Intelligent visual detection system and method for special light source switching power supply

Abstract

The invention provides an intelligent visual detection system and method for a special light source switching power supply, and relates to the technical field of instrument detection. The system comprises a non-intrusive signal sensing layer, a data processing layer and an intelligent application layer. The sensing layer uses a high-frequency current clamp and a high-isolation voltage probe to collect electrical signals through electromagnetic induction and capacitive coupling on the premise of uninterruptible power supply; the data layer executes adaptive signal conditioning and digital processing; and the application layer performs health assessment, fault prediction and visual interaction based on a multi-parameter fusion algorithm and an aging model. The problems of potential safety hazards and low efficiency of traditional intrusive detection are solved, automation and predictive maintenance of power supply detection are achieved, and detection precision and safety are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of instrument testing technology, specifically to an intelligent visual testing system and method for a special light source switching power supply. Background Technology

[0002] In the fields of analytical chemistry instruments and biochemical medical devices, special light sources are widely used in various testing instruments. These special light sources include ultraviolet-visible (UV) lamps, infrared lamps, xenon lamps, and metal halide lamps. Ultraviolet light sources, with their main wavelengths ranging from 100 to 400 nanometers, are widely used in important scientific and production fields such as organic chemical analysis, drug testing, medical devices, food safety testing, environmental monitoring, and medical sterilization. They also play a crucial role in aerospace and military industries. Deuterium lamps, as the core light source in analytical instruments such as UV-Vis spectrophotometers and high-performance liquid chromatographs, rely heavily on the performance and reliability of their power supplies, which directly determine the analytical accuracy and stability of the entire instrument. Inferior power supplies accelerate deuterium lamp aging, cause fluctuations in output light intensity, and introduce significant measurement errors. Therefore, comprehensive and rigorous testing of power supplies before they leave the factory is a critical step in ensuring the quality of the final product.

[0003] Specialty light source switching power supplies typically operate under special conditions of high frequency, high voltage, and high current. Their testing and diagnosis have traditionally relied on the experience of specialized engineers and expensive imported testing equipment. However, current testing methods for specialty light source switching power supplies suffer from numerous technical deficiencies, severely limiting testing efficiency and reliability.

[0004] Existing technologies lack a unified testing platform, resulting in a fragmented testing process. After completing one test, manual switching of wiring and instruments is required before proceeding to the next test. This discrete operating mode is not only inefficient, but the frequent plugging and unplugging can easily introduce contact resistance and human error, leading to poor consistency in measurement results.

[0005] Current testing methods heavily rely on specialized experience and expensive equipment, including high-end benchtop instruments such as oscilloscopes, power analyzers, and spectrum analyzers. Operators must possess deep expertise to correctly configure instrument parameters and interpret complex data. This not only increases labor and equipment costs but also makes standardization of the testing process difficult, resulting in inconsistent test results.

[0006] Traditional testing often focuses on basic electrical parameters, separating electromagnetic compatibility testing, safety testing, and performance testing, thus lacking a systematic safety and performance assessment. This segmented testing approach cannot simulate the real operating conditions of a power supply in complex electromagnetic environments, nor can it systematically assess the safety and stability risks of its long-term operation.

[0007] Due to the cumbersome and inefficient nature of the above processes, traditional testing methods are time-consuming and cannot be integrated into the fast pace of modern automated production lines, failing to meet production cycle requirements and becoming a bottleneck for increasing production capacity and achieving large-scale manufacturing.

[0008] Furthermore, existing testing technologies generally employ invasive measurement methods, requiring disconnection of the circuit before connecting the sensor. This is not only complex and poses safety risks, but it also disrupts the power supply's true operating condition, introducing measurement errors. Simultaneously, traditional testing methods focus on a "reactive" approach, lacking the ability to quantitatively assess the power supply's health status, failing to provide early warnings of potential faults, and unable to predict the power supply's remaining lifespan, thus hindering predictive maintenance.

[0009] In summary, the inherent defects of existing testing technologies, such as process fragmentation, reliance on professional personnel, incomplete evaluation, low efficiency, and inability to achieve predictive maintenance, have become key obstacles restricting the improvement of the quality and industrialization of special light source switching power supplies. Summary of the Invention

[0010] The purpose of this invention is to provide an intelligent visual inspection system and method for special light source switching power supplies, which can integrate multiple testing functions, adopt non-invasive testing methods, have intelligent diagnostic and predictive capabilities, and can seamlessly connect with the production process.

[0011] To achieve the above objectives, the present invention provides the following technical solution: An intelligent visual inspection system for a special light source switching power supply includes: A non-invasive signal sensing layer is used to couple the electrical signal of the power supply under test without disconnecting the circuit of the power supply under test; the non-invasive signal sensing layer includes: High-frequency current clamp is used to couple the output current signal of the power supply under test through the principle of electromagnetic induction. A high-isolation voltage probe is used to sense the output voltage signal of the power supply under test through capacitive coupling. The data processing layer is used to receive analog signals from the non-invasive signal sensing layer and convert them into digital signals for analysis and processing. The intelligent application layer is used to perform health status assessment, fault prediction, and result visualization interaction based on the analysis results output by the data processing layer.

[0012] Furthermore, the high-frequency current clamp uses nanocrystalline magnetic core material, and the voltage divider capacitor of the high isolation voltage probe uses C0G material.

[0013] Furthermore: the high isolation voltage probe includes: The voltage divider network uses a low-loss tangent high-voltage ceramic capacitor; The protection circuit includes a gas discharge tube and a transient voltage suppression diode arranged in series. An isolation amplifier uses capacitive or magnetic isolation technology to achieve isolated signal transmission.

[0014] Further: The data processing layer includes: The signal conditioning module is used to filter and amplify the signal from the non-invasive signal sensing layer. The data acquisition module is used to convert the conditioned analog signal into a digital signal; The core module of the algorithm is used to perform ripple decomposition, transient response modeling, and spectral feature extraction.

[0015] Further: the signal conditioning module includes an adaptive signal conditioning circuit, the adaptive signal conditioning circuit including: The intelligent range switching unit is used to automatically switch the range of the programmable gain amplifier according to the signal amplitude. An adaptive filtering unit is used to dynamically adjust the cutoff frequency of the anti-aliasing filter according to the spectral characteristics of the signal. An automatic calibration unit is used to compensate for gain and offset errors using a built-in precision reference source.

[0016] Furthermore: the intelligent application layer includes: The performance evaluation and fault prediction unit is used to perform health status evaluation and remaining service life prediction based on a multi-parameter fusion algorithm. The central control unit is used to coordinate the synchronous operation of the programmable power supply, electronic load, and data acquisition module. The human-computer interaction interface is used for parameter setting, data visualization, and diagnostic report generation.

[0017] Furthermore, the performance evaluation and fault prediction unit uses a weighted geometric mean model to calculate the comprehensive health index, which is calculated based on the peak-to-peak value of the output ripple voltage, the on-resistance of the power devices, the series resistance of the filter capacitor, and the temperature of the heat sink.

[0018] Furthermore, it also includes a security protection module, which comprises: The first layer of protection circuit includes a gas discharge tube connected in parallel at the signal input terminal for surge suppression; The second layer of protection circuitry includes transient voltage suppression diodes for transient voltage clamping; The third layer of protection circuitry includes overvoltage and overcurrent protection integrated circuits and solid-state relays, which are used to actively shut down fault paths.

[0019] A smart visual detection method for special light source switching power supplies includes the following steps: S1: Couple the electrical signal of the power supply under test through a non-invasive sensor without disconnecting the circuit of the power supply under test; S2: Conditioning, acquiring, and digitizing the coupled electrical signals; S3: Perform ripple characteristic analysis and transient response analysis on the digitized signal; S4: Based on the analysis results, perform health status assessment and fault prediction, and generate a visual diagnostic report.

[0020] Further: In step S1, the current signal is coupled based on the principle of electromagnetic induction by a high-frequency current clamp, and the voltage signal is sensed based on the principle of capacitive coupling by a high-isolation voltage probe.

[0021] Further: Step S2 includes: S201: Monitors signal amplitude. When the signal approaches saturation, it automatically switches to a higher range. When the signal is too weak, it automatically switches to a higher sensitivity range. S202: Perform a Fast Fourier Transform on the signal to identify the highest effective frequency component in the signal; S203: Dynamically adjust the cutoff frequency of the anti-aliasing filter according to the highest effective frequency component.

[0022] Further: the health status assessment in step S4 includes: The peak-to-peak value of the output ripple voltage, the on-resistance of the power devices, the series resistance of the filter capacitor, and the temperature of the heat sink are collected as evaluation parameters. The current measured values ​​of each evaluation parameter are compared with the factory calibration values ​​to calculate the degree of degradation of each parameter; A weighted geometric mean model is used to calculate the degree of degradation of each parameter to obtain a comprehensive health index.

[0023] Further: the fault prediction in step S4 includes: The degree of capacitor damage was calculated based on the Arrhenius thermal aging model; Damage to power devices is calculated based on a power cycling model; The capacitor damage and power device damage are accumulated, and the remaining service life is predicted based on the recent average damage rate.

[0024] Compared with the prior art, the present invention has the following advantages: I. This invention employs a non-invasive signal sensing layer based on a high-frequency current clamp and a high-isolation voltage probe. It achieves electrical signal coupling through the principles of electromagnetic induction and capacitive coupling, enabling online detection without disconnecting the power supply circuit under test. This overcomes the technical defects of traditional invasive measurement methods, which require disconnecting the circuit to connect the sensor, resulting in complex operation, safety risks, and measurement errors caused by disrupting the actual working state of the power supply.

[0025] Second, this invention uses nanocrystalline magnetic core material to make high-frequency current clamps and C0G material to make voltage divider capacitors, so that the system maintains a flat frequency response in a wide frequency band from power frequency to megahertz. This effectively overcomes the inherent defects of traditional ferrite magnetic cores in the sharp decay of permeability at high frequencies and the parameter drift of ordinary ceramic capacitors, and achieves high precision and high fidelity in high-frequency signal measurement.

[0026] Third, the data processing layer of this invention integrates intelligent range switching, adaptive filtering and automatic calibration functions, which can dynamically adjust the measurement parameters according to the signal characteristics. This solves the problem that traditional testing relies on professional engineers to manually configure instrument parameters, realizes the standardization and automation of the testing process, and eliminates the problem of inconsistent test results caused by differences in personnel experience.

[0027] Fourth, the intelligent application layer of this invention uses a weighted geometric mean model based on multi-parameter fusion for health status assessment, and combines the Arrhenius thermal aging model and power cycle model for remaining service life prediction. This realizes the transformation of the operation and maintenance mode from the traditional "post-maintenance" to "pre-prediction", which can detect hidden faults in advance and provide early warning, providing a technical basis for predictive maintenance.

[0028] Fifth, this invention integrates non-invasive signal sensing, data processing and analysis, and intelligent diagnostic evaluation into a unified three-layer architecture, realizing one-stop comprehensive testing of special light source switching power supplies. It solves the problems of fragmented testing processes and frequent switching of wiring and instruments in existing technologies, significantly improving testing efficiency and meeting the fast-paced needs of modern automated production lines. Attached Figure Description

[0029] Figure 1 This is an overall architecture diagram of the intelligent visual detection system for special light source switching power supplies of the present invention; Figure 2 Block diagram for high isolation voltage probe circuit design; Figure 3 Design block diagram for adaptive signal conditioning circuit; Figure 4 This is a flowchart of an intelligent visual detection method applicable to special light source switching power supplies. Detailed Implementation

[0030] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0032] The intelligent visual testing system for special light source switching power supplies provided by this invention adopts a three-layer architecture design, including a non-invasive signal sensing layer, a data processing layer, and an intelligent application layer. This system can perform one-stop comprehensive testing and intelligent diagnosis of independent special light source switching power supply modules, power supply boards integrated into analytical instruments, and their loads.

[0033] The non-invasive signal sensing layer is used to couple the electrical signals of the power supply under test (DUT) without disconnecting the DUT circuit. Its core principle lies in achieving fundamental electrical isolation between the test system and the DUT through the physical principles of magnetic and capacitive coupling. This layer includes a high-frequency current clamp and a high-isolation voltage probe.

[0034] High-frequency current clamps are used to couple the output current signal of a power supply under test using the principle of electromagnetic induction. Their core employs a high-performance magnetic core, which clamps around the current-carrying conductor, inducing an alternating magnetic field around the conductor and linearly converting the magnetic field strength into a corresponding voltage signal for measurement. Throughout the process, there is no electrical connection between the measurement circuit and the high-voltage current loop under test; energy and signal are transferred only through the magnetic field, thus achieving safe electrical isolation. The high-frequency current clamp uses a nanocrystalline magnetic core material with extremely high permeability and saturation magnetic induction, enabling it to maintain a flat frequency response and extremely low core loss across a wide frequency band from power frequency to megahertz, fundamentally overcoming the inherent defect of traditional ferrite cores where permeability decays sharply at high frequencies. The core structure features an adjustable precision air gap for sensitivity calibration and dynamic range extension. The high-frequency current clamp is also equipped with a dedicated conditioning chip, employing a zero-drift operational amplifier or a dedicated current sensor interface integrated circuit, integrating automatic zeroing technology to eliminate DC offset, and a built-in temperature compensation circuit to compensate for changes in core characteristics with temperature.

[0035] High-isolation voltage probes are used to sense the output voltage signal of the power supply under test through capacitive coupling. Internally, a high-impedance capacitive voltage divider network senses the high voltage at the test point. The current shifts very slightly between the capacitors, generating a precisely scaled-down signal on the low-voltage side. This design ensures that there is only capacitive field contact between the probe tip and the subsequent processing circuitry, with no DC path, thus achieving reliable electrical isolation. The voltage divider capacitors in the high-isolation voltage probe are made of C0G material. These capacitors have a near-zero loss factor and an extremely low temperature coefficient, ensuring that the voltage division ratio remains highly stable under different temperatures and voltages. This effectively avoids the high-frequency signal attenuation and distortion caused by the inherent losses and parameter drift of ordinary high-dielectric-constant ceramic capacitors.

[0036] The high-isolation voltage probe specifically comprises a voltage divider network, a protection circuit, and an isolation amplifier. The voltage divider network employs ultra-low loss tangent high-voltage ceramic capacitors to ensure accurate voltage division ratios at high frequencies, and is equipped with a voltage-controlled variable capacitor to automatically compensate for voltage division ratio drift caused by temperature and frequency variations through feedback. The protection circuit employs a multi-layer protection design, including a gas discharge tube and transient voltage suppression diodes arranged in series, providing comprehensive protection from slow overvoltage to electrostatic discharge. The isolation amplifier uses capacitive isolation or magnetic isolation technology, transmitting signals through digital encoding on both sides of the isolation barrier, greatly improving the common-mode rejection ratio. The isolation power supply is based on a flyback converter or push-pull converter, using an isolation transformer to provide a fully floating front-end power supply, and employs spread spectrum modulation technology to reduce switching noise interference to sensitive measurement circuits.

[0037] The non-invasive signal sensing layer may also include temperature sensors and light intensity sensors to assist in monitoring the temperature of the special light source tube shell and the output light intensity, providing more comprehensive data support for subsequent health status assessment.

[0038] The data processing layer is used to receive analog signals from the non-invasive signal sensing layer and convert them into digital signals for analysis and processing. It includes a signal conditioning module, a data acquisition module, and an algorithm core module.

[0039] The signal conditioning module is used to filter and amplify signals from the non-invasive signal sensing layer. The signal conditioning module includes an adaptive signal conditioning circuit, which possesses intelligent signal processing capabilities. The adaptive signal conditioning circuit includes an intelligent range switching unit, an adaptive filtering unit, and an automatic calibration unit.

[0040] The intelligent range switching unit automatically switches the range of the programmable gain amplifier based on the signal amplitude. The microcontroller monitors the signal amplitude and automatically switches to a higher range when the signal approaches saturation, and automatically switches to a more sensitive range when the signal is too weak. The programmable gain amplifier has multiple preset ranges, and the microcontroller achieves automatic range switching by controlling the resistance value of the switching resistor array. This function ensures that the system always adjusts the signal within the optimal measurement range of the analog-to-digital converter. For signals with drastic amplitude fluctuations over a wide range, it can automatically lock into the optimal range in a very short time, avoiding the overload and resolution waste that can occur with traditional manual switching, and completely solving the problem of measurement inaccuracy or reduced precision caused by excessively large signal dynamic range.

[0041] The adaptive filtering unit dynamically adjusts the cutoff frequency of the anti-aliasing filter based on the signal's spectral characteristics. The microcontroller first sets the filter to its widest bandwidth, acquires a signal segment, and performs a Fast Fourier Transform (FFT) to obtain the signal's spectrum. It then identifies the highest effective frequency component (RFC) and, based on this RFC, consults a pre-stored empirical table to set the filter's cutoff frequency to the RFC multiplied by a safety factor. During continuous measurements, the microcontroller periodically repeats these steps to adapt to changes in the signal's frequency components. This function achieves "signal-specific" filtering, effectively filtering out high-frequency noise while fully preserving the signal's fundamental and lower harmonics, significantly improving the signal-to-noise ratio and resolving the inherent trade-off between noise suppression and signal fidelity in fixed-cutoff-frequency filters.

[0042] The automatic calibration unit compensates for gain and offset errors using a built-in precision reference source. The system periodically initiates a self-calibration process. The microcontroller first controls an internal relay array to short-circuit the signal input to ground to establish a zero-input reference. It reads the analog-to-digital converter (ADC) output value as the zero-point value to measure the system's DC offset. Then, it controls the relay array to connect a precision reference voltage to the signal chain to provide a known full-scale standard. It reads the ADC output value as the full-scale value to measure the system's actual gain and calculates the calibration coefficient (actual gain = reference voltage divided by the difference between the full-scale value and the zero-point value). The zero-point value and calibration coefficient are stored in non-volatile memory. In subsequent measurements, the calibration formula is applied to all sampled values ​​for real-time software compensation to eliminate hardware errors. Testing has shown that, over a wide operating temperature range, this system can stabilize the gain error of the measurement channels within a very small range through automatic calibration without any external standards or manual intervention. This overcomes parameter drift caused by temperature changes and aging of analog components, ensuring the long-term reliability and accuracy of the measurement data.

[0043] The data acquisition module is used to convert the conditioned analog signal into a digital signal, and a high-precision analog-to-digital converter is used to synchronously acquire multiple signals.

[0044] The algorithm core module is used to perform ripple decomposition, transient response modeling, and spectral feature extraction. This module deeply analyzes the collected voltage and current signals, extracts ripple characteristic parameters, transient response parameters, and spectral feature parameters, providing a data basis for subsequent intelligent diagnosis.

[0045] The intelligent application layer is used to perform health status assessment, fault prediction, and result visualization interaction based on the analysis results output by the data processing layer. It includes a performance evaluation and fault prediction unit, a central control unit, and a human-machine interaction interface.

[0046] The performance evaluation and fault prediction unit is used to perform health status assessment and remaining useful life prediction based on a multi-parameter fusion algorithm. This unit calculates the comprehensive health index using a weighted geometric mean model HI , and the comprehensive health index is calculated based on the peak-to-peak value of the output ripple voltage, the on-resistance of the power device, the series resistance of the filter capacitor, and the radiator temperature. The algorithm reads the above-mentioned preprocessed electrical parameters in real time, where represents the peak-to-peak value of the output ripple voltage, represents the on-resistance of the power MOSFET, ESR represents the series resistance of the main filter capacitor, represents the radiator temperature. The calculation formula for the comprehensive health index HI is as follows:

[0047] where, represents the factory calibration value, represents the current measured value, is the weight coefficient, [[ID=二十六]] is the adjustment exponent, used to reflect the non-linear degradation characteristics of different parameters. When the comprehensive health index HI≥0.8, the system reports that the health status is good; when 0.5≤HI<0.8, the system prompts that there is performance degradation that needs attention; when HI<0.5, the system determines that the health status has seriously deteriorated and prompts that a fault is about to occur and immediate maintenance is required. In the accelerated aging test, the same type of power supply was continuously monitored. The traditional method reported qualified for a long time, while this algorithm can give an early warning based on the rapid increase of the series resistance of the filter capacitor for a long time in advance. Subsequent disassembly verification confirmed that the electrolytic capacitors of these power supplies had indeed shown early dryness, indicating that this algorithm can detect hidden faults in advance and provides a key technical basis for realizing predictive maintenance.

[0048] The performance evaluation and fault prediction unit also uses a remaining useful life prediction algorithm based on a stress model. This algorithm combines the Miner linear cumulative damage model and the Arrhenius thermal aging model for prediction. The algorithm reads the capacitor thermal stress data and directly monitors or estimates the core temperature of the electrolytic capacitor through a temperature sensor , and at the same time records the switching times and average junction temperature of the power device .

[0049] The Arrhenius model is used to calculate the capacitor lifetime. The formula for calculating the capacitor lifetime L(T) at the current temperature is as follows:

[0050] in, Rated temperature The rated lifespan. Capacitor damage per unit of time. The calculation formula is:

[0051] The switching transistor lifetime is calculated based on a power cycle model, according to the power cycle curve provided by the manufacturer, and is influenced by junction temperature fluctuations. Damage for a single cycle can be obtained by looking up a table. Cumulative damage to power devices The calculation formula is:

[0052] Total damage It equals the sum of capacitor damage and power device damage:

[0053] Remaining service life The calculation formula is as follows:

[0054] in This represents the recent average damage rate. To verify the accuracy of the algorithm, accelerated aging experiments were conducted on the power supply at high temperatures. The prediction error between the average lifespan predicted by the algorithm and the actual average power supply failure time was within an acceptable range, proving that the algorithm can dynamically predict the remaining lifespan of the power supply with high accuracy. The prediction results can be used to generate scientific spare parts procurement plans and trigger early warnings, arranging shutdown maintenance before failures to avoid interrupting critical experiments or production processes.

[0055] The central control unit coordinates the synchronous operation of the programmable power supply, electronic load, and data acquisition module, and manages the automated testing process. When testing the entire power supply, the system can operate it under full load, light load, and dynamic load conditions to perform precise measurements and in-depth analysis of electrical performance, ripple characteristics, transient response, and electromagnetic interference.

[0056] The human-machine interface (HMI) is used for parameter setting, data visualization, and diagnostic report generation. It includes a local touchscreen and a cloud-based monitoring platform, enabling real-time interaction between test results and reports. The HMI can generate intelligent diagnostic reports that include health status scores, fault risk points, and maintenance recommendations.

[0057] The detection system of this invention also includes a security protection module, which constructs a multi-layered protection system from the external port to the internal core based on the defense-in-depth concept. The security protection module includes a first-layer protection circuit, a second-layer protection circuit, and a third-layer protection circuit.

[0058] The first layer of protection circuitry is used for surge suppression, including a gas discharge tube connected in parallel between the signal input terminal and the chassis ground. When encountering extremely high voltage, rapidly rising transient pulses such as lightning strikes or power grid surges, the gas discharge tube quickly breaks down, forming a short circuit and discharging most of the energy to the ground. The gas discharge tube can be used in series with a varistor, utilizing the fast response of the gas discharge tube and the strong current-carrying capacity of the varistor to form a synergistic protection system.

[0059] The second layer of protection circuitry is used for transient voltage clamping, including a transient voltage suppressor diode connected in parallel between the signal line and signal ground, located after the gas discharge tube and varistor. For pulses with lower amplitude but faster speed that the first layer of protection fails to fully absorb, the transient voltage suppressor diode precisely clamps the voltage within the safe range of subsequent circuitry with a picosecond-level response speed. A bidirectional transient voltage suppressor diode array is used to effectively suppress both positive and negative pulses, and a fast-blow fuse is connected in series to disconnect the circuit and prevent short circuits and fires if the transient voltage suppressor diode fails due to sustained overvoltage.

[0060] The third layer of protection circuitry is used to actively shut down fault paths, including an overvoltage and overcurrent protection integrated circuit and a high-speed solid-state relay. The protection integrated circuit continuously monitors the input voltage and current. When the voltage or current exceeds the user-defined safety window, the integrated circuit drives the solid-state relay to disconnect within microseconds, physically isolating subsequent circuits from the dangerous input. This protection is self-resetting or resettable; once the fault condition is removed, the microcontroller can attempt to automatically or manually reclose the solid-state relay without replacing components.

[0061] The safety protection module also includes an electrical isolation barrier. Signal isolation employs capacitive or magnetic isolation chips, with rated isolation voltage, creepage distance, and clearance all meeting relevant international safety standards. This ensures that fault voltage on the high-voltage side cannot enter the low-voltage side's human-machine interface via signal paths. Power isolation utilizes an isolated DC-DC converter module with a transformer for energy transfer, providing a fully floating power supply to the high-voltage side's front-end circuitry to cut off common-mode voltage paths. Pulse voltage testing of this invention showed that when contact discharge was applied to the input port, the tested system did not restart, showed no damage, and the measured data was normal. When a combined surge was applied, the protection circuit acted quickly, successfully limiting the voltage across the downstream core circuitry to a safe range.

[0062] The detection system of this invention also possesses system functional safety and self-diagnostic capabilities. Upon system power-on and operation, the microcontroller periodically executes self-diagnostic procedures, including power monitoring, sensor open / short circuit detection, processor operating status monitoring, temperature monitoring, and protection circuit self-testing. Regarding power monitoring, the microcontroller monitors the voltage of all power rails and compares it to preset ranges. If any power rail exceeds the tolerance, the high-voltage output is immediately cut off, and a power fault is displayed on the interface. For sensor open / short circuit detection, the microcontroller applies test current or voltage to the sensor to check if the response is within a reasonable range. If an abnormality is detected, the relevant channel is disabled, and an alarm is triggered indicating sensor malfunction. Regarding processor operating status monitoring, an independent watchdog timer requires the microcontroller to periodically feed the watchdog. If the program crashes without timely watchdog feeding, the watchdog forces a system hardware reset to prevent system malfunction. Regarding temperature monitoring, a temperature sensor monitors the power devices and the temperature inside the chassis in real time. When the temperature exceeds the first-level threshold, the fan speed is automatically increased; when it exceeds the second-level threshold, the system operates at a reduced rated rate or is safely shut down. Regarding the self-testing of the protection circuit, the microcontroller periodically controls the relay to inject known small voltage or current signals into the protection circuit to verify its response. If the protection circuit does not operate as expected, the system is locked in a safe state and an alarm is triggered indicating that the protection function has failed and maintenance is required.

[0063] The detection system of this invention also possesses an accuracy assurance system, ensuring long-term measurement stability through the synergistic effect of software algorithm compensation and hardware closed-loop design. The system employs a multi-point temperature sensor network and calibration data table, placing multiple high-precision digital temperature sensors at key locations on the circuit board. Before leaving the factory, the entire system is placed in a temperature chamber for full-range calibration at multiple temperature points. The gain correction coefficient and offset correction coefficient for each temperature point and each range are recorded, forming a multi-dimensional calibration table stored in the microcontroller's non-volatile memory. During system operation, the microcontroller reads the values ​​of each temperature sensor in real time and calculates the effective average temperature. Using this effective temperature as an index, it queries the pre-stored calibration table. If the current temperature is exactly at the calibration point, the corresponding coefficient is directly obtained. If it falls between two calibration points, a linear interpolation algorithm is used to calculate the accurate compensation coefficient at the current temperature. Then, the compensation formula is applied to all raw sampled data. Testing revealed that the uncompensated system exhibited significant errors in the voltage measurement channel over a wide operating temperature range. After applying temperature compensation, the measurement error across the entire temperature range was stably controlled to a very small extent, reducing the impact of temperature drift by an order of magnitude and significantly improving the long-term measurement consistency of the system under different environments.

[0064] The detection system of this invention also employs a method based on... Modulation and digital feedback closed-loop isolation measurement technology solves the problems of nonlinearity, gain drift with temperature and aging inherent in traditional analog isolation amplifiers. In the voltage measurement channel, the analog input signal on the high-voltage side is first... The modulator converts the signal into a one-bit high-speed digital bitstream, the duty cycle of which is precisely proportional to the input voltage. The digital stream is transmitted to the low-voltage side via a capacitive digital isolator. Since the transmitted signal is digital, its amplitude and timing are unaffected by the drift of the isolation components themselves, fundamentally eliminating errors introduced by the isolation stage. On the low-voltage side, the digital stream is reconstructed into high-resolution digital code by the demodulator and simultaneously converted into an analog voltage by a high-precision, low-drift digital-to-analog converter. This analog voltage is fed back to the high-voltage side and compared with the original input voltage at the summation point. Any errors generated in the forward path are corrected in real time through the negative feedback loop. The overall gain and linearity of the system are no longer determined by unstable analog isolation components, but by the high stability of the low-voltage side reference voltage source and the accuracy of the precision digital-to-analog converter. In long-term aging tests compared to traditional open-loop isolation amplifiers, the gain drift and nonlinearity of this invention are significantly better than those of the traditional solution, and its long-term stability is improved by more than an order of magnitude, ensuring measurement accuracy throughout the entire product lifecycle.

[0065] The detection system of this invention features a modular structure. Non-invasive sensors can be quickly connected and replaced via standard interfaces, adapting to the testing needs of special light source power supplies with different power levels and packaging forms. The system can also be designed as an integrated portable structure, internally integrating a programmable power supply and electronic load, suitable for various scenarios such as laboratories, production lines, and field services. The system also possesses data traceability and comparative analysis functions, enabling the establishment of a full lifecycle performance profile for the power supply and tracking performance degradation trends.

[0066] The present invention also provides an intelligent visual detection method suitable for special light source switching power supplies, comprising the following steps.

[0067] S1: The electrical signal of the power supply under test is coupled using a non-invasive sensor without disconnecting the circuit. Specifically, a high-frequency current clamp couples the current signal based on the principle of electromagnetic induction. The high-frequency current clamp is placed around the conductor through which the current flows, inducing an alternating magnetic field around the conductor and converting it into a voltage signal. A high-isolation voltage probe, based on the principle of capacitive coupling, senses the voltage signal. The high-isolation voltage probe senses the high voltage at the test point through a high-impedance capacitive voltage divider network and generates a precisely scaled-down signal on the low-voltage side.

[0068] S2: Conditions, acquires, and digitizes the coupled electrical signals. Specifically, this includes monitoring the signal amplitude, automatically switching to a higher range when the signal approaches saturation, and automatically switching to a higher sensitivity range when the signal is too weak. It performs a Fast Fourier Transform (FFT) on the signal to identify the highest effective frequency component and dynamically adjusts the cutoff frequency of the anti-aliasing filter based on this component. It performs periodic automatic calibration using a built-in precision reference source to compensate for gain and offset errors. The conditioned signal is then converted to a digital signal by a high-precision analog-to-digital converter.

[0069] S3: Perform ripple characteristic analysis and transient response analysis on the digitized signal. Ripple decomposition is performed on voltage and current signals to extract parameters such as peak-to-peak ripple voltage. Transient response modeling is used to analyze the dynamic characteristics of the power supply under sudden load changes. Spectral feature extraction is performed to analyze the frequency domain characteristics of the signal to identify abnormal frequency components.

[0070] S4: Based on the analysis results, perform health status assessment and fault prediction, and generate a visualized diagnostic report. Health status assessment includes collecting peak-to-peak output ripple voltage, power device on-resistance, filter capacitor series resistance, and heatsink temperature as assessment parameters. The current measured values ​​of each parameter are compared with the factory calibration values ​​to calculate the degree of degradation. A weighted geometric mean model is used to fuse the degradation degrees of each parameter to obtain a comprehensive health index. The comprehensive health index is used to determine the power supply's health status level. Fault prediction includes calculating capacitor damage based on the Arrhenius thermal aging model and power device damage based on the power cycling model. The capacitor damage and power device damage are accumulated, and the remaining service life is predicted based on the recent average damage rate. The diagnostic report includes a health status score, fault risk points, and maintenance recommendations, and can be displayed and interacted with via a local touchscreen or cloud monitoring platform.

[0071] The application of this system and method has transformed the operation and maintenance model of special light source power supplies from "post-event maintenance" to "pre-event prediction," significantly improving product reliability, stability, and market competitiveness. This strongly supports their large-scale reliable application in high-end analytical instruments, medical devices, and military testing. The automated test sequence and intelligent report generation functions save significant manpower and time costs associated with complex wiring, manual recording, and data processing in traditional testing, achieving the goal of cost reduction and efficiency improvement for enterprises.

[0072] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent transformations or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An intelligent visual detection system for a special light source switching power supply, characterized in that, include: A non-invasive signal sensing layer is used to couple the electrical signals of the power supply under test without disconnecting the circuit of the power supply under test. The non-invasive signal sensing layer includes: High-frequency current clamp is used to couple the output current signal of the power supply under test through the principle of electromagnetic induction. A high-isolation voltage probe is used to sense the output voltage signal of the power supply under test through capacitive coupling. The data processing layer is used to receive analog signals from the non-invasive signal sensing layer and convert them into digital signals for analysis and processing. The intelligent application layer is used to perform health status assessment, fault prediction, and result visualization interaction based on the analysis results output by the data processing layer.

2. The intelligent visual detection system according to claim 1, characterized in that, The high-frequency current clamp uses nanocrystalline magnetic core material, and the voltage divider capacitor of the high isolation voltage probe is made of C0G material.

3. The intelligent visual detection system according to claim 1, characterized in that, The high isolation voltage probe includes: The voltage divider network uses a low-loss tangent high-voltage ceramic capacitor; The protection circuit includes a gas discharge tube and a transient voltage suppression diode arranged in series. An isolation amplifier uses capacitive or magnetic isolation technology to achieve isolated signal transmission.

4. The intelligent visual detection system according to claim 1, characterized in that, The data processing layer includes: The signal conditioning module is used to filter and amplify the signal from the non-invasive signal sensing layer. The data acquisition module is used to convert the conditioned analog signal into a digital signal; The core module of the algorithm is used to perform ripple decomposition, transient response modeling, and spectral feature extraction.

5. The intelligent visual detection system according to claim 4, characterized in that, The signal conditioning module includes an adaptive signal conditioning circuit, which includes: The intelligent range switching unit is used to automatically switch the range of the programmable gain amplifier according to the signal amplitude. An adaptive filtering unit is used to dynamically adjust the cutoff frequency of the anti-aliasing filter according to the spectral characteristics of the signal. An automatic calibration unit is used to compensate for gain and offset errors using a built-in precision reference source.

6. The intelligent visual detection system according to claim 1, characterized in that, The intelligent application layer includes: The performance evaluation and fault prediction unit is used to perform health status evaluation and remaining service life prediction based on a multi-parameter fusion algorithm. The central control unit is used to coordinate the synchronous operation of the programmable power supply, electronic load, and data acquisition module. The human-computer interaction interface is used for parameter setting, data visualization, and diagnostic report generation.

7. The intelligent visual detection system according to claim 6, characterized in that, The performance evaluation and fault prediction unit uses a weighted geometric mean model to calculate the comprehensive health index, which is based on the peak-to-peak value of the output ripple voltage, the on-resistance of the power devices, the series resistance of the filter capacitor, and the temperature of the heat sink.

8. The intelligent visual detection system according to any one of claims 1 to 7, characterized in that, It also includes a security protection module, which includes: The first layer of protection circuit includes a gas discharge tube connected in parallel at the signal input terminal for surge suppression; The second layer of protection circuitry includes transient voltage suppression diodes for transient voltage clamping; The third layer of protection circuitry includes overvoltage and overcurrent protection integrated circuits and solid-state relays, which are used to actively shut down fault paths.

9. An intelligent visual detection method suitable for special light source switching power supplies, characterized in that, Includes the following steps: S1: Couple the electrical signal of the power supply under test through a non-invasive sensor without disconnecting the circuit of the power supply under test; S2: Conditioning, acquiring, and digitizing the coupled electrical signals; S3: Perform ripple characteristic analysis and transient response analysis on the digitized signal; S4: Based on the analysis results, perform health status assessment and fault prediction, and generate a visual diagnostic report.

10. The intelligent visual detection method according to claim 9, characterized in that, In step S1, the current signal is coupled based on the principle of electromagnetic induction using a high-frequency current clamp, and the voltage signal is sensed based on the principle of capacitive coupling using a high-isolation voltage probe.

11. The intelligent visual detection method according to claim 9, characterized in that, Step S2 includes: S201: Monitors signal amplitude. When the signal approaches saturation, it automatically switches to a higher range. When the signal is too weak, it automatically switches to a higher sensitivity range. S202: Perform a Fast Fourier Transform on the signal to identify the highest effective frequency component in the signal; S203: Dynamically adjust the cutoff frequency of the anti-aliasing filter according to the highest effective frequency component.

12. The intelligent visual detection method according to claim 9, characterized in that, The health status assessment in step S4 includes: The peak-to-peak value of the output ripple voltage, the on-resistance of the power devices, the series resistance of the filter capacitor, and the temperature of the heat sink are collected as evaluation parameters. The current measured values ​​of each evaluation parameter are compared with the factory calibration values ​​to calculate the degree of degradation of each parameter; A weighted geometric mean model is used to calculate the degree of degradation of each parameter to obtain a comprehensive health index.

13. The intelligent visual detection method according to claim 9, characterized in that, The fault prediction in step S4 includes: The degree of capacitor damage was calculated based on the Arrhenius thermal aging model; Damage to power devices is calculated based on a power cycling model; The capacitor damage and power device damage are accumulated, and the remaining service life is predicted based on the recent average damage rate.

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