High-frequency switching power supply aging monitoring system and method

Abstract

The invention aims to provide the high-frequency switching power supply aging monitoring system and the high-frequency switching power supply aging monitoring method which comprehensively reflect the switching state and are high in precision. The high-frequency switching power supply aging monitoring system comprises a data acquisition module, a data processing and fusion module and an early warning decision module. The data acquisition module is used for acquiring electrical parameters and physical parameters of the high-frequency switching power supply in the aging process in real time; the data processing and fusion module is used for receiving the original data of the data acquisition module, performing preprocessing and feature extraction, and performing comprehensive state evaluation by adopting a multi-parameter fusion algorithm; the early warning decision module is used for automatically matching a threshold value according to the model of the module, and carrying out equipment real-time uploading alarm and platform real-time operation alarm; the high-frequency switching power supply aging monitoring method is realized based on the high-frequency switching power supply aging monitoring system. The method is applied to the technical field of aging tests.

Description

Technical Field

[0001] This invention relates to the technical field of aging testing, and in particular to a high-frequency switching power supply aging monitoring system and method. Background Technology

[0002] In the production process of high-frequency switching power supplies, aging testing is an essential step in screening for early failures and ensuring long-term reliability. However, existing power supply aging monitoring systems are currently mainly divided into the following categories: Manual timed recording: Current aging tests mostly rely on manual timed recording of data such as voltage, temperature, and faults, typically at intervals of 1.5 hours, 3 hours, or 6 hours. However, this timed recording method cannot capture and record intermittent or transient fault symptoms, leading to the accumulation of potential risks. For example, if the aging equipment goes through over-temperature protection and then restarts aging after the temperature returns to normal, abnormal data cannot be obtained in a timely manner or due to a lack of recording. In addition, it is impossible to monitor the entire process data in real time, and manual recording may result in errors or inaccurate data.

[0003] Single-parameter threshold monitoring: This method relies on a limited set of electrical parameters, such as voltage and current, for judgment and comparison with preset thresholds. This fails to comprehensively reflect the aging state of the power supply. It only provides threshold alarms (e.g., overvoltage, overcurrent) and lacks analysis of parameter change trends. For example, it fails to dynamically determine the thresholds for key parameters like current sharing and voltage regulation accuracy, making it unable to predict potential, gradual faults.

[0004] Basic online monitoring systems, such as traditional intelligent online detection devices for high-frequency switching power supplies, can collect operating status characteristic signals from multiple switching power supply modules and perform simple fault diagnosis and performance ranking. However, their data analysis capabilities are limited, and they lack accurate prediction of the power supply aging process.

[0005] Therefore, there is an urgent need for an intelligent monitoring solution that can monitor the status of switching power supplies in real time and in multiple dimensions, and provide early and accurate warnings based on data analysis. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a high-frequency switching power supply aging monitoring system and method that comprehensively reflects the switching status and has high accuracy.

[0007] The technical solution adopted in this invention is as follows: the high-frequency switching power supply aging monitoring system includes a data acquisition module, a data processing and fusion module, and an early warning decision module; The data acquisition module is used to collect electrical and physical parameters of the high-frequency switching power supply in real time during the aging process. The data processing and fusion module is used to receive raw data from the data acquisition module, perform preprocessing and feature extraction, and use a multi-parameter fusion algorithm for comprehensive status evaluation. The early warning decision module is used to automatically match thresholds based on the module model, and to send alarms to the device in real time and to calculate alarms in real time on the platform.

[0008] As can be seen from the above scheme, the data acquisition module realizes real-time acquisition of electrical and physical parameters, and the data processing and fusion module records and evaluates the data. It monitors and provides early warnings for intermittent and transient fault symptoms, achieving fully automated monitoring of the aging process, reducing manual intervention, and improving testing efficiency and data reliability. Simultaneously, it collects and correlates multiple parameters such as current sharing, voltage regulation accuracy, and ripple, providing a more comprehensive data foundation for power supply aging status assessment. The module performs threshold alarms and trend warnings to promptly detect potential faults, improving the power supply module's quality and reliability assessment capabilities. The early warning decision module automatically loads corresponding thresholds based on different power supply module models, achieving accurate early warnings.

[0009] In a preferred embodiment, the data acquisition module includes an RS485 serial port connection unit and a CAN interface connection unit. The RS485 serial port connection unit is connected to the acquisition unit and acquires the AC input voltage, output bus current, ripple, and cabinet temperature. The CAN interface connection unit is connected to the acquisition unit and acquires the voltage, current, internal temperature, and communication address of the switching power supply.

[0010] In a preferred embodiment, the data processing and fusion module includes an aging monitoring server and a database. The aging monitoring server receives data collected by the data acquisition module, performs calculations using a multi-parameter fusion algorithm, and conducts data analysis. The database stores the data collected during the aging process, alarm records, and analysis results generated by the aging monitoring server.

[0011] In a preferred embodiment, the early warning decision module matches a preset threshold based on the model of the test product, compares the data collected by the data acquisition module with the threshold, and issues an alarm when an anomaly occurs.

[0012] The high-frequency switching power supply aging monitoring method includes the following specific steps: Step S1: The data acquisition module connects the acquisition device and the power supply module under test through a communication network to provide feedback on AC input voltage, output bus current, output bus voltage, ripple, temperature, as well as the voltage, current, internal temperature, and communication address of the power supply module under test. Step S2: Before testing, the operator identifies the coding information of the power module under test using a barcode scanner. The data processing and fusion module obtains relevant information and test parameters of the power module under test based on the coding information, and generates an aging task sheet based on the obtained information. Step S3: After starting the test, the data processing and fusion module receives the data transmitted by the data acquisition module through the communication network. The data processing and fusion module performs the test and records the data through a multi-parameter fusion algorithm. The early warning decision module issues an alarm during the test. Step S4: Upon completion of the test, the early warning decision module generates an aging report and archives it.

[0013] As can be seen from the above scheme, the aging assessment of the power module under test is achieved by using a multi-parameter fusion algorithm, thereby comprehensively reflecting the aging status of the power module and the risks that occur during the aging process, ensuring the reliability and accuracy of the product aging test.

[0014] A preferred embodiment is that the multi-parameter fusion algorithm in step S3 includes the following specific steps: Step a, real-time acquisition of voltage, current, internal temperature, system ambient temperature, bus voltage, and ripple value measured by the acquisition device; Step b: Aggregate data by cabinet dimension and refresh at a set interval; Step c: Calculate key parameters; key parameters include current sharing imbalance, voltage regulation accuracy, and ripple value. Attached Figure Description

[0015] Figure 1 This is a system block diagram of the high-frequency switching power supply aging monitoring system; Figure 2 This is a flowchart of the high-frequency switching power supply aging monitoring method; Figure 3 This is a schematic diagram of the aging monitoring interface in real time. Figure 4 This is a schematic diagram of the flow imbalance in the aging monitoring interface. Detailed Implementation

[0016] like Figure 1 As shown, in this embodiment, the high-frequency switching power supply aging monitoring system includes a data acquisition module 1, a data processing and fusion module 2, and an early warning decision module; The data acquisition module 1 is used to collect electrical and physical parameters of the high-frequency switching power supply in real time during the aging process. The data acquisition module 1 includes an RS485 serial port connection unit and a CAN interface connection unit. The RS485 serial port connection unit connects to the data acquisition unit and collects AC input voltage, output bus current, ripple, and cabinet temperature. The CAN interface connection unit connects to the data acquisition unit and collects the voltage, current, internal temperature, and communication address of the switching power supply. The RS485 serial port connection unit is used for measuring electrical parameters, and the CAN interface connection unit is used for collecting data from the aging cabinet and the power supply module under test and sending feedback. The data acquisition unit can be a voltage, current, or temperature acquisition unit, such as a voltmeter, ammeter, or temperature sensor. The RS485 serial port connection unit and the CAN interface connection unit communicate with a host computer via a switch to upload the data to the host computer or server.

[0017] The data processing and fusion module 2 receives raw data from the data acquisition module 1, performs preprocessing and feature extraction, and uses a multi-parameter fusion algorithm for comprehensive status evaluation. The data processing and fusion module 2 includes an aging monitoring server and a database. The aging monitoring server receives data collected by the data acquisition module 1, performs calculations using the multi-parameter fusion algorithm, and conducts data analysis. The database stores data collected during the aging process, alarm records, and analysis results generated by the aging monitoring server. Through the collaborative operation of the aging monitoring server and the database, data processing and storage are achieved, and communication with external interactive terminals enables operators to obtain real-time aging data and stored historical data.

[0018] The early warning decision module is used to automatically match thresholds based on the module model, enabling real-time alarm transmission from the device and real-time alarm calculation by the platform. The early warning decision module matches preset thresholds based on the model of the tested product and compares the data collected by the data acquisition module 1 with the thresholds, issuing an alarm when an anomaly occurs. A barcode is provided on the power supply module under test, which the operator scans to bind the module. Before aging, the early warning decision module communicates with the data processing and fusion module 2. The data processing and fusion module 2 sets various aging parameters, such as output voltage range, current limit, and aging time, in the corresponding aging task sheet based on the characteristics and aging requirements of the power supply module. The early warning decision module then sets the thresholds for key parameters in the multi-parameter fusion algorithm and triggers a warning based on the results from the data processing and fusion module 2.

[0019] like Figures 1 to 4 As shown, the high-frequency switching power supply aging monitoring method includes the following specific steps: Step S1: The data acquisition module 1 connects the acquisition device and the power supply module under test through a communication network to provide feedback on AC input voltage, output bus current, output bus voltage, ripple, temperature, and the voltage, current, internal temperature, and communication address of the power supply module under test. During the access operation, network address allocation and configuration are required to ensure that the acquisition device can communicate normally in the network. Step S2: Set up and start the monitoring process on the aging monitoring server. Before the test, the operator identifies the code or barcode information of the power module under test through a barcode scanner. The data processing and fusion module 2 obtains relevant information and test parameters of the power module under test according to the code or barcode information, and generates an aging task sheet according to the obtained information. At the same time, the early warning decision module sets the threshold for triggering alarms according to the information of the power module under test. Step S3: After starting the test, the data processing and fusion module 2 receives the data transmitted by the data acquisition module 1 through the communication network. The data processing and fusion module 2 performs the test and records the data using a multi-parameter fusion algorithm. The early warning decision module monitors and issues early warnings during the test. The monitoring and early warnings include types such as abnormal system ambient temperature, overvoltage / undervoltage, abnormal ripple, excessive current sharing, and communication abnormalities. Alarm levels and priorities are set according to the type, and only the highest level alarm is alerted at the same time, thus achieving hierarchical early warning and ensuring that alarm types with more serious problems are handled first. Alarm methods may include audible alarms and displaying alarm information on the interface. The thresholds corresponding to different specifications of the power supply modules under test are updated and stored in the database, and the corresponding configuration parameters are called during the test. The alarm information is updated at a set interval, which is 1 minute in this embodiment. Step S4: Upon completion of the test, the early warning decision module generates an aging report and archives it.

[0020] During testing, operators can start, pause, resume, and end the aging process via an interactive terminal. Pause time is included in the total duration. The aging time can be set from 1 to 72 hours, with automatic timing and automatic stop upon completion, or it can be manually ended early. Pre-start parameter confirmation includes setting the output voltage to 80%Un to 110%Un and the current to 20%In to 100%In.

[0021] The interactive terminal's interface displays in real-time various data collected by the data acquisition module 1 during the aging process, such as the voltage, current, and temperature change curves of the power module, and real-time values ​​of key indicators like current sharing and voltage regulation accuracy. It also intuitively displays the operating status of each power module, such as normal operation, alarm, and fault. Simultaneously, it displays the aging progress, such as the time elapsed and remaining time.

[0022] After the aging test is completed, the data processing and fusion module 2 automatically generates an aging report based on the collected data and calculated indicators. The aging report should include the product model, aging temperature curve, current sharing imbalance, voltage regulation accuracy result, and ripple result. It relies on a multi-parameter fusion engine to analyze current sharing imbalance and voltage regulation accuracy to determine whether the module has passed the aging test. Users can review and confirm the generated aging report. After confirmation, the aging report is archived for subsequent querying and analysis.

[0023] Through the above steps, aging tasks are generated, multi-parameter real-time fusion acquisition and intelligent analysis are achieved, the comprehensiveness of monitoring and the authenticity of data are improved, and accurate early warning and real-time anomaly response are realized. A flexible aging pause mechanism is provided, and the removal of abnormal modules does not interrupt the overall aging process, ensuring the continuity and validity of test data. The system automatically generates structured reports, and process data and reports are automatically archived for a long time for subsequent query and analysis.

[0024] The multi-parameter fusion algorithm described in step S3 includes the following specific steps: Step a: Real-time acquisition of voltage, current, internal temperature, system ambient temperature, bus voltage, and ripple value measured by the acquisition device; Step b: Aggregate data by cabinet dimension and refresh at a set interval; Step c: Calculate key parameters; key parameters include current sharing imbalance, voltage regulation accuracy, and ripple value.

[0025] Specifically, the flow imbalance δ I A key feature point is dynamically calculated every 15 minutes, satisfying δ. I =(I M -I PJ ) / I E ×100%; Where, δ I : Flow imbalance, I M : Single module output current limit (i.e., maximum or minimum value), I PJ : Average output current of the module, I E (Rated output current value for a single module). δ I The requirement is ≤±5%. If it exceeds 5%, an abnormal alarm will be generated.

[0026] Voltage regulation accuracy δ U A key feature point is dynamically calculated every 15 minutes, satisfying δ. U = (U M - U z ) / U z ×100%; Where, δ U Voltage regulation accuracy; UM : Output voltage limit (i.e., maximum or minimum value) during aging process; U Z Aging setpoint. δ U The error should be within ±0.5%; an alarm should be triggered if it exceeds ±0.5%.

[0027] Ripple anomaly detection: After 1.5 hours of aging, a baseline ripple value is obtained. The value of the acquisition device is then compared every 15 minutes. If the value exceeds the baseline by more than double, an alarm is triggered, and key ripple feature points are identified.

[0028] A fusion analysis was performed on parameters such as current sharing imbalance, voltage regulation accuracy, and ripple. Current sharing imbalance, voltage regulation accuracy, and ripple were selected as three key parameters. Fuzzy logic fusion was employed; for example, if either current sharing imbalance or voltage regulation accuracy did not meet the specified value, the aging test would fail. After 1.5 hours of aging test, a ripple alarm was generated every 15 minutes if the ripple value exceeded the baseline ripple value.

[0029] The high-frequency switching power supply aging monitoring method described above breaks through the traditional mode of only monitoring voltage / current, solving the problem of lacking monitoring for intermittent and transient fault symptoms. It achieves fully automated monitoring of the aging process, reducing manual intervention and improving testing efficiency and data reliability. Simultaneously, it collects and correlates multiple parameters such as current sharing, voltage regulation accuracy, and ripple, providing a more comprehensive data foundation for power supply aging status assessment. A threshold alarm combined with trend warning mechanism is employed to promptly detect potential faults, improving the power module quality and reliability assessment capabilities. Corresponding thresholds are automatically loaded according to different voltage module models to achieve accurate warnings. A "cloud-edge-device" collaborative architecture is adopted. Computationally intensive analysis is placed in the cloud, reducing the cost and complexity of field equipment while achieving centralized data management and remote monitoring.

[0030] Although the embodiments of the present invention are described with reference to actual solutions, they do not constitute a limitation on the meaning of the present invention. Modifications to the embodiments and combinations with other solutions based on this specification will be obvious to those skilled in the art.

Claims

1. A high-frequency switching power supply aging monitoring system, characterized in that: It includes a data acquisition module (1), a data processing and fusion module (2), and an early warning decision module; The data acquisition module (1) is used to collect electrical and physical parameters of the high-frequency switching power supply in real time during the aging process. The data processing and fusion module (2) is used to receive the raw data from the data acquisition module (1), perform preprocessing and feature extraction, and use a multi-parameter fusion algorithm to perform comprehensive state evaluation. The early warning decision module is used to automatically match thresholds based on the module model, and to send alarms to the device in real time and to calculate alarms in real time on the platform.

2. The high-frequency switching power supply aging monitoring system according to claim 1, characterized in that: The data acquisition module (1) includes an RS485 serial port connection unit and a CAN interface connection unit. The RS485 serial port connection unit is connected to the acquisition unit and acquires AC input voltage, output bus current, ripple and cabinet temperature. The CAN interface connection unit is connected to the acquisition unit and acquires voltage, current, internal temperature and communication address of the switching power supply.

3. The high-frequency switching power supply aging monitoring system according to claim 1, characterized in that: The data processing and fusion module (2) includes an aging monitoring server and a database. The aging monitoring server receives the data collected by the data acquisition module (1), performs multi-parameter fusion algorithm calculations, and conducts data analysis. The database stores the data collected during the aging process, alarm records, and analysis results generated by the aging monitoring server.

4. The high-frequency switching power supply aging monitoring system according to claim 1, characterized in that: The early warning decision module matches a preset threshold based on the model of the test product, and compares the data collected by the data acquisition module (1) with the threshold, and issues an alarm when an abnormality occurs.

5. A method for monitoring the aging of a high-frequency switching power supply, implemented using the high-frequency switching power supply aging monitoring system described in any one of claims 1 to 4, characterized in that, It includes the following specific steps: Step S1: The data acquisition module (1) connects the acquisition device and the power supply module under test through the communication network to provide feedback on AC input voltage, output bus current, output bus voltage, ripple, temperature, and the voltage, current, internal temperature and communication address of the power supply module under test. Step S2: Before testing, the operator identifies the coding information of the power module under test using a barcode scanner. The data processing and fusion module (2) obtains relevant information and test parameters of the power module under test based on the coding information, and generates an aging task sheet based on the obtained information. Step S3: After starting the test, the data processing and fusion module (2) receives the data transmitted by the data acquisition module (1) through the communication network. The data processing and fusion module (2) performs the test and records the data through the multi-parameter fusion algorithm. The early warning decision module issues an alarm during the test. Step S4: Upon completion of the test, the early warning decision module generates an aging report and archives it.

6. The high-frequency switching power supply aging monitoring method according to claim 5, characterized in that, The multi-parameter fusion algorithm described in step S3 includes the following specific steps: Step a: Real-time acquisition of voltage, current, internal temperature, system ambient temperature, bus voltage, and ripple value measured by the acquisition device; Step b: Aggregate data by cabinet dimension and refresh at a set interval; Step c: Calculate key parameters; key parameters include current sharing imbalance, voltage regulation accuracy, and ripple value.

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