Transit monitoring method, system, medium, electronic device and program product

Abstract

The present disclosure relates to a transfer monitoring method, system, medium, electronic device and program product. The method includes: obtaining relevant operation data of the transfer, the relevant operation data including temperature data, rotation speed data and vibration data; if the vibration data does not exceed the preset vibration range, determining whether the transfer is abnormal based on the relevant operation data and the pre-built transfer dynamic model; if the transfer is abnormal, determining abnormal diagnosis information. In this way, when there is no obvious abnormality in the vibration data, it is possible to further determine whether the transfer has potential abnormalities that are not easy to detect through the pre-built transfer dynamic model, and then take preventive maintenance measures according to the abnormal diagnosis information to improve the reliability and stability of the equipment, so as to avoid the increase of maintenance costs due to sudden failures, realize the effective control of maintenance costs, realize the automation and informatization of transfer management, improve work efficiency, avoid the waste of computing resources, and reduce human errors and duplication of work.

Description

Technical Field

[0001] The present disclosure relates to the field of transfer, and in particular, to a transfer monitoring method, system, medium, electronic device and program product. Background Art

[0002] Turnaround is closely related to production, but currently most companies still use the traditional method of regular inspections to perform preventive maintenance on turnarounds. There is a lack of real-time analysis of the turnaround status, and timely adjustments cannot be made based on actual conditions and scientific theories. Problems with the turnaround may be realized only after a delay, and inadequate monitoring of the turnaround status has seriously restricted on-site production. Summary of the invention

[0003] The purpose of the present disclosure is to provide a transfer monitoring method, system, medium, electronic device and program product to timely discover transfer failures, and then take corresponding preventive maintenance measures to improve the reliability and stability of the equipment.

[0004] In order to achieve the above-mentioned object, the present disclosure provides a first aspect of a transfer monitoring method, comprising:

[0005] Acquire relevant operation data of the transfer machine, wherein the relevant operation data includes temperature data, rotation speed data, and vibration data;

[0006] If the vibration data does not exceed the preset vibration range, determining whether the transfer is abnormal based on the relevant operation data and a pre-built transfer dynamic model;

[0007] If the transition is abnormal, abnormal diagnosis information is determined.

[0008] Optionally, the method further comprises:

[0009] Corresponding analysis and processing are performed on each of the related operating data.

[0010] Optionally, performing corresponding analysis and processing on the temperature data includes:

[0011] The temperature data is preprocessed, and the preprocessed temperature data is uploaded to a target terminal so that the temperature data is analyzed by the target terminal.

[0012] Optionally, performing corresponding analysis and processing on the rotation speed data includes:

[0013] Preprocessing the speed data;

[0014] Determining the wear coefficient of the rotor according to the preprocessed rotation speed data, wherein the wear coefficient is used to characterize the influence of the rotor rotation speed on the rotor wear degree;

[0015] A first degree of wear is determined based on the wear coefficient.

[0016] Optionally, determining the wear coefficient of the rotor according to the preprocessed rotation speed data includes:

[0017] The wear coefficient λ of the rotor is determined by the following formula:

[0018]

[0019] Among them, K is the rotor torque, P is the rotor torque of the rotor, L is the rotating outer diameter, and N is the rotor stiffness of the rotor.

[0020] Optionally, performing corresponding analysis and processing on the vibration data includes:

[0021] Preprocessing the vibration data;

[0022] If the preprocessed vibration data exceeds the preset vibration range, it is determined that the transfer is abnormal;

[0023] The abnormality diagnosis information is determined based on the frequency component of the vibration data.

[0024] Optionally, if the vibration data does not exceed the preset vibration range, determining whether the transfer is abnormal according to the relevant operation data and a pre-trained transfer dynamic model includes:

[0025] If the preprocessed vibration data does not exceed the preset vibration range, it is determined whether the transfer is abnormal based on the relevant operation data and the pre-trained transfer dynamic model.

[0026] Optionally, the relevant operation data also includes oil; and performing corresponding analysis and processing on the oil includes:

[0027] The second wear degree and wear location of the rotor are determined by spectral analysis of the metal elements, pollutant elements and additive concentrations in the oil, and the failure degree and error degree of the oil are determined by compensation for the pollutant elements.

[0028] Optionally, the relevant operation data also includes oil; and performing corresponding analysis and processing on the oil includes:

[0029] The shape, quantity, size and distribution format of the metal particles in the oil are analyzed by ferrography to determine the wear form of the rotating body of the rotor, the third wear degree of the rotor and the cause of wear.

[0030] Optionally, the method further comprises:

[0031] The analysis and processing results corresponding to each of the relevant operating data are classified and managed to obtain basic data for updating the turnaround dynamic model.

[0032] Optionally, the objective function X of the transfer dynamic model is:

[0033]

[0034] Where M is the input data error loss compensation, N j The correction coefficient for the hth group of input data, N j-1 is the correction coefficient for the j-1th group of input data, n is the total number of moments, α i is the difference between the input data at the i-th moment and the (i+1)-th moment, where the input data is any one of temperature data, rotation speed data and vibration data.

[0035] Optionally, the constraints of the transfer dynamic model are:

[0036]

[0037] N≥0

[0038] Wherein, N is the input data correction coefficient, and the value range of j is [1, n].

[0039] Optionally, determining whether the transfer is abnormal based on the relevant operation data and a pre-built transfer dynamic model includes:

[0040] Preprocessing the relevant operation data;

[0041] Inputting the pre-processed relevant operation data into the transfer dynamic model to obtain an abnormality judgment result;

[0042] If the transfer is abnormal, determining abnormal diagnosis information includes:

[0043] If the abnormality judgment result indicates that the transfer is abnormal, uploading the abnormality judgment result and the data input into the transfer dynamic model to the cloud interconnection diagnosis platform;

[0044] Obtain abnormal diagnosis information issued by the cloud interconnection diagnosis platform.

[0045] Optionally, the method further comprises:

[0046] The relevant operating data and the abnormal diagnosis information are sent to a visualization screen so as to display the relevant operating data and the abnormal diagnosis information through the visualization screen.

[0047] A second aspect of the present disclosure provides a transfer monitoring system, comprising:

[0048] A data acquisition module, used to obtain relevant operating data of the transfer machine, wherein the relevant operating data includes temperature data, rotation speed data, and vibration data;

[0049] The transfer monitoring module is used to determine whether the transfer is abnormal based on the relevant operating data and a pre-built transfer dynamic model if the vibration data does not exceed a preset vibration range; if the transfer is abnormal, determine abnormal diagnosis information.

[0050] Optionally, the transfer monitoring system further includes:

[0051] The data processing module is used to perform corresponding analysis and processing on each of the related operation data.

[0052] Optionally, the transfer monitoring system further includes:

[0053] The data management module is used to classify and manage the analysis and processing results corresponding to each of the relevant operating data to obtain basic data for updating the turnaround dynamic model.

[0054] Optionally, the transfer monitoring system further includes:

[0055] A visualization module is used to display the operation data and the abnormal diagnosis information.

[0056] A third aspect of the present disclosure provides a non-temporary computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect of the present disclosure.

[0057] A fourth aspect of the present disclosure provides an electronic device, comprising

[0058] a memory having a computer program stored thereon;

[0059] A processor is used to execute a computer program in a memory to implement the steps of the method provided in the first aspect of the present disclosure.

[0060] A fifth aspect of the present disclosure provides a computer program product, including a computer program, which implements the steps provided in the first aspect of the disclosure when executed by a processor.

[0061] In the above technical solution, the relevant operating data of the transfer machine is obtained, and the relevant operating data includes temperature data, speed data, and vibration data; if the vibration data does not exceed the preset vibration range, it is determined whether the transfer machine has an abnormality based on the relevant operating data and the pre-built transfer machine dynamic model; if the transfer machine has an abnormality, the abnormal diagnosis information is determined. In this way, when there is no obvious abnormality in the vibration data, the pre-built transfer machine dynamic model can be used to further determine whether the transfer machine has potential abnormalities that are not easy to detect, and then preventive maintenance measures can be taken according to the abnormal diagnosis information to improve the reliability and stability of the equipment, so as to avoid the increase in maintenance costs caused by sudden failures, realize effective control of maintenance costs, realize automation and informatization of transfer management, improve work efficiency, avoid waste of computing resources, and reduce human errors and repetitive work.

[0062] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0064] Figure 1 is a flow chart of a transfer monitoring method provided by an exemplary embodiment of the present disclosure.

[0065] Figure 2 is a flow chart of a transfer monitoring method provided by an exemplary embodiment of the present disclosure.

[0066] Figure 3 is a block diagram of a transfer monitoring system provided by an exemplary embodiment of the present disclosure.

[0067] Figure 4 is a block diagram of a transfer monitoring system provided by an exemplary embodiment of the present disclosure.

[0068] Figure 5 is a block diagram of an electronic device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0069] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0070] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0071] Figure 1 FIG. 1 is a flow chart of a transfer monitoring method provided by an exemplary embodiment of the present disclosure. Figure 1 As shown, the method may include S101 to S103.

[0072] S101, obtaining relevant operation data of the transfer.

[0073] The relevant operating data may include temperature data, rotation speed data, and vibration data.

[0074] For example, the temperature of the transfer machine can be collected by a pre-set temperature sensor, and the collected temperature signal can be converted into an electrical signal. The electrical signal can then be pre-processed by filtering, denoising, etc. to obtain temperature data with higher accuracy.

[0075] For example, the rotation speed of the rotating machine can be collected by a pre-set photoelectric sensor, and the collected optical signal can be converted into an electrical signal. The electrical signal can then be pre-processed by filtering, denoising, etc. to obtain rotation speed data with higher accuracy.

[0076] For example, the vibration of the rotating machine can be collected by a pre-set photoelectric sensor, and the collected vibration signal can be converted into an electrical signal. The electrical signal can then be pre-processed by filtering, denoising, etc. to obtain vibration data with higher accuracy.

[0077] S102: If the vibration data does not exceed the preset vibration range, determine whether the transfer is abnormal based on relevant operation data and a pre-built transfer dynamic model.

[0078] For example, it can be determined whether the vibration data exceeds the preset vibration range to determine whether there are obvious abnormalities in the transfer. Among them, the vibration range can be preset based on actual needs. If the vibration data does not exceed the preset vibration range, it can be determined that there is no obvious abnormality in the transfer, but the transfer may have potential abnormalities that have not yet been fed back in the vibration data. When the vibration data does not exceed the preset vibration range, the relevant operating data can be input into the pre-built transfer dynamic model, and the transfer dynamic model can output the judgment result of whether there is a potential abnormality in the transfer. When determining whether there is an abnormality in the transfer through the transfer dynamic model, the computing resources occupied are relatively large. Therefore, when the vibration data does not exceed the preset vibration range, enabling the transfer dynamic model can not only timely and accurately determine whether there are potential abnormalities in the transfer that are not easy to detect, but also effectively avoid the waste of computing resources.

[0079] S103, if there is an abnormality in the transfer, determine the abnormality diagnosis information.

[0080] For example, the abnormality diagnosis information may include any one or more of the fault location, fault degree, fault cause and fault form. For example, based on the relevant operation data, the turnaround dynamic model can not only obtain the judgment result of whether the turnaround is abnormal, but also obtain the aforementioned abnormality diagnosis information synchronously through the turnaround dynamic model.

[0081] In the above technical solution, the relevant operating data of the transfer machine is obtained, and the relevant operating data includes temperature data, speed data, and vibration data; if the vibration data does not exceed the preset vibration range, it is determined whether the transfer machine has an abnormality based on the relevant operating data and the pre-built transfer machine dynamic model; if the transfer machine has an abnormality, the abnormal diagnosis information is determined. In this way, when there is no obvious abnormality in the vibration data, the pre-built transfer machine dynamic model can be used to further determine whether the transfer machine has potential abnormalities that are not easy to detect, and then preventive maintenance measures can be taken according to the abnormal diagnosis information to improve the reliability and stability of the equipment, so as to avoid the increase in maintenance costs caused by sudden failures, realize effective control of maintenance costs, realize automation and informatization of transfer management, improve work efficiency, avoid waste of computing resources, and reduce human errors and repetitive work.

[0082] In an optional embodiment, the transfer monitoring method provided by the present disclosure may further include:

[0083] Corresponding analysis and processing are performed on each relevant operating data.

[0084] In one embodiment, the temperature data is analyzed and processed accordingly, including:

[0085] The temperature data is preprocessed and uploaded to a target terminal so that the temperature data can be analyzed by the target terminal.

[0086] For example, preprocessing may include filtering, smoothing, and denoising. Preprocessing the temperature data can effectively improve the reliability of the temperature data, thereby improving the reliability of the subsequent turnaround dynamic model output results and reducing the possibility of misjudgment. The target terminal may be a PC, and the preprocessed temperature data may be uploaded to the PC to analyze the temperature through the PC, and the analysis results may be displayed through the visualization screen described below.

[0087] In addition, corresponding labels can be added to the temperature data based on the value of the temperature data, such as adding a high temperature label to temperature data whose value is greater than a first temperature threshold, and adding a low temperature label to temperature data whose value is less than a second temperature threshold.

[0088] In one embodiment, the speed data is analyzed and processed accordingly, including:

[0089] Preprocess the speed data;

[0090] Determine the wear coefficient of the rotary machine according to the preprocessed rotation speed data, wherein the wear coefficient is used to characterize the influence of the rotary machine rotation speed on the rotary machine wear degree;

[0091] Based on the wear coefficient, a first wear degree is determined.

[0092] For example, preprocessing may include filtering, smoothing, and denoising. Preprocessing the speed data can effectively improve the reliability of the speed data, thereby improving the reliability of the subsequent output results of the turnaround dynamic model and reducing the possibility of misjudgment.

[0093] For example, if the actual speed of the rotor is lower than the rated speed, the speed is low, the rated speed is input to the rotor, and the rotor gear is decelerated to output a lower rotor operating speed, and the rotor wear is reduced; if the actual speed of the rotor is higher than the rated speed, the speed is high, the rated speed is input to the rotor, and the rotor is accelerated to output a higher rotor operating speed, and the rotor wear is increased. In other words, the speed of the rotor affects the wear of the rotor. Therefore, the wear degree can be determined by the speed data, and specifically, the wear coefficient of the rotor can be determined according to the pre-processed speed data, so as to determine the first wear degree of the rotor according to the wear coefficient.

[0094] For example, the wear coefficient λ of the rotating machine can be determined by the following formula:

[0095]

[0096] Among them, K is the torque of the machine, { is the rotational moment of the machine rotating body, L is the rotational outer diameter, and N is the rotor stiffness of the machine rotating body. As the speed increases, the rotational momentum increases, the time required for the rotation to stop increases, and the angle of the rotating body increases, so the machine torque increases, and the machine torque and the wear coefficient λ are positively correlated. Therefore, there is a positive correlation between the speed data and the first wear degree.

[0097] The wear degree of the transfer machine is linearly related to the dynamic balance of the transfer machine system. If the system has a poor dynamic balance, the destructive force is small, the collision and friction between the transfer machine gears are small, and the wear is reduced. Therefore, the wear degree of the transfer machine can affect the operation of the transfer machine. In other words, the operating status of the transfer machine can be clarified by determining the wear degree of the transfer machine.

[0098] For example, the correspondence between the wear coefficient and the first wear degree can be preset by test results, and the correspondence can be represented by a function, a mapping table, etc. The first wear degree corresponding to the wear coefficient can be determined by searching the preset correspondence.

[0099] In this way, by preprocessing the rotation speed data and determining the first wear degree, the wear degree of the rotor can be determined, and then the health of the rotor can be reflected by the wear degree.

[0100] In addition, corresponding labels can be added to the rotational speed data based on the value of the rotational speed data and the value of the first wear degree, such as adding a high-speed label to the rotational speed data whose value is greater than the first rotational speed threshold, and adding a high-wear label to the rotational speed data whose value of the corresponding first wear degree data is greater than the first wear degree threshold.

[0101] In one embodiment, the vibration data is analyzed and processed accordingly, including:

[0102] Preprocess the vibration data;

[0103] If the preprocessed vibration data exceeds the preset vibration range, it is determined that the transfer is abnormal;

[0104] Based on the frequency components of the vibration data, abnormality diagnosis information is determined.

[0105] For example, preprocessing may include filtering, smoothing, and denoising. Preprocessing vibration data can effectively improve the reliability of vibration data, thereby improving the reliability of subsequent output results of the dynamic model of the transfer machine, and reducing the possibility of misjudgment. In this way, it is possible to simply and quickly determine whether the transfer machine vibration has obvious abnormalities by presetting the vibration range, and then accurately determine the corresponding abnormality diagnosis information when there are obvious abnormalities in the transfer machine.

[0106] For example, the fault source that causes abnormal vibration will generate vibration with a certain frequency component. Therefore, the abnormal diagnostic information can be determined based on the frequency component of the vibration data. The correspondence between the composition of the vibration frequency and the abnormal diagnostic information can be preset by the test results, and the correspondence can be represented by a mapping table or the like. The abnormal diagnostic information corresponding to the frequency component of the current vibration data can be determined by searching the preset correspondence. The abnormal diagnostic information may include the cause of the fault, and the cause of the fault may include at least one of the rotor mass imbalance, the time domain waveform error and the axis trajectory error of the rotor, and the mechanical looseness of the rotor.

[0107] Correspondingly, in S102, if the vibration data does not exceed the preset vibration range, determining whether the transfer is abnormal based on the relevant operation data and the pre-built transfer dynamic model includes:

[0108] If the preprocessed vibration data does not exceed the preset vibration range, it is determined whether the transfer is abnormal based on the relevant operating data and the pre-trained transfer dynamic model.

[0109] In one embodiment, the relevant operating data may also include oil; and the corresponding analysis and processing of the oil may include:

[0110] Determine the secondary wear degree and wear location of the transfer machine by spectral analysis of metal elements, contaminant elements and additive concentrations in the oil, and determine the degree of oil failure and error by compensating for contaminant elements; and / or,

[0111] The shape, quantity, size and distribution pattern of metal particles in the oil are analyzed by ferrography to determine the wear form of the rotating body of the rotor, the third wear degree of the rotor and the cause of wear.

[0112] For example, the concentration of wear metal elements contained in the oil, such as iron, chromium, copper, aluminum, etc., pollutant elements silicon, sodium, etc., catalyst elements phosphorus, zinc, calcium, magnesium, etc., can be analyzed by spectroscopy. According to the size and change trend of the concentration values ​​of various elements, the wear degree (second wear degree) of each friction pair of the equipment can be determined, and the fault location can be determined. At the same time, the failure degree and contamination degree of the lubricating oil can be determined.

[0113] In this way, by analyzing the oil status to identify potential problems, we can ensure that we can grasp the timing of maintenance, eliminate insufficient or excessive maintenance, and repair it just in time before a failure occurs. This can reduce machinery maintenance costs and downtime, and improve equipment utilization and safety.

[0114] In an optional embodiment, the transfer monitoring method provided by the present disclosure may further include:

[0115] The analysis and processing results corresponding to each relevant operation data are classified and managed to obtain basic data for updating the turnaround dynamic model.

[0116] For example, classification management may include abnormal integration and abnormal analysis of temperature anomalies, speed anomalies, vibration anomalies, and oil composition anomalies. Abnormal integration may include classification and labeling of temperature anomalies, speed anomalies, vibration anomalies, and oil composition anomalies, and classification and labeling may be achieved by setting corresponding tags; abnormal analysis may include analyzing the fault location, fault degree, fault cause, and fault form of the transfer machine. Through classification management, basic data for updating the transfer machine dynamic model may be obtained, and the basic data may include at least temperature data, speed data, and vibration data. In this way, the transfer machine dynamic model may be used to monitor and predict data such as temperature, speed, and vibration of the transfer machine in real time.

[0117] In an optional embodiment, the objective function X of the transfer dynamic model is:

[0118]

[0119] Where M is the input data error loss compensation, N j The correction coefficient for the jth group of input data, N j-1 is the correction coefficient for the j-1th group of input data, n is the total number of moments, αi is the difference between the input data at the i-th time and the i+1-th time, and the input data is any one of temperature data, rotation speed data and vibration data.

[0120] In an optional embodiment, the constraints of the turnaround dynamic model are:

[0121]

[0122] N≥0

[0123] Where N is the input data correction coefficient, and the value range of j is [1, n]; if It can be determined that the rotation is abnormal, and the faulty rotating body can be cut off or other protective measures can be taken.

[0124] In an optional embodiment, in S102, determining whether there is an abnormality in the transfer according to relevant operation data and a pre-built transfer dynamic model includes:

[0125] Preprocess relevant operation data;

[0126] The pre-processed relevant operation data is input into the turnaround dynamic model to obtain the abnormality judgment result.

[0127] For example, the abnormality judgment result may include the presence of an abnormality or the absence of an abnormality. The loss value may be determined by the loss function of the transfer dynamic model. The transfer dynamic model may output the corresponding loss value while outputting the abnormality judgment result, so as to characterize the reliability of the abnormality judgment result by the loss value. The transfer dynamic model may be used to monitor the transfer rotating body in real time. When the temperature, speed and vibration are abnormal, the transfer dynamic model outputs abnormal data and cuts off the faulty rotating body to avoid further aggravation of the transfer abnormality.

[0128] Correspondingly, in S103, if there is an abnormality in the transfer, abnormality diagnosis information is determined, including:

[0129] If the abnormality judgment result indicates that the transfer is abnormal, the abnormality judgment result and the data input into the transfer dynamic model are uploaded to the cloud interconnection diagnosis platform;

[0130] Obtain abnormal diagnosis information issued by the cloud interconnection diagnosis platform.

[0131] In this way, the fault type can be diagnosed and solutions can be sought through cloud interconnection, and then preventive maintenance measures can be taken based on the abnormal diagnostic information to improve the reliability and stability of the equipment.

[0132] In an optional embodiment, the transfer monitoring method provided by the present disclosure may further include:

[0133] The relevant operating data and abnormal diagnosis information are sent to the visualization screen so as to display the relevant operating data and abnormal diagnosis information through the visualization screen.

[0134] In this way, relevant personnel can timely and accurately understand the operating status of the transfer through the information displayed on the visual screen, and take corresponding measures in time to ensure the safety of equipment operation.

[0135] Figure 2 is a flow chart of a transfer monitoring method provided by an exemplary embodiment of the present disclosure. Figure 2 , the implementation process of the transfer monitoring method provided by the present disclosure can be more clearly understood. Figure 2 As shown, the method may include S201 to S210.

[0136] S201, obtaining relevant operation data of the transfer.

[0137] Among them, relevant operating data may include temperature data, speed data, vibration data and oil.

[0138] S202, pre-processing each relevant operation data.

[0139] S203, uploading the preprocessed temperature data to the target terminal so that the target terminal analyzes the temperature data.

[0140] S204: Determine the wear coefficient and the first wear degree of the rotor according to the rotation speed data.

[0141] S205, determining the wear form, third wear degree and wear cause of the rotating body of the rotor by analyzing the shape, quantity, size and distribution format of the metal particles in the oil through iron spectrum; and / or, determining the second wear degree and wear location of the rotor through spectral analysis of the metal elements, contaminant elements and additive concentrations in the oil, and determining the failure degree and error degree of the oil through contaminant element compensation.

[0142] S206: If the preprocessed vibration data exceeds a preset vibration range, abnormality diagnosis information is determined according to the frequency components of the vibration data.

[0143] S207, if the pre-processed vibration data does not exceed the preset vibration range, determine whether the transfer is abnormal based on the relevant operation data and the pre-trained transfer dynamic model.

[0144] S208, if the abnormality judgment result indicates that the transfer is abnormal, the abnormality judgment result and the data input into the transfer dynamic model are uploaded to the cloud interconnection diagnosis platform.

[0145] S209, obtaining abnormal diagnosis information sent by the cloud interconnection diagnosis platform.

[0146] S210, sending the relevant operating data, abnormal diagnosis information and the analysis and processing results corresponding to each relevant operating data to the visualization screen, so as to display the relevant operating data and abnormal diagnosis information through the visualization screen.

[0147] In this way, comprehensive monitoring of the transfer operation status can be achieved, and when there is no obvious abnormality in the vibration data, the pre-built transfer dynamic model can be used to further determine whether there are potential abnormalities in the transfer that are not easy to detect. Then, preventive maintenance measures can be taken based on the abnormal diagnosis information to improve the reliability and stability of the equipment, avoid increased maintenance costs due to sudden failures, achieve effective control of maintenance costs, realize automation and informatization of transfer management, improve work efficiency, avoid waste of computing resources, and reduce human errors and repetitive work.

[0148] Based on the same inventive concept, the present disclosure also provides a transfer monitoring system. Figure 3 is a block diagram of a transfer monitoring system 300 provided by an exemplary embodiment of the present disclosure. Figure 3 , the transfer monitoring system 300 may include:

[0149] The data acquisition module 301 is used to obtain relevant operation data of the transfer machine, and the relevant operation data includes temperature data, rotation speed data, and vibration data;

[0150] The transfer monitoring module 302 is used to determine whether the transfer is abnormal based on the relevant operating data and a pre-built transfer dynamic model if the vibration data does not exceed a preset vibration range; if the transfer is abnormal, determine abnormality diagnosis information.

[0151] In this way, when there is no obvious abnormality in the vibration data, the pre-built transfer dynamic model can be used to further determine whether there are potential abnormalities in the transfer that are not easy to detect. Then, preventive maintenance measures can be taken based on the abnormal diagnosis information to improve the reliability and stability of the equipment, so as to avoid increased maintenance costs due to sudden failures, achieve effective control of maintenance costs, realize automation and informatization of transfer management, improve work efficiency, avoid waste of computing resources, and reduce human errors and repetitive work.

[0152] like Figure 4 As shown, the transfer monitoring system 300 may further include:

[0153] The data processing module 303 is used to perform corresponding analysis and processing on each of the relevant operation data;

[0154] The data management module 304 is used to classify and manage the analysis and processing results corresponding to each of the relevant operation data to obtain basic data for updating the turnaround dynamic model;

[0155] The visualization module 305 is used to display the operation data and the abnormal diagnosis information.

[0156] Optionally, the data processing module 303 may include:

[0157] The temperature processing module 3031 is used to pre-process the temperature data and upload the pre-processed temperature data to the target terminal so that the target terminal can analyze the temperature data.

[0158] Optionally, the data processing module 303 may include:

[0159] The speed processing module 3032 is used to pre-process the speed data; determine the wear coefficient of the rotor according to the pre-processed speed data, wherein the wear coefficient is used to characterize the influence of the rotor speed on the rotor wear degree; and determine the first wear degree according to the wear coefficient.

[0160] Optionally, the speed processing module 3032 is used to determine the wear coefficient λ of the rotor by the following formula:

[0161]

[0162] Among them, K is the rotor torque, P is the rotor torque of the rotor, L is the rotating outer diameter, and N is the rotor stiffness of the rotor.

[0163] Optionally, the data processing module 303 may include:

[0164] The vibration processing module 3033 is used to pre-process the vibration data; if the pre-processed vibration data exceeds a preset vibration range, it is determined that the transfer is abnormal; and the abnormality diagnosis information is determined according to the frequency component of the vibration data.

[0165] Optionally, the transfer monitoring module 302 is used to determine whether the transfer is abnormal in the following manner:

[0166] If the preprocessed vibration data does not exceed the preset vibration range, it is determined whether the transfer is abnormal based on the relevant operation data and the pre-trained transfer dynamic model.

[0167] Optionally, the relevant operating data also includes oil; the data processing module 303 may include:

[0168] The oil processing module 3034 is used to determine the second wear degree and wear location of the rotor by spectral analysis of the metal elements, contaminant elements and additive concentrations in the oil, and determine the oil failure degree and error degree by compensating for the contaminant elements; and / or, determine the wear form of the rotor rotating body, the third wear degree of the rotor and the cause of wear by ferrography analysis of the shape, quantity, size and distribution format of the metal particles in the oil.

[0169] Optionally, the objective function X of the transfer dynamic model is:

[0170]

[0171] Where M is the input data error loss compensation, N j The correction coefficient for the jth group of input data, N j-1 is the correction coefficient for the j-1th group of input data, n is the total number of moments, α i is the difference between the input data at the i-th moment and the (i+1)-th moment, where the input data is any one of temperature data, rotation speed data and vibration data.

[0172] Optionally, the constraints of the transfer dynamic model are:

[0173]

[0174] N≥0

[0175] Wherein, N is the input data correction coefficient, and the value range of j is [1, n].

[0176] Optionally, the transfer monitoring module 302 is used to input the pre-processed relevant operating data into the transfer dynamic model to obtain an abnormality judgment result; if the abnormality judgment result indicates that the transfer is abnormal, the abnormality judgment result and the data input into the transfer dynamic model are uploaded to the cloud interconnection diagnosis platform; and the abnormality diagnosis information issued by the cloud interconnection diagnosis platform is obtained.

[0177] Optionally, the transfer monitoring module 302 is further configured to send the relevant operating data and the abnormal diagnosis information to a visualization screen, so as to display the relevant operating data and the abnormal diagnosis information through the visualization screen.

[0178] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0179] Figure 5 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 5As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0180] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned transfer monitoring method. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0181] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned transfer monitoring method.

[0182] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned transfer monitoring method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned transfer monitoring method.

[0183] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned transfer monitoring method when executed by the programmable device.

[0184] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0185] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0186] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A transfer monitoring method, characterized in that: include: Acquire relevant operation data of the transfer machine, wherein the relevant operation data includes temperature data, rotation speed data, and vibration data; Performing corresponding analysis and processing on each of the relevant operation data; The performing corresponding analysis and processing on each of the related operation data respectively includes: pre-processing the rotation speed data, the vibration data and the temperature data respectively; The corresponding analysis and processing of the speed data also includes: Determining the wear coefficient of the rotor according to the preprocessed rotation speed data, wherein the wear coefficient is used to characterize the influence of the rotor rotation speed on the rotor wear degree; Determining a first degree of wear according to the wear coefficient; Determining the wear coefficient of the rotating machine according to the preprocessed rotation speed data includes: The wear coefficient of the rotary machine is determined by the following formula: : in, is the rotation torque, is the rotational torque of the rotating body of the machine, is the outer diameter of rotation, is the rotor stiffness of the rotating body of the machine; The corresponding analysis and processing of the vibration data further includes: if the pre-processed vibration data exceeds a preset vibration range, determining that the transfer machine is abnormal, and determining abnormality diagnosis information based on the frequency component of the vibration data; If the preprocessed vibration data does not exceed the preset vibration range, determining whether the transfer is abnormal based on the relevant operation data and the pre-built transfer dynamic model, and if the transfer is abnormal, determining abnormality diagnosis information; The relevant operating data also includes oil; and the corresponding analysis and processing of each of the relevant operating data also includes: Determine the second degree of wear and the location of wear of the rotor by spectral analysis of the metal elements, contaminant elements and additive concentrations in the oil, and determine the degree of oil failure and error by compensating for the contaminant elements; and / or determine the form of wear of the rotor, the third degree of wear and the cause of wear of the rotor by ferrography analysis of the shape, quantity, size and distribution format of metal particles in the oil; Classify and manage the analysis and processing results corresponding to each of the relevant operating data to obtain basic data for updating the turnaround dynamic model; The objective function of the turnaround dynamic model for: in, To compensate for input data error loss, For the Set input data correction coefficient, For the Set input data correction coefficient, is the total number of moments, For the The moment and The input data is any one of temperature data, rotation speed data and vibration data; The constraints of the turnaround dynamic model are: in, is the input data correction factor, The value range is [1, ].

2. The method according to claim 1, characterized in that Performing corresponding analysis and processing on the temperature data also includes: The preprocessed temperature data is uploaded to the target terminal so that the temperature data can be analyzed by the target terminal.

3. The method according to claim 1, characterized in that The determining whether the transfer is abnormal according to the relevant operation data and the pre-built transfer dynamic model includes: Inputting the pre-processed relevant operation data into the transfer dynamic model to obtain an abnormality judgment result; If the transfer is abnormal, determining abnormal diagnosis information includes: If the abnormality judgment result indicates that the transfer is abnormal, uploading the abnormality judgment result and the data input into the transfer dynamic model to the cloud interconnection diagnosis platform; Obtain abnormal diagnosis information issued by the cloud interconnection diagnosis platform.

4. The method according to claim 1, characterized in that: The method further comprises: The relevant operating data and the abnormal diagnosis information are sent to a visualization screen so as to display the relevant operating data and the abnormal diagnosis information through the visualization screen.

5. A transfer monitoring system, characterized in that: include: A data acquisition module, used to obtain relevant operating data of the transfer machine, wherein the relevant operating data includes temperature data, rotation speed data, and vibration data; A data processing module, used to perform corresponding analysis and processing on each of the relevant operating data; wherein the data processing module is used to pre-process the speed data, the vibration data and the temperature data respectively; The data processing module includes: a speed processing module, used to determine the wear coefficient of the rotor according to the preprocessed speed data, wherein the wear coefficient is used to characterize the influence of the rotor speed on the rotor wear degree; determine the first wear degree according to the wear coefficient; The speed processing module is used to determine the wear coefficient of the rotating machine by the following formula: : in, is the rotation torque, is the rotational torque of the rotating body of the machine, is the outer diameter of rotation, is the rotor stiffness of the rotating body of the machine; The data processing module includes: a vibration processing module for determining that the transfer machine is abnormal if the preprocessed vibration data exceeds a preset vibration range, and determining abnormality diagnosis information based on the frequency component of the vibration data; A transfer monitoring module, for determining whether the transfer is abnormal according to the relevant operation data and a pre-built transfer dynamic model if the pre-processed vibration data does not exceed the preset vibration range, and determining abnormality diagnosis information if the transfer is abnormal; Relevant operating data also includes oil; the data processing module includes: An oil processing module is used to determine the second wear degree and wear location of the rotor by spectral analysis of metal elements, contaminant elements and additive concentrations in the oil, and to determine the oil failure degree and error degree by compensating for contaminant elements; and / or to determine the wear form of the rotor rotating body, the third wear degree of the rotor and the cause of wear by ferrography analysis of the shape, quantity, size and distribution format of metal particles in the oil; The transfer monitoring system also includes: A data management module, used for classifying and managing the analysis and processing results corresponding to each of the relevant operation data, and obtaining basic data for updating the turnaround dynamic model; The objective function of the turnaround dynamic model for: in, To compensate for input data error loss, For the Set input data correction coefficient, For the Set input data correction coefficient, is the total number of moments, For the The moment and The input data is any one of temperature data, rotation speed data and vibration data; The constraints of the turnaround dynamic model are: in, is the input data correction factor, The value range is [1, ].

6. The system according to claim 5, characterized in that The transfer monitoring system further comprises: A visualization module is used to display the relevant operating data and the abnormal diagnosis information.

7. A non-transitory 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 according to any one of claims 1 to 4 are implemented.

8. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 4.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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