CN114137338A - Equipment running state monitoring method, system and storage medium - Google Patents
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Abstract
The application discloses a method and a system for monitoring the running state of equipment and a storage medium, which are used for improving the instantaneity of equipment fault monitoring and saving the labor cost. The method comprises the following steps: acquiring a vibration signal in the running process of equipment; decomposing the vibration signal into k modal components, wherein the k value is automatically determined by the sample entropy of the modal components and the center frequency ratio of the modal components; determining Hilbert time frequency spectrums corresponding to the k modal components; comparing the Hilbert time frequency spectrums corresponding to the k modal components with a preset Hilbert time frequency spectrum; when the difference value between the Hilbert time frequency spectrum corresponding to the k modal components and the preset Hilbert time frequency spectrum is smaller than the preset difference value, determining that the equipment does not have a fault; and when the difference value between the Hilbert time frequency spectrum corresponding to the k modal components and the preset Hilbert time frequency spectrum is greater than the preset difference value, determining that the equipment fails. By adopting the scheme provided by the application, the labor cost is saved, and the instantaneity is improved.
Description
Technical Field
The present disclosure relates to the field of fault monitoring technologies, and in particular, to a method, a system, and a storage medium for monitoring an operating status of a device.
Background
At present, fault monitoring of some equipment is mainly performed in a manual troubleshooting mode, for example, for fault detection of a vibrating feeder, a maintainer is required to troubleshoot each detail one by one, but all possible fault conditions cannot be troubleshooted in power failure diagnosis, and the maintainer is required to troubleshoot potential safety hazards which may appear in operation, the maintainer who works for a long time in the first line can roughly judge the operation condition of the vibrating feeder through ears but cannot qualitatively judge internal faults of the vibrating feeder, the commonly used diagnosis method depends on subjective judgment and personal experience of technicians to a great extent, sounds are large when the vibrating feeder operates, are influenced by surrounding, are difficult to accurately judge a single fault machine, have great uncertainty, and are time-consuming and labor-consuming, when a fault occurs, if no inspection personnel is nearby, the fault can be difficult to find in time.
Therefore, the existing mode of monitoring equipment faults through manual troubleshooting is poor in instantaneity and wastes labor cost, and therefore the method for automatically monitoring the running state of the equipment is provided, instantaneity of monitoring the equipment faults is improved, and labor cost is saved.
Disclosure of Invention
The application provides a method and a system for monitoring the running state of equipment and a storage medium, which are used for improving the instantaneity of equipment fault monitoring and saving the labor cost.
The application provides a method for monitoring the running state of equipment, which comprises the following steps:
acquiring a vibration signal in the running process of equipment;
decomposing the vibration signal into k modal components, wherein the k value is automatically determined by the sample entropy of the modal components and the center frequency ratio of the modal components;
determining Hilbert time spectrums corresponding to the k modal components;
comparing the Hilbert time frequency spectrums corresponding to the k modal components with a preset Hilbert time frequency spectrum, wherein the preset Hilbert time frequency spectrum is the Hilbert time frequency spectrum corresponding to the modal component of the vibration signal when the equipment normally operates;
when the difference value between the Hilbert-time frequency spectrum corresponding to the k modal components and a preset Hilbert-time frequency spectrum is smaller than a preset difference value, determining that the equipment does not have a fault;
and when the difference value between the Hilbert time frequency spectrum corresponding to the k modal components and the preset Hilbert time frequency spectrum is greater than the preset difference value, determining that the equipment fails.
The beneficial effect of this application lies in: the Hilbert-time frequency spectrum of the characteristic modal component of the vibration signal under the condition that the equipment is in the running state can be compared with the Hilbert-time frequency spectrum corresponding to the modal component of the vibration signal when the preset equipment runs normally, so that whether the equipment breaks down is automatically judged, equipment failure monitoring is not required to be carried out through manual troubleshooting, the labor cost is saved, and in addition, whether the equipment breaks down or not is judged by the equipment through automatic judgment, and the instantaneity of equipment failure monitoring is improved for a manual troubleshooting scheme.
In one embodiment, the determining hilbert-time spectrums corresponding to the k modal components includes:
determining a single frequency spectrum corresponding to each mode in the k mode components;
modulating the single term spectrum corresponding to each mode to a corresponding baseband frequency by mixing with an index tuned to the respective estimated center frequency;
and calculating Hilbert time spectrums corresponding to modal components of the modes modulated to the corresponding baseband frequencies.
In one embodiment, the determining the single-term spectrum corresponding to each of the k modal components includes:
determining a single-term spectrum corresponding to each mode in the k mode components according to the following formula:
wherein z isk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a hilbert time spectrum corresponding to the modal component of each mode.
In one embodiment, the modulating the single spectrum corresponding to each mode to the corresponding baseband frequency includes:
modulating the corresponding single-term spectrum of each mode to the corresponding baseband frequency according to the following formula:
wherein z isk(t) a unidirectional spectrum corresponding to each mode,
is an index of the center frequency.
In one embodiment, calculating hilbert-time spectra corresponding to modal components of respective modalities modulated to corresponding baseband frequencies comprises:
adding all components to be equal to the original signal as a constraint condition, and establishing the following constraint variation models related to each mode:
s.t.∑kuk＝f；
wherein, { uk}:＝{u1,…,uK}，{ωk}:＝{ω1,…,ωKIs the optimal solution set of all modal Hilbert-time spectra and its center frequency set, zk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a Hilbert-time spectrum corresponding to a modal component of each mode,
is an index of the center frequency;
introducing a Lagrange multiplier lambda (t) and a secondary penalty factor alpha to optimize the constraint variational model so as to obtain the following optimization model:
solving the optimization model to obtain the following optimal solution expression of the Hilbert time spectrum:
wherein the content of the first and second substances,
and the optimal solution of the Hilbert time spectrum corresponding to the modal components of each mode is obtained.
In one embodiment, the central frequency updating formula of each hilbert time spectrum is:
wherein the content of the first and second substances,
the center frequency of each hilbert time spectrum.
In one embodiment, the k value is determined as follows:
setting an initial value of the k value;
decomposing the vibration signal in the operation process of the equipment according to the initial value to obtain a decomposed first modal component;
calculating sample entropy and center frequency between the decomposed first modal components;
and when the sample entropy difference value reaches a first preset value and the center frequency is smaller than a second preset value, determining the initial value as a final k value.
In one embodiment, when the sample entropy difference value does not reach the first preset value, or the center frequency is greater than the second preset value, the method further includes:
step 1, adjusting a k value according to a preset step length;
step 2, decomposing the vibration signal in the operation process of the equipment according to the adjusted k value to obtain a decomposed second modal component;
step 3, calculating sample entropy and center frequency between the decomposed second modal components;
and 4, when the sample entropy difference value does not reach the first preset value or the center frequency is greater than the second preset value, repeating the steps 1-3 until the k value is obtained when the sample entropy difference value reaches the first preset value and the center frequency is less than the second preset value.
The beneficial effects of this embodiment lie in, can solve out optimum k value based on the central frequency between the sample entropy difference value between the modal component and the modal component, need not to rely on expert's experience just can obtain optimum modal component number, subjectivity when having reduced manual judgement, and the decomposition number of having confirmed the appropriate modal component can effectively reduce under-decomposing and over-decomposition phenomenon, effectively rejects the false component, and then has promoted the accuracy of equipment failure judgement.
The present application further provides an apparatus operating state monitoring system, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the device operation state monitoring method according to any one of the above embodiments.
The present application further provides a computer-readable storage medium, wherein when instructions in the storage medium are executed by a processor corresponding to the device operation state monitoring system, the device operation state monitoring system is enabled to implement the device operation state monitoring method described in any one of the above embodiments.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present application is further described in detail by the accompanying drawings and examples.
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The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiment(s) of the application and together with the description serve to explain the application and not limit the application. In the drawings:
fig. 1 is a flowchart of a method for monitoring an operating state of a device according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for monitoring the operational status of a device according to another embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for monitoring the operating status of a device according to another embodiment of the present application;
fig. 4 is a schematic diagram of a hardware structure of the system for monitoring an operating state of a device according to the present application.
Detailed Description
The preferred embodiments of the present application will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein only to illustrate and explain the present application and not to limit the present application.
Fig. 1 is a flowchart of a method for monitoring an operation state of a device according to an embodiment of the present application, and as shown in fig. 1, the method may be implemented as the following steps S11-S16:
in step S11, acquiring a vibration signal during the operation of the device;
decomposing the vibration signal into k modal components in step S12, wherein the k value is automatically determined by the sample entropy of the modal component and the center frequency ratio of the modal component;
in step S13, determining hilbert-time spectrums corresponding to the k modal components;
in step S14, comparing the hilbert-time frequency spectrums corresponding to the k modal components with a preset hilbert-time frequency spectrum, where the preset hilbert-time frequency spectrum is a hilbert-time frequency spectrum corresponding to a modal component of a vibration signal when the device normally operates;
in step S15, when a difference value between the hilbert-time frequency spectrum corresponding to the k modal components and a preset hilbert-time frequency spectrum is smaller than a preset difference value, it is determined that the device does not fail;
in step S16, when a difference between the hilbert-time frequency spectrum corresponding to the k modal components and a preset hilbert-time frequency spectrum is greater than a preset difference, it is determined that the device has a fault.
In the embodiment, a vibration signal in the operation process of the equipment is obtained; taking a vibrating feeder equipped with vibrating motors as an example, according to the plane tone quality body vibration synchronization theory, when two vibrating motors perform reverse self-synchronization operation at the same angle, the component forces of the inertia forces in the direction of the connecting line of the rotating centers of the vibrating motors are equal in magnitude and opposite in direction, and are mutually offset, while the component forces in the direction perpendicular to the connecting line of the centers are the same in direction and are mutually superposed, and under the action of the inertia forces, the feeding groove performs simple harmonic vibration along the direction of resultant force, thereby achieving the purpose of conveying materials. This process produces a fixed frequency band vibratory sound signal.
Decomposing the vibration signal into k modal components, wherein the k value is automatically determined by the sample entropy of the modal components and the center frequency ratio of the modal components. The expression for the kth IMF component may be expressed as:
uk(t)＝At(t)cos(φk(t)); formula (1)
Wherein K is 1,2, …, K; a. thek(t) is uk(t) instantaneous amplitude; omegak(t)＝φ′k(t) is uk(t) instantaneous frequency of and Ak(t) and ωk(t) phase vsk(t) is slowly varying.
Determining Hilbert time spectrums corresponding to the k modal components; specifically, determining a single spectrum corresponding to each mode in the k mode components; determining a single-term spectrum corresponding to each mode in the k mode components according to the following formula:
wherein z isk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a hilbert time spectrum corresponding to the modal component of each mode.
Modulating the single term spectrum corresponding to each mode to a corresponding baseband frequency by mixing with an index tuned to the respective estimated center frequency; modulating the corresponding single-term spectrum of each mode to the corresponding baseband frequency according to the following formula:
wherein z isk(t) a unidirectional spectrum corresponding to each mode,
is an index of the center frequency.
Calculating a hilbert time spectrum corresponding to modal components of each mode modulated to a corresponding baseband frequency, specifically, adding all the components to equal to an original signal as a constraint condition, establishing a constraint variation model related to each mode, specifically, calculating a gradient of a demodulated signal, and estimating a bandwidth of each modal signal by using an L2 norm square, wherein the sum of all the components to equal to the original signal as the constraint condition is described as follows:
s.t.∑kukf; formula (4)
Wherein, { uk}:＝{u1,…,uK}，{ωk}:＝{ω1,…,ωKAre all the modalities, respectivelySet of optimal solutions for the Lambert-time spectrum and its set of center frequencies, zk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a Hilbert-time spectrum corresponding to a modal component of each mode,
is an index of the center frequency;
introducing a Lagrange multiplier lambda (t) and a secondary penalty factor alpha to optimize the constraint variational model so as to obtain the following optimization model:
after the lagrangian multiplier lambda (t) and the secondary penalty factor alpha are introduced to optimize the constraint variable model, the constraint variable problem can be changed into the non-constraint variable problem. Wherein, λ (t) can enhance the constraint strictness, and α can effectively reduce the gaussian noise interference. Equation (5) above, i.e., the optimization model, is an extended lagrangian expression.
Solving the optimization model to obtain the following optimal solution expression of the Hilbert time spectrum:
wherein the content of the first and second substances,
and the optimal solution of the Hilbert time spectrum corresponding to the modal components of each mode is obtained.
The center frequency updating formula of the modal component in this formula 6 is:
it can be understood that the characteristic frequency of the original vibration signal when the vibrating feeder is in a fault is different from the characteristic frequency of the original vibration signal when the vibrating feeder is in a normal state, and therefore after the hilbert time frequency spectrums corresponding to k modal components are determined, the hilbert time frequency spectrums corresponding to the k modal components are compared with a preset hilbert time frequency spectrum, wherein the preset hilbert time frequency spectrum is the hilbert time frequency spectrum corresponding to the modal component of the vibration signal when the device is in a normal operation; when the difference value between the Hilbert-time frequency spectrum corresponding to the k modal components and a preset Hilbert-time frequency spectrum is smaller than a preset difference value, determining that the equipment does not have a fault; and when the difference value between the Hilbert time frequency spectrum corresponding to the k modal components and the preset Hilbert time frequency spectrum is greater than the preset difference value, determining that the equipment fails.
From the decomposition principle, the decomposition precision of the scheme is influenced by the number K of modal components, a penalty parameter alpha, a discrimination precision epsilon and a fidelity coefficient tau. The judgment precision epsilon and the fidelity coefficient tau have small influence on the signal decomposition effect, and a default value is usually adopted during research; and the number K of modal components and the penalty parameter α have a large influence on the signal decomposition. The method is characterized in that the concept of decomposition evaluation coefficients is provided based on the information entropy theory, and the value of the number k of modal components is selected more scientifically by taking the concept as a standard, so that the optimal decomposition effect is ensured, and the operation efficiency is considered. The mode of determining the number k of modal components is as follows:
firstly, it can be understood that if K is smaller than the number of useful components in the processed signal, insufficient data decomposition will result; if K is greater than the number of useful components in the processed signal, over-resolution occurs, resulting in spurious components. The optimal K value is reasonably determined by utilizing the sample entropy, the center frequency ratio and the correlation coefficient, the phenomena of under-decomposition and over-decomposition can be solved to a certain extent, and the false component is effectively eliminated. The sample entropy can effectively represent the complexity of the time series, and the more complex the time series, the larger the corresponding sample entropy. When effective component signals in the complex mixed signals can be extracted well, the sample entropy of the effective modal components is small, the sample entropies of adjacent components are different remarkably, the entropy value of the noise component is large generally, and therefore when the sample entropies of the adjacent modal components are close to each other, the over-decomposition phenomenon can be judged to occur. Therefore, the sample entropy can be used to analyze whether the extracted modal components have over-decomposition phenomenon. When over-resolution occurs, the center frequencies of the modal components are close, so that the adjacent center frequencies can also be used as an index of over-resolution, and a center frequency ratio greater than 0.9 is usually used as an over-resolution determination criterion. The correlation coefficient can be used to effectively analyze the correlation between the component and the original signal, and further, the correlation coefficient can be used to determine whether the modal component is a useful component and a noise component, and usually, the correlation coefficient is less than 0.1 as a false component and noise component rejection standard. The original signal is subjected to spectrum analysis, and the frequency with more obvious energy value is extracted as the initial value of the central frequency, so that the decomposition efficiency and the precision of the original signal can be improved.
Therefore, the present application may be embodied as: setting an initial value of the k value; for example, k ═ 2;
decomposing the vibration signal in the operation process of the equipment according to the initial value to obtain a decomposed first modal component;
calculating sample entropy and center frequency between the decomposed first modal components;
and when the sample entropy difference value reaches a first preset value and the center frequency is smaller than a second preset value, determining the initial value as a final k value.
When the sample entropy difference value does not reach a first preset value or the center frequency is greater than a second preset value, the method further comprises the following steps of 1-4:
step 1, adjusting a k value according to a preset step length; for example, if the preset step size is 1, k is set to 3.
Step 2, decomposing the vibration signal in the operation process of the equipment according to the adjusted k value to obtain a decomposed second modal component;
step 3, calculating sample entropy and center frequency between the decomposed second modal components;
and 4, when the sample entropy difference value does not reach the first preset value or the center frequency is greater than the second preset value, repeating the steps 1-3 until the k value is obtained when the sample entropy difference value reaches the first preset value and the center frequency is less than the second preset value. For example, when k is 3, the sample entropy difference value does not reach the first preset value, or the center frequency is greater than the second preset value, k is continuously adjusted to k is 4 according to the preset step length, and so on.
The beneficial effect of this application lies in: the Hilbert-time frequency spectrum of the characteristic modal component of the vibration signal under the condition that the equipment is in the running state can be compared with the Hilbert-time frequency spectrum corresponding to the modal component of the vibration signal when the preset equipment runs normally, so that whether the equipment breaks down is automatically judged, equipment failure monitoring is not required to be carried out through manual troubleshooting, the labor cost is saved, and in addition, whether the equipment breaks down or not is judged by the equipment through automatic judgment, and the instantaneity of equipment failure monitoring is improved for a manual troubleshooting scheme.
In one embodiment, as shown in FIG. 2, the above step S13 can be implemented as the following steps S21-S23:
in step S21, determining a single spectrum corresponding to each mode in the k mode components;
modulating the single term spectrum corresponding to each mode to a corresponding baseband frequency by mixing with an index tuned to the corresponding estimated center frequency in step S22;
in step S23, hilbert-time spectra corresponding to modal components of the respective modes modulated to the corresponding baseband frequencies are calculated.
In one embodiment, the above step S21 can be implemented as the following steps:
determining a single-term spectrum corresponding to each mode in the k mode components according to the following formula:
wherein z isk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a mode of each modeThe hubert time spectrum to which the component corresponds.
In one embodiment, the above step S22 can be implemented as the following steps:
modulating the corresponding single-term spectrum of each mode to the corresponding baseband frequency according to the following formula:
wherein z isk(t) a unidirectional spectrum corresponding to each mode,
is an index of the center frequency.
In one embodiment, the above step S23 may be implemented as the following steps A1-A3:
in step a1, adding all components equal to the original signal as a constraint condition, and establishing the following constraint variation models related to each mode:
s.t.∑kuk＝f；
wherein, { uk}:＝{u1,…,uK}，{ωk}:＝{ω1,…,ωKIs the optimal solution set of all modal Hilbert-time spectra and its center frequency set, zk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a Hilbert-time spectrum corresponding to a modal component of each mode,
is an index of the center frequency;
in step a2, introducing a lagrange multiplier λ (t) and a secondary penalty factor α to optimize the constraint variational model, so as to obtain the following optimization models:
in step a3, the optimization model is solved to obtain the following optimal solution expression of hilbert-time spectrum:
wherein the content of the first and second substances,
and the optimal solution of the Hilbert time spectrum corresponding to the modal components of each mode is obtained.
In one embodiment, the central frequency updating formula of each hilbert time spectrum is:
wherein the content of the first and second substances,
the center frequency of each hilbert time spectrum.
In one embodiment, as shown in FIG. 3, the k value determination process may be implemented as the following steps S31-S34:
in step S31, an initial value of the k value is set;
in step S32, decomposing the vibration signal during the operation of the device according to the initial value to obtain a decomposed first modal component;
in step S33, calculating sample entropies between the decomposed first modal components and their center frequencies;
in step S34, when the sample entropy difference value reaches a first preset value and the center frequency is smaller than a second preset value, the initial value is determined as a final k value.
In one embodiment, when the sample entropy difference value does not reach the first preset value, or the center frequency is greater than the second preset value, the method may further be implemented as the following steps:
step 1, adjusting a k value according to a preset step length;
step 2, decomposing the vibration signal in the operation process of the equipment according to the adjusted k value to obtain a decomposed second modal component;
step 3, calculating sample entropy and center frequency between the decomposed second modal components;
and 4, when the sample entropy difference value does not reach the first preset value or the center frequency is greater than the second preset value, repeating the steps 1-3 until the k value is obtained when the sample entropy difference value reaches the first preset value and the center frequency is less than the second preset value.
The beneficial effects of this embodiment lie in, can solve out optimum k value based on the central frequency between the sample entropy difference value between the modal component and the modal component, need not to rely on expert's experience just can obtain optimum modal component number, subjectivity when having reduced manual judgement, and the decomposition number of having confirmed the appropriate modal component can effectively reduce under-decomposing and over-decomposition phenomenon, effectively rejects the false component, and then has promoted the accuracy of equipment failure judgement.
Fig. 4 is a schematic diagram of a hardware structure of an apparatus operation state monitoring system according to the present application, as shown in fig. 4, including:
at least one processor 420; and the number of the first and second groups,
a memory 404 communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the device operation state monitoring method according to any one of the above embodiments.
Referring to fig. 4, the device operating condition monitoring system 400 may include one or more of the following components: processing components 402, memory 404, power components 406, multimedia components 408, audio components 410, input/output (I/O) interfaces 412, sensor components 414, and communication components 416.
The processing component 402 generally controls the overall operation of the device health monitoring system 400. The processing component 402 may include one or more processors 420 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 402 can include one or more modules that facilitate interaction between the processing component 402 and other components. For example, the processing component 402 can include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.
The memory 404 is configured to store various types of data to support operation of the monitoring system 400 in device health. Examples of such data include instructions for any application or method operating on device health monitoring system 400, such as text, pictures, video, and so forth. The memory 404 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 406 provides power to the various components of the device health monitoring system 400. Power components 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power supplies for device health monitoring system 400.
The multimedia component 408 includes a screen that provides an output interface between the device health monitoring system 400 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 may also include a front facing camera and/or a rear facing camera. When the device operation state monitoring system 400 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 410 is configured to output and/or input audio signals. For example, the audio component 410 includes a Microphone (MIC) configured to receive an external audio signal when the device operational status monitoring system 400 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 404 or transmitted via the communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.
The I/O interface 412 provides an interface between the processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 414 includes one or more sensors for providing various aspects of condition assessment for the plant operating condition monitoring system 400. For example, the sensor component 414 may include an acoustic sensor. Additionally, the sensor component 414 can detect the open/closed status of the device operating condition monitoring system 400, the relative positioning of components, such as a display and keypad of the device operating condition monitoring system 400, the sensor component 414 can also detect a change in the position of the device operating condition monitoring system 400 or a component of the device operating condition monitoring system 400, the presence or absence of user contact with the device operating condition monitoring system 400, the orientation or acceleration/deceleration of the device operating condition monitoring system 400, and a change in the temperature of the device operating condition monitoring system 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 416 is configured to enable the device operational status monitoring system 400 to provide wired or wireless communication capabilities with other devices and cloud platforms. The device health monitoring system 400 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the device operation state monitoring system 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described device operation state monitoring method.
The present application further provides a computer-readable storage medium, wherein when instructions in the storage medium are executed by a processor corresponding to the device operation state monitoring system, the device operation state monitoring system is enabled to implement the device operation state monitoring method described in any one of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. An equipment running state monitoring method is characterized by comprising the following steps:
acquiring a vibration signal in the running process of equipment;
decomposing the vibration signal into k modal components, wherein the k value is automatically determined by the sample entropy of the modal components and the center frequency ratio of the modal components;
determining Hilbert time spectrums corresponding to the k modal components;
comparing the Hilbert time frequency spectrums corresponding to the k modal components with a preset Hilbert time frequency spectrum, wherein the preset Hilbert time frequency spectrum is the Hilbert time frequency spectrum corresponding to the modal component of the vibration signal when the equipment normally operates;
when the difference value between the Hilbert-time frequency spectrum corresponding to the k modal components and a preset Hilbert-time frequency spectrum is smaller than a preset difference value, determining that the equipment does not have a fault;
and when the difference value between the Hilbert time frequency spectrum corresponding to the k modal components and the preset Hilbert time frequency spectrum is greater than the preset difference value, determining that the equipment fails.
2. The method of claim 1, wherein the determining the hilbert-time spectra corresponding to the k modal components comprises:
determining a single frequency spectrum corresponding to each mode in the k mode components;
modulating the single term spectrum corresponding to each mode to a corresponding baseband frequency by mixing with an index tuned to the respective estimated center frequency;
and calculating Hilbert time spectrums corresponding to modal components of the modes modulated to the corresponding baseband frequencies.
3. The method of claim 2, wherein the determining the single-term spectrum for each modality of the k-modality components comprises:
determining a single-term spectrum corresponding to each mode in the k mode components according to the following formula:
wherein z isk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a hilbert time spectrum corresponding to the modal component of each mode.
4. The method of claim 2, wherein the modulating the singles spectrum corresponding to each modality to a corresponding baseband frequency comprises:
modulating the corresponding single-term spectrum of each mode to the corresponding baseband frequency according to the following formula:
wherein z isk(t) a unidirectional spectrum corresponding to each mode,
is an index of the center frequency.
5. The method of claim 2, wherein calculating the hilbert-time spectrum for modal components of each mode modulated to the corresponding baseband frequency comprises:
adding all components to be equal to the original signal as a constraint condition, and establishing the following constraint variation models related to each mode:
s.t.∑kuk＝f；
wherein, { uk}：＝{u1，…，uK}，{ωk}：＝{ω1，…，ωKIs the optimal solution set of all modal Hilbert-time spectra and its center frequency set, zk(t) is the unidirectional spectrum corresponding to each mode, σ (t) is the impulse function, uk(t) is a Hilbert-time spectrum corresponding to a modal component of each mode,
is an index of the center frequency;
introducing a Lagrange multiplier lambda (t) and a secondary penalty factor alpha to optimize the constraint variational model so as to obtain the following optimization model:
solving the optimization model to obtain the following optimal solution expression of the Hilbert time spectrum:
wherein the content of the first and second substances,
and the optimal solution of the Hilbert time spectrum corresponding to the modal components of each mode is obtained.
6. The method of claim 2, wherein the center frequency update formula of each hubert time spectrum is:
wherein the content of the first and second substances,
for the centre of the respective Hilbert time spectrumFrequency.
7. The method of claim 1, wherein the k value is determined as follows:
setting an initial value of the k value;
decomposing the vibration signal in the operation process of the equipment according to the initial value to obtain a decomposed first modal component;
calculating sample entropy and center frequency between the decomposed first modal components;
and when the sample entropy difference value reaches a first preset value and the center frequency is smaller than a second preset value, determining the initial value as a final k value.
8. The method of claim 7, wherein when the sample entropy difference value does not reach a first preset value or the center frequency is greater than a second preset value, further comprising:
step 1, adjusting a k value according to a preset step length;
step 2, decomposing the vibration signal in the operation process of the equipment according to the adjusted k value to obtain a decomposed second modal component;
step 3, calculating sample entropy and center frequency between the decomposed second modal components;
and 4, when the sample entropy difference value does not reach the first preset value or the center frequency is greater than the second preset value, repeating the steps 1-3 until the k value is obtained when the sample entropy difference value reaches the first preset value and the center frequency is less than the second preset value.
9. An equipment operating condition monitoring system, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to perform a method of monitoring operational status of a device as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor corresponding to the device operation state monitoring system, enable the device operation state monitoring system to implement the device operation state monitoring method according to any one of claims 1 to 8.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106055734A (en) * | 2016-05-13 | 2016-10-26 | 浙江理工大学 | Method for failure diagnosis of wind power gearbox |
| CN109029987A (en) * | 2018-09-10 | 2018-12-18 | 北航（天津武清）智能制造研究院有限公司 | Epicyclic gearbox gear distress detection method and system |
| CN109682600A (en) * | 2018-09-14 | 2019-04-26 | 温州大学 | A kind of improvement variation mode decomposition diagnostic method for Main Shaft Bearing of Engine fault diagnosis |
| CN109829402A (en) * | 2019-01-21 | 2019-05-31 | 福州大学 | Different operating condition lower bearing degree of injury diagnostic methods based on GS-SVM |
| CN110909480A (en) * | 2019-11-29 | 2020-03-24 | 华北水利水电大学 | Denoising method and denoising device for water turbine vibration signal |
| CN112232244A (en) * | 2020-10-22 | 2021-01-15 | 昆明理工大学 | Fault diagnosis method for rolling bearing |
| CN112326236A (en) * | 2020-11-02 | 2021-02-05 | 北京信息科技大学 | Gear box operation state online monitoring method and system and storage medium |
-
2021
- 2021-11-19 CN CN202111375883.0A patent/CN114137338B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106055734A (en) * | 2016-05-13 | 2016-10-26 | 浙江理工大学 | Method for failure diagnosis of wind power gearbox |
| CN109029987A (en) * | 2018-09-10 | 2018-12-18 | 北航（天津武清）智能制造研究院有限公司 | Epicyclic gearbox gear distress detection method and system |
| CN109682600A (en) * | 2018-09-14 | 2019-04-26 | 温州大学 | A kind of improvement variation mode decomposition diagnostic method for Main Shaft Bearing of Engine fault diagnosis |
| CN109829402A (en) * | 2019-01-21 | 2019-05-31 | 福州大学 | Different operating condition lower bearing degree of injury diagnostic methods based on GS-SVM |
| CN110909480A (en) * | 2019-11-29 | 2020-03-24 | 华北水利水电大学 | Denoising method and denoising device for water turbine vibration signal |
| CN112232244A (en) * | 2020-10-22 | 2021-01-15 | 昆明理工大学 | Fault diagnosis method for rolling bearing |
| CN112326236A (en) * | 2020-11-02 | 2021-02-05 | 北京信息科技大学 | Gear box operation state online monitoring method and system and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 杨大为，等: "VMD样本熵特征提取方法及其在行星变速箱故障诊断中的应用", 《振动与冲击》, vol. 37, no. 16, pages 198 - 205 * |
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