Equipment internal overheating fault monitoring system and method

Abstract

The invention belongs to the technical field of high-voltage power equipment monitoring, and particularly relates to an equipment internal overheating fault monitoring system and method, and the system comprises a fiber bragg grating direct temperature measurement module, a characteristic gas indirect diagnosis module, an intelligent fusion early warning module and a system reliability guarantee module. The fiber bragg grating direct temperature measurement module realizes accurate measurement of the temperature of an internal hot spot; the characteristic gas indirect diagnosis module assists in identifying the fault type by monitoring the insulating medium decomposition gas; the intelligent fusion early warning module constructs a diagnosis model based on multi-source data, and realizes fault trend prediction and dynamic threshold early warning; and the system reliability guarantee module ensures long-term stable operation in a severe environment. According to the method, the measurement precision reaches + / -0.3 DEG C, the fault type identification accuracy is greater than or equal to 92%, early warning can be carried out in advance, the average fault-free operation time of the system exceeds 8000 hours or above, the internal overheating fault occurrence rate and economic loss of the high-voltage power equipment can be effectively reduced, and safe and stable operation of a power grid is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of high-voltage power equipment monitoring technology, specifically relating to a system and method for monitoring internal overheating faults in equipment. Background Technology High-voltage power equipment (such as transformers, cable joints, GIS contacts, reactors, etc.) is the core equipment in the power grid's transmission, transformation, and distribution links. Its internal heating state is directly related to its insulation performance and service life. Internal overheating hidden faults refer to a type of fault in which local high temperatures are generated in critical parts of the equipment due to increased contact resistance, insulation aging, abnormal load, etc., while external temperature monitoring shows no obvious abnormalities.

[0002] Statistical data shows that a large number of sudden failures in high-voltage power equipment are caused by hidden internal overheating faults. Taking transformers as an example, for every 6°C increase in the hot spot temperature of the windings, the insulation life will be shortened by 50%. When the internal overheating temperature exceeds 140°C, the insulation paper will age and decompose rapidly, potentially causing major accidents such as insulation breakdown and oil explosions within hours. Traditional monitoring methods are unable to detect these hidden faults that are "cold on the surface but hot on the inside," often only being discovered after the fault has occurred, resulting in serious consequences such as large-scale power outages and equipment damage. The economic loss from a single fault can reach millions of yuan.

[0003] With the increasing peak-valley difference in power grid load and the intensified impact of new energy grid integration, high-voltage power equipment is frequently operating under heavy load and overload conditions, significantly increasing the risk of internal overheating. Traditional monitoring methods (such as external infrared thermography and oil temperature monitoring) can only reflect the surface or overall temperature of the equipment, failing to reach the core internal heat points; offline detection (such as DC resistance testing) has a long cycle (six months to one year) and requires power outages, making it difficult to track overheating trends in real time. Therefore, developing technical solutions that can directly monitor internal hotspots and accurately diagnose overheating faults has become a core requirement for ensuring the safe operation of the power grid. Summary of the Invention

[0004] This invention provides a system and method for monitoring internal overheating faults in equipment, which solves the problems in the prior art such as the inability to directly measure the temperature of internal hot spots, the difficulty in identifying the type of overheating fault, the inability to predict the development trend, and the failure of monitoring under special operating conditions.

[0005] It includes a fiber optic grating direct temperature measurement module, a characteristic gas indirect diagnostic module, an intelligent fusion early warning module, and a system reliability assurance module, with each module connected to the signal in sequence. The fiber optic grating direct temperature measurement module is used to directly measure the temperature of the core heating point inside high-voltage power equipment. The characteristic gas indirect diagnostic module is used to monitor the characteristic gases generated by the decomposition of the equipment's insulating medium, and to assist in identifying the type of overheating fault. The intelligent fusion early warning module is used to fuse multi-source data to build a diagnostic model, thereby enabling fault development trend prediction and dynamic threshold-based early warning. The system reliability assurance module is used to ensure that the system operates stably for a long time under harsh operating environments.

[0006] Based on the above embodiments, the fiber Bragg grating direct temperature measurement module further includes: The fiber optic grating sensor uses a core wavelength of 1550nm, a measurement range of -50℃ to 300℃, and an accuracy of ±0.3℃. Customized packaging is available for different equipment types: transformer windings are packaged with "insulating coating + metal skeleton" and pre-embedded between the winding coils; cable connectors are packaged with "embedded" and implanted between the conductor and the insulation layer; and GIS contacts are packaged with "probe type" and extend into the contact area through a dedicated interface. The signal transmission unit uses single-mode optical fiber as the transmission medium, and the signal transmission loss is <0.2dB / km; The data acquisition unit is equipped with a fiber optic grating demodulator with an adjustable sampling frequency of 1Hz to 10Hz. It converts the acquired wavelength change data into temperature values ​​through a wavelength-temperature calibration curve. The multi-point measurement unit places sensors at locations where equipment failures are frequent: one sensor is placed at the beginning, end, and middle of the high-voltage winding of the transformer, and one sensor is placed at the conductor connection point and the insulation shielding layer of the cable joint.

[0007] Based on the above technical features, the feature gas indirect diagnostic module further includes: The oil immersion equipment monitoring unit is equipped with an online oil chromatography monitoring device to detect characteristic gases dissolved in the oil, such as hydrogen (H2), methane (CH4), ethylene (C2H4), ethane (C2H6), and acetylene (C2H2), and to determine the superheat temperature level by the ratio of gas components. The gas insulation equipment monitoring unit is equipped with an SF6 gas decomposition product monitoring device to detect characteristic gases such as SO2, H2S, and CO, and to identify the cause of overheating by the type and concentration of the gas. The data calibration unit establishes a "temperature-gas production rate" correlation model and combines equipment load and operating years parameters to calibrate monitoring data, thereby improving the accuracy of fault diagnosis.

[0008] Based on the above technical features, the intelligent fusion early warning module further includes: The multi-dimensional data acquisition unit collects fiber optic grating temperature measurement data (hot spot temperature, temperature rise rate), characteristic gas data (component concentration, gas production rate), and equipment operating condition data (load rate, voltage, years of operation) to construct a multi-dimensional feature vector. The fault diagnosis model unit adopts an improved BP neural network algorithm and is trained based on more than 1,000 sets of laboratory simulated fault sample data. The fault type identification accuracy rate is over 92%, and it can identify overheating causes such as poor contact, insulation aging, and iron core grounding. The fault development assessment unit calculates the fault development index by combining the gas production rate and the temperature rise rate, and divides it into three stages: the budding stage (index < 0.3), the development stage (0.3 ≤ index < 0.7), and the critical stage (index ≥ 0.7). The dynamic threshold early warning unit establishes dynamic temperature thresholds based on the equipment insulation level and years of operation. The hot spot temperature threshold for new transformer windings is set at 110℃, while the threshold for old transformers that have been in operation for more than 10 years is lowered to 90℃. Three levels of early warning are set: Level 1 (emergency stage, pushing monitoring reports), Level 2 (development stage, arranging special inspections), and Level 3 (critical stage, emergency shutdown for maintenance).

[0009] Based on the above technical features, the system reliability assurance module further includes: The environmentally adaptable design unit features sensors encapsulated in oil-resistant and SF6-corrosion-resistant polytetrafluoroethylene, while the demodulator is housed in an IP65-rated enclosure. The redundant backup unit uses dual sensors for key measurement points and employs a dual-link backup of "fiber optic + wireless" for data transmission. The unit is calibrated periodically. The fiber optic grating sensor is calibrated for temperature every six months, and the oil chromatography and SF6 decomposition product monitoring devices are calibrated for accuracy every year.

[0010] The present invention also provides a method for monitoring internal overheating faults in equipment, comprising the following steps: S1: The temperature of the core heating point inside the high-voltage power equipment is directly measured through the fiber optic grating direct temperature measurement module to obtain hot spot temperature and temperature rise rate data. S2: Monitor the characteristic gases generated by the decomposition of the equipment's insulating medium through the characteristic gas indirect diagnostic module, and obtain data on gas component concentration and gas generation rate. S3: Collect equipment operating data through the intelligent fusion early warning module, integrate the data obtained in steps S1 and S2 to construct a multi-dimensional feature vector, input it into the improved BP neural network diagnostic model, identify the fault type and calculate the fault development index; S4: Establish dynamic temperature thresholds based on equipment insulation class and years of operation, and combine them with the fault development index to provide a three-level early warning; S5: The system reliability assurance module ensures long-term stable operation of each component and regularly calibrates equipment to ensure data accuracy.

[0011] Based on the above embodiments, in step S1, after the fiber optic grating sensor collects temperature data, it transmits the data to the demodulator through a single-mode fiber. The demodulator converts the wavelength change data into temperature values ​​with a measurement accuracy of ±0.3℃, which can capture minute temperature rises of more than 10℃.

[0012] Based on the above embodiments, in step S2, the oil-immersed equipment detects five characteristic gases using an oil chromatography monitoring device, and the gas-insulated equipment detects three characteristic gases using an SF6 decomposition product monitoring device, combined with the calibration data of the "temperature-gas production rate" correlation model.

[0013] Based on the above embodiments, in step S3, the improved BP neural network diagnostic model is trained on 1000+ sets of laboratory simulated fault samples, with a fault type identification accuracy of ≥92%, and the fault development index is calculated based on the gas production rate and the temperature rise rate.

[0014] Based on the above embodiments, in step S4, the dynamic temperature threshold is adjusted according to the equipment's operating years, and the three-level early warning corresponds to the fault initiation stage, development stage, and critical stage, respectively, and corresponding handling suggestions are pushed.

[0015] This invention employs a three-pronged technical approach: direct fiber optic grating temperature measurement, indirect characteristic gas diagnosis, and intelligent fusion early warning. This approach constructs a comprehensive, high-precision, and highly reliable internal overheating concealed fault monitoring system. The direct fiber optic grating temperature measurement module overcomes traditional monitoring blind spots, enabling direct measurement of internal hotspot temperatures. The characteristic gas indirect diagnosis module analyzes the decomposition products of the insulating medium to assist in identifying fault types. The intelligent fusion early warning module integrates multi-source data to achieve accurate prediction and dynamic early warning of fault trends. The system reliability assurance module ensures long-term stable operation in harsh environments through multiple protection designs.

[0016] The beneficial effects of this invention are: Precise measurement of internal hot spot temperature: The measurement accuracy reaches ±0.3℃, which can capture minute temperature rises of more than 10℃, solving the blind spot problem of "cold surface and hot interior" in traditional external temperature measurement; Accurate identification of overheating fault types: Combining temperature and characteristic gas data, the accuracy of fault type identification is ≥92%, avoiding blind repairs; Accurate prediction of fault development trends: The severity of overheating is quantified by the fault development index, and early warnings are issued 1-3 months in advance to allow sufficient time for maintenance. High system reliability: The sensor has a service life of more than 5 years in harsh environments such as oil immersion and SF6 gas, and the mean time between failures exceeds 8,000 hours. Significant economic and social benefits: greatly improves the reliability of power grid supply.

[0017] Other features and beneficial effects of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects of the invention and other beneficial effects may be realized and obtained by means of the structures particularly pointed out in the description and claims. Attached Figure Description

[0018] Figure 1 This is a structural diagram of the equipment's internal overheating fault monitoring system. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. The technical features designed in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be noted that all terms used in this invention (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and should not be construed as limiting the invention; it should be further understood that the terms used in this invention should be understood to have the same meaning as those in the context of this specification and in the relevant field, and should not be understood in an idealized or overly formal sense, except as expressly defined in this invention.

[0021] The present invention provides the following embodiments: like Figure 1 As shown, a high-voltage power equipment internal overheating fault monitoring system includes a fiber optic grating direct temperature measurement module, a characteristic gas indirect diagnostic module, an intelligent fusion early warning module, and a system reliability assurance module, with each module connected in sequence.

[0022] The fiber Bragg grating direct temperature measurement module employs a high-temperature resistant and interference-resistant fiber Bragg grating sensor to achieve direct measurement of internal hot spot temperatures. The sensor uses a core wavelength of 1550nm, with a measurement range of -50℃ to 300℃ and an accuracy of ±0.3℃. For 110kV oil-immersed transformers, the sensor uses an "insulating coating + metal frame" encapsulation and is pre-embedded at the beginning, middle, and end of the high-voltage winding. The signal is transmitted through single-mode optical fiber with a loss of <0.2dB / km, effectively avoiding strong electromagnetic interference. The fiber Bragg grating demodulator is deployed externally to the equipment, with an adjustable sampling frequency of 1Hz to 10Hz, converting wavelength change data into temperature values.

[0023] The characteristic gas indirect diagnostic module deploys an online oil chromatography monitoring device for oil-immersed equipment to detect characteristic gases dissolved in the oil, such as H2, CH4, C2H4, C2H6, and C2H2, and determines the overheating temperature level by using the C2H4 / C2H6 ratio. For gas-insulated equipment, it deploys an SF6 decomposition product monitoring device to detect gases such as SO2, H2S, and CO to identify the cause of overheating. The data calibration unit improves diagnostic accuracy by using a temperature-gas production rate correlation model combined with calibration data based on equipment load and years of operation.

[0024] The intelligent fusion early warning module's multi-dimensional data acquisition unit collects temperature measurement data, gas data, and operating condition data to construct a multi-dimensional feature vector; the fault diagnosis model unit adopts an improved BP neural network algorithm, trained based on 1000+ sets of laboratory simulation samples, including different models of equipment, which can accurately identify fault types such as poor contact and insulation aging, with an accuracy rate of ≥92%; the fault development assessment unit calculates the development index by combining the gas production rate and the temperature rise rate, and divides it into three stages; the dynamic threshold early warning unit adjusts the temperature threshold according to the equipment's operating years, sets three levels of early warning, and pushes handling suggestions.

[0025] The environmental adaptability design unit of the system reliability assurance module 4 adopts oil-resistant and corrosion-resistant encapsulation and IP65 protective shell to adapt to harsh environments; the redundancy backup unit ensures that data is not lost through dual sensors and dual transmission links; the periodic calibration unit calibrates the sensors every six months and the gas monitoring device every year to ensure data reliability.

[0026] In the application of this invention on a 110kV oil-immersed transformer, three fiber optic grating sensors are pre-embedded at the beginning, middle, and end of the high-voltage winding, respectively. An online oil chromatography monitoring device is installed on the oil tank. Each module is connected to the intelligent fusion early warning platform through a data transmission link to achieve 24-hour continuous monitoring.

[0027] The effectiveness of the present invention is verified through specific embodiments below: Example 1: Internal Overheat Monitoring of a 110kV Oil-Immersed Transformer Test conditions: A 110kV oil-immersed transformer (12 years of service life, rated capacity 63MVA) in a substation experienced localized overheating due to loose winding conductor joints. The hot spot temperature reached a maximum of 120℃, while the external oil tank temperature was only 38℃.

[0028] The monitoring system of this invention pre-embeds one fiber optic grating sensor at each of the beginning, end, and middle of the high-voltage winding of the transformer, deploys an online oil chromatography monitoring device, and connects to an intelligent fusion early warning platform.

[0029] Results: On the third day of system operation, the winding hot spot temperature was detected to rise from 75℃ to 90℃. Oil chromatography data showed an increase in CH4 and C2H4 content, with a gas production rate of 8% / month. On the fifth day, the fault type was identified as "poor conductor contact", with a development index of 0.4, and a level-two warning was issued. The maintenance personnel accurately located and tightened the loose joint, which took 2 hours. The equipment was restored to normal and there was no recurrence during the one-year trial period.

[0030] Example 2: Overheat monitoring inside a 10kV cable joint Test conditions: A 10kV cable line intermediate joint in an industrial park (5 years of operation, load rate fluctuation of 40%-95%) was overheated due to insulation aging, with the hot spot temperature reaching 110℃, while the external temperature was only 45℃.

[0031] The monitoring system of this invention embeds one fiber optic grating sensor at each of the conductor connection point and the insulating shielding layer, and deploys a data acquisition terminal to access the early warning platform.

[0032] Results: The system detected a conductor temperature of 95℃ (dynamic threshold 90℃) when the load rate was 85%, and issued a level one warning. Based on historical data, it was determined to be "insulation aging" with a development index of 0.5. Maintenance personnel replaced the connector during the off-peak period, which took 1.5 hours, preventing the fault from escalating. The system had no false alarms.

[0033] Example 3: Overheating Monitoring of 220kV GIS Contactors Test conditions: A 220kV GIS switchgear in a converter station (8 years of operation, rated current 3150A) overheated internally due to contact wear, reaching a temperature of 130℃, while the external cabinet temperature was 42℃ and the surrounding electromagnetic field strength was 25kV / m.

[0034] The monitoring system of this invention uses a probe-type fiber optic grating sensor installed on the GIS contact point, deploys an SF6 decomposition product monitoring device, and activates an anti-interference mode.

[0035] Results: On the 7th day of system operation, the contact temperature was detected to rise to 100℃, and the SO2 concentration in the SF6 decomposition products reached 2.5μL / L; on the 10th day, it was identified as "poor contact", with a development index of 0.8, and a level 3 warning was issued; the maintenance personnel urgently replaced the contact, which took 4 hours, without causing insulation damage, and the system data acquisition was stable and without distortion.

[0036] The results of the above embodiments show that the high-voltage power equipment internal overheating fault monitoring system and method of the present invention performs excellently in terms of internal hot spot temperature measurement accuracy, fault identification accuracy, early warning timeliness and system reliability, and has extremely high engineering feasibility and promotion value.

[0037] The sensors and hardware units used in the technical solution of this invention are commercially available products and are existing technologies. The results of the above embodiments demonstrate that the high-voltage power equipment internal overheating fault monitoring system and method of this invention possesses extremely high engineering feasibility and promotional value.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for monitoring internal overheating faults in equipment, characterized in that, It includes a fiber optic grating direct temperature measurement module, a characteristic gas indirect diagnostic module, an intelligent fusion early warning module, and a system reliability assurance module, with each module communicating with each other; The fiber optic grating direct temperature measurement module is used to directly measure the temperature of the core heating point inside high-voltage power equipment. The characteristic gas indirect diagnostic module is used to monitor the characteristic gases generated by the decomposition of the equipment's insulating medium, and to assist in identifying the type of overheating fault. The intelligent fusion early warning module is used to fuse multi-source data to build a diagnostic model, thereby enabling fault development trend prediction and dynamic threshold-based early warning. The system reliability assurance module is used to ensure that the system operates stably for a long time under harsh operating environments.

2. The equipment internal overheating fault monitoring system according to claim 1, characterized in that, The fiber Bragg grating direct temperature measurement module includes: The fiber optic grating sensor has a core wavelength of 1200~1550nm, a measurement range of -50℃~300℃, and an accuracy of ±0.3℃. The fiber optic grating sensor includes a transformer winding, a cable connector, and a GIS contact. The transformer winding is encapsulated with an insulating coating and a metal frame and embedded between the winding coils. The cable connector is embedded between the conductor and the insulating layer. The GIS contact is encapsulated with a probe and extends into the contact area. The signal transmission unit uses single-mode optical fiber as the transmission medium, and the signal transmission loss is <0.2dB / km; The data acquisition unit is equipped with a fiber optic grating demodulator with an adjustable sampling frequency of 1Hz to 10Hz. It converts the acquired wavelength change data into temperature values ​​through a wavelength-temperature calibration curve. The multi-point measurement unit includes temperature sensors placed at locations prone to equipment failure. The arrangement is as follows: one temperature sensor is placed at the beginning, end, and middle of the transformer winding, and one temperature sensor is placed at the conductor connection point of the cable joint and the insulation shielding layer.

3. The equipment internal overheating fault monitoring system according to claim 1, characterized in that, The characteristic gas indirect diagnostic module includes: The oil immersion equipment monitoring unit is equipped with an online oil chromatography monitoring device; the oil immersion equipment monitoring unit can detect characteristic gases such as hydrogen, methane, ethylene, ethane and acetylene dissolved in the oil, and determine the superheat temperature level by the ratio of gas components; The gas insulation equipment monitoring unit is equipped with an SF6 gas decomposition product monitoring device to detect characteristic gases such as SO2, H2S, and CO, and to identify the cause of overheating by the type and concentration of the gas. The data calibration unit establishes a "temperature-gas production rate" correlation model and combines equipment load and operating years parameters to calibrate monitoring data, thereby improving the accuracy of fault diagnosis.

4. The equipment internal overheating fault monitoring system according to claim 1, characterized in that, The intelligent fusion early warning module includes: The multi-dimensional data acquisition unit collects fiber optic temperature measurement data, characteristic gas data, and equipment operating condition data, and constructs feature vectors. The fault diagnosis model unit adopts an improved BP neural network algorithm and is trained based on more than 1,000 sets of laboratory simulated fault sample data. The fault type identification accuracy rate is over 92%, and it can identify the causes of poor contact, insulation aging, and overheating due to iron core grounding. The fault development assessment unit calculates the fault development index by combining the gas production rate and the temperature rise rate, and divides it into three stages: the budding stage, the development stage, and the critical stage. The dynamic threshold early warning unit establishes dynamic temperature thresholds based on the equipment insulation level and years of operation. The hot spot temperature threshold for new transformer windings is set at 110℃, while the threshold for old transformers that have been in operation for more than 10 years is lowered to 90℃. Three levels of early warning are set: Level 1, Level 2, and Level 3.

5. The equipment internal overheating fault monitoring system according to claim 1, characterized in that, The system reliability assurance module includes: The environmentally adaptable design unit features sensors encapsulated in oil-resistant and SF6-corrosion-resistant polytetrafluoroethylene, and the demodulator is housed in an IP65-rated enclosure. Redundant backup unit, with key measuring points using dual-sensor redundancy; The unit is calibrated periodically. The fiber optic grating sensor is calibrated for temperature every six months, and the oil chromatography and SF6 decomposition product monitoring devices are calibrated for accuracy every year.

6. A method for monitoring internal overheating faults in equipment, characterized in that, Includes the following steps: S1: The temperature of the core heating point inside the high-voltage power equipment is directly measured through the fiber optic grating direct temperature measurement module to obtain hot spot temperature and temperature rise rate data. S2: Monitor the characteristic gases generated by the decomposition of the equipment's insulating medium through the characteristic gas indirect diagnostic module, and obtain data on gas component concentration and gas generation rate. S3: Collect equipment operating condition data through the intelligent fusion early warning module, integrate the data obtained in steps S1 and S2 to construct a feature vector, input it into the improved BP neural network diagnostic model, identify the fault type and calculate the fault development index; S4: Establish dynamic temperature thresholds based on equipment insulation class and years of operation, and combine them with the fault development index to provide a three-level early warning; S5: The system reliability assurance module ensures long-term stable operation of each component and regularly calibrates equipment to ensure data accuracy.

7. The method for monitoring internal overheating faults in equipment according to claim 6, characterized in that, In step S1, after the fiber optic grating sensor collects temperature data, it transmits the data to the demodulator via a single-mode fiber. The demodulator converts the wavelength change data into temperature values ​​with a measurement accuracy of ±0.3℃, and can capture minute temperature rises above 10℃.

8. The method for monitoring internal overheating faults in equipment according to claim 6, characterized in that, In step S2, the oil-immersed equipment detects five characteristic gases using an oil chromatography monitoring device, and the gas-insulated equipment detects three characteristic gases using an SF6 decomposition product monitoring device, combined with the calibration data from the "temperature-gas production rate" correlation model.

9. The method for monitoring internal overheating faults in equipment according to claim 6, characterized in that, In step S3, the improved BP neural network diagnostic model is trained based on more than 1,000 sets of laboratory simulated fault samples, and the fault development index is calculated based on the gas production rate and the temperature rise rate.

10. The method for monitoring internal overheating faults in equipment according to claim 6, characterized in that, In step S4, the dynamic temperature threshold is adjusted according to the equipment's operating years, and the three-level early warning corresponds to the fault initiation stage, development stage, and critical stage, respectively, and pushes corresponding handling suggestions.

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