Device and method for noise detection

Abstract

The present disclosure relates to a device and a method for noise detection. The noise detection device comprises: a plurality of sensors configured to detect signals for a test subject; and a processor configured to calculate signal-to-noise ratios (SNRs) for a plurality of signals input from the plurality of sensors, analyze a frequency spectrum for each signal for the plurality of signals to detect noise, and remove noise from the plurality of signals based on at least one of the SNRs, a specific signal, and a specific frequency band. The present disclosure can effectively remove noise contained in a signal and improve signal quality.

Description

Area

[0001] Exemplary embodiments of the present disclosure relate to a device and a method for noise detection that removes noise from signals measured by a plurality of sensors. Discussion of the background

[0002] A vehicle consists of a multitude of components, and each of the components works organically together to enable the vehicle to be driven.

[0003] Each of the majority of components intended for use in the vehicle is tested to check its performance with regard to noise and vibration.

[0004] The test can be performed in the form of a coupled model, where each component is connected to a connector and a clamping device. The test is conducted by evaluating performance with respect to noise and vibration by detecting an acceleration response based on signals measured by multiple sensors installed on or adjacent to the component while it is in operation.

[0005] To test the performance of a component, it is necessary to select a number of sensor signals to measure data related to the component. The signal selection process involves simultaneously measuring multiple sensor signals, comparing signal strength and noise levels to determine the usefulness of the data, and identifying a useful signal.

[0006] A test device can then use the detected signal to predict the component's performance with respect to noise and vibration.

[0007] However, the process of individually comparing a plurality of sensor signals and checking the signal-to-noise ratio (SNR) of a large amount of data is carried out by humans, which is time-consuming and labor-intensive.

[0008] Accordingly, there is a need for a means and a procedure to check the SNR of a plurality of sensor signals in order to determine the usefulness of data.

[0009] The prior art of the present disclosure is disclosed in Korean patent application publication no. 10-2024-0048109 (titled “Leak detection system and method therefor”). Summary

[0010] One objective of the present disclosure is to provide a device and method for noise detection that removes noise by removing a specific frequency band or signal based on a signal-to-noise ratio (SNR) for a plurality of sensor signals.

[0011] A noise detection device according to one aspect of the present disclosure comprises: a plurality of sensors configured to detect signals for a test subject; and a processor configured to calculate SNRs for a plurality of signals input from the plurality of sensors, analyze a frequency spectrum for each signal for the plurality of signals in order to detect noise, and remove noise from the plurality of signals based on at least one of the SNR, a specific signal, and a specific frequency band.

[0012] When a reference SNR is set, the processor removes noise by batch-deleting a range from the majority of signals where the SNR is lower than the reference SNR.

[0013] When the specific signal is selected, the processor removes interference by eliminating a signal range corresponding to the specific signal from the majority of signals.

[0014] When the specific frequency band is selected, the processor removes interference by eliminating a frequency range corresponding to the specific frequency band from the majority of signals.

[0015] The processor detects a signal with an SNR that is less than a specified value for the majority of signals and performs a control to change the position of a sensor according to the signal.

[0016] The majority of sensors are installed on the test object or are installed at a location next to the test object and input acceleration signals along the x-axis, y-axis and z-axis of the test object into the processor.

[0017] A method for noise detection according to one aspect of the present disclosure comprises: analyzing a plurality of signals inputted by a plurality of sensors by a processor when signals for a test object are inputted by the plurality of sensors; calculating SNRs for the plurality of signals by the processor and analyzing a frequency spectrum for each signal by the processor; detecting noise from the plurality of signals by the processor and removing noise from the plurality of signals by the processor based on at least one of the SNR, a specific signal and a specific frequency band.

[0018] When removing background noise, the processor sets a reference SNR based on the input data and batch-deletes a range where the SNR is lower than the reference SNR from the majority of signals.

[0019] When removing background noise, if a specific signal is selected based on the input data, the processor deletes a signal range corresponding to the selected signal from the majority of signals, and if a specific frequency band is selected, the processor deletes a frequency range corresponding to the selected frequency band from the majority of signals.

[0020] The analysis includes: detecting a signal with an SNR that is less than a specified value for the majority of signals and performing a control to change the position of a sensor according to the signal.

[0021] According to one aspect of the present disclosure, the device and method for noise detection of the present disclosure can detect and easily remove specific noise from a plurality of signals input by a plurality of sensors.

[0022] According to one aspect of the present disclosure, the device and method for noise detection of the present disclosure can list and quantitatively compare signals received by a plurality of sensors and can selectively filter the signals for each frequency band or based on a required condition.

[0023] According to one aspect of the present disclosure, the device and method for noise detection of the present disclosure can easily and quickly determine the usefulness of reaction data from an accelerometer.

[0024] According to one aspect of the present disclosure, the device and method for noise detection of the present disclosure can check the status of a plurality of sensors based on an SNR and optimize the positions of the sensors. Brief description of the drawings Fig. Figure 1 is a block diagram schematically showing a configuration of a noise detection device comprising a plurality of sensors according to an embodiment of the present disclosure. Fig. Figure 2 is a block diagram schematically showing a configuration of the noise detection device according to an embodiment of the present disclosure. Fig. Figure 3 is a view illustrating acceleration signals from sensors according to an embodiment of the present disclosure. Fig. Figure 4 is a view showing the signal-to-noise ratio (SNR) of sensor signals according to an embodiment of the present disclosure. Fig. 5A and Fig. Figure 5B are views illustrating the frequency spectrum and SNR for a plurality of sensors according to an embodiment of the present disclosure. Fig. 6A and Fig. Figure 6B are views illustrating the frequency spectrum and SNR in relation to the stack-wise removal of unwanted noise according to an embodiment of the present disclosure. Fig. Figures 7A to 7C are views illustrating the frequency spectrum and SNR in relation to selective noise removal according to an embodiment of the present disclosure. Fig. Figures 8A to 8C are views illustrating the SNR with respect to the selective removal of background noise according to an embodiment of the present disclosure. Fig. Figure 9 is a flowchart illustrating a noise detection method using the noise detection device according to an embodiment of the present disclosure. Detailed description of the illustrated embodiments

[0025] The components described in the exemplary embodiments can be implemented by hardware components, including, for example, at least one digital signal processor (DSP), a processor, a controller, an application-specific integrated circuit (ASIC), a programmable logic element such as an FPGA, other electronic devices, or combinations thereof. At least some of the functions or processes described in the exemplary embodiments can be implemented by software, and the software can be recorded on a recording medium. The components, functions, and processes described in the exemplary embodiments can be implemented by a combination of hardware and software.

[0026] The method according to exemplary embodiments can be executed as a program that can be run by a computer and can be implemented as various recording media, such as a magnetic storage medium, an optical reading medium and a digital storage medium.

[0027] Various techniques described herein can be implemented as digital electronic circuits or as computer hardware, firmware, software, or combinations thereof. The techniques can be implemented as a computer program product, that is, as a computer program tangibly embodied in an information carrier, such as a machine-readable storage device (for example, a computer-readable medium) or as a propagated signal for processing by or controlling the operation of a data processing device, such as a programmable processor, a computer, or multiple computers.A computer program can be written in any form of a programming language, including compiled or interpreted languages, and can be deployed in any form, including a standalone program or a module, component, subroutine, or other unit suitable for use in a computer environment. A computer program can be deployed to run on one computer, on multiple computers at one location, or distributed across multiple locations and interconnected by a communication network.

[0028] Processors capable of executing a computer program include, for example, general-purpose processors, specialized microprocessors, and one or more processors of any digital computer. Generally, a processor receives instructions and data from read-only memory, random-access memory, or both. Elements of a computer may include at least one processor for executing instructions and one or more storage devices for storing instructions and data. Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic, magneto-optical, or optical media, or is connected to these devices to receive data from them, send data to them, or both.Examples of information carriers suitable for embodying computer program instructions and data include semiconductor storage devices, such as magnetic media like a hard drive, a floppy disk, and a magnetic tape; optical media like a Compact Disc Read Only Memory (CD-ROM), a Digital Video Disc (DVD), etc.; and magneto-optical media like a floppy disk, a read-only memory (ROM), a random access memory (RAM), flash memory, an erasable programmable ROM (EPROM), and an electrically erasable programmable ROM (EEPROM), and any other known computer-readable medium. A processor and memory can be supplemented by or integrated into a special logic circuit.

[0029] The processor can execute an operating system (OS) and one or more software applications that run on the OS. The processor device can also access, store, modify, process, and create data in response to software execution. For simplicity, the description of a processor device is used in the singular; however, a person skilled in the art will recognize that a processor device can include multiple processing elements and / or multiple types of processing elements. For example, a processor device can include multiple processors or a processor and a controller. Furthermore, various processing configurations are possible, such as parallel processors.

[0030] Furthermore, non-volatile computer-readable media can be any available media that a computer can access, and can include both computer storage media and transmission media.

[0031] The present description contains details of a number of specific implementations; however, it should be clear that these details do not limit the invention or what can be claimed in the description, but rather describe features of the specific example implementation. Features described in the description within the context of individual example implementations can be implemented as a combination in a single example implementation. Conversely, different features described in the description within the context of a single example implementation can be implemented individually or in a suitable subcombination in several example implementations.Furthermore, the features may function in a specific combination and may initially be described as claimed in the combination, but in some cases one or more features may be excluded from the claimed combination, and the claimed combination may be changed to a subcombination or a modification of a subcombination.

[0032] Even if processes are described in the drawings in a specific order, this should not be interpreted as meaning that the processes must be carried out in that specific order or sequence to achieve the desired results, or that all processes must be performed. In certain cases, multitasking and parallel processing may be advantageous. Furthermore, this should not be interpreted as requiring the separation of different device components in all the example implementations described above, and it should be understood that the program components and devices described above may be integrated into a single software product or bundled across multiple software products.

[0033] It is understood that the exemplary embodiments disclosed herein are merely illustrative and are not intended to limit the scope of the invention. It is obvious to a person skilled in the art that various modifications of the exemplary embodiments can be made without departing from the spirit and scope of the claims and their equivalents.

[0034] The following sections describe in detail embodiments of the present disclosure with reference to the accompanying drawings, so that a person skilled in the art can easily implement the present disclosure. However, the present disclosure can be implemented in many different forms and is not limited to the embodiments described herein.

[0035] In the following description of embodiments of the present disclosure, a detailed description of the known functions and configurations contained herein is omitted if this might further obscure the subject matter of the present disclosure. Parts that do not relate to the description of the present disclosure in the drawings are omitted, and identical parts are identified by similar reference numbers.

[0036] In this disclosure, components that are distinct from one another are intended to clearly illustrate each feature. However, this does not necessarily mean that the components are separate. That is to say, a plurality of components may be integrated into a hardware or software unit, or a single component may be distributed across a plurality of hardware or software units. Unless otherwise stated, such integrated or distributed embodiments are therefore also within the scope of this disclosure.

[0037] In the present disclosure, the components described in the various embodiments are not necessarily essential components, and some may be optional components. Accordingly, embodiments consisting of a subset of the components described in one embodiment also fall within the scope of the present disclosure. Furthermore, embodiments comprising additional components beyond those described in the various embodiments also fall within the scope of the present disclosure.

[0038] The following sections describe in detail embodiments of the present disclosure with reference to the accompanying drawings, so that a person skilled in the art can easily implement the present disclosure. However, the present disclosure can be implemented in many different forms and is not limited to the embodiments described herein.

[0039] In the following description of embodiments of the present disclosure, a detailed description of the known functions and configurations contained herein is omitted if this might further obscure the subject matter of the present disclosure. Parts that do not relate to the description of the present disclosure in the drawings are omitted, and identical parts are identified by similar reference numbers.

[0040] When the present disclosure refers to a component as being "linked," "coupled," or "connected" to another component, it is understood that this may include not only a direct connection but also an indirect connection via an intermediate component. Furthermore, when a component is referred to as "comprising" or "featuring" another component, this may mean the inclusion of another component, not its exclusion, unless expressly stated otherwise.

[0041] In this disclosure, the terms “first”, “second”, etc. are used only to distinguish one component from another and do not restrict the order or importance of components, etc., unless expressly stated otherwise. Therefore, within the scope of this disclosure, a first component in one exemplary embodiment may be referred to as a second component in another embodiment, and similarly, a second component in one exemplary embodiment may be referred to as a first component.

[0042] In this disclosure, components that are distinct from one another are intended to clearly illustrate each feature. However, this does not necessarily mean that the components are separate. That is to say, a plurality of components may be integrated into a hardware or software unit, or a single component may be distributed across a plurality of hardware or software units. Unless otherwise stated, such integrated or distributed embodiments are therefore also within the scope of this disclosure.

[0043] In the present disclosure, the components described in the various embodiments are not necessarily essential components, and some may be optional components. Accordingly, embodiments consisting of a subset of the components described in one embodiment also fall within the scope of the present disclosure. Furthermore, exemplary embodiments that include additional components beyond those described in the various embodiments also fall within the scope of the present disclosure.

[0044] Fig. Figure 1 is a block diagram schematically showing a configuration of a noise detection device comprising a plurality of sensors according to an embodiment of the present disclosure.

[0045] With reference to Fig. 1. A noise detection device 100 according to the present embodiment detects, by means of a plurality of sensor units 140, noise and vibrations generated during the operation of a test object 10, wherein the test object 10 is a component installed in a vehicle. In this case, one of the methods for predicting the performance of the test object 10 is to calculate a black force for the test object 10.

[0046] Based on the acquired signals, a test device (not shown) can predict the performance of each component by analyzing the black force, which is a model that converts the vibration of the test object 10 during its operation into a force at a coupling point.

[0047] The test instrument can apply the black force to the frequency response function-based substructuring (FBS) to predict the final performance in the final coupled model. Computing the black force requires noise responses from multiple sensors.

[0048] The test equipment can perform a test by either increasing the driving force of a component relative to the background noise or decreasing the background noise relative to the driving force. Increasing the driving force is generally not possible, and therefore the test equipment can perform the test by reducing the background noise.

[0049] To calculate the black force, it is necessary to reduce background noise by selecting a particularly useful signal from a multitude of sensor signals. Accordingly, the noise detection device 100 can calculate a signal-to-noise ratio (SNR) and perform a control operation to ensure that the value is equal to or less than a reference value. In this case, if the SNR does not meet a specified value that is lower than the reference value, a sensor position can be repositioned.

[0050] The noise detection device 100 can analyze signals input from multiple sensors to select highly useful data and apply it to the test device. Accordingly, the test device can determine or predict the performance of the test object 10 with respect to noise and vibration based on the filtered signals.

[0051] A plurality of sensors 141 to 149 can be installed on a component that is the test object 10, or can be installed at a position next to the test object 10. The plurality of sensors 141 to 149 can detect acceleration signals along the x-axis, y-axis, and z-axis for the test object 10.

[0052] The test object 10 can be an electric power steering system, a compressor or the like, which is mounted on a connector, such as a socket, or a receiver, such as a clamping device.

[0053] The majority of sensors 141 to 149 detect disturbances and vibrations while the test object 10 is in operation, as well as disturbances while the test object 10 is not in operation, and transmit the detected disturbances and vibrations to the disturbance detection device 100.

[0054] The noise detection device 100 lists the signals input by the majority of sensors 141 to 149 and analyzes the frequency spectrum and the SNR. The noise detection device 100 can select signals based on the SNR. The SNR is calculated based on a value obtained by dividing a signal strength Ps by a noise strength Pn and can indicate a relative signal strength by determining the signal strength relative to the noise.

[0055] The noise detection device 100 can detect noise contained in a signal and either control the signal containing the noise or remove a frequency band corresponding to the noise.

[0056] The noise detection device 100 can list and compare all data in the form of an "absolute value diagram" and an "SNR diagram" corresponding to multiple sensor signals, and determine the strength of each signal and the influence of background noise. The noise detection device 100 can determine, for each signal or frequency band, whether a signal is useful.

[0057] During the signal analysis process, the noise detection device 100 can optimize a sensor position by changing the sensor position in real time. If a signal-to-noise ratio (SNR) does not meet a defined condition, the noise detection device 100 can change the sensor position for the relevant signal to optimize the sensor position based on whether the SNR changes.

[0058] The noise detection device 100 can select a signal with a signal-to-noise ratio (SNR) equal to or higher than a specified SNR. If the SNR of a particular signal does not meet a specified condition, the noise detection device 100 can delete the signal. The noise detection device 100 can also select a signal with an SNR equal to or higher than a specified value through filtering, thus enabling accurate noise analysis and improving data quality.

[0059] The noise detection device 100 of the present disclosure can provide high-quality data for performance verification of the virtual component coupling for a dynamic substructuring, thereby improving signal processing efficiency and data consistency.

[0060] The noise detection device 100 can be used in an evaluation procedure (e.g. MODAL, ODS, BF-TPA etc.) that simultaneously measures a plurality of sensors for noise analysis of a test object (e.g. a vehicle component).

[0061] In some cases, the noise detection device 100 may be included in a test device.

[0062] Fig. Figure 2 is a block diagram schematically showing a configuration of the noise detection device according to an embodiment of the present disclosure.

[0063] With reference to Fig. 2 The noise detection device 100 can comprise a communication part 130, a memory 120, the sensor unit 140, an input part 170, an output part 180 and a processor 110.

[0064] The sensor unit 140 can comprise the majority of sensors 141 to 149. The sensor unit 140 can be installed on the test object 10 or at a location adjacent to the test object 10. The sensor unit 140 can measure noise or vibrations for the test object 10 and input the measured noise or vibrations into the processor 110. The sensor unit 140 can input acceleration signals along three axes of the test object 10 into the processor 110.

[0065] The input unit 170 can input setting data for signal analysis and noise detection, as well as status data for signal filtering. The input unit 170 can include at least one of the following input devices: a button; a switch; and a touchpad.

[0066] For example, the input part 170 can receive an input from at least one of the following elements: a filtered signal, a reference value of an SNR (reference SNR) for filtering, a signal to be filtered, and a frequency band.

[0067] The output unit 180 can include at least one of the following output devices: a loudspeaker; a display; and an indicator light. The output unit 180 can output a status based on noise detection in response to a control command from the processor 110. The output unit 180 can output a frequency spectrum graph or a signal-to-noise ratio (SNR) of sensor signals.

[0068] The output unit 180 can, in response to a control command from the processor 110, output data regarding the signal filtered as a result of noise detection in at least one of the following forms: a sound effect; a warning tone; and a voice instruction. The output unit 180 can output an instruction message, a warning message, or a warning light.

[0069] The communication unit 130 comprises a wired or wireless communication module. The communication unit 130 can input signals from the majority of sensors 141 to 149 in response to a control command from the processor 110 to the processor 110. The communication unit 130 can also convert signals received from the majority of sensors 141 to 149 into a specific format.

[0070] The communication unit 130 can communicate using Wi-Fi, Ethernet, Bluetooth, mobile communication (5G, LTE, CDMA and GSM), wireless short-range communication, serial communication, parallel communication, powerline communication and the like.

[0071] Memory 120 can store data on at least one of the following elements: the majority of sensors 141 to 149; installation locations of the majority of sensors 141 to 149; acceleration signals received by the majority of sensors 141 to 149; a frequency spectrum; and an SNR of the signals.

[0072] Memory 120 can store data for at least one of the following elements: a signal analysis algorithm, a noise detection algorithm, an SNR calculation algorithm, a signal filter algorithm, and a noise removal algorithm.

[0073] The Memory 120 can include storage media such as random access memory (RAM), non-volatile memory such as read-only memory (ROM) and electrically erased programmable ROM (EEPROM), flash memory, HDD, SSD and SDS.

[0074] The processor 110 can include at least one microprocessor and can operate on the basis of data stored in memory 120.

[0075] The processor 110 can analyze the signals input from the majority of sensors 141 to 149 to generate a frequency spectrum and calculate a signal-to-noise ratio (SNR). The processor 110 can then specify a signal or frequency band to be filtered based on the frequency spectrum and the SNR.

[0076] The Processor 110 can delete a signal range for a signal selected from a plurality of signals or a frequency range for a selected frequency band.

[0077] Furthermore, the Processor 110 can batch-delete signals that are equal to or less than a specified reference SNR. The Processor 110 can use a filter to delete a specific range of signals from the majority of signals.

[0078] Fig. Figure 3 is a view showing acceleration signals from sensors according to an embodiment of the present disclosure.

[0079] With reference to Fig. 3. The processor 110 receives and processes acceleration signals input from the majority of the sensors 141 to 149.

[0080] The acceleration signals are used to determine the performance of the test object 10, and the processor 110 can remove noise from the majority of signals (acceleration signals) to select a signal that is highly useful as data. The processor 110 can determine the usefulness of a signal based on the signal-to-noise ratio (SNR).

[0081] The Processor 110 can compare and analyze the signal and noise levels when the absolute signal strength is low and the noise level is also low, or when the absolute signal strength is high and the noise level is high. In this case, the noise level can be determined as a ratio relative to the signal strength. The noise level can be defined as high if it is equal to or greater than a first ratio based on the maximum signal strength; and the noise level can be defined as low if it is less than a second ratio that is less than the first. The first and second ratios can be adjusted depending on the settings.

[0082] The Processor 110 can list the absolute response strength of all signals and perform an initial comparison and analysis of the absolute strength of each signal.

[0083] The processor 110 can list and display the acceleration response strength for the total acceleration signals input from the majority of sensors. The processor 110 can analyze a first segment A1 and a second segment B1 with respect to signal strength. In this case, it can be observed that a first signal 11 has a greater strength than a second signal 12, and that background noise 13 has a lesser strength than both the first signal 11 and the second signal 12.

[0084] Processor 110 can assume that an anomaly exists in the first segment A1 and the second segment B1 due to significant fluctuations in the acceleration response strength of the first segment A1 and the second segment B1. However, the absolute strength of the signal alone cannot determine its usefulness, and therefore Processor 110 can analyze the signal by calculating a signal-to-noise ratio (SNR), as shown in Fig. 4 shown, which is described further below.

[0085] Fig. Figure 4 is a view illustrating the signal-to-noise ratio (SNR) of sensor signals according to an embodiment of the present disclosure.

[0086] With reference to Fig. 4. The processor 110 can generate a diagram by calculating the SNR for a plurality of signals.

[0087] The processor 110 can define the strength of the signals Ps measured by the sensors while the test object 10 is not in operation as background noise and calculate the SNR based on the strength of the signals Ps and the strength of the background noise Pn measured by the sensors while the test object 10 is in operation.

[0088] Based on the SNR diagram, the processor 110 can compare the signal strength Ps with the noise strength Pn while the test object 10 is in operation and compare the difference for each sensor.

[0089] The processor 110 can compare a first SNR 21 of the first signal 11, input from a first sensor 141, and a second SNR 22 of the second signal 12, input from a second sensor 142. The processor 110 can compare the first SNR 21 and the second SNR 22 based on a reference value 24 for signal quality. In this case, the reference value 24 of the SNR is set to approximately 60 dB, which is only an example and can vary. The reference value can vary depending on the conditions required to determine the usefulness of the signal.

[0090] Processor 110 can determine that an anomaly exists in the first SNR 21 of the first signal 11 and in the second SNR 22 of the second signal 12 for the second segment B1, where the SNR becomes less than the reference value 24. Furthermore, processor 110 can determine that an anomaly exists in the second SNR 22 for a third segment C1, where the SNR becomes less than the reference value 24.

[0091] This means that the processor 110 can be used based on the signal strength in Fig. Assuming an anomaly in the first segment A1 and the second segment B1, processor 110 can determine, based on the SNR analysis, that the first segment A1 is normal and the second segment B1 contains an anomaly. Furthermore, processor 110 can detect an unexpected anomaly in the third segment C1 based on the strength comparison.

[0092] Accordingly, the processor 110 can analyze the SNR instead of the absolute strength of the signals to determine the usefulness of the data for the majority of signals input from the majority of sensors.

[0093] The Processor 110 can set a reference value for the SNR and filter out from the signals a signal with an SNR below the reference value, a specific signal, or a specific frequency band.

[0094] In addition, the processor can detect 110 signals 23 with an SNR that does not meet a reference value (required condition) and perform further analysis.

[0095] Fig. 5A and Fig. Figure 5B are views illustrating the frequency spectrum and SNR for a plurality of sensors according to an embodiment of the present disclosure.

[0096] With reference to Fig. 5A, the processor can detect 110 interference noises from signals input by the majority of sensors 141 to 149.

[0097] The processor 110 can calculate the signal-to-noise ratio (SNR) for all signals input from the majority of sensors 141 to 149 and analyze the signals for each signal or for each frequency band. The processor 110 can generate a frequency spectrum for each signal and compare the generated frequency spectra.

[0098] The processor 110 can successively list and analyze a spectrum for each frequency of the x-axis, y-axis and z-axis of the first sensor 141, a spectrum for each frequency of the x-axis, y-axis and z-axis of the second sensor 142 and a spectrum for each frequency of the x-axis, y-axis and z-axis of the Nth sensor 149.

[0099] Processor 110 can display the SNR for the majority of sensors 141 to 149 in color. Processor 110 can compare sections with low and high SNR in color. In this case, the first area 31 is an area with a high SNR, and the second area 32 is an area with the lowest SNR among all sensors.

[0100] For example, the first region 31 can correspond to the y-axis signal of a 21st sensor and the x-axis, y-axis, and z-axis signals of a 25th sensor, exhibiting the highest SNR of approximately 90 dB in a frequency band from 1500 Hz to 2300 Hz. Furthermore, in the second region 32, it can be observed that the x-axis, y-axis, and z-axis signals of a 59th sensor exhibit the lowest values ​​in the entire frequency band.

[0101] The Processor 110 can perform an analysis for each signal and frequency range. If a signal from a specific sensor does not meet a defined SNR value, the Processor 110 can initiate a control action to change the sensor's location. The Processor 110 can generate a prompt or warning regarding a sensor position change and output this prompt or warning via the Output Unit 180.

[0102] If the sensor position changes, the processor 110 can repeat the SNR analysis to optimize the sensor position. Once the sensor location optimization is complete and the SNR of a signal is lower than a reference value (reference SNR), the processor 110 can filter out the signal.

[0103] The Processor 110 can select signals that have a segment with a signal-to-noise ratio (SNR) lower than the reference value. For the selected signals, the Processor 110 can batch delete signals in a frequency band where the SNR is lower than the reference value.

[0104] Furthermore, the processor 110 can select a specific signal or selectively delete signals within a particular frequency band. For example, the processor 110 can batch-delete signals corresponding to the second area 32. Additionally, the processor 110 can batch-delete signals from a majority of signals in either a low-frequency or high-frequency band. That is, the processor 110 can batch-delete signals along the x-axis, y-axis, and z-axis of the 59th sensor located in the second area 32.

[0105] While the signal from the anomalous sensor is being cleared, the processor 110 can change the sensor's position to check if the SNR improves and to verify if the sensor is indeed anomalous. In this way, the processor 110 can calculate the SNRs for signals input from multiple sensors and compare the differences for each signal or for each frequency band.

[0106] With reference to Fig. 5B allows the processor 110 to generate a diagram for the SNRs for signals from the majority of sensors and to compare the SNRs.

[0107] The Processor 110 can check for changes in the SNR strength for each frequency band. The Processor 110 can filter out a signal with an SNR lower than a reference value.

[0108] Fig. 6A and Fig. Figure 6B are views illustrating the frequency spectrum and SNR in relation to stack-wise noise removal according to an embodiment of the present disclosure.

[0109] With reference to Fig. 6A The processor 110 can perform a task to use the sensor signals as actual data by comparing and analyzing the signal-to-noise ratios (SNRs). The processor 110 can filter the signals based on the SNR from the majority of signals input from the majority of sensors.

[0110] The processor 110 can select a range in the frequency spectrum where the SNR is lower than a reference value and delete a signal and a frequency band within that range. For example, the processor 110 can batch delete signals and frequency bands 33 where the SNR is lower than the reference value.

[0111] In this case, processor 110 cannot delete the entire single signal, but only a portion of the signal where the SNR is lower than the reference value. For example, processor 110 can delete the 50–100 Hz band of a first signal, the 0–200 Hz and 2000–2500 Hz bands of a fifth signal, and the 0–450 Hz, 550–1500 Hz, and 2000–3000 Hz bands of a 59th signal.

[0112] By deleting signal and frequency segments for a given SNR, the processor 110 can batch-delete a range 34 where the SNR is smaller than a reference value, as in Fig. 6B shown.

[0113] Fig. Figures 7A to 7C are views illustrating the frequency spectrum and SNR in relation to selective noise removal according to an embodiment of the present disclosure.

[0114] The processor 110 can delete a specific signal or a signal in a specific frequency band based on the data entered via the input part 170.

[0115] As in Fig. As shown in Figure 7A, the processor 110 can generate a frequency spectrum for the majority of sensors 141 to 149 and output the generated frequency spectrum via the output part 180.

[0116] If a reference value for the SNR is entered via the input part 170, the processor 110 can select a frequency band of a signal with an SNR that is smaller than the reference value and display the selected frequency band on a screen.

[0117] If a specific signal is selected via the input part 170, the processor 110 can delete a signal area 35 corresponding to the selected signal, as shown in Fig. 7B shown.

[0118] Furthermore, if a specific frequency band is selected, as in Fig. As shown in Figure 7C, processor 110 can delete a frequency range corresponding to the selected frequency band for the entire signal. For example, processor 110 can delete frequency range 36 corresponding to the frequency band from 0 to 400 Hz and frequency range 37 corresponding to the frequency band from 3000 to 4000 Hz.

[0119] The processor 110 can delete the designated frequency ranges 36 and 37 for the entire signal.

[0120] Fig. Figures 8A to 8C are views illustrating the SNR with respect to selective noise removal according to an embodiment of the present disclosure.

[0121] The Processor 110 can delete a specific signal or a particular frequency band. SNR graphs for this are in Fig. 8A to 8C are shown.

[0122] If processor 110 deletes a specific signal from a graph of the SNR for the majority of signals, as in Fig. As shown in Figure 8A, processor 110 can clear a diagram of the SNR according to a relevant signal 41, as shown in Fig. 8B shown.

[0123] Furthermore, when deleting a specific frequency band, the processor 110 can delete sections from all signals corresponding to the relevant frequency bands 42 and 43, as shown in Fig. 8C shown. That is, the processor 110 can batch clear signal values ​​in a range where the SNR is less than a fixed value based on the SNR, and can clear a signal range for a signal selected from the plurality of signals, or clear a frequency range according to a fixed frequency band for the plurality of signals.

[0124] Based on a distribution of the SNR, the processor can remove 110 noises based on at least one SNR, one signal and one frequency band.

[0125] Fig. Figure 9 is a flowchart illustrating a noise detection method using the noise detection device according to an embodiment of the present disclosure.

[0126] With reference to Fig. 9. The noise detection device can detect 100 noises by analyzing the SNR for signals input from the majority of sensors 141 to 149 and removing the noise. The noise detection device can remove noises based on at least one SNR, a signal, and a frequency band.

[0127] The processor 110 receives signals (acceleration signals) for the test object 10 from the majority of sensors 141 to 149 (S310).

[0128] The processor 110 analyzes the majority of signals to calculate the SNR (S320).

[0129] The processor 110 determines whether a signal with an SNR below a specified value is present (S330), detects a signal with an SNR below the specified value and performs a control to change a position of the sensor (S340).

[0130] After the sensor position has been changed, the processor 110 calculates the SNR of the signal input from the sensor and determines again whether the SNR is lower than the set value. If the SNR is lower than the set value, the processor 110 performs another control operation to change the sensor position.

[0131] In this way, the processor 110 can optimize the sensor position by repeatedly changing the sensor location for a signal with an SNR that is below a specified value.

[0132] In some cases, before performing a test on the test object 10, the processor 110 can first perform sensor location optimization and then detect interference noise from the sensor once the optimization is complete.

[0133] The processor 110 can determine that a sensor whose SNR remains below the set value even after a certain number of changes to the sensor location is faulty, and perform a sensor fault check.

[0134] The Processor 110 can set a noise removal mode based on a distribution of the SNR of the majority of signals if the SNR of the input signal is equal to or higher than a set value.

[0135] When a reference SNR (reference value) is entered via input part 170, the processor 110 can determine that the noise reduction mode is in a first mode (S350).

[0136] Processor 110 sets a reference SNR based on the data input via input unit 170 (S360). In this case, the reference SNR can be set to a value higher than the set value.

[0137] The Processor 110 detects a signal or frequency band with an SNR that is smaller than the reference SNR for the majority of signals.

[0138] The 110 processor batch-deletes signals from the majority of signals in a range where the SNR is lower than the reference SNR (S370). A portion of a single signal can be deleted for each frequency band, as described above. Fig. 6A and Fig. 6B shown.

[0139] When a specific signal or frequency band is input via the input part 170, the processor 110 can set a second noise removal mode.

[0140] The processor 110 sets the specific signal or frequency band entered via the input part 170 for erasure (S380).

[0141] Processor 110 can delete the specific signal set from the majority of signals. Processor 110 can delete the designated signal range 35, as described above. Fig. 7B shown.

[0142] Furthermore, the processor 110 can delete all frequency ranges defined for the majority of signals. The processor 110 can delete the specified frequency ranges 36 and 37, as described above. Fig. 7C shown.

[0143] The Processor 110 can remove noise from the majority of signals based on at least one SNR, a signal and a frequency band, thereby improving signal quality.

[0144] The processor 110 processes the filtered signal and outputs it (S400).

[0145] The noise detection device 100 can send the filtered signal to the test device. Accordingly, the test device can predict the performance of the test object 10 based on the filtered signal.

[0146] Therefore, according to one aspect of the present disclosure, the device and method for noise detection can easily detect a sensor with an anomaly, improve the reliability of the filtered signal by eliminating a low SNR area or signal, and improve the accuracy of the determination result due to the reduced noise. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] KR 10-2024-0048109

[0009]

Claims

[1] Noise detection device comprising: a plurality of sensors designed to detect signals for a test subject; and a processor configured to: calculate signal-to-noise ratios (SNRs) for a plurality of signals input from a plurality of sensors; analyze a frequency spectrum for each signal for the plurality of signals to detect noise; and remove noise from the plurality of signals based on at least one of the SNR, a specific signal, and a specific frequency band. [2] Noise detection device according to claim 1, wherein when a reference SNR is set, the processor removes noise by batch-removing from the plurality of signals a range in which the SNR is smaller than the reference SNR. [3] Noise detection device according to claim 1 or 2, wherein when the specific signal is selected, the processor removes noise by removing a signal range corresponding to the specific signal from the plurality of signals. [4] Noise detection device according to one of claims 1 to 3, wherein when the specific frequency band is selected, the processor removes noise by removing a frequency range corresponding to the specific frequency band from the plurality of signals. [5] Noise detection device according to any one of claims 1 to 4, wherein the processor detects a signal with an SNR that is less than a specified value for the plurality of signals and performs a control to change a position of a sensor according to the signal. [6] Noise detection device according to one of claims 1 to 5, wherein the plurality of sensors is installed on the test object or is installed at a location next to the test object and inputs acceleration signals along the x-axis, y-axis and z-axis of the test object into the processor. [7] Incident detection methods encompassing: Analyzing a plurality of signals inputted by a plurality of sensors by a processor when signals for a test object are input from the plurality of sensors; Calculating SNRs for the majority of signals by the processor and analyzing a frequency spectrum for each signal by the processor; The processor detects background noise from the majority of signals and Removal of noise from the majority of signals by the processor based on at least one of the SNR, a specific signal and a specific frequency band. [8] Noise detection method according to claim 7, wherein when removing noise, the processor sets a reference SNR based on the input data and batch-deletes a range in which an SNR is smaller than the reference SNR from the plurality of signals. [9] Noise detection method according to claim 7 or 8, wherein when removing noise, if a specific signal is selected based on the input data, the processor deletes a signal range corresponding to the specific signal from the plurality of signals, and if a specific frequency band is selected, the processor deletes a frequency range corresponding to the specified frequency band from the plurality of signals. [10] Noise detection method according to any one of claims 7 to 9, wherein the analysis comprises: detecting a signal with an SNR that is less than a specified value for the plurality of signals and performing a control to change a position of a sensor according to the signal.

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