Fault diagnosis method, device and equipment

Abstract

The invention discloses a fault diagnosis method, device and equipment, and relates to the technical field of fault detection. The method is applied to a nuclear power plant primary loop coolant ion on-line monitor, and comprises the following steps: under the condition that a test result of a target on-line monitor indicates abnormity, determining a fault alarm monitoring result, the fault alarm monitoring result being used for indicating whether a fault alarm exists; and determining a target fault type of the target online monitor according to the fault alarm monitoring result. According to the embodiment of the invention, on one hand, requirements of manual intervention and manual access to a radiation area can be reduced, personnel safety is guaranteed, and fault diagnosis efficiency is improved; and on the other hand, faults can be accurately positioned, namely, the accuracy of fault diagnosis can be improved, and on-site blind troubleshooting is avoided.

Description

Technical Field

[0001] This application belongs to the field of fault detection technology, specifically relating to a fault diagnosis method, apparatus and equipment. Background Technology

[0002] The primary loop of a nuclear power plant is the core of the nuclear reactor cooling system. It is responsible for removing the heat generated during nuclear fission from the reactor core and transferring it to the water in the secondary loop via a steam generator to drive the turbine generator to generate electricity.

[0003] In nuclear power plants, as the capacity of individual generator units continues to increase, the systems operate under extremely high temperatures and pressures, leading to increasingly stringent requirements for water quality. Due to the addition of coolant and makeup water to the reactor, some ions in these waters can corrode the surfaces of components in contact with the water under high temperatures and pressures. Therefore, online monitoring instruments based on ion chromatography are needed to monitor anions such as fluoride, chloride, and sulfate, as well as cations such as sodium and lithium ions in the coolant, and anions such as chloride and sulfate, as well as sodium ions, in the primary loop makeup water. If the concentrations of anions and cations deviate from the permissible range, timely intervention by personnel is required. Boric acid is used as a soluble neutron absorber in the primary loop of nuclear power plants. The high boric acid matrix and large concentration variations can easily affect the monitoring results of anion and cation concentrations. When online monitoring instrument test results are abnormal, besides ruling out the possibility that the abnormal results are genuine, diagnosing faults in the online monitoring instrument requires manual on-site inspection to troubleshoot and accurately pinpoint the fault type. Due to the high radiation risk in the primary loop of nuclear power plants, manual on-site inspection is inconvenient, and the efficiency and accuracy of manual troubleshooting are low. Therefore, there is an urgent need for a fault diagnosis method that reduces the need for manual entry into radiation areas, ensures personnel safety, and improves the efficiency and accuracy of fault diagnosis. Summary of the Invention

[0004] The technical problem to be solved by this application is to provide a fault diagnosis method, apparatus and equipment to address the above-mentioned shortcomings of the existing technology. Using this method, on the one hand, it can reduce the need for manual intervention and manual entry into the radiation area, ensure personnel safety and improve the efficiency of fault diagnosis; on the other hand, it can accurately locate the fault, that is, improve the accuracy of fault diagnosis and avoid blind on-site troubleshooting.

[0005] In a first aspect, embodiments of this application provide a fault diagnosis method applied to an online coolant ion monitoring instrument in the primary loop of a nuclear power plant. The method includes:

[0006] If the test results of the target online monitoring instrument indicate an abnormality, the fault alarm monitoring results are determined, and the fault alarm monitoring results are used to indicate whether a fault alarm exists.

[0007] Based on the fault alarm monitoring results, determine the target fault type of the online monitoring instrument. The target fault type includes one of the following: communication fault, low pressure fault, high pressure fault, insufficient eluent fault, eluent generator depletion or fault, leakage fault, matrix influence fault, column fault, suppressor fault, detector fault, software peak determination fault, injection fault, and occasional fault.

[0008] In some embodiments of the first aspect, the target fault type of the target online monitoring instrument is determined based on the fault alarm monitoring results, specifically including:

[0009] If the fault monitoring results indicate the presence of a fault alarm, determine the target fault type of the online monitoring instrument based on the alarm information;

[0010] If the fault monitoring results indicate that there is no fault alarm, the boric acid matrix quality control sample is retested using the target online monitoring instrument to obtain the retest results. Based on the retest results, the target fault type of the target online monitoring instrument is determined. The retest results are used to indicate whether the boric acid matrix quality control sample is qualified.

[0011] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0012] If the retest results indicate that the boric acid matrix quality control sample is unqualified, the boric acid matrix of the boric acid matrix quality control sample is removed to obtain the target quality control sample;

[0013] The target quality control sample is tested using an online monitoring instrument to obtain the first test result, which is used to indicate whether the target quality control sample is qualified.

[0014] If the first test result indicates that the target quality control sample is qualified, the target fault type of the online monitoring instrument is determined to be a matrix-affected fault.

[0015] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0016] If the retest results indicate that the boric acid matrix quality control sample is unqualified, and the peak shape determination results indicate that the peak shape is abnormal, then the target fault type of the target online monitoring instrument is determined to be a column fault.

[0017] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0018] If the retest results indicate that the boric acid matrix quality control sample is unqualified, and the background conductivity value determination results indicate that the background conductivity value is abnormal, then the target fault type of the target online monitoring instrument is determined to be a suppressor fault.

[0019] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0020] If the retest results indicate that the boric acid matrix quality control sample is unqualified, and the baseline noise determination results indicate that the baseline noise is abnormal, then the target fault type of the target online monitoring instrument is determined to be a detector fault.

[0021] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0022] If the retest results indicate that the boric acid matrix quality control sample is qualified, but the software peak determination results indicate that the software peak determination is abnormal, then the target fault type of the target online monitoring instrument is determined to be a software peak determination fault.

[0023] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0024] If the retest results indicate that the boric acid matrix quality control sample is qualified, but the sample injection inspection results indicate that the sample injection is abnormal, then the target fault type of the target online monitoring instrument is determined to be an injection fault.

[0025] In some implementations of the first aspect, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0026] If the retest results indicate that the boric acid-based quality control sample is qualified, and the re-injection test results are used to confirm that the re-injected sample is qualified, then the target fault type of the target online monitoring instrument is determined to be an intermittent fault.

[0027] In some implementations of the first aspect, the target fault type of the online target monitoring instrument is determined based on alarm information, specifically including:

[0028] When the alarm message is a communication interruption alarm, the target fault type of the online target monitoring instrument is determined to be a communication fault;

[0029] When the alarm message is a low-voltage alarm, the target fault type of the online monitoring instrument is determined to be a low-voltage fault.

[0030] When the alarm message is an overpressure alarm, the target fault type of the online monitoring instrument is determined to be a high-pressure fault;

[0031] When the alarm message is a low fluid alarm, the target fault type of the online monitoring instrument is determined to be insufficient rinsing fluid fault.

[0032] If the alarm message is a suppressor current alarm, the target fault type of the online monitoring instrument is determined to be a suppressor fault;

[0033] When the alarm message is "rinsing fluid generator concentration alarm", the target fault type of the target online monitoring instrument is determined to be "rinsing fluid generator depleted or malfunctioning".

[0034] If the alarm message is a liquid leakage alarm, the target fault type of the online monitoring instrument is determined to be a liquid leakage fault.

[0035] Based on the same inventive concept, in a second aspect, embodiments of this application also provide a fault diagnosis device applied to an online monitoring instrument for coolant ions in the primary loop of a nuclear power plant. The device includes:

[0036] The first determining module is used to determine the fault alarm monitoring result when the test result of the target online monitoring instrument indicates an abnormality. The fault alarm monitoring result is used to indicate whether a fault alarm exists.

[0037] The second determination module is used to determine the target fault type of the target online monitoring instrument based on the fault alarm monitoring results. The target fault type includes one of the following: communication fault, low pressure fault, high pressure fault, insufficient eluent fault, eluent generator depletion or fault, leakage fault, matrix influence fault, column fault, suppressor fault, detector fault, software peak determination fault, injection fault, and occasional fault.

[0038] Based on the same inventive concept, in a third aspect, embodiments of this application provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to implement a fault diagnosis method as described in any of the first aspects.

[0039] According to the fault diagnosis method, apparatus, and equipment provided in the embodiments of this application, when the test results of the target online monitoring instrument indicate an abnormality, the fault alarm monitoring result is first determined, and then the target fault type of the target online monitoring instrument is determined based on the fault alarm monitoring result. In other words, the embodiments of this application automatically identify the target fault type of the target online monitoring instrument based on the test results and fault alarm monitoring results. Compared to manually checking faults on-site one by one, this approach reduces the need for manual intervention and entry into the radiation zone, ensuring personnel safety and improving fault diagnosis efficiency. Furthermore, since fault diagnosis is based on objective data such as fault alarm monitoring results and target online monitoring instruments, compared to manually checking faults on-site one by one, it can accurately locate the fault, thus improving the accuracy of fault diagnosis and avoiding blind on-site troubleshooting. Attached Figure Description

[0040] Figure 1 This illustration shows a flowchart of a fault diagnosis method provided in an embodiment of this application.

[0041] Figure 2 This illustration shows another flowchart of the fault diagnosis method provided in an embodiment of this application;

[0042] Figure 2a for Figure 2 Enlarged view of point A1 in the middle;

[0043] Figure 2b for Figure 2 Enlarged view of point A2 in the middle;

[0044] Figure 2c for Figure 2 Enlarged view of point A3 in the middle;

[0045] Figure 2d for Figure 2 Enlarged view of section A4 in the middle;

[0046] Figure 3 This illustration shows a structural schematic diagram of a fault diagnosis device provided in an embodiment of this application;

[0047] Figure 4 This illustration shows a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions of this application, the application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0049] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used merely 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 "comprising," "including," 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. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0051] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0052] Example 1

[0053] The fault diagnosis method provided in this application can be applied to the online monitoring instrument for coolant ions in the primary loop of a nuclear power plant, that is, it is suitable for fault diagnosis of the online monitoring instrument for coolant ions in the primary loop of a nuclear power plant. This fault diagnosis method can be executed by a fault diagnosis device and electronic equipment. The following description uses the execution of this fault diagnosis method by electronic equipment as an example.

[0054] like Figure 1 As shown, the fault diagnosis method provided in this application embodiment may include steps S110 to S120.

[0055] S110. If the test results of the target online monitoring instrument indicate an abnormality, determine the fault alarm monitoring results. The fault alarm monitoring results are used to indicate whether a fault alarm exists.

[0056] S120. Based on the fault alarm monitoring results, determine the target fault type of the target online monitoring instrument. The target fault type includes one of the following: communication fault, low pressure fault, high pressure fault, insufficient eluent fault, eluent generator depletion or fault, leakage fault, matrix influence fault, column fault, suppressor fault, detector fault, software peak determination fault, injection fault, and occasional fault.

[0057] According to the fault diagnosis method provided in this application embodiment, when the test results of the target online monitoring instrument indicate an abnormality, the fault alarm monitoring result is first determined. Then, based on the fault alarm monitoring result, the target fault type of the target online monitoring instrument is determined. In other words, this application embodiment automatically identifies the target fault type of the target online monitoring instrument based on the test results and fault alarm monitoring results. Compared to manual on-site troubleshooting, this method reduces the need for manual intervention and entry into the radiation zone, ensuring personnel safety and improving fault diagnosis efficiency. Furthermore, since fault diagnosis is based on objective data such as fault alarm monitoring results and target online monitoring instrument data, it allows for more precise fault location compared to manual on-site troubleshooting, thus improving the accuracy of fault diagnosis and avoiding blind on-site troubleshooting.

[0058] The specific implementation methods for each of the above steps are described below.

[0059] In step S110, the target online monitoring instrument can be any online monitoring instrument that requires fault diagnosis.

[0060] For example, excluding the fact that the test results of the target online monitoring instrument are the actual sample test results, if the test results of the target online monitoring instrument fluctuate within the normal range of single ion detection values ​​by no more than a first threshold (e.g., 10%), the test results of the target online monitoring instrument are determined to be normal; if the test results of the target online monitoring instrument fluctuate within the normal range of single ion detection values ​​by more than the first threshold, the test results of the target online monitoring instrument are determined to be abnormal. This avoids subjective judgment differences.

[0061] In other words, the test results of the online monitoring instrument are obtained, and if the test results of the online monitoring instrument indicate an abnormality, it is determined whether there is a fault alarm, so as to obtain the fault alarm monitoring results.

[0062] In step S120, when the test result of the target online monitoring instrument indicates an abnormality, after determining the fault alarm monitoring result, the electronic device can also determine the target fault type of the target online monitoring instrument based on the fault alarm monitoring result.

[0063] In the embodiments of this application, the target fault type is classified according to the cause of the fault.

[0064] In some implementations, the target fault type of the online monitoring instrument is determined based on the fault alarm monitoring results, specifically including:

[0065] If the fault monitoring results indicate the presence of a fault alarm, determine the target fault type of the online monitoring instrument based on the alarm information;

[0066] If the fault monitoring results indicate that there is no fault alarm, the boric acid matrix quality control sample is retested using the target online monitoring instrument to obtain the retest results. Based on the retest results, the target fault type of the target online monitoring instrument is determined. The retest results are used to indicate whether the boric acid matrix quality control sample is qualified.

[0067] In this embodiment, retesting the borate-based quality control sample simulates a real fault diagnosis scenario in the primary loop of a nuclear power plant. This serves to differentiate between instrument malfunctions and temporary interference, preventing misdiagnosis. Retesting the borate-based quality control sample enables stratified fault screening, gradually narrowing down the fault range and improving fault diagnosis efficiency. Simultaneously, it avoids misdiagnosis due to occasional errors, thereby improving the accuracy of fault diagnosis.

[0068] For example, the alarm information may be a communication interruption alarm, low pressure alarm, overpressure alarm, low liquid alarm, suppressor current alarm, rinsing fluid generator concentration alarm, or leakage alarm.

[0069] For example, a low-pressure alarm is triggered when the pressure is below a first pressure threshold (e.g., 1 MPa); an overpressure alarm is triggered when the pressure is above a second pressure threshold (e.g., 20 MPa); and a suppressor current alarm is triggered when the actual suppressor current is less than a set value (e.g., 5 mA).

[0070] In some examples, the target fault type of the online monitoring instrument is determined based on the alarm information, specifically including:

[0071] When the alarm message is a communication interruption alarm, the target fault type of the online target monitoring instrument is determined to be a communication fault;

[0072] When the alarm message is a low-voltage alarm, the target fault type of the online monitoring instrument is determined to be a low-voltage fault.

[0073] When the alarm message is an overpressure alarm, the target fault type of the online monitoring instrument is determined to be a high-pressure fault;

[0074] When the alarm message is a low fluid alarm, the target fault type of the online monitoring instrument is determined to be insufficient rinsing fluid fault.

[0075] If the alarm message is a suppressor current alarm, the target fault type of the online monitoring instrument is determined to be a suppressor fault;

[0076] When the alarm message is "rinsing fluid generator concentration alarm", the target fault type of the target online monitoring instrument is determined to be "rinsing fluid generator depleted or malfunctioning".

[0077] If the alarm message is a liquid leakage alarm, the target fault type of the online monitoring instrument is determined to be a liquid leakage fault.

[0078] In this implementation, different fault types are matched for seven common alarm types: communication interruption alarm, low pressure alarm, overpressure alarm, low liquid alarm, suppressor current alarm, rinsing fluid generator concentration alarm, or system leakage alarm. This provides a classification and handling approach to avoid misjudgment or omission of faults, and to handle the alarm type in a targeted manner, thus avoiding blind operation.

[0079] For example, when the alarm information is a suppressor current alarm, the target fault type of the target online monitoring instrument is determined to be the suppressor internal resistance increase fault in the suppressor fault.

[0080] For example, if the retest results indicate that the boric acid-based quality control sample is qualified, it means that the target online monitoring instrument is operating normally, which can rule out instrument hardware failures such as chromatographic columns, suppressors, and detectors, and narrow down the scope of the problem search.

[0081] In some examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0082] If the retest results indicate that the boric acid matrix quality control sample is unqualified, the boric acid matrix of the boric acid matrix quality control sample is removed to obtain the target quality control sample;

[0083] The target quality control sample is tested using an online monitoring instrument to obtain the first test result, which is used to indicate whether the target quality control sample is qualified.

[0084] If the first test result indicates that the target quality control sample is qualified, the target fault type of the online monitoring instrument is determined to be a matrix-affected fault.

[0085] For example, if the first test result is within the normal range of a single ion detection value and fluctuates within a first threshold, the first test result is determined to be normal; if the first test result is within the normal range of a single ion detection value and fluctuates within a first threshold, the first test result is determined to be abnormal.

[0086] For example, borate-based quality control samples and borate-removed quality control samples can be automatically prepared using an online monitoring instrument's automatic dilution module, which is described in reference to a Chinese patent publication.

[0087] CN118465134A discloses an online dilution method in an online detection system for anions and cations in radioactive samples. This method enables the automatic online preparation and injection analysis of standard sample solutions of different concentrations, calculates the first test result of the target quality control sample, automatically determines whether the target quality control sample is qualified, and uploads the determination result.

[0088] It should be noted that routine fault detection of instruments in conventional industrial water quality testing does not require the use of boric acid-based quality control samples. Ordinary industrial water quality testing does not involve the high-concentration boric acid environment and its associated complex interferences specific to nuclear power plants. This application's embodiment addresses the unique scenario of high-concentration boric acid matrix in the primary loop water of nuclear power plants. It simulates a boric acid-based environment by preparing boric acid-based quality control samples perfectly matched to the primary loop, actively inducing interferences specific to nuclear power plants, such as baseline drift and poor peak separation. This accurately reproduces the nuclear power plant-grade interference scenario, facilitating precise fault location and calibrating the instrument's resistance to boric acid interference. In other words, the fault diagnosis method provided in this application's embodiment simulates the real-world scenario of a nuclear power plant's primary loop, which is susceptible to interference from high-boric acid matrix. By comparing boric acid-based and non-boric acid-based quality control samples, it accurately distinguishes between instrument malfunctions and matrix interference, improving the accuracy of fault diagnosis.

[0089] In the embodiments of this application, if the first test result of the target quality control sample returns to normal (i.e. qualified) after removing the boric acid matrix, the fault is identified as originating from the boric acid matrix effect. If the first test result of the target quality control sample is still abnormal (i.e. unqualified) after removing the boric acid matrix, the investigation can turn to hardware such as chromatographic columns, suppressors, and detectors. This achieves precise location of faults in online detectors.

[0090] In other examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0091] If the retest results indicate that the boric acid matrix quality control sample is unqualified, and the peak shape determination results indicate that the peak shape is abnormal, then the target fault type of the target online monitoring instrument is determined to be a column fault.

[0092] In this example, peak shape is used to identify abnormal peak shapes, directly pinpointing the cause of the malfunction as a problem with the chromatographic column. This avoids blindly checking other parts and improves the efficiency of fault diagnosis.

[0093] For example, if the retest results indicate that the boric acid matrix quality control sample is unqualified, it is determined whether the peak shape is abnormal, and a peak shape determination result is obtained. The peak shape determination result is used to indicate whether the peak shape is abnormal.

[0094] Furthermore, if the peak shape exhibits phenomena such as tailing peaks, split peaks, or peak broadening, the peak shape determination result indicates an anomaly; otherwise, the risk peak shape determination result indicates normality.

[0095] For example, the formula for calculating the tailing factor T of the tailing peak is shown in formula (1).

[0096]

[0097] Among them, W 0.05 A represents the peak width at 5% of the peak height; A represents the horizontal distance from the peak front to the peak apex at 5% of the peak height.

[0098] Specifically, during the calculation, the retention time tR and peak height h of the target peak are recorded at the apex of the chromatogram. A horizontal line is drawn at 5% of the peak height (i.e., 0.05h), intersecting the peak leading edge and the peak trailing edge. The horizontal distance A between the intersection point of the peak leading edge and the peak apex, and the total width W between the intersection points of the peak leading edge and the peak trailing edge are measured. 0.05 Substitute the values ​​into formula (1) to calculate the tailing factor T. In this embodiment, a T value > 1.5 indicates significant tailing, which may be due to decreased column efficiency from prolonged use or column contamination caused by the adsorption of strongly retained substances. Regeneration of the packing material may be considered in the future.

[0099] For example, the separation degree R of the split peaks s The calculation formula is shown in formula (2).

[0100]

[0101] Among them, t R1 t R2 W represents the retention times of the first and second peaks, respectively. b1 W b2 These represent the baseline peak widths of the first and second peaks, respectively.

[0102] Specifically, during the calculation, the retention time t of peak 1 (i.e., the first peak) is recorded in the chromatogram. R1 And peak width W b1 Record the retention time t of peak 2 (i.e., the second peak). R2 And peak width W b2 Substitute into formula (2) to calculate the separation degree R. s In this embodiment of the application, R s A resolution of ≤1.2 indicates poor separation, which may be due to column packing collapse. In such cases, replacing the column or regenerating the packing material may be considered.

[0103] For example, the formula for calculating the theoretical plate number N of peak broadening is shown in formula (3).

[0104]

[0105] Among them, t R Indicates the retention time of the target peak; W b This indicates the baseline peak width of the target peak.

[0106] Specifically, during the calculation, the retention time t of the target peak is recorded in the chromatogram. R And peak width W b Substitute into formula (3) to calculate the theoretical plate number N. In this embodiment, the calculated theoretical plate number N is >20% lower than the theoretical plate number provided by the new column. This is due to the column efficiency decreasing or the packing material collapsing after prolonged use. In this case, the column can be replaced or the packing material can be regenerated.

[0107] Optionally, the tailing factor, separation degree, and theoretical plate number mentioned above can be directly read from the online monitoring instrument software.

[0108] It should be noted that if the retest results indicate that the boric acid matrix quality control sample is qualified, and the peak shape determination results indicate that the peak shape is normal, then the target fault type is determined not to be a column fault. The specific target fault type still needs further judgment.

[0109] Optionally, after determining that the target fault type of the online monitoring instrument is a column fault, it is possible to further analyze whether the column fault is caused by reasons such as decreased column efficiency, packing collapse, or contamination.

[0110] In other examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0111] If the retest results indicate that the boric acid matrix quality control sample is unqualified, and the background conductivity value determination results indicate that the background conductivity value is abnormal, then the target fault type of the target online monitoring instrument is determined to be a suppressor fault.

[0112] For example, if the retest results indicate that the boric acid matrix quality control sample is unqualified, it is determined whether the background conductivity value is abnormal, and the background conductivity value determination result is obtained. The background conductivity value determination result is used to indicate whether the background conductivity value is abnormal.

[0113] Specifically, if the background conductivity value is greater than the baseline background conductivity value (e.g., 5 μS / cm), then the background conductivity value determination result indicates an abnormal background conductivity value. In this case, it can be considered that the fault is caused by the aging of the suppressor membrane, leakage of the suppressor, or contamination. That is, the target fault type is determined to be suppressor fault.

[0114] It should be noted that if the retest results indicate that the boric acid matrix quality control sample is qualified, and the background conductivity value determination results indicate that the background conductivity value is normal, then the target fault type is determined not to be a suppressor fault. The specific target fault type still needs to be further determined.

[0115] In other examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0116] If the retest results indicate that the boric acid matrix quality control sample is unqualified, and the baseline noise determination results indicate that the baseline noise is abnormal, then the target fault type of the target online monitoring instrument is determined to be a detector fault.

[0117] For example, baseline noise refers to baseline spikes, periodic fluctuations, and baseline drift. Baseline noise analysis can pinpoint detector malfunctions.

[0118] For example, if the retest results indicate that the borate-based quality control sample is unqualified, it is determined whether the baseline noise is abnormal, and a baseline noise determination result is obtained. The baseline noise determination result is used to indicate whether the baseline noise is abnormal.

[0119] For example, if the baseline noise is greater than a second threshold (e.g., 2 nS / cm) and the baseline drift is greater than a drift threshold (e.g., 1 μS / cm * 30 min), the baseline noise determination result indicates that the baseline noise is abnormal; otherwise, the baseline noise determination result indicates that the baseline noise is normal.

[0120] It should be noted that if the retest results indicate that the boric acid matrix quality control sample is qualified, and the baseline noise determination results indicate that the background conductivity value is normal, then the target fault type is determined to be not a baseline noise fault. The specific target fault type still needs to be further determined.

[0121] Understandably, in the embodiments of this application, if the retest results indicate that the boric acid-based quality control sample is unqualified, the target fault type can be determined by combining at least two of the following: the first test result, the peak shape determination result, the background conductivity determination result, and the baseline noise determination result. If the target fault type still cannot be determined by combining the first test result, the peak shape determination result, the background conductivity determination result, and the baseline noise determination result, the target fault type can be further determined through manual intervention.

[0122] In other examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0123] If the retest results indicate that the boric acid matrix quality control sample is qualified, but the software peak determination results indicate that the software peak determination is abnormal, then the target fault type of the target online monitoring instrument is determined to be a software peak determination fault.

[0124] Understandably, if the retest results indicate that the boric acid matrix quality control is qualified, it means that the online monitoring instrument is operating normally, and hardware failures such as chromatographic columns, suppressors, and detectors can be ruled out, allowing for further analysis of software failures.

[0125] For example, if the retest results indicate that the boric acid matrix quality control sample is qualified, it is determined whether the software peak determination is abnormal, and the software peak determination result is obtained. The software peak determination result is used to indicate whether the software peak determination is abnormal.

[0126] Specifically, if the software peak determination has issues such as peak identification omissions, baseline misjudgments, or integration errors, the software peak determination result indicates that the software peak determination is abnormal; otherwise, the software peak determination result indicates that the software peak determination is normal.

[0127] Understandably, peak identification omissions, baseline misjudgments, and integration errors can lead to data distortion, directly affecting the accuracy of the analysis results. Independently verifying the peak identification logic can eliminate faults caused by software settings.

[0128] It should be noted that if the retest results indicate that the boric acid matrix quality control sample is qualified, and the software peak determination results indicate that the software peak determination is normal, then the target fault type is determined not to be a software peak determination fault. The specific target fault type still needs to be further determined.

[0129] If the retest results indicate that the boric acid matrix quality control sample is qualified, but the software peak determination results indicate that the software peak determination is abnormal, then the target fault type of the target online monitoring instrument is determined to be a software peak determination fault.

[0130] In other examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0131] If the retest results indicate that the boric acid matrix quality control sample is qualified, but the sample injection inspection results indicate that the sample injection is abnormal, then the target fault type of the target online monitoring instrument is determined to be an injection fault.

[0132] For example, if the retest results indicate that the boric acid matrix quality control sample is qualified, the sample injection is checked for abnormalities to obtain the sample injection inspection results, which are used to indicate whether the sample injection is abnormal.

[0133] Specifically, faults in the sample injection process can be directly located by checking for issues such as the accuracy of the injection volume, needle blockage, sample residue, and abnormal switching of the six-way valve. That is, if the accuracy of the injection volume, the needle blockage, the sample residue, or the six-way valve switching is abnormal, the sample injection check result indicates that the sample injection is abnormal; otherwise, the sample injection check result indicates that the sample injection is normal.

[0134] It should be noted that if the retest results indicate that the boric acid matrix quality control sample is qualified, and the sample injection inspection results indicate that the sample injection is normal, then the target fault type is determined not to be an injection fault. The specific target fault type still needs to be further determined.

[0135] In other examples, the target fault type of the online monitoring instrument is determined based on the retest results, specifically including:

[0136] If the retest results indicate that the boric acid-based quality control sample is qualified, and the re-injection test results are used to confirm that the re-injected sample is qualified, then the target fault type of the target online monitoring instrument is determined to be an intermittent fault.

[0137] For example, if the retest result indicates that the boric acid-based quality control sample is qualified, the re-injected sample is tested to obtain the re-injection test result, which is used to indicate whether the re-injected sample is qualified. The re-injected sample is the sample with the boric acid-based quality control sample removed.

[0138] It should be noted that if the retest result indicates that the boric acid matrix quality control sample is qualified, but the re-injection test result indicates that the re-injected sample is unqualified, then the process will proceed to the step of "determining the fault alarm monitoring result".

[0139] Understandably, re-injecting samples for testing can prevent occasional issues such as injection errors and temporary fluctuations from being misdiagnosed as online monitoring instrument malfunctions, thus reducing unnecessary maintenance or component replacements. In other words, by taking steps such as re-injection testing, occasional anomalies such as injection errors and temporary fluctuations are eliminated, reducing the false alarm rate.

[0140] To better understand the fault diagnosis method provided in the embodiments of this application, a specific implementation method will be described below.

[0141] like Figures 2 to 2d As shown in the embodiment of this application, the fault diagnosis method for the online monitoring instrument of the primary coolant ion in a nuclear power plant includes steps S1 to S9.

[0142] like Figure 2a As shown, step S1. Obtain abnormal information of the test results of the online monitoring instrument, determine whether there is a fault alarm, and output the judgment result. If the output judgment result is negative, perform retesting of the boric acid matrix quality control sample; if the output judgment result is positive, identify the specific fault type according to different alarm information.

[0143] Optionally, in step S1, the different alarm information may be communication interruption alarm, low pressure alarm, overpressure alarm, low liquid alarm, suppressor current alarm, rinsing liquid generator concentration alarm, or leakage alarm.

[0144] Further optional, such as Figure 2b As shown, in step S1, the alarm information is a communication interruption alarm, which is determined to be a communication fault, and the fault diagnosis is completed; the alarm information is a low pressure alarm (pressure below 1MPa), which is determined to be a low pressure fault, and the fault diagnosis is completed; the alarm information is an overpressure alarm (pressure above 20MPa), which is determined to be a high pressure fault, and the fault diagnosis is completed; the alarm information is a low liquid alarm, which is determined to be a insufficient rinsing fluid fault, and the fault diagnosis is completed; the alarm information is an suppressor current alarm (the actual suppressor current is more than 5mA less than the set value), which is determined to be an increased internal resistance of the suppressor fault, and the fault diagnosis is completed; the alarm information is a rinsing fluid generator concentration alarm, which is determined to be a depletion or fault of the rinsing fluid generator, and the fault diagnosis is completed; the alarm information is a leakage alarm, which is determined to be a leakage fault, and the fault diagnosis is completed.

[0145] In this embodiment, step S1 automatically determines the cause of abnormality in the online monitoring instrument through preset logic, avoiding manual on-site investigation and thus preventing harm to personnel from radiation in the primary loop of nuclear power plants. Simultaneously, excluding abnormal test results from actual sample measurements, it clarifies that a normal test result is defined as a fluctuation of 10% above or below the normal range for a single ion detection value. If the fluctuation exceeds 10%, the test result is considered abnormal, thus avoiding subjective judgment discrepancies. It provides categorized handling methods for seven common alarm types: communication interruption alarm, low pressure alarm, overpressure alarm, low liquid alarm, suppressor current alarm, eluent generator concentration alarm, and system leakage alarm. This avoids misjudgment or omission of faults, allows for targeted handling after identifying the alarm type, and prevents blind operation.

[0146] Optionally, the process also includes: Step S2. Retesting the boric acid-based quality control sample to determine if the quality control result is acceptable, and outputting the result. If the output result is negative, the boric acid-based quality control sample is removed and tested again. If the output result is positive, it indicates that the instrument is operating normally, ruling out instrument hardware malfunctions. Further investigation is needed to determine if the software peak judgment is correct, and the next step is to investigate the sample introduction system. In step S2, the normal test result of the boric acid-based quality control sample is defined as a fluctuation of 10% above or below the normal range of single ion detection values. If the fluctuation exceeds 10%, the test result of the boric acid-based quality control sample is abnormal.

[0147] In this embodiment, step S2 simulates a real fault-finding scenario in a nuclear power plant's primary loop by retesting the boric acid matrix quality control sample. This serves to distinguish between instrument malfunction and temporary interference, preventing misjudgment. If the boric acid matrix quality control sample result is unqualified, the sample is removed and tested again. Further evaluation is then conducted to determine if the removed sample is qualified, thus pinpointing the cause of the fault. If the boric acid matrix quality control is qualified, it indicates that the instrument is operating normally, ruling out hardware malfunctions such as the column, suppressor, and detector, thus narrowing down the search for the fault. In this embodiment, step S2 achieves stratified fault screening through quality control sample retesting, gradually narrowing down the fault range, improving troubleshooting efficiency, and avoiding misjudgments caused by occasional errors.

[0148] Optional, such as Figure 2a and Figure 2d As shown, the method further includes: Step S3. Testing the boric acid matrix quality control sample, determining whether the boric acid matrix quality control result is qualified, and outputting the judgment result. If the output judgment result is negative, then determining whether the peak shape is normal; if the output judgment result is positive, then the fault is determined to be caused by matrix influence, and the fault diagnosis is completed. In step S3, the normal test result of the boric acid matrix quality control sample is defined as a fluctuation of 10% above or below the normal range of single ion detection values. If the fluctuation exceeds 10%, then the test result of the boric acid matrix quality control sample is abnormal.

[0149] The boric acid-based quality control samples and the boric acid-removed quality control samples can be automatically prepared by the online monitoring instrument's automatic dilution module. The automatic dilution module refers to the online dilution method in Chinese Patent CN118465134A, "An Online Detection System and Dilution Method for Anions and Cations in Radioactive Detection Samples," which enables the online automatic preparation of standard sample solutions of different concentrations, automatic sample injection and analysis, calculation of quality control sample results, automatic determination of whether the quality control samples are qualified, and uploading of the determination results.

[0150] In routine instrument fault detection for conventional industrial water quality, there is no need to test quality control samples containing boric acid matrices. Ordinary industrial water quality testing does not involve the high-concentration boric acid environment and its associated complex interferences specific to nuclear power plants. This application addresses the unique scenario of high-concentration boric acid matrices in the primary loop water of nuclear power plants. It simulates a boric acid-containing environment by preparing boric acid-matrice quality control samples perfectly matched to the primary loop, actively inducing interferences specific to nuclear power plants, such as baseline drift and poor peak resolution. This accurately reproduces the nuclear power plant-grade interference scenario, facilitating precise fault location and calibrating the instrument's resistance to boric acid interference. In step S3 of this application, if the quality control sample results return to normal after removing the boric acid matrix, the fault is identified as originating from the boric acid matrix effect. If the results remain abnormal after removing the boric acid matrix, the investigation shifts to hardware such as the chromatographic column, suppressor, and detector. This achieves precise fault localization.

[0151] Optionally, the method further includes: step S4. Determine whether the peak shape is normal and output the determination result. If the output determination result is negative, it is determined that the chromatographic column is faulty and the fault diagnosis is completed. If the output determination result is positive, determine whether the background conductivity value is normal.

[0152] In this embodiment, step S4 directly identifies the problem with the chromatographic column by detecting abnormal peak shapes, such as tailing peaks, split peaks, or peak broadening, thus avoiding blindly checking other parts and further analyzing whether the problem is a decrease in column efficiency, packing collapse, or contamination.

[0153] The formula for calculating the tailing factor of the tailing peak is shown in formula (1):

[0154]

[0155] W 0.05: Peak width at 5% of peak height;

[0156] A: The horizontal distance from the peak front to the peak apex at 5% peak height.

[0157] During calculation, the retention time tR and peak height h of the target peak are recorded at the apex of the chromatogram. A horizontal line is drawn at 5% of the peak height (i.e., 0.05h), intersecting the peak leading edge and the peak trailing edge. The horizontal distance A between the intersection point of the peak leading edge and the peak apex, and the total width W ≤ 0.05 between the intersection points of the peak leading edge and the peak trailing edge are measured and substituted into formula (1) to calculate the tailing factor T. In the embodiments of this application, a T value > 1.5 indicates significant tailing, which may be due to a decrease in column efficiency after prolonged use or a column contamination caused by the adsorption of strongly retained substances. Regeneration of the packing material may be considered in the future.

[0158] The formula for calculating the separation degree of the split peak is shown in formula (2):

[0159]

[0160] tR1, tR2: Retention times of the two peaks;

[0161] Wb1, Wb2: Baseline peak widths of the two peaks.

[0162] During the calculation, the retention time tR1 and peak width Wb1 of peak 1 and the retention time tR2 and peak width Wb2 of peak 2 are recorded in the chromatogram. The resolution Rs is calculated by substituting them into formula (2). In the embodiments of this application, Rs≤1.2 indicates poor resolution. The failure caused by the collapse of the column packing will be considered. The column can be replaced or the packing can be regenerated in the future.

[0163] The formula for calculating the theoretical plate number of peak broadening is shown in formula (3):

[0164]

[0165] tR: Retention time of the target peak;

[0166] Wb: Baseline peak width.

[0167] During the calculation, the retention time tR and peak width Wb of the target peak are recorded in the chromatogram and substituted into formula (3) to calculate the theoretical plate number N. In the embodiments of this application, the calculated theoretical plate number N is >20% lower than the theoretical plate number provided by the new chromatographic column. This is due to the failure caused by the decline in column efficiency or the collapse of the packing material after prolonged use. In the future, it is advisable to replace the chromatographic column or regenerate the packing material.

[0168] The tailing factor, separation degree, and theoretical plate number mentioned above can be directly read from the online monitoring instrument software.

[0169] Optionally, it also includes: step S5. Determine whether the background conductivity value is normal, output the determination result, if the output determination result is negative, determine that the suppressor is faulty, and the fault diagnosis is completed; if the output determination result is positive, then determine whether the baseline noise is normal.

[0170] In this embodiment of the application, step S5 can determine the problem of the lock-up suppressor by the conductivity value. Generally, when the background conductivity value is greater than the basic background conductivity value by 5 μS / cm, it is considered that the fault is caused by the aging of the suppressor membrane, leakage of the suppressor or contamination.

[0171] Optionally, it also includes: step S6. Determine whether the baseline noise is normal and output the determination result. If the output determination result is negative, it is determined that the detector is faulty and the fault diagnosis is completed. If the output determination result is positive, then manually investigate the cause of the abnormality, determine the cause of the fault, and the fault diagnosis is completed.

[0172] In this embodiment, baseline noise in step S6 refers to baseline spikes, periodic fluctuations, and baseline drift. Baseline noise analysis can pinpoint detector malfunctions. Baseline noise > 2 nS / cm and baseline drift > 1 μS / cm * 30 min indicate abnormal baseline noise. If the baseline is normal but the test results are still abnormal, manual intervention for further analysis is required.

[0173] Optional, such as Figure 2c As shown, the method further includes: step S7. After further eliminating instrument hardware faults in step S2, determine whether the software peak judgment is normal and output the judgment result. If the output judgment result is negative, it is determined that the fault is caused by the software peak judgment and the fault diagnosis is completed; if the output judgment result is positive, then check the sample injection judgment.

[0174] In this embodiment, errors in peak determination in step S7, such as missed peak identification, baseline misjudgment, or integration errors, may lead to data distortion and directly affect the accuracy of the analysis results. Independent verification of the peak determination logic can eliminate faults caused by software settings.

[0175] Optionally, it also includes: step S8. Check whether the sample injection is abnormal, output the judgment result. If the output judgment result is yes, it is determined that the fault is caused by the injection, and the fault diagnosis is completed; if the output judgment result is no, retest the boric acid-free matrix quality control sample and judge the result of the re-injected sample.

[0176] In this embodiment of the application, step S8 directly locates the fault in the injection process by checking for problems such as the accuracy of the injection volume, blockage of the injection needle, sample residue, and abnormal switching of the six-way valve.

[0177] Optionally, it also includes: Step S9. Determine whether the result of the re-injected sample is appropriate, and output the determination result. If the output determination result is negative, then execute step S1; if the output determination result is positive, it is determined that the fault is caused by an occasional reason, and the fault diagnosis is completed. The re-injected sample in step S9 is a sample with boric acid matrix removed from the quality control sample.

[0178] In this embodiment of the application, step S9 prevents occasional problems such as injection errors and temporary fluctuations from being judged as online monitoring instrument malfunctions, thereby reducing unnecessary maintenance or component replacement.

[0179] It should be noted that the execution order of steps S2 to S6 and steps S7 to S9 can be set according to actual needs and is not limited here.

[0180] Compared with related technologies, the embodiments of this application have at least the following beneficial effects:

[0181] 1. The fault diagnosis method for the online coolant ion monitoring instrument in the primary loop of a nuclear power plant provided in this application embodiment addresses the issue that, when the online monitoring instrument test results are abnormal and the influence of actual samples is ruled out, traditional methods require manual on-site troubleshooting. However, the primary loop environment of a nuclear power plant carries a high radiation risk, making manual troubleshooting inefficient and increasing the risk of radiation exposure to personnel. This application embodiment addresses the issue by remotely and accurately locating faults, automatically identifying fault types based on alarm information and quality control sample testing, and quickly pinpointing faulty components through parameter analysis such as peak shape, conductivity, and noise. This reduces manual intervention and the need for personnel to enter the radiation zone, ensuring personnel safety. Furthermore, the fault location is accurate, avoiding blind on-site troubleshooting.

[0182] 2. The fault diagnosis method for the online coolant ion monitoring instrument in the primary loop of a nuclear power plant provided in this application simulates the real scenario of the primary loop of a nuclear power plant, which is susceptible to interference from high borate matrix. By comparing quality control samples containing borate matrix and quality control samples without borate matrix, the method can accurately distinguish whether the fault is due to instrument failure or matrix interference, thereby improving the accuracy of diagnosis.

[0183] 3. The fault diagnosis method for the online coolant ion monitor in the primary loop of a nuclear power plant provided in this application eliminates occasional anomalies, such as injection errors and temporary fluctuations, and reduces the false alarm rate by taking steps such as re-injection testing.

[0184] Example 2

[0185] This application also provides a fault diagnosis device for use in an online monitoring instrument for coolant ions in the primary loop of a nuclear power plant. The device includes a first determination module 310 and a second determination module 320.

[0186] The first determining module 310 is used to determine the fault alarm monitoring result when the test result of the target online monitoring instrument indicates an abnormality. The fault alarm monitoring result is used to indicate whether a fault alarm exists.

[0187] The second determining module 320 is used to determine the target fault type of the target online monitoring instrument based on the fault alarm monitoring results. The target fault type includes one of the following: communication fault, low pressure fault, high pressure fault, insufficient eluent fault, eluent generator depletion or fault, leakage fault, matrix influence fault, column fault, suppressor fault, detector fault, software peak determination fault, injection fault, and occasional fault.

[0188] According to the fault diagnosis device provided in this application embodiment, when the test results of the target online monitoring instrument indicate an abnormality, the fault alarm monitoring result is first determined, and then the target fault type of the target online monitoring instrument is determined based on the fault alarm monitoring result. In other words, this application embodiment automatically identifies the target fault type of the target online monitoring instrument based on the test results and fault alarm monitoring results. Compared to manual on-site troubleshooting, this reduces the need for manual intervention and entry into the radiation area, ensuring personnel safety and improving fault diagnosis efficiency. Furthermore, since fault diagnosis is based on objective data such as fault alarm monitoring results and target online monitoring instrument data, it can accurately locate the fault compared to manual on-site troubleshooting, thus improving the accuracy of fault diagnosis and avoiding blind on-site troubleshooting.

[0189] In some embodiments, the second determining module 320 is specifically used for: when the fault monitoring result indicates that a fault alarm exists, determining the target fault type of the target online monitoring instrument based on the alarm information; when the fault monitoring result indicates that no fault alarm exists, retesting the boric acid matrix quality control sample using the target online monitoring instrument, obtaining the retest result, and determining the target fault type of the target online monitoring instrument based on the retest result; wherein the retest result is used to indicate whether the boric acid matrix quality control sample is qualified.

[0190] In some embodiments, the second determining module 320 is specifically used for: removing the boric acid matrix of the boric acid matrix quality control sample to obtain a target quality control sample when the retest result indicates that the boric acid matrix quality control sample is unqualified; testing the target quality control sample using an online monitoring instrument to obtain a first test result, the first test result being used to indicate whether the target quality control sample is qualified; and determining the target fault type of the target online monitoring instrument as a matrix influence fault when the first test result indicates that the target quality control sample is qualified.

[0191] In some implementations, the second determining module 320 is specifically used to: if the retest result indicates that the boric acid matrix quality control sample is unqualified, and the peak shape determination result indicates that the peak shape is abnormal, then determine that the target fault type of the target online monitoring instrument is a column fault.

[0192] In some implementations, the second determining module 320 is specifically used to: if the retest result indicates that the boric acid matrix quality control sample is unqualified, and the background conductivity value determination result indicates that the background conductivity value is abnormal, then determine that the target fault type of the target online monitoring instrument is a suppressor fault.

[0193] In some implementations, the second determining module 320 is specifically used to: if the retest result indicates that the borate matrix quality control sample is unqualified, and the baseline noise determination result indicates that the baseline noise is abnormal, then determine that the target fault type of the target online monitoring instrument is a detector fault.

[0194] In some implementations, the second determining module 320 is specifically used to: if the retest result indicates that the boric acid matrix quality control sample is qualified, and the software peak determination result indicates that the software peak determination is abnormal, then determine that the target fault type of the target online monitoring instrument is a software peak determination fault.

[0195] In some implementations, the second determining module 320 is specifically used to: if the retest result indicates that the boric acid matrix quality control sample is qualified, and the sample injection inspection result indicates that the sample injection is abnormal, then determine that the target fault type of the target online monitoring instrument is an injection fault.

[0196] In some implementations, the second determining module 320 is specifically used to: if the retest result indicates that the boric acid matrix quality control sample is qualified, and if the re-injection test result is used to determine that the target fault type of the target online monitoring instrument is an intermittent fault.

[0197] In some embodiments, the second determining module 320 is specifically used to: determine the target fault type of the target online monitoring instrument as a communication fault when the alarm information is a communication interruption alarm; determine the target fault type of the target online monitoring instrument as a low-pressure fault when the alarm information is a low-pressure alarm; determine the target fault type of the target online monitoring instrument as a high-pressure fault when the alarm information is an overpressure alarm; determine the target fault type of the target online monitoring instrument as a insufficient eluent fault when the alarm information is a low-liquid alarm; determine the target fault type of the target online monitoring instrument as a suppressor fault when the alarm information is a suppressor current alarm; determine the target fault type of the target online monitoring instrument as an eluent generator depletion or fault when the alarm information is an eluent generator concentration alarm; and determine the target fault type of the target online monitoring instrument as a leakage fault when the alarm information is a leakage alarm.

[0198] The fault diagnosis device provided in this application has the beneficial effects and implementation methods of the fault diagnosis method provided in Embodiment 1 of this application. For details, please refer to the specific description of the fault diagnosis method in Embodiment 1 above. This embodiment will not repeat the description here.

[0199] Example 3

[0200] like Figure 4 As shown, this application provides an electronic device including a memory 41 and a processor 42. The memory 41 stores a computer program, and the processor 42 is configured to run the computer program to perform the fault diagnosis method in embodiment 1.

[0201] The memory 41 is connected to the processor 42. The memory 41 can be a flash memory, a read-only memory or other memory, and the processor 42 can be a central processing unit or a microcontroller.

[0202] Example 4

[0203] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the fault diagnosis method in Embodiment 1 above.

[0204] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0205] Example 5

[0206] This application also provides a computer program product in which, when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs the fault diagnosis method as described in Embodiment 1.

[0207] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A failure diagnosis method characterized by comprising: The method is applied to a nuclear power plant primary coolant ion online monitor, and the method comprises: In the case that the test result of the target online monitor indicates an abnormality, a fault alarm monitoring result is determined, the fault alarm monitoring result being used to indicate whether there is a fault alarm; According to the fault alarm monitoring result, a target fault type of the target online monitor is determined, the target fault type comprising one of a communication fault, a low pressure fault, a high pressure fault, a lack of eluent fault, an eluent generator exhaustion or fault, a liquid leakage fault, a matrix influence fault, a chromatographic column fault, an inhibitor fault, a detector fault, a software peak judgment fault, a sample injection fault, and an occasional fault.

2. The method of claim 1, wherein, The determination of the target fault type of the target online monitor according to the fault alarm monitoring result specifically comprises: In the case that the fault monitoring result indicates that there is a fault alarm, a target fault type of the target online monitor is determined according to alarm information; In the case that the fault monitoring result indicates that there is no fault alarm, a boron acid matrix quality control sample is retested by the target online monitor to obtain a retest result, and a target fault type of the target online monitor is determined according to the retest result; wherein the retest result is used to indicate whether the boron acid matrix quality control sample is qualified.

3. The method of claim 2, wherein, The determination of the target fault type of the target online monitor according to the retest result specifically comprises: In the case that the retest result indicates that the boron acid matrix quality control sample is unqualified, a boron acid matrix of the boron acid matrix quality control sample is removed to obtain a target quality control sample; The target quality control sample is tested by the online monitor to obtain a first test result, and the first test result is used to indicate whether the target quality control sample is qualified; In the case that the first test result indicates that the target quality control sample is qualified, a target fault type of the target online monitor is determined to be a matrix influence fault.

4. The method of claim 2, wherein, The determination of the target fault type of the target online monitor according to the retest result specifically comprises: In the case that the retest result indicates that the boron acid matrix quality control sample is unqualified, if a peak shape determination result indicates that a peak shape is abnormal, a target fault type of the target online monitor is determined to be a chromatographic column fault.

5. The method of claim 2, wherein, The determination of the target fault type of the target online monitor according to the retest result specifically comprises: In the case that the retest result indicates that the boron acid matrix quality control sample is unqualified, if a background conductance value determination result indicates that a background conductance value is abnormal, a target fault type of the target online monitor is determined to be an inhibitor fault.

6. The method of claim 2, wherein, The determination of the target fault type of the target online monitor according to the retest result specifically comprises: In the case that the retest result indicates that the boron acid matrix quality control sample is unqualified, if a baseline noise determination result indicates that a baseline noise is abnormal, a target fault type of the target online monitor is determined to be a detector fault.

7. The method of claim 2, wherein, The determination of the target fault type of the target online monitor according to the retest result specifically comprises: In a case where the retest result indicates that the borate matrix quality control sample is qualified, if the software peak determination result indicates that software peak determination is abnormal, it is determined that the target fault type of the target online monitor is a software peak determination fault.

8. The method of claim 2, wherein, The method further includes determining the target fault type of the target online monitor according to the retest result, and the determining includes: In a case where the retest result indicates that the borate matrix quality control sample is qualified, if the sample injection inspection result indicates that sample injection is abnormal, it is determined that the target fault type of the target online monitor is a sample injection fault.

9. The method of claim 2, wherein, The method further includes determining the target fault type of the target online monitor according to the retest result, and the determining includes: In a case where the retest result indicates that the borate matrix quality control sample is qualified, if the re-injection test result indicates that the re-injection sample is qualified, it is determined that the target fault type of the target online monitor is an occasional fault.

10. The method of claim 2, wherein, The method further includes determining the target fault type of the target online monitor according to the alarm information, and the determining includes: In a case where the alarm information is a communication interruption alarm, it is determined that the target fault type of the target online monitor is a communication fault; In a case where the alarm information is a low pressure alarm, it is determined that the target fault type of the target online monitor is a low pressure fault; In a case where the alarm information is an overpressure alarm, it is determined that the target fault type of the target online monitor is a high pressure fault; In a case where the alarm information is a liquid deficiency alarm, it is determined that the target fault type of the target online monitor is a eluent deficiency fault; In a case where the alarm information is a suppressor current alarm, it is determined that the target fault type of the target online monitor is a suppressor fault; In a case where the alarm information is a eluent generator concentration alarm, it is determined that the target fault type of the target online monitor is a eluent generator exhaustion or fault; In a case where the alarm information is a liquid leakage alarm, it is determined that the target fault type of the target online monitor is a liquid leakage fault.

11. A failure diagnosing apparatus characterized by comprising: The device is applied to a primary loop coolant ion online monitor of a nuclear power plant, and the device includes: A first determining module is configured to determine a fault alarm monitoring result in a case where a test result of a target online monitor indicates abnormality, the fault alarm monitoring result being used to indicate whether there is a fault alarm; A second determining module is configured to determine a target fault type of the target online monitor according to the fault alarm monitoring result, the target fault type including one of a communication fault, a low pressure fault, a high pressure fault, a eluent deficiency fault, a eluent generator exhaustion or fault, a liquid leakage fault, a matrix influence fault, a chromatographic column fault, a suppressor fault, a detector fault, a software peak determination fault, a sample injection fault, and an occasional fault.

12. An electronic device, comprising: The electronic device includes a memory and a processor, the memory storing a computer program, and the processor is configured to run the computer program to implement the fault diagnosis method according to any one of claims 1-10.

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