and determines the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute nodes, and then determines the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information; acquires the fault identification features of the wireless transmitter under variable operating conditions, and determines the unbalanced data segment of the wireless transmitter under variable operating conditions based on the fault identification features and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation, and then performs proximity compensation on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation; determines the response trend value of the wireless transmitter when a fault occurs based on the fault offset domain and the dynamic compensation amount; and classifies the faults of the wireless transmitter according to the response trend value.

[0050] Therefore, this application demonstrates that dynamic compensation can be achieved for fault analysis of wireless transmitters under complex operating conditions. Specifically, by performing outlier detection and fault attribute node determination on historical operating data, faults in the wireless transmitter can be accurately identified and located, overcoming the problem of incomplete fault feature identification in traditional methods and ensuring the accuracy of fault diagnosis. Furthermore, through a dynamic compensation mechanism, proximity compensation is performed on unbalanced data segments, solving the problem of insufficient data processing under varying operating conditions in traditional methods. This method improves adaptability to changes in actual operating conditions, reduces data errors, and enhances the accuracy of fault analysis. By classifying faults according to response trend values, a systematic management of faults is achieved, solving the problems of coarse fault classification and insufficiently refined processing strategies in existing methods, making fault management more effective and orderly. Combining fault response coefficients, fault matching information, and fault offset domains provides real-time and accurate fault responses, improving the timeliness of fault response and processing in traditional methods. Finally, the faults of the wireless transmitter are classified according to the aforementioned response trend values. Attached Figure Description

[0051] Figure 1 This is a flowchart of a wireless transmitter fault analysis method for oil and gas field systems provided in this application;

[0052] Figure 2 This is a module structure diagram of a wireless transmitter fault analysis system for oil and gas field systems provided in this application. Detailed Implementation

[0053] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0054] This application discloses a method and system for fault analysis of wireless transmitters in oil and gas field systems. The core of this method is to acquire historical operating data of wireless transmitters in the oil and gas field system; perform outlier detection on the historical operating data to obtain multiple fault outliers, and then determine the fault attribute nodes of the wireless transmitter based on all fault outliers; determine the fault response coefficient of the wireless transmitter under steady-state conditions, and determine the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute nodes, and then determine the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information; acquire fault identification features of the wireless transmitter under variable operating conditions, and determine the unbalanced data segment of the wireless transmitter under variable operating conditions through the fault identification features and the compatibility adjustment parameters of the wireless transmitter when it becomes unstable; then perform proximity compensation on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation; determine the response trend value of the wireless transmitter when a fault occurs based on the fault offset domain and the dynamic compensation amount; and classify the faults of the wireless transmitter according to the response trend value. The above scheme can achieve adaptive grading for fault analysis of wireless transmitters under complex working conditions, thereby improving the reliability of fault analysis of wireless transmitters.

[0055] Example 1

[0056] To better understand the above technical solutions, a detailed explanation will be provided below with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in the figure, this is an exemplary flowchart of a wireless transmitter fault analysis method for oil and gas field systems according to this embodiment of the present application. The analysis method includes the following steps:

[0057] In step S1, historical operating data of the wireless transmitter in the oil and gas field system is acquired.

[0058] It should be noted that a wireless transmitter is a device used to collect physical parameters and transmit the data to a central control system via wireless communication. The function of this wireless transmitter is to enable remote monitoring and data transmission of distributed equipment.

[0059] In practice, the data acquisition module periodically reads the operating data of the wireless transmitter. The data can include parameters related to oil and gas field production, such as pressure, temperature, and flow rate. The data is transmitted to the central control system via wireless communication protocols (such as Zigbee, LoRa, etc.) and stored in the database of the oil and gas field system. The historical operating data of the wireless transmitter in the oil and gas field system can be obtained by reading the database. The reading method can be SQL query or NoSQL database for data management and retrieval, which is not limited here.

[0060] It should be noted that, in this application, historical operating data refers to a continuous set of data obtained by monitoring and recording the operating status of the wireless transmitter over a period of time. This data typically includes various operating parameters (such as pressure, temperature, flow rate, etc.) and equipment status information (such as start-up, stop, alarm, etc.), which are used to analyze the operating patterns of the equipment, identify faults, and predict performance changes. The aim is to provide a basis for fault diagnosis, performance evaluation, and maintenance decisions, and help improve the reliability and stability of the system.

[0061] In step S2, outlier detection is performed on the historical operating data to obtain multiple fault outliers, and then the fault attribute node of the wireless transmitter is determined based on all fault outliers.

[0062] In this embodiment, outlier detection of the historical operating data to obtain multiple fault outliers can be achieved through the following steps:

[0063] Identify outlier characteristics of wireless transmitters during faulty operation.

[0064] The historical operating data is analyzed and verified using the outlier characteristic factors to obtain multiple fault outliers.

[0065] In practice, the process begins by analyzing historical operating data of the wireless transmitter under both normal and fault conditions to identify key parameters that exhibit abnormal changes during fault occurrences. These parameters may include signal strength, data transmission delay, ambient temperature, pressure, and flow rate. Then, statistical analysis or machine learning algorithms, such as Principal Component Analysis (PCA) or feature selection techniques, are used to extract the feature values ​​that best reflect the fault state from these key parameters. By comparing data under normal and fault conditions, it is determined that certain feature values ​​show significant deviations under fault conditions, and these feature values ​​are designated as outlier factors. Next, outlier factors can be used as a criterion for outlier detection on the historical operating data of the wireless transmitter. The detection algorithm model can employ Isolation Forest, DBSCAN (density-based clustering), or Z-score-based statistical detection methods. Specifically, historical data is input into the outlier detection model, and based on the outlier factor, an anomaly score or outlier value is calculated for each data point. A reasonable threshold is then set; when the anomaly score exceeds the threshold, the data point is identified as a fault outlier. All detected outliers are then aggregated to obtain multiple fault outlier quantities.

[0066] It should be noted that, in this application, outlier characteristic factor refers to the key parameter used to identify obvious abnormal changes in the wireless transmitter during fault operation, which aims to locate and identify the potential fault state of the equipment; fault outlier quantity is the set of abnormal data points identified by analyzing historical operating data through outlier characteristic factor, which is used to quantify and locate the abnormal performance when the fault occurs, and to provide a basis for further fault diagnosis.

[0067] In this embodiment, determining the fault attribute node of the wireless transmitter based on all fault outliers can be achieved using the following steps:

[0068] Determine the abnormal characteristic identifier when the wireless transmitter malfunctions based on all fault outliers.

[0069] The fault attribute structure of the wireless transmitter is determined by the abnormal feature identifier;

[0070] The fault attribute nodes of the wireless transmitter are extracted from the fault attribute structure.

[0071] In practice, firstly, outliers with common or significant characteristics are identified among all fault outliers, including sudden changes in specific parameters, frequency anomalies, and numerical jumps. Then, cluster analysis (such as K-means clustering) or pattern recognition algorithms are used to group and classify all fault outliers, identifying the key features corresponding to each type of fault. Significant features for each type of fault are extracted through cluster centers or pattern matching; these features are the outlier identifiers. Next, correlation analysis is performed between the outlier identifiers and the operating parameters of the wireless transmitter to determine the correlation characteristics between the outlier identifiers and different components or functional modules of the transmitter. For example, a specific anomaly identifier may be mainly related to a fault in the data transmission module, while another identifier may be related to a fault in the sensor module. A multi-dimensional fault attribute structure model is then constructed, which associates the anomaly identifier with the various functional modules of the wireless transmitter, forming a hierarchical or network structure of faults, thus obtaining the fault attribute structure of the wireless transmitter. Finally, fault attribute nodes corresponding to specific functional modules or components are identified and extracted from the fault attribute structure, that is, the fault attribute structure is decomposed to determine the fault manifestation of each key functional module, and the fault manifestation is mapped to the corresponding fault attribute node.

[0072] It should be noted that, in this application, the abnormal feature identifier refers to the key features extracted by analyzing fault outliers, used to represent the characteristic behavior of the wireless transmitter under fault conditions, and can serve as a marker for fault identification, helping to quickly locate and diagnose faults; the fault attribute structure refers to the association model established based on the abnormal feature identifier, which shows the relationship between different fault characteristics of the wireless transmitter and its functional modules, providing an overall framework for fault identification, and helping to analyze and diagnose faults more systematically; the fault attribute node represents the key node in the fault attribute structure that characterizes the failure of a specific functional module or component, and can provide clear fault identification points, helping to locate the fault source and guide repair actions.

[0073] In step S3, the fault response coefficient of the wireless transmitter under steady-state conditions is determined, and the fault matching information of the wireless transmitter under steady-state conditions is determined based on the fault response coefficient and the fault attribute node. Then, the fault offset domain when the wireless transmitter undergoes a working state transition is determined based on the fault matching information.

[0074] In specific implementation, the fault response coefficient of the wireless transmitter under steady-state conditions can be determined in the following way: First, historical operating data of the wireless transmitter under normal and fault conditions needs to be collected. By comparing and analyzing these data under steady-state conditions, key parameters affecting the transmitter performance can be identified. Next, regression analysis or system identification technology is applied to establish a mathematical model between these parameters and the degree of fault. The coefficients of the model are the fault response coefficients, which represent the sensitivity and impact of parameter changes on the fault. Finally, through model verification and optimization, the accuracy and stability of the fault response coefficients are ensured, providing a reliable basis for fault prediction and diagnosis. In other embodiments, other methods can also be used to determine the fault response coefficients, which are not limited here.

[0075] It should be noted that, in this application, the fault response coefficient represents the sensitivity and impact of changes in specific key parameters on the occurrence of faults under the steady-state operating conditions of the wireless transmitter.

[0076] In this embodiment, determining the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node can be achieved through the following steps:

[0077] The fault distribution dispersion is determined based on the fault response coefficient and the fault attribute nodes;

[0078] Determine the fault matching confidence of the wireless transmitter under steady-state conditions;

[0079] The fault matching information of the wireless transmitter under steady-state conditions is determined based on the fault distribution dispersion and the fault matching confidence.

[0080] In practice, firstly, by combining the fault response coefficient and fault attribute nodes, the distribution of each fault under different operating parameters is calculated. This can be done using a linear fitting algorithm. Then, the dispersion of the distribution is analyzed to measure the relationship between the range of variation of the fault attribute nodes and the fault response coefficient. Standard deviation, coefficient of variation, or dispersion indices from statistics can be used to quantify the distribution dispersion of each fault, thus obtaining the fault distribution dispersion. Next, a preliminary confidence model is established, using the fault distribution dispersion as input. The confidence level of each fault attribute node is calculated based on historical data and statistical methods. Finally, by combining the fault distribution dispersion and the fault matching confidence level, the fault matching information of the wireless transmitter under steady-state conditions is comprehensively calculated. Weighted averaging or fuzzy logic methods can be used to combine the fault distribution dispersion and the matching confidence level, ultimately yielding the fault matching information.

[0081] It should be noted that, in this application, the fault distribution dispersion reflects the degree of dispersion of the distribution of fault attribute nodes on different parameters, and is used to assess the fluctuation range of each parameter when a fault occurs; the fault matching confidence represents the confidence of the degree of matching between a specific fault attribute node and the actual fault under steady-state conditions, and is used to quantify the reliability of fault prediction; the fault matching information represents the set of degree of matching evaluation of various faults of the wireless transmitter under steady-state conditions.

[0082] In this embodiment, determining the fault offset domain when the wireless transmitter undergoes a state transition based on the fault matching information can be achieved through the following steps:

[0083] The steady-state operating range when the wireless converter undergoes a working state transition is determined based on the fault matching information.

[0084] Obtain the buffer deviation coefficient when the wireless transmitter undergoes a state transition;

[0085] The fault offset domain when the wireless transmitter undergoes a working state transition is determined based on the steady-state operating range and the buffer deviation coefficient.

[0086] In practical implementation, firstly, fault matching information is analyzed to determine the steady-state operating characteristics of the wireless transmitter under different operating conditions. The steady-state operating range refers to the parameter range within which the transmitter maintains stable operation when switching between different operating states. Then, the fault matching information is segmented to identify the key parameter change ranges before and after state transitions. Boundary analysis or transition range analysis methods are used to determine the specific parameter value range within the key parameter change range that allows the wireless transmitter to maintain steady-state operation. This parameter value range is then taken as the steady-state operating range. Next, when the wireless transmitter undergoes a state transition, the system parameters will experience temporary fluctuations and adjustments. A buffer deviation coefficient is used to quantify the impact of these fluctuations on equipment stability. Further analysis of the parameters within the steady-state operating range identifies temporary deviations that occur during state transitions. Through statistical data analysis, the fluctuation amplitude of each parameter during the state transition process is calculated (the fluctuation amplitude can be represented by variance, which is not limited here). These fluctuations are then compared with the steady-state operating range to obtain the buffer deviation coefficient. Finally, using the steady-state operating range as a basis, the range of parameters related to the normal operation of the equipment is identified. The buffer deviation coefficient is then used to adjust the boundary of the steady-state operating range to reflect the fluctuations and anomalies that may occur during state transitions. By superimposing the steady-state operating range and the buffer deviation coefficient, the range of parameters that may fail during state transitions is determined, i.e., the fault offset domain.

[0087] It should be noted that, in this application, the steady-state operating range represents the parameter range within which the wireless transmitter can maintain stable operation during the transition of operating states, aiming to ensure stable operation of the equipment during the state transition process and reduce the occurrence of potential failures; the buffer deviation coefficient is used to quantify the parameter fluctuation amplitude of the wireless transmitter during the transition of operating states, which facilitates the assessment of the stability of the equipment during the state transition process; the fault offset domain is the parameter range within which the wireless transmitter may fail during the transition of operating states, determined by the steady-state operating range and the buffer deviation coefficient, which facilitates the prediction and prevention of possible equipment failures during the state transition process and improves the reliability of system operation.

[0088] In step S4, the fault identification features of the wireless transmitter under varying operating conditions are obtained. The unbalanced data segment of the wireless transmitter under varying operating conditions is determined by the fault identification features and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation. Then, the unbalanced data segment is compensated for by proximity to obtain the dynamic compensation amount of the wireless transmitter during fault operation.

[0089] In practice, obtaining fault identification features of a wireless transmitter under varying operating conditions can be achieved in the following way: First, operate the wireless transmitter under varying operating conditions and collect its operating data. This data should include various operating parameters (such as pressure, temperature, flow rate, etc.) and status information. Then, clean and standardize the data to remove noise and outliers to ensure data quality. Next, extract features that may be related to the fault from the collected data. Feature selection techniques, such as principal component analysis (PCA) or information gain analysis, can be used to identify key parameters related to the occurrence of the fault. These parameters can characterize abnormal patterns, trend changes, or inconsistencies in the data. Finally, analyze the extracted features to determine the feature identifiers that best reflect the fault state under varying operating conditions. These feature identifiers are usually determined through statistical analysis or pattern recognition algorithms (such as support vector machines (SVM) or decision trees), which are not limited here.

[0090] It should be noted that, in this application, fault identification features refer to key parameters or data patterns that reflect the fault status during the operation of the wireless transmitter, which facilitates providing clear indicators for fault diagnosis and prediction.

[0091] In this embodiment, determining the unbalanced data segment of the wireless transmitter under varying operating conditions by using the fault identification features and the compatibility adjustment parameters of the wireless transmitter during operational instability can be achieved through the following steps:

[0092] Extract the associated difference sequence of the wireless transmitter operating under varying conditions from the fault identification features;

[0093] The compatibility correction factor for fault identification of the wireless transmitter during operational instability is determined based on the compatibility adjustment parameters of the wireless transmitter during operational instability.

[0094] The unbalanced data segment of the wireless transmitter under varying operating conditions is determined based on the associated difference sequence and the compatibility correction factor.

[0095] In practice, firstly, fault identification features are used to analyze the operating data of the wireless transmitter under varying operating conditions. These features include key parameters or data patterns that reflect the fault state of the equipment. Then, by comparing the operating data under varying operating conditions with data under normal operating conditions, correlation differences are calculated. Statistical methods (such as mean difference, standard deviation), time series analysis, or other data mining techniques can be used to extract these differences. These correlation differences are then organized according to time or state order to obtain a correlation difference sequence. Next, the compatibility adjustment parameters of the wireless transmitter during operational instability are collected and analyzed. These parameters are used to adjust the equipment to adapt to varying operating conditions. The compatibility adjustment parameters can include… This process includes adjusting thresholds and setting correction factors. Then, by comparing the actual adjustment parameters during operational instability with the standard parameters under normal operating conditions, a compatibility correction factor is determined. Regression analysis or optimization algorithms can be used to quantify the impact of the compatibility correction factor. Finally, the associated difference sequence is combined with the compatibility correction factor to analyze the unbalanced portion of the data under varying operating conditions. Data fitting or compensation algorithms can be used to calculate the actual value of the unbalanced data segment. Based on the calculation results, compensation is applied to the unbalanced data segment of the wireless transmitter under varying operating conditions. Compensation algorithms can include interpolation, smoothing, or other data adjustment techniques to obtain the unbalanced data segment of the wireless transmitter under varying operating conditions.

[0096] It should be noted that, in this application, the associated difference sequence represents the sequence of data differences between the wireless transmitter under varying operating conditions and under normal operating conditions, characterizing the device's variation pattern under different operating conditions; the compatibility correction factor refers to the parameter used to adjust the fault identification process when the wireless transmitter becomes unstable; and the unbalanced data segment represents the abnormal data segment generated by the wireless transmitter under varying operating conditions due to faults or instability. These are used to identify and adjust the unbalanced portions of the data to improve the stability of equipment operation and the reliability of the data.

[0097] In this embodiment, the dynamic compensation amount of the wireless transmitter during fault operation can be obtained by performing proximity compensation on the unbalanced data segment using the following steps:

[0098] Obtain the nearest missing index among the data in the unbalanced data segment;

[0099] Determine the fault update parameters for the wireless transmitter during fault operation;

[0100] The dynamic compensation amount of the wireless transmitter during fault operation is obtained by fitting the nearest missing index and the fault update parameters.

[0101] In practice, firstly, missing data points or segments are identified in the unbalanced data segment, caused by faults or transmission problems. For each missing data point, its similarity or distance to surrounding known data points is calculated, and the calculation results are summarized to form a dataset of the nearest-negative missing index, i.e., the nearest-negative missing index. The nearest-negative missing index can be represented using statistical methods (such as mean difference, variance) or distance metrics (such as Euclidean distance, Manhattan distance). Secondly, the data of the wireless transmitter during fault operation is analyzed to determine the key parameters affecting equipment performance. These parameters may include temperature, pressure, flow rate, etc. Statistical analysis or machine learning algorithms are used to identify and calculate the main parameters affecting equipment performance during fault operation. Machine learning algorithms can be used to simulate the main parameters affecting equipment performance during fault operation, and the simulation results are used as the fault update parameters of the wireless transmitter during fault operation. Finally, the nearest-negative missing index and fault update parameters are combined, and regression analysis, interpolation, or other fitting techniques are used to establish a compensation model. This model is used to calculate the actual value of the missing data segment, and the actual value of the missing data segment is linearly fitted. The fitting result is used as the dynamic compensation amount of the wireless transmitter during fault operation, which will not be elaborated here.

[0102] It should be noted that, in this application, the proximity missing index is an indicator that measures the similarity or distance between missing data points in an unbalanced data segment and surrounding known data points; the fault update parameter is a key indicator used to describe the performance changes of the wireless transmitter during fault operation, which can accurately reflect the impact of the fault on the equipment and help to carry out effective dynamic compensation; the dynamic compensation amount is a measure for compensating the unbalanced data segment by fitting the proximity missing index and the fault update parameter, which aims to fill in the data missing, correct the data deviation caused by the fault, and ensure the accuracy and integrity of the data.

[0103] In step S5, the response trend value when the wireless transmitter malfunctions is determined based on the fault offset domain and the dynamic compensation amount; the faults of the wireless transmitter are classified and categorized according to the response trend value.

[0104] In this embodiment, determining the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount can be achieved through the following steps:

[0105] The fault judgment level when the wireless transmitter malfunctions is determined based on the fault offset domain.

[0106] The response trend characteristics of the wireless transmitter when a fault occurs are generated based on the dynamic compensation amount.

[0107] The response trend value when the wireless transmitter malfunctions is determined by the fault judgment level and the response trend characteristics.

[0108] In practical implementation, firstly, a fault judgment hierarchy is established based on the fault offset domain. The fault offset domain determines the severity of the equipment fault and its deviation from the normal operating state. Standards for fault judgment levels are established, such as minor fault, moderate fault, and severe fault. Based on the data in the fault offset domain, the severity of the equipment fault is determined, and thresholds for each level are set according to the range of the offset domain. The severity of the fault is then classified into different judgment levels according to the set thresholds. Secondly, dynamic compensation quantities are used to analyze the response behavior of the wireless transmitter under fault conditions, including the data change trend after compensation when the fault occurs. Response trend features are extracted from the dynamic compensation quantities, such as response time, changes in compensation effect, and data fluctuations, which will not be elaborated here. Finally, the fault judgment level (fault severity) and response trend features (dynamic response of the equipment) are comprehensively analyzed. Statistical methods or data models are used to calculate the final response trend value based on these data. Weighted average, regression analysis, and other methods can be used. The calculated response trend value is then verified to ensure its accuracy and reliability.

[0109] It should be noted that in this application, the fault judgment hierarchy is a hierarchical system used to classify the severity of faults, which facilitates the systematic assessment of the impact of faults; the response trend feature is characteristic data describing the response behavior of the wireless transmitter when a fault occurs; the response trend value is a quantitative value describing the response of the wireless transmitter when a fault occurs, which is used to quantify the response performance of the equipment under fault conditions and support fault handling and decision-making.

[0110] In practice, the classification of faults in the wireless transmitter based on the response trend value can be achieved in the following way: First, define the fault classification standard, which is determined based on the response trend value. For example, a threshold range can be set to classify the response trend value into "minor fault", "moderate fault" and "serious fault". Then, match the calculated response trend value with the set classification standard. According to the threshold range in which the response trend value is located, map the response trend value of each fault to the corresponding level, thus completing the classification of faults in the wireless transmitter. This will not be elaborated further here.

[0111] It should be noted that in this application, by classifying and categorizing the response trend values, a systematic assessment of faults is achieved, which helps to more effectively manage and handle faults of wireless transmitters, prioritize the handling of serious faults, optimize maintenance and repair strategies, and improve the reliability and operating efficiency of the equipment.

[0112] Therefore, this application demonstrates that dynamic compensation can be achieved for fault analysis of wireless transmitters under complex operating conditions. Specifically, by performing outlier detection and fault attribute node determination on historical operating data, faults in the wireless transmitter can be accurately identified and located, overcoming the problem of incomplete fault feature identification in traditional methods and ensuring the accuracy of fault diagnosis. Furthermore, through a dynamic compensation mechanism, proximity compensation is performed on unbalanced data segments, solving the problem of insufficient data processing under varying operating conditions in traditional methods. This method improves adaptability to changes in actual operating conditions, reduces data errors, and enhances the accuracy of fault analysis. By classifying faults according to response trend values, a systematic management of faults is achieved, solving the problems of coarse fault classification and insufficiently refined processing strategies in existing methods, making fault management more effective and orderly. Combining fault response coefficients, fault matching information, and fault offset domains provides real-time and accurate fault responses, improving the timeliness of fault response and processing in traditional methods. Finally, the faults of the wireless transmitter are classified according to the aforementioned response trend values. In summary, the technical solution adopted in this application can achieve adaptive grading for fault analysis of wireless transmitters under complex working conditions, thereby improving the reliability of fault analysis of wireless transmitters.

[0113] Example 2

[0114] Based on Example 1, this example discloses a wireless transmitter fault analysis system for oil and gas field systems, referencing... Figure 2 As shown in the figure, this is a schematic diagram of the analysis system according to this embodiment of the present application. The analysis system includes:

[0115] Initialization module 100 is used to acquire historical operating data of wireless transmitters in oil and gas field systems;

[0116] The fault attribute identification module 200 is used to perform outlier detection on the historical operating data to obtain multiple fault outliers, and then determine the fault attribute node of the wireless transmitter based on all fault outliers.

[0117] The fault matching module 300 is used to determine the fault response coefficient of the wireless transmitter under steady-state conditions, determine the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node, and then determine the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information.

[0118] The dynamic compensation module 400 is used to acquire the fault identification characteristics of the wireless transmitter under varying operating conditions, determine the unbalanced data segment of the wireless transmitter under varying operating conditions through the fault identification characteristics and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation, and then perform proximity compensation on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation.

[0119] The fault classification module 500 is used to determine the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount; and to classify the faults of the wireless transmitter according to the response trend value.

[0120] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products 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.

[0121] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0122] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for fault analysis of wireless transmitters in oil and gas field systems, characterized in that, The wireless transmitter fault analysis method includes: S1: Acquire historical operating data of wireless transmitters in oil and gas field systems; S2: Perform outlier detection on the historical operating data to obtain multiple fault outliers, and then determine the fault attribute node of the wireless transmitter based on all fault outliers. S3: Determine the fault response coefficient of the wireless transmitter under steady-state conditions, determine the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node, and then determine the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information. S4: Obtain the fault identification features of the wireless transmitter under varying operating conditions. Determine the unbalanced data segment of the wireless transmitter under varying operating conditions by using the fault identification features and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation. Then, perform proximity compensation on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation. S5: Determine the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount; classify the faults of the wireless transmitter according to the response trend value.

2. The wireless transmitter fault analysis method as described in claim 1, characterized in that, In step S2, outlier detection is performed on the historical operating data to obtain multiple fault outliers, specifically including: Identify outlier characteristics of wireless transmitters during faulty operation. The historical operating data is analyzed and verified using the outlier characteristic factors to obtain multiple fault outliers.

3. The wireless transmitter fault analysis method as described in claim 2, characterized in that, In step S2, determining the fault attribute nodes of the wireless transmitter based on all fault outliers specifically includes: Determine the abnormal characteristic identifier when the wireless transmitter malfunctions based on all fault outliers. The fault attribute structure of the wireless transmitter is determined by the abnormal feature identifier; The fault attribute nodes of the wireless transmitter are extracted from the fault attribute structure.

4. The wireless transmitter fault analysis method as described in claim 1, characterized in that, In step S3, determining the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node specifically includes: The fault distribution dispersion is determined based on the fault response coefficient and the fault attribute nodes; Determine the fault matching confidence of the wireless transmitter under steady-state conditions; The fault matching information of the wireless transmitter under steady-state conditions is determined based on the fault distribution dispersion and the fault matching confidence.

5. The wireless transmitter fault analysis method as described in claim 4, characterized in that, In step S3, determining the fault offset domain when the wireless transmitter undergoes a state transition based on the fault matching information specifically includes: The steady-state operating range when the wireless converter undergoes a working state transition is determined based on the fault matching information. Obtain the buffer deviation coefficient when the wireless transmitter undergoes a state transition; The fault offset domain when the wireless transmitter undergoes a working state transition is determined based on the steady-state operating range and the buffer deviation coefficient.

6. The wireless transmitter fault analysis method as described in claim 1, characterized in that, In step S4, determining the unbalanced data segment of the wireless transmitter under varying operating conditions through the fault identification characteristics and the compatibility adjustment parameters of the wireless transmitter during operational instability specifically includes: Extract the associated difference sequence of the wireless transmitter operating under varying conditions from the fault identification features; The compatibility correction factor for fault identification of the wireless transmitter during operational instability is determined based on the compatibility adjustment parameters of the wireless transmitter during operational instability. The unbalanced data segment of the wireless transmitter under varying operating conditions is determined based on the associated difference sequence and the compatibility correction factor.

7. The wireless transmitter fault analysis method as described in claim 6, characterized in that, In step S4, proximity compensation is performed on the unbalanced data segment to obtain the dynamic compensation amount of the wireless transmitter during fault operation. Specifically, this includes: Obtain the nearest missing index among the data in the unbalanced data segment; Determine the fault update parameters for the wireless transmitter during fault operation; The dynamic compensation amount of the wireless transmitter during fault operation is obtained by fitting the nearest missing index and the fault update parameters.

8. The wireless transmitter fault analysis method as described in claim 1, characterized in that, In step S5, determining the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount specifically includes: The fault judgment level when the wireless transmitter malfunctions is determined based on the fault offset domain. The response trend characteristics of the wireless transmitter when a fault occurs are generated based on the dynamic compensation amount. The response trend value when the wireless transmitter malfunctions is determined by the fault judgment level and the response trend characteristics.

9. The wireless transmitter fault analysis method as described in claim 1, characterized in that, In step S1, historical operating data of wireless transmitters in the oil and gas field system are obtained by reading the database of the oil and gas field system.

10. A wireless transmitter fault analysis system for oil and gas field systems, characterized in that, The wireless transmitter fault analysis system is used to execute the wireless transmitter fault analysis method according to any one of claims 1 to 9. The wireless transmitter fault analysis system includes: The initialization module is used to acquire historical operating data of wireless transmitters in oil and gas field systems; The fault attribute identification module is used to perform outlier detection on the historical operating data to obtain multiple fault outliers, and then determine the fault attribute node of the wireless transmitter based on all fault outliers. The fault matching module is used to determine the fault response coefficient of the wireless transmitter under steady-state conditions, determine the fault matching information of the wireless transmitter under steady-state conditions based on the fault response coefficient and the fault attribute node, and then determine the fault offset domain when the wireless transmitter undergoes a working state transition based on the fault matching information. The dynamic compensation module is used to acquire the fault identification characteristics of the wireless transmitter under varying operating conditions. Based on the fault identification characteristics and the compatibility adjustment parameters of the wireless transmitter when it is unstable during operation, the unbalanced data segment of the wireless transmitter under varying operating conditions is determined. Then, the unbalanced data segment is compensated for by proximity to obtain the dynamic compensation amount of the wireless transmitter during fault operation. The fault classification module is used to determine the response trend value when the wireless transmitter malfunctions based on the fault offset domain and the dynamic compensation amount; and to classify the faults of the wireless transmitter according to the response trend value.

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