A method and system for detecting an automatic drain valve

Abstract

The disclosure provides a detection method and system of an automatic drain valve, and relates to the technical field of device testing. The method comprises the following steps: collecting working data of a drain valve to be detected; determining a flow peak change trend and a drain efficiency attenuation degree based on flow data recorded in continuous multiple drainages, and generating an impurity deposition index; generating an impurity stripping index based on the instantaneous change rate of flow rate data and the change amount of flow data, and determining a first impurity deposition degree in combination with the impurity deposition index; determining a vibration anomaly factor and an acoustic anomaly factor based on vibration data and acoustic data, and determining an impurity anomaly score in combination with the first impurity deposition degree; determining working intensity based on vibration data, pressure data and liquid level data, obtaining the drainage frequency of the drain valve to be detected since the last maintenance, determining a failure probability based on the working intensity, the drainage frequency and the impurity anomaly score; and predicting the failure condition of the drain valve to be detected based on the failure probability. The disclosure improves the failure detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of equipment testing technology, and specifically to a testing method and system for an automatic drain valve. Background Technology

[0002] Automatic drain valves are critical components in industrial fluid systems, and their operating status directly determines the system's energy efficiency and operational safety. During long-term operation, industrial systems continuously generate condensate due to temperature fluctuations or medium condensation. This condensate often contains impurities such as dust from the air, rust from pipe corrosion, residual oil mist from system lubrication, and scale formed by medium crystallization. If this impurity-laden condensate is not drained in time, it will accumulate within the system, leading to a series of serious problems: on the one hand, it accelerates corrosion of the inner walls of pipes, shortening their service life; on the other hand, it causes functional failures such as pneumatic component jamming and failure, significant reduction in heat exchanger efficiency, and decreased vacuum levels in vacuum pumps. In extreme cases, it can cause the entire industrial system to shut down, resulting in significant economic losses to production and operations.

[0003] Related technologies typically employ traditional manual methods to inspect automatic drain valves, including visually checking the drainage status, manually starting and stopping the valve to test its response, and listening to determine if the valve's operating sound is abnormal. However, these inspection methods mainly rely on reactive maintenance or periodic inspections. From a timeliness perspective, reactive maintenance only intervenes after the drain valve becomes clogged or the system malfunctions due to drainage problems, lacking early warning capabilities and failing to prevent system damage before failures occur. While periodic inspections are conducted at fixed intervals, a sudden and rapid accumulation of impurities between inspections can create blind spots, leading to undetected faults and an expanded impact. Summary of the Invention

[0004] In view of this, the present disclosure proposes an automatic drain valve detection method and system to solve the problems in related technologies where post-maintenance strategies cannot avoid system losses before failure occurs; and where periodic inspection strategies can easily create detection blind spots when impurities accumulate rapidly between two inspections, leading to failure to detect faults in time and thus expanding the scope of impact.

[0005] According to the first aspect of this disclosure, a method for detecting an automatic drain valve is provided, and the specific technical solution adopted is as follows:

[0006] Collect the operating data of the drain valve to be tested, including flow rate data, flow velocity data, vibration data, acoustic data, pressure data, and liquid level data;

[0007] Based on the flow data from multiple consecutive drainage records, the peak flow rate variation trend and the degree of drainage efficiency decay are determined, and an impurity deposition index is generated based on the peak flow rate variation trend and the degree of drainage efficiency decay.

[0008] An impurity stripping index is generated based on the instantaneous rate of change of the flow velocity data and the corresponding change in the flow rate data, and the degree of first impurity deposition is determined based on the impurity deposition index and the impurity stripping index.

[0009] Based on the vibration data and the acoustic data, a vibration anomaly factor and an acoustic anomaly factor are determined, and based on the vibration anomaly factor, the acoustic anomaly factor, and the first impurity deposition degree, the impurity anomaly score of the drain valve to be tested is determined.

[0010] The working intensity is determined based on the vibration data, the pressure data, and the liquid level data. The drainage frequency of the drain valve to be tested since the last maintenance is obtained. The failure probability is determined based on the working intensity, the drainage frequency, and the impurity anomaly score.

[0011] The failure status of the drain valve to be tested is predicted based on the failure probability.

[0012] For example, determining the flow peak change trend and drainage efficiency decay degree based on the flow data from multiple consecutive drainage records, and generating an impurity deposition index based on the flow peak change trend and drainage efficiency decay degree, includes: obtaining the flow peak in the flow data from multiple consecutive drainage records, and constructing a flow peak change curve with the number of drainages as the independent variable and the flow peak corresponding to the number of drainages as the dependent variable; performing linear fitting on the flow peak change curve and determining the slope of the fitted line, which is recorded as the flow peak change trend; obtaining the initial drainage of the first drainage from the drain valve to be tested since the last maintenance. The system calculates the initial drainage volume and duration, and determines the initial drainage efficiency based on the initial drainage volume and duration; it also obtains the current drainage volume and duration of the current drainage from the drain valve under test since the last maintenance, and determines the current drainage efficiency based on the current drainage volume and duration; it determines the effective drainage time based on the flow data during the current drainage process; it determines the degree of drainage efficiency decay based on the initial drainage efficiency, the current drainage efficiency, and the effective drainage time; and it calculates the product of the absolute value of the flow peak change trend and the degree of drainage efficiency decay to generate the impurity deposition index.

[0013] For example, determining the effective drainage time based on the flow data in the current drainage process includes: obtaining the system design maximum flow of the drain valve to be detected, calculating the product of the system design maximum flow and a preset ratio to obtain a flow threshold; obtaining target flow data that is not less than the flow threshold in the current drainage process, determining the sum of drainage times corresponding to the target flow data, and recording it as the effective drainage time.

[0014] For example, the step of generating an impurity stripping index based on the instantaneous rate of change of the flow velocity data and the corresponding change in the flow rate data, and determining a first degree of impurity deposition based on the impurity deposition index and the impurity stripping index, includes: for each sampling time in the current drainage, acquiring the flow rate data and the flow velocity data at the current sampling time and the previous sampling time; determining the instantaneous rate of change of the flow velocity data at the current sampling time based on the difference between the flow velocity data at the current sampling time and the previous sampling time and the sampling interval, and determining a flow velocity inflection point based on the instantaneous rate of change of the flow velocity data; determining the difference between the instantaneous rate of change of the flow velocity data at the current sampling time and the previous sampling time corresponding to the flow velocity inflection point, denoted as a first change; determining the difference between the flow rate data at the current sampling time and the previous sampling time corresponding to the flow velocity inflection point, denoted as a second change; determining the product of the first change and the second change to obtain the impurity stripping index; and determining the product of the impurity deposition index and the impurity stripping index to obtain the first degree of impurity deposition.

[0015] For example, the vibration data is valve vibration data collected by a vibration sensor installed on the outer wall of the drain valve under test during the drainage process; the acoustic data is sound data emitted by the valve during the drainage process collected by an acoustic sensor installed on the outer wall of the drain valve under test; the determination of vibration anomaly factors and acoustic anomaly factors based on the vibration data and the acoustic data includes: for each sampling time in the current drainage, obtaining the actual vibration amplitude and actual vibration frequency collected by the vibration sensor, and the actual noise intensity and actual noise frequency collected by the acoustic sensor; obtaining the set upper limit of the vibration amplitude of the drain valve under test and the set upper limit of the vibration amplitude. A fixed upper limit for vibration frequency is defined. The difference between the actual vibration amplitude and the set upper limit for vibration amplitude, and the difference between the actual vibration frequency and the set upper limit for vibration frequency are calculated. The product of the difference in vibration amplitude and the difference in vibration frequency is determined and denoted as the vibration anomaly factor. A set upper limit for noise intensity and a set upper limit for noise frequency of the drain valve to be tested are obtained. The difference between the actual noise intensity and the set upper limit for noise intensity, and the difference between the actual noise frequency and the set upper limit for noise frequency are calculated. The product of the difference in noise intensity and the difference in noise frequency is determined and denoted as the acoustic anomaly factor.

[0016] For example, determining the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, the acoustic anomaly factor, and the first impurity deposition degree includes: determining the mean value of the vibration anomaly factor at each sampling time during the current drainage process, and recording it as the vibration anomaly score of the current drainage; determining the mean value of the acoustic anomaly factor at each sampling time during the current drainage process, and recording it as the acoustic anomaly score of the current drainage; determining the vibration anomaly score and the anomaly correlation factor of the acoustic anomaly score; determining the second impurity deposition degree based on the anomaly correlation factor, the vibration anomaly score, and the acoustic anomaly score; determining the confidence level of the first impurity deposition degree based on the first impurity deposition degree and the second impurity deposition degree, and recording the product of the first impurity deposition degree and the confidence level as the impurity anomaly score.

[0017] For example, determining the abnormal correlation factor of the vibration anomaly score and the acoustic anomaly score includes: determining the product of the vibration anomaly score and the acoustic anomaly score, denoted as the sensor anomaly score product; determining the absolute value of the difference between the vibration anomaly score and the acoustic anomaly score, denoted as the absolute value of the sensor anomaly score difference; and determining the ratio of the sensor anomaly score product to the absolute value of the sensor anomaly score difference to obtain the abnormal correlation factor of the vibration anomaly score and the acoustic anomaly score.

[0018] For example, the vibration data is valve vibration data collected by a vibration sensor installed on the outer wall of the drain valve under test during the drainage process; the pressure data is pressure data inside the valve under test collected by a pressure sensor installed inside the drain valve under test during the drainage process; the liquid level data is the liquid level height of the water accumulated inside the system to which the drain valve under test belongs, collected by a liquid level sensor built into the inside of the drain valve under test; the determination of working intensity based on the vibration data, the pressure data, and the liquid level data includes: for the current drainage, acquiring the first pressure data collected by the pressure sensor before the current drainage and the second pressure data collected after the current drainage, and calculating the first pressure... The difference between the force data and the second pressure data is recorded as the pressure difference before and after drainage; the average vibration amplitude obtained by the vibration sensor during the current drainage process is acquired, and the equipment operating intensity is determined based on the average vibration amplitude and the pressure difference before and after drainage; the maximum liquid level data of the water storage device in the system to which the drain valve to be tested belongs and the current liquid level data collected by the liquid level sensor are acquired, and the theoretical drainage volume is determined based on the maximum liquid level data, the current liquid level data, and the volume of the water storage device; the actual drainage volume of the current drainage is acquired, and the difference between the actual drainage volume and the theoretical drainage volume is recorded as the impurity residue value; the working intensity is determined based on the effective drainage time, the equipment operating intensity, and the impurity residue value.

[0019] For example, predicting the failure status of the drain valve under test based on the failure probability includes: acquiring the liquid level data before the next drainage; predicting the increase in the failure probability of the next drainage relative to the current drainage based on the liquid level data before the next drainage and the failure probability of the current drainage; determining the failure probability of the next drainage based on the failure probability of the current drainage and the increase in the failure probability; and if the failure probability of the next drainage is greater than a preset threshold, sending a stop operation command to the drain valve under test and performing maintenance on the drain valve under test.

[0020] According to a second aspect of this disclosure, an automatic drain valve detection system is provided, as follows:

[0021] The data acquisition module is used to collect the working data of the drain valve to be tested. The working data includes flow rate data, flow velocity data, vibration data, acoustic data, pressure data, and liquid level data.

[0022] The data processing module is used to determine the flow peak change trend and the degree of drainage efficiency decay based on the flow data from multiple consecutive drainage records, and to generate an impurity deposition index based on the flow peak change trend and the degree of drainage efficiency decay.

[0023] The data processing module is further configured to generate an impurity stripping index based on the instantaneous change rate of the flow velocity data and the corresponding change in the flow rate data, and to determine the first impurity deposition degree based on the impurity deposition index and the impurity stripping index.

[0024] The data processing module is further configured to determine vibration anomaly factor and acoustic anomaly factor based on the vibration data and the acoustic data, and to determine the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, the acoustic anomaly factor and the first impurity deposition degree.

[0025] The data processing module is also used to determine the working intensity based on the vibration data, the pressure data and the liquid level data, obtain the drainage frequency of the drain valve to be tested since the last maintenance, and determine the failure probability based on the working intensity, the drainage frequency and the impurity abnormality score.

[0026] The fault detection module is used to predict the fault condition of the drain valve to be detected based on the fault probability.

[0027] This invention may have some or all of the following beneficial effects:

[0028] In the automatic drain valve detection method provided by this invention, the flow peak change trend and the degree of drainage efficiency decay are determined by the flow data from multiple consecutive drainage records. An impurity deposition index is generated based on the flow peak change trend and the degree of drainage efficiency decay. An impurity stripping index is generated based on the instantaneous change rate of flow velocity data and the corresponding change in flow rate data. The first degree of impurity deposition is determined based on the impurity deposition index and the impurity stripping index. Therefore, the degree of impurity deposition at the valve of the drain valve can be analyzed by utilizing the changes in flow rate and flow velocity data during the operation of the drain valve, improving the accuracy of predicting the degree of blockage in the drain valve. Furthermore, this invention... Furthermore, based on vibration and acoustic data, vibration anomaly factors and acoustic anomaly factors are determined. Based on these factors and the degree of deposition of the first impurity, an impurity anomaly score is determined for the drain valve under test. This allows for the verification of the degree of deposition of the first impurity using multimodal data, including vibration and acoustic data, further improving the reliability of fault prediction for the drain valve under test. Simultaneously, this invention also determines the working intensity based on vibration, pressure, and liquid level data, obtains the drainage frequency of the drain valve under test since the last maintenance, determines the fault probability based on the working intensity, drainage frequency, and impurity anomaly score, and predicts the fault condition of the drain valve under test based on the fault probability. By dynamically evaluating the working intensity of the drain valve under test through changes in liquid level and pressure before and after operation, and calculating the fault probability by combining the drainage frequency and impurity anomaly score, early prediction of drain valve faults is achieved, effectively solving the lag of manual maintenance.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart of a detection method for an automatic drain valve according to an exemplary embodiment of the present disclosure is shown;

[0032] Figure 2 A schematic diagram of the flow peak variation curve in the detection method of the automatic drain valve according to an exemplary embodiment of the present disclosure is shown;

[0033] Figure 3 A schematic block diagram of a detection system for an automatic drain valve according to an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automatic drain valve detection method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] The following description, in conjunction with the accompanying drawings, details the specific scheme of the detection method and system for an automatic drain valve provided by the present invention.

[0037] Please see Figure 1 It shows a flowchart of a method for detecting an automatic drain valve according to an embodiment of the present invention, as follows. Figure 1 As shown, the testing method for this automatic drain valve specifically includes the following steps:

[0038] S110: Collects the operating data of the drain valve under test, including flow rate data, velocity data, vibration data, acoustic data, pressure data, and liquid level data;

[0039] S120: Determine the peak flow rate change trend and the degree of drainage efficiency decay based on the flow rate data from multiple consecutive drainage records, and generate an impurity deposition index based on the peak flow rate change trend and the degree of drainage efficiency decay.

[0040] S130: Generate an impurity stripping index based on the instantaneous rate of change of the flow velocity data and the corresponding change in the flow rate data, and determine the degree of first impurity deposition based on the impurity deposition index and the impurity stripping index.

[0041] S140: Determine the vibration anomaly factor and acoustic anomaly factor based on vibration data and acoustic data, and determine the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, acoustic anomaly factor and the degree of first impurity deposition.

[0042] S150: Determine the working intensity based on vibration data, pressure data and liquid level data, obtain the drainage frequency of the drain valve under test since the last maintenance, and determine the failure probability based on working intensity, drainage frequency and impurity abnormality score.

[0043] S160: Predict the failure status of the drain valve under test based on failure probability.

[0044] The following is a detailed explanation of each step in the above-mentioned testing method for automatic drain valves:

[0045] In step S110, the working data of the drain valve to be tested is collected. The working data includes flow rate data, flow velocity data, vibration data, acoustic data, pressure data, and liquid level data.

[0046] In this embodiment, the drain valve to be tested can be any automatic drain valve deployed in an industrial system, used to discharge liquids such as condensate from its system. During drainage, the discharged liquid may contain impurities such as particulate matter, oily substances, and crystals. When the liquid passes through the valve, these impurities are adsorbed and deposited on the valve wall or filter screen. As the liquid continues to flow through the automatic drain valve, the impurity layer gradually thickens until it blocks the entire liquid channel of the drain valve. Furthermore, due to the periodic opening of the drain valve, the instantaneous increase in water flow impacts the outermost layer of impurities, carrying away the loose impurities. The deeper deposits are compressed and solidified, resulting in a denser impurity layer, eventually causing the drain valve to become completely blocked at some point. The automatic drain valve testing method provided in this embodiment assesses the likelihood of blockage in the drain valve and performs maintenance on it.

[0047] In this embodiment, the aforementioned working data refers to various data generated by the drain valve under test during the drainage process. For example, the working data may include flow rate data, flow velocity data, vibration data, acoustic data, pressure data, and liquid level data.

[0048] In this embodiment, the flow rate data refers to the water flow rate of the drain valve under test during the drainage process. For example, this flow rate data can be collected by a flow sensor at the drain valve under test, reflecting the unobstructedness of the valve passage and the impact of impurities deposited in the valve on the water flow rate.

[0049] In this embodiment, the flow velocity data refers to the speed at which water flows through the drain valve to be tested during the drainage process. For example, this flow velocity data can be calculated from flow rate data and valve cross-sectional area based on fundamental fluid mechanics formulas, or it can be directly collected by a flow velocity sensor to determine the condition of the valve by analyzing changes in flow velocity.

[0050] In this embodiment, the vibration data includes the vibration amplitude and frequency generated by the drain valve under test during the drainage process. Exemplarily, this vibration data can be collected by a vibration sensor installed on the outer wall of the drain valve under test.

[0051] In this embodiment, the acoustic data refers to the sound intensity and frequency generated by the water flow impact on the valve of the drain valve to be tested during the drainage process. Exemplarily, this acoustic data can be collected by an acoustic sensor installed on the outer wall of the drain valve to be tested.

[0052] In this embodiment, the pressure data refers to the pressure exerted on the inner side of the drain valve during the drainage process. Exemplarily, this pressure data can be collected by a pressure sensor installed inside the valve.

[0053] In this embodiment, the aforementioned liquid level data is used to characterize the height of the water level inside the system to which the drain valve under test belongs. For example, this liquid level data can be obtained in real time through a liquid level sensor built into the drain valve under test.

[0054] In this embodiment, the acquisition frequency of each sensor is set based on the actual scenario requirements. For example, in one specific implementation, the data acquisition frequency of each sensor is specified as 10Hz.

[0055] In this embodiment, after acquiring real-time operating data of the drain valve to be tested, the acquired real-time operating data is standardized to achieve data dimension unification, and the real-time operating data is temporarily stored in a temporary storage area designated by the system. The maximum storage time of this temporary storage area can be set based on the needs of the actual scenario. For example, in the above specific implementation, the maximum storage time of this temporary storage area can be set to 10 minutes. When the drain valve to be tested is opened, all data in the above temporary storage area is transferred to the real-time storage area designated by the system, and the subsequently acquired operating data is also stored in the real-time storage area until the drain valve to be tested is closed. After the drain valve to be tested is closed, the operating data acquired in real time afterward is stored in the above temporary storage area.

[0056] This application embodiment achieves time-series management of data acquisition through the aforementioned regional dynamic storage mechanism. This ensures complete recording of data throughout the entire working cycle before and after valve opening, while also preventing invalid data from occupying storage space for extended periods through a temporary storage mechanism. Furthermore, data partitioning storage is achieved using the valve opening / closing status as the trigger point, facilitating accurate extraction and analysis of drainage process data in the future, thereby improving the efficiency and relevance of data management.

[0057] In addition, for the data in the aforementioned real-time storage area, this application embodiment can also use the time from the opening to the closing of the drain valve to be detected as a time window to preprocess the acquired real-time working data, and integrate the preprocessed data into a dataset to obtain the working dataset of the corresponding drainage event.

[0058] In step S120, the flow rate peak change trend and the degree of drainage efficiency decay are determined based on the flow rate data from multiple consecutive drainage records, and an impurity deposition index is generated based on the flow rate peak change trend and the degree of drainage efficiency decay.

[0059] In this embodiment, the aforementioned flow peak value variation trend reflects the variation trend of the flow peak value with the number of drainage events; wherein, the flow peak value is the maximum value of the water flow rate during the corresponding drainage event. Specifically, taking the i-th drainage as an example, the flow peak value of the i-th drainage is the maximum value of the flow data collected by the flow sensor during the i-th drainage process.

[0060] During the operation of the drain valve under test, impurities in the liquid deposit on the valve wall or filter screen, reducing the valve passage area. According to fluid mechanics principles, if the valve passage area of ​​the drain valve under test is reduced, the flow rate through the drain valve will decrease within the same drainage time. Therefore, the degree of impurity deposition in the drain valve under test can be measured by the trend of the aforementioned flow rate peak value. Specifically, when draining multiple times consecutively, if impurities are deposited in the drain valve under test, the peak flow rate of each drainage will gradually decrease with the increase of the number of drainages.

[0061] For example, the aforementioned flow peak variation trend can be determined by the following method: obtaining the flow peak from the flow data of multiple consecutive drainage records, and constructing a flow peak variation curve with the number of drainages as the independent variable and the flow peak corresponding to the number of drainages as the dependent variable. Specifically, this flow peak variation curve can be as follows: Figure 2 As shown; perform linear fitting on the above flow peak change curve, and determine the slope of the fitted line, which is denoted as the flow peak change trend.

[0062] In this embodiment, the aforementioned degree of drainage efficiency decay is a quantitative indicator of the decrease in drainage efficiency of the drain valve under test due to impurity deposition. Drainage efficiency is a quantitative indicator of the drainage volume of the drain valve under test per unit time, reflecting its drainage capacity. When the cross-sectional area of ​​the valve channel of the drain valve under test shrinks due to impurity deposition, the total drainage volume within the same drainage time decreases, and the drainage efficiency also decreases. Therefore, the degree of impurity deposition in the drain valve under test can be measured by the degree of drainage efficiency decay over different drainage cycles.

[0063] For example, the aforementioned degree of drainage efficiency attenuation can be achieved in the following way: obtain the initial drainage volume and duration of the first drainage of the drain valve under test since the last maintenance, and calculate the ratio of the initial drainage volume to the initial drainage duration, which is recorded as the initial drainage efficiency; obtain the current drainage volume and duration of the current drainage of the drain valve under test since the last maintenance, and calculate the ratio of the current drainage volume to the current drainage duration, which is recorded as the current drainage efficiency; determine the effective drainage time based on the flow data during the current drainage process; and determine the degree of drainage efficiency attenuation based on the initial drainage efficiency, the current drainage efficiency, and the effective drainage time.

[0064] Specifically, taking the i-th drainage after the last maintenance of the self-monitoring drain valve as an example, the drainage efficiency of this i-th drainage can be calculated using the following formula:

[0065]

[0066] in, Let be the drainage efficiency of the i-th drainage; Let i be the amount of water discharged during the i-th drainage. Let be the duration of the i-th drainage.

[0067] In this embodiment, the effective drainage time is the sum of the time periods during which the drain valve under test is in a high flow state during the drainage process. The high flow state refers to the drainage state in which the real-time flow of the drain valve under test is not less than the flow threshold. The flow threshold can be determined based on the maximum flow designed for the system to which the drain valve under test belongs.

[0068] For example, the above-mentioned determination of effective drainage time based on flow data in the current drainage process can be achieved by the following method: obtaining the system design maximum flow of the drain valve to be tested, calculating the product of the system design maximum flow and a preset ratio to obtain the flow threshold; obtaining the target flow data that is not less than the flow threshold in the current drainage process, determining the sum of the drainage times corresponding to the target flow data, and recording it as the effective drainage time.

[0069] Specifically, taking the i-th drainage since the last maintenance of the drain valve under test as an example, assuming the system to which the drain valve under test belongs has a designed maximum flow rate of [missing information]. The preset ratio is 0.8, which is for the real-time traffic at time t. ,like If the time is high, then mark that time as the efficient working time; count all efficient working times during the i-th drainage process, and record the sum of all efficient working times as the effective drainage time of the i-th drainage. .

[0070] In this embodiment, the initial drainage efficiency can also be determined based on the initial drainage volume and duration corresponding to the first drainage after the last maintenance of the drain valve under test, according to the formula for calculating the drainage efficiency of the i-th drainage. Taking the current drainage as the i-th drainage since the last maintenance of the drain valve to be tested as an example, the degree of decrease in drainage efficiency of the i-th drainage since the last maintenance of the drain valve to be tested can be calculated by the following formula:

[0071]

[0072] in, The degree of efficiency degradation during the i-th drainage run; The initial drainage efficiency is the first drainage operation. Let be the drainage efficiency of the i-th drainage; Let be the effective drainage time for the i-th drainage. From the above formula, we can see that if the drainage efficiency of the i-th drainage is significantly lower than that of the initial drainage, and the effective drainage time of the i-th drainage is relatively long, then it can be determined that the decrease in drainage efficiency is less likely to be caused by the accumulation of impurities. Combining the effective drainage time with the judgment can avoid misjudging the degree of impurity accumulation in the drain valve to be tested.

[0073] In this embodiment, the aforementioned impurity deposition index is an indicator used to measure the degree of impurity deposition on the drain valve to be tested. For example, this impurity deposition index can be determined by calculating the product of the absolute value of the peak flow rate change trend and the degree of drainage efficiency decline.

[0074] Specifically, taking the i-th drainage after the last maintenance of the self-testing drain valve as an example, the above-mentioned impurity deposition index can be calculated using the following formula:

[0075]

[0076] in, The impurity deposition index for the i-th drainage; The trend of peak flow rate variation for multiple consecutive drainage operations; The degree of efficiency degradation during the i-th drainage run; The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0077] In step S130, an impurity stripping index is generated based on the instantaneous rate of change of the flow velocity data and the corresponding change in the flow rate data, and the degree of first impurity deposition is determined based on the impurity deposition index and the impurity stripping index.

[0078] In this embodiment of the application, the instantaneous change rate of the above-mentioned flow velocity data is the rate of change of the flow velocity data collected at adjacent sampling times, which can be used to measure the degree of impurity stripping at the initial opening of the drain valve to be tested.

[0079] This application embodiment can determine the flow velocity inflection point based on the instantaneous rate of change of flow velocity data. Before this inflection point, the flow velocity data increases due to the decrease in the cross-sectional area of ​​the drain valve under test. As the water flow washes away the loose sediment on the outermost layer of impurities, after reaching the inflection point, the cross-sectional area of ​​the drain valve under test increases, and the flow velocity data gradually decreases. By analyzing the flow velocity change at the initial opening of the drain valve under test, the amount of impurities stripped can be determined based on the magnitude of the flow velocity reduction.

[0080] For example, the instantaneous rate of change of the above flow velocity data can be determined by the following method: for each sampling time in the current drainage, obtain the flow velocity data at the current sampling time and the previous sampling time; determine the ratio of the difference between the flow velocity data at the current sampling time and the previous sampling time to the sampling interval, and record it as the instantaneous rate of change of the flow velocity data at the current sampling time.

[0081] Taking the i-th drainage process after the last maintenance of the self-monitoring drain valve at time t as an example, the instantaneous rate of change of the flow velocity data at time t can be calculated using the following formula:

[0082]

[0083] in, Let be the instantaneous rate of change of the flow velocity data at time t during the i-th drainage process; The flow velocity data at time t during the i-th drainage process; For the t-th drainage process of the i-th time Flow rate data at any given time; This represents the sampling interval between adjacent sampling times.

[0084] In this embodiment of the application, after determining the instantaneous rate of change of the flow velocity data corresponding to each sampling time through the above method, the flow velocity inflection point can be further determined based on the instantaneous rate of change of the flow velocity data.

[0085] Specifically, taking the i-th drainage after the last maintenance of the self-testing drain valve as an example, if and Then, the time t in the i-th drainage process is defined as the velocity inflection point; where, The instantaneous rate of change of the flow velocity data at time t-1 during the i-th drainage process after the last maintenance of the drain valve under test; The instantaneous rate of change of the flow velocity data at time t during the i-th drainage process after the last maintenance of the drain valve under test.

[0086] In this embodiment of the application, the aforementioned impurity stripping index is used to quantify the degree to which the water flow disperses the loose outer layer of impurities in the initial stage of opening the drain valve to be tested.

[0087] For example, the above-mentioned impurity removal index can be determined by the following method: determining the difference between the instantaneous rate of change of the flow rate data at the current sampling time and the previous sampling time corresponding to the flow rate inflection point, and recording it as the first change; determining the difference between the flow rate data at the current sampling time and the previous sampling time corresponding to the flow rate inflection point, and recording it as the second change; determining the product of the first change and the second change to obtain the impurity removal index.

[0088] Specifically, taking the time t during the i-th drainage process after the last maintenance of the drain valve under test as the flow velocity inflection point, the above-mentioned impurity stripping index can be calculated in the following way:

[0089]

[0090] in, This is an indicator of impurity stripping at the flow velocity inflection point. Let be the flow rate data at time t during the i-th drainage process. The data represents the flow rate at time t-1 during the i-th drainage process. This is the second change mentioned above; Let be the instantaneous rate of change of the flow velocity data at time t-1 during the i-th drainage process. Let be the instantaneous rate of change of the flow velocity data at time t during the i-th drainage process. This refers to the first change mentioned above; It is a normalization function with a range of [0,1].

[0091] In this embodiment, the first impurity deposition degree is used to quantify the degree of blockage caused by impurity deposition in the drain valve under test. For example, the determination of the first impurity deposition degree based on the impurity deposition index and the impurity stripping index can be achieved by the following method: determining the product of the impurity deposition index and the impurity stripping index to obtain the first impurity deposition degree.

[0092] Specifically, taking the i-th drainage after the last maintenance of the self-testing drain valve as an example, the degree of deposition of the first impurity can be calculated using the following formula:

[0093]

[0094] in, The degree of deposition of the first impurity in the i-th drainage; The impurity deposition index for the i-th drainage; This is the impurity stripping index at the velocity inflection point during the i-th drainage process. The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0095] In step S140, vibration anomaly factor and acoustic anomaly factor are determined based on vibration data and acoustic data, and impurity anomaly score of the drain valve to be tested is determined based on vibration anomaly factor, acoustic anomaly factor and first impurity deposition degree.

[0096] In this embodiment of the application, the above-mentioned vibration anomaly factor is used to quantify the degree of abnormality of mechanical vibration during the operation of the drain valve under test.

[0097] For example, the above-mentioned vibration anomaly factor can be implemented by the following method: for each sampling time in the current drainage, obtain the actual vibration amplitude and actual vibration frequency collected by the vibration sensor; obtain the set upper limit of vibration amplitude and set upper limit of vibration frequency of the drainage valve to be tested, calculate the difference between the actual vibration amplitude and the set upper limit of vibration amplitude, and the difference between the actual vibration frequency and the set upper limit of vibration frequency; determine the product of the difference in vibration amplitude and the difference in vibration frequency, and record it as the vibration anomaly factor.

[0098] Specifically, taking time t during the i-th drainage process after the last maintenance of the self-tested drain valve as an example, the vibration anomaly factor at that time can be calculated using the following formula:

[0099]

[0100] in, Let be the vibration anomaly factor at time t during the i-th drainage process; Let be the vibration amplitude collected by the vibration sensor at time t during the i-th drainage process; This is the upper limit of the normal vibration amplitude when the drain valve under test is working (that is, the upper limit of the vibration amplitude set above). Let be the vibration frequency collected by the vibration sensor at time t during the i-th drainage process; This is the upper limit of the normal vibration frequency of the drain valve under test when it is working (that is, the upper limit of the vibration frequency set above). The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0101] In this embodiment, the aforementioned acoustic anomaly factor is used to quantify the degree of noise anomaly during the operation of the drain valve under test.

[0102] For example, the aforementioned acoustic anomaly factor can be determined by the following method: for each sampling time in the current drainage, obtain the actual noise intensity and actual noise frequency collected by the acoustic sensor; obtain the set noise intensity upper limit and set noise frequency upper limit of the drain valve to be tested, calculate the difference between the actual noise intensity and the set noise intensity upper limit, and the difference between the actual noise frequency and the set noise frequency upper limit; determine the product of the difference in noise intensity and the difference in noise frequency, and record it as the acoustic anomaly factor.

[0103] Specifically, taking time t during the i-th drainage process after the last maintenance of the self-tested drain valve as an example, the acoustic anomaly factor at that time can be calculated using the following formula:

[0104]

[0105] in, Let be the acoustic anomaly factor at time t during the i-th drainage process; Let be the noise intensity collected by the acoustic sensor at time t during the i-th drainage process; This is the upper limit of the normal noise intensity when the drain valve under test is working (that is, the upper limit of the noise intensity set above). Let be the noise frequency collected by the acoustic sensor at time t during the i-th drainage process; This is the upper limit of the normal noise frequency when the drain valve under test is working (that is, the upper limit of the noise frequency set above). The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0106] It should be noted that during the calculation of the vibration anomaly factor or acoustic anomaly factor, if the real-time value of the vibration data or acoustic data collected by the vibration sensor or acoustic sensor does not exceed the corresponding set upper limit value, the difference between the real-time value and the set value of the corresponding vibration data or acoustic data is marked as 1. The vibration amplitude at time t during the i-th drainage process collected by the vibration sensor is used as an example. For example, if Then record .

[0107] In this embodiment of the application, the above-mentioned impurity abnormality score is an indicator used to comprehensively evaluate the degree of abnormality in the working state of the drain valve to be tested.

[0108] For example, the above-mentioned determination of the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, acoustic anomaly factor, and the degree of first impurity deposition can be achieved as follows: determine the mean value of the vibration anomaly factor at each sampling moment during the current drainage process, and record it as the vibration anomaly score of the current drainage; determine the mean value of the acoustic anomaly factor at each sampling moment during the current drainage process, and record it as the acoustic anomaly score of the current drainage; determine the anomaly correlation factor of the vibration anomaly score and the acoustic anomaly score; determine the degree of second impurity deposition based on the anomaly correlation factor, the vibration anomaly score, and the acoustic anomaly score; determine the confidence level of the degree of first impurity deposition based on the degree of first impurity deposition and the degree of second impurity deposition, and record the product of the degree of first impurity deposition and the confidence level as the impurity anomaly score.

[0109] Specifically, taking the i-th drainage after the last maintenance of the drain valve under test as an example, after calculating the vibration anomaly factor and acoustic anomaly factor at each sampling time during the i-th drainage process through the above process, the average value of the vibration anomaly factor at each sampling time can be calculated to obtain the vibration anomaly score of the i-th drainage. The acoustic anomaly score for the i-th drainage is obtained by taking the average of the acoustic anomaly factors at each sampling time and the average of the acoustic anomaly factors at each sampling time. Among them, the above-mentioned vibration anomaly score Used to measure the degree of vibration abnormality of the drain valve under test during the i-th drainage process; the above acoustic abnormality score Used to measure the degree of noise abnormality of the drain valve under test during the i-th drainage process.

[0110] In this embodiment, the aforementioned abnormal correlation factor is a parameter used to measure the degree of abnormal correlation between vibration abnormality factors and acoustic abnormality factors. The higher the value of the abnormal correlation factor, the higher the probability that vibration data and acoustic data are simultaneously abnormal. For example, impurity deposition reduces the cross-sectional area of ​​the valve, and changes in liquid flow rate can simultaneously trigger increased vibration and abnormal noise.

[0111] For example, the process of determining the above-mentioned abnormal correlation factor based on the vibration abnormality score and the acoustic abnormality score can be implemented by the following method: determining the product of the vibration abnormality score and the acoustic abnormality score, denoted as the sensor abnormality score product; determining the absolute value of the difference between the vibration abnormality score and the acoustic abnormality score, denoted as the absolute value of the sensor abnormality score difference; and determining the abnormal correlation factor of the vibration abnormality score and the acoustic abnormality score based on the sensor abnormality score product and the absolute value of the sensor abnormality score difference.

[0112] Specifically, taking the i-th drainage after the last maintenance of the self-monitoring drain valve as an example, the above-mentioned abnormal correlation factor can be calculated using the following formula:

[0113]

[0114] in, is the abnormal correlation factor for the i-th drainage; Let be the vibration anomaly score for the i-th drainage; Let be the acoustic anomaly score for the i-th drainage.

[0115] In this embodiment, the second impurity deposition degree is the impurity deposition degree of the valve to be tested determined based on the abnormal data of the vibration sensor and the acoustic sensor. By comparing the consistency between the second impurity deposition degree and the first impurity deposition degree, the confidence level of the first impurity deposition degree can be determined. Specifically, the higher the consistency between the second impurity deposition degree and the first impurity deposition degree, the higher the confidence level of the first impurity deposition degree.

[0116] Specifically, taking the i-th drainage after the last maintenance of the self-tested drain valve as an example, the confidence level of the aforementioned first impurity deposition degree can be calculated using the following formula:

[0117]

[0118] in, Let be the confidence level of the degree of deposition of the first impurity during the i-th drainage process; The degree of deposition of the first impurity during the i-th drainage process; Let be the vibration anomaly score for the i-th drainage; Let be the acoustic anomaly score for the i-th drainage; The vibration anomaly score during the i-th drainage process Harmony and acoustic anomaly score Abnormal correlation factors; Used to characterize the consistency between the degree of deposition of the first impurity and the degree of deposition of the second impurity; The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0119] In this embodiment of the application, after determining the confidence level of the first impurity deposition degree, the above-mentioned impurity anomaly score can be determined by calculating the product of the first impurity deposition degree and the confidence level.

[0120] Specifically, taking the i-th drainage after the last maintenance of the self-testing drain valve as an example, the above-mentioned impurity abnormality score can be calculated using the following formula:

[0121]

[0122] in, Let be the impurity anomaly score for the i-th drainage; Let be the confidence level of the degree of deposition of the first impurity during the i-th drainage process; The degree of first impurity deposition during the i-th drainage.

[0123] In step S150, the working intensity is determined based on vibration data, pressure data and liquid level data, the drainage frequency of the drain valve to be tested since the last maintenance is obtained, and the failure probability is determined based on the working intensity, drainage frequency and impurity abnormality score.

[0124] In this embodiment, the aforementioned working intensity is used to quantify the working pressure related to impurity deposition experienced by the drain valve under test during the drainage process. Specifically, the greater the total amount of liquid to be drained from the system, the longer the effective drainage time of the drain valve under test lasts, and the higher the working intensity of the drain valve under test; the greater the total amount of impurities in the liquid, the smaller the cross-sectional area of ​​the valve channel will be due to impurity deposition, thus increasing the water flow resistance when the liquid passes through the valve under test, requiring the valve under test to provide a greater pressure difference to drive the liquid flow, resulting in even higher working intensity.

[0125] For example, the above-mentioned determination of working intensity based on vibration data, pressure data, and liquid level data can be achieved by the following method: For the current drainage, obtain the first pressure data collected by the pressure sensor before the current drainage and the second pressure data collected after the current drainage, and calculate the difference between the first pressure data and the second pressure data, which is recorded as the pressure difference before and after drainage; obtain the average vibration amplitude obtained by the vibration sensor during the current drainage process, and determine the equipment operating intensity based on the average vibration amplitude and the pressure difference before and after drainage; obtain the maximum liquid level data of the water storage device in the system to which the drain valve under test belongs and the current liquid level data collected by the liquid level sensor, and determine the theoretical drainage volume based on the maximum liquid level data, the current liquid level data, and the volume of the water storage device; obtain the actual drainage volume of the current drainage, and record the difference between the actual drainage volume and the theoretical drainage volume as the impurity residue value; determine the working intensity based on the effective drainage time, equipment operating intensity, and impurity residue value.

[0126] In this embodiment of the application, the above-mentioned equipment operating intensity is an indicator used to reflect the operating load of the drain valve under test due to pressure changes and vibrations during the drainage process.

[0127] Specifically, taking the current drainage as the i-th drainage since the last maintenance of the drain valve to be tested as an example, the operating intensity of the above equipment can be calculated by the following formula:

[0128]

[0129] in, The equipment operating intensity of the drain valve to be tested during the i-th drainage process; It is the difference between the pressure data collected by the pressure sensor before and after the i-th drainage (that is, the pressure difference before and after drainage mentioned above). This represents the average vibration amplitude collected by the vibration sensor during the i-th drainage process; The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0130] In this embodiment of the application, the maximum liquid level data is the maximum value of the liquid level data of the water storage device deployed in the system to which the drain valve to be tested belongs (the liquid to be discharged in the system is discharged through the drain valve to be tested). For example, the maximum liquid level data can be the height of the water storage device.

[0131] In this embodiment, the theoretical drainage volume is the volume of liquid to be discharged during the drainage process determined based on the liquid level in the water storage device. For example, it can be determined by the proportion of the current liquid level data collected by the liquid level sensor to the maximum liquid level data and the volume of the water storage device, wherein the current liquid level data refers to the height of the liquid to be discharged in the water storage device during the current drainage process.

[0132] In this embodiment, the actual drainage volume refers to the volume of liquid to be discharged that is actually removed during the drainage process. Exemplarily, this can be obtained by integrating or summing drainage flow data collected by a flow sensor.

[0133] In this embodiment, the aforementioned residual impurity value is an indicator used to reflect the impurity content in the water and the risk of valve deposition during the drainage process. For example, it can be determined by the difference between the actual drainage volume and the theoretical drainage volume during the corresponding drainage process.

[0134] Specifically, taking the current drainage as the i-th drainage since the last maintenance of the drain valve to be tested as an example, the above-mentioned residual impurity value can be calculated by the following formula:

[0135]

[0136] in, Let be the residual value of impurities during the i-th drainage process; The actual drainage volume of the i-th drainage; This refers to the volume of the aforementioned water storage device; The liquid level data for the i-th drainage is collected by the liquid level sensor. The height of the aforementioned water storage device; Let be the theoretical drainage volume of the i-th drainage; The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0137] The equipment operating intensity of the valve to be tested during the i-th drainage process is determined through the above process. and residual value of impurities Furthermore, the working intensity of the valve to be tested during the i-th drainage process can be calculated using the following formula:

[0138]

[0139] in, The working intensity of the valve to be tested during the i-th drainage process; The effective drainage time during the i-th drainage process; Let be the residual value of impurities during the i-th drainage process; The operating intensity of the drain valve to be tested during the i-th drainage process is denoted as .

[0140] In this embodiment, the aforementioned failure probability is used to quantify the likelihood of a blockage failure occurring in the valve under test during drainage. For example, this failure probability can be determined based on workload, the drainage frequency of the drain valve under test since its last maintenance, and the impurity anomaly score; wherein, the drainage frequency of the drain valve under test since its last maintenance can be determined by the ratio of the number of times the drain valve under test has drained since its last maintenance to the time interval from the last maintenance to the current moment.

[0141] Specifically, taking the i-th drainage after the last maintenance of the aforementioned self-testing drain valve as an example, the above-mentioned failure probability can be calculated using the following formula:

[0142]

[0143] in, Let be the failure probability of the drain valve to be tested during the i-th drainage process; The drain frequency of the drain valve to be tested from the last maintenance to the i-th draining time; The working intensity of the valve to be tested during the i-th drainage process; Let be the impurity anomaly score for the i-th drainage; The normalization function is a linear normalization function in this embodiment of the invention. Other normalization functions may also be used in other embodiments of the invention, which will not be elaborated or limited here.

[0144] In step S160, the failure condition of the drain valve to be tested is predicted based on the failure probability.

[0145] In this embodiment of the application, after determining the failure probability of the current drainage through the above process, the probability of the drain valve to be detected becoming blocked during the next drainage process can be predicted based on the failure probability, so that timely treatment measures can be taken to avoid the lag of manual maintenance.

[0146] For example, the above-mentioned prediction of the failure status of the drain valve under test based on the failure probability can be achieved as follows: acquiring the liquid level data collected by the liquid level sensor before the next drainage; predicting the increase in the failure probability of the next drainage relative to the current drainage based on the liquid level data before the next drainage and the failure probability of the current drainage; determining the failure probability of the next drainage based on the failure probability of the current drainage and the increase in the failure probability; if the failure probability of the next drainage is greater than a preset threshold, sending a stop operation command to the drain valve under test and performing maintenance on the drain valve under test.

[0147] Specifically, artificial intelligence algorithms (such as convolutional neural networks) can be used to predict the increase in the failure probability based on the failure probability of the current drainage and the liquid level data before the next drainage of the valve under test. The sum of the failure probability of the current drainage and the predicted increase in the failure probability is then recorded as the predicted failure probability value for the next drainage. Taking the current drainage as the i-th drainage since the last maintenance of the valve under test as an example, if the predicted failure probability of the next drainage... (The preset threshold is set to 0.6 here.) If it is believed that the continued operation of the drain valve under test will cause equipment damage, a stop operation command is sent to control the equipment or system to stop running and to perform maintenance on the drain valve under test. It should be noted that if the failure probability of the current drainage is greater than the preset threshold, a stop operation command should also be sent to the drain valve under test.

[0148] The foregoing primarily describes the solutions provided by the embodiments of the present invention from a methodological perspective. To achieve the aforementioned functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0149] Correspondingly, embodiments of this disclosure also provide a detection system for an automatic drain valve, referencing... Figure 3 As shown, the detection system 300 for the automatic drain valve may include a data acquisition module 310, a data processing module 320, and a fault detection module 330, wherein:

[0150] The data acquisition module is used to collect the working data of the drain valve under test. The working data includes flow rate data, flow velocity data, vibration data, acoustic data, pressure data, and liquid level data.

[0151] The data processing module is used to determine the flow peak change trend and the degree of drainage efficiency decay based on the flow data of multiple consecutive drainage records, and to generate impurity deposition index based on the flow peak change trend and the degree of drainage efficiency decay.

[0152] The data processing module is also used to generate an impurity stripping index based on the instantaneous change rate of the flow velocity data and the corresponding change in the flow rate data, and to determine the degree of first impurity deposition based on the impurity deposition index and the impurity stripping index.

[0153] The data processing module is also used to determine the vibration anomaly factor and the acoustic anomaly factor based on the vibration data and acoustic data, and to determine the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, the acoustic anomaly factor and the degree of first impurity deposition.

[0154] The data processing module is also used to determine the working intensity based on vibration data, pressure data and liquid level data, obtain the drainage frequency of the drain valve under test since the last maintenance, and determine the failure probability based on the working intensity, drainage frequency and impurity abnormality score.

[0155] The fault detection module is used to predict the fault conditions of the drain valve under test based on the fault probability.

[0156] The specific implementation details of the above-mentioned automatic drain valve detection system have been explained in detail in the corresponding section of the automatic drain valve detection method, so they will not be repeated here.

[0157] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0158] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

Claims

1. A method for detecting an automatic drain valve, characterized in that, The method includes: The system collects operational data from the drain valve under test, including flow rate, velocity, vibration, acoustic data, pressure, and liquid level data. The vibration data is collected by a vibration sensor installed on the outer wall of the drain valve during the drainage process. The pressure data is collected by a pressure sensor installed on the inner side of the drain valve during the drainage process. The liquid level data is collected by a liquid level sensor built into the inner side of the drain valve, showing the height of the water level inside the system to which the drain valve belongs. Based on the flow data from multiple consecutive drainage records, the trend of peak flow variation and the degree of drainage efficiency decay are determined, and an impurity deposition index is generated based on the trend of peak flow variation and the degree of drainage efficiency decay. An impurity stripping index is generated based on the instantaneous rate of change of flow velocity data and the corresponding change in flow rate data. The degree of first impurity deposition is determined based on the impurity deposition index and the impurity stripping index. Based on vibration and acoustic data, vibration anomaly factors and acoustic anomaly factors are determined, and based on vibration anomaly factors, acoustic anomaly factors, and the degree of first impurity deposition, the impurity anomaly score of the drain valve to be tested is determined. The working intensity is determined based on vibration data, pressure data, and liquid level data, including: For the current drainage, acquire the first pressure data collected by the pressure sensor before the current drainage and the second pressure data collected after the current drainage, and calculate the difference between the first pressure data and the second pressure data, which is recorded as the pressure difference before and after drainage. The average vibration amplitude obtained by the vibration sensor during the current drainage process is acquired, and the operating intensity of the equipment is determined based on the average vibration amplitude and the pressure difference before and after drainage. Obtain the maximum liquid level data of the water storage device in the system to which the drain valve to be tested belongs, as well as the current liquid level data collected by the liquid level sensor. Determine the theoretical drainage volume based on the maximum liquid level data, the current liquid level data, and the volume of the water storage device. Obtain the actual discharge volume of the current drainage, and record the difference between the actual discharge volume and the theoretical discharge volume as the impurity residue value; The workload is determined based on the effective drainage time, equipment operating intensity, and residual impurities; the effective drainage time is determined based on the flow rate data during the current drainage process. Obtain the drainage frequency of the drain valve to be tested since the last maintenance, and determine the failure probability based on the workload, drainage frequency and impurity abnormality score. The failure status of the drain valve under test is predicted based on the failure probability.

2. The detection method for the automatic drain valve according to claim 1, characterized in that, The process of determining the peak flow rate variation trend and the degree of drainage efficiency decay based on the flow rate data from multiple consecutive drainage records, and generating an impurity deposition index based on the peak flow rate variation trend and the degree of drainage efficiency decay, includes: Obtain the peak flow rate from the flow rate data of multiple consecutive drainage records, and construct a flow rate peak change curve with the number of drainages as the independent variable and the peak flow rate corresponding to the number of drainages as the dependent variable. The flow peak change curve is fitted with a straight line, and the slope of the fitted straight line is determined and denoted as the flow peak change trend. The initial drainage volume and duration of the first drainage of the drain valve under test since the last maintenance are obtained, and the initial drainage efficiency is determined based on the initial drainage volume and the initial drainage duration. Obtain the current drainage volume and current drainage duration of the drain valve under test since the last maintenance, and determine the current drainage efficiency based on the current drainage volume and current drainage duration. The degree of drainage efficiency decay is determined based on the initial drainage efficiency, the current drainage efficiency, and the effective drainage time. The impurity deposition index is generated by multiplying the absolute value of the peak flow rate change trend with the degree of drainage efficiency decline.

3. The detection method for the automatic drain valve according to claim 2, characterized in that, Determining the effective drainage time based on the flow data during the current drainage process includes: Obtain the system design maximum flow rate of the drain valve to be tested, calculate the product of the system design maximum flow rate and a preset ratio, and obtain the flow rate threshold. Obtain the target flow rate data that is not less than the flow rate threshold during the current drainage process, determine the sum of the drainage times corresponding to the target flow rate data, and record it as the effective drainage time.

4. The detection method for the automatic drain valve according to claim 1, characterized in that, The process of generating an impurity stripping index based on the instantaneous change rate of the flow velocity data and the corresponding change in the flow rate data, and determining the first impurity deposition degree based on the impurity deposition index and the impurity stripping index, includes: For each sampling time in the current drainage, acquire the flow rate data and the flow velocity data at the current sampling time and the previous sampling time; Based on the flow velocity data and sampling interval at the current sampling time and the previous sampling time, the instantaneous rate of change of the flow velocity data at the current sampling time is determined, and the flow velocity inflection point is determined based on the instantaneous rate of change of the flow velocity data. The difference between the instantaneous rate of change of the flow velocity data at the current sampling time and the previous sampling time corresponding to the flow velocity inflection point is determined and denoted as the first change amount; The difference between the flow rate data at the current sampling time and the previous sampling time corresponding to the flow rate inflection point is determined and denoted as the second change. The impurity stripping index is obtained by determining the product of the first change and the second change. The product of the impurity deposition index and the impurity stripping index is determined to obtain the first impurity deposition degree.

5. The detection method for the automatic drain valve according to claim 1, characterized in that, The vibration data refers to the valve vibration data collected by a vibration sensor installed on the outer wall of the drain valve under test during the drainage process; the acoustic data refers to the sound data emitted by the valve during the drainage process collected by an acoustic sensor installed on the outer wall of the drain valve under test; the determination of vibration anomaly factors and acoustic anomaly factors based on the vibration data and the acoustic data includes: For each sampling moment in the current drainage, the actual vibration amplitude and actual vibration frequency collected by the vibration sensor, and the actual noise intensity and actual noise frequency collected by the acoustic sensor are obtained; Obtain the upper limit of the set vibration amplitude and the upper limit of the set vibration frequency of the drain valve to be tested, and calculate the difference between the actual vibration amplitude and the upper limit of the set vibration amplitude, as well as the difference between the actual vibration frequency and the upper limit of the set vibration frequency. The product of the difference in vibration amplitude and the difference in vibration frequency is defined as the vibration anomaly factor. Obtain the upper limit of the set noise intensity and the upper limit of the set noise frequency of the drain valve to be tested, and calculate the difference between the actual noise intensity and the upper limit of the set noise intensity, as well as the difference between the actual noise frequency and the upper limit of the set noise frequency. The product of the difference in noise intensity and the difference in noise frequency is determined and denoted as the acoustic anomaly factor.

6. The detection method for the automatic drain valve according to claim 5, characterized in that, The determination of the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, the acoustic anomaly factor, and the degree of the first impurity deposition includes: The mean value of the vibration anomaly factor at each sampling time during the current drainage process is determined and denoted as the vibration anomaly score of the current drainage. The mean value of the acoustic anomaly factor at each sampling time during the current drainage process is determined and denoted as the acoustic anomaly score of the current drainage. Determine the vibration anomaly score and the anomaly correlation factor of the acoustic anomaly score; The degree of second impurity deposition is determined based on the abnormal correlation factor, the vibration anomaly score, and the acoustic anomaly score. The confidence level of the first impurity deposition degree is determined based on the first impurity deposition degree and the second impurity deposition degree, and the product of the first impurity deposition degree and the confidence level is recorded as the impurity anomaly score.

7. The method for detecting an automatic drain valve according to claim 6, characterized in that, The abnormal correlation factors for determining the vibration anomaly score and the acoustic anomaly score include: The product of the vibration anomaly score and the acoustic anomaly score is determined and denoted as the sensor anomaly score product. The absolute value of the difference between the vibration anomaly score and the acoustic anomaly score is determined and denoted as the absolute value of the sensor anomaly score difference; The abnormal correlation factor of the vibration abnormality score and the acoustic abnormality score is determined based on the product of the abnormality scores of the sensors and the absolute value of the difference between the abnormality scores of the sensors.

8. The method for detecting an automatic drain valve according to claim 1, characterized in that, The method of predicting the failure status of the drain valve under test based on the failure probability includes: Obtain the liquid level data before the next drainage; Based on the liquid level data before the next drainage and the failure probability of the current drainage, the increase in the failure probability of the next drainage relative to the current drainage is predicted. The failure probability of the next drainage is determined based on the failure probability of the current drainage and the increase in the failure probability. If the probability of failure in the next drainage is greater than a preset threshold, a stop operation command is sent to the drain valve to be tested, and the drain valve to be tested is maintained.

9. A detection system for an automatic drain valve, characterized in that, The system includes: The data acquisition module is used to collect the operating data of the drain valve under test. The operating data includes flow rate data, flow velocity data, vibration data, acoustic data, pressure data, and liquid level data. The vibration data is the valve vibration data during the drainage process collected by the vibration sensor installed on the outer wall of the drain valve under test. The pressure data is the pressure data inside the valve during the drainage process collected by the pressure sensor installed on the inner side of the drain valve under test. The liquid level data is the liquid level height of the water inside the system to which the drain valve belongs, collected by the liquid level sensor built into the inner side of the drain valve under test. The data processing module is used to determine the flow peak change trend and the degree of drainage efficiency decay based on the flow data from multiple consecutive drainage records, and to generate an impurity deposition index based on the flow peak change trend and the degree of drainage efficiency decay. The data processing module is further configured to generate an impurity stripping index based on the instantaneous change rate of the flow velocity data and the corresponding change in the flow rate data, and to determine the first impurity deposition degree based on the impurity deposition index and the impurity stripping index. The data processing module is further configured to determine vibration anomaly factor and acoustic anomaly factor based on the vibration data and the acoustic data, and to determine the impurity anomaly score of the drain valve to be tested based on the vibration anomaly factor, the acoustic anomaly factor and the first impurity deposition degree. The data processing module is also used to determine the working intensity based on the vibration data, the pressure data and the liquid level data, obtain the drainage frequency of the drain valve to be tested since the last maintenance, and determine the failure probability based on the working intensity, the drainage frequency and the impurity abnormality score. The data processing module is also used to acquire, for the current drainage, the first pressure data collected by the pressure sensor before the current drainage and the second pressure data collected after the current drainage, and calculate the difference between the first pressure data and the second pressure data, which is recorded as the pressure difference before and after drainage. The average vibration amplitude obtained by the vibration sensor during the current drainage process is acquired, and the operating intensity of the equipment is determined based on the average vibration amplitude and the pressure difference before and after drainage. Obtain the maximum liquid level data of the water storage device in the system to which the drain valve to be tested belongs, as well as the current liquid level data collected by the liquid level sensor. Determine the theoretical drainage volume based on the maximum liquid level data, the current liquid level data, and the volume of the water storage device. Obtain the actual discharge volume of the current drainage, and record the difference between the actual discharge volume and the theoretical discharge volume as the impurity residue value; The workload is determined based on the effective drainage time, equipment operating intensity, and residual impurities; the effective drainage time is determined based on the flow rate data during the current drainage process. The fault detection module is used to predict the fault condition of the drain valve to be detected based on the fault probability.

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