Method and device for identifying operating state of loading arm circuit

Abstract

The present invention discloses a method and device for identifying the operating state of a loading arm circuit. The method includes: establishing an operating state evaluation model in advance based on the monitoring data of the loading arm circuit; during the operation of the loading arm circuit, monitoring the operating parameters of the loading arm circuit in real time; and determining the operating state of the loading arm circuit by using the operating parameters and the operating state evaluation model. By using the solution of the present invention, the operating state of the loading arm circuit can be monitored in real time, potential faults can be discovered and processed in time, and production safety and efficiency can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation and equipment monitoring, and particularly to a method and device for identifying the operating state of a loading arm circuit. Background Art

[0002] In the modern industrial production process, as a key material conveying equipment, the stability and reliability of the loading arm are crucial for ensuring the continuity and safety of the production process. The loading arm realizes the material loading and unloading of containers such as storage tanks, transport vehicles or ships through its flexible arm pipe system. However, during the long-term operation of the loading arm motor, limit switch and other circuit components, faults may occur due to wear, aging or external environmental factors. These faults will not only cause material leakage and equipment damage, but may also lead to safety accidents, seriously affecting personnel safety and enterprise economic benefits.

[0003] At present, the monitoring and fault diagnosis of the loading arm mainly rely on regular manual inspections and simple sensor monitoring. These methods have problems such as slow response, low accuracy, inability to monitor and diagnose in real time, etc. Especially in complex working conditions, traditional monitoring means are difficult to meet the requirements of effectively predicting and diagnosing the operating state of the loading arm. Summary of the Invention

[0004] The present invention provides a method and device for identifying the operating state of a loading arm circuit, so as to monitor the operating state of the loading arm circuit in real time, discover and handle potential faults in time, and ensure production safety and efficiency.

[0005] To this end, the present invention provides the following technical solutions:

[0006] A method for identifying the operating state of a loading arm circuit, the method comprising:

[0007] Pre-establish an operating state evaluation model based on the monitoring data of the loading arm circuit;

[0008] During the operation of the loading arm circuit, real-time monitor the operating parameters of the loading arm circuit;

[0009] Use the operating parameters and the operating state evaluation model to determine the operating state of the loading arm circuit.

[0010] Optionally, the establishing an operating state evaluation model based on the monitoring data of the loading arm circuit includes:

[0011] Obtain the monitoring data of the loading arm circuit;

[0012] Determine the fault characteristic parameters of the loading arm circuit according to the monitoring data, and the fault characteristic parameters include any one or more of the following: the state of the limit switch of the loading arm circuit, the working current, voltage and power of the motor;

[0013] Normalize the fault characteristic parameters;

[0014] Establish an operation status evaluation model based on the normalized fault characteristic parameters.

[0015] Optionally, the normalization of the fault characteristic parameters includes: normalizing the fault characteristic parameters using the relative deterioration degree formula.

[0016] Optionally, the establishment of the operation status evaluation model based on the normalized fault characteristic parameters includes:

[0017] Establish an operation status matrix of the loading arm circuit based on the normalized fault characteristic parameters;

[0018] Determine the weights of each fault symptom in the operation status matrix of the loading arm circuit;

[0019] Determine the calculation formula of the connection number of the operation status according to the weights and the operation status matrix of the loading arm circuit, and use the connection number calculation formula as the operation status evaluation model.

[0020] Optionally, the establishment of the operation status matrix of the loading arm circuit includes:

[0021] Determine the membership function of each state, where the states include: healthy, sub-healthy, abnormal, and faulty;

[0022] Establish an operation status matrix of the loading arm circuit according to the membership function :

[0023]

[0024] where is the th fault symptom of the th fault; are the membership degrees of healthy, sub-healthy, abnormal, and faulty respectively.

[0025] Optionally, the determination of the weights of each fault symptom in the operation status matrix of the loading arm circuit includes: determining the weights of each fault symptom in the operation status matrix of the loading arm circuit using the entropy weight method.

[0026] Optionally, the determination of the operation status of the loading arm circuit using the operation parameters and the operation status evaluation model includes:

[0027] Input the operation parameters into the operation status evaluation model and calculate the values of the connection numbers of each state;

[0028] Determine the operation status of the loading arm circuit according to the values of the connection numbers.

[0029] An identifying device for the operating state of a loading arm circuit, the device comprising:

[0030] A model establishment module, configured to pre-establish an operating state evaluation model based on the monitoring data of the loading arm circuit;

[0031] An operating parameter acquisition module, configured to monitor the operating parameters of the loading arm circuit in real time during the operation of the loading arm circuit;

[0032] A state identification module, configured to determine the operating state of the loading arm circuit by using the operating parameters and the operating state evaluation model.

[0033] Optionally, the model establishment module includes:

[0034] A data acquisition unit, configured to obtain the monitoring data of the loading arm circuit;

[0035] A feature extraction unit, configured to determine the fault feature parameters of the loading arm circuit according to the monitoring data, where the fault feature parameters include any one or more of the following: the state of the limit switch of the loading arm circuit, the operating current of the motor, voltage, and power;

[0036] A data processing unit, configured to perform normalization processing on the fault feature parameters;

[0037] A model establishment unit, configured to establish an operating state evaluation model according to the normalized fault feature parameters.

[0038] Optionally, the model establishment unit includes:

[0039] A matrix establishment subunit, configured to establish a loading arm circuit operating state matrix according to the normalized fault feature parameters;

[0040] A weight determination subunit, configured to determine the weights of each fault symptom in the loading arm circuit operating state matrix;

[0041] A connection number calculation subunit, configured to determine a connection number calculation formula for the operating state according to the weights and the loading arm circuit operating state matrix, and use the connection number calculation formula as the operating state evaluation model.

[0042] A computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the steps of the method for identifying the operating state of the loading arm circuit.

[0043] The method and device for identifying the operating state of the loading arm circuit provided by the present invention pre - establish an operating state evaluation model based on the monitoring data of the loading arm circuit. During the operation of the loading arm circuit, the operating parameters of the loading arm circuit are monitored in real - time, and the operating state of the loading arm circuit is determined by using the monitored current operating parameters and the operating state evaluation model. By using the solution of the present invention, the operating state of the loading arm circuit can be accurately identified, the real - time monitoring of the loading arm circuit can be realized, potential faults can be discovered and processed in time, and the production safety and efficiency can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart for establishing an operating state evaluation model in an embodiment of the present invention;

[0046] Figure 2 It is a schematic diagram of the membership function curve in an embodiment of the present invention;

[0047] Figure 3 It is a flowchart of a method for identifying the operating state of the loading arm circuit in an embodiment of the present invention;

[0048] Figure 4 It is a schematic structural diagram of a device for identifying the operating state of the loading arm circuit in an embodiment of the present invention;

[0049] Figure 5 It is a schematic structural diagram of a model - building module in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will detail the specific embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific embodiments described here are only for explaining and illustrating the present invention, and are not used to limit the present invention.

[0051] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Aiming at the problem that traditional monitoring means are difficult to meet the needs of effectively predicting and diagnosing the operating state of the loading arm under complex working conditions, an embodiment of the present invention provides a method and device for identifying the operating state of the loading arm circuit. An operating state evaluation model is established in advance based on the monitoring data of the loading arm circuit. During the operation of the loading arm circuit, the operating state of the loading arm circuit can be determined in real time by using this operating state evaluation model, providing an effective guarantee for the normal operation of the loading arm.

[0053] First, the establishment process of the operating state evaluation model will be described in detail below.

[0054] As Figure 1 shown, it is a flowchart for establishing an operating state evaluation model in an embodiment of the present invention, including the following steps:

[0055] Step 101, obtain the monitoring data of the loading arm circuit.

[0056] The monitoring data may include the working parameters and actual monitoring data of the loading arm circuit, such as data like voltage and current.

[0057] Step 102, determine the fault characteristic parameters of the loading arm circuit according to the monitoring data.

[0058] Specifically, by analyzing the monitoring data of the loading arm, different operating states of the loading arm circuit can be summarized. For example, in some embodiments, its operating state can be divided into the following four types:

[0059] (1) Healthy state: The equipment parameter values are within the rated value range, the equipment operates normally, and the probability of failure is extremely low.

[0060] (2) Sub-healthy state: The equipment parameter values deviate from the rated values, the equipment does not operate very normally, and it needs to be concerned about and monitored.

[0061] (3) Abnormal state: The equipment parameter values deviate significantly from the rated values and fluctuate, the equipment is in a high-risk state, the probability of failure is very high, and it is necessary to strengthen monitoring and repair as soon as possible.

[0062] (4) Fault state: The equipment has failed and must be repaired.

[0063] Of course, in specific implementation, it is not limited to the above-mentioned division method of operating state types. According to the actual application requirements, there can be other division methods. For example, the operating state can be divided into two types: normal and abnormal.

[0064] In the following descriptions, the above four state types are used as examples for illustration.

[0065] For different operating states, some relevant parameters of the loading arm circuit usually change. For example, when a line fault occurs in the limit switch, its current and voltage will change. Therefore, the current and voltage of the limit switch can be used as its fault symptoms. In the embodiments of the present invention, the fault characteristic parameters may include, but are not limited to, any one or more of the following: the state of the limit switch of the loading arm circuit, the operating current, voltage, and power of the motor, etc.

[0066] Step 103: Normalize the fault characteristic parameters.

[0067] In a non-limiting embodiment, the relative deterioration degree formula can be used to normalize the fault characteristic parameters. For example, there are the following several ways:

[0068] (1) For the type where the larger the index, the better, the relative deterioration degree is:

[0069]

[0070] (2) For the type where the smaller the index, the better, the relative deterioration degree is:

[0071]

[0072] (3) For the intermediate type index, the relative deterioration degree is:

[0073]

[0074] Among them, is the relative deterioration degree of the fault symptom; , are the limit values (endpoint values) of each corresponding index interval, , is the intermediate type index, taking the endpoint values of the optimal interval value.

[0075] Step 104: Establish an operating state evaluation model based on the normalized fault characteristic parameters.

[0076] A non-limiting embodiment of establishing an operating state evaluation model may include the following steps:

[0077] (1) Establish a loading arm circuit operating state matrix based on the normalized fault characteristic parameters;

[0078] (2) Determine the weights of each fault symptom in the loading arm circuit operating state matrix;

[0079] (3) Determine the connection number calculation formula of the operating state according to the weights and the loading arm circuit operating state matrix, and use the connection number calculation formula as the operating state evaluation model.

[0080] Among them, the operation state matrix of the loading arm circuit can be established by using membership functions. The commonly used types of membership functions include matrix type, semi-trapezoidal and trapezoidal distributions, k-th parabola type, normal distribution type, triangle type, and ridge type. The ridge type membership function is known for its relatively wide main value interval and gentle transition, and is suitable for occasions where quantitative calculations are required. Therefore, in the embodiments of the present invention, the ridge type membership function can be preferentially selected.

[0081] Taking the above four operation state types as examples, the membership function curves corresponding to each state are as Figure 2 shown.

[0082] In the healthy state, the membership function is:

[0083]

[0084] In the sub-healthy state, the membership function is:

[0085]

[0086] In the abnormal state, the membership function is:

[0087]

[0088] In the faulty state, the membership function is:

[0089]

[0090] Using the above membership functions, an operation state matrix is established for the loading arm circuit :

[0091]

[0092] Among them, is the th fault symptom of the th fault; are the membership degrees of healthy, sub-healthy, abnormal, and faulty states respectively.

[0093] In some embodiments, the entropy weight method (Entropy Weight Method, abbreviated as EWM) can be used to determine the weights of each fault symptom. The entropy weight method is a method for determining the weights of each index in a multi-index evaluation system. Its core idea is to measure the information volume of each index according to the size of the information entropy, and thereby determine the importance of the index. The smaller the entropy value, the greater the information volume and the higher the weight should be. This method can well avoid the interference of subjective factors and make the determination of weights more scientific and objective.

[0094] Information entropy :

[0095]

[0096] Among them, is the number of data, is the th probability of the data occurrence. If a certain data does not occur, its probability is set to 0.

[0097] Weight of fault symptom :

[0098]

[0099] Among them, is the weight of the th fault symptom, is the th information entropy of the fault symptom.

[0100] In the embodiments of the present invention, an accurate operation state evaluation model can be constructed through the connection number equation. The connection number is a structure function used to describe the certainty and uncertainty in things and their interaction. It is usually composed of two parts: the certainty measure and the uncertainty measure of the thing relative to a certain reference thing.

[0101] Specifically as follows:

[0102]

[0103] Among them, is the connection number of the operation state; is the weight of the fault symptom; is the operation state matrix; is the coefficient of the connection number.

[0104] During the operation of the loading arm circuit, the above operation state evaluation model can be used to realize the identification of the operation state of the loading arm circuit, timely discover potential faults and abnormalities, and provide an effective guarantee for the normal operation of the loading arm.

[0105] As Figure 3 shown, it is a flowchart of a method for identifying the operation state of the loading arm circuit in the embodiments of the present invention, including the following steps:

[0106] Step 301, during the operation of the loading arm circuit, real-time monitor the operation parameters of the loading arm circuit.

[0107] The operation parameters of the loading arm circuit may include but are not limited to any one or more of the following: current, voltage, power, etc. when the limit switch and the motor of the loading arm circuit are working.

[0108] Step 302: Determine the operating state of the loading arm circuit by using the operating parameters and the operating state evaluation model established in advance based on the monitoring data of the loading arm circuit.

[0109] Specifically, input the operating parameters into the operating state evaluation model, and calculate the connection number values of each state; determine the operating state of the loading arm circuit according to the connection number values.

[0110] For example, the range of the connection number is [-1, 1]. According to the four operating states of healthy, sub-healthy, abnormal, and faulty, the connection number is divided into four intervals (0.5, 1], (0, 0.5], (-0.5, 0], [-1, -0.5], corresponding to the healthy, sub-healthy, abnormal, and faulty states in sequence.

[0111] For the m-th operating state, calculate the corresponding connection number based on the current operating parameters :

[0112]

[0113] When it is in the interval [-1, -0.5], it is determined that the loading arm circuit is in a faulty state.

[0114] Furthermore, in some embodiments, the monitored operating state of the loading arm circuit can also be displayed in real time. Once it is monitored that the operating state of the loading arm circuit is any state other than the healthy state, an alarm can also be triggered (for example, relevant operators can be notified by means of sound and light alarms, text messages, or cloud platform push), ensuring timely response; at the same time, the status information (such as including timestamp, status flag, etc.) can also be recorded in a log file or database to provide corresponding data support for subsequent problem troubleshooting and analysis, etc.

[0115] Correspondingly, an embodiment of the present invention also provides a device for identifying the operating state of a loading arm circuit, as Figure 4 shown, which is a schematic structural diagram of the device.

[0116] The device 400 for identifying the operating state of the loading arm circuit includes the following modules:

[0117] A model establishment module 401, configured to establish an operating state evaluation model 40 in advance based on the monitoring data of the loading arm circuit;

[0118] An operating parameter acquisition module 402, configured to monitor the operating parameters of the loading arm circuit in real time during the operation of the loading arm circuit;

[0119] A state identification module 403, configured to determine the operating state of the loading arm circuit by using the operating parameters and the operating state evaluation model.

[0120] In some embodiments, the crane pipe circuit operating state recognition device 400 may further include: a display module and / or an exception handling module. Among them:

[0121] The display module is used to display the operating state of the crane pipe circuit recognized by the state recognition module 403 in real time.

[0122] The exception handling module is used to trigger an alarm when the state recognition module 403 recognizes that the operating state of the crane pipe circuit is any state other than the healthy state, and record the exception information in a log file or a database.

[0123] Figure 5 Fig. shows a schematic structural diagram of the model establishment module in an embodiment of the present invention.

[0124] The model establishment module 401 includes the following units:

[0125] The data acquisition unit 411 is used to obtain the monitoring data of the crane pipe circuit;

[0126] The feature extraction unit 412 is used to determine the fault feature parameters of the crane pipe circuit according to the monitoring data, and the fault feature parameters include any one or more of the following: the state of the limit switch of the crane pipe circuit, the operating current, voltage and power of the motor;

[0127] The data processing unit 413 is used to perform normalization processing on the fault feature parameters;

[0128] The model establishment unit 414 is used to establish an operating state evaluation model according to the normalized fault feature parameters.

[0129] Among them, the model establishment unit 414 may include the following sub-units:

[0130] The matrix establishment sub-unit is used to establish a crane pipe circuit operating state matrix according to the normalized fault feature parameters;

[0131] The weight determination sub-unit is used to determine the weights of each fault symptom in the crane pipe circuit operating state matrix;

[0132] The connection number calculation sub-unit is used to determine the connection number calculation formula of the operating state according to the weight and the crane pipe circuit operating state matrix, and use the connection number calculation formula as the operating state evaluation model.

[0133] The specific implementation manners of the above-mentioned modules, units, and sub-units may refer to the corresponding descriptions in the method embodiment of the present invention above, and will not be repeated here.

[0134] The method and device for identifying the operating state of the loading arm circuit provided by the embodiments of the present invention establish an operating state evaluation model through accurate data analysis and state evaluation. This model can effectively predict and identify potential problems in the loading arm circuit, thereby taking preventive measures to avoid the occurrence of faults and ensuring the stable operation of the system.

[0135] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0136] The present invention also provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program runs, it can execute Figure 1 or Figure 3 some or all of the steps of the method shown in

[0137] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data provider to another website, computer, server, or data provider in a wired or wireless manner.

[0138] The above has introduced the embodiments of the present invention in detail. In this article, specific implementation manners are used to expound the present invention. The description of the above embodiments is only used to help understand the method and system of the present invention. They are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention. The content of this specification should not be construed as a limitation to the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying the operating status of a crane circuit, characterized in that: The method comprises: Establish an operation status assessment model in advance based on the monitoring data of the crane circuit; During the operation of the crane circuit, the operation parameters of the crane circuit are monitored in real time; Determine the operation state of the crane circuit by using the operation parameters and the operation state evaluation model; The establishment of the operation status evaluation model based on the monitoring data of the crane circuit includes: Obtain monitoring data of crane circuit; Determine the fault characteristic parameters of the crane circuit according to the monitoring data, wherein the fault characteristic parameters include any one or more of the following: the limit switch state of the crane circuit, the motor operating current, voltage and power; Normalizing the fault characteristic parameters; Establishing an operation status assessment model based on normalized fault characteristic parameters; Wherein, establishing the operation status assessment model according to the normalized fault characteristic parameters includes: The operation state matrix of the crane circuit is established according to the normalized fault characteristic parameters; Determining the weight of each fault symptom in the crane circuit operation state matrix; Determine a connection number calculation formula for the operation state according to the weight and the crane circuit operation state matrix, and use the connection number calculation formula as an operation state evaluation model; The establishment of the crane circuit operation state matrix comprises: Determine a membership function for each state, wherein the states include: healthy, sub-healthy, abnormal, and faulty; According to the membership function, the crane circuit operation state matrix is ​​established. : in, For the The first fault Item malfunction symptoms; They are the membership degrees of health, sub-health, abnormality and failure respectively.

2. The method for identifying the operation status of a crane circuit according to claim 1, characterized in that: The normalizing process of the fault characteristic parameters comprises: The fault characteristic parameters are normalized using a relative degradation degree formula.

3. The method for identifying the operation status of a crane circuit according to claim 2, characterized in that: The step of determining the weight of each fault symptom in the crane circuit operation state matrix includes: The entropy weight method is used to determine the weight of each fault symptom in the crane circuit operation state matrix.

4. The method for identifying the operation status of a crane circuit according to claim 3, characterized in that: Determining the operation state of the crane circuit by using the operation parameters and the operation state evaluation model includes: Inputting the operating parameters into the operating state evaluation model to calculate the value of the connection number of each state; The operating state of the crane circuit is determined according to the value of the connection number.

5. A device for identifying the operation status of a crane circuit, characterized in that: The device comprises: A model building module is used to build an operation status evaluation model based on the monitoring data of the crane circuit in advance; The operating parameter acquisition module is used to monitor the operating parameters of the crane circuit in real time during the operation of the crane circuit; A state identification module, used to determine the operation state of the crane circuit by using the operation parameters and the operation state evaluation model; The model building module includes: A data acquisition unit, used to obtain monitoring data of the crane circuit; A feature extraction unit, used to determine the fault characteristic parameters of the crane circuit according to the monitoring data, wherein the fault characteristic parameters include any one or more of the following: the limit switch state of the crane circuit, the motor operating current, voltage and power; A data processing unit, used for normalizing the fault characteristic parameters; A model building unit, used to build an operation status evaluation model according to the normalized fault characteristic parameters; Wherein, establishing the operation status assessment model according to the normalized fault characteristic parameters includes: The operation state matrix of the crane circuit is established according to the normalized fault characteristic parameters; Determining the weight of each fault symptom in the crane circuit operation state matrix; Determine a connection number calculation formula for the operation state according to the weight and the crane circuit operation state matrix, and use the connection number calculation formula as an operation state evaluation model; The establishment of the crane circuit operation state matrix comprises: Determine a membership function for each state, wherein the states include: healthy, sub-healthy, abnormal, and faulty; According to the membership function, the crane circuit operation state matrix is ​​established. : in, For the The first fault Item malfunction symptoms; They are the membership degrees of health, sub-health, abnormality and failure respectively.

6. The device for identifying the operation status of a crane circuit according to claim 5, characterized in that: The model building unit comprises: A matrix establishment subunit is used to establish a crane circuit operation state matrix according to normalized fault characteristic parameters; A weight determination subunit, used to determine the weight of each fault symptom in the crane circuit operation state matrix; The connection number calculation subunit is used to determine the connection number calculation formula of the operating state according to the weight and the crane circuit operating state matrix, and use the connection number calculation formula as the operating state evaluation model.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying the operating status of a crane circuit according to any one of claims 1 to 4 are executed.

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