Simulation test method and simulation device for on-line monitoring system of main ventilator

Abstract

The invention relates to a main ventilator on-line monitoring system simulation test method and a simulation device. The method comprises the following steps: carrying out abnormal condition simulation on a main ventilator on a test bench and collecting a vibration signal at a main bearing position; decomposing the vibration signal to obtain a spectrogram of the vibration signal; determining a suspected resonance area on the spectrogram; determining a frequency range of the suspected resonance area and constructing a suspected resonance extraction range by using the frequency range; extracting the vibration signal by using the suspected resonance extraction range to obtain a feature signal; and extracting an envelope line of the characteristic signal and performing Fourier transform on the envelope line to obtain a characteristic frequency spectrum and a test result. According to the simulation test method and the simulation device for the on-line monitoring system of the main ventilator, the simulation test can be carried out on the on-line monitoring system of the main ventilator by building a real scene and an actual detection mode, and the test method is realized based on the real scene and can directly reflect the related performance of the on-line monitoring system of the main ventilator.

Description

Technical Field

[0001] This invention relates to the field of simulation testing technology, and in particular to a simulation testing method and simulation device for an online monitoring system of a main ventilation fan. Background Technology

[0002] Simulation testing of the main ventilation fan online monitoring system refers to a series of activities that comprehensively verify and evaluate the various functions, performance, and reliability of the online monitoring system by creating a highly realistic virtual simulation environment (or "digital twin") without affecting the actual operation of the main ventilation fan.

[0003] However, a specific problem with simulation testing is that the accuracy of the simulation model (a ventilation fan is a complex mechatronic system subject to the coupling effects of multiple physical fields such as fluid mechanics, structural mechanics, thermodynamics, and electromagnetics) depends entirely on the precision of its mathematical model, making it extremely difficult to construct an identical model. Furthermore, the issue of fault injection must be considered. Current fault injection methods often employ simplification, leading to deviations from actual faults and rendering the simulation results unable to reflect real-world operating conditions.

[0004] In addition, the issue of universality needs to be considered. The scope of a single model is limited, and the test data provided can only reflect the processing method of the main ventilation fan online monitoring system under limited conditions. Summary of the Invention

[0005] This invention provides a simulation test method and device for an online monitoring system of a main ventilation fan. It can simulate and test the online monitoring system of the main ventilation fan by building a real scenario and conducting actual testing. This test method is based on a real scenario and can directly reflect the relevant performance of the online monitoring system of the main ventilation fan.

[0006] The above-mentioned objective of the present invention is achieved through the following technical solution: In a first aspect, the present invention provides a simulation testing method for an online monitoring system of a main ventilation fan, comprising: An abnormal condition simulation was performed on the main fan on the test bench, and vibration signals were collected at the location of the main bearing. The vibration signal is decomposed to obtain its spectrum. Identify suspected resonance regions on the spectrogram; Determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; The vibration signal is extracted using the suspected resonance extraction range to obtain the characteristic signal; Extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the feature spectrum; Determine the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system, and provide the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system.

[0007] In one possible implementation of the first aspect, the simulation of abnormal conditions for the main ventilator on the test bench further includes: Gradually increase the main bearing speed: Obtain the spectrum of the vibration signal at each rotational speed at the position of the main bearing; Based on the linear variation law, the frequency region in the spectrum corresponding to the vibration of the main bearing is determined and denoted as the vibration frequency region.

[0008] In one possible implementation of the first aspect, determining the suspected resonance region on the spectrogram includes: Identify the peak points included in the vibration frequency region; there are multiple peak points. Based on the peak point, the frequency region corresponding to the peak point is determined and the frequency region is regarded as the suspected resonance region. Each frequency region includes only one peak point.

[0009] One possible implementation of the first aspect also includes: After obtaining the characteristic spectrum using the suspected resonance region, the suspected resonance region is processed to reduce its size, and the process of obtaining the characteristic spectrum is repeated. The signal-to-noise ratio of the obtained feature spectra is determined by comparing multiple feature spectra. The narrowing range of the suspected resonance region is determined based on the signal-to-noise ratio.

[0010] In one possible implementation of the first aspect, after obtaining the characteristic signal, it further includes: Determine the interval of occurrence of the characteristic signal; The verification signal is obtained by extracting data from the vibration signal based on the interval of occurrence. The verification signal is processed. If the verification signal includes a feature signal, the feature signal is discarded. If the verification signal does not include a feature signal, the feature signal is retained.

[0011] In one possible implementation of the first aspect, when the rotational speed of the main bearing is increased in stages, it further includes: Vibration signals at the location of the main bearing are collected and decomposed to obtain the spectrum of the vibration signals; Identify suspected resonance regions on the spectrogram; Determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; The vibration signal is extracted using the suspected resonance extraction range to obtain the characteristic signal; Extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the intrinsic feature spectrum; After obtaining the inherent characteristic spectrum, the characteristic spectrum is then used to filter the characteristic spectrum.

[0012] In one possible implementation of the first aspect, filtering the feature spectrum using the inherent feature spectrum includes: Adjust the frequencies in the inherent characteristic spectrum according to the rotational speed; The amplitude variation coefficient is determined based on the performance of the inherent characteristic spectrum at different frequencies, and the inherent characteristic spectrum is adjusted again based on the amplitude variation coefficient to obtain the inherent characteristic updated spectrum; Remove the portion of the feature spectrum that is identical to the intrinsic feature update spectrum.

[0013] Secondly, the present invention provides a simulation testing device for an online monitoring system of a main ventilation fan, comprising: The simulation unit is used to simulate abnormal conditions of the main fan on the test bench and to collect vibration signals at the location of the main bearing. The signal processing unit is used to decompose the vibration signal to obtain the spectrum of the vibration signal; The first processing unit is used to determine the suspected resonance region on the spectrogram; The second processing unit is used to determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range. The feature signal extraction unit is used to extract the vibration signal using the suspected resonance extraction range to obtain the feature signal; The third processing unit is used to extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the feature spectrum. The fourth processing unit is used to determine the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system, and to give the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system.

[0014] Thirdly, the present invention provides a simulation test system for an online monitoring system of a main ventilation fan, the system comprising: One or more memories for storing instructions; and One or more processors are configured to retrieve and execute the instructions from the memory to perform the methods described in the first aspect and any possible implementation thereof.

[0015] Fourthly, the present invention provides a computer-readable storage medium comprising: The program, when run by a processor, is executed as described in the first aspect and any possible implementation thereof.

[0016] Fifthly, the present invention provides a computer program product, including program instructions that, when the program instructions are run by a computing device, execute the method described in the first aspect and any possible implementation thereof.

[0017] In a sixth aspect, the present invention provides a chip system including a processor for implementing the functions involved in the foregoing aspects, such as generating, receiving, transmitting, or processing data and / or information involved in the foregoing methods.

[0018] This chip system can consist of chips or include chips and other discrete components.

[0019] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and located on different devices, connected via wired or wireless means, or the processor and the memory can be coupled to the same device. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the steps of a simulation test method for an online monitoring system of a main ventilation fan provided by the present invention.

[0021] Figure 2 This is a schematic diagram of a testing process provided by the present invention.

[0022] Figure 3 This is a comparative diagram of test results provided by the present invention.

[0023] Figure 4 This is a spectrum diagram of a vibration signal provided by the present invention.

[0024] Figure 5 Based on Figure 4 A schematic diagram showing the location of the suspected resonance region on the given spectrum.

[0025] Figure 6 This is a schematic diagram of an extraction of feature signals to obtain the envelope of a pair, provided by the present invention.

[0026] Figure 7 This is a schematic diagram illustrating the relationship between natural vibration frequency and rotational speed provided by the present invention.

[0027] Figure 8 This is a schematic diagram of using a verification signal to verify a feature signal, as provided by the present invention. Detailed Implementation

[0028] To better understand the technical solutions in this invention, the relevant technologies will be described first.

[0029] For simulation testing of the main ventilation fan online monitoring system, one approach is to input the test signal into the main ventilation fan online monitoring system and then examine the output of the main ventilation fan online monitoring system. The specific process of this approach is as follows: The simulation engine runs a "virtual wind turbine" model and calculates the theoretical values ​​of all sensors under the current state.

[0030] Data conversion and output: These theoretical values ​​are converted into standard physical signals (such as 4-20mA current, ±5V voltage, Modbus / TCP protocol, etc.) through hardware interfaces (such as DAQ cards).

[0031] Monitoring system response: The actual monitoring system collects these signals just as if it were connected to a real wind turbine. It will display, store, analyze, and judge the data, and may issue control commands (such as "start", "stop", "alarm").

[0032] Command reception and feedback: The simulation engine receives control commands from the real monitoring system, uses them as new input conditions, changes the operating state of the "virtual wind turbine", and then starts a new round of calculations.

[0033] Observation and evaluation: Testers simultaneously observe the internal state of the simulation model (i.e., "truth") and the display and response of the monitoring system (i.e., "cognition"), and compare whether the two are consistent, thereby evaluating the performance of the monitoring system.

[0034] Analysis of actual use shows that the testing process often involves modifying one or more parameters, but this can lead to "model mismatch". This can cause a monitoring system that performs well in simulation to misjudge or fail when faced with dynamic characteristics that are not modeled in the real world (such as special turbulence, flow, non-uniform material aging, and unexpected structural resonance).

[0035] Furthermore, there are still ambiguous areas regarding the interrelationships between parameters. Current research is not in-depth on the specific values ​​of parameter modifications and their correlations. This makes it impossible to form a unified standard when modifying parameters, resulting in the value of test results being discussed only within a limited scope.

[0036] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings.

[0037] This invention discloses a simulation test method for an online monitoring system of a main ventilation fan. Please refer to [link / reference]. Figure 1 In some examples, the simulation test method for the main ventilation fan online monitoring system disclosed in this invention includes the following steps: S101, simulate abnormal conditions of the main fan on the test bench and collect vibration signals at the location of the main bearing; S102, decompose the vibration signal to obtain the spectrum of the vibration signal; S103, identify the suspected resonance region on the spectrogram; S104, determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; S105, use the suspected resonance extraction range to extract the vibration signal and obtain the feature signal; S106, extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the feature spectrum; S107, determine the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system, and give the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system.

[0038] First, it should be noted that the simulation testing method for the main ventilation fan online monitoring system disclosed in this invention is based on a real-world test scenario (test bench). The main ventilation fan online monitoring system is directly connected to the test bench for data processing. Simultaneously, this invention also provides a real-time test server, which simultaneously collects and analyzes data from the test bench. Figure 2 As shown.

[0039] For the same scenario, the main ventilation fan online monitoring system will output a judgment result and a processing time, and the test server will also output a judgment result and a processing time. Specifically, the following situations may occur: If the two judgment results are the same, it indicates that the data analysis process of the main ventilation fan online monitoring system is correct. At this point, the judgment is based on the two processing times. Figure 3 As shown, the details are as follows: The processing time delay of the main ventilation fan online monitoring system is 1 second, but the required processing time delay is 0.5 seconds. This indicates that the response speed of the main ventilation fan online monitoring system is insufficient. If the two judgments are different, it indicates that the data analysis process of the main ventilation fan online monitoring system is incorrect.

[0040] The core advantage of the test server mentioned earlier is that it knows the results of the test process, and the key is to determine the processing time. The results of the test process are unknown to the main ventilation fan online monitoring system, and data analysis is required.

[0041] There are no restrictions on the data acquisition method for the main ventilation fan online monitoring system.

[0042] It should also be noted that the processing time is an unknown quantity in the method given in this invention. This is because the processing time is also uncontrollable when the main ventilator on the test bench is different and the test parameters are different. In other words, in this invention, the processing time needs to be provided by the test server in each test process.

[0043] The test scenario addressed by this invention is the stall scenario of the main ventilation fan. Severe stall can lead to surge and other problems in the main ventilation fan. The test bench directly simulates various stall scenarios, and there are no restrictions on the main ventilation fan participating in the test. In other words, the test bench provides a rich variety of test scenarios that are more realistic.

[0044] In step S101, the test server simulates an abnormal condition of the main ventilator on the test bench and collects vibration signals at the location of the main bearing. Then, the vibration signals are decomposed in the next step to obtain the spectrum of the vibration signals, as shown below. Figure 4 As shown, the suspected resonance regions on the spectrogram are then identified, such as... Figure 5 As shown.

[0045] This section first describes the stall situation. Main fan stall refers to an abnormal operating state where, when airflow impacts the fan blades at an excessive angle of attack, severe airflow separation occurs on the non-working surface of the blades (usually the suction surface), leading to a sharp deterioration in the fan's aerodynamic performance, a sudden drop in air pressure, and a drastic decrease in efficiency. Once large-scale airflow separation occurs, the lift effect of the blades weakens drastically, making it unable to effectively propel the gas, ultimately causing the fan to be unable to maintain the required pressure.

[0046] During a stall, a stall cluster is generated. This stall cluster is a persistent airflow separation zone that appears in one or more areas within the fan impeller. This airflow separation zone is dynamic and impacts the blades of the main fan, which can lead to resonance.

[0047] Therefore, in step S103, a suspected resonance region on the spectrum is determined. Then, in S104, the frequency range of the suspected resonance region is determined and the frequency range is used to construct the suspected resonance extraction range. Then, the vibration signal obtained in step S101 is extracted using the suspected resonance extraction range. At this time, the feature signal is obtained, which is the content in step S105.

[0048] Next, in step S106, the envelope of the feature signal is extracted (e.g., Figure 6 (As shown) and perform a Fourier transform on the envelope to obtain the characteristic spectrum. The specific process here is as follows: For a real signal y(t), its analytic signal z(t) is defined as: z(t) = y(t) + j * H[y(t)] Where j is the imaginary unit, and H[y(t)] is the Hilbert transform of y(t).

[0049] Extract the envelope e(t), e(t) = |z(t)| = sqrt( y²(t) + H²[y(t)]), e(t) is a low-frequency signal that varies with time, which accurately depicts the amplitude fluctuation profile of the original high-frequency vibration signal.

[0050] Performing an FFT on the envelope signal e(t) yields its spectrum E(f), which displays periodic impulse characteristics (in the form of spikes).

[0051] Finally, in step S107, the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system are determined, and the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system is given.

[0052] The final step, S107, is a comparison step. This step is used to evaluate the alarm timeliness of the main ventilation fan online monitoring system. If the time difference is within the allowable range or occurs earlier than the time of occurrence of the characteristic spectrum, it means that the main ventilation fan online monitoring system has passed the simulation test; otherwise, it has failed the simulation test.

[0053] Meanwhile, this invention does not impose any restrictions on the triggering scenarios of stall (system resistance, fan-related reasons, operational reasons) or the analysis methods of the main ventilation fan online monitoring system (airflow analysis, voltage and current analysis, curve analysis).

[0054] In some cases, simulating abnormal conditions for the main ventilator on the test bench also includes the following steps: Gradually increase the main bearing speed: Obtain the spectrum of the vibration signal at each rotational speed at the position of the main bearing; Based on the linear variation law, the frequency region in the spectrum corresponding to the vibration of the main bearing is determined and denoted as the vibration frequency region.

[0055] The purpose of these steps is to determine the natural vibration frequency of the main bearing. It should be understood that the natural vibration frequency of an object, simply put, is the "natural" frequency of its vibration when it is not subject to continuous external disturbance. At different rotational speeds, the natural vibration frequency of the main bearing is different and exhibits a linear change, such as... Figure 7 As shown.

[0056] Based on this principle, the frequency range corresponding to the vibration of the main bearing can be determined by gradually increasing the rotational speed and linearly changing the main bearing during the simulation test process described above. This facilitates the rapid identification of suspected resonance regions.

[0057] In some cases, the specific methods for identifying suspected resonance regions on the spectrogram are as follows: Identify the peak points included in the vibration frequency region; there are multiple peak points. Based on the peak point, the frequency region corresponding to the peak point is determined and the frequency region is regarded as the suspected resonance region. Each frequency region includes only one peak point.

[0058] The suspected resonance region obtained in this way is a maximized acquisition method, which inevitably introduces some noise and makes the content displayed on the final characteristic spectrum unclear. Therefore, the following method is used to process it here: After obtaining the characteristic spectrum using the suspected resonance region, the suspected resonance region is processed to reduce its size, and the process of obtaining the characteristic spectrum is repeated. The signal-to-noise ratio of the obtained feature spectra is determined by comparing multiple feature spectra. The narrowing range of the suspected resonance region is determined based on the signal-to-noise ratio.

[0059] One way to determine the signal-to-noise ratio is to use wavelet decomposition to decompose the envelope of the feature signal. The resulting decomposition includes some regular spikes and some clutter, and then the clutter ratio is calculated.

[0060] Finally, the reduction range of the suspected resonance region is determined based on the signal-to-noise ratio. Specifically, in the process of processing and reducing the suspected resonance region, several locations are first selected, and then the clutter ratio is calculated at each location. Then, a suitable reduction range is determined based on the size of the clutter ratio, in order to reduce the clutter ratio as much as possible.

[0061] In some cases, after obtaining the characteristic signal, the following steps are added: Determine the interval of occurrence of the characteristic signal; The verification signal is obtained by extracting data from the vibration signal based on the interval of occurrence. The verification signal is processed. If the verification signal includes a feature signal, the feature signal is discarded. If the verification signal does not include a feature signal, the feature signal is retained.

[0062] This section identifies the authenticity of feature signals. Specifically, it verifies the signal based on the timing of its appearance. First, the interval between the appearance of the feature signals is determined. Then, a verification signal is extracted from the vibration signal based on this interval. The verification signal must have a duration shorter than the interval between the appearance of the feature signals, and it must also be located between the appearance times of the two feature signals. Figure 8 As shown.

[0063] If the verification signal includes a feature signal, the feature signal is discarded; if the verification signal does not include a feature signal, the feature signal is retained.

[0064] In some cases, when the main bearing speed is increased in stages, the following method is used: S201, Collect the vibration signal at the main bearing location and decompose the vibration signal to obtain the spectrum of the vibration signal; S202, identify the suspected resonance region on the spectrogram; S203, determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; S204, use the suspected resonance extraction range to extract the vibration signal and obtain the feature signal; S205, extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the inherent feature spectrum; After obtaining the inherent characteristic spectrum, the characteristic spectrum is then used to filter the characteristic spectrum.

[0065] The content of steps S201 to S205 is to obtain other resonance signals on the main fan in the early stage. As mentioned above, the present invention is based on the resonance caused by the impact of the stall cluster on the fan blade. However, resonance phenomena may also occur at other locations. Therefore, at this point, the inherent characteristic spectrum will be established. The resonance phenomena corresponding to these inherent characteristic spectra are a collective term.

[0066] Next, the feature spectrum is filtered using inherent feature spectra, as follows: Adjust the frequencies in the inherent characteristic spectrum according to the rotational speed; The amplitude variation coefficient is determined based on the performance of the inherent characteristic spectrum at different frequencies, and the inherent characteristic spectrum is adjusted again based on the amplitude variation coefficient to obtain the inherent characteristic updated spectrum; Remove the portion of the feature spectrum that is identical to the intrinsic feature update spectrum.

[0067] First, the frequencies in the inherent characteristic spectrum are adjusted according to the rotational speed. The adjustment method is based on the linear relationship between vibration frequency and rotational speed, as described above. Then, the amplitude variation coefficient is determined based on the performance of the inherent characteristic spectrum at different frequencies, and the inherent characteristic spectrum is adjusted again based on the amplitude variation coefficient to obtain the updated inherent characteristic spectrum. Here, the adjustment is also based on the linear relationship.

[0068] Finally, the part of the feature spectrum that is the same as the intrinsic feature update spectrum is removed. This can provide the accuracy of the final judgment result because resonance is a widespread phenomenon. By eliminating other resonance phenomena, the resonance phenomenon caused by stall clusters can be judged more accurately.

[0069] The present invention also provides a simulation test device for an online monitoring system of a main ventilation fan, comprising: The simulation unit is used to simulate abnormal conditions of the main fan on the test bench and to collect vibration signals at the location of the main bearing. The signal processing unit is used to decompose the vibration signal to obtain the spectrum of the vibration signal; The first processing unit is used to determine the suspected resonance region on the spectrogram; The second processing unit is used to determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range. The feature signal extraction unit is used to extract the vibration signal using the suspected resonance extraction range to obtain the feature signal; The third processing unit is used to extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the feature spectrum. The fourth processing unit is used to determine the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system, and to give the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system.

[0070] Furthermore, when simulating abnormal conditions of the main ventilation fan on the test bench, it also includes: Gradually increase the main bearing speed: Obtain the spectrum of the vibration signal at each rotational speed at the position of the main bearing; Based on the linear variation law, the frequency region in the spectrum corresponding to the vibration of the main bearing is determined and denoted as the vibration frequency region.

[0071] Furthermore, the suspected resonance regions on the spectrogram were identified as follows: Identify the peak points included in the vibration frequency region; there are multiple peak points. Based on the peak point, the frequency region corresponding to the peak point is determined and the frequency region is regarded as the suspected resonance region. Each frequency region includes only one peak point.

[0072] Furthermore, it also includes: After obtaining the characteristic spectrum using the suspected resonance region, the suspected resonance region is processed to reduce its size, and the process of obtaining the characteristic spectrum is repeated. The signal-to-noise ratio of the obtained feature spectra is determined by comparing multiple feature spectra. The narrowing range of the suspected resonance region is determined based on the signal-to-noise ratio.

[0073] Furthermore, after obtaining the characteristic signal, it also includes: Determine the interval of occurrence of the characteristic signal; The verification signal is obtained by extracting data from the vibration signal based on the interval of occurrence. The verification signal is processed. If the verification signal includes a feature signal, the feature signal is discarded. If the verification signal does not include a feature signal, the feature signal is retained.

[0074] Furthermore, when increasing the main bearing speed in stages, it also includes: Vibration signals at the location of the main bearing are collected and decomposed to obtain the spectrum of the vibration signals; Identify suspected resonance regions on the spectrogram; Determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; The vibration signal is extracted using the suspected resonance extraction range to obtain the characteristic signal; Extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the intrinsic feature spectrum; After obtaining the inherent characteristic spectrum, the characteristic spectrum is then used to filter the characteristic spectrum.

[0075] Furthermore, filtering the feature spectrum using inherent feature spectra includes: Adjust the frequencies in the inherent characteristic spectrum according to the rotational speed; The amplitude variation coefficient is determined based on the performance of the inherent characteristic spectrum at different frequencies, and the inherent characteristic spectrum is adjusted again based on the amplitude variation coefficient to obtain the inherent characteristic updated spectrum; Remove the portion of the feature spectrum that is identical to the intrinsic feature update spectrum.

[0076] In one example, the unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0077] For example, when the units in the device can be implemented through a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these units can be integrated together to form a system-on-a-chip (SOC).

[0078] In this invention, various objects such as messages / information / devices / network elements / systems / devices / actions / operations / processes / concepts may be named. It is understood that these specific names do not constitute a limitation on the relevant objects. The names may be changed depending on the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this invention should be mainly determined from their functions and technical effects embodied / performed in the technical solution.

[0079] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0083] It should also be understood that in the various embodiments of the present invention, "first," "second," etc., are merely used to indicate that multiple objects are different. For example, a first time window and a second time window are only used to indicate different time windows, and should not have any effect on the time window itself. The aforementioned "first," "second," etc., should not impose any limitations on the embodiments of the present invention.

[0084] It should also be understood that, in the various embodiments of the present invention, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions between different embodiments are consistent and can be referenced by each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] This invention also provides a simulation test system for an online monitoring system of a main ventilation fan, the system comprising: The present invention also provides one or more memories for storing instructions; and One or more processors are configured to retrieve and execute the instructions from the memory, performing the methods described above.

[0087] The present invention also provides a computer program product including instructions that, when executed, cause the terminal device and the network device to perform operations corresponding to the methods described above.

[0088] The present invention also provides a chip system including a processor for implementing the functions involved in the above description, such as generating, receiving, transmitting, or processing the data and / or information involved in the above methods.

[0089] This chip system can consist of chips or include chips and other discrete components.

[0090] The processor mentioned above can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits that execute a program to control the method of transmitting the feedback information described above.

[0091] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and located on different devices, connected via wired or wireless means to support the chip system in implementing the various functions described in the above embodiments. Alternatively, the processor and the memory can also be coupled to the same device.

[0092] Optionally, the computer instructions are stored in memory.

[0093] Optionally, the memory can be a storage unit within the chip, such as a register or cache. Alternatively, the memory can be a storage unit located outside the chip within the terminal, such as a ROM or other types of static storage devices that can store static information and instructions, such as RAM.

[0094] It is understood that the memory in this invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0095] Non-volatile memory can be ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0096] Volatile memory can be RAM, which is used as an external cache. There are many different types of RAM, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory.

[0097] The embodiments described herein are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A simulation test method for an online monitoring system of a main ventilation fan, characterized in that, include: An abnormal condition simulation was performed on the main fan on the test bench, and vibration signals were collected at the location of the main bearing. The vibration signal is decomposed to obtain its spectrum. Identify suspected resonance regions on the spectrogram; Determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; The vibration signal is extracted using the suspected resonance extraction range to obtain the characteristic signal; Extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the feature spectrum; Determine the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system, and provide the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system.

2. The simulation test method for the online monitoring system of the main ventilation fan according to claim 1, characterized in that, When simulating abnormal conditions of the main ventilation fan on the test bench, it also includes: Gradually increase the main bearing speed: Obtain the spectrum of the vibration signal at each rotational speed at the position of the main bearing; Based on the linear variation law, the frequency region in the spectrum corresponding to the vibration of the main bearing is determined and denoted as the vibration frequency region.

3. The simulation test method for the online monitoring system of the main ventilation fan according to claim 2, characterized in that, The suspected resonance regions on the spectrogram include: Identify the peak points included in the vibration frequency region; there are multiple peak points. Based on the peak point, the frequency region corresponding to the peak point is determined and the frequency region is regarded as the suspected resonance region. Each frequency region includes only one peak point.

4. The simulation test method for the online monitoring system of the main ventilation fan according to claim 3, characterized in that, Also includes: After obtaining the characteristic spectrum using the suspected resonance region, the suspected resonance region is processed to reduce its size, and the process of obtaining the characteristic spectrum is repeated. The signal-to-noise ratio of the obtained feature spectra is determined by comparing multiple feature spectra. The narrowing range of the suspected resonance region is determined based on the signal-to-noise ratio.

5. The simulation test method for the online monitoring system of the main ventilation fan according to any one of claims 1 to 4, characterized in that, After obtaining the characteristic signal, it also includes: Determine the interval of occurrence of the characteristic signal; The verification signal is obtained by extracting data from the vibration signal based on the interval of occurrence. The verification signal is processed. If the verification signal includes a feature signal, the feature signal is discarded. If the verification signal does not include a feature signal, the feature signal is retained.

6. The simulation test method for the online monitoring system of the main ventilation fan according to claim 2, characterized in that, When gradually increasing the speed of the main bearing, it also includes: Vibration signals at the location of the main bearing are collected and decomposed to obtain the spectrum of the vibration signals; Identify suspected resonance regions on the spectrogram; Determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range; The vibration signal is extracted using the suspected resonance extraction range to obtain the characteristic signal; Extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the intrinsic feature spectrum; After obtaining the inherent characteristic spectrum, the characteristic spectrum is then used to filter the characteristic spectrum.

7. The simulation test method for the online monitoring system of the main ventilation fan according to claim 6, characterized in that, Filtering feature spectra using inherent feature spectra includes: Adjust the frequencies in the inherent characteristic spectrum according to the rotational speed; The amplitude variation coefficient is determined based on the performance of the inherent characteristic spectrum at different frequencies, and the inherent characteristic spectrum is adjusted again based on the amplitude variation coefficient to obtain the inherent characteristic updated spectrum; Remove the portion of the feature spectrum that is identical to the intrinsic feature update spectrum.

8. A simulation test device for an online monitoring system of a main ventilation fan, characterized in that, include: The simulation unit is used to simulate abnormal conditions of the main fan on the test bench and to collect vibration signals at the location of the main bearing. The signal processing unit is used to decompose the vibration signal to obtain the spectrum of the vibration signal; The first processing unit is used to determine the suspected resonance region on the spectrogram; The second processing unit is used to determine the frequency range of the suspected resonance region and use the frequency range to construct the suspected resonance extraction range. The feature signal extraction unit is used to extract the vibration signal using the suspected resonance extraction range to obtain the feature signal; The third processing unit is used to extract the envelope of the feature signal and perform a Fourier transform on the envelope to obtain the feature spectrum. The fourth processing unit is used to determine the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system, and to give the time difference between the occurrence time of the characteristic spectrum and the alarm time of the main ventilation fan online monitoring system.

9. A simulation test system for an online monitoring system of a main ventilation fan, characterized in that, The system includes: One or more memories for storing instructions; and One or more processors are configured to retrieve and execute the instructions from the memory to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes: The program, when run by a processor, executes the method as described in any one of claims 1 to 7.

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