Fault detection method and system for CT energy taking equipment

Abstract

The invention relates to the technical field of CT energy taking equipment fault detection, and relates to a CT energy taking equipment fault detection method and system, and the method comprises the steps: collecting the voltage and current signals of an energy storage element, building a health state reference model, extracting dynamic response characteristics, comparing a baseline parameter set, judging the performance degradation trend, and determining the specific degradation index and degree. The system comprises an acquisition module, a reference model module, a feature extraction module, a comparison module and a judgment module. According to the method, through multi-dimensional verification and periodic retest, the performance degradation area is accurately positioned, the maintenance scheme is generated, and meanwhile, the quality of the maintenance process is monitored in real time. According to the invention, the accuracy and efficiency of fault detection of the CT energy taking equipment can be improved, and reliable operation of the equipment is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of power equipment monitoring and fault diagnosis technology, specifically a fault detection method and system for CT energy harvesting equipment. Background Technology

[0002] In the field of power equipment, CT energy harvesting equipment is an important energy harvesting device, and the performance of its core component, supercapacitor or rechargeable battery, directly affects the stable operation of the equipment.

[0003] As usage time increases, these energy storage components gradually deteriorate, primarily manifested as increased internal resistance and reduced capacity. When this deterioration accumulates to a certain extent, the equipment may experience voltage instability or even power outages, leading to system failure. However, traditional methods typically only detect problems after obvious equipment malfunctions occur, making it difficult to provide early warnings of performance degradation trends.

[0004] Currently, common detection methods mostly rely on periodic manual inspections or simple voltage monitoring. Manual inspections require shutdown operations, which are not only inefficient but may also affect the normal operation of the equipment. Voltage monitoring methods can only reflect the current output status and cannot comprehensively assess the health status of energy storage components.

[0005] For example, when the internal resistance of an energy storage element just begins to increase, the output voltage may still be within the normal range, but at this point the element has already entered its performance degradation phase. Furthermore, existing technologies lack effective online quantification methods for capacity changes in energy storage elements, making it difficult to detect potential problems in a timely manner.

[0006] In recent years, some studies have attempted to assess the health status of energy storage devices through indirect methods, such as estimation based on parameters like temperature and the number of charge-discharge cycles.

[0007] However, these methods often have limitations, failing to directly measure key performance indicators of energy storage components, such as equivalent series resistance and capacity changes. Furthermore, external environmental factors, such as temperature fluctuations, can interfere with the evaluation results, further reducing the accuracy of the measurements.

[0008] Furthermore, existing technologies lack the ability to dynamically track the performance degradation trends of energy storage components, making it difficult to provide maintenance recommendations based on real-time data.

[0009] Current technologies still have shortcomings in the health management of energy storage components. Testing results are mostly limited to post-fault diagnosis, failing to upgrade from fault detection to health management. Especially during equipment operation, how to efficiently and accurately assess the health status of energy storage components and provide early warnings of performance degradation remains a pressing issue.

[0010] With the increasingly widespread application of CT energy harvesting devices, traditional detection methods and existing technologies are no longer sufficient to meet the needs of online quantitative assessment of the health status of energy storage components. A health management method that can monitor in real time, analyze accurately, and provide early warnings is needed. Summary of the Invention

[0011] The purpose of this invention is to provide a fault detection method and system for CT energy harvesting equipment to solve the problems mentioned in the background art.

[0012] To achieve the above objectives, the present invention provides a fault detection method for a CT energy harvesting device, the method comprising: The voltage and current signals across the energy storage element are collected. The energy storage element is the core component, and its performance degradation is manifested by increased internal resistance and reduced capacity. The voltage and current signals contain dynamic response data of the energy storage element under different operating conditions. Establish a health status reference model; the health status reference model includes a baseline parameter set and a temperature compensation rule set. The baseline parameter set includes the initial values ​​of the equivalent series resistance and the initial capacity obtained during factory testing. The temperature compensation rule set is generated based on the relationship between ambient temperature and the performance changes of energy storage components. Extract the dynamic response characteristics of the energy storage element, including transient voltage drop amplitude, recovery time constant, and charge / discharge curve slope; The dynamic response characteristics are compared with the baseline parameter set in the health status reference model to determine whether the energy storage element has a performance degradation trend. If so, determine the specific indicators of performance degradation and the corresponding degree of degradation based on the comparison results.

[0013] Preferably, the extraction of the dynamic response characteristics of the energy storage element includes the following steps: A high-current pulse with a duration in the microsecond range is injected into the energy storage element; the high-current pulse is generated by the control unit, and its amplitude and duration are set according to the maximum allowable current and thermal stability of the energy storage element. The voltage change curves across the energy storage element before and after the pulse are sampled using a high-speed analog-to-digital converter; the voltage change curves include data points for the transient drop phase and the recovery phase; Calculate the transient voltage drop amplitude; the transient voltage drop amplitude is the difference between the pulse injection moment and the steady-state voltage; The voltage change curve during the recovery phase is fitted to obtain the recovery time constant; the recovery time constant is obtained by fitting an exponential function and is used to characterize the dynamic recovery capability of the energy storage element. The charge-discharge curves of the energy storage element are analyzed, and the slope of the charge-discharge curves is extracted. The slope of the charge-discharge curves is calculated by a linear regression algorithm and used to evaluate the capacity change of the energy storage element.

[0014] Preferably, after comparing the dynamic response features with the baseline parameter set in the health status reference model, the method further includes the following steps: If at least two baseline parameters deviate from the dynamic response characteristics beyond a first preset range, a performance degradation candidate set is generated. Calculate the mean and standard deviation of the deviation for each baseline parameter in the candidate set of performance degradation; The baseline parameter with the largest mean deviation and the smallest standard deviation is used as the target degradation index.

[0015] Preferably, after selecting the baseline parameter with the largest mean deviation and the smallest standard deviation as the target degradation index, the method further includes the following steps: Obtain the set of degradation degree judgment thresholds corresponding to the target degradation index, wherein the set of degradation degree judgment thresholds includes resistance increment threshold, capacity decay percentage threshold and temperature compensation coefficient; Calculate the actual parameter values ​​corresponding to the degradation degree judgment threshold set in the dynamic response characteristics. The actual parameter values ​​include the change of the current equivalent series resistance relative to the initial value, the attenuation ratio of the current capacity relative to the initial value, and the correction value after temperature compensation. The actual parameter values ​​are compared with the set of degradation degree judgment thresholds to determine the degradation degree of the target degradation index.

[0016] Preferably, after generating the candidate set of performance degradation, the method further includes the following steps: Obtain a performance correlation rule base from historical operating data, wherein the performance correlation rule base records the co-occurrence probability of different performance degradation indicators; If two performance degradation indicators in the candidate set have a co-occurrence probability exceeding the second preset range, then multi-dimensional verification is initiated. Cross-validate the dynamic response characteristics corresponding to the two performance degradation indicators. If the consistency of the cross-validation results exceeds the third preset range, both performance degradation indicators will be included in the target degradation indicators.

[0017] Preferably, after determining whether the energy storage element exhibits a performance degradation trend, the method further includes the following steps: If there is no performance degradation trend, a no-degradation test report is generated, which includes the location information of the energy storage element, the test time, and the dynamic response feature extraction results. If a performance degradation trend exists, a performance degradation location map is generated based on the target degradation index and the degree of degradation. The performance degradation location map indicates the distribution area of ​​performance degradation in the energy storage element and the degree of degradation. By associating the performance degradation location map with the maintenance suggestion set in the health status reference model, a preliminary maintenance plan is generated.

[0018] Preferably, after generating the preliminary maintenance plan, the following steps are also included: Obtain the physical structure parameters of the energy storage element, including electrode material type, packaging form and designed service life; The physical structure parameters are input into the maintenance scheme optimization model, which is trained and generated based on historical maintenance case data. The maintenance cycle and replacement strategy in the preliminary maintenance plan are adjusted based on the output of the optimization model to obtain the final maintenance plan.

[0019] Preferably, the method further includes the following steps: The energy storage element is periodically retested at preset time intervals, and the retest signals are collected. The dynamic response characteristics of the first detection signal and the retest signal are compared. The changes include the rate of increase of equivalent series resistance, the rate of capacity decay, and the fluctuation of temperature compensation correction value. If any change exceeds the warning range of the corresponding performance degradation indicator, a performance tracking alarm will be triggered.

[0020] Preferably, the method further includes the following steps: During the implementation of the final maintenance plan, maintenance process signals are collected in real time; Key maintenance node features are extracted from the maintenance process signals. These key maintenance node features include electrode contact quality features, packaging sealing features, and temperature distribution uniformity features. The key maintenance node characteristics are compared with the maintenance quality requirements in the health status reference model. If the comparison results do not meet the requirements, maintenance adjustment suggestions are generated.

[0021] Preferably, the present invention further includes a fault detection system for a CT energy harvesting device, used to implement the fault detection method for a CT energy harvesting device as described above, the system comprising: The acquisition module is configured to acquire voltage and current signals across the energy storage element. The energy storage element is a core component, and its performance degradation is manifested by increased internal resistance and reduced capacity. The voltage and current signals contain dynamic response data of the energy storage element under different operating conditions. A reference model module is configured to store a health state reference model. The health state reference model includes a baseline parameter set and a temperature compensation rule set. The baseline parameter set includes the initial value of the equivalent series resistance and the initial value of the capacity obtained during factory testing. The temperature compensation rule set is generated based on the relationship between ambient temperature and the performance changes of the energy storage element. The feature extraction module is configured to extract the dynamic response features of the energy storage element, including transient voltage drop amplitude, recovery time constant, and charge / discharge curve slope. The comparison module is configured to compare the dynamic response characteristics with the baseline parameter set in the health status reference model to determine whether the energy storage element has a performance degradation trend. The determination module determines the specific indicators of performance degradation and the corresponding degree of degradation based on the comparison results.

[0022] Compared with the prior art, the beneficial effects of the present invention are: This invention establishes a health state reference model including a baseline parameter set and a temperature compensation rule set. Combined with dynamic response characteristics such as transient voltage drop amplitude, recovery time constant, and charge / discharge curve slope, it can accurately capture early degradation signals of energy storage components, such as increased internal resistance and decreased capacity. Compared to traditional post-fault diagnosis or single voltage monitoring, it can identify performance degradation trends in advance. For example, when the internal resistance has just begun to increase but the output voltage is still normal, the problem can be detected through deviations in dynamic response characteristics. This prevents sudden failures such as voltage instability and power outages due to accumulated degradation, ensuring the stable operation of CT energy harvesting equipment.

[0023] This invention, through multi-dimensional verification and calculation of mean and standard deviation of deviations, can accurately determine the target degradation indicators and their degree of degradation, and generate a performance degradation location map marking the degradation distribution area. Simultaneously, a maintenance scheme optimization model trained based on the physical structural parameters of energy storage components and historical maintenance cases can adjust maintenance cycles and replacement strategies, generating a personalized final maintenance plan. This avoids the blindness and downtime losses of traditional manual periodic inspections, and prevents over-maintenance or under-maintenance, significantly reducing maintenance costs and the impact of equipment downtime. Attached Figure Description

[0024] Figure 1 A flowchart of a fault detection method for a CT energy harvesting device provided in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the dynamic response feature extraction process for energy storage components.

[0026] Figure 3 A schematic diagram of the structure of a health status reference model.

[0027] Figure 4 This is a module architecture diagram of a fault detection system for CT energy harvesting equipment. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0029] like Figure 1 As shown, the entire process of this method, from signal acquisition to performance degradation determination, is clearly demonstrated. The whole process is completed collaboratively by multiple modules, including an acquisition module, a reference model module, a feature extraction module, a comparison module, and a determination module. The connections, positions, and cooperation between these modules will be described in detail below.

[0030] First, the acquisition module, as the core input component of the system, is used to acquire the voltage and current signals across the energy storage element. The energy storage element is a critical component in CT energy harvesting equipment, and its performance degradation is mainly manifested in increased internal resistance and reduced capacity. The acquisition module uses high-precision sensors to monitor the dynamic response data of the energy storage element in real time under different operating conditions. This data includes transient voltage changes and current fluctuations during charging and discharging. The acquisition module and the energy storage element are directly connected via wires to ensure the stability and accuracy of signal transmission. Furthermore, the acquisition module is also connected to the feature extraction module, transmitting the acquired raw signals to the feature extraction module for subsequent processing.

[0031] The main task of the feature extraction module is to extract the dynamic response characteristics of the energy storage element from the acquired voltage and current signals. For example... Figure 2 As shown, the module triggers the dynamic response behavior of the energy storage element by injecting a large current pulse with a duration on the order of microseconds.

[0032] The high-current pulse is generated by the control unit, and its amplitude and duration are set according to the maximum allowable current and thermal stability of the energy storage element to avoid irreversible damage to the energy storage element. After the pulse is injected, the voltage change curves across the energy storage element before and after the pulse are sampled by a high-speed analog-to-digital converter.

[0033] The voltage change curve contains data points for the transient voltage drop phase and the recovery phase. The feature extraction module calculates the transient voltage drop amplitude based on these data, fits the voltage change curve of the recovery phase to obtain the recovery time constant, and analyzes the slope of the charge and discharge curve through a linear regression algorithm.

[0034] These characteristic parameters together constitute the dynamic response characteristics of energy storage elements, providing a basis for subsequent performance degradation judgment.

[0035] The reference model module stores a health state reference model, which includes a baseline parameter set and a temperature compensation rule set. For example... Figure 3 As shown, the baseline parameter set includes the initial values ​​of the equivalent series resistance and the initial capacity obtained during factory testing. These parameters serve as a benchmark for judging the performance degradation of energy storage components.

[0036] The temperature compensation rule set is generated based on the relationship between ambient temperature and changes in the performance of energy storage components. It is used to correct the dynamic response characteristics for temperature variations, ensuring the accuracy of evaluation results under different environmental conditions. The reference model module is connected to the comparison module, providing the baseline parameter set and temperature compensation rule set from the health state reference model to the comparison module for comparative analysis of dynamic response characteristics.

[0037] The comparison module receives dynamic response features from the feature extraction module and baseline parameter sets from the reference model module, and compares the two to determine whether there is a performance degradation trend in the energy storage element.

[0038] During the comparison process, if at least two baseline parameters deviate from the dynamic response characteristics beyond a first preset range, a performance degradation candidate set is generated.

[0039] The mean deviation and standard deviation of each baseline parameter in the performance degradation candidate set are calculated, and the baseline parameter with the largest mean deviation and the smallest standard deviation is determined as the target degradation index.

[0040] The comparison module is connected to the judgment module, and transmits the target degradation index to the judgment module for further analysis.

[0041] The judgment module determines the specific indicators of performance degradation and the corresponding degree of degradation based on the results of the comparison module. For example... Figure 4 As shown, the determination module first obtains the set of degradation degree determination thresholds corresponding to the target degradation index. The set of thresholds includes the resistance increment threshold, the capacity decay percentage threshold, and the temperature compensation coefficient.

[0042] Subsequently, the determination module calculates the actual parameter values ​​corresponding to the degradation degree determination threshold set in the dynamic response characteristics, including the change in the current equivalent series resistance relative to the initial value, the attenuation ratio of the current capacity relative to the initial value, and the correction value after temperature compensation.

[0043] By comparing actual parameter values ​​with a set of degradation severity thresholds, the degradation severity of the target degradation index is ultimately determined. Furthermore, the determination module can generate a performance degradation location map, marking the distribution area and degree of degradation within the energy storage element, and correlate it with the maintenance recommendation set in the health status reference model to generate a preliminary maintenance plan.

[0044] After the initial maintenance plan is generated, the system further optimizes the maintenance strategy. Specifically, the system acquires the physical structural parameters of the energy storage elements, including electrode material type, packaging form, and designed service life, and inputs these parameters into the maintenance plan optimization model.

[0045] The maintenance plan optimization model is trained and generated based on historical maintenance case data. It can adjust the maintenance cycle and replacement strategy in the initial maintenance plan according to the output of the optimization model, thereby obtaining the final maintenance plan. This process ensures the scientific nature and relevance of the maintenance plan.

[0046] To enable long-term monitoring of the performance of energy storage components, the system periodically retests them at preset time intervals. The acquisition module re-acquires the retest signals and compares their dynamic response characteristics with those of the initial detection signals.

[0047] The comparison includes the rate of increase in equivalent series resistance, the rate of capacity decay, and fluctuations in temperature compensation correction values. If any change exceeds the warning range of the corresponding performance degradation indicator, a performance tracking alarm is triggered. This mechanism can promptly detect potential performance degradation problems and prevent the fault from escalating.

[0048] During the final maintenance implementation, the system can also collect maintenance process signals in real time. The feature extraction module extracts key maintenance node features from the maintenance process signals, including electrode contact quality features, packaging sealing features, and temperature distribution uniformity features.

[0049] These characteristics are compared with the maintenance quality requirements in the health status reference model. If the comparison results do not meet the requirements, maintenance adjustment suggestions are generated. This process ensures the quality and effectiveness of maintenance operations.

[0050] Through the coordinated operation of the above modules, this invention achieves comprehensive detection and precise location of performance degradation of energy storage components in CT energy harvesting equipment.

[0051] The acquisition module is responsible for signal acquisition, the feature extraction module is responsible for dynamic response feature extraction, the reference model module provides a health status reference model, the comparison module performs feature comparison, and the judgment module determines the degradation index and degree. All modules are tightly connected via data transmission lines to ensure smooth information flow. This modular design not only improves the system's scalability and flexibility but also enhances the accuracy and reliability of the detection results.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting a failure of a CT pickup device, characterized by, The method comprises the following steps: Collecting voltage signals and current signals at both ends of the energy storage element; The energy storage element is a core component, and its performance degradation is manifested as an increase in internal resistance and a decrease in capacity. The voltage signals and current signals contain dynamic response data of the energy storage element in different working states; A health state reference model is established. The health state reference model contains a baseline parameter set and a temperature compensation rule set. The baseline parameter set includes an initial value of the equivalent series resistance and an initial value of the capacity obtained during factory testing. The temperature compensation rule set is generated according to the relationship between the environmental temperature and the performance change of the energy storage element; Dynamic response characteristics of the energy storage element are extracted. The dynamic response characteristics include a transient voltage drop amplitude, a recovery time constant, and a charge-discharge curve slope. The dynamic response characteristics are compared with the baseline parameter set in the health state reference model to determine whether the energy storage element has a performance degradation trend. Based on the comparison result, specific indicators of performance degradation and corresponding degradation degrees are determined.

2. The method of claim 1, wherein: The dynamic response characteristics of the energy storage element are extracted by the following steps: A large current pulse with a duration of microseconds is injected into the energy storage element. The large current pulse is generated by a control unit, and its amplitude and duration are set according to the maximum allowable current and thermal stability of the energy storage element; The voltage change curve at both ends of the energy storage element before and after the pulse is sampled by a high-speed analog-to-digital converter. The voltage change curve contains data points in the transient drop stage and the recovery stage; The transient voltage drop amplitude is calculated. The transient voltage drop amplitude is the difference between the pulse injection time and the steady-state voltage; The voltage change curve in the recovery stage is fitted to obtain the recovery time constant. The recovery time constant is obtained by exponential function fitting; The charge-discharge curve slope of the energy storage element is extracted by analyzing the charge-discharge curve. The charge-discharge curve slope is calculated by a linear regression algorithm.

3. The method of claim 1, wherein: The following steps are also included: If the deviations of at least two baseline parameters from the dynamic response characteristics exceed a first preset range, a performance degradation candidate set is generated; The mean and standard deviation of the deviation of each baseline parameter in the performance degradation candidate set are calculated; The baseline parameter with the maximum mean deviation and the minimum standard deviation is taken as the target degradation indicator.

4. The method of claim 3, wherein: After taking the baseline parameter with the maximum mean deviation and the minimum standard deviation as the target degradation indicator, the following steps are also included: Obtain the degradation degree judgment threshold set corresponding to the target degradation indicator. The degradation degree judgment threshold set includes a resistance increment threshold, a capacity attenuation percentage threshold, and a temperature compensation coefficient; Calculate the actual parameter values corresponding to the degradation degree judgment threshold set in the dynamic response characteristics. The actual parameter values include the change amount of the current equivalent series resistance relative to the initial value, the decay ratio of the current capacity relative to the initial value, and the modified value after temperature compensation; Compare the actual parameter values with the degradation degree judgment threshold set to determine the degradation degree of the target degradation indicator.

5. The method of claim 3, wherein Also included are: Obtain the performance correlation rule library in the historical operation data. The performance correlation rule library records the co-occurrence probability of different performance degradation indicators; If there are two performance degradation indicators in the performance degradation candidate set with a co-occurrence probability exceeding a second preset range, start multi-dimensional verification; The dynamic response characteristics corresponding to the two performance degradation indicators are cross-verified, and if the consistency of the cross-verification result exceeds a third preset range, both of the two performance degradation indicators are included in the target degradation indicator.

6. The CT energization apparatus fault detection method of claim 1, wherein, Also comprising: If there is no performance degradation trend, a no-degradation detection report is generated, which contains the position information of the energy storage element, the detection time and the dynamic response characteristic extraction result; If there is a performance degradation trend, a performance degradation positioning map is generated according to the target degradation indicator and the degradation degree, and the performance degradation positioning map labels the distribution area of the performance degradation in the energy storage element and the degradation degree identifier; The performance degradation positioning map is associated with the maintenance suggestion set in the health state reference model to generate a preliminary maintenance scheme.

7. The CT energization apparatus failure detection method according to claim 6, characterized by, Also comprising the following steps: Obtain the physical structure parameters of the energy storage element, including the electrode material type, the packaging form and the designed service life; Input the physical structure parameters into the maintenance scheme optimization model, which is generated based on historical maintenance case data training; Adjust the maintenance cycle and replacement strategy in the preliminary maintenance scheme according to the optimization model output result to obtain the final maintenance scheme.

8. The method of claim 1, wherein, Also comprising the following steps: Periodically retest the energy storage element at a preset time interval and collect the retest signals; Compare the dynamic response characteristic change amount of the first detection signal and the retest signal, including the equivalent series resistance increment rate, the capacity attenuation rate and the temperature compensation correction value fluctuation; If any change amount exceeds the warning range of the corresponding performance degradation indicator, a performance tracking alarm is triggered.

9. The method of claim 7, wherein: Also comprising the following steps: During the implementation of the final maintenance scheme, real-time maintenance process signals are collected; Extract key maintenance node features from the maintenance process signals, including electrode contact quality features, packaging sealing features and temperature distribution uniformity features; Compare the key maintenance node features with the maintenance quality requirements in the health state reference model, and if the comparison result does not meet the requirements, generate a maintenance adjustment suggestion.

10. A fault detection system of a CT pick-up device, for implementing the fault detection method of the CT pick-up device according to any one of claims 1 to 9, characterized in that: a collection module, configured to collect voltage signals and current signals across an energy storage element; the energy storage element is a core component, and its performance degradation is manifested as an increase in internal resistance and a decrease in capacity, and the voltage signals and current signals contain dynamic response data of the energy storage element under different working states; a reference model module, configured to store a health state reference model; wherein the health state reference model contains a baseline parameter set and a temperature compensation rule set, the baseline parameter set includes an initial equivalent series resistance value and an initial capacity value obtained during factory testing, and the temperature compensation rule set is generated according to the relationship between environmental temperature and performance change of the energy storage element; a feature extraction module, configured to extract dynamic response characteristics of the energy storage element, including transient voltage drop amplitude, recovery time constant and charge-discharge curve slope; a comparison module configured to compare the dynamic response characteristic with a baseline parameter set in a health state reference model to determine whether the energy storage element has a performance degradation trend; a determination module configured to determine a specific index of performance degradation and a corresponding degradation degree based on the comparison result.

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