System, method, equipment and media for determining the operating status parameters of industrial centrifuges

Abstract

This application provides a system, method, device, and medium for determining the operating state parameters of an industrial centrifuge. The system includes a signal acquisition module for acquiring at least one operating state signal generated by the industrial centrifuge during operation; a signal transmission module for inputting each of the at least one operating state signal into its corresponding feature layer in the feature extraction module; a feature extraction module for extracting signal features from each feature layer based on the operating state signals contained within each feature layer; and a parameter prediction module for inputting the signal features from each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge, thereby obtaining the operating state parameters of the industrial centrifuge. This system is used to determine the operating state parameters of an industrial centrifuge.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment control, and more specifically, to a system, method, equipment, and medium for determining the operating status parameters of an industrial centrifuge. Background Technology

[0002] Industrial centrifuges are machines that use centrifugal force to accelerate the separation of different materials. Centrifuges are widely used in chemical, petroleum, food, pharmaceutical, mineral processing, coal, water treatment, and shipbuilding industries. The inventors discovered that when centrifuges malfunction in industrial production, they may fail to meet production requirements, thus reducing efficiency. To avoid such malfunctions, it is necessary to determine the operating parameters of industrial centrifuges in a timely manner. This allows for accurate assessment of the centrifuge's operating status, enabling managers to perform timely maintenance. Therefore, determining the operating parameters of industrial centrifuges has become a pressing issue. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a system, method, device and medium for determining the operating status parameters of an industrial centrifuge, so as to determine the operating status parameters of an industrial centrifuge.

[0004] In a first aspect, embodiments of this application provide an industrial centrifuge operating status parameter determination system, the system comprising a signal acquisition module, a signal transmission module, a feature extraction module, and a parameter prediction module;

[0005] The signal acquisition module is used to acquire at least one operating status signal generated by the industrial centrifuge during operation;

[0006] The signal transmission module is used to input each of the at least one operating status signal into its corresponding feature layer in the feature extraction module;

[0007] The feature extraction module is used to extract the signal features of each feature layer based on the running status signals contained in each feature layer;

[0008] The parameter prediction module is used to input the signal features of each feature layer into a trained parameter prediction model for predicting the operating status parameters of the industrial centrifuge, so as to obtain the operating status parameters of the industrial centrifuge.

[0009] Optionally, the at least one operating state includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal, and rotational angle signal.

[0010] Optionally, the feature layers in the feature extraction module include a time-frequency feature layer, a frequency domain feature layer, a time domain feature layer, a feature trend layer, a kurtosis feature layer, and a distribution feature layer;

[0011] When the signal transmission module is used to input each of the at least one operating status signal into its corresponding feature layer in the feature extraction module, it is specifically used for:

[0012] The vibration signal is input to the time-frequency feature layer, the frequency domain feature layer, and the time domain feature layer;

[0013] The temperature signal, the pressure signal, and the flow rate signal are input to the feature trend layer;

[0014] The rotational acceleration signal is input to the kurtosis feature layer;

[0015] The rotation speed signal is input to the distribution feature layer.

[0016] Optionally, when the feature extraction module extracts signal features for each feature layer based on the running state signals contained in each feature layer, it is specifically used for:

[0017] The time-frequency characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-frequency feature layer based on the vibration signal;

[0018] The frequency domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the frequency domain feature layer based on the vibration signal.

[0019] The time-domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-domain feature layer based on the vibration signal.

[0020] Based on the temperature signal, the pressure signal, and the flow signal, the signal features of the feature trend layer are extracted to obtain the feature trend of the operating status signal generated by the industrial centrifuge during operation;

[0021] Based on the rotational acceleration signal, the kurtosis features of the kurtosis feature layer are extracted to obtain the kurtosis features of the operating status signal generated by the industrial centrifuge during operation;

[0022] The distribution characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the distribution feature layer based on the rotation speed signal.

[0023] Optionally, the parameter prediction model includes a data analysis model and an industrial mechanism model;

[0024] The parameter prediction module, when inputting the signal features of each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge, specifically obtains the operating state parameters of the industrial centrifuge, as follows:

[0025] The signal characteristics of each feature layer are input into a preset data analysis model to obtain the first operating parameters of the industrial centrifuge;

[0026] The second operating parameters of the industrial centrifuge are obtained by inputting the signal features of each feature layer into a preset industrial mechanism model;

[0027] The operating status parameters are determined using a preset industrial mechanism data intelligent fusion algorithm based on the first operating parameters and the second operating parameters.

[0028] Optionally, the data analysis model includes at least one of the following:

[0029] Machine learning models, deep learning models, Gaussian process analysis models, hidden Markov process analysis models, wavelet analysis models, and optimization analysis models;

[0030] The industrial mechanism model includes at least one of the following:

[0031] Rotor dynamics model, structural modal dynamics model, electromagnetic coupling drive model, vibration transmission model, transient thermodynamics model, and fluid dynamics model.

[0032] Optionally, the system further includes a signal filtering module;

[0033] The signal filtering module is used to filter the operating status signal acquired by the signal acquisition module and then send it to the feature extraction module.

[0034] Secondly, embodiments of this application provide a method for determining the operating status parameters of an industrial centrifuge, applied to an industrial centrifuge operating status parameter determination system. The system includes a signal acquisition module, a signal transmission module, a feature extraction module, and a parameter prediction module. The method includes:

[0035] The signal acquisition module acquires at least one operating status signal generated by the industrial centrifuge during operation;

[0036] The signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module;

[0037] The feature extraction module extracts the signal features of each feature layer based on the running status signals contained in each feature layer;

[0038] The parameter prediction module inputs the signal features of each feature layer into a trained parameter prediction model for predicting the operating status parameters of the industrial centrifuge, thereby obtaining the operating status parameters of the industrial centrifuge.

[0039] Optionally, the at least one operating state includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal, and rotational angle signal.

[0040] Optionally, the feature layers in the feature extraction module include a time-frequency feature layer, a frequency domain feature layer, a time domain feature layer, a feature trend layer, a kurtosis feature layer, and a distribution feature layer;

[0041] The signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module, including:

[0042] The vibration signal is input to the time-frequency feature layer, the frequency domain feature layer, and the time domain feature layer;

[0043] The temperature signal, the pressure signal, and the flow rate signal are input to the feature trend layer;

[0044] The rotational acceleration signal is input to the kurtosis feature layer;

[0045] The rotation speed signal is input to the distribution feature layer.

[0046] Optionally, when the feature extraction module extracts the signal features of each feature layer based on the running state signals contained in each feature layer, it includes:

[0047] The time-frequency characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-frequency feature layer based on the vibration signal;

[0048] The frequency domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the frequency domain feature layer based on the vibration signal.

[0049] The time-domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-domain feature layer based on the vibration signal.

[0050] Based on the temperature signal, the pressure signal, and the flow signal, the signal features of the feature trend layer are extracted to obtain the feature trend of the operating status signal generated by the industrial centrifuge during operation;

[0051] Based on the rotational acceleration signal, the kurtosis features of the kurtosis feature layer are extracted to obtain the kurtosis features of the operating status signal generated by the industrial centrifuge during operation;

[0052] The distribution characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the distribution feature layer based on the rotation speed signal.

[0053] Optionally, the parameter prediction model includes a data analysis model and an industrial mechanism model;

[0054] The parameter prediction module inputs the signal features of each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge. When obtaining the operating state parameters of the industrial centrifuge, the module includes:

[0055] The signal characteristics of each feature layer are input into a preset data analysis model to obtain the first operating parameters of the industrial centrifuge;

[0056] The second operating parameters of the industrial centrifuge are obtained by inputting the signal features of each feature layer into a preset industrial mechanism model;

[0057] The operating status parameters are determined using a preset industrial mechanism data intelligent fusion algorithm based on the first operating parameters and the second operating parameters.

[0058] Optionally, the data analysis model includes at least one of the following:

[0059] Machine learning models, deep learning models, Gaussian process analysis models, hidden Markov process analysis models, wavelet analysis models, and optimization analysis models;

[0060] The industrial mechanism model includes at least one of the following:

[0061] Rotor dynamics model, structural modal dynamics model, electromagnetic coupling drive model, vibration transmission model, transient thermodynamics model, and fluid dynamics model.

[0062] Optionally, the system further includes a signal filtering module, and after the signal acquisition module acquires at least one operating status signal generated by the industrial centrifuge during operation, the method includes:

[0063] The signal filtering module filters the operating status signal acquired by the signal acquisition module and then sends it to the feature extraction module.

[0064] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the method for determining the operating state parameters of an industrial centrifuge as described in any of the optional embodiments of the first aspect are executed.

[0065] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method for determining the operating state parameters of an industrial centrifuge as described in any of the optional embodiments of the first aspect.

[0066] The technical solution provided in this application includes, but is not limited to, the following beneficial effects:

[0067] The signal acquisition module is used to acquire at least one operating status signal generated by the industrial centrifuge during operation. Through the above means, the operating signals of the industrial centrifuge can be acquired in real time, providing a data source for subsequent signal feature extraction based on the operating signals.

[0068] The signal transmission module is used to input each of the at least one operating status signals into its corresponding feature layer in the feature extraction module; the feature extraction module is used to extract the signal features of each feature layer based on the operating status signals contained in each feature layer; through the above means, signal feature extraction based on the operating status signals can be realized to obtain various signal features used to indicate the operating status of the industrial centrifuge.

[0069] The parameter prediction module is used to input the signal features of each feature layer into a trained parameter prediction model for predicting the operating status parameters of the industrial centrifuge, thereby obtaining the operating status parameters of the industrial centrifuge. Through the above means, the current operating status parameters of the industrial centrifuge can be determined based on the signal features and the parameter prediction model.

[0070] Using the above system, after extracting the signal features of the industrial centrifuge's operating status signal, the operating status parameters of the industrial centrifuge are predicted using a parameter prediction model based on the signal features, so as to determine the operating status parameters of the industrial centrifuge.

[0071] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0072] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This diagram illustrates the structure of an industrial centrifuge operating status parameter determination system provided in Embodiment 1 of the present invention.

[0074] Figure 2 This shows a schematic diagram of the structure of a feature extraction module provided in Embodiment 1 of the present invention;

[0075] Figure 3 A flowchart of a signal transmission method provided in Embodiment 1 of the present invention is shown;

[0076] Figure 4 A flowchart of a signal feature method provided in Embodiment 1 of the present invention is shown;

[0077] Figure 5 A flowchart of a method for setting operating parameters provided in Embodiment 1 of the present invention is shown;

[0078] Figure 6 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention is shown. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0080] Example 1

[0081] To facilitate understanding of this application, the following is combined with... Figure 1 The schematic diagram of the system for determining the operating status parameters of an industrial centrifuge provided in Embodiment 1 of the present invention illustrates the content of Embodiment 1 of this application in detail.

[0082] See Figure 1 As shown, Figure 1 A schematic diagram of an industrial centrifuge operating status parameter determination system provided in Embodiment 1 of the present invention is shown. The system includes a signal acquisition module 101, a signal transmission module 102, a feature extraction module 103, and a parameter prediction module 104.

[0083] The signal acquisition module is used to acquire at least one operating status signal generated by the industrial centrifuge during operation.

[0084] Specifically, during operation, various sensors installed on the industrial centrifuge collect real-time operating status signals. At least one operating status signal includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal, and rotational speed signal.

[0085] The signal transmission module is used to input each of the at least one operating status signal into its corresponding feature layer in the feature extraction module.

[0086] Specifically, each operating status signal is input into a matching feature layer in the feature extraction module based on its different time and frequency characteristics. The preset matching rules include matching vibration signals with time-frequency feature layers, frequency domain feature layers, and time domain feature layers; matching temperature signals, pressure signals, and flow signals with feature trend layers; matching rotational acceleration signals with kurtosis features; and matching rotational speed signals with distribution feature layers.

[0087] The feature extraction module is used to extract the signal features of each feature layer based on the running status signals contained in each feature layer.

[0088] Specifically, signal features are extracted for each feature layer. Specific extraction methods include wavelet coefficient algorithm, wavelet transform algorithm, wavelet packet energy algorithm, Hilbert spectrum algorithm, ARMA model, fast Fourier transform algorithm, etc.

[0089] The parameter prediction module is used to input the signal features of each feature layer into a trained parameter prediction model for predicting the operating status parameters of the industrial centrifuge, so as to obtain the operating status parameters of the industrial centrifuge.

[0090] Specifically, a parameter prediction model is pre-trained to predict the operating status parameters of the industrial centrifuge. The signal features of each feature layer are input into the parameter prediction model to obtain the current operating status parameters of the industrial centrifuge.

[0091] In one feasible implementation, the at least one operating state includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal, and rotational angle signal.

[0092] In one feasible implementation plan, see Figure 2 As shown, Figure 2 The diagram shows a feature extraction module according to Embodiment 1 of the present invention. The feature layers in the feature extraction module 103 include a time-frequency feature layer 201, a frequency domain feature layer 202, a time domain feature layer 203, a feature trend layer 204, a kurtosis feature layer 205, and a distribution feature layer 206.

[0093] Specifically, different feature layers contain different signal data, and signal features in different feature layers can be extracted based on different signal data.

[0094] See Figure 3 As shown, Figure 3 The flowchart of a signal transmission method provided in Embodiment 1 of the present invention is shown. Specifically, when the signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module, it performs steps S301 to S304:

[0095] S301: Input the vibration signal to the time-frequency feature layer, the frequency domain feature layer and the time domain feature layer.

[0096] S302: Input the temperature signal, the pressure signal, and the flow rate signal into the feature trend layer.

[0097] S303: Input the rotational acceleration signal into the kurtosis feature layer.

[0098] S304: Input the rotation speed signal to the distribution feature layer.

[0099] Specifically, each operating status signal is input into its corresponding matching feature layer based on the signal characteristics of the operating status signal.

[0100] In one feasible implementation plan, see Figure 4 As shown, Figure 4 The flowchart of a signal feature method provided in Embodiment 1 of the present invention is shown. Specifically, when the feature extraction module extracts signal features from each feature layer based on the operating state signals contained in each feature layer, it executes steps S401 to S406:

[0101] S401: Extract the signal features of the time-frequency feature layer based on the vibration signal to obtain the time-frequency features of the operating status signal generated by the industrial centrifuge during operation.

[0102] S402: Extract the signal features of the frequency domain feature layer based on the vibration signal to obtain the frequency domain features of the operating status signal generated by the industrial centrifuge during operation.

[0103] S403: Extract the signal features of the time-domain feature layer based on the vibration signal to obtain the time-domain features of the operating status signal generated by the industrial centrifuge during operation.

[0104] S404: Extract the signal features of the feature trend layer based on the temperature signal, the pressure signal and the flow signal to obtain the feature trend of the operating status signal generated by the industrial centrifuge during operation.

[0105] S405: Extract the signal features of the kurtosis feature layer based on the rotational acceleration signal to obtain the kurtosis features of the operating status signal generated by the industrial centrifuge during operation.

[0106] S406: Extract the signal features of the distribution feature layer based on the rotation speed signal to obtain the distribution features of the operating status signal generated by the industrial centrifuge during operation.

[0107] Specifically, the specific signal feature extraction method can be selected based on actual needs.

[0108] After obtaining the time-frequency characteristics, frequency domain characteristics, time domain characteristics, characteristic trends, kurtosis characteristics, and distribution characteristics of the operating status signal, the time-frequency characteristics are input into the feature analysis module for time-frequency analysis, the frequency domain characteristics are input into the feature analysis module for spectrum analysis, the time domain characteristics are input into the feature analysis module for stationarity analysis, the characteristic trends are input into the feature analysis module for trend analysis, the kurtosis characteristics are input into the feature analysis module for kurtosis analysis, and the distribution characteristics are input into the feature analysis module for statistical analysis.

[0109] In one feasible implementation, the parameter prediction model includes a data analysis model and an industrial mechanism model.

[0110] Specifically, the data analysis model mainly provides an intelligent data model for industrial centrifuges for feature analysis, serving as a reference for intelligent learning and optimization analysis of centrifuge status monitoring; the data analysis models include machine learning models, deep learning models, Gaussian process analysis, hidden Markov process analysis, wavelet analysis, optimization analysis, etc.

[0111] The industrial mechanism model mainly provides a physical model of the industrial centrifuge for feature analysis, serving as a practical reference for centrifuge condition monitoring.

[0112] See Figure 5 As shown, Figure 5 The flowchart of an operating parameter method provided in Embodiment 1 of the present invention is shown. Specifically, when the parameter prediction module inputs the signal features of each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge to obtain the operating state parameters of the industrial centrifuge, it executes steps S501 to S503:

[0113] S501: Input the signal features of each feature layer into the preset data analysis model to obtain the first operating parameters of the industrial centrifuge.

[0114] S502: The second operating parameters of the industrial centrifuge are obtained by inputting the signal features of each feature layer into the preset industrial mechanism model.

[0115] S503: Based on the first operating parameters and the second operating parameters, the operating status parameters are determined using a preset industrial mechanism data intelligent fusion algorithm.

[0116] Specifically, the main function of the industrial mechanism data intelligent fusion algorithm is to integrate the results of the feature analysis layer data processing of the industrial mechanism model and the data-driven model, extract the operating status parameters of the centrifuge, and provide parameter data for the intelligent maintenance decision-making system.

[0117] The intelligent fusion algorithm for industrial mechanism data includes, but is not limited to, calculating the average value of the first operating parameter and the second operating parameter to obtain the operating status parameter.

[0118] In one feasible implementation, the data analysis model includes at least one of the following: machine learning model, deep learning model, Gaussian process analysis model, hidden Markov process analysis model, wavelet analysis model, and optimization analysis model;

[0119] The industrial mechanism model includes at least one of the following: rotor dynamics model, structural modal dynamics model, electromagnetic coupling drive model, vibration transmission model, transient thermodynamics model, and fluid dynamics model.

[0120] In one feasible implementation, the system further includes a signal filtering module;

[0121] The signal filtering module is used to filter the operating status signal acquired by the signal acquisition module and then send it to the feature extraction module.

[0122] Specifically, after the signal acquisition module acquires at least one operating status signal generated by the industrial centrifuge during operation, the signal filtering module filters the at least one operating status signal acquired by the signal acquisition module and sends it to the feature extraction module. The filtering method includes, but is not limited to, low-pass filtering and Kalman filtering.

[0123] The industrial centrifuge operating status parameter determination system also includes a decision determination module. The decision determination module performs further decision-level processing on the centrifuge's operating status parameters. It typically combines decision information databases provided by experts and decision information databases accumulated from human experience to formulate maintenance strategies, generate optimal intelligent decisions, and issue maintenance task work orders.

[0124] Specifically, the decision determination module is used to determine the operating status parameters from a preset decision database based on the operating status parameters after the parameter prediction module determines the operating status parameters using a preset industrial mechanism data intelligent fusion algorithm based on the first operating parameters and the second operating parameters, and then send the target decision to the client for display so that the client's management personnel can maintain the industrial centrifuge based on the target decision.

[0125] Example 2

[0126] The present invention provides a method for determining the operating status parameters of an industrial centrifuge, applicable to an industrial centrifuge operating status parameter determination system. The system includes a signal acquisition module, a signal transmission module, a feature extraction module, and a parameter prediction module. The method includes:

[0127] The signal acquisition module acquires at least one operating status signal generated by the industrial centrifuge during operation;

[0128] The signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module;

[0129] The feature extraction module extracts the signal features of each feature layer based on the running status signals contained in each feature layer;

[0130] The parameter prediction module inputs the signal features of each feature layer into a trained parameter prediction model for predicting the operating status parameters of the industrial centrifuge, thereby obtaining the operating status parameters of the industrial centrifuge.

[0131] In one feasible implementation, the at least one operating state includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal, and rotational angle signal.

[0132] In one feasible implementation, the feature layers in the feature extraction module include a time-frequency feature layer, a frequency domain feature layer, a time domain feature layer, a feature trend layer, a kurtosis feature layer, and a distribution feature layer;

[0133] The signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module, including:

[0134] The vibration signal is input to the time-frequency feature layer, the frequency domain feature layer, and the time domain feature layer;

[0135] The temperature signal, the pressure signal, and the flow rate signal are input to the feature trend layer;

[0136] The rotational acceleration signal is input to the kurtosis feature layer;

[0137] The rotation speed signal is input to the distribution feature layer.

[0138] In a feasible implementation, when the feature extraction module extracts the signal features of each feature layer based on the running state signals contained in each feature layer, it includes:

[0139] The time-frequency characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-frequency feature layer based on the vibration signal;

[0140] The frequency domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the frequency domain feature layer based on the vibration signal.

[0141] The time-domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-domain feature layer based on the vibration signal.

[0142] Based on the temperature signal, the pressure signal, and the flow signal, the signal features of the feature trend layer are extracted to obtain the feature trend of the operating status signal generated by the industrial centrifuge during operation;

[0143] Based on the rotational acceleration signal, the kurtosis features of the kurtosis feature layer are extracted to obtain the kurtosis features of the operating status signal generated by the industrial centrifuge during operation;

[0144] The distribution characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the distribution feature layer based on the rotation speed signal.

[0145] In one feasible implementation, the parameter prediction model includes a data analysis model and an industrial mechanism model;

[0146] The parameter prediction module inputs the signal features of each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge. When obtaining the operating state parameters of the industrial centrifuge, the module includes:

[0147] The signal characteristics of each feature layer are input into a preset data analysis model to obtain the first operating parameters of the industrial centrifuge;

[0148] The second operating parameters of the industrial centrifuge are obtained by inputting the signal features of each feature layer into a preset industrial mechanism model;

[0149] The operating status parameters are determined using a preset industrial mechanism data intelligent fusion algorithm based on the first operating parameters and the second operating parameters.

[0150] In one feasible implementation, the data analysis model includes at least one of the following:

[0151] Machine learning models, deep learning models, Gaussian process analysis models, hidden Markov process analysis models, wavelet analysis models, and optimization analysis models;

[0152] The industrial mechanism model includes at least one of the following:

[0153] Rotor dynamics model, structural modal dynamics model, electromagnetic coupling drive model, vibration transmission model, transient thermodynamics model, and fluid dynamics model.

[0154] In one feasible implementation, the system further includes a signal filtering module, and after the signal acquisition module acquires at least one operating status signal generated by the industrial centrifuge during operation, the method includes:

[0155] The signal filtering module filters the operating status signal acquired by the signal acquisition module and then sends it to the feature extraction module.

[0156] Example 3

[0157] Based on the same application concept, see [link / reference] Figure 6 As shown, Figure 6 A schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention is shown, wherein, as Figure 6 As shown, the computer device 600 provided in Embodiment 3 of this application includes:

[0158] The computer device 600 includes a processor 601, a memory 602, and a bus 603. The memory 602 stores machine-readable instructions that can be executed by the processor 601. When the computer device 600 is running, the processor 601 communicates with the memory 602 through the bus 603. When the machine-readable instructions are executed by the processor 601, the steps of the method for determining the operating status parameters of an industrial centrifuge as described in Embodiment 1 are executed.

[0159] Example 4

[0160] Based on the same concept, this application also provides a computer-readable storage medium storing a computer program, which, when run by a processor, executes the steps of the method for determining the operating state parameters of an industrial centrifuge as described in any of the above embodiments.

[0161] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0162] The computer program product for determining the operating status parameters of an industrial centrifuge provided in this embodiment of the invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0163] The industrial centrifuge operating status parameter determination system provided in this embodiment of the invention can be specific hardware on the equipment or software or firmware installed on the equipment. The device provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiments. For the sake of brevity, any parts not mentioned in the device embodiments can be referred to the corresponding content in the aforementioned method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0164] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some communication interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] In addition, the functional units in the embodiments provided by the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0167] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0168] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0169] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A system for determining the operating status parameters of an industrial centrifuge, characterized in that, The system includes a signal acquisition module, a signal transmission module, a feature extraction module, and a parameter prediction module; The signal acquisition module is used to acquire at least one operating status signal generated by the industrial centrifuge during operation, wherein the at least one operating status includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal and rotation angle signal; The signal transmission module is used to input each of the at least one operating status signal into its corresponding feature layer in the feature extraction module; The feature extraction module is used to extract the signal features of each feature layer based on the running status signals contained in each feature layer; The parameter prediction module is used to input the signal features of each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge, thereby obtaining the operating state parameters of the industrial centrifuge. The parameter prediction model includes a data analysis model and an industrial mechanism model. The data analysis model includes at least one of the following: machine learning model, deep learning model, Gaussian process analysis model, hidden Markov process analysis model, wavelet analysis model, and optimization analysis model. The industrial mechanism model includes at least one of the following: rotor dynamics model, structural modal dynamics model, electromagnetic coupling drive model, vibration transmission model, transient thermodynamics model, and fluid dynamics model. Specifically, the parameter prediction module is used to: input the signal features of each feature layer into a preset data analysis model to obtain the first operating parameters of the industrial centrifuge; input the signal features of each feature layer into a preset industrial mechanism model to obtain the second operating parameters of the industrial centrifuge; and determine the operating state parameters based on the first and second operating parameters using a preset industrial mechanism data intelligent fusion algorithm. The feature layers in the feature extraction module include a time-frequency feature layer, a frequency domain feature layer, a time domain feature layer, a feature trend layer, a kurtosis feature layer, and a distribution feature layer. When the signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module, it specifically performs the following: inputting the vibration signal into the time-frequency feature layer, the frequency domain feature layer, and the time domain feature layer; inputting the temperature signal, the pressure signal, and the flow rate signal into the feature trend layer; inputting the rotational acceleration signal into the kurtosis feature layer; and inputting the rotational speed signal into the distribution feature layer.

2. The system according to claim 1, characterized in that, When the feature extraction module extracts signal features for each feature layer based on the running state signals contained in each feature layer, it is specifically used for: The time-frequency characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-frequency feature layer based on the vibration signal; The frequency domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the frequency domain feature layer based on the vibration signal. The time-domain features of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the time-domain feature layer based on the vibration signal. Based on the temperature signal, the pressure signal, and the flow signal, the signal features of the feature trend layer are extracted to obtain the feature trend of the operating status signal generated by the industrial centrifuge during operation; Based on the rotational acceleration signal, the kurtosis features of the kurtosis feature layer are extracted to obtain the kurtosis features of the operating status signal generated by the industrial centrifuge during operation; The distribution characteristics of the operating status signal generated by the industrial centrifuge during operation are obtained by extracting the signal features of the distribution feature layer based on the rotation speed signal.

3. The system according to claim 1, characterized in that, The system also includes a signal filtering module; The signal filtering module is used to filter the operating status signal acquired by the signal acquisition module and then send it to the feature extraction module.

4. A method for determining the operating status parameters of an industrial centrifuge, characterized in that, A system for determining the operating status parameters of an industrial centrifuge is provided. The system includes a signal acquisition module, a signal transmission module, a feature extraction module, and a parameter prediction module. The method includes: The signal acquisition module acquires at least one operating status signal generated by the industrial centrifuge during operation, wherein the at least one operating status includes vibration signal, temperature signal, pressure signal, flow signal, rotational acceleration signal and rotation angle signal; The signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module; The feature extraction module extracts the signal features of each feature layer based on the running status signals contained in each feature layer; The parameter prediction module inputs the signal features of each feature layer into a trained parameter prediction model for predicting the operating state parameters of the industrial centrifuge, thereby obtaining the operating state parameters of the industrial centrifuge. The parameter prediction model includes a data analysis model and an industrial mechanism model. The data analysis model includes at least one of the following: machine learning model, deep learning model, Gaussian process analysis model, hidden Markov process analysis model, wavelet analysis model, and optimization analysis model. The industrial mechanism model includes at least one of the following: rotor dynamics model, structural modal dynamics model, electromagnetic coupling drive model, vibration transmission model, transient thermodynamics model, and fluid dynamics model. Specifically, the parameter prediction module inputs the signal features of each feature layer into a preset data analysis model to obtain the first operating parameters of the industrial centrifuge; inputs the signal features of each feature layer into a preset industrial mechanism model to obtain the second operating parameters of the industrial centrifuge; and determines the operating state parameters based on the first and second operating parameters using a preset industrial mechanism data intelligent fusion algorithm. The feature extraction module includes a time-frequency feature layer, a frequency domain feature layer, a time domain feature layer, a feature trend layer, a kurtosis feature layer, and a distribution feature layer. The signal transmission module inputs each of the at least one operating status signal into its corresponding feature layer in the feature extraction module, including: inputting the vibration signal into the time-frequency feature layer, the frequency domain feature layer, and the time domain feature layer; inputting the temperature signal, the pressure signal, and the flow rate signal into the feature trend layer; inputting the rotational acceleration signal into the kurtosis feature layer; and inputting the rotational speed signal into the distribution feature layer.

5. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the method for determining the operating state parameters of an industrial centrifuge as described in claim 4 are performed.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for determining the operating state parameters of an industrial centrifuge as described in claim 4.

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