Electric valve abnormal working condition monitoring method and system

Abstract

The invention relates to the technical field of electric valves, in particular to an electric valve abnormal working condition monitoring method and system. Comprising the steps of setting a plurality of monitoring points based on equipment parameters of the electric valve; operation deviation values of all the monitoring points are generated according to a preset state evaluation model, and a first-level diagnosis strategy is set according to all the operation deviation values; generating an abnormal risk value according to the primary diagnosis strategy, and judging whether an early warning instruction is generated or not according to the abnormal risk value; a plurality of monitoring points are established based on the equipment parameters and the historical fault parameters of the electrically operated valve, and the abnormal operation state of each monitoring point is analyzed and pre-warned in time by setting the diagnosis sub-model of each monitoring point, so that the pre-warning efficiency of the abnormal working condition of the electrically operated valve is improved, the fault hidden danger is eliminated in time, and the safe operation of the electrically operated valve is ensured.

Description

Technical Field

[0001] This application relates to the field of electric valve technology, and in particular to a method and system for monitoring abnormal operating conditions of electric valves. Background Technology

[0002] Electric valves, as key actuators in industrial process control systems, are widely used in process industries such as petrochemicals, power generation, and water treatment. Their operating status directly affects production safety and efficiency.

[0003] Currently, anomaly monitoring of electric valves mainly relies on fixed thresholds for valve motor current, stroke time, or torque to identify abnormalities, or on-site testing or disassembly by maintenance personnel. However, industrial site conditions are complex, and factors such as load fluctuations, voltage disturbances, and mechanical wear can easily lead to poor adaptability of fixed thresholds, high false alarm rates, and an inability to identify early, progressive faults. This results in problems such as response lag, high labor costs, inability to monitor in real time, and insufficient early warning capabilities for sudden faults or latent defects. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for monitoring abnormal operating conditions of electric valves in order to solve the above-mentioned technical problems, thereby improving the early warning efficiency for abnormal operating conditions of electric valves, eliminating potential faults in a timely manner, and ensuring the safe operation of electric valves.

[0005] In some embodiments of this application, multiple monitoring points are established based on the equipment parameters and historical fault parameters of the electric valve. By setting up diagnostic sub-models for each monitoring point, the abnormal operating status of each monitoring point is analyzed and warned in a timely manner, thereby improving the early warning efficiency for abnormal operating conditions of the electric valve, eliminating potential faults in a timely manner, and ensuring the safe operation of the electric valve.

[0006] In some embodiments of this application, by establishing the correlation between various monitoring points, auxiliary data packets of primary anomalies with potential fault risks can be obtained in a timely manner, thereby realizing multi-dimensional data analysis of primary anomalies, improving the diagnostic accuracy of abnormal operating conditions, and reducing the overall monitoring cost.

[0007] In some embodiments of this application, a method for monitoring abnormal operating conditions of an electric valve is provided, including:

[0008] Multiple monitoring points are set for the equipment parameters based on electric valves;

[0009] The operational deviation value of each monitoring point is generated based on the preset state evaluation model, and a first-level diagnostic strategy is set based on all operational deviation values.

[0010] Anomaly risk values ​​are generated based on the primary diagnostic strategy, and a warning instruction is generated based on the anomaly risk values.

[0011] When setting multiple monitoring points, the following are included:

[0012] Establish a sequence of monitoring points A, where A = (a1, a2, ..., a3) i …a n ), where a i Let be the i-th monitoring point; n is the number of monitoring points.

[0013] In some embodiments of this application, establishing a state diagnosis model includes:

[0014] Based on the monitoring point sequence A, a is set sequentially. i For target monitoring points;

[0015] Generate historical fault data packets for the target monitoring points based on historical monitoring parameters;

[0016] Generate multiple primary operational indicators for the target monitoring points based on historical fault data packets;

[0017] Set standard reference values ​​for each primary operational indicator;

[0018] Construct a state sub-model for the target monitoring points based on all primary operational indicators;

[0019] Construct state sub-models for each monitoring point in sequence;

[0020] Construct a sequence of state sub-models P, P = (p1, p2, ..., p3) i …p n ), where p i Let n be the i-th state sub-model; n is the number of state sub-models.

[0021] A state evaluation model is generated based on all state sub-models.

[0022] In some embodiments of this application, the generation of operational deviation values ​​for each monitoring point includes:

[0023] Multiple feedback time points can be preset;

[0024] Based on the monitoring point sequence A, a is set sequentially. i Monitoring points to be diagnosed;

[0025] Generate monitoring evaluation values ​​for the monitoring points to be diagnosed, and set the number of data collection times r for the monitoring points to be diagnosed based on the monitoring evaluation values;

[0026] Obtain the monitoring data packet of the monitoring point to be diagnosed at the current feedback time point;

[0027] Set the state sub-model corresponding to the monitoring point to be diagnosed as the target state sub-model;

[0028] Based on the target state sub-model and monitoring data package, deviation evaluation values ​​for each primary operational indicator of the monitoring point to be diagnosed are generated;

[0029] Generate the operational deviation value b of the monitoring point to be diagnosed at the current feedback time node based on all operational deviation values;

[0030]

[0031] Where θ1 represents the number of primary operational indicators for the monitoring points to be diagnosed; β i c is the influencing factor of the i-th primary operational indicator of the monitoring point to be diagnosed; i The deviation evaluation value of the i-th primary operational indicator of the monitoring point to be diagnosed at the current feedback time node;

[0032] The operational deviation values ​​of each monitoring point at the current feedback time node are generated sequentially;

[0033] Establish a sequence of deviation values ​​B, B = (b1, b2, ..., bb2) i …b n ), where b i is the operational deviation value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points.

[0034] In some embodiments of this application, the generation of deviation evaluation values ​​for each primary operational indicator includes:

[0035] Establish a primary operational indicator series W for the monitoring points to be diagnosed, where W = (w1, w2, ..., w i …wθ1), where w i θ1 represents the i-th primary operational indicator of the monitoring point to be diagnosed; θ1 represents the number of primary operational indicators of the monitoring point to be diagnosed.

[0036] Based on the primary operational indicator series W, w is set sequentially. i Target operating indicators;

[0037] Generate the deviation evaluation value c of the target operating indicator;

[0038]

[0039] Where e1 is the preset first fixed coefficient; e2 is the preset second coefficient; Q1 is the preset second fixed coefficient; Q2 is the preset second fixed coefficient; r is the number of data collection times for the monitoring point to be diagnosed; j i denoted as , where is the real-time value of the i-th collected target operational indicator; j' is the standard reference value of the target operational indicator; and Δj is the average of all real-time reference values ​​of the target operational indicator collected at the current feedback time node.

[0040] In some embodiments of this application, when setting a first-level diagnostic strategy based on all operational deviation values, the following are included:

[0041] Obtain the sequence B of running deviation values;

[0042] The first operational deviation threshold B1 is preset.

[0043] If b i <B1, the i-th monitoring point is a normal monitoring point;

[0044] If b i >B1, set the i-th monitoring point as a first-level anomaly;

[0045] Get all first-level anomalies at the current feedback time point;

[0046] Establish a first-level anomaly sequence A1, A1 = (a 11 a 12 …a 1i …a 1n1 ), where a 1i n1 represents the i-th primary anomaly at the current feedback time point; n1 represents the number of primary anomalies at the current feedback time point.

[0047] A first-level diagnostic strategy is generated based on all first-level anomalies.

[0048] In some embodiments of this application, when generating a first-level diagnostic strategy based on all first-level anomalies, the following are included:

[0049] Based on the sequence of first-level anomalies A1, a is set sequentially. 1i The target is the anomaly.

[0050] Multiple auxiliary evaluation indicators are set based on the historical fault data packets of the target anomaly point;

[0051] Generate correlation evaluation values ​​between target anomalies and each monitoring point based on all auxiliary evaluation indicators;

[0052] Establish a correlation evaluation value sequence H, H = (h1, h2…h i …h n ), where h i This is the correlation evaluation value between the target anomaly and the i-th monitoring point;

[0053] Preset the threshold value H1 for correlation evaluation;

[0054] If h i >H1, set the i-th monitoring point as the associated monitoring point of the target anomaly point;

[0055] Configure sub-strategies for collecting each associated anomaly point;

[0056] Generate auxiliary data packets for the target anomalies based on the collection sub-strategy;

[0057] Auxiliary data packets for each primary anomaly point are generated sequentially.

[0058] In some embodiments of this application, when determining whether to generate a warning instruction based on an abnormal risk value, including

[0059] Based on the sequence of first-level anomalies A1, a is set sequentially. i These are abnormal points to be diagnosed.

[0060] Obtain auxiliary data packets for the anomalies to be diagnosed;

[0061] Generate the anomaly risk value f of the anomaly point to be diagnosed based on the auxiliary data package;

[0062] Generate the anomaly risk value for each primary anomaly point in sequence;

[0063] Establish a sequence F of abnormal risk values ​​at the current feedback time point, where F = (f1, f2, ..., f...). i …f n1 ), where f i Let be the abnormal risk value of the i-th first-level anomaly at the current feedback time point;

[0064] Preset an abnormal risk threshold F1;

[0065] If f i >F1 generates a first-level warning instruction for the i-th first-level anomaly point.

[0066] In some embodiments of this application, generating the abnormal risk value f of the abnormal point to be diagnosed includes:

[0067]

[0068] Where, e3 is the preset third weighting coefficient; e4 is the preset fourth weighting coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; b' is the operational deviation value of the anomaly to be diagnosed at the current feedback time node; θ2 is the number of auxiliary evaluation indicators for the anomaly to be diagnosed; η i k is the influencing factor of the i-th auxiliary evaluation index of the abnormality to be diagnosed. i k' is the real-time reference value of the i-th auxiliary evaluation index for the abnormality to be diagnosed; i This is the standard reference value for the i-th auxiliary evaluation index of the abnormal point to be diagnosed.

[0069] In some embodiments of this application, an abnormal operating condition monitoring system for electric valves is provided, including:

[0070] The central control unit is used to set multiple monitoring points according to the equipment parameters of the electric valve;

[0071] The monitoring unit includes multiple monitoring sub-modules, which are located at various monitoring points.

[0072] The central control unit includes:

[0073] The first processing module is used to generate the operational deviation value of each monitoring point according to the preset state evaluation model;

[0074] The second processing module is used to set a primary diagnostic strategy based on all operational deviation values ​​and to generate abnormal risk values ​​based on the primary diagnostic strategy.

[0075] The early warning module is used to determine whether to generate an early warning command based on abnormal risk values.

[0076] The third processing module is used to establish a sequence of monitoring points A, where A = (a1, a2, ..., a...). i …a n ), where a i Let be the i-th monitoring point; n is the number of monitoring points.

[0077] In some embodiments of this application, the first processing module is further configured to:

[0078] Based on the monitoring point sequence A, a is set sequentially. i For target monitoring points;

[0079] Generate historical fault data packets for the target monitoring points based on historical monitoring parameters;

[0080] Generate multiple primary operational indicators for the target monitoring points based on historical fault data packets;

[0081] Set standard reference values ​​for each primary operational indicator;

[0082] Construct a state sub-model for the target monitoring points based on all primary operational indicators;

[0083] Construct state sub-models for each monitoring point in sequence;

[0084] Construct a sequence of state sub-models P, P = (p1, p2, ..., p3) i …p n ), where p i Let n be the i-th state sub-model; n is the number of state sub-models.

[0085] A state evaluation model is generated based on all state sub-models.

[0086] Compared with the prior art, the beneficial effects of the electric valve abnormal operating condition monitoring method and system of this application are as follows:

[0087] Multiple monitoring points are established based on the equipment parameters and historical fault parameters of the electric valve. By setting up diagnostic sub-models for each monitoring point, abnormal operating states of each monitoring point can be analyzed and warned in a timely manner, thereby improving the early warning efficiency for abnormal operating conditions of electric valves, eliminating potential faults in a timely manner, and ensuring the safe operation of electric valves.

[0088] By establishing the correlation between various monitoring points, auxiliary data packets of primary anomalies with potential fault risks can be obtained in a timely manner, enabling multi-dimensional data analysis of primary anomalies, improving the diagnostic accuracy of abnormal operating conditions, and reducing the overall monitoring cost. Attached Figure Description

[0089] Figure 1 This is a flowchart illustrating a method for monitoring abnormal operating conditions of an electric valve in a preferred embodiment of this application. Detailed Implementation

[0090] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0091] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0092] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0093] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0094] like Figure 1As shown in the preferred embodiment of this application, a method for monitoring abnormal operating conditions of an electric valve includes:

[0095] S101: Multiple monitoring points are set for equipment parameters based on electric valves;

[0096] S102: Generate the operational deviation value of each monitoring point according to the preset state evaluation model, and set the first-level diagnostic strategy according to all operational deviation values;

[0097] S103: Generate abnormal risk values ​​based on the primary diagnostic strategy, and determine whether to generate an early warning instruction based on the abnormal risk values;

[0098] When setting multiple monitoring points, the following are included:

[0099] Establish a sequence of monitoring points A, where A = (a1, a2, ..., a3) i …a n ), where a i Let be the i-th monitoring point; n is the number of monitoring points.

[0100] Specifically, by analyzing the equipment parameters and historical fault data of electric valves, monitoring points are set up for each equipment point that is prone to operational failure and the equipment points that are associated with operational failure.

[0101] Specifically, establishing a state diagnosis model includes:

[0102] Based on the monitoring point sequence A, a is set sequentially. i For target monitoring points;

[0103] Generate historical fault data packets for the target monitoring points based on historical monitoring parameters;

[0104] Generate multiple primary operational indicators for the target monitoring points based on historical fault data packets;

[0105] Set standard reference values ​​for each primary operational indicator;

[0106] Construct a state sub-model for the target monitoring points based on all primary operational indicators;

[0107] Construct state sub-models for each monitoring point in sequence;

[0108] Construct a sequence of state sub-models P, P = (p1, p2, ..., p3) i …p n ), where p i Let n be the i-th state sub-model; n is the number of state sub-models.

[0109] A state evaluation model is generated based on all state sub-models.

[0110] Specifically, the data types to be collected are set according to the structural location of the equipment corresponding to the monitoring point, thereby generating multiple primary operational indicators. By analyzing the operating values ​​of each primary operational indicator when no operational failure occurs, its corresponding standard reference value is set, and a state sub-model of the monitoring point is constructed based on all standard reference values.

[0111] Specifically, the data types include, but are not limited to, current changes, temperature, valve opening, pipeline pressure, motor torque, valve response time, vibration parameters, etc. The primary operating indicators of the monitoring points are set according to the data types that the monitoring points need to collect.

[0112] Specifically, by establishing state sub-models for each monitoring point, timely warnings can be issued for abnormal operating states of each monitoring point.

[0113] It is understood that in the above embodiments, multiple monitoring points are set based on historical fault parameters, and by setting the state sub-model corresponding to each monitoring point, early warnings are given in a timely manner for abnormal operating states that occur at the monitoring points, thereby improving the monitoring efficiency for various faults of electric valves and ensuring the stable operation of electric valves.

[0114] In a preferred embodiment of this application, generating the operational deviation values ​​for each monitoring point includes:

[0115] Multiple feedback time points can be preset;

[0116] Based on the monitoring point sequence A, a is set sequentially. i Monitoring points to be diagnosed;

[0117] Generate monitoring evaluation values ​​for the monitoring points to be diagnosed, and set the number of data collection times r for the monitoring points to be diagnosed based on the monitoring evaluation values;

[0118] Obtain the monitoring data packet of the monitoring point to be diagnosed at the current feedback time point;

[0119] Set the state sub-model corresponding to the monitoring point to be diagnosed as the target state sub-model;

[0120] Based on the target state sub-model and monitoring data package, deviation evaluation values ​​for each primary operational indicator of the monitoring point to be diagnosed are generated;

[0121] Generate the operational deviation value b of the monitoring point to be diagnosed at the current feedback time node based on all operational deviation values;

[0122]

[0123] Where θ1 represents the number of primary operational indicators for the monitoring points to be diagnosed; β i c is the influencing factor of the i-th primary operational indicator of the monitoring point to be diagnosed; iThe deviation evaluation value of the i-th primary operational indicator of the monitoring point to be diagnosed at the current feedback time node;

[0124] The operational deviation values ​​of each monitoring point at the current feedback time node are generated sequentially;

[0125] Establish a sequence of deviation values ​​B, B = (b1, b2, ..., bb2) i …b n ), where b i is the operational deviation value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points.

[0126] Specifically, the greater the correlation between the primary operating indicators and the faults at the monitoring points to be diagnosed, the greater the corresponding influence factor. The specific value of the influence factor can be set based on historical parameters. For example, when it is determined that a valve has a blockage fault, the pressure parameters in the pipeline are highly responsive to the fault, and the correlation between the two is strong, so the reference value of the corresponding influence factor is greater.

[0127] Specifically, the larger the deviation value, the greater the possibility that the monitoring point to be diagnosed has abnormal operation and potential failure risk.

[0128] Specifically, monitoring evaluation values ​​for the monitoring points to be diagnosed are generated based on historical monitoring parameters. The higher the monitoring evaluation value, the higher the failure frequency and misjudgment frequency of the monitoring point to be diagnosed, and the more data collection times are required. By dynamically adjusting the number of data collection times, the accuracy of judging the abnormal operating status of the monitoring points to be diagnosed is improved.

[0129] Specifically, when generating deviation evaluation values ​​for each primary operational indicator, the following are included:

[0130] Establish a primary operational indicator series W for the monitoring points to be diagnosed, where W = (w1, w2, ..., w i …wθ1), where w i θ1 represents the i-th primary operational indicator of the monitoring point to be diagnosed; θ1 represents the number of primary operational indicators of the monitoring point to be diagnosed.

[0131] Based on the primary operational indicator series W, w is set sequentially. i Target operating indicators;

[0132] Generate the deviation evaluation value c of the target operating indicator;

[0133]

[0134] Where e1 is the preset first fixed coefficient; e2 is the preset second coefficient; Q1 is the preset second fixed coefficient; Q2 is the preset second fixed coefficient; r is the number of data collection times for the monitoring point to be diagnosed; j idenoted as , where is the real-time value of the i-th collected target operational indicator; j' is the standard reference value of the target operational indicator; and Δj is the average of all real-time reference values ​​of the target operational indicator collected at the current feedback time node.

[0135] Specifically, the greater the deviation from the evaluation value, the greater the possibility of abnormal operation of the corresponding primary operating indicator.

[0136] Specifically, by analyzing time-series data, we can promptly diagnose various operational indicators of the nodes to be diagnosed and issue early warnings of abnormal risks in the nodes to be diagnosed.

[0137] Specifically, standard reference values ​​for each operating indicator are set based on the historical monitoring parameters of the node to be diagnosed. These standard reference values ​​refer to the operating values ​​of each operating indicator of the electric valve system during normal operation.

[0138] Specifically, all parameters in the model are normalized by pre-setting a first fixed coefficient and a second fixed coefficient, thereby ensuring that all parameters in the model are within the same value range. It can be understood that in the above embodiment, multiple monitoring points are established based on the equipment parameters and historical fault parameters of the electric valve. By setting diagnostic sub-models for each monitoring point, abnormal operating states of each monitoring point are analyzed and warned in a timely manner, improving the early warning efficiency for abnormal operating conditions of the electric valve, promptly eliminating potential faults, and ensuring the safe operation of the electric valve.

[0139] In a preferred embodiment of this application, when setting a primary diagnostic strategy based on all operational deviation values, the following is included:

[0140] Obtain the sequence B of running deviation values;

[0141] The first operational deviation threshold B1 is preset.

[0142] If b i <B1, the i-th monitoring point is a normal monitoring point;

[0143] If b i >B1, set the i-th monitoring point as a first-level anomaly;

[0144] Get all first-level anomalies at the current feedback time point;

[0145] Establish a first-level anomaly sequence A1, A1 = (a 11 a 12 …a 1i …a 1n1 ), where a 1i n1 represents the i-th primary anomaly at the current feedback time point; n1 represents the number of primary anomalies at the current feedback time point.

[0146] A first-level diagnostic strategy is generated based on all first-level anomalies.

[0147] Specifically, a first operational deviation threshold is set based on historical monitoring parameters. When the real-time operational deviation value is greater than the preset first operational deviation threshold, it indicates that the corresponding monitoring point has abnormal operation and potential fault risk.

[0148] Specifically, when generating a first-level diagnostic strategy based on all first-level anomalies, it includes:

[0149] Based on the sequence of first-level anomalies A1, a is set sequentially. 1i The target is the anomaly.

[0150] Multiple auxiliary evaluation indicators are set based on the historical fault data packets of the target anomaly point;

[0151] Generate correlation evaluation values ​​between target anomalies and each monitoring point based on all auxiliary evaluation indicators;

[0152] Establish a correlation evaluation value sequence H, H = (h1, h2…h i …h n ), where h i This is the correlation evaluation value between the target anomaly and the i-th monitoring point;

[0153] Preset the threshold value H1 for correlation evaluation;

[0154] If h i >H1, set the i-th monitoring point as the associated monitoring point of the target anomaly point;

[0155] Configure sub-strategies for collecting each associated anomaly point;

[0156] Generate auxiliary data packets for the target anomalies based on the collection sub-strategy;

[0157] Auxiliary data packets for each primary anomaly point are generated sequentially.

[0158] Specifically, a correlation evaluation value is set between the target anomaly and each monitoring point based on the fault type of the target anomaly. The larger the correlation evaluation value, the greater the correlation between the operation monitoring data of the monitoring point and the fault diagnosis of the target anomaly.

[0159] Specifically, a threshold for correlation evaluation values ​​is set based on historical monitoring parameters. When the real-time correlation evaluation value exceeds the threshold, it indicates that the monitoring point can collect the fault characteristic parameters required for the target anomaly. Corresponding auxiliary data packets are generated by collecting fault characteristic parameters from each correlated monitoring point. By combining the fault characteristic parameters in all auxiliary data packets with the primary operational indicator parameters collected from the target anomaly, a precise analysis of the fault risk of the target anomaly is performed.

[0160] It is understandable that, in the above embodiments, by establishing the correlation between various monitoring points, auxiliary data packets of primary anomalies with potential fault risks can be obtained in a timely manner, thereby realizing multi-dimensional data analysis of primary anomalies, improving the diagnostic accuracy of abnormal operating conditions, and reducing the overall monitoring cost.

[0161] In a preferred embodiment of this application, when determining whether to generate a warning instruction based on an abnormal risk value, the following are included:

[0162] Based on the sequence of first-level anomalies A1, a is set sequentially. i These are abnormal points to be diagnosed.

[0163] Obtain auxiliary data packets for the anomalies to be diagnosed;

[0164] Generate the anomaly risk value f of the anomaly point to be diagnosed based on the auxiliary data package;

[0165] Generate the anomaly risk value for each primary anomaly point in sequence;

[0166] Establish a sequence F of abnormal risk values ​​at the current feedback time point, where F = (f1, f2, ..., f...). i …f n1 ), where f i Let be the abnormal risk value of the i-th first-level anomaly at the current feedback time point;

[0167] Preset an abnormal risk threshold F1;

[0168] If f i >F1 generates a first-level warning instruction for the i-th first-level anomaly point.

[0169] Specifically, the threshold for abnormal risk values ​​can be set based on historical parameters.

[0170] Specifically, a Level 1 early warning instruction indicates that the Level 1 anomaly point has abnormal operating conditions and fault risks, and needs to be repaired in a timely manner.

[0171] Specifically, the abnormal risk threshold F1 can be set based on historical parameters.

[0172] Specifically, generating the abnormal risk value f for the abnormal point to be diagnosed includes:

[0173]

[0174] Where, e3 is the preset third weighting coefficient; e4 is the preset fourth weighting coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; b' is the operational deviation value of the anomaly to be diagnosed at the current feedback time node; θ2 is the number of auxiliary evaluation indicators for the anomaly to be diagnosed; η ik is the influencing factor of the i-th auxiliary evaluation index of the abnormality to be diagnosed. i k' is the real-time reference value of the i-th auxiliary evaluation index for the abnormality to be diagnosed; i This is the standard reference value for the i-th auxiliary evaluation index of the abnormal point to be diagnosed.

[0175] Specifically, auxiliary evaluation indicators are fault characteristic parameters set based on the fault type of the abnormal point to be diagnosed. For example, if the abnormal point to be diagnosed may have a fault of valve not operating, the corresponding fault characteristic parameters include, but are not limited to, multiple parameters such as power supply operating status, control signal transmission command, and mechanical jamming. Each of these auxiliary evaluation indicators cannot be directly collected at the monitoring point to be diagnosed, but is distributed at various related monitoring points.

[0176] Specifically, the influence factors of each auxiliary evaluation indicator can be set according to the significance of the failure risk of the abnormal point to be diagnosed. The greater the significance, the larger the value of the corresponding influence factor.

[0177] Specifically, the standard reference values ​​for each auxiliary evaluation indicator are the operating values ​​of the electric valve under normal operating conditions.

[0178] Specifically, by pre-setting a third and a fourth fixed coefficient, all parameters in the model are normalized, so that all parameters in the model are within the same range of values.

[0179] It is understandable that in the above embodiments, by monitoring the primary operating indicators of each monitoring point, early warnings are given for abnormal states of each monitoring point in a timely manner. When an abnormal state exists at a monitoring point, accurate diagnosis of the fault is achieved by collecting auxiliary evaluation indicators from each related monitoring point.

[0180] In another preferred embodiment of the electric valve abnormal operating condition monitoring method based on any of the above preferred embodiments, this preferred embodiment provides an electric valve abnormal operating condition monitoring system, including:

[0181] The central control unit is used to set multiple monitoring points according to the equipment parameters of the electric valve;

[0182] The monitoring unit includes multiple monitoring sub-modules, which are set up at various monitoring points;

[0183] Specifically, the monitoring submodule is preferably equipped with various data acquisition devices, and the corresponding data acquisition device is set according to the data type that needs to be collected at the monitoring point.

[0184] The central control unit includes:

[0185] The first processing module is used to generate the operational deviation value of each monitoring point according to the preset state evaluation model;

[0186] The second processing module is used to set a primary diagnostic strategy based on all operational deviation values ​​and to generate abnormal risk values ​​based on the primary diagnostic strategy.

[0187] The early warning module is used to determine whether to generate an early warning command based on abnormal risk values.

[0188] The third processing module is used to establish a sequence of monitoring points A, where A = (a1, a2, ..., a...). i …a n ), where a i Let be the i-th monitoring point; n is the number of monitoring points.

[0189] In a preferred embodiment of this application, the first processing module is further configured to:

[0190] Based on the monitoring point sequence A, a is set sequentially. i For target monitoring points;

[0191] Generate historical fault data packets for the target monitoring points based on historical monitoring parameters;

[0192] Generate multiple primary operational indicators for the target monitoring points based on historical fault data packets;

[0193] Set standard reference values ​​for each primary operational indicator;

[0194] Construct a state sub-model for the target monitoring points based on all primary operational indicators;

[0195] Construct state sub-models for each monitoring point in sequence;

[0196] Construct a sequence of state sub-models P, P = (p1, p2, ..., p3) i …p n ), where p i Let n be the i-th state sub-model; n is the number of state sub-models.

[0197] A state evaluation model is generated based on all state sub-models.

[0198] Based on the first concept of the application, multiple monitoring points are established based on the equipment parameters and historical fault parameters of the electric valve. By setting up a diagnostic sub-model for each monitoring point, the abnormal operating status of each monitoring point can be analyzed and warned in a timely manner, thereby improving the early warning efficiency for abnormal operating conditions of the electric valve, eliminating potential faults in a timely manner, and ensuring the safe operation of the electric valve.

[0199] According to the second concept of this application, by establishing the correlation between various monitoring points, auxiliary data packets of primary anomalies with potential fault risks can be obtained in a timely manner, thereby realizing multi-dimensional data analysis of primary anomalies, improving the diagnostic accuracy of abnormal operating conditions, and reducing the overall monitoring cost.

[0200] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for monitoring abnormal operating conditions of an electric valve, characterized in that, include: Multiple monitoring points are set for the equipment parameters based on electric valves; The operational deviation value of each monitoring point is generated based on the preset state evaluation model, and a first-level diagnostic strategy is set based on all operational deviation values. Anomaly risk values ​​are generated based on the primary diagnostic strategy, and a warning instruction is generated based on the anomaly risk values. When setting multiple monitoring points, the following are included: Establish a sequence of monitoring points A, where A = (a1, a2, ..., a3) i …a n ), where a i Let be the i-th monitoring point; n is the number of monitoring points.

2. The method for monitoring abnormal operating conditions of electric valves as described in claim 1, characterized in that, When establishing a state diagnosis model, the following should be included: Based on the monitoring point sequence A, a is set sequentially. i For target monitoring points; Generate historical fault data packets for the target monitoring points based on historical monitoring parameters; Generate multiple primary operational indicators for the target monitoring points based on historical fault data packets; Set standard reference values ​​for each primary operational indicator; Construct a state sub-model for the target monitoring points based on all primary operational indicators; Construct state sub-models for each monitoring point in sequence; Construct a sequence of state sub-models P, P = (p1, p2, ..., p3) i …p n ), where p i Let n be the i-th state sub-model; n is the number of state sub-models. A state evaluation model is generated based on all state sub-models.

3. The method for monitoring abnormal operating conditions of electric valves as described in claim 2, characterized in that, When generating the operational deviation values ​​for each monitoring point, the following are included: Multiple feedback time points can be preset; Based on the monitoring point sequence A, a is set sequentially. i Monitoring points to be diagnosed; Generate monitoring evaluation values ​​for the monitoring points to be diagnosed, and set the number of data collection times r for the monitoring points to be diagnosed based on the monitoring evaluation values; Obtain the monitoring data packet of the monitoring point to be diagnosed at the current feedback time point; Set the state sub-model corresponding to the monitoring point to be diagnosed as the target state sub-model; Based on the target state sub-model and monitoring data package, deviation evaluation values ​​for each primary operational indicator of the monitoring point to be diagnosed are generated; Generate the operational deviation value b of the monitoring point to be diagnosed at the current feedback time node based on all operational deviation values; Where θ1 represents the number of primary operational indicators for the monitoring points to be diagnosed; β i c is the influencing factor of the i-th primary operational indicator of the monitoring point to be diagnosed; i The deviation evaluation value of the i-th primary operational indicator of the monitoring point to be diagnosed at the current feedback time node; The operational deviation values ​​of each monitoring point at the current feedback time node are generated sequentially; Establish a sequence of deviation values ​​B, B = (b1, b2, ..., bb2) i …b n ), where b i is the operational deviation value of the i-th monitoring point at the current feedback time node; n is the number of monitoring points.

4. The method for monitoring abnormal operating conditions of electric valves as described in claim 3, characterized in that, When generating deviation evaluation values ​​for each primary operational indicator, the following are included: Establish a primary operational indicator series W for the monitoring points to be diagnosed, where W = (w1, w2, ..., w i …wθ1), where w i θ1 represents the i-th primary operational indicator of the monitoring point to be diagnosed; θ1 represents the number of primary operational indicators of the monitoring point to be diagnosed. Based on the primary operational indicator series W, w is set sequentially. i Target operating indicators; Generate the deviation evaluation value c of the target operating indicator; Where e1 is the preset first fixed coefficient; e2 is the preset second coefficient; Q1 is the preset second fixed coefficient; Q2 is the preset second fixed coefficient; r is the number of data collection times for the monitoring point to be diagnosed; j i denoted as , where is the real-time value of the i-th collected target operational indicator; j' is the standard reference value of the target operational indicator; and Δj is the average of all real-time reference values ​​of the target operational indicator collected at the current feedback time node.

5. The method for monitoring abnormal operating conditions of electric valves as described in claim 4, characterized in that, When setting a Level 1 diagnostic strategy based on all operational deviations, it includes: Obtain the sequence B of running deviation values; The first operational deviation threshold B1 is preset. If b i <B1, the i-th monitoring point is a normal monitoring point; If b i >B1, set the i-th monitoring point as a first-level anomaly; Get all first-level anomalies at the current feedback time point; Establish a first-level anomaly sequence A1, A1 = (a 11 a 12 …a 1i …a 1n1 ), where a 1i n1 represents the i-th primary anomaly at the current feedback time point; n1 represents the number of primary anomalies at the current feedback time point. A first-level diagnostic strategy is generated based on all first-level anomalies.

6. The method for monitoring abnormal operating conditions of electric valves as described in claim 5, characterized in that, When generating a first-level diagnostic strategy based on all first-level anomalies, it includes: Based on the sequence of first-level anomalies A1, a is set sequentially. 1i The target is the anomaly. Multiple auxiliary evaluation indicators are set based on the historical fault data packets of the target anomaly point; Generate correlation evaluation values ​​between target anomalies and each monitoring point based on all auxiliary evaluation indicators; Establish a correlation evaluation value sequence H, H = (h1, h2…h i …h n ), where h i This is the correlation evaluation value between the target anomaly and the i-th monitoring point; Preset the threshold value H1 for correlation evaluation; If h i >H1, set the i-th monitoring point as the associated monitoring point of the target anomaly point; Configure sub-strategies for collecting each associated anomaly point; Generate auxiliary data packets for the target anomalies based on the collection sub-strategy; Auxiliary data packets for each primary anomaly point are generated sequentially.

7. The method for monitoring abnormal operating conditions of electric valves as described in claim 6, characterized in that, When determining whether to generate an early warning instruction based on abnormal risk values, including Based on the sequence of first-level anomalies A1, a is set sequentially. i These are abnormal points to be diagnosed. Obtain auxiliary data packets for the anomalies to be diagnosed; Generate the anomaly risk value f of the anomaly point to be diagnosed based on the auxiliary data package; Generate the anomaly risk value for each primary anomaly point in sequence; Establish a sequence F of abnormal risk values ​​at the current feedback time point, where F = (f1, f2, ..., f...). i …f n1 ), where f i Let be the abnormal risk value of the i-th first-level anomaly at the current feedback time point; Preset anomaly risk threshold F1; If f i >F1 generates a first-level warning instruction for the i-th first-level anomaly point.

8. The method for monitoring abnormal operating conditions of electric valves as described in claim 7, characterized in that, When generating the abnormal risk value f for the abnormal point to be diagnosed, the following are included: Where, e3 is the preset third weighting coefficient; e4 is the preset fourth weighting coefficient; Q3 is the preset third fixed coefficient; Q4 is the preset fourth fixed coefficient; b' is the operational deviation value of the anomaly to be diagnosed at the current feedback time node; θ2 is the number of auxiliary evaluation indicators for the anomaly to be diagnosed; η i k is the influencing factor of the i-th auxiliary evaluation index of the abnormality to be diagnosed. i k' is the real-time reference value of the i-th auxiliary evaluation index for the abnormality to be diagnosed; i This is the standard reference value for the i-th auxiliary evaluation index of the abnormal point to be diagnosed.

9. An abnormal operating condition monitoring system for electric valves, employing the abnormal operating condition monitoring method for electric valves as described in any one of claims 1-8, characterized in that, include: The central control unit is used to set multiple monitoring points according to the equipment parameters of the electric valve; The monitoring unit includes multiple monitoring sub-modules, which are located at various monitoring points. The central control unit includes: The first processing module is used to generate the operational deviation value of each monitoring point according to the preset state evaluation model; The second processing module is used to set a primary diagnostic strategy based on all operational deviation values ​​and to generate abnormal risk values ​​based on the primary diagnostic strategy. The early warning module is used to determine whether to generate an early warning instruction based on abnormal risk values; The third processing module is used to establish a sequence of monitoring points A, where A = (a1, a2, ..., a...). i …a n ), where a i Let be the i-th monitoring point; n is the number of monitoring points.

10. The electric valve abnormal operating condition monitoring system as described in claim 9, characterized in that, The first processing module is also used for: Based on the monitoring point sequence A, a is set sequentially. i For target monitoring points; Generate historical fault data packets for the target monitoring points based on historical monitoring parameters; Generate multiple primary operational indicators for the target monitoring points based on historical fault data packets; Set standard reference values ​​for each primary operational indicator; Construct a state sub-model for the target monitoring points based on all primary operational indicators; Construct state sub-models for each monitoring point in sequence; Construct a sequence of state sub-models P, P = (p1, p2, ..., p3) i …p n ), where p i Let n be the i-th state sub-model; n is the number of state sub-models. A state evaluation model is generated based on all state sub-models.

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