Oil leakage detection system and method for oil charging equipment

Abstract

The invention relates to the field of equipment detection, in particular to an oil leakage detection system and method for oil charging equipment. The system comprises a hydrocarbon sensing cable, the center of the hydrocarbon sensing cable is provided with a sensing line, the outer side of the sensing line is wrapped with an adsorption part, the adsorption part is used for adsorbing leaked liquid hydrocarbon, and the sensing line is used for detecting resistance change; the hydrocarbon sensor is connected with the hydrocarbon sensing cable and is used for converting resistance change caused by adsorbed hydrocarbon into a digital signal and sending the digital signal to the sensor module; the sensor module communicates with the background system monitoring host and is used for detecting the oil leakage condition of the oil charging equipment; the background system monitoring host is used for receiving and processing signals sent by the sensor module. And missed inspection caused by insufficient manual inspection is reduced. According to the invention, the plurality of sensor modules are used for covering key parts of the oil filling equipment, especially areas which are difficult to observe, so that dead-angle-free monitoring is ensured.

Description

Technical Field

[0001] The invention relates to the field of equipment detection, and in particular to a system and method for detecting oil leakage of oil-filled equipment. Background Art

[0002] In the modern industrial field, oil-filled equipment such as transformers play a vital role in the power supply system. The normal operation of these equipment is essential to ensure the stability and safety of the power system. However, the oil leakage problem of oil-filled equipment has always been one of the main factors affecting equipment performance and environmental safety. Traditional oil leakage detection of oil-filled equipment mainly relies on regular manual inspections, which is not only inefficient but also difficult to detect hidden leakage points in time, especially for those located at high places or difficult to observe. In addition, since liquid hydrocarbons such as transformer oil are usually colorless, this further increases the difficulty of detection.

[0003] In the prior art, although some automated detection systems have been proposed, they often have some limitations. For example, some systems rely on a single type of sensor, which limits their ability to detect different types of oil leaks. In addition, these systems lack sufficient sensitivity and accuracy to detect small-scale leaks at an early stage. Some systems are also unable to adapt to changing environmental conditions, such as changes in temperature and humidity, which may lead to false alarms or missed alarms.

[0004] In addition, some existing systems also have deficiencies in data processing and analysis. They cannot effectively integrate data from multiple sensors, or lack advanced data analysis tools to identify and predict leakage trends. These limitations not only affect the reliability of the detection system, but also increase the operation and maintenance costs and the risk of equipment failure. For example, patent CN210014883U describes an oil leakage detection device for oil-filled equipment based on ultraviolet fluorescence method, which realizes rapid detection through the principle of reflected fluorescence after ultraviolet light irradiation, but may be limited by light conditions and equipment layout. Patent CN114062224A proposes an oil leakage detection system and method for oil-immersed power equipment, which uses ultraviolet light sources and shooting equipment to identify oil leakage, but may require additional equipment and complex image processing algorithms, and it is difficult to detect hidden leakage points in time and has poor adaptability to environmental conditions. Therefore, there is an urgent need for an oil leakage detection system and method for oil-filled equipment. Summary of the invention

[0005] The purpose of the present invention is to provide an oil leakage detection system and method for oil-filled equipment to solve the problems in the prior art of reliance on manual inspection, low detection efficiency, difficulty in timely detection of hidden leakage points, and poor adaptability to environmental conditions.

[0006] To achieve the above purpose, the following technical solution is adopted.

[0007] An oil leakage detection system for oil-filled equipment, comprising:

[0008] A hydrocarbon sensing cable, wherein a sensing line is arranged at the center, an adsorption part is wrapped around the outside of the sensing line, the adsorption part is used to adsorb leaked liquid hydrocarbons, and the sensing line is used to detect resistance changes;

[0009] a hydrocarbon sensor connected to the hydrocarbon sensing cable and used to convert the resistance change caused by the adsorbed hydrocarbons into a digital signal and send it to the sensor module;

[0010] The sensor module communicates with the background system monitoring host and is used to detect oil leakage of the oil-filled equipment;

[0011] The background system monitoring host is used to receive and process the signal sent by the sensor module.

[0012] Optionally, there are multiple hydrocarbon sensing cables, each of which is connected to a hydrocarbon sensor, multiple hydrocarbon sensors are connected to one sensor module, and multiple sensor modules communicate with the background system monitoring host.

[0013] Optionally, the sensing wire of the hydrocarbon sensing cable is provided with a braided layer on its outer layer, and the braided layer has gaps for the leaked liquid hydrocarbons to contact the sensing wire.

[0014] Optionally, the hydrocarbon sensor includes a signal conversion unit, a signal amplification circuit, an analog-to-digital converter, a signal processing algorithm and a communication module.

[0015] The signal conversion unit is used to convert the resistance change detected by the sensor cable into an electrical signal;

[0016] The output of the signal conversion unit is connected to the signal amplification circuit to enhance the strength of the electrical signal;

[0017] The output of the signal amplification circuit is connected to the analog-to-digital converter, for converting the enhanced analog electrical signal into a digital signal;

[0018] The signal processing algorithm is built into the microprocessor in the hydrocarbon sensor and is used to receive and analyze the digital signal and identify whether hydrocarbon leakage has occurred, and obtain the analysis result;

[0019] The communication module is used to send the analysis result of the microprocessor to the sensor module.

[0020] Optionally, the sensor module includes:

[0021] A sensor identification unit: used to assign a unique identifier to each hydrocarbon sensor and number it;

[0022] A data receiving unit: used for receiving a digital signal with a unique identifier from the hydrocarbon sensor;

[0023] Data processing unit: used to further analyze the received digital signal with a unique identifier to identify leakage events;

[0024] Intelligent alarm unit: used for sending out an alarm signal of corresponding level according to the severity and urgency of the event when a leakage event is detected, and indicating the position of the hydrocarbon sensor of the alarm;

[0025] Data storage unit: used to store processed leakage detection data and alarm records, including the unique identifier of the hydrocarbon sensor and event details;

[0026] Communication management unit: used to manage data transmission between the background system monitoring host.

[0027] Optionally, the background system monitoring host includes:

[0028] A data integration unit is used to receive analysis results and alarm signals from multiple sensor modules, perform centralized data integration, and obtain integrated data;

[0029] Advanced analytics engines for in-depth analysis of the integrated data, including trend analysis, predictive analysis, and correlation analysis to identify potential systemic problems or long-term maintenance needs;

[0030] Alarm verification and upgrade system, used to verify the alarm signal issued by the sensor module and adjust or confirm the alarm level according to the systemic risk assessment;

[0031] Decision support system, used to provide maintenance and response decision support based on the analysis results of the advanced analysis engine and the alarm verification results;

[0032] User interaction interface, used to provide visual display of system status, historical data analysis results, alarm records and maintenance suggestions;

[0033] Remote monitoring and control interface, used to remotely access the backend system monitoring host through a secure network connection for real-time monitoring and control;

[0034] Data storage and management unit, used to store long-term historical data, analysis results, alarm records and system logs, and support data retrieval, backup and recovery;

[0035] The system health monitoring unit is used to monitor the operating status of the background system monitoring host itself, including hardware status, software performance and network connection status.

[0036] Optionally, the background system monitoring host also includes an adaptive learning algorithm for automatically adjusting the sensitivity and alarm threshold of the sensor module according to historical leakage events and environmental changes, so as to optimize the response performance of the detection system.

[0037] A method for detecting oil leakage of oil-filled equipment comprises the following steps:

[0038] Install the hydrocarbon sensor cable at the leak-prone part of the oil-filled equipment and ensure that the sensor line and the adsorption part are correctly configured. Start the sensor module and the background system monitoring host, establish communication connection, and perform initial system configuration and calibration;

[0039] The hydrocarbon sensor collects a resistance change signal caused by the adsorption portion, and converts the signal into a digital signal and sends it to the sensor module;

[0040] In the sensor module, the received digital signals are numbered, the leakage events are analyzed and identified, and an alarm signal is issued;

[0041] The sensor module analysis and identification results and alarm signals are sent to the background system monitoring host for in-depth analysis to provide maintenance and response decision support.

[0042] Optionally, the step of analyzing and identifying the leakage event specifically includes:

[0043] receiving a digital signal from the hydrocarbon sensor and performing filtering and noise suppression processing to obtain a pre-processed digital signal;

[0044] Extract characteristic parameters related to oil leakage events from the pre-processed digital signals;

[0045] Establish a signal baseline under normal operating conditions based on historical data and environmental parameters;

[0046] Compare the extracted feature parameters with the signal baseline to detect abnormal changes in the digital signal;

[0047] Conduct correlation analysis of time series and spatial distribution of detected abnormal changes;

[0048] Use the preset threshold to judge the correlation analysis results and confirm whether a leakage event has occurred.

[0049] Optionally, the following steps are also included:

[0050] Collect historical leakage event data and environmental parameter data to obtain data sets for adaptive learning algorithm training and parameter adjustment;

[0051] Analyze the collected data sets, identify the characteristics of leakage events and patterns of environmental changes, and establish a characteristic model of leakage events. Based on the results of pattern recognition, automatically adjust the sensitivity and alarm threshold of the sensor module;

[0052] Under the adjusted sensitivity and alarm threshold settings, oil leakage of oil-filled equipment can be monitored in real time;

[0053] Evaluate the performance of the adjusted system in real-time monitoring, including sensitivity, accuracy and false alarm rate, and obtain evaluation results; iteratively optimize the parameters of the adaptive learning algorithm and the adjustment strategy of sensitivity and alarm threshold according to the effect evaluation results;

[0054] Update the optimized adaptive learning algorithm and parameters to the background system monitoring host and implement new adjustment strategies.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention monitors the oil leakage of the oil filling equipment in real time through an automated sensor system, reducing missed inspections caused by insufficient manual inspections. Multiple sensor modules are used to cover key parts of the oil filling equipment, especially those areas that are difficult to observe, to ensure monitoring without blind spots.

[0057] By using hydrocarbon sensing cables and dedicated hydrocarbon sensors, the detection sensitivity of liquid hydrocarbon leaks is significantly improved. The adsorption part of the sensing cable can effectively absorb the leaking oil, while the sensing line monitors the resistance changes in real time. These changes are directly converted into digital signals, providing accurate data input for the background system. This enables the system to respond quickly to even small leaks, improving the accuracy and reliability of detection.

[0058] By setting braids and gaps in the hydrocarbon sensing cable, the system can adapt to detection needs in different environmental conditions. The braid design not only enhances the durability of the sensing cable, but also allows liquid hydrocarbons to contact the sensing wire more effectively, thereby improving the performance of the system in changing environments.

[0059] The backend system monitoring host integrates an advanced analysis engine and decision support system, which enables the system to conduct in-depth analysis and processing of the collected data. This advanced data processing capability not only improves the accuracy of leak detection, but also helps predict potential maintenance needs and systemic problems, thereby providing more comprehensive decision support for operation and maintenance personnel.

[0060] The adaptive learning algorithm can automatically adjust the sensitivity and alarm threshold of the sensor module according to historical leakage events and environmental changes, allowing the system to continuously optimize its performance. This adaptive capability ensures that the system maintains efficient and accurate detection in long-term operation, while reducing false alarms and missed alarms, and improving the overall effectiveness of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of an oil leakage detection system for oil-filled equipment according to an embodiment of the present invention.

[0062] Figure 2 It is a schematic flow chart of the steps of an embodiment of a method for detecting oil leakage of oil-filled equipment according to the present invention. DETAILED DESCRIPTION

[0063] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0064] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0065] Example 1

[0066] like Figure 1 As shown, an oil leakage detection system for oil-filled equipment includes a hydrocarbon sensor cable, a hydrocarbon sensor, a sensor module and a background system monitoring host.

[0067] Specifically, the hydrocarbon sensor cable is a special sensor device, the core of which is a sensor wire designed to detect changes in resistance. The outer side of the sensor wire is wrapped with an adsorption part, which is made of a material with high adsorption capacity for liquid hydrocarbons and can efficiently adsorb liquid hydrocarbons leaked due to leakage of oil-filled equipment. This adsorption causes the resistance around the sensor wire to change, so it can be used to detect oil leakage.

[0068] The hydrocarbon sensor is a device connected to the hydrocarbon sensing cable, including a signal conversion unit, a signal amplification circuit, an analog-to-digital converter, and a communication module. The signal conversion unit is responsible for converting the resistance change detected by the sensing cable into an electrical signal. The signal amplification circuit enhances the strength of these electrical signals to ensure the stability and reliability of the signal during transmission. The analog-to-digital converter converts the enhanced analog electrical signal into a digital signal for subsequent digital processing. The communication module is responsible for sending the digital signal to the sensor module for further analysis.

[0069] The sensor module is a device that communicates with the backend system monitoring host. It is responsible for collecting digital signals from hydrocarbon sensors and performing preliminary data processing. The sensor module is able to time-stamp and perform preliminary analysis on the received signals to determine if there are signs of oil leakage. The sensor module is also responsible for sending the processed data to the backend system monitoring host for more in-depth analysis and decision support.

[0070] The backend system monitoring host is the central processing unit of the system, responsible for receiving data from the sensor modules and performing advanced data analysis. It includes a data integration unit, an advanced analysis engine, an alarm verification and upgrade system, a decision support system, a user interface, a remote monitoring and control interface, a data storage and management unit, and a system health monitoring unit. The backend system monitoring host can integrate data from multiple sensor modules and perform trend analysis, predictive analysis, and correlation analysis to identify potential systemic problems or long-term maintenance needs. It can also verify the alarm signals issued by the sensor modules and adjust or confirm the alarm levels based on systemic risk assessments. In addition, the backend system monitoring host provides a user interface that enables operation and maintenance personnel to monitor the system status in real time, view historical data analysis results, alarm records, and maintenance recommendations. The remote monitoring and control interface allows authorized operation and maintenance personnel to remotely access the backend system monitoring host through the network for real-time monitoring and control. The data storage and management unit is responsible for storing long-term historical data, analysis results, alarm records, and system logs, and supports data retrieval, backup, and recovery. The system health monitoring unit is used to monitor the operating status of the backend system monitoring host itself to ensure the stable operation of the system.

[0071] The hydrocarbon sensing cable is installed in the leak-prone parts of the oil-filled equipment, such as the flange of the high-pressure bushing riser, the flange of the pressure relief valve, the core clamp down-lead copper bar porcelain column riser, the upper and lower oil tank flanges, the bottom radiator oil pipe flange, the emergency oil drain valve flange and the lower edge of the manhole cover. The adsorption part of the sensing cable is in close contact with the surface of the equipment to ensure that any leaked liquid hydrocarbons can be adsorbed. The sensing cable is connected to the sensor module through the hydrocarbon sensor, and the sensor module further communicates with the background system monitoring host.

[0072] When liquid hydrocarbons are adsorbed by the adsorption part, the resistance detected by the sensing line changes, and this change is captured by the hydrocarbon sensor and converted into an electrical signal. After amplification and analog-to-digital conversion, the signal is sent to the sensor module in the form of a digital signal. The sensor module pre-processes and preliminarily analyzes the received digital signal to identify possible leakage events. Once signs of leakage are detected, the sensor module sends this information to the background system monitoring host.

[0073] The backend system monitoring host receives data from the sensor module and uses its advanced analysis engine to perform in-depth analysis. It compares the analysis results with historical data and environmental parameters to determine whether there is a real leakage event. If a leakage is confirmed, the backend system monitoring host will trigger the corresponding alarm through the alarm verification and upgrade system, and provide a decision support system to help operation and maintenance personnel formulate response measures. At the same time, the user interaction interface will display relevant alarm information and maintenance suggestions, and the remote monitoring and control interface allows operation and maintenance personnel to remotely access the system for real-time monitoring and control. All relevant data and event records will be stored by the data storage and management unit for future analysis and auditing.

[0074] In addition, the adaptive learning algorithm in the background system monitoring host automatically adjusts the sensitivity and alarm threshold of the sensor module according to historical leakage events and environmental changes to optimize the response performance of the detection system. This ensures that the system can adapt to different operating conditions and continuously improve its detection accuracy and efficiency.

[0075] Through this specific implementation method, the present invention provides an efficient, reliable and intelligent oil leakage detection system for oil-filling equipment, which can significantly improve the operating safety and environmental friendliness of the oil-filling equipment, while reducing operation and maintenance costs and workload.

[0076] Example 2

[0077] As a preferred example, there are multiple hydrocarbon sensing cables, each hydrocarbon sensing cable is connected to a hydrocarbon sensor, multiple hydrocarbon sensors are connected to a sensor module, and multiple sensor modules communicate with a background system monitoring host.

[0078] The oil-filled equipment oil leakage detection system is deployed through multiple hydrocarbon sensor cables to achieve comprehensive monitoring of potential leakage points in oil-filled equipment. These sensor cables are configured in different parts of the oil-filled equipment, such as oil tanks, radiators, casings, valves and other key areas to ensure comprehensive coverage and monitoring.

[0079] The oil-filled equipment oil leakage detection system is deployed through multiple hydrocarbon sensor cables to achieve comprehensive monitoring of potential leakage points in oil-filled equipment. These sensor cables are configured in different parts of the oil-filled equipment, such as oil tanks, radiators, casings, valves and other key areas to ensure comprehensive coverage and monitoring.

[0080] Each sensing cable is wrapped or fixed to a specific location on the oil-filled equipment, such as the bottom of the tank, the edge of the radiator, the joint of the casing, etc., to maximize the possibility of adsorbing and detecting liquid hydrocarbons. The arrangement of the sensing cables takes into account the geometry of the equipment and potential leak paths.

[0081] The adsorption part in the sensing cable is made of high-performance materials to ensure high adsorption of various liquid hydrocarbons. The sensing wire fits tightly with the adsorption part to accurately detect the resistance change caused by adsorption.

[0082] Each sensing cable is connected to a dedicated hydrocarbon sensor. This connection is achieved via an electrical interface, ensuring accurate signal transmission.

[0083] The hydrocarbon sensor is responsible for converting the resistance changes detected by the sensing cable into electrical signals and converting these signals into digital signals through the built-in signal amplification circuit and analog-to-digital converter.

[0084] The sensor module integrates multiple hydrocarbon sensors and is responsible for collecting, preprocessing and preliminarily analyzing the digital signals sent by these sensors. The sensor module analyzes the received digital signals through the internal data processing unit to identify possible leakage events. Confirmed leakage events and related data are sent to the background system monitoring host through the communication management unit.

[0085] The backend system monitoring host receives data from multiple sensor modules and uses its data integration unit for centralized processing. The advanced analysis engine conducts in-depth analysis of this data to identify and predict leakage trends.

[0086] Once a leakage event is detected, the background system monitoring host triggers the corresponding alarm through the alarm verification and upgrade system, and provides a decision support system to help operation and maintenance personnel formulate response measures.

[0087] The user interface allows maintenance personnel to monitor system status in real time, view historical data analysis results, alarm records and maintenance suggestions. The remote monitoring and control interface allows maintenance personnel to remotely access the backend system monitoring host through a secure network connection.

[0088] The oil leakage detection system for oil-filled equipment of the present invention can realize comprehensive, efficient and accurate monitoring of oil-filled equipment, timely discover and respond to leakage events, and ensure the safe and stable operation of the equipment.

[0089] Example 3

[0090] As a preferred example, the outer layer of the sensing wire of the hydrocarbon sensing cable is provided with a braided layer, and the braided layer has gaps for the leaked liquid hydrocarbon to contact the sensing wire.

[0091] Specifically, the sensing wire is tightly wrapped in the braided layer, and its position and gap configuration are optimized to ensure that oil leakage in any direction can be quickly detected. The adsorption part uses a polymer material with high adsorption to liquid hydrocarbons. This material can quickly form an adsorption complex when in contact with oil molecules, resulting in a change in resistance. The sensing cable is installed in key parts of the oil-filled equipment, such as the flange of the high-pressure bushing riser, the flange of the pressure relief valve, the copper bar porcelain column riser of the core clamp lead-down wire, the flange of the upper and lower oil tanks, the flange of the bottom radiator oil pipe, the flange of the emergency drain valve and the lower edge of the manhole cover. These locations are common places for oil leakage, so installing them here can maximize the coverage and efficiency of detection. The sensing cable is installed in the flange position of the equipment prone to oil leakage by winding, and a magnetic buckle is used to fix it every 0.2m to ensure that the cable is stable and tightly attached to the monitoring surface. Each sensing cable is connected to the sensor module through its connected hydrocarbon sensor. The sensor module is responsible for collecting data from each sensor and performing preliminary processing. The sensor module sends the processed data to the background system monitoring host through the communication management unit, and the background system performs further data analysis and decision support.

[0092] The oil leakage detection system for oil-filled equipment of the present invention can realize comprehensive, efficient and accurate monitoring of oil-filled equipment, timely discover and respond to leakage events, and ensure the safe and stable operation of the equipment. In addition, the design of the braided layer and the selection of the installation position enable the system to adapt to various environmental conditions, thereby improving the practicality and reliability of the system.

[0093] Example 4

[0094] As a preferred example, the hydrocarbon sensor includes a signal conversion unit, a signal amplification circuit, an analog-to-digital converter, a signal processing algorithm and a communication module.

[0095] Specifically, the hydrocarbon sensor includes: a signal conversion unit, which is used to convert the resistance change detected by the sensor cable into an electrical signal; and convert the physical change of the sensor cable into a processable electrical signal.

[0096] The signal amplification circuit is connected to the output of the signal conversion unit and is used to enhance the strength of the electrical signal; the enhanced electrical signal improves the quality of the signal and provides a clearer input for subsequent analog-to-digital conversion.

[0097] The analog-to-digital converter is connected to the output of the signal amplifying circuit and is used to convert the enhanced analog electrical signal into a digital signal; the digital signal is convenient for the microprocessor to analyze and process, providing accurate data for oil leakage detection.

[0098] Signal Processing Algorithm The microprocessor built into the hydrocarbon sensor executes a signal processing algorithm to analyze the digital signal and identify whether a hydrocarbon leak has occurred; through algorithm analysis, the sensor can intelligently identify and determine whether an oil leak has occurred.

[0099] The communication module is used to send the analysis results of the microprocessor to the sensor module for further data processing and decision support; it ensures effective communication between the sensor and the sensor module so that the detection results can be transmitted to the monitoring system in a timely manner.

[0100] Through the combination of signal conversion unit and signal amplification circuit, the system can detect small resistance changes, thereby improving the detection sensitivity of oil leakage. The signal amplification circuit ensures that a clear signal can be obtained even in a noisy environment, reducing the possibility of false alarms and missed alarms. The built-in microprocessor and signal processing algorithm enable the sensor to perform intelligent data analysis, improving the accuracy and response speed of the system. The communication module ensures real-time communication between the sensor and the sensor module, so that the test results can be quickly transmitted to the monitoring system, facilitating timely response measures. The design and implementation of the system take into account a variety of environmental conditions, so that the sensor can operate stably in different working environments. The design of the sensor module is easy to maintain and calibrate, reducing the maintenance cost and workload in long-term operation.

[0101] Through the combination of these technical features, the present invention not only improves the accuracy and efficiency of oil leakage detection of oil-filled equipment, but also enhances the reliability and practicality of the system, providing strong technical support for the safe management of oil-filled equipment.

[0102] Example 5

[0103] As a preferred example, the sensor module includes:

[0104] Sensor identification unit: used to assign a unique identifier to each hydrocarbon sensor and number it;

[0105] Data receiving unit: used for receiving a digital signal with a unique identifier from a hydrocarbon sensor;

[0106] Data processing unit: used to further analyze the received digital signal with a unique identifier to identify leakage events;

[0107] Intelligent alarm unit: used to send out an alarm signal of corresponding level according to the severity and urgency of the event when a leakage event is detected, and indicate the location of the hydrocarbon sensor of the alarm;

[0108] Data storage unit: used to store processed leak detection data and alarm records, including hydrocarbon sensor unique identifier and event details;

[0109] Communication management unit: used to manage data transmission between the background system monitoring host.

[0110] Specifically, the sensor module includes: a sensor identification unit for assigning a unique identifier to each hydrocarbon sensor and numbering it to ensure that the data of each sensor in the system can be accurately tracked and identified; each sensor has a unique identifier, making the source and processing of data more precise.

[0111] The data receiving unit is used to receive a digital signal with a unique identifier from a hydrocarbon sensor to ensure that the source of the signal is clear and facilitate subsequent processing; the received signal contains the unique identifier of the sensor, providing a basis for data analysis.

[0112] The data processing unit is used to further analyze the received digital signal with a unique identifier, identify leakage events, analyze signal changes through algorithms, and determine whether there is leakage; the data processing unit analyzes the signal and generates a report on the leakage status.

[0113] Intelligent alarm unit: When a leakage event is detected, it sends out an alarm signal of the corresponding level according to the severity and urgency of the event, and indicates the location of the hydrocarbon sensor that alarms; the intelligent alarm unit sends out an alarm according to the severity of the leakage and provides leakage location information for timely response.

[0114] The data storage unit is used to store processed leak detection data and alarm records, including hydrocarbon sensor unique identifiers and event details, to provide data support for historical data analysis and system optimization; all relevant data and events are recorded and stored for future review and analysis.

[0115] The communication management unit is used to manage data transmission between the background system monitoring host and ensure the stability and security of data transmission; the communication management unit ensures that data is accurately and securely transmitted to the background system for further analysis and decision-making.

[0116] The accuracy and traceability of sensor data are ensured by the allocation and use of unique identifiers. The data processing unit can perform in-depth analysis of the signal, improving the accuracy of leak detection. The intelligent alarm unit can issue appropriate alarms according to the severity of the leak, improving the response efficiency. The introduction of the data storage unit makes it possible to analyze and optimize the system's historical data. The communication management unit ensures the stability and security of data transmission and improves the overall reliability of the system.

[0117] These effects work together to make the oil leakage detection system for oil-filled equipment of the present invention more efficient, accurate and reliable in practical applications.

[0118] Example 6

[0119] As a preferred example, the background system monitoring host includes:

[0120] A data integration unit is used to receive analysis results and alarm signals from multiple sensor modules, perform centralized data integration, and obtain integrated data;

[0121] Advanced analytics engines for in-depth analysis of the integrated data, including trend analysis, predictive analysis, and correlation analysis to identify potential systemic problems or long-term maintenance needs;

[0122] Alarm verification and upgrade system, used to verify the alarm signal issued by the sensor module and adjust or confirm the alarm level according to the systemic risk assessment;

[0123] Decision support system, used to provide maintenance and response decision support based on the analysis results of the advanced analysis engine and the alarm verification results;

[0124] User interaction interface, used to provide visual display of system status, historical data analysis results, alarm records and maintenance suggestions;

[0125] Remote monitoring and control interface, used to remotely access the backend system monitoring host through a secure network connection for real-time monitoring and control;

[0126] Data storage and management unit, used to store long-term historical data, analysis results, alarm records and system logs, and support data retrieval, backup and recovery;

[0127] The system health monitoring unit is used to monitor the operating status of the background system monitoring host itself, including hardware status, software performance and network connection status.

[0128] Specifically, the background system monitoring host is designed as an integrated data processing and analysis platform, which receives data from various sensor modules through the data integration unit. These data include resistance change signals detected by the sensors, environmental parameters, and alarm signals generated by the sensor modules. The data integration unit integrates these scattered data streams into a unified data set, providing a basis for subsequent analysis.

[0129] The advanced analysis engine is the core of the backend system. It uses machine learning and data mining technology to conduct in-depth analysis of the integrated data. Through trend analysis, the engine can identify the performance trend of oil-filled equipment; predictive analysis predicts possible leakage events in the future; and correlation analysis identifies the correlation between different parameters, thereby providing a more comprehensive diagnosis of systemic problems.

[0130] The alarm verification and upgrade system verifies the alarm signals sent by the sensor module to ensure the accuracy of the alarm. The systematic risk assessment model evaluates the severity of the alarm and adjusts the alarm level based on the assessment results to ensure that the operation and maintenance personnel can give priority to the most urgent events.

[0131] The decision support system provides decision support for operation and maintenance personnel based on the analysis results of the advanced analysis engine and the alarm verification results. The system provides maintenance suggestions, response strategies and optimization plans to help operation and maintenance personnel develop effective maintenance plans and response measures.

[0132] The user interface provides an intuitive operation platform, enabling operation and maintenance personnel to monitor system status in real time, view historical data analysis results, alarm records and maintenance suggestions. The interface is designed with user experience in mind, ensuring the readability of information and the convenience of operation.

[0133] The remote monitoring and control interface allows maintenance personnel to remotely access the backend system monitoring host through the network. Through this interface, maintenance personnel can monitor and control the system from any location, improving the flexibility and response speed of the system.

[0134] The data storage and management unit is responsible for storing all data generated during system operation, including historical data, analysis results, alarm records and system logs. These data are used for long-term analysis, trend prediction and maintenance decisions. The data storage and management unit also supports data retrieval, backup and recovery, ensuring data security and reliability.

[0135] The system health monitoring unit monitors the operating status of the backend system monitoring host, including hardware status, software performance and network connection status. This unit can detect and report potential problems in a timely manner, allowing operation and maintenance personnel to take quick measures to prevent system failures.

[0136] The background system monitoring host of the present invention not only improves the accuracy and efficiency of oil leakage detection of oil filling equipment, but also enhances the reliability of the system and user experience.

[0137] Example 7

[0138] As a preferred example, the background system monitoring host also includes an adaptive learning algorithm for automatically adjusting the sensitivity and alarm threshold of the sensor module according to historical leakage events and environmental changes, so as to optimize the response performance of the detection system.

[0139] Specifically, the adaptive learning algorithm of the present invention can automatically adjust the working parameters of the sensor module according to the actual situation of the oil-filling device and environmental changes, thereby optimizing the response performance of the entire detection system and ensuring the safe and stable operation of the oil-filling device.

[0140] Data collection and preprocessing: Provide accurate input data for adaptive learning algorithms.

[0141] Feature extraction: Identify key features of leakage events to provide a basis for model training.

[0142] Adaptive learning algorithm training: Train a model that can predict leakage events.

[0143] Sensitivity and alarm threshold adjustment: Dynamically adjust sensor module parameters based on model prediction results.

[0144] Performance evaluation and optimization: Continuously improve the adaptive learning algorithm to ensure system performance.

[0145] Real-time monitoring and feedback: Apply algorithm adjustment results to real-time monitoring to improve system reliability.

[0146] Applying the adaptive learning algorithm to the oil leakage detection system of oil-filled equipment enables the system to dynamically adjust the working parameters of the sensor module according to historical data and real-time data. This adaptive adjustment mechanism improves the detection accuracy and response speed of the system, reduces false positives and false negatives, and improves the overall performance of the system.

[0147] Example 8

[0148] like Figure 2 As shown, a method for detecting oil leakage of oil-filled equipment comprises the following steps:

[0149] Install the hydrocarbon sensor cable at the leak-prone part of the oil-filled equipment and ensure that the sensor line and the adsorption part are correctly configured.

[0150] Specifically, the hydrocarbon sensor cable is installed at the leakage-prone parts of the oil-filled equipment, such as the high-pressure bushing riser flange, the pressure relief valve flange, the core clamp downlead copper bar porcelain column riser, the upper and lower oil tank flanges, the bottom radiator oil pipe flange, the emergency oil drain valve flange and the lower edge of the manhole cover, etc., and ensure that the sensor line and the adsorption part are correctly configured to effectively adsorb the leaked liquid hydrocarbons.

[0151] Start the sensor module and the background system monitoring host, establish communication connections, and perform initial system configuration and calibration;

[0152] Specifically, the sensor module and the background system monitoring host are started, a communication connection is established between the two via wireless or wired means, and initial system configuration and calibration are performed to ensure the accuracy of data transmission and the response performance of the system.

[0153] The hydrocarbon sensor collects a resistance change signal caused by the adsorption portion, and converts the signal into a digital signal and sends it to the sensor module;

[0154] Specifically, the hydrocarbon sensor collects resistance change signals caused by the adsorption portion, and the signal conversion unit converts these analog signals into digital signals, which are then sent to the sensor module through the communication module.

[0155] In the sensor module, the received digital signals are numbered, the leakage events are analyzed and identified, and an alarm signal is issued;

[0156] Specifically, in the sensor module, a data processing unit is used to pre-process and analyze the received digital signal, identify the leakage event caused by the resistance change, and number the signal according to the characteristics of the signal.

[0157] The sensor module analysis and identification results and alarm signals are sent to the background system monitoring host for in-depth analysis to provide maintenance and response decision support.

[0158] Specifically, when the sensor module identifies a leakage event, the intelligent alarm unit will issue an alarm signal of the corresponding level according to the severity and urgency of the event, and indicate the specific location of the alarm; at the same time, the analysis and identification results and the alarm signal will be sent to the background system monitoring host for in-depth analysis and decision support for maintenance response.

[0159] Specifically, first, the technician installs the hydrocarbon sensor cable at the predetermined monitoring position of the oil-filled equipment. During installation, ensure that the sensor line and the adsorption part are correctly configured so that they can maximize contact and adsorb the possible leaked liquid hydrocarbons.

[0160] After the installation is complete, start the sensor module and the background system monitoring host. Through the system's built-in boot program, establish a communication connection between the sensor module and the background system monitoring host, and perform necessary system configuration and calibration to ensure that the system can accurately detect and respond to leakage events.

[0161] During system operation, the hydrocarbon sensor continuously collects resistance change signals caused by the adsorption part. These signals are converted into digital signals by the signal conversion unit and sent to the sensor module through the communication module.

[0162] After the sensor module receives the digital signal, the data processing unit pre-processes and analyzes the received signal. Using the adaptive learning algorithm, the data processing unit can automatically adjust the sensitivity and alarm threshold according to historical data and environmental changes to optimize the response performance of the detection system.

[0163] Once the data processing unit identifies a leakage event, the intelligent alarm unit will issue an alarm signal of the corresponding level according to the severity of the event and indicate the specific location of the alarm. At the same time, the sensor module will send the analysis results and alarm signals to the background system monitoring host for in-depth analysis and decision support for maintenance response.

[0164] After receiving the data sent by the sensor module, the backend system monitoring host uses its advanced analysis engine to conduct in-depth analysis of the data to identify potential systemic problems or long-term maintenance needs. Based on the analysis results, the decision support system provides maintenance and response decision support to operation and maintenance personnel, helping them to develop effective maintenance strategies.

[0165] The oil leakage detection method for oil-filled equipment of the present invention not only improves the accuracy and efficiency of detection, but also optimizes the response performance of the system through an adaptive learning algorithm, thereby ensuring the safe and stable operation of the oil-filled equipment.

[0166] Example 9

[0167] As a preferred example, the step of analyzing and identifying leakage events specifically includes:

[0168] receiving a digital signal from a hydrocarbon sensor and performing filtering and noise suppression processing to obtain a pre-processed digital signal;

[0169] Extract characteristic parameters related to oil leakage events from the pre-processed digital signals;

[0170] Establish a signal baseline under normal operating conditions based on historical data and environmental parameters;

[0171] Compare the extracted feature parameters with the signal baseline to detect abnormal changes in the digital signal;

[0172] Conduct correlation analysis of time series and spatial distribution of detected abnormal changes;

[0173] Use the preset threshold to judge the correlation analysis results and confirm whether a leakage event has occurred.

[0174] Specifically, the sensor module receives digital signals from the hydrocarbon sensor through its communication management unit, which are direct responses of the sensor to the change in resistance after the adsorption part adsorbs liquid hydrocarbons.

[0175] The sensor module's data processing unit filters and performs noise suppression on the received digital signal. This step includes using digital filters to remove high-frequency noise and interference, and using signal smoothing techniques to reduce random fluctuations.

[0176] The data processing unit further analyzes the pre-processed signal to extract characteristic parameters related to the leakage event. These parameters may include statistical characteristics of resistance change, such as mean, variance, peak value, etc.

[0177] The advanced analytics engine on the backend system monitoring host uses historical data and environmental parameters to establish a signal baseline. This baseline reflects the expected range of sensor signals under normal operating conditions.

[0178] The data processing unit compares the extracted characteristic parameters with the signal baseline to identify any significant deviations that may indicate a leakage event.

[0179] The background system monitoring host conducts in-depth analysis of the detected abnormal changes, considering the correlation of time series and spatial distribution. This helps to distinguish between real leakage events and signal changes caused by other factors.

[0180] The advanced analysis engine uses preset thresholds to judge the correlation analysis results. These thresholds are determined based on historical data analysis and expert knowledge to ensure high sensitivity and low false alarm rate of the system.

[0181] Once a leakage event is determined, the alarm verification and upgrade system of the background system monitoring host triggers an alarm. The alarm signal is displayed through the user interface and can be sent to the operation and maintenance personnel through the remote monitoring and control interface.

[0182] The data storage and management unit records alarm events and related data and generates detection reports. These records and reports can be used for post-analysis and maintenance decisions.

[0183] Example 10

[0184] As a preferred example, the method further includes the following steps:

[0185] Collect historical leakage event data and environmental parameter data to obtain data sets for adaptive learning algorithm training and parameter adjustment;

[0186] Analyze the collected data sets, identify the characteristics of leakage events and patterns of environmental changes, and establish a characteristic model of leakage events. Based on the results of pattern recognition, automatically adjust the sensitivity and alarm threshold of the sensor module;

[0187] Under the adjusted sensitivity and alarm threshold settings, oil leakage of oil-filled equipment can be monitored in real time;

[0188] Evaluate the performance of the adjusted system in real-time monitoring, including sensitivity, accuracy and false alarm rate, and obtain evaluation results; iteratively optimize the parameters of the adaptive learning algorithm and the adjustment strategy of sensitivity and alarm threshold according to the effect evaluation results;

[0189] Update the optimized adaptive learning algorithm and parameters to the background system monitoring host and implement new adjustment strategies.

[0190] Specifically, the sensor module receives digital signals from the hydrocarbon sensor through its communication management unit, which are direct responses of the sensor to the change in resistance after the adsorption part adsorbs liquid hydrocarbons.

[0191] The sensor module's data processing unit filters and performs noise suppression on the received digital signal. This step includes using digital filters to remove high-frequency noise and interference, and using signal smoothing techniques to reduce random fluctuations.

[0192] The data processing unit further analyzes the pre-processed signal to extract characteristic parameters related to the leakage event. These parameters may include statistical characteristics of resistance change, such as mean, variance, peak value, etc.

[0193] The advanced analytics engine on the backend system monitoring host uses historical data and environmental parameters to establish a signal baseline. This baseline reflects the expected range of sensor signals under normal operating conditions.

[0194] The data processing unit compares the extracted characteristic parameters with the signal baseline to identify any significant deviations that may indicate a leakage event.

[0195] The background system monitoring host conducts in-depth analysis of the detected abnormal changes, considering the correlation of time series and spatial distribution. This helps to distinguish between real leakage events and signal changes caused by other factors.

[0196] The advanced analysis engine uses preset thresholds to judge the correlation analysis results. These thresholds are determined based on historical data analysis and expert knowledge to ensure high sensitivity and low false alarm rate of the system.

[0197] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. An oil leakage detection system for oil-filled equipment, characterized in that: include, A hydrocarbon sensing cable, wherein a sensing line is arranged at the center, an adsorption part is wrapped around the outside of the sensing line, the adsorption part is used to adsorb leaked liquid hydrocarbons, and the sensing line is used to detect resistance changes; a hydrocarbon sensor connected to the hydrocarbon sensing cable and used to convert the resistance change caused by the adsorbed hydrocarbons into a digital signal and send it to the sensor module; The sensor module communicates with the background system monitoring host and is used to detect oil leakage of the oil-filled equipment; The background system monitoring host is used to receive and process the signal sent by the sensor module.

2. The oil leakage detection system for oil-filled equipment according to claim 1, characterized in that: There are multiple hydrocarbon sensor cables, each of which is connected to one hydrocarbon sensor, multiple hydrocarbon sensors are connected to one sensor module, and multiple sensor modules communicate with the background system monitoring host.

3. The oil leakage detection system for oil-filled equipment according to claim 1, characterized in that: The sensing wire of the hydrocarbon sensing cable is provided with a braided layer on its outer layer, and the braided layer has gaps for leaking liquid hydrocarbons to contact the sensing wire.

4. The oil leakage detection system for oil-filled equipment according to claim 1, characterized in that: The hydrocarbon sensor includes a signal conversion unit, a signal amplification circuit, an analog-to-digital converter, a signal processing algorithm and a communication module. The signal conversion unit is used to convert the resistance change detected by the sensor cable into an electrical signal; The output of the signal conversion unit is connected to the signal amplification circuit to enhance the strength of the electrical signal; The output of the signal amplification circuit is connected to the analog-to-digital converter, for converting the enhanced analog electrical signal into a digital signal; The signal processing algorithm is built into the microprocessor in the hydrocarbon sensor and is used to receive and analyze the digital signal and identify whether hydrocarbon leakage has occurred, and obtain the analysis result; The communication module is used to send the analysis result of the microprocessor to the sensor module.

5. The oil leakage detection system for oil-filled equipment according to claim 1, characterized in that: The sensor module comprises: A sensor identification unit, used to assign a unique identifier to each hydrocarbon sensor and number it; a data receiving unit, configured to receive a digital signal with a unique identifier from the hydrocarbon sensor; A data processing unit, used for further analyzing the received digital signal with the unique identifier to identify the leakage event; An intelligent alarm unit, for, when a leakage event is detected, issuing an alarm signal of a corresponding level according to the severity and urgency of the event, and indicating the location of the hydrocarbon sensor of the alarm; a data storage unit for storing processed leak detection data and alarm records, including the hydrocarbon sensor unique identifier and event details; The communication management unit is used to manage the data transmission between the background system monitoring host.

6. The oil leakage detection system for oil-filled equipment according to claim 1, characterized in that: The background system monitoring host includes: A data integration unit is used to receive analysis results and alarm signals from multiple sensor modules, perform centralized data integration, and obtain integrated data; Advanced analytics engines for in-depth analysis of the integrated data, including trend analysis, predictive analysis, and correlation analysis to identify potential systemic problems or long-term maintenance needs; Alarm verification and upgrade system, used to verify the alarm signal issued by the sensor module and adjust or confirm the alarm level according to the systemic risk assessment; Decision support system, used to provide maintenance and response decision support based on the analysis results of the advanced analysis engine and the alarm verification results; User interaction interface, used to provide visual display of system status, historical data analysis results, alarm records and maintenance suggestions; Remote monitoring and control interface, used to remotely access the backend system monitoring host through a secure network connection for real-time monitoring and control; Data storage and management unit, used to store long-term historical data, analysis results, alarm records and system logs, and support data retrieval, backup and recovery; The system health monitoring unit is used to monitor the operating status of the background system monitoring host itself, including hardware status, software performance and network connection status.

7. The oil leakage detection system for oil-filled equipment according to claim 1, characterized in that: The background system monitoring host also includes an adaptive learning algorithm for automatically adjusting the sensitivity and alarm threshold of the sensor module according to historical leakage events and environmental changes, and optimizing the response performance of the detection system.

8. A method for detecting oil leakage of oil-filled equipment, based on an oil leakage detection system for oil-filled equipment according to any one of claims 1 to 7, characterized in that: The following steps are included: Install the hydrocarbon sensor cable at the leak-prone part of the oil-filled equipment and ensure that the sensor line and the adsorption part are correctly configured; Start the sensor module and the background system monitoring host, establish communication connections, and perform initial system configuration and calibration; The hydrocarbon sensor collects a resistance change signal caused by the adsorption portion, and converts the signal into a digital signal and sends it to the sensor module; In the sensor module, the received digital signals are numbered, the leakage events are analyzed and identified, and an alarm signal is issued; The sensor module analysis and identification results and alarm signals are sent to the background system monitoring host for in-depth analysis to provide maintenance and response decision support.

9. A method for detecting oil leakage of oil-filled equipment according to claim 8, characterized in that: The steps to analyze and identify leakage incidents include: receiving a digital signal from the hydrocarbon sensor and performing filtering and noise suppression processing to obtain a pre-processed digital signal; Extract characteristic parameters related to oil leakage events from the pre-processed digital signals; Establish a signal baseline under normal operating conditions based on historical data and environmental parameters; Compare the extracted feature parameters with the signal baseline to detect abnormal changes in the digital signal; Conduct correlation analysis of time series and spatial distribution of detected abnormal changes; Use the preset threshold to judge the correlation analysis results and confirm whether a leakage event has occurred.

10. The method for detecting oil leakage of oil-filled equipment according to claim 8, characterized in that: The following steps are also included: Collect historical leakage event data and environmental parameter data to obtain data sets for adaptive learning algorithm training and parameter adjustment; Analyze the collected data sets, identify the characteristics of leakage events and patterns of environmental changes, and establish a characteristic model of leakage events; Automatically adjust the sensitivity and alarm threshold of the sensor module according to the results of pattern recognition; Under the adjusted sensitivity and alarm threshold settings, oil leakage of oil-filled equipment can be monitored in real time; Evaluate the performance of the adjusted system in real-time monitoring, including sensitivity, accuracy and false alarm rate, and obtain evaluation results; According to the effect evaluation results, iteratively optimize the parameters and sensitivity of the adaptive learning algorithm and the adjustment strategy of the alarm threshold; Update the optimized adaptive learning algorithm and parameters to the background system monitoring host and implement new adjustment strategies.

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