Aviation excitation system fault early warning method based on holographic current

Abstract

The invention relates to the technical field of excitation systems, in particular to an aviation excitation system fault early warning method based on holographic current. According to the method, the operation information of the aviation excitation system and multiple types of sensor data including high-bandwidth current, magnetic resistance, high-frequency voltage and infrared thermal imaging data are obtained firstly, holographic monitoring data are formed through preprocessing, component details and parameter changes are clearly captured, a traditional operation and maintenance information blind area is eliminated, and then through the holographic monitoring data and the operation information, the real-time monitoring of the aviation excitation system is achieved. Three-dimensional holographic current data are generated, the limitation of a single dimension is broken through, and a current track is constructed from the space-time dimension. And then electromagnetic coupling information is focused to extract abnormal features, so that fault analysis is turned to targeted positioning, and the recognition efficiency and accuracy are improved. Finally, fault types are determined based on abnormal characteristics, maintenance decision instructions are generated, passive operation and maintenance are converted into an active early warning closed-loop system, the processing time is shortened, potential anomalies are found in the early stage, the shutdown probability is reduced, and the power supply stability of aviation equipment is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of excitation system technology, and in particular to a fault early warning method for aerospace excitation systems based on holographic current. Background Technology

[0002] Holography is a technique that records and reproduces the complete wavefront information of an object, including amplitude and phase, based on the principles of interference and diffraction. Its core value lies in breaking through the two-dimensional limitations of traditional imaging and achieving three-dimensional global reconstruction of physical fields. In the aerospace field, this technological concept has been revolutionaryly extended to the electromagnetic field, providing a completely new approach to the state monitoring of complex electrical systems, thereby using holography to monitor current changes and obtain holographic current data.

[0003] As a core component of the aircraft power generation system, the excitation system's operational status directly impacts flight safety and operational efficiency. With the increasing integration and power density of aviation electrical systems, the failure modes of the excitation system are becoming increasingly complex. Traditional fault monitoring technologies are no longer sufficient to meet practical application requirements, revealing numerous technical bottlenecks that urgently need to be addressed.

[0004] High fault misdiagnosis rate: Existing technologies mostly rely on single electrical parameters, such as the effective value of current, for status judgment, ignoring the strong electromagnetic coupling relationship between internal components of the system. When a single fault causes multiple parameter linkage anomalies, such as a fault in the excitation regulator, it is easily misdiagnosed as a rotor fault, leading to misjudgment, significantly prolonging the maintenance cycle, and affecting normal flight operations;

[0005] Lack of fault prediction capability: Traditional technologies can only respond passively to faults that have already occurred, and cannot predict the development trend of faults or the remaining safe operating time of the system. This directly leads to situations where aircraft maintenance bases often experience sudden faults before takeoff due to the failure to provide early warning of potential risks. Summary of the Invention

[0006] The main objective of this invention is to provide a fault early warning method for aerospace excitation systems based on holographic current, aiming to solve the technical problems in the prior art.

[0007] This invention proposes a fault early warning method for aerospace excitation systems based on holographic current, comprising: Obtain operational information from the aircraft excitation system; Acquire sensor data, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data; The sensor data is preprocessed to obtain holographic monitoring data; Based on holographic monitoring data, operational information is monitored to obtain three-dimensional holographic current data of the aerospace excitation system; Electromagnetic coupling information is extracted from the three-dimensional holographic current data, and fault analysis is performed based on the electromagnetic coupling information to obtain multiple abnormal operation feature information. Based on multiple abnormal operation characteristics, obtain the corresponding fault type information and generate fault warning and maintenance decision instructions.

[0008] Preferably, the step of preprocessing the sensor data to obtain holographic monitoring data includes: Acquire multiple sensor data corresponding to multiple sensors, wherein the multiple sensors are distributed and arranged within the aerospace excitation system; Initial timestamp synchronization is performed on the data from multiple sensors, and a timestamp synchronization data matrix is ​​obtained; The installation location of the generator stator of the aerospace excitation system is obtained, and space field sensing nodes are deployed at the stator installation location to collect space electromagnetic field distribution data; The position of the rotor excitation winding lead of the aerospace excitation system is obtained, and time-domain sensing nodes are deployed at the lead positions to capture current waveform data; The key locations of the cooling duct of the aerospace excitation system are obtained, and frequency domain sensing nodes are deployed at the key locations of the duct to monitor harmonic energy distribution data. Holographic monitoring data is generated based on spatial electromagnetic field distribution data, current waveform data, and harmonic energy distribution data.

[0009] Preferably, the step of monitoring operational information based on holographic monitoring data to obtain three-dimensional holographic current data of the airborne excitation system includes: Obtain the corresponding spatial magnetic field distribution data based on the holographic monitoring data; Obtain the corresponding current density distribution information based on the spatial magnetic field distribution data; Obtain the corresponding transient current characteristic information based on the holographic monitoring data; Obtain the corresponding thermal current loss compensation information based on the holographic monitoring data; Three-dimensional holographic current data is obtained based on the current density distribution information, current transient characteristic information, and thermally induced current loss compensation information.

[0010] Preferably, the step of extracting electromagnetic coupling information from the three-dimensional holographic current data includes: Obtain the corresponding current density distribution information based on the three-dimensional holographic current data; Based on the current density distribution information, obtain the corresponding magnetic field strength information, and based on the magnetic field strength information, obtain the corresponding magnetic flux density change information; Obtain the corresponding eddy current loss information based on the magnetic flux density change information; The corresponding electromagnetic coupling strength information is obtained based on the eddy current loss information. Based on the electromagnetic coupling strength information, obtain the corresponding electromagnetic coupling region information; Obtain the corresponding electromagnetic coupling position information based on the electromagnetic coupling region information; The corresponding electromagnetic coupling information is obtained based on the electromagnetic coupling position information and the corresponding electromagnetic coupling strength information.

[0011] Preferably, the step of performing fault analysis based on electromagnetic coupling information to obtain multiple operational anomaly characteristic information includes: The electromagnetic coupling information of the excitation system is obtained, wherein the electromagnetic coupling information includes electromagnetic field strength data and current distribution data; The electromagnetic coupling information is analyzed in the time domain, frequency domain, and spatial domain to obtain the corresponding transient anomaly characteristic information, harmonic distortion characteristic information, and spatial distribution anomaly characteristic information. Anomaly monitoring is performed based on the transient anomaly characteristic information, harmonic distortion characteristic information, and spatial distribution anomaly characteristic information to obtain multiple operational anomaly characteristic information.

[0012] Preferably, the step of obtaining corresponding fault type information based on multiple operational anomaly characteristic information and generating fault early warning maintenance decision instructions includes: Based on multiple operational anomaly characteristics, insulation degradation level, eddy current loss intensity, and electromagnetic interference risk index are obtained; Based on the insulation degradation level, eddy current loss intensity, and electromagnetic interference risk index, obtain the corresponding excitation system fault type information; Based on the fault type information, obtain the corresponding fault location information; Predict the remaining safe operating time based on the fault type information and the corresponding fault location information; Based on the remaining safe operating time and the corresponding fault location information, a fault early warning and maintenance decision instruction is generated.

[0013] This application also provides a fault early warning system for an airborne excitation system based on holographic current, including: The first acquisition module acquires the operating information of the aircraft excitation system; The second acquisition module acquires sensor data, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data. The first processing module preprocesses the sensor data to obtain holographic monitoring data; The first monitoring module monitors operational information based on holographic monitoring data, and obtains three-dimensional holographic current data of the aerospace excitation system; The first extraction module extracts electromagnetic coupling information from the three-dimensional holographic current data and performs fault analysis based on the electromagnetic coupling information to obtain multiple abnormal operation feature information. The third acquisition module obtains the corresponding fault type information based on multiple abnormal operation feature information and generates fault early warning maintenance decision instructions.

[0014] Preferably, the first processing module includes: The first acquisition unit acquires multiple sensor data corresponding to multiple sensors, wherein the multiple sensors are distributed and arranged within the aerospace excitation system; The second acquisition unit performs initial timestamp synchronization on the data from the multiple sensors and acquires a timestamp synchronization data matrix. The third acquisition unit acquires the installation position of the generator stator of the aerospace excitation system and deploys a space field sensing node at the stator installation position to collect space electromagnetic field distribution data. The fourth acquisition unit acquires the position of the rotor excitation winding lead of the aerospace excitation system and deploys time-domain sensing nodes at the lead positions to capture current waveform data. The fifth acquisition unit acquires the key locations of the cooling duct of the aerospace excitation system and deploys frequency domain sensing nodes at the key locations of the duct to monitor harmonic energy distribution data. The first generation unit generates holographic monitoring data based on spatial electromagnetic field distribution data, current waveform data, and harmonic energy distribution data.

[0015] Preferably, the first monitoring module includes: The sixth acquisition unit acquires the corresponding spatial magnetic field distribution data based on the holographic monitoring data; The seventh acquisition unit acquires the corresponding current density distribution information based on the spatial magnetic field distribution data; The eighth acquisition unit acquires the corresponding transient current characteristic information based on the holographic monitoring data; The ninth acquisition unit acquires the corresponding thermal current loss compensation information based on the holographic monitoring data; The tenth acquisition unit acquires three-dimensional holographic current data based on the current density distribution information, current transient characteristic information, and thermally induced current loss compensation information.

[0016] Preferably, the first extraction module includes: The eleventh acquisition unit acquires the corresponding current density distribution information based on the three-dimensional holographic current data; The twelfth acquisition unit acquires the corresponding magnetic field strength information based on the current density distribution information, and acquires the corresponding magnetic flux density change information based on the magnetic field strength information; The thirteenth acquisition unit acquires the corresponding eddy current loss information based on the magnetic flux density change information; The fourteenth acquisition unit acquires the corresponding electromagnetic coupling strength information based on the eddy current loss information; The fifteenth acquisition unit acquires the corresponding electromagnetic coupling region information based on the electromagnetic coupling strength information. The sixteenth acquisition unit acquires the corresponding electromagnetic coupling position information based on the electromagnetic coupling region information. The seventeenth acquisition unit acquires the corresponding electromagnetic coupling information based on the electromagnetic coupling position information and the corresponding electromagnetic coupling strength information.

[0017] The beneficial effects of this invention are as follows: By integrating the operational information of the aviation excitation system and collecting high-bandwidth current, magnetoresistive, and other data through multiple sensors, this invention processes the data to form comprehensive and standardized holographic monitoring data. This clearly captures the operational details and key parameter changes of each component, eliminating the information blind spots of traditional operation and maintenance. On this basis, the holographic monitoring data is deeply integrated with the operational information to generate three-dimensional holographic current data that breaks through the limitations of a single dimension. This constructs the current trajectory from spatial and temporal dimensions. At the same time, it focuses on key information of electromagnetic coupling to accurately extract abnormal features, enabling fault analysis to shift from blind investigation to targeted localization, significantly improving the accuracy and efficiency of anomaly identification. Finally, based on the abnormal features, the fault type is determined and maintenance decision instructions are generated, opening up the entire chain of information acquisition, analysis and diagnosis, and decision execution. This transforms the traditional passive response operation and maintenance mode into a closed-loop system of proactive early warning and precise execution. This process effectively shortens fault handling time and avoids the risk of fault escalation. Through full-dimensional information control and precise fault analysis, potential system anomalies are detected early. Combined with timely maintenance instructions, small faults are prevented from evolving into large faults, significantly reducing the probability of system downtime or abnormal operation caused by excitation problems, and providing a solid guarantee for the overall power supply stability of aviation equipment. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] like Figure 1 As shown, this application provides a fault early warning method for an airborne excitation system based on holographic current, including: S1. Obtain the operating information of the aircraft excitation system; S2. Acquire sensor data, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data. S3. Preprocess the sensor data to obtain holographic monitoring data; S4. Based on holographic monitoring data, the operation information is monitored to obtain three-dimensional holographic current data of the aerospace excitation system; S5. Extract the electromagnetic coupling information from the three-dimensional holographic current data, and perform fault analysis based on the electromagnetic coupling information to obtain multiple abnormal operation feature information; S6. Obtain the corresponding fault type information based on multiple abnormal operation feature information and generate fault early warning maintenance decision instructions.

[0023] The aerospace excitation system involved in this invention is equipped with multiple sensors, including but not limited to a high-bandwidth current sensor, a magnetoresistive sensor, a high-frequency voltage sensor, and an infrared thermal imager. The high-bandwidth current sensor has a sampling rate ≥1MHz and is installed on the excitation winding and stator output terminal. It is used to collect dynamic characteristics such as instantaneous current values, peak values, and rise times, and also to capture microsecond-level fault pulses, such as instantaneous current spikes caused by insulation breakdown. Multiple magnetoresistive sensors can be deployed in an array around the rotor to collect spatial magnetic field distribution data with a resolution of 0.1mT. This spatial magnetic field distribution data can reflect magnetic field distortion problems caused by winding short circuits, rotor eccentricity, etc. The high-frequency voltage sensor has a bandwidth ≥10MHz and is connected to the regulator output terminal of the aerospace excitation system. It is used to record high-frequency voltage fluctuations, such as PWM signal distortion, and also to identify switching device faults. The infrared thermal imager has a frame rate ≥30fps and is used to capture the surface temperature field of the aerospace excitation system and the relationship between associated electrical anomalies and thermal states. Its resolution for capturing local hot spots is 1℃.

[0024] As described in step S1 above, the operating information of the airborne excitation system is obtained. The operating information includes multiple parameters and states that are necessary to maintain the operation of the airborne excitation system, such as the voltage information, current information, and operating status of each component. By obtaining the operating information of the airborne excitation system, the staff can gain a preliminary understanding of the overall operating status of the system and know whether the airborne excitation system is in a basic operating state, providing original information support for subsequent more in-depth data analysis.

[0025] As described in step S2 above, sensor data is acquired, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data. Existing technologies generally monitor faults directly based on operational information. However, relying solely on the basic operational information acquired in S1 cannot comprehensively reflect the specific operational details of each component in the system. For example, it cannot know the high-frequency changes in local current, the temperature distribution of components, etc. The information dimension is relatively singular, making it difficult to meet the needs of in-depth analysis. This results in inaccurate fault prediction in existing technologies. Therefore, this invention acquires multiple types of sensor data, characterizing the system's operation from multiple dimensions such as current, magnetoresistive data, voltage, and temperature. This allows personnel to understand the system's more detailed operational status, such as the real-time temperature of a component, high-frequency fluctuations in current, etc., comprehensively improving information coverage. Furthermore, different types of sensor data reflect the operational details of the aerospace excitation system from different angles. For example, high-bandwidth current data accurately reflects the dynamic changes in current, and infrared thermal imaging data can intuitively present the temperature of each component in the system. Compared to relying solely on the basic operational information in S1, S2 provides more comprehensive and detailed information, offering more sufficient data support for subsequent accurate analysis.

[0026] As described in step S3 above, the sensor data is preprocessed to obtain holographic monitoring data. After acquiring a large amount of sensor data in S2, this data may contain noise, redundancy, or inconsistent formats. Preprocessing operations, such as noise removal, data normalization, and integration of data from different formats, can eliminate invalid information, making the data cleaner and more standardized, thus ensuring the reliability of subsequent analyses based on this data. If unprocessed sensor data is used directly for analysis, the analysis results may be biased due to data errors. Processed sensor data is used as holographic monitoring data, which refers to a comprehensive monitoring data set covering the multi-dimensional operating status of the aerospace excitation system, meeting data quality standards, and possessing correlation. Its core lies in holography—by integrating and optimizing multi-source sensor data, a panoramic record of the operating status of the aerospace excitation system is achieved. This includes both quantitative data of key parameters and characteristic information of component states, providing standardized, high-quality basic data for subsequent three-dimensional holographic analysis and fault diagnosis.

[0027] As described in step S4 above, monitoring the operational information based on holographic monitoring data and obtaining three-dimensional holographic current data has the advantage of achieving in-depth data correlation. The three-dimensional holographic current data is not a simple data superposition, but a comprehensive data with spatial and temporal dimensions formed by deeply integrating the holographic monitoring data with the operational information obtained in S1. It can reflect the current operation in the aerospace excitation system from a more three-dimensional and comprehensive perspective. Compared with single-dimensional data, it can better reveal the inherent laws of system operation and provide a high-quality data carrier for subsequent extraction of key information. The three-dimensional holographic current data refers to a set of three-dimensional current information that is formed in step S4 after multi-dimensional analysis and reconstruction of the system operation information based on holographic monitoring data, with current as the core and integrating spatial distribution, temporal dynamics and related states.

[0028] As described in step S5 above, electromagnetic coupling information is extracted from the three-dimensional holographic current data, and fault analysis is performed based on this information to obtain multiple operational anomaly features. The advantage of extracting electromagnetic coupling information from the three-dimensional holographic current data and performing fault analysis accordingly is that it focuses on key information and can accurately locate the root cause of the anomaly. Electromagnetic coupling is a critical link in the operation of the aerospace excitation system, and many faults are related to electromagnetic coupling anomalies. By extracting and analyzing this key information, fault-related anomalies can be extracted from complex three-dimensional holographic current data, avoiding blindly searching for anomalies in massive amounts of data and improving the targeting and accuracy of fault analysis. In the above-mentioned monitoring and fault analysis process of the aerospace excitation system, electromagnetic coupling information refers to the relevant information on the interaction, energy transfer, and signal correlation between various electromagnetic components within the aerospace excitation system, such as excitation windings, generator stator / rotor, iron core, and sensor coils, caused by electromagnetic induction, capacitance effect, inductance effect, or electromagnetic radiation. This information reflects the distribution pattern of electromagnetic energy within the system, the strength of electromagnetic correlation between components, and dynamic interaction relationships. It is the core basis for judging whether the electromagnetic performance of the system is normal. Since the aerospace excitation system is essentially a core device for electromagnetic energy conversion and regulation, the electromagnetic coupling state between components directly determines the system's operational stability. Once the coupling is abnormal, it may cause faults such as current distortion and magnetic field imbalance. Therefore, extracting this kind of information is a key step in fault analysis. Using the electromagnetic coupling information during normal system operation as a benchmark, by comparing and analyzing the currently acquired electromagnetic coupling information such as coupling strength, phase correlation, and spatial distribution, abnormal coupling states that deviate from the normal range are identified. These abnormal states are then transformed into specific and quantifiable characteristic descriptions, i.e., operational abnormality characteristic information.

[0029] For example, when the excitation winding is normal, the spacing between each coil turn is uniform and the insulation is intact. The magnetic field generated after applying alternating current is symmetrically distributed within the winding. The electromagnetic coupling information at this time is as follows: Inductive coupling strength: The inductive coupling strength between adjacent turns of the coil is stable at 20-25mV, which is determined by the number of turns and the spacing. Phase correlation: The phase difference between the winding current and the generated magnetic field is stable at 85°-90°, which conforms to the law of electromagnetic induction; Spatial distribution: The coupling strength is uniformly distributed along the axial length of the winding, and the strength difference within any 10cm interval does not exceed 2mV.

[0030] When a fault occurs, abnormal operation characteristics are displayed in the electromagnetic coupling information.

[0031] For example, when an inter-turn short circuit occurs in a section of the winding, such as when the insulation of turns 10-12 is damaged, a local current concentration will form at the short circuit point, leading to disordered magnetic field distribution and the following abnormalities in electromagnetic coupling information: Inductive coupling strength: The coupling strength between adjacent turns in the 10th-12th turn region increases sharply to 50-60mV due to the surge in current density caused by the short circuit, resulting in a significant increase in magnetic field strength; Phase correlation: In this region, the phase difference between the current and the magnetic field drops to 60°-70°. Short circuits disrupt the normal electromagnetic induction relationship, and phase synchronization decreases. Spatial distribution: The axial coupling strength of the winding shows a sudden peak, and the strength difference between the short-circuit region and the adjacent 10cm region reaches 40mV, far exceeding the normal 2mV.

[0032] By comparing normal and abnormal electromagnetic coupling information, it can be analyzed that: a sudden increase in coupling strength, a decrease in phase difference, and abrupt peaks in spatial distribution are all caused by electromagnetic coupling anomalies due to local inter-turn short circuits. Converting these anomalies into specific characteristics yields the operational anomaly characteristic information.

[0033] As described in step S6 above, obtaining corresponding fault type information based on multiple operational anomaly characteristic information and generating fault early warning maintenance decision instructions serves as the final step in the entire process. Its advantage lies in achieving a closed loop from information acquisition to decision generation. By accurately determining the fault type using the anomaly characteristic information obtained in the preceding steps, specific maintenance decision instructions are directly generated, allowing staff to quickly understand what measures to take to handle the situation. This avoids a disconnect between fault analysis and maintenance decision-making, greatly improving the efficiency and timeliness of the operation and maintenance of the aviation excitation system. The multiple operational anomaly characteristic information represents the symptoms of the fault, while the fault type information represents the essence of the fault. The core of this step is to accurately locate the essential problem through the combination of symptoms. Because a single abnormal feature may correspond to multiple faults, such as increased local coupling strength, inter-turn short circuit, or local core saturation, but the combination of multiple features is unique, and the combinations of abnormal features for different faults are different. This difference can be used to pinpoint the specific fault type. This process usually relies on a fault feature-type association library, which can be built based on historical fault cases or expert experience. The type association library pre-stores feature combination templates corresponding to various typical faults. The currently acquired multiple abnormal feature information is compared one by one with the templates in the library. The fault type corresponding to the template with the highest matching degree is the corresponding fault type information. After determining the fault type, it needs to be further transformed into actionable instructions, including two parts: fault warning and maintenance decision. The first part, fault warning, is to indicate the urgency and scope of the fault, so that relevant personnel can quickly know the severity of the fault, such as high priority requiring immediate handling. The second part, maintenance decision instructions, is a specific operation plan formulated for the fault type, which clarifies what to do, how to do it, and when to do it, to ensure that the maintenance action is accurate and effective.

[0034] Step S1 in this application lays the foundation for the basic operational information framework. Step S2 fills the information gaps using high-bandwidth current and magnetoresistance data from various sensors. Finally, the holographic monitoring data obtained through preprocessing in Step S3 transforms the system's operational information from fragmented and partial to comprehensive and standardized. This allows for the clear capture of operational details and key parameter changes of each system component, eliminating information blind spots in traditional operation and maintenance. Step S4 deeply integrates the holographic monitoring data with operational information to generate three-dimensional holographic current data, breaking through the limitations of traditional single-dimensional data and constructing the current trajectory from spatial and temporal dimensions. Step S5 focuses on key information about electromagnetic coupling, accurately extracting abnormal features, and enabling fault analysis to move beyond blind troubleshooting. By focusing on targeted localization, the S6 significantly improves the accuracy and efficiency of anomaly identification. Based on anomaly characteristics, it determines the fault type and generates maintenance decision instructions, connecting the entire chain of information acquisition, analysis and diagnosis, and decision execution. This transforms the traditional passive response maintenance model into a closed-loop system of proactive early warning and precise execution, effectively shortening fault handling time and avoiding the risk of fault escalation. Through comprehensive information control and precise fault analysis, potential system anomalies can be detected early. Combined with the timely maintenance instructions of the S6, small faults can be prevented from evolving into major faults, significantly reducing the probability of system downtime or abnormal operation caused by excitation problems, and providing a solid guarantee for the overall power supply stability of aviation equipment.

[0035] In one embodiment, the step of preprocessing the sensor data to obtain holographic monitoring data includes: S201. Acquire multiple sensor data corresponding to multiple sensors, wherein the multiple sensors are distributed and arranged within the aerospace excitation system; S202. Perform initial timestamp synchronization on the data from the multiple sensors and obtain a timestamp synchronization data matrix; S203. Obtain the installation position of the generator stator of the aerospace excitation system, and deploy space field sensing nodes at the stator installation position to collect space electromagnetic field distribution data. S204. Obtain the position of the rotor excitation winding lead of the aerospace excitation system, and deploy time-domain sensing nodes at the lead positions to capture current waveform data. S205. Obtain the key location of the cooling duct of the aviation excitation system, and deploy frequency domain sensing nodes at the key location of the duct to monitor harmonic energy distribution data. S206. Generate holographic monitoring data based on spatial electromagnetic field distribution data, current waveform data, and harmonic energy distribution data.

[0036] As described in steps S201-S206 above, the present invention extends the monitoring range from the entire system to the local components through a distributed sensor layout. The monitoring range covers core parts such as the stator, rotor leads, and cooling air ducts, ensuring that the operating status of each component is supported by data. S203-S205 target the deployment of sensor nodes for the fault characteristics of different parts—the spatial field sensor node accurately collects the 360° electromagnetic field distribution around the stator, the time domain sensor node captures the microsecond-level transient waveform of the rotor leads current, and the frequency domain sensor node monitors the proportion of each harmonic energy in the cooling air duct. The three types of data together fill the feature blind spots of traditional monitoring and improve the capture rate of early fault signals. Among them, the timestamp synchronization in S202 controls the time deviation of the data from each sensor to within ±1μs, and the generated timestamp synchronization data matrix realizes the accurate binding of data from different parts at the same time.

[0037] For example, when a current spike occurs in the rotor lead, it can be synchronously correlated with the magnetic field distortion at the corresponding position of the stator and the sudden increase in harmonic energy in the cooling air duct. This clearly restores the causal chain from lead fault to current transient, then to magnetic field distortion, and finally to harmonic anomaly. This solves the problem of disordered parameter change sequence in traditional monitoring, improves the accuracy of dynamic coupling process analysis, and S206 constructs a holographic monitoring data system that includes spatial field, time domain, and frequency domain by integrating three types of data. The data not only includes independent features of each dimension such as magnetic field symmetry, current spike amplitude, and the proportion of the third harmonic, but also establishes a correlation through timestamps and spatial tags.

[0038] The step of "preprocessing the sensor data to obtain holographic monitoring data" is the core link connecting the original sensor data acquisition with multi-dimensional data fusion. Its essence is to eliminate the defects of the original data, unify the data standards, and strengthen the data correlation through a series of targeted processing operations, so as to generate holographic monitoring data that can fully reflect the state of the aerospace excitation system.

[0039] In one embodiment, the step of monitoring operational information based on holographic monitoring data to obtain three-dimensional holographic current data of the airborne excitation system includes: S301. Obtain the corresponding spatial magnetic field distribution data based on the holographic monitoring data; S302. Obtain the corresponding current density distribution information based on the spatial magnetic field distribution data; S303. Obtain the corresponding transient current characteristic information based on the holographic monitoring data; S304. Obtain the corresponding thermal current loss compensation information based on the holographic monitoring data; S305. Obtain three-dimensional holographic current data based on the current density distribution information, current transient characteristic information, and thermally induced current loss compensation information.

[0040] As described in steps S301-S305 above, this invention first extracts spatial magnetic field distribution data, such as the magnetic field strength and direction data at each point within a 360° radius around the stator, from the holographic monitoring data in S301. This lays the foundation for obtaining current density distribution information in S302. Furthermore, based on the coupling relationship between magnetic field and current according to the law of electromagnetic induction, the specific distribution of current in space can be inferred from the magnetic field distribution. This process overcomes the limitation that direct measurement can only cover a limited number of measurement points, extending current monitoring from points / lines to surfaces / volumes. The final generated current density distribution information can be visualized through a heat map, improving the positioning accuracy of anomalies such as local current concentration. Then, in S303, transient current characteristic information, such as the peak amplitude, rise time, and pulse frequency of rotor lead current, and the transient distortion rate of stator winding current, is extracted from the holographic monitoring data and correlated with the steady-state current density distribution obtained in S302.

[0041] For example, when a spike of 1.5 μs in rotor lead current with an amplitude 1.8 times the rated value is detected, the current density distribution of the stator winding at the time of the transient can be simultaneously examined. This reveals a sudden increase in current density in the corresponding region from 2.2 A / mm² to 2.7 A / mm², thus clarifying the impact of the transient current on the spatial distribution. This correlation breaks down the traditional separation of transient and steady-state recording, enabling quantifiable analysis of the dynamic changes and spatial response patterns of the current, and improving the accuracy of assessing the impact of transient anomalies on the system.

[0042] Subsequently, S304 extracts thermally induced current loss compensation information from the holographic monitoring data. Combining the temperature data of the cooling duct and the temperature-resistance characteristic curve of the winding material, it calculates the current loss correction value at different temperatures and incorporates this compensation information into the fusion process of S305. The resulting three-dimensional holographic current data can eliminate the measurement deviation caused by temperature and reduce the measurement error of the current amplitude, providing an accurate data benchmark for fault diagnosis based on current parameters. Finally, by fusing current density distribution information (spatial dimension), current transient characteristic information (time dimension), and thermally induced loss compensation information (correction dimension), the three-dimensional holographic current data formed by S305 is no longer a single numerical set, but a three-dimensional data containing spatial distribution, temporal dynamics, and accuracy correction. The above steps not only solve the inherent limitations of traditional current data, but also provide core data support for the stable operation and intelligent management of the system.

[0043] In one embodiment, the steps of extracting electromagnetic coupling information from three-dimensional holographic current data and obtaining multiple operational anomaly characteristic information by analyzing the electromagnetic coupling information include: S401. Obtain the corresponding current density distribution information based on the three-dimensional holographic current data; S402. Obtain the corresponding magnetic field strength information based on the current density distribution information, and obtain the corresponding magnetic flux density change information based on each magnetic field strength information. S403. Obtain the corresponding eddy current loss information based on the magnetic flux density change information, and obtain the corresponding electromagnetic coupling strength information based on the eddy current loss information. S404. Obtain the corresponding electromagnetic coupling region information based on the electromagnetic coupling strength information; S405. Obtain the corresponding electromagnetic coupling position information based on the electromagnetic coupling region information; S406. Obtain the corresponding electromagnetic coupling information based on the electromagnetic coupling position information and the corresponding electromagnetic coupling strength information.

[0044] As described in steps S401-S406 above, the present invention obtains current density distribution information such as the current density of each region of the stator winding and the current distribution of the rotor leads from three-dimensional holographic current data, providing a benchmark for subsequent derivation of operating data, because the essence of electromagnetic coupling is the interaction of magnetic fields generated by current, and the current density distribution directly determines the basic characteristics of the magnetic field distribution. S402 derives magnetic field strength and flux density change information based on current density distribution, establishing a clear causal chain between the magnetic field and current. S403 further derives eddy current loss information through flux density change, and then obtains electromagnetic coupling strength information through correlation. This step ensures that electromagnetic coupling information is no longer an isolated measurement value, but a complete chain traceable to current distribution, magnetic field change, and loss response. The technical chain is the correlation between current density anomaly causing magnetic field change, which in turn leads to coupling anomaly, avoiding the blindness of traditional methods that only measure coupling strength. S404-S405 progressively derive and pinpoint the specific range of coupling: S404 determines the coupling region information based on electromagnetic coupling strength information, excluding non-correlated regions; S405 further locates the specific electromagnetic coupling position within the coupling region. In this process, the coupling information is refined from the overall system to the centimeter-level position. S406 integrates electromagnetic coupling position information and strength information, and the final electromagnetic coupling information is no longer a single parameter, but a three-dimensional information containing the correlation between strength, position, and loss.

[0045] For example, when the stator is at a 90° position, the electromagnetic coupling strength is 18% higher than the reference value, corresponding to an increase in eddy current loss to 80W (normally 50W), located at a 0.3mm gap between the winding and the core. This information clearly indicates the strength of the coupling, where the coupling anomaly occurs, and how much loss it causes, providing a more complete picture compared to traditional coupling information that only contains strength.

[0046] This application links the specific location of electromagnetic coupling with the magnitude of the coupling strength at that location, transforming the dispersed location and strength parameters into comprehensive information that fully reflects the electromagnetic coupling state of the system. This clarifies not only where the coupling anomaly occurs but also how severe it is, ultimately forming a complete characterization of the electromagnetic coupling state with precise location and quantitative description, providing a direct basis for subsequent fault analysis.

[0047] In one embodiment, the step of performing fault analysis based on electromagnetic coupling information to obtain multiple operational anomaly characteristic information includes: S501. Obtain electromagnetic coupling information of the excitation system, wherein the electromagnetic coupling information includes electromagnetic field strength data and current distribution data; S502. Perform time-domain analysis, frequency-domain analysis, and spatial-domain analysis on the electromagnetic coupling information to obtain the corresponding transient anomaly characteristic information, harmonic distortion characteristic information, and spatial distribution anomaly characteristic information. S503. Based on the transient abnormality feature information, harmonic distortion feature information and spatial distribution abnormality feature information, perform abnormality monitoring to obtain multiple operational abnormality feature information.

[0048] As described in steps S501-S503 above, this invention first forms a full-state record of electromagnetic coupling by synchronously collecting electromagnetic field strength and current distribution data. Through S502, it achieves full coverage of multi-dimensional abnormal features, improving the comprehensiveness of feature extraction. Among them, the three types of analysis, transient abnormal feature information, harmonic distortion feature information, and spatial distribution abnormal feature information, respectively cover the anomalies of "time dynamics, frequency components, and spatial distribution". The time domain analysis can capture transient fault signals such as microsecond-level current spikes due to poor lead contact; the frequency domain analysis can identify harmonic-related faults such as the third harmonic distortion of thyristor triggering abnormalities; and the spatial domain analysis can locate structural-related faults such as magnetic field asymmetry due to rotor eccentricity. Combining the three can improve the coverage of abnormal features. Finally, through S503, it achieves accurate anomaly identification, reducing the false judgment or missed detection rate. Through multi-feature correlation verification, it can effectively distinguish between interference and real faults and accurately obtain the abnormal operation features.

[0049] For example, a single current spike caused by sensor noise will not be accompanied by harmonic changes or magnetic field anomalies and will be excluded, while a real lead short circuit will simultaneously show "current spike + harmonic surge + local magnetic field distortion", thus being accurately identified.

[0050] For example, the electromagnetic coupling information at a certain moment includes both the symmetrical distribution of the stator magnetic field and the uniformity of the corresponding winding current density. The correlation between the two, such as the synchronous increase of magnetic field strength when the current density increases, provides a reference logic for subsequent judgment of whether there is an anomaly, avoiding the blind guessing of faults based on a single data.

[0051] In one embodiment, the step of obtaining corresponding fault type information based on multiple operational anomaly characteristic information and generating fault early warning maintenance decision instructions includes: S601. Obtain insulation degradation level, eddy current loss intensity and electromagnetic interference risk index based on multiple operational anomaly characteristic information; S602. Obtain the fault type information of the corresponding excitation system based on the insulation degradation level, eddy current loss intensity and electromagnetic interference risk index. S603. Obtain the corresponding fault location information based on the fault type information; S604. Predict the remaining safe operating time based on the fault type information and the corresponding fault location information; S605. Generate a fault early warning maintenance decision instruction based on the remaining safe operating time and the corresponding fault location information.

[0052] As described in steps S601-S605 above, this invention solves the problems of feature ambiguity and incomparability by converting features into three types of indicators: level, intensity, and index. This shifts anomaly assessment from qualitative description to quantitative calculation. Subsequently, multiple indicators are combined and matched, such as linking insulation, loss, and interference to generate fault type information. Furthermore, an indicator-fault association library is used, which can be established based on historical faults. This library solves the problem of misjudgment based on a single feature, shifting fault type identification from experience-based guessing to data matching. Even if the fault type is clear, if the specific location cannot be determined, maintenance still requires a comprehensive disassembly and investigation, which is inefficient. Then, in step S603, by combining the typical location of the fault type with the previous spatial distribution characteristics, the fault type is accurately located. Location-based fault diagnosis solves the problems of ambiguous location and blind maintenance, and also refines fault localization from the component level to the point level. If we only know that there is a fault but do not know when it will worsen, it is easy to cause premature maintenance, which wastes resources, or premature maintenance, which leads to downtime. In this case, S604 can predict the remaining safe operating time, which solves the problem of unknown fault development rhythm, and shifts the maintenance timing from passive response to proactive planning. Furthermore, if the initial analysis only outputs the fault type without converting it into specific operations, the analysis results will not be able to guide maintenance. For example, maintenance personnel still need to judge how to repair and when to repair, which requires the judgment of maintenance personnel's experience. By generating a complete instruction with warning, operation and time limit through S605, the problem of disconnect between analysis and maintenance is solved, and fault handling is shifted from decentralized decision-making to closed-loop instruction.

[0053] like Figure 2 As shown, this application also provides a fault early warning system for an airborne excitation system based on holographic current, comprising: The first acquisition module 1 acquires the operating information of the aircraft excitation system; The second acquisition module 2 acquires sensor data, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data. The first processing module 3 preprocesses the sensor data to obtain holographic monitoring data; The first monitoring module 4 monitors the operation information based on holographic monitoring data and obtains three-dimensional holographic current data of the aerospace excitation system; The first extraction module 5 extracts electromagnetic coupling information from the three-dimensional holographic current data and performs fault analysis based on the electromagnetic coupling information to obtain multiple abnormal operation feature information. The third acquisition module 6 acquires the corresponding fault type information based on multiple abnormal operation feature information and generates fault early warning maintenance decision instructions.

[0054] Preferably, the first processing module includes: The first acquisition unit acquires multiple sensor data corresponding to multiple sensors, wherein the multiple sensors are distributed and arranged within the aerospace excitation system; The second acquisition unit performs initial timestamp synchronization on the data from the multiple sensors and acquires a timestamp synchronization data matrix. The third acquisition unit acquires the installation position of the generator stator of the aerospace excitation system and deploys a space field sensing node at the stator installation position to collect space electromagnetic field distribution data. The fourth acquisition unit acquires the position of the rotor excitation winding lead of the aerospace excitation system and deploys time-domain sensing nodes at the lead positions to capture current waveform data. The fifth acquisition unit acquires the key locations of the cooling duct of the aerospace excitation system and deploys frequency domain sensing nodes at the key locations of the duct to monitor harmonic energy distribution data. The first generation unit generates holographic monitoring data based on spatial electromagnetic field distribution data, current waveform data, and harmonic energy distribution data.

[0055] Preferably, the first monitoring module includes: The sixth acquisition unit acquires the corresponding spatial magnetic field distribution data based on the holographic monitoring data; The seventh acquisition unit acquires the corresponding current density distribution information based on the spatial magnetic field distribution data; The eighth acquisition unit acquires the corresponding transient current characteristic information based on the holographic monitoring data; The ninth acquisition unit acquires the corresponding thermal current loss compensation information based on the holographic monitoring data; The tenth acquisition unit acquires three-dimensional holographic current data based on the current density distribution information, current transient characteristic information, and thermally induced current loss compensation information.

[0056] Preferably, the first extraction module includes: The eleventh acquisition unit acquires the corresponding current density distribution information based on the three-dimensional holographic current data; The twelfth acquisition unit acquires the corresponding magnetic field strength information based on the current density distribution information, and acquires the corresponding magnetic flux density change information based on the magnetic field strength information; The thirteenth acquisition unit acquires the corresponding eddy current loss information based on the magnetic flux density change information; The fourteenth acquisition unit acquires the corresponding electromagnetic coupling strength information based on the eddy current loss information; The fifteenth acquisition unit acquires the corresponding electromagnetic coupling region information based on the electromagnetic coupling strength information. The sixteenth acquisition unit acquires the corresponding electromagnetic coupling position information based on the electromagnetic coupling region information. The seventeenth acquisition unit acquires the corresponding electromagnetic coupling information based on the electromagnetic coupling position information and the corresponding electromagnetic coupling strength information.

[0057] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, database, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus RAM (RDRAM), direct memory bus RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0058] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0059] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A fault early warning method for an airborne excitation system based on holographic current, characterized in that, include: Obtain operational information from the aircraft excitation system; Acquire sensor data, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data; The sensor data is preprocessed to obtain holographic monitoring data; Based on holographic monitoring data, operational information is monitored to obtain three-dimensional holographic current data of the aerospace excitation system; Electromagnetic coupling information is extracted from the three-dimensional holographic current data, and fault analysis is performed based on the electromagnetic coupling information to obtain multiple abnormal operation feature information. Based on multiple abnormal operation characteristics, obtain the corresponding fault type information and generate fault warning and maintenance decision instructions.

2. The fault early warning method for aerospace excitation systems based on holographic current according to claim 1, characterized in that, The step of preprocessing the sensor data to obtain holographic monitoring data includes: Acquire multiple sensor data corresponding to multiple sensors, wherein the multiple sensors are distributed and arranged within the aerospace excitation system; Initial timestamp synchronization is performed on the data from multiple sensors, and a timestamp synchronization data matrix is ​​obtained; The installation location of the generator stator of the aerospace excitation system is obtained, and space field sensing nodes are deployed at the stator installation location to collect space electromagnetic field distribution data; The position of the rotor excitation winding lead of the aerospace excitation system is obtained, and time-domain sensing nodes are deployed at the lead positions to capture current waveform data; The key locations of the cooling duct of the aerospace excitation system are obtained, and frequency domain sensing nodes are deployed at the key locations of the duct to monitor harmonic energy distribution data. Holographic monitoring data is generated based on spatial electromagnetic field distribution data, current waveform data, and harmonic energy distribution data.

3. The fault early warning method for aerospace excitation systems based on holographic current according to claim 1, characterized in that, The step of monitoring operational information based on holographic monitoring data to obtain three-dimensional holographic current data of the airborne excitation system includes: Obtain the corresponding spatial magnetic field distribution data based on the holographic monitoring data; Obtain the corresponding current density distribution information based on the spatial magnetic field distribution data; Obtain the corresponding transient current characteristic information based on the holographic monitoring data; Obtain the corresponding thermal current loss compensation information based on the holographic monitoring data; Three-dimensional holographic current data is obtained based on the current density distribution information, current transient characteristic information, and thermally induced current loss compensation information.

4. The fault early warning method for aerospace excitation systems based on holographic current according to claim 1, characterized in that, The step of extracting electromagnetic coupling information from the three-dimensional holographic current data includes: Obtain the corresponding current density distribution information based on the three-dimensional holographic current data; Based on the current density distribution information, obtain the corresponding magnetic field strength information, and based on the magnetic field strength information, obtain the corresponding magnetic flux density change information; Obtain the corresponding eddy current loss information based on the magnetic flux density change information; The corresponding electromagnetic coupling strength information is obtained based on the eddy current loss information. Based on the electromagnetic coupling strength information, obtain the corresponding electromagnetic coupling region information; Obtain the corresponding electromagnetic coupling position information based on the electromagnetic coupling region information; The corresponding electromagnetic coupling information is obtained based on the electromagnetic coupling position information and the corresponding electromagnetic coupling strength information.

5. The fault early warning method for aerospace excitation systems based on holographic current according to claim 1, characterized in that, The step of performing fault analysis based on electromagnetic coupling information to obtain multiple operational anomaly characteristic information includes: The electromagnetic coupling information of the excitation system is obtained, wherein the electromagnetic coupling information includes electromagnetic field strength data and current distribution data; The electromagnetic coupling information is analyzed in the time domain, frequency domain, and spatial domain to obtain the corresponding transient anomaly characteristic information, harmonic distortion characteristic information, and spatial distribution anomaly characteristic information. Anomaly monitoring is performed based on the transient anomaly characteristic information, harmonic distortion characteristic information, and spatial distribution anomaly characteristic information to obtain multiple operational anomaly characteristic information.

6. The fault early warning method for aerospace excitation systems based on holographic current according to claim 1, characterized in that, The step of obtaining corresponding fault type information based on multiple operational anomaly characteristic information and generating fault early warning maintenance decision instructions includes: Based on multiple operational anomaly characteristics, insulation degradation level, eddy current loss intensity, and electromagnetic interference risk index are obtained; Based on the insulation degradation level, eddy current loss intensity, and electromagnetic interference risk index, obtain the corresponding excitation system fault type information; Based on the fault type information, obtain the corresponding fault location information; Predict the remaining safe operating time based on the fault type information and the corresponding fault location information; generate fault early warning maintenance decision instructions based on the remaining safe operating time and the corresponding fault location information.

7. A fault early warning system for an aircraft excitation system based on holographic current, characterized in that, include: The first acquisition module acquires the operating information of the aircraft excitation system; The second acquisition module acquires sensor data, including high-bandwidth current data, magnetoresistive data, high-frequency voltage data, and infrared thermal imaging data. The first processing module preprocesses the sensor data to obtain holographic monitoring data; The first monitoring module monitors operational information based on holographic monitoring data, and obtains three-dimensional holographic current data of the aerospace excitation system; The first extraction module extracts electromagnetic coupling information from the three-dimensional holographic current data and performs fault analysis based on the electromagnetic coupling information to obtain multiple abnormal operation feature information. The third acquisition module obtains the corresponding fault type information based on multiple abnormal operation feature information and generates fault early warning maintenance decision instructions.

8. A fault early warning system for an aircraft excitation system based on holographic current according to claim 7, characterized in that, The first processing module includes: The first acquisition unit acquires multiple sensor data corresponding to multiple sensors, wherein the multiple sensors are distributed and arranged within the aerospace excitation system; The second acquisition unit performs initial timestamp synchronization on the data from the multiple sensors and acquires a timestamp synchronization data matrix. The third acquisition unit acquires the installation position of the generator stator of the aerospace excitation system and deploys a space field sensing node at the stator installation position to collect space electromagnetic field distribution data. The fourth acquisition unit acquires the position of the rotor excitation winding lead of the aerospace excitation system and deploys time-domain sensing nodes at the lead positions to capture current waveform data. The fifth acquisition unit acquires the key locations of the cooling duct of the aerospace excitation system and deploys frequency domain sensing nodes at the key locations of the duct to monitor harmonic energy distribution data. The first generation unit generates holographic monitoring data based on spatial electromagnetic field distribution data, current waveform data, and harmonic energy distribution data.

9. The fault early warning system for an airborne excitation system based on holographic current according to claim 7, characterized in that, The first monitoring module includes: The sixth acquisition unit acquires the corresponding spatial magnetic field distribution data based on the holographic monitoring data; The seventh acquisition unit acquires the corresponding current density distribution information based on the spatial magnetic field distribution data; The eighth acquisition unit acquires the corresponding transient current characteristic information based on the holographic monitoring data; The ninth acquisition unit acquires the corresponding thermal current loss compensation information based on the holographic monitoring data; The tenth acquisition unit acquires three-dimensional holographic current data based on the current density distribution information, current transient characteristic information, and thermally induced current loss compensation information.

10. The fault early warning system for an aircraft excitation system based on holographic current according to claim 7, characterized in that, The first extraction module includes: The eleventh acquisition unit acquires the corresponding current density distribution information based on the three-dimensional holographic current data; The twelfth acquisition unit acquires the corresponding magnetic field strength information based on the current density distribution information, and acquires the corresponding magnetic flux density change information based on the magnetic field strength information; The thirteenth acquisition unit acquires the corresponding eddy current loss information based on the magnetic flux density change information; The fourteenth acquisition unit acquires the corresponding electromagnetic coupling strength information based on the eddy current loss information; The fifteenth acquisition unit acquires the corresponding electromagnetic coupling region information based on the electromagnetic coupling strength information. The sixteenth acquisition unit acquires the corresponding electromagnetic coupling position information based on the electromagnetic coupling region information. The seventeenth acquisition unit acquires the corresponding electromagnetic coupling information based on the electromagnetic coupling position information and the corresponding electromagnetic coupling strength information.

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