Signal monitoring method, system and equipment and storage medium

Abstract

The invention discloses a signal monitoring method, system and device and a storage medium, and relates to the field of engines, and the method comprises the steps: carrying out the adaptive filtering processing of a rotating speed signal in a collection period, and obtaining the filtering signal data; performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; performing frequency domain analysis on the time domain signal data to identify a target frequency in a set frequency range; and if the accumulatively identified target frequency reaches a set alarm condition, triggering an alarm process. At the adaptive filtering position, noise and interference components in the signals can be effectively removed; local features of the signals can be extracted by performing time domain analysis on the filtered signals by using a sliding window technology. Furthermore, the target frequency in the set frequency range is identified through frequency domain analysis, and the periodic mode and key frequency components in the engine rotating speed signal can be accurately captured. The abnormal state of the engine in operation is effectively recognized, and potential faults or damage is prevented.

Description

Technical Field

[0001] The present invention relates to the field of engines, and in particular to a signal monitoring method, system, device and storage medium. Background Art

[0002] In the relevant technical field, engine design flaws or improper assembly of components often lead to a series of serious problems. Especially during the startup phase, these defects may cause abnormal vibrations in the flywheel housing or the speed sensor mounting bracket, which in turn affects the stability of the speed sensor signal and produces abnormal waveforms. This signal abnormality not only hinders the normal startup and operation of the vehicle, but may also cause faults in the synchronization signal, seriously threatening the driver's life safety and causing irreversible consequences. Summary of the invention

[0003] The main purpose of the present invention is to provide a signal monitoring method, system, device and storage medium to identify and analyze the time domain characteristics in the speed signal and the specific frequencies that may indicate abnormal conditions, monitor and identify possible abnormal conditions, so as to take timely measures to prevent potential failures or damages.

[0004] In order to achieve the above objectives, the present application provides the following technical solutions: According to a first aspect of an embodiment of the present application, a signal monitoring method is provided, the method comprising: In response to the enable signal, the engine speed signal is collected in real time; Adaptively filter the speed signal within the acquisition period to obtain filtered signal data; Performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; Performing frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range; When the cumulative identified target frequency reaches the set alarm condition, the alarm process is triggered.

[0005] Optionally, performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data includes: Dynamically adjust the sliding window size according to the signal properties of the filtered signal data; Applying a sliding window to the filtered signal data based on the sliding window size, and calculating a sliding average of the signal data within each sliding window; Calculate the difference between each signal data and the corresponding sliding average value; the difference includes a pair difference or a standardized difference; Signal data whose difference value corresponding to the signal data exceeds a set sliding threshold value is deleted from the filtered signal data to obtain the time domain signal data; the set sliding threshold value is dynamically calculated according to the signal attribute of the filtered signal data.

[0006] Optionally, after obtaining the time domain signal data, the method further includes: Extracting classification features from the time domain signal data, the classification features including statistical features, spectral features and time-frequency features; Inputting the classification features into a trained signal classification model to classify the time domain signal data and identify normal vibration signals and abnormal vibration signals; The data of the abnormal vibration signal is deleted.

[0007] Optionally, performing frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range includes: Performing Fourier transform on the time domain signal data to obtain a transformed spectrum diagram; Identifying a frequency domain signal in the spectrogram; Determining an adaptive filter according to the attribute of the frequency domain signal; Based on the adaptive filter identifying the frequency of the frequency domain signal, the frequency domain signal in the set frequency range is determined as the target frequency.

[0008] Optionally, before performing Fourier transform on the time domain signal data, the method further includes: Calculate the coefficient of each position in the window according to the window function; The coefficient of each position in the window is multiplied by the time domain signal data so that the edge of the time domain signal data is smooth when it is truncated in the time domain, thereby obtaining the processed time domain signal data.

[0009] Optionally, the adaptive filtering of the speed signal within the acquisition period to obtain filtered signal data includes: Performing wavelet filtering on the speed signal collected during the collection period to obtain a processed speed signal; Inputting the processed speed signal into an adaptive filter to obtain a filtered speed signal; Calculating the error between the filtered speed signal and the expected speed signal to construct an adaptive error function, and assigning weights to various parameters of the adaptive filter; Correcting various parameters of the adaptive filter by a minimization algorithm to minimize the adaptive error function; Repeat the above steps until the adaptive error function meets the preset conditions, and output the filtered signal data.

[0010] Optionally, the cumulatively identified target frequency reaches a set alarm condition, including: If the target frequency is detected to be within the set frequency range, the fault cumulative count increases by 1; If the cumulative fault count reaches or exceeds the preset alarm threshold, it is determined that the set alarm condition is met. According to a second aspect of the embodiment of the present application, a signal monitoring system is provided, the system comprising: A signal acquisition module, used for collecting the engine speed signal in real time in response to the enable signal; A filtering processing module is used to perform adaptive filtering on the speed signal within the acquisition period to obtain filtered signal data; A time domain analysis module, used for performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; A frequency domain analysis module, used to perform frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range; The alarm module is used to trigger the alarm process when the cumulative identified target frequency reaches the set alarm condition.

[0011] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0012] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. The computer-readable instructions can be executed by a processor to implement the method described in the first aspect above.

[0013] In summary, the embodiments of the present application provide a signal monitoring method, system, device and storage medium. By responding to an enable signal, the engine speed signal is collected in real time; the speed signal within the collection period is adaptively filtered to obtain filtered signal data; the filtered signal data is analyzed in time domain based on a sliding window to obtain time domain signal data; the time domain signal data is analyzed in frequency domain to identify the target frequency within a set frequency range; when the cumulative identified target frequency reaches the set alarm condition, the alarm process is triggered. By responding to the enable signal and collecting the engine speed signal in real time, combined with adaptive filtering, the noise and interference components in the signal can be effectively removed; the time domain analysis of the filtered signal using sliding window technology can extract the local features of the signal. Further, the target frequency within the set frequency range is identified through frequency domain analysis, which can accurately capture the periodic pattern and key frequency components in the engine speed signal. This analysis method combining time and frequency domains can effectively identify abnormal conditions in engine operation and prevent potential failures or damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0015] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0016] Figure 1 A schematic diagram of a signal monitoring method flow chart provided in an embodiment of the present application; Figure 2 Abnormal signal detection flow chart provided for the embodiment of the present application; Figure 3 A schematic diagram of a signal monitoring system provided in an embodiment of the present application; Figure 4 A structural diagram of an electronic device provided in an embodiment of the present application is shown; Figure 5 A diagram showing a computer-readable storage medium provided by an embodiment of the present application.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0020] In addition, in the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0021] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0023] Figure 1 The signal monitoring method provided by an embodiment of the present application is shown, and the method includes: Step S101: In response to an enable signal, collecting the engine speed signal in real time; Step S102: performing adaptive filtering on the speed signal within the acquisition period to obtain filtered signal data; Step S103: performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; Step S104: performing frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range; Step S105: When the cumulative identified target frequency reaches the set alarm condition, the alarm process is triggered.

[0024] Suppose a large factory uses multiple heavy machines, each of which is equipped with an engine to drive its operation. The factory needs to ensure that the engines of these machines are always in the best working condition to avoid production interruptions and mechanical damage. A speed sensor is installed on each machine to collect the engine speed signal in real time. This data will be used to monitor the operating status of the engine. By responding to the enable signal to collect the engine speed signal in real time, combined with adaptive filtering processing, the noise and interference components in the signal can be effectively removed, thereby improving the signal-to-noise ratio and purity of the signal. Using the sliding window technology to perform time domain analysis on the filtered signal, the local characteristics of the signal, such as statistical characteristics such as mean and standard deviation, can be extracted. Further, the target frequency within the set frequency range is identified through frequency domain analysis, which can accurately capture the periodic pattern and key frequency components in the engine speed signal. This time-frequency domain combined analysis method can effectively identify abnormal conditions in engine operation, such as speed fluctuations, stalling, etc. If the target frequency identified in the frequency domain analysis reaches or exceeds the preset alarm condition, the system will automatically trigger the alarm process to notify the maintenance personnel to conduct inspections and repairs to prevent potential mechanical failures.

[0025] In a possible implementation, in step S102, adaptive filtering is performed on the speed signal within the acquisition period to obtain filtered signal data, including: The speed signal collected during the acquisition period is subjected to wavelet filtering to obtain a processed speed signal; the processed speed signal is input into an adaptive filter to obtain a filtered speed signal; the error between the filtered speed signal and the expected speed signal is calculated to construct an adaptive error function, and weights are assigned to various parameters of the adaptive filter; various parameters of the adaptive filter are corrected by a minimization algorithm to minimize the adaptive error function; the above steps are repeated until the adaptive error function meets a preset condition, and the filtered signal data is output.

[0026] Through the combination of wavelet filtering and adaptive filter, random noise and periodic interference in the speed signal can be effectively removed, the signal-to-noise ratio of the signal can be improved, and an accurate data basis can be provided for subsequent analysis. The adaptive filter can dynamically adjust parameters according to the changes in the signal, and correct various coefficients through the minimization algorithm to minimize the adaptive error function. It can ensure that the filter can maintain the best filtering effect under different working conditions, and improve the robustness and adaptability of the system.

[0027] Suppose that the engine speed of a car is being monitored. During driving, the engine speed signal will be affected by various noises and interferences, such as road bumps, wind noise, interference from other electronic devices, etc. Through the above-mentioned adaptive filtering processing, these noises and interferences can be effectively removed to obtain a purer and more stable speed signal. For example, when a car is driving on a bumpy road, the speed signal may have large fluctuations and noise. Through wavelet filtering, these random noises can be removed to obtain a smoother signal. Then, the processed speed signal is input into the adaptive filter, and the coefficients of the filter are corrected by the minimization algorithm to minimize the adaptive error function. After multiple iterations, the adaptive filter can dynamically adjust the parameters to adapt to the changes in the signal, and finally output high-quality filtered signal data.

[0028] In a possible implementation, in step S103, the time domain analysis of the filtered signal data is performed based on a sliding window to obtain the time domain signal data, including: dynamically adjusting the sliding window size according to the signal attributes of the filtered signal data; applying the sliding window to the filtered signal data based on the sliding window size to calculate the sliding average of the signal data in each sliding window; calculating the difference between each signal data and the corresponding sliding average; the difference includes a pair difference or a standardized difference; deleting signal data in the filtered signal data whose difference corresponding to the signal data exceeds a set sliding threshold to obtain the time domain signal data; the set sliding threshold is dynamically calculated according to the signal attributes of the filtered signal data.

[0029] The sliding window size and sliding threshold are dynamically adjusted according to the signal properties of the filtered signal data (such as signal frequency, fluctuation amplitude, etc.), and can adapt to signal changes under different working conditions. For example, if the signal frequency is high, the sliding window can be set smaller; if the signal frequency is low, the sliding window can be set larger. This dynamic adjustment mechanism can ensure that time domain analysis can maintain the best effect in different scenarios and improve the adaptability and flexibility of the system. By calculating the difference (or standardized difference) between each signal data and the sliding average value, and deleting the signal data that exceeds the set sliding threshold, abnormal data points can be effectively identified and removed. By calculating the sliding average value and difference through the sliding window, the local time domain characteristics of the signal, such as fluctuation amplitude, trend change, etc., can be extracted. These characteristics are of great significance for subsequent frequency domain analysis and fault diagnosis.

[0030] Apply a sliding window to the filtered signal data and calculate the sliding average of the signal data in each sliding window. Then calculate the difference (or standardized difference) between each signal data and the corresponding sliding average. For example, assuming that the sliding window size is 5 data points, the signal data in the current window is [100, 102, 105, 103, 101], and the sliding average is 102.6. The difference of each data point is -2.6, -0.6, 2.4, 0.4, and -1.6. According to the dynamically calculated sliding threshold (for example, set to a certain percentage or fixed value of the sliding average), delete the signal data points whose difference exceeds the sliding threshold. Assuming the sliding threshold is ±3, the data point with a difference of 2.4 (i.e., 105) will be deleted because its difference exceeds the threshold. After the above processing, the obtained time domain signal data is purer, the abnormal data points are removed, and the main features of the signal are retained. These time domain signal data can be used for subsequent frequency domain analysis to identify potential fault frequencies.

[0031] In a possible implementation manner, after obtaining the time domain signal data in step S103, the method further includes: Extracting classification features from the time domain signal data, the classification features include statistical features, spectral features and time-frequency features; inputting the classification features into a trained signal classification model to classify the time domain signal data and identify normal vibration signals and abnormal vibration signals; and deleting the data of the abnormal vibration signal.

[0032] Extract classification features from time domain signal data, including statistical features, spectral features, and time-frequency features, in order to use these features to characterize the characteristics of the signal and provide necessary information for signal classification. Input the extracted classification features into the trained signal classification model in order to distinguish normal vibration signals from abnormal vibration signals for further processing. Delete the data of the identified abnormal vibration signal in order to purify the data set and retain the signal data under normal operating conditions for subsequent analysis and monitoring.

[0033] Suppose a factory needs to monitor the machines and equipment on its production line to ensure that the equipment is operating normally and prevent production interruptions caused by equipment failure. From the time domain signal data processed by time domain analysis, extract key information that can represent the signal characteristics, such as statistical features (mean, variance, etc.), spectral features (frequency distribution, etc.) and time-frequency features (short-time Fourier transform, etc.). These features can capture different aspects of the signal and provide rich information for classification. Input these features into a pre-trained classification model, which may be based on machine learning algorithms such as support vector machines, random forests, or neural networks. The model can identify which signals belong to normal vibration and which belong to abnormal vibration based on the features. Once the model identifies abnormal vibration signals, the data of these signals will be deleted from the data set. Doing so can reduce noise and interference in subsequent analysis and ensure the accuracy and reliability of data analysis.

[0034] In a possible implementation, in step S104, frequency domain analysis is performed on the time domain signal data to identify a target frequency within a set frequency range, including: Performing Fourier transform on the time domain signal data to obtain a transformed spectrum diagram; identifying the frequency domain signal in the spectrum diagram; determining an adaptive filter according to the property of the frequency domain signal; identifying the frequency of the frequency domain signal based on the adaptive filter, and determining the frequency domain signal in the set frequency range as the target frequency.

[0035] By performing Fourier transform on the time domain signal data, the signal is converted from the time domain to the frequency domain, so that the frequency components in the signal can be identified. Combined with the adaptive filter, the target frequency within the set frequency range can be accurately identified, providing key information for fault diagnosis.

[0036] In a possible implementation, before performing Fourier transform on the time domain signal data, the method further includes: calculating the coefficient of each position in the window according to the window function; multiplying the coefficient of each position in the window with the time domain signal data so that the time domain signal data has smooth edges when it is truncated in the time domain, thereby obtaining processed time domain signal data.

[0037] Before Fourier transform, the time domain signal data is processed by the window function so that the edge of the signal is smooth when it is truncated in the time domain. This processing can effectively reduce spectrum leakage, improve the accuracy of frequency domain analysis, and avoid spectrum distortion caused by signal truncation. Dynamically adjusting the parameters of the adaptive filter according to the properties of the frequency domain signal can further optimize the frequency domain analysis process and improve the recognition accuracy of the target frequency. This adaptive mechanism can adapt to signal changes under different working conditions and improve the robustness and flexibility of the system.

[0038] Through window function processing, Fourier transform and adaptive filter optimization, this technical solution can accurately identify the target frequency, reduce spectrum leakage, and improve the accuracy and reliability of frequency domain analysis. This frequency domain analysis method can be effectively applied to engine fault diagnosis and early warning, improving the operational reliability and safety of the system.

[0039] In a possible implementation, in step S105, the cumulatively identified target frequency reaches a set alarm condition, including: if a target frequency within a set frequency range is detected, the cumulative fault count increases by 1; if the cumulative fault count reaches or exceeds a preset alarm threshold, it is determined that the set alarm condition is reached.

[0040] By detecting the target frequency within the set frequency range, the purpose is to monitor and record the occurrence of abnormal frequencies, which may indicate equipment failure or performance degradation. Each time the target frequency is detected, the cumulative fault count increases by 1, with the purpose of quantifying the frequency of abnormal frequencies and providing a basis for further decision-making. If the cumulative fault count reaches or exceeds the preset alarm threshold, it is determined that the set alarm condition has been met, with the purpose of automatically identifying when action needs to be taken to prevent potential equipment failures or performance issues.

[0041] Figure 2 The flowchart of abnormal vibration detection of the flywheel housing provided by the embodiment of the present application is shown, which specifically includes: Step 1: Check whether T50 is enabled to trigger the acquisition of the speed signal. When a certain condition (T50) is met, the system starts to execute the acquisition of the speed signal.

[0042] Step 2: Speed ​​signal acquisition: Once T50 is enabled, the acquisition module starts to collect the engine speed signal.

[0043] Step 3: Time>Tmax: During the acquisition process, check whether the acquisition time exceeds the preset maximum time Tmax. If not, continue to acquire the signal. If yes, the acquisition ends and proceed to the next step.

[0044] Step 4: Signal acquisition completed: Confirm that signal acquisition has been completed.

[0045] Step 5: Signal preprocessing: Adaptively filter the speed signal within the acquisition period to obtain filtered signal data; or preprocess the acquired signal, which may include using filtering algorithms such as wavelet filtering to eliminate high-frequency interference in the signal.

[0046] Step 6: Time domain analysis: Perform time domain analysis on the preprocessed speed signal, such as using a sliding window average algorithm to identify and remove outlier data, thereby reducing glitches in the signal.

[0047] Frequency domain analysis: Use Fourier transform to perform frequency domain analysis on the signal to find possible abnormal frequency f0.

[0048] Step 7: Abnormal frequency f0 exists: Check whether the abnormal frequency f0 exists in the analysis result. If not, the process ends. If it exists, proceed to the next step.

[0049] Step 8: Fault cumulative count n=n+1: If an abnormal frequency is detected, the fault cumulative count n increases by 1.

[0050] Step 9: n≥N: Check whether the fault cumulative count n has reached the preset threshold value N. If not, return to the check enabled by T50 and restart the signal acquisition and analysis process. If yes, proceed to the next step.

[0051] Step 10: Fault alarm: If the accumulated fault count reaches or exceeds the threshold value N, a fault alarm is triggered to confirm the fault.

[0052] By collecting speed sensor data from real vehicles and analyzing it using this method, abnormal frequency points can be effectively detected and an alarm can be issued after the fault is confirmed. This method helps to detect potential mechanical problems in advance, thereby improving the reliability and safety of the engine.

[0053] The present application provides a method for detecting abnormal vibration of a speed sensor or a flywheel housing, which is started under the trigger of a T50 enable signal, activates an acquisition module to capture the speed signal of the engine, and performs preliminary data preprocessing on it. Once the acquisition time exceeds the preset maximum value Tmax, the acquisition process comes to an end. After the data acquisition phase is over, the speed signal within the acquisition period is adaptively filtered to obtain filtered signal data. The pre-processed signal will enter the time domain analysis phase, where a sliding window averaging algorithm will be used to identify and exclude outliers to smooth the signal and reduce burrs. Subsequently, the signal is analyzed in the frequency domain through Fourier transform to identify possible abnormal frequencies f0. Once an abnormal frequency is found, the fault accumulation counter n will increase by 1, and the above steps will be executed repeatedly until the fault accumulation count n reaches or exceeds the preset threshold value N. This process ensures accurate identification of faults and ultimately triggers a fault alarm mechanism to ensure that faults can be discovered and reported in a timely manner.

[0054] In summary, the embodiment of the present application provides a signal monitoring method, which collects the engine speed signal in real time in response to the enable signal; performs adaptive filtering processing on the speed signal within the collection period to obtain filtered signal data; performs time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; performs frequency domain analysis on the time domain signal data to identify the target frequency within the set frequency range; and triggers the alarm process when the cumulatively identified target frequency reaches the set alarm condition. By collecting the engine speed signal in real time in response to the enable signal and combining it with adaptive filtering processing, the noise and interference components in the signal can be effectively removed; the local features of the signal can be extracted by performing time domain analysis on the filtered signal using sliding window technology. Further, the target frequency within the set frequency range is identified through frequency domain analysis, which can accurately capture the periodic pattern and key frequency components in the engine speed signal. This analysis method combining time and frequency domains can effectively identify abnormal conditions in engine operation and prevent potential failures or damage.

[0055] Based on the same technical concept, the embodiment of the present application also provides a signal monitoring system, such as Figure 3 As shown, the system comprises: The signal acquisition module 301 is used to collect the engine speed signal in real time in response to the enable signal; The filtering processing module 302 is used to perform adaptive filtering on the speed signal within the acquisition period to obtain filtered signal data; A time domain analysis module 303, configured to perform time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; A frequency domain analysis module 304 is used to perform frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range; The alarm module 305 is used to trigger the alarm process when the cumulatively identified target frequency reaches the set alarm condition.

[0056] The present application also provides an electronic device corresponding to the method provided in the above embodiment. Figure 4 , which shows an electronic device diagram provided by some embodiments of the present application. The electronic device 20 may include: a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected via the bus 202; the memory 201 stores a computer program that can be run on the processor 200, and the processor 200 executes the method provided by any of the aforementioned embodiments of the present application when running the computer program.

[0057] The memory 201 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one physical port (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0058] The bus 202 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs, and the processor 200 executes the programs after receiving execution instructions. The method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 200, or implemented by the processor 200.

[0059] The processor 200 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 200. The above processor 200 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the above method in combination with its hardware.

[0060] The electronic device provided in the embodiment of the present application and the method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0061] The present application also provides a computer-readable storage medium corresponding to the method provided in the above embodiment. Figure 5 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the method provided in any of the aforementioned embodiments is executed.

[0062] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0063] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0064] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0065] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

[0066] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A signal monitoring method, characterized in that: The method comprises: In response to the enable signal, the engine speed signal is collected in real time; Adaptively filter the speed signal within the acquisition period to obtain filtered signal data; Performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; Performing frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range; When the cumulative identified target frequency reaches the set alarm condition, the alarm process is triggered.

2. The method according to claim 1, characterized in that The performing time domain analysis on the filtered signal data based on the sliding window to obtain time domain signal data includes: Dynamically adjust the sliding window size according to the signal properties of the filtered signal data; Applying a sliding window to the filtered signal data based on the sliding window size, and calculating a sliding average of the signal data within each sliding window; Calculate the difference between each signal data and the corresponding sliding average value; the difference includes a pair difference or a standardized difference; Signal data whose difference value corresponding to the signal data exceeds a set sliding threshold value is deleted from the filtered signal data to obtain the time domain signal data; the set sliding threshold value is dynamically calculated according to the signal attribute of the filtered signal data.

3. The method according to claim 2, characterized in that After obtaining the time domain signal data, the method further includes: Extracting classification features from the time domain signal data, the classification features including statistical features, spectral features and time-frequency features; Inputting the classification features into a trained signal classification model to classify the time domain signal data and identify normal vibration signals and abnormal vibration signals; The data of the abnormal vibration signal is deleted.

4. The method according to claim 1, characterized in that Performing frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range includes: Performing Fourier transform on the time domain signal data to obtain a transformed spectrum diagram; Identifying a frequency domain signal in the spectrogram; Determining an adaptive filter according to the attribute of the frequency domain signal; Based on the adaptive filter identifying the frequency of the frequency domain signal, the frequency domain signal in the set frequency range is determined as the target frequency.

5. The method according to claim 4, characterized in that Before performing Fourier transform on the time domain signal data, the method further includes: Calculate the coefficient of each position in the window according to the window function; The coefficient of each position in the window is multiplied by the time domain signal data so that the edge of the time domain signal data is smooth when it is truncated in the time domain, thereby obtaining the processed time domain signal data.

6. The method according to claim 1, characterized in that The adaptive filtering process is performed on the speed signal within the acquisition period to obtain the filtered signal data, including: Performing wavelet filtering on the speed signal collected during the collection period to obtain a processed speed signal; Inputting the processed speed signal into an adaptive filter to obtain a filtered speed signal; Calculating the error between the filtered speed signal and the expected speed signal to construct an adaptive error function, and assigning weights to various parameters of the adaptive filter; Correcting various parameters of the adaptive filter by a minimization algorithm to minimize the adaptive error function; Repeat the above steps until the adaptive error function meets the preset conditions, and output the filtered signal data.

7. The method according to claim 1, characterized in that The cumulatively identified target frequency reaches the set alarm condition, including: If the target frequency is detected to be within the set frequency range, the fault cumulative count increases by 1; If the cumulative fault count reaches or exceeds the preset alarm threshold, it is determined that the set alarm condition is met.

8. A signal monitoring system, characterized in that: The system comprises: A signal acquisition module, used for collecting the engine speed signal in real time in response to the enable signal; A filtering processing module is used to perform adaptive filtering on the speed signal within the acquisition period to obtain filtered signal data; A time domain analysis module, used for performing time domain analysis on the filtered signal data based on a sliding window to obtain time domain signal data; A frequency domain analysis module, used to perform frequency domain analysis on the time domain signal data to identify a target frequency within a set frequency range; The alarm module is used to trigger the alarm process when the cumulative identified target frequency reaches the set alarm condition.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1-7.

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