Fault diagnosis method, device and equipment for motor equipment of cigarette making machine and medium

Abstract

The invention discloses a fault diagnosis method and device for cigarette making machine motor equipment, equipment and a medium. The method comprises the following steps: determining a current working condition label based on current speed information corresponding to motor equipment of a target cigarette making machine and a current equipment operation working condition, and determining conversion speed information based on current acceleration data and a preset frequency domain integration algorithm; analyzing and processing the current acceleration data and the conversion speed information based on a preset frequency domain and time domain analysis algorithm to obtain time domain and frequency domain features, and performing data enhancement processing on the current speed information based on a preset overlapping sampling technology to obtain frequency spectrum data; obtaining an anomaly judgment result based on anomaly judgment time domain and frequency domain characteristics of a preset anomaly detection model, and obtaining a fault prediction result based on fault detection frequency spectrum data of a preset fault prediction model; and processing the abnormal judgment result and the fault prediction result based on a preset early warning mechanism combination to generate a fault diagnosis result. Through the technical scheme of the invention, fault early warning of the motor equipment can be realized, and the risk of non-planned shutdown of the equipment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment status monitoring, and particularly to a fault diagnosis method, device, equipment and medium for a cigarette machine motor device. Background Art

[0002] A cigarette machine is one of the important tobacco mechanical equipment in the existing tobacco industry. Generally, a cigarette machine includes a feeding and strip-forming machine, a rolling and forming machine, and a tipping machine. In cigarette production activities, the health status of the cigarette machine directly affects cigarette production activities, and the reliable operation of its key components such as motors is crucial for ensuring production efficiency and product quality. Therefore, it is very important to monitor the status and identify faults of key equipment such as cigarette machine motors.

[0003] In the prior art, the fault diagnosis method for cigarette machine motor devices mostly adopts regular maintenance and manual inspection. However, when using the regular maintenance and manual inspection method, there are problems of untimely fault discovery and high operation and maintenance costs, which easily lead to unplanned downtime of the equipment, and some faults are caused by improper operation during regular maintenance. Therefore, how to achieve fault warning for motor devices, reduce the risk of unplanned downtime of the equipment, and improve the lean management level of motor devices is an urgent problem to be solved at present. Summary of the Invention

[0004] The present invention provides a fault diagnosis method, device, equipment and medium for a cigarette machine motor device, which can solve the problem of relatively high risk of unplanned downtime of the cigarette machine motor device.

[0005] According to one aspect of the present invention, there is provided a fault diagnosis method for a cigarette machine motor device, including:

[0006] Obtaining current speed information corresponding to a target cigarette machine motor device; wherein, the current speed information includes current acceleration data and current rotational speed data;

[0007] Determining a current condition label corresponding to the current speed information based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor device, and determining conversion speed information corresponding to the current speed information based on the current acceleration data and a preset frequency domain integration algorithm;

[0008] Analyzing and processing the current acceleration data and conversion speed information corresponding to the current condition label based on a preset frequency domain and time domain analysis algorithm to obtain time domain and frequency domain characteristics corresponding to the current speed information, and performing data enhancement processing on the current speed information corresponding to the current condition label based on a preset overlapping sampling technique to obtain spectrum data corresponding to the current speed information;

[0009] Based on a preset anomaly detection model, perform anomaly judgment on the time-domain and frequency-domain features to obtain an anomaly judgment result corresponding to the current speed information, and based on a preset fault prediction model, perform fault detection on the spectrum data to obtain a fault prediction result corresponding to the current speed information;

[0010] Based on a preset early warning mechanism, combine and process the anomaly judgment result and the fault prediction result to generate a fault diagnosis result corresponding to the target cigarette machine motor equipment.

[0011] According to another aspect of the present invention, there is provided a fault diagnosis device for a cigarette machine motor equipment, including:

[0012] A data acquisition module, configured to acquire current speed information corresponding to a target cigarette machine motor equipment; wherein, the current speed information includes current acceleration data and current rotation speed data;

[0013] A first data processing module, configured to determine a current working condition label corresponding to the current speed information based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor equipment, and determine conversion speed information corresponding to the current speed information based on the current acceleration data and a preset frequency-domain integration algorithm;

[0014] A second data processing module, configured to analyze and process the current acceleration data and conversion speed information corresponding to the current working condition label based on a preset time-domain and frequency-domain analysis algorithm to obtain time-domain and frequency-domain features corresponding to the current speed information, and perform data enhancement processing on the current speed information corresponding to the current working condition label based on a preset overlapping sampling technique to obtain spectrum data corresponding to the current speed information;

[0015] A model prediction module, configured to perform anomaly judgment on the time-domain and frequency-domain features based on a preset anomaly detection model to obtain an anomaly judgment result corresponding to the current speed information, and perform fault detection on the spectrum data based on a preset fault prediction model to obtain a fault prediction result corresponding to the current speed information;

[0016] A result generation module, configured to combine and process the anomaly judgment result and the fault prediction result based on a preset early warning mechanism to generate a fault diagnosis result corresponding to the target cigarette machine motor equipment.

[0017] According to another aspect of the present invention, there is provided an electronic device, the electronic device includes:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program executable by the at least one processor. When executed by the at least one processor, the computer program enables the at least one processor to execute the fault diagnosis method for a cigarette machine motor device according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the fault diagnosis method for a cigarette machine motor device according to any embodiment of the present invention when executed.

[0022] According to another aspect of the present invention, there is provided a computer program product including a computer program which implements the fault diagnosis method for a cigarette machine motor device according to any embodiment of the present invention when executed by a processor.

[0023] The technical solution of the embodiment of the present invention determines a current working condition label corresponding to the current speed information based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor device, and determines a converted speed information corresponding to the current speed information based on the current acceleration data and a preset frequency-domain integration algorithm. Furthermore, based on a preset frequency-domain time-domain analysis algorithm, the current acceleration data and the converted speed information corresponding to the current working condition label are analyzed and processed to obtain time-domain frequency-domain features corresponding to the current speed information, and data enhancement processing is performed on the current speed information corresponding to the current working condition label based on a preset overlapping sampling technique to obtain spectrum data corresponding to the current speed information. Further, an anomaly judgment is made on the time-domain frequency-domain features based on a preset anomaly detection model to obtain an anomaly judgment result corresponding to the current speed information, and a fault detection is performed on the spectrum data based on a preset fault prediction model to obtain a fault prediction result corresponding to the current speed information. Finally, based on a preset warning mechanism, the anomaly judgment result and the fault prediction result are combined and processed to generate a fault diagnosis result corresponding to the target cigarette machine motor device. Since the preset anomaly detection model, the preset fault prediction model, and the preset warning mechanism are used to perform fault warning on the motor device, the problem of a relatively high risk of unplanned shutdown of the cigarette machine motor device is solved, the fault warning of the motor device can be realized, the risk of unplanned shutdown of the device is reduced, and the lean management level of the motor device is improved.

[0024] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of a fault diagnosis method for a cigarette machine motor device according to Embodiment 1 of the present invention;

[0027] Figure 2 It is a flowchart of a fault diagnosis method for a cigarette machine motor device according to Embodiment 2 of the present invention;

[0028] Figure 3 It is a structural schematic diagram of a preset fault prediction model according to Embodiment 2 of the present invention;

[0029] Figure 4 It is a flowchart of an optional fault diagnosis method for a cigarette machine motor device according to Embodiment 2 of the present invention;

[0030] Figure 5 It is a structural schematic diagram of a fault diagnosis device for a cigarette machine motor device according to Embodiment 3 of the present invention;

[0031] Figure 6 It is a structural schematic diagram of an electronic device for implementing the fault diagnosis method of the cigarette machine motor device in the embodiments of the present invention. Detailed implementation manners

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", "target", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 As shown in the flowchart of a fault diagnosis method for a cigarette machine motor device provided in Embodiment 1 of the present invention, this embodiment is applicable to the situation of real-time fault diagnosis of a cigarette machine motor device. This method can be executed by a fault diagnosis device of the cigarette machine motor device. The fault diagnosis device of the cigarette machine motor device can be implemented in the form of hardware and / or software, and the fault diagnosis device of the cigarette machine motor device can be configured in an electronic device. As Figure 1 shown, this method includes:

[0036] S110. Obtain the current speed information corresponding to the target cigarette machine motor device; wherein, the current speed information includes current acceleration data and current rotation speed data.

[0037] Among them, the target cigarette machine motor device may refer to the cigarette machine motor device that needs to be fault diagnosed. The current acceleration data may refer to the operating acceleration of the cigarette machine motor device corresponding to the current moment. Usually, the current acceleration data may be a set of acceleration waveform data with the current moment as the acquisition point. The current rotation speed data may refer to the operating rotation speed of the cigarette machine motor device corresponding to the current moment. The current speed information may refer to a data group composed of the current acceleration data and the current rotation speed data corresponding to the current moment.

[0038] S120. Determine the current condition label corresponding to the current speed information based on the current speed information and the current device operating condition corresponding to the target cigarette machine motor device, and determine the converted speed information corresponding to the current speed information based on the current acceleration data and a preset frequency domain integration algorithm.

[0039] Among them, the operating conditions of the equipment can refer to the operating condition information of the cigarette machine motor equipment. Exemplarily, the operating conditions of the equipment can include equipment operation data, equipment shutdown data, etc. The current operating conditions of the equipment can refer to the operating conditions of the equipment corresponding to the current moment. The current condition label can refer to the condition category determined according to the current speed information and the current operating conditions of the equipment. Exemplarily, the current condition label can be 0 representing the shutdown condition, 1 representing the low-speed operation condition, or 2 representing the high-speed operation condition. Usually, each condition can be matched with the corresponding label in advance according to actual application requirements.

[0040] Among them, the preset frequency-domain integration algorithm can refer to the algorithm preset for performing frequency-domain integration calculation. Exemplarily, the preset frequency-domain integration algorithm can be a first-order frequency-domain integration algorithm or a second-order frequency-domain integration algorithm. The converted speed information can refer to the speed information obtained by performing frequency-domain integration calculation on the current acceleration data according to the preset frequency-domain integration algorithm.

[0041] S130. Analyze and process the current acceleration data and the converted speed information corresponding to the current condition label based on the preset frequency-domain and time-domain analysis algorithm to obtain the time-domain and frequency-domain characteristics corresponding to the current speed information, and perform data enhancement processing on the current speed information corresponding to the current condition label based on the preset overlapping sampling technique to obtain the spectral data corresponding to the current speed information.

[0042] Among them, the preset frequency-domain and time-domain analysis algorithm can refer to the algorithm preset for performing time-domain analysis and frequency-domain analysis. Usually, the preset frequency-domain and time-domain analysis algorithm includes a time-domain analysis algorithm and a frequency-domain analysis algorithm. Exemplarily, the time-domain analysis algorithm can be a peak determination method, and the frequency-domain analysis algorithm can be a Fourier transform. The time-domain and frequency-domain characteristics can refer to the set of characteristics calculated according to the preset frequency-domain and time-domain analysis algorithm. Usually, the time-domain and frequency-domain characteristics include time-domain characteristics and frequency-domain characteristics. Exemplarily, the time-domain characteristics can be the acceleration peak value, the square root value of the speed, the displacement peak value, etc. The frequency-domain characteristics can be the Fast Fourier Transformation (FFT) and the acceleration envelope spectrum, etc.

[0043] Among them, the preset overlapping sampling technique can refer to the method preset for performing data overlapping sampling. The spectral data can refer to the representation form of the time-domain data in the frequency domain.

[0044] S140. Perform anomaly judgment on the time-domain and frequency-domain characteristics based on the preset anomaly detection model to obtain the anomaly judgment result corresponding to the current speed information, and perform fault detection on the spectral data based on the preset fault prediction model to obtain the fault prediction result corresponding to the current speed information.

[0045] Among them, the preset anomaly detection model can refer to a pre-set model for anomaly detection of the target cigarette machine motor equipment. Exemplarily, the preset anomaly detection model can be a mechanism-based early warning model. The anomaly judgment result can refer to the judgment result generated after anomaly judgment based on the preset anomaly detection model. Exemplarily, the anomaly judgment result can be that the equipment is normal or the equipment is abnormal. Usually, the number 0 can be used to represent that the equipment is normal, and the number 1 can be used to represent that the equipment is abnormal.

[0046] Among them, the preset fault prediction model can refer to a pre-set model for fault detection of the target cigarette machine motor equipment. Exemplarily, the preset fault prediction model can be a residual neural network. The fault prediction result can refer to the detection result generated after fault detection based on the preset fault prediction model. Exemplarily, the fault prediction result can be the specific fault reason. For example, the inner ring fault, outer ring fault or rolling element fault of the motor bearing, etc.

[0047] S150. Combine and process the anomaly judgment result and the fault prediction result based on the preset early warning mechanism to generate a fault diagnosis result corresponding to the target cigarette machine motor equipment.

[0048] Among them, the preset early warning mechanism can refer to a pre-set rule for defining the combination method of the anomaly judgment result and the fault prediction result. Exemplarily, the preset early warning mechanism can be a dot multiplication method. The fault diagnosis result can refer to the summary result generated after combining the anomaly judgment result and the fault prediction result based on the preset early warning mechanism.

[0049] In the technical solution of the embodiment of the present invention, the current condition label corresponding to the current speed information is determined based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor equipment, and the converted speed information corresponding to the current speed information is determined based on the current acceleration data and the preset frequency domain integration algorithm. Furthermore, the current acceleration data and the converted speed information corresponding to the current condition label are analyzed and processed based on the preset frequency domain and time domain analysis algorithm to obtain the time domain and frequency domain characteristics corresponding to the current speed information, and the current speed information corresponding to the current condition label is enhanced based on the preset overlapping sampling technique to obtain the spectrum data corresponding to the current speed information. Further, an abnormality judgment is made on the time domain and frequency domain characteristics based on the preset abnormality detection model to obtain the abnormality judgment result corresponding to the current speed information, and a fault detection is performed on the spectrum data based on the preset fault prediction model to obtain the fault prediction result corresponding to the current speed information. Finally, the abnormality judgment result and the fault prediction result are combined and processed based on the preset warning mechanism to generate the fault diagnosis result corresponding to the target cigarette machine motor equipment. Since the preset abnormality detection model, the preset fault prediction model and the preset warning mechanism are used to perform fault warning on the motor equipment, the problem of high risk of unplanned shutdown of the cigarette machine motor equipment is solved, the fault warning of the motor equipment can be realized, the risk of unplanned shutdown of the equipment is reduced, and the lean management level of the motor equipment is improved.

[0050] Embodiment 2

[0051] Figure 2 FIG. is a flowchart of a fault diagnosis method for a cigarette machine motor equipment provided in Embodiment 2 of the present invention. This embodiment is refined based on the above embodiment. Specifically, in this embodiment, the operation of obtaining the current speed information corresponding to the target cigarette machine motor equipment is refined, which may specifically include: obtaining the current acceleration data corresponding to the target cigarette machine motor equipment based on the preset general frequency acceleration sensor; obtaining the current rotation speed data corresponding to the target cigarette machine motor equipment based on the preset proximity signal sensor; and generating the current speed information corresponding to the target cigarette machine motor equipment by summarizing and processing the current acceleration data and the current rotation speed data based on the acquisition time. As Figure 2 shown, the method includes:

[0052] S210. Obtain the current acceleration data corresponding to the target cigarette machine motor equipment based on the preset general frequency acceleration sensor.

[0053] Among them, the general frequency acceleration sensor may refer to a sensor for measuring the acceleration of the motor equipment. The preset general frequency acceleration sensor may refer to a preset general frequency acceleration sensor. Exemplarily, the preset general frequency acceleration sensor may be a general frequency acceleration sensor installed at the target cigarette machine motor equipment.

[0054] S220. Obtain the current rotational speed data corresponding to the target cigarette machine motor equipment based on a preset proximity signal sensor.

[0055] Among them, the proximity signal sensor can refer to a sensor for the purpose of detecting without contacting the detection object. Generally, the proximity signal sensor can detect the movement information and presence information of the object and convert them into electrical signals. In the embodiment of the present invention, the proximity signal sensor can be used to measure the rotational speed of the motor equipment. The preset proximity signal sensor can refer to a pre-set proximity signal sensor. Exemplarily, the preset proximity signal sensor can be a proximity signal sensor installed at the target cigarette machine motor equipment.

[0056] S230. Aggregate and process the current acceleration data and the current rotational speed data based on the acquisition time, and generate the current speed information corresponding to the target cigarette machine motor equipment.

[0057] Among them, the acquisition time can refer to the time point when data is collected. Exemplarily, the acquisition time can be the time point when the preset general frequency acceleration sensor and the preset proximity signal sensor trigger the acquisition action.

[0058] Specifically, after collecting the current acceleration data and the current rotational speed data through the preset general frequency acceleration sensor and the preset proximity signal sensor installed at the target cigarette machine motor equipment, the current acceleration data and the current rotational speed data at the same acquisition time can be aggregated and processed according to the acquisition time of each sensor. Thus, the current speed information corresponding to the target cigarette machine motor equipment is generated, providing an effective basis for subsequent operations.

[0059] It should be noted that in the embodiment of the present invention, after generating the current speed information corresponding to the target cigarette machine motor equipment, the current speed information can also be processed based on edge analysis technology, and then the processed data is uploaded to the local database memory for subsequent operation use.

[0060] S240. Obtain the current equipment operating condition corresponding to the target cigarette machine motor equipment based on a preset data acquisition system.

[0061] Among them, the preset data acquisition system can refer to a pre-set operating data acquisition device. Generally, the preset data acquisition system includes a data acquisition interface, and through this data acquisition interface, the equipment operating data can be obtained.

[0062] S250. Perform a threshold judgment on the current speed information based on a preset condition threshold and the current equipment operating condition, generate a threshold judgment result, and determine the current condition label corresponding to the current speed information based on the threshold judgment result.

[0063] Among them, the preset operating condition threshold can refer to a threshold set in advance for evaluating the current speed information. Exemplarily, the preset operating condition threshold can include the motor speed threshold and acceleration threshold in the shutdown state, the motor speed threshold and acceleration threshold in the low-speed operation state, and the motor speed threshold and acceleration threshold in the high-speed operation state. The threshold judgment result can refer to the comparison result obtained after comparing data based on the preset operating condition threshold and the current equipment operating condition. Exemplarily, the threshold judgment result can be that both the motor speed and acceleration are less than the motor speed threshold and acceleration threshold in the shutdown state.

[0064] Specifically, after generating the current speed information corresponding to the target cigarette machine motor equipment, the preset data acquisition system can be used to collect data on the current equipment operating condition corresponding to the target cigarette machine motor equipment. Furthermore, in combination with the current equipment operating condition, the preset operating condition threshold is used to perform threshold judgment on the current speed information to generate a threshold judgment result. Exemplarily, the current speed information can be first compared with the preset operating condition threshold, and then the comparison result can be verified using the current equipment operating condition. If the verification passes, for example, the comparison result conforms to the current equipment operating condition, then the comparison result is used as the threshold judgment result. Finally, based on the threshold judgment result, the current condition label corresponding to the current speed information is determined. Exemplarily, if the threshold judgment result is that both the motor speed and acceleration are less than the motor speed threshold and acceleration threshold in the shutdown state, then the current condition label is set to 0 representing the shutdown condition; if the threshold judgment result is that both the motor speed and acceleration are less than the motor speed threshold and acceleration threshold in the low-speed operation state and are greater than the motor speed threshold and acceleration threshold in the shutdown state, then the current condition label is set to 1 representing the low-speed operation condition; if the threshold judgment result is that both the motor speed and acceleration are greater than the motor speed threshold and acceleration threshold in the high-speed operation state, then the current condition label is set to 2 representing the high-speed operation condition. Thus, the current condition label corresponding to the current speed information in different conditions is generated, facilitating subsequent fault identification by condition.

[0065] S260. Integrate and convert the current acceleration data based on the first-order frequency-domain integration algorithm to generate the current speed data corresponding to the current speed information.

[0066] Among them, the first-order frequency-domain integration algorithm can refer to an algorithm for performing first-order integration conversion on the current acceleration data. The current speed data can refer to the conversion result generated after performing first-order integration conversion on the current speed information.

[0067] S270. Integrate and convert the current acceleration data based on the second-order frequency-domain integration algorithm to generate the current displacement data corresponding to the current speed information.

[0068] Among them, the second-order frequency-domain integration algorithm may refer to an algorithm for performing second-order integration conversion on the current acceleration data. The current displacement data may refer to the conversion result generated by performing second-order integration conversion on the current velocity information.

[0069] S280. Combine and process the current velocity data and the current displacement data to generate conversion velocity information corresponding to the current velocity information.

[0070] Specifically, after generating the current working condition label corresponding to the current velocity information, first-order integration conversion and second-order integration conversion may be performed on the current velocity information with different current working condition labels to obtain the current velocity data and the current displacement data, and the current velocity data and the current displacement data are combined to generate conversion velocity information. Thus, the conversion velocity information corresponding to the current velocity information under the current working condition label is obtained.

[0071] S290. Analyze and process the current acceleration data and the conversion velocity information corresponding to the current working condition label based on a preset frequency-domain time-domain analysis algorithm to obtain the time-domain frequency-domain characteristics corresponding to the current velocity information.

[0072] It should be noted that in the embodiment of the present invention, after generating the time-domain frequency-domain characteristics, the time-domain frequency-domain characteristics may also be uploaded to the local database memory for subsequent operation and use.

[0073] S2100. Resample the current velocity information based on a preset overlapping sampling technique to generate resampled data corresponding to the current velocity information.

[0074] Among them, the resampled data may refer to the resampling result generated by resampling the current velocity information.

[0075] Specifically, taking the data set under the current working condition label i as D i (x) = {x 1 , x 2 ,..., x n}, where x n represents the current velocity information of the nth sampling signal; the overlapping sampling rate of the preset overlapping sampling technique is initialized to 0.5. Among them, L may represent the sliding window length and is initialized to 512, and D may represent the expected overlapping sample data volume as an example. If the current velocity information is resampled based on the preset overlapping sampling technique, resampled data in the form of: D i (X) = {X 1 , X 2 ,..., X N} can be generated. Among them, X j (j = 1,..., N) may represent the resampling of D iEach window data generated after resampling the data in (x) according to a preset overlapping sampling technique, where the number of samples N after overlapping sampling can be calculated by the formula: where n can represent the length of the sampling signal and s can represent the moving step size of the sliding window.

[0076] S2110. Perform frequency-domain conversion on the resampled data based on a preset spectrum transformation algorithm to generate spectrum data corresponding to the current speed information.

[0077] Among them, the preset spectrum transformation algorithm can refer to an algorithm preset for performing frequency-domain conversion on resampled data. Exemplarily, the preset spectrum transformation algorithm can be FFT. Spectrum data can refer to the conversion result generated after performing frequency-domain conversion on resampled data.

[0078] S2120. Perform anomaly judgment on the time-domain and frequency-domain features based on a preset anomaly detection model to obtain an anomaly judgment result corresponding to the current speed information, and perform fault detection on the spectrum data based on a preset fault prediction model to obtain a fault prediction result corresponding to the current speed information.

[0079] In an alternative embodiment, the preset anomaly detection model includes: the association relationship between the current working condition label and the corresponding warning threshold. Among them, the warning threshold can refer to the set threshold corresponding to each time-domain feature under different working conditions. Specifically, after generating the time-domain and frequency-domain features, the time-domain and frequency-domain features can be input into the preset anomaly detection model M od (x), where x can represent the time-domain feature in the time-domain and frequency-domain features. The preset anomaly detection model performs anomaly judgment on the time-domain feature based on the set threshold corresponding to each time-domain feature under different working conditions, and thus, obtains an anomaly judgment result Among them, can represent that the device is normal, can represent that the device is abnormal, and i can represent the current working condition label.

[0080] Figure 3 The following shows a schematic structural diagram of a preset fault prediction model provided by an embodiment of the present invention. Specifically, in the embodiment of the present invention, the preset fault prediction model can be a one-dimensional residual network (Residual Network, ResNet) including a convolutional layer, a batch normalization layer, a residual block, a global average pooling layer, a max pooling layer, a fully connected layer, and a rectified linear unit (ReLU). Exemplarily, it can be represented by M fd (x). Among them, the max pooling layer is the output of each layer of the neural network. For the untrained preset fault prediction model M fd(x), whose learning rate ε can be initialized to 1e-3; the number of epochs for the entire training dataset can be initialized to 100, where one epoch means that each sample in the training set has participated in the training once; the batch size batch can be initialized to 256. Based on the above model training configuration, training and validating the model M fd (x) can obtain the optimized and trained M fd (x) model. Thus, after generating the spectral data D i '(x), the spectral data D i '(x) can be input into the trained M fd (x) model to obtain the corresponding fault prediction result

[0081] S2130. Combine and process the abnormal judgment result and the fault prediction result based on a preset warning mechanism to generate a fault diagnosis result corresponding to the target cigarette machine motor equipment.

[0082] Specifically, taking the abnormal judgment result corresponding to the current speed information as the fault prediction result as and the preset warning mechanism as the dot product method as an example, the fault diagnosis result y i (x) corresponding to the target cigarette machine motor equipment under the current working condition label i can be expressed as

[0083] S2140. Visualize and display the fault diagnosis result, the abnormal judgment result, and the fault prediction result.

[0084] Specifically, after generating the fault diagnosis result, the fault diagnosis result can be pushed and displayed together with the corresponding abnormal judgment result and fault prediction result, enabling the user to timely obtain the warning information.

[0085] The technical solution of the embodiment of the present invention is as follows: based on a preset general frequency acceleration sensor, the current acceleration data corresponding to the target cigarette machine motor equipment is obtained; based on a preset proximity signal sensor, the current rotational speed data corresponding to the target cigarette machine motor equipment is obtained; and based on the acquisition time, the current acceleration data and the current rotational speed data are summarized and processed to generate the current speed information corresponding to the target cigarette machine motor equipment. Furthermore, based on a preset data acquisition system, the current equipment operating condition corresponding to the target cigarette machine motor equipment is obtained; based on a preset condition threshold and the current equipment operating condition, a threshold judgment is made on the current speed information to generate a threshold judgment result; and based on the threshold judgment result, the current condition label corresponding to the current speed information is determined. Further, based on the first-order frequency-domain integration algorithm, the current acceleration data is integrated and converted to generate the current speed data corresponding to the current speed information; based on the second-order frequency-domain integration algorithm, the current acceleration data is integrated and converted to generate the current displacement data corresponding to the current speed information; and the current speed data and the current displacement data are combined and processed to generate the converted speed information corresponding to the current speed information. Further, based on a preset frequency-domain time-domain analysis algorithm, the current acceleration data and the converted speed information corresponding to the current condition label are analyzed and processed to obtain the time-domain frequency-domain characteristics corresponding to the current speed information; based on a preset overlapping sampling technique, the current speed information is resampled to generate the resampled data corresponding to the current speed information; and based on a preset spectrum transformation algorithm, the resampled data is frequency-domain converted to generate the spectrum data corresponding to the current speed information. Finally, based on a preset anomaly detection model, an anomaly judgment is made on the time-domain frequency-domain characteristics to obtain the anomaly judgment result corresponding to the current speed information; based on a preset fault prediction model, a fault detection is performed on the spectrum data to obtain the fault prediction result corresponding to the current speed information; based on a preset warning mechanism, the anomaly judgment result and the fault prediction result are combined and processed to generate the fault diagnosis result corresponding to the target cigarette machine motor equipment; and the fault diagnosis result, the anomaly judgment result and the fault prediction result are visually displayed. Since the preset anomaly detection model, the preset fault prediction model and the preset warning mechanism are used to perform fault warning on the motor equipment, the problem of high risk of unplanned shutdown of the cigarette machine motor equipment is solved, the fault warning of the motor equipment can be realized, the risk of unplanned shutdown of the equipment is reduced, and the lean management level of the motor equipment is improved.

[0086] Figure 4The figure shows a flowchart of an optional fault diagnosis method for a cigarette machine motor device provided by an embodiment of the present invention. Specifically, first, the current acceleration data and the current rotational speed data are collected through a preset general frequency acceleration sensor and a preset proximity signal sensor installed at the target cigarette machine motor device, and based on the acquisition time of each sensor, the current acceleration data and the current rotational speed data at the same acquisition time are summarized and processed to generate the current speed information corresponding to the target cigarette machine motor device. Furthermore, based on a preset operating condition threshold and the current device operating condition, a threshold judgment is made on the current speed information to generate a threshold judgment result, and based on the threshold judgment result, the current operating condition label corresponding to the current speed information is determined to achieve device operating condition identification. Further, the current speed information under the current operating condition label is respectively subjected to abnormal diagnosis and fault prediction to generate an abnormal judgment result and a fault prediction result corresponding to the current speed information. Finally, based on a preset warning mechanism, the abnormal judgment result and the fault prediction result are combined and processed to generate a fault diagnosis result corresponding to the target cigarette machine motor device, and the fault diagnosis result, the abnormal judgment result, and the fault prediction result are visually displayed.

[0087] Among them, the abnormal diagnosis process is as follows: First, low-pass data filtering is performed on the acceleration signal in the current speed information collected by the sensor to filter the data that needs to be focused on and analyzed. Furthermore, based on the current acceleration data after data filtering and a preset frequency-domain integration algorithm, the conversion speed information corresponding to the current speed information is determined, and vibration data is calculated. Further, based on a preset frequency-domain time-domain analysis algorithm, the current acceleration data and the conversion speed information corresponding to the current operating condition label are analyzed and processed to obtain the time-domain frequency-domain characteristics corresponding to the current speed information. Finally, the time-domain frequency-domain characteristics are input into a preset abnormal detection model, and based on the preset abnormal detection model, an abnormal judgment is made on the time-domain frequency-domain characteristics to obtain the abnormal judgment result corresponding to the current speed information.

[0088] The fault prediction process is as follows: First, the current speed information is divided into data sets according to the current operating condition label. Furthermore, based on a preset oversampling technique, the current speed information corresponding to the current operating condition label is resampled to generate resampled data corresponding to the current speed information, and based on a preset spectrum transformation algorithm, the resampled data is subjected to frequency-domain conversion to generate spectrum data corresponding to the current speed information. Finally, the spectrum data is input into a preset fault prediction model, and based on the preset fault prediction model, a fault detection is performed on the spectrum data to obtain the fault prediction result corresponding to the current speed information.

[0089] It should be noted that, in the embodiments of the present invention, the abnormal diagnosis process and the fault prediction process are data processing processes that are executed in parallel. Generally, when the collected data is insufficient, abnormal detection can be performed only through a preset abnormal detection model based on the mechanism; when the collected data is sufficient, a preset fault prediction model can be introduced to perform fault early warning on the operating state of the device. Moreover, the abnormal judgment result and the fault prediction result can be comprehensively considered to give the device fault diagnosis result and device maintenance suggestions, which are not elaborated additionally in the embodiments of the present invention. Thus, aiming at the problems of poor data quality, lack of data annotation, and difficult feature extraction in actual industrial operations, the technical solution of the embodiments of the present invention improves the accuracy of fault early warning and reduces the situation of false alarms.

[0090] Embodiment III

[0091] Figure 5 FIG. is a schematic structural diagram of a fault diagnosis device for a cigarette machine motor device provided in Embodiment III of the present invention. As Figure 5 shown, the device includes: a data acquisition module 310, a first data processing module 320, a second data processing module 330, a model prediction module 340, and a result generation module 350;

[0092] Among them, the data acquisition module 310 is used to acquire the current speed information corresponding to the target cigarette machine motor device; wherein, the current speed information includes current acceleration data and current rotation speed data;

[0093] The first data processing module 320 is used to determine the current working condition label corresponding to the current speed information based on the current speed information and the current device operating condition corresponding to the target cigarette machine motor device, and determine the converted speed information corresponding to the current speed information based on the current acceleration data and a preset frequency domain integration algorithm;

[0094] The second data processing module 330 is used to analyze and process the current acceleration data and the converted speed information corresponding to the current working condition label based on a preset frequency domain time domain analysis algorithm to obtain the time domain frequency domain characteristics corresponding to the current speed information, and perform data enhancement processing on the current speed information corresponding to the current working condition label based on a preset overlapping sampling technique to obtain the spectrum data corresponding to the current speed information;

[0095] The model prediction module 340 is used to perform abnormal judgment on the time domain frequency domain characteristics based on a preset abnormal detection model to obtain the abnormal judgment result corresponding to the current speed information, and perform fault detection on the spectrum data based on a preset fault prediction model to obtain the fault prediction result corresponding to the current speed information;

[0096] A result generation module 350 is configured to generate a fault diagnosis result corresponding to the target cigarette machine motor device by combining and processing the abnormal judgment result and the fault prediction result based on a preset warning mechanism.

[0097] In the technical solution of the embodiment of the present invention, the current condition label corresponding to the current speed information is determined based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor device, and the converted speed information corresponding to the current speed information is determined based on the current acceleration data and a preset frequency-domain integration algorithm. Furthermore, the current acceleration data and the converted speed information corresponding to the current condition label are analyzed and processed based on a preset time-frequency domain analysis algorithm to obtain the time-frequency domain characteristics corresponding to the current speed information, and the current speed information corresponding to the current condition label is subjected to data enhancement processing based on a preset overlapping sampling technique to obtain the spectrum data corresponding to the current speed information. Further, an abnormal judgment is made on the time-frequency domain characteristics based on a preset abnormal detection model to obtain an abnormal judgment result corresponding to the current speed information, and a fault prediction is made on the spectrum data based on a preset fault prediction model to obtain a fault prediction result corresponding to the current speed information. Finally, a fault diagnosis result corresponding to the target cigarette machine motor device is generated by combining and processing the abnormal judgment result and the fault prediction result based on a preset warning mechanism. Since the preset abnormal detection model, the preset fault prediction model, and the preset warning mechanism are used to perform fault warning on the motor device, the problem of a relatively high risk of unplanned shutdown of the cigarette machine motor device is solved, the fault warning of the motor device can be realized, the risk of unplanned shutdown of the device is reduced, and the lean management level of the motor device is improved.

[0098] Optionally, the data acquisition module 310 may specifically be configured to:

[0099] Obtain the current acceleration data corresponding to the target cigarette machine motor device based on a preset general-purpose acceleration sensor;

[0100] Obtain the current rotational speed data corresponding to the target cigarette machine motor device based on a preset proximity signal sensor;

[0101] Generate the current speed information corresponding to the target cigarette machine motor device by summarizing and processing the current acceleration data and the current rotational speed data based on the acquisition time.

[0102] Optionally, the first data processing module 320 may specifically be configured to:

[0103] Obtain the current equipment operating condition corresponding to the target cigarette machine motor device based on a preset data acquisition system;

[0104] Perform a threshold judgment on the current speed information based on a preset condition threshold and the current equipment operating condition to generate a threshold judgment result, and determine the current condition label corresponding to the current speed information based on the threshold judgment result.

[0105] Optionally, the first data processing module 320 may specifically be configured to:

[0106] Integrate and convert the current acceleration data based on a first-order frequency-domain integration algorithm to generate current velocity data corresponding to the current velocity information;

[0107] Integrate and convert the current acceleration data based on a second-order frequency-domain integration algorithm to generate current displacement data corresponding to the current velocity information;

[0108] Combined process the current velocity data and the current displacement data to generate conversion velocity information corresponding to the current velocity information.

[0109] Optionally, the second data processing module 330 may specifically be configured to:

[0110] Resample the current velocity information based on a preset overlapping sampling technique to generate resampled data corresponding to the current velocity information;

[0111] Perform frequency-domain conversion on the resampled data based on a preset spectrum transformation algorithm to generate spectrum data corresponding to the current velocity information.

[0112] Optionally, the preset anomaly detection model includes: the association relationship between the current working condition label and the corresponding warning threshold.

[0113] Optionally, the fault diagnosis device for a cigarette-making machine motor device may further include: a visualization module, configured to visually display the fault diagnosis result, the anomaly judgment result, and the fault prediction result after combining and processing the anomaly judgment result and the fault prediction result based on the preset warning mechanism to generate a fault diagnosis result corresponding to the target cigarette-making machine motor device.

[0114] The fault diagnosis device for a cigarette-making machine motor device provided by the embodiments of the present invention may execute the fault diagnosis method for a cigarette-making machine motor device provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0115] Embodiment 4

[0116] Figure 6The structural schematic diagram of an electronic device 410 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0117] As Figure 6 shown, the electronic device 410 includes at least one processor 420, and a memory communicatively connected to the at least one processor 420, such as a read-only memory (ROM) 430, a random access memory (RAM) 440, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 420 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 430 or the computer program loaded from the storage unit 490 into the random access memory (RAM) 440. In the RAM 440, various programs and data required for the operation of the electronic device 410 can also be stored. The processor 420, the ROM 430, and the RAM 440 are connected to each other through a bus 450. The input / output (I / O) interface 460 is also connected to the bus 450.

[0118] A plurality of components in the electronic device 410 are connected to the I / O interface 460, including: an input unit 470, such as a keyboard, a mouse, etc.; an output unit 480, such as various types of displays, speakers, etc.; a storage unit 490, such as a magnetic disk, an optical disc, etc.; and a communication unit 4100, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 4100 allows the electronic device 410 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The processor 420 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 420 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 420 executes the various methods and processes described above, such as the fault diagnosis method for the cigarette machine motor device.

[0120] The method includes:

[0121] Obtain the current speed information corresponding to the target cigarette-making machine motor equipment; wherein, the current speed information includes current acceleration data and current rotational speed data;

[0122] Based on the current speed information and the current equipment operating conditions corresponding to the target cigarette-making machine motor equipment, determine the current condition label corresponding to the current speed information, and based on the current acceleration data and a preset frequency-domain integration algorithm, determine the converted speed information corresponding to the current speed information;

[0123] Based on a preset frequency-domain time-domain analysis algorithm, analyze and process the current acceleration data and the converted speed information corresponding to the current condition label to obtain the time-domain frequency-domain characteristics corresponding to the current speed information, and based on a preset overlapping sampling technique, perform data enhancement processing on the current speed information corresponding to the current condition label to obtain the spectrum data corresponding to the current speed information;

[0124] Based on a preset anomaly detection model, perform anomaly judgment on the time-domain frequency-domain characteristics to obtain the anomaly judgment result corresponding to the current speed information, and based on a preset fault prediction model, perform fault detection on the spectrum data to obtain the fault prediction result corresponding to the current speed information;

[0125] Based on a preset warning mechanism, combine and process the anomaly judgment result and the fault prediction result to generate a fault diagnosis result corresponding to the target cigarette-making machine motor equipment.

[0126] In some embodiments, the fault diagnosis method of the cigarette-making machine motor equipment can be implemented as a computer program, which is tangibly included in a computer-readable storage medium, such as the storage unit 490. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 410 via the ROM 430 and / or the communication unit 4100. When the computer program is loaded into the RAM 440 and executed by the processor 420, one or more steps of the above-described fault diagnosis method of the cigarette-making machine motor equipment can be executed. Alternatively, in other embodiments, the processor 420 can be configured to execute the fault diagnosis method of the cigarette-making machine motor equipment in any other suitable manner (e.g., by means of firmware).

[0127] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0128] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0129] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0132] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0133] An embodiment of this application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the fault diagnosis method for the cigarette machine motor device provided in any embodiment of this application. This program product and the fault diagnosis method for the cigarette machine motor device disclosed in each embodiment of this application belong to the same inventive concept, and thus will not be elaborated herein.

[0134] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0135] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for diagnosing a fault of a cigarette making machine motor, characterized in that: include: Obtaining current speed information corresponding to the target cigarette machine motor device; wherein the current speed information includes current acceleration data and current rotation speed data; Determine a current operating condition label corresponding to the current speed information based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor equipment, and determine conversion speed information corresponding to the current speed information based on the current acceleration data and a preset frequency domain integration algorithm; Based on a preset frequency domain and time domain analysis algorithm, current acceleration data and conversion speed information corresponding to the current working condition label are analyzed and processed to obtain time domain and frequency domain features corresponding to the current speed information, and based on a preset overlapping sampling technology, data enhancement processing is performed on the current speed information corresponding to the current working condition label to obtain frequency spectrum data corresponding to the current speed information; Performing abnormality judgment on the time-domain and frequency-domain features based on a preset abnormality detection model to obtain an abnormality judgment result corresponding to the current speed information, and performing fault detection on the spectrum data based on a preset fault prediction model to obtain a fault prediction result corresponding to the current speed information; The abnormality judgment result and the fault prediction result are processed in combination based on a preset early warning mechanism to generate a fault diagnosis result corresponding to the target cigarette making machine motor equipment.

2. The method according to claim 1, characterized in that The obtaining of current speed information corresponding to the motor device of the target cigarette making machine includes: Acquire current acceleration data corresponding to the motor equipment of the target cigarette making machine based on a preset general frequency acceleration sensor; Acquiring current speed data corresponding to the motor device of the target cigarette making machine based on a preset proximity signal sensor; The current acceleration data and the current rotation speed data are aggregated and processed based on the collection time to generate the current speed information corresponding to the motor equipment of the target cigarette making machine.

3. The method according to claim 1, characterized in that The determining of the current operating condition label corresponding to the current speed information based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor equipment includes: Acquire the current equipment operating condition corresponding to the target cigarette machine motor equipment based on the preset data acquisition system; A threshold judgment is performed on the current speed information based on a preset operating condition threshold and the current equipment operating condition, a threshold judgment result is generated, and a current operating condition label corresponding to the current speed information is determined based on the threshold judgment result.

4. The method according to claim 1, characterized in that: The determining, based on the current acceleration data and a preset frequency domain integration algorithm, the conversion speed information corresponding to the current speed information includes: Integrate and convert the current acceleration data based on a first-order frequency domain integration algorithm to generate current speed data corresponding to the current speed information; Integrate and convert the current acceleration data based on a second-order frequency domain integration algorithm to generate current displacement data corresponding to the current velocity information; The current speed data and the current displacement data are combined and processed to generate conversion speed information corresponding to the current speed information.

5. The method according to claim 1, characterized in that The performing data enhancement processing on the current speed information corresponding to the current working condition label based on the preset overlapping sampling technology to obtain the spectrum data corresponding to the current speed information includes: Resampling the current speed information based on a preset overlapping sampling technique to generate resampled data corresponding to the current speed information; The resampled data is converted into a frequency domain based on a preset spectrum conversion algorithm to generate spectrum data corresponding to the current speed information.

6. The method according to claim 1, characterized in that The preset anomaly detection model includes: an association relationship between a current operating condition label and a corresponding warning threshold.

7. The method according to claim 1, characterized in that After the abnormality judgment result and the fault prediction result are processed based on the preset early warning mechanism to generate the fault diagnosis result corresponding to the target cigarette machine motor equipment, the method further includes: The fault diagnosis results, abnormality judgment results and fault prediction results are displayed visually.

8. A fault diagnosis device for a cigarette making machine motor, characterized in that: include: A data acquisition module is used to acquire the current speed information corresponding to the motor equipment of the target cigarette making machine; wherein the current speed information includes current acceleration data and current rotation speed data; A first data processing module, used to determine a current operating condition label corresponding to the current speed information based on the current speed information and the current equipment operating condition corresponding to the target cigarette machine motor equipment, and to determine conversion speed information corresponding to the current speed information based on the current acceleration data and a preset frequency domain integration algorithm; A second data processing module is used to analyze and process the current acceleration data and the conversion speed information corresponding to the current working condition label based on a preset frequency domain and time domain analysis algorithm to obtain the time domain and frequency domain features corresponding to the current speed information, and to perform data enhancement processing on the current speed information corresponding to the current working condition label based on a preset overlapping sampling technology to obtain the frequency spectrum data corresponding to the current speed information; A model prediction module, used to perform abnormality judgment on the time-domain and frequency-domain features based on a preset abnormality detection model to obtain an abnormality judgment result corresponding to the current speed information, and perform fault detection on the spectrum data based on a preset fault prediction model to obtain a fault prediction result corresponding to the current speed information; The result generation module is used to process the abnormality judgment result and the fault prediction result based on a preset early warning mechanism combination to generate a fault diagnosis result corresponding to the target cigarette machine motor equipment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault diagnosis method for the motor equipment of the cigarette making machine according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the fault diagnosis method for a cigarette making machine motor device according to any one of claims 1 to 7 when executed.

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