CN111947903B - Vibration abnormity positioning method and device - Google Patents
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Abstract
The invention belongs to the technical field of nuclear power maintenance, and particularly relates to a vibration abnormity positioning method and device. Carrying out abnormal value removal processing and smoothing processing on an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, and the target set comprises a plurality of target vibration data; determining a plurality of target subsets according to the target set, the time length of the analysis window and the time length of the sliding step length; determining a Pearson correlation coefficient of target vibration data change in each target subset and change at a moment; determining a fluctuation threshold value according to the determined plurality of Pearson correlation coefficients; and setting the time corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the time of the abnormal vibration. The embodiment of the disclosure can accurately position the moment corresponding to the abnormal vibration, has a good effect on the aspect of abnormal vibration detection, and provides data support for early warning and diagnosis of mechanical equipment.
Description
Technical Field
The invention belongs to the technical field of nuclear power maintenance, and particularly relates to a vibration abnormity positioning method and device.
Background
In a nuclear power plant, rotary mechanical equipment represented by a main pump, a steam turbine and a circulating water pump is heart equipment in the nuclear power plant, and plays an irreplaceable role in the operation and production process of the nuclear power plant. In order to guarantee the safe operation of a main pump, the existing main pump monitoring system monitors the state of the main pump by accessing information acquired by sensors such as pressure, flow, temperature, vibration and the like in the operation process of the main pump, wherein the vibration reflects key information of the health state of main pump equipment and is often used as an index for evaluating the fault of the main pump equipment. However, the number of field vibration data is huge and the regularity is checked, so how to efficiently locate the abnormal vibration from a large amount of unknown state data becomes an urgent problem to be solved.
Disclosure of Invention
In order to overcome the problems in the related art, a method and a device for positioning the vibration abnormity are provided.
According to an aspect of the embodiments of the present disclosure, there is provided a method for locating a vibration abnormality, the method including:
removing abnormal values and smoothing an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, the target set comprises a plurality of target vibration data, different original vibration data correspond to different moments, and different target vibration data correspond to different moments;
determining a plurality of target subsets according to the target set, the duration of the analysis window and the duration of the sliding step length;
determining a Pearson correlation coefficient of target vibration data change in each target subset and change at a moment, wherein the moment corresponding to each target vibration data in each target subset corresponds to the Pearson correlation coefficient of the target subset;
determining a fluctuation threshold value according to the determined Pearson correlation coefficients;
and setting the time corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the time of the abnormal vibration.
In a possible implementation manner, the removing an abnormal value and smoothing the original set to obtain a target set includes:
taking original vibration data with the numerical value smaller than Q1-1.5IQR or larger than Q3+1.5IQR as abnormal data, wherein Q1 is the upper quartile of a box chart of an original set, Q3 is the lower quartile of the box chart of the original set, and IQR = Q3-Q1;
taking the original vibration data with the time difference from the adjacent original vibration data larger than the preset time as abnormal data;
removing the determined abnormal data from the original set to form a set to be smoothed;
dividing original vibration data in a set to be smoothed into a plurality of subsets to be smoothed, taking an average value of the original vibration data in each subset to be smoothed as target vibration data, and determining the time corresponding to the target vibration data according to the time corresponding to each original vibration data in the subset to be smoothed;
and forming the determined plurality of target vibration data into a target set.
In a possible implementation manner, the duration of the analysis window is the minimum duration between peaks of corresponding amplitude values of the original vibration data.
In one possible implementation, the step size of the sliding is determined according to the following equation:
s＝2×(1-α)T
wherein S is the sliding step length, T is the analysis window duration, and alpha is an adjustment factor.
In one possible implementation, determining the fluctuation threshold according to the determined plurality of pearson correlation coefficients includes:
determining the number of the Pearson correlation coefficients with different values according to the Pearson correlation coefficients;
and determining a fluctuation threshold value according to the number of the Pearson correlation coefficients with different values.
According to another aspect of the embodiments of the present disclosure, there is provided a vibration abnormality locating apparatus, the apparatus including:
the processing module is used for removing abnormal values and smoothing an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, the target set comprises a plurality of target vibration data, different original vibration data correspond to different moments, and different target vibration data correspond to different moments;
the first determining module is used for determining a plurality of target subsets according to the target set, the duration of the analysis window and the duration of the sliding step length;
the second determining module is used for determining the Pearson correlation coefficient of the change of the target vibration data in each target subset and the change of the time, and the time corresponding to each target vibration data in each target subset corresponds to the Pearson correlation coefficient of the target subset;
the third determining module is used for determining a fluctuation threshold value according to the plurality of determined Pearson correlation coefficients;
and the fourth determining module is used for taking the moment corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the moment of the abnormal vibration.
In one possible implementation, the processing module includes:
the first determination submodule is used for taking original vibration data with the value smaller than Q1-1.5IQR or larger than Q3+1.5IQR as abnormal data, wherein Q1 is the upper quartile of a box diagram of an original set, Q3 is the lower quartile of the box diagram of the original set, and IQR = Q3-Q1;
the second determining submodule is used for taking the original vibration data of which the time difference with the adjacent original vibration data is greater than the preset time length as abnormal data;
a third determining submodule, configured to remove the determined abnormal data from the original set, and form a set to be smoothed;
the fourth determining submodule is used for dividing the original vibration data in the set to be smoothed into a plurality of subsets to be smoothed, taking the average value of the original vibration data in each subset to be smoothed as target vibration data, and determining the time corresponding to the target vibration data according to the time corresponding to each original vibration data in the subset to be smoothed;
and the fifth determining submodule is used for forming the determined plurality of target vibration data into a target set.
In a possible implementation manner, the duration of the analysis window is a minimum duration between peaks of corresponding amplitude values of the original vibration data.
In one possible implementation, the sliding step is determined according to the following equation:
s＝2×(1-α)T
wherein S is the sliding step length, T is the analysis window duration, and alpha is an adjustment factor.
In one possible implementation manner, the third determining module includes:
a sixth determining submodule, configured to determine, according to the plurality of pearson correlation coefficients, the number of pearson correlation coefficients of different values;
and the seventh determining submodule is used for determining the fluctuation threshold value according to the number of the Pearson correlation coefficients with different values.
According to another aspect of the embodiments of the present disclosure, there is provided a wireless control device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
the above method is performed.
According to another aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having instructions therein, which when executed by a processor, enable the processor to perform the above-described method.
The invention has the beneficial effects that: the vibration change trend is measured by adopting the Pearson coefficient method, the individualized threshold values are provided for different equipment individuals, the moment corresponding to the abnormal vibration can be accurately positioned, a good effect is achieved in the aspect of abnormal vibration detection, and data support is provided for early warning and diagnosis of mechanical equipment.
Drawings
FIG. 1 is a flow chart illustrating a method of locating a vibration anomaly in accordance with an exemplary embodiment.
Fig. 2 is a histogram of a number distribution of determined pearson coefficients, shown in accordance with an exemplary embodiment.
FIG. 3 is a schematic illustration of the location of a vibration anomaly shown in accordance with an exemplary embodiment.
FIG. 4 is a block diagram illustrating a vibration anomaly locating device according to an exemplary embodiment.
FIG. 5 is a block diagram illustrating a vibration anomaly locating device according to an exemplary embodiment.
Detailed Description
The invention is described in further detail below with reference to the figures and the embodiments.
FIG. 1 is a flow chart illustrating a method of locating a vibration anomaly in accordance with an exemplary embodiment. The method disclosed by the invention can be applied to terminal equipment such as a desktop computer, a notebook computer or a server, and the type of the terminal equipment is not limited by the embodiment of the disclosure. As shown in fig. 1, the method may include:
100, performing outlier removal processing and smoothing processing on an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, the target set comprises a plurality of target vibration data, different original vibration data correspond to different moments, and different target vibration data correspond to different moments;
103, determining a fluctuation threshold value according to the plurality of determined Pearson correlation coefficients;
and step 104, setting the time corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the time of the abnormal vibration.
As an example of the embodiment, the terminal device may be connected to a vibration sensor disposed on the object to be measured, and acquire a plurality of raw vibration data to form a raw set. The absolute value of the original vibration data may represent the amplitude of the object at the time corresponding to the original data, and different original vibration data may correspond to a different time.
The terminal device may determine an upper quartile Q1 of the original set of box plots, determine a lower quartile Q3 of the original set of box plots, and determine IQR = Q3-Q1. The terminal equipment can take the original vibration data with the numerical value smaller than Q1-1.5IQR or larger than Q3+1.5IQR as abnormal data, so that the original vibration data with the numerical value too large or too small can be effectively removed, and the accuracy of the data is improved.
The terminal device may further determine a difference between each original vibration data and the adjacent original vibration data, and may use the original vibration data having a difference between the adjacent original vibration data and the adjacent original vibration data larger than a preset time period as the abnormal data. Therefore, data with overlarge sampling intervals can be effectively removed, and the accuracy of the data is further improved. For example, the preset duration may be, for example, 0.25 times the duration of the analysis window. And the data in the same analysis window are effectively prevented from being too little.
The terminal equipment can remove the determined abnormal data from the original set after determining one or more abnormal data to form a set to be smoothed;
the terminal device may divide the original vibration data in the set to be smoothed into a plurality of subsets to be smoothed (e.g., the original vibration data may be equally divided into the plurality of subsets to be smoothed according to the number of the original vibration data in the set to be smoothed). Then, the average value of the original vibration data in each subset to be smoothed may be used as a target vibration data (for example, for each subset to be smoothed, the original vibration data with the largest value and the smallest value of the subset to be smoothed may be removed, and the simple average value of the remaining original vibration data in the subset to be smoothed may also be used as a target vibration data, and finally, the terminal device may determine the time corresponding to the target vibration data according to the time corresponding to each original vibration data in the subset to be smoothed, (for example, the terminal device may use the earliest time, the latest time, or the time with the same time interval as the earliest time and the latest time corresponding to the original vibration data in the subset to be smoothed as the time corresponding to the target vibration data, which is not limited by the embodiment of the present disclosure).
The terminal device forms the determined plurality of target vibration data into a target set.
Then, the terminal device may determine a plurality of target subsets according to the target set, the duration of the analysis window, and the duration of the sliding step, for example, the terminal device may determine each vibration peak formed by the raw vibration data (for example, the terminal device may apply the raw vibration data a to the raw vibration data a) t As a peak, wherein A t >A t-1 And A is t >A t+1 And t is the sequence of the moments corresponding to the original vibration data), and determining the minimum time length of the intervals between the wave crests, and taking the minimum time length as the time length of the analysis window, so that the analysis window can intercept data which are continuously in a fluctuation state.
Then, the terminal device may determine the sliding step according to the following formula:
s＝2×(1-α)T
wherein S is the sliding step length, T is the analysis window duration, and alpha is an adjustment factor. The method avoids overlarge calculated amount caused by too small sliding step length, and further ensures that the analysis window intercepts data which are continuously in a fluctuation state. In one possible implementation, α is 0.95.
After determining a plurality of target subsets, the terminal device may determine the pearson correlation coefficient ρ between the target vibration data change and the time change in the target subsets according to the following formula X,Z The time instants corresponding to the respective target vibration data in each target subset may correspond to the pearson correlation coefficients for that target subset.
Wherein cov (X, Z) is the covariance, σ, of the target vibration data variable X and the corresponding time variable Z within the analysis window T X For each vibration data variable X, sigma Y Is the standard deviation of each time variable Z.
Then, the terminal device may determine a fluctuation threshold value according to the determined plurality of pearson correlation coefficients.
For example, the terminal device may determine the number of different values of pearson correlation coefficients according to the plurality of pearson correlation coefficients; and determining a fluctuation threshold value according to the number of the Pearson correlation coefficients with different values.
Fig. 2 is a histogram of a number distribution of determined pearson coefficients, shown in accordance with an exemplary embodiment. As shown in fig. 2, the terminal device may determine the number of pearson correlation coefficients of different values and may determine a plurality of peaks (e.g., the terminal device may determine the number B i As a peak, wherein B i >B i-j And B is i >B i+j I is the pearson correlation coefficient, j is the increment step of each pearson correlation coefficient), the terminal device may use the peak corresponding to the smallest pearson correlation coefficient as the target peak (e.g., R in fig. 2), and use 2 times the pearson coefficient corresponding to the peak (e.g., 2R in fig. 2) as the fluctuation threshold.
If the vibration data are continuously in a certain variation trend, the absolute value of the Pearson correlation coefficient is continuously increased; if the vibration fluctuation is smaller, the absolute value of the Pearson correlation coefficient is reduced; if the vibration is in a steady trend. The pearson coefficient is close to 0. In the historical monitoring vibration data, the vibration data with large fluctuation and the stable vibration data as well as the vibration data with a slight fluctuation trend between the vibration data and the stable vibration data exist, and in order to accurately distinguish the vibration data with large fluctuation from the stable vibration data and locate the abnormal vibration, a fluctuation threshold value needs to be determined.
As shown in fig. 2, the closer the pearson coefficient value is to 0, the smoother the vibration is; the closer the pearson coefficient is to 1, the more severe the vibration fluctuation is. Experience shows that most of the monitoring data belong to normal data, so that under the condition that the historical monitoring data are enough, the first peak R in the Pearson coefficient distribution histogram represents the center of the vibration stationary data, if more abnormal data exist in the historical monitoring data, the second peak appears, and at the moment, the area between the two peaks is the data with slight fluctuation. Since the normal distribution model has symmetry and data outside the 2R range can be considered as abnormal fluctuation, the pearson coefficient corresponding to 2R is used as a fluctuation threshold.
FIG. 3 is a schematic illustration of the location of a vibration anomaly shown in accordance with an exemplary embodiment. As shown in fig. 3, the terminal device may take a time corresponding to the pearson correlation coefficient larger than the fluctuation threshold as a time of the abnormal vibration.
The vibration change trend is measured by adopting the Pearson coefficient method, the individualized threshold values are provided for different equipment individuals, the moment corresponding to the abnormal vibration can be accurately positioned, a good effect is achieved in the aspect of abnormal vibration detection, and data support is provided for early warning and diagnosis of mechanical equipment.
In one possible implementation, there is provided a vibration anomaly locating apparatus, the apparatus comprising:
the processing module is used for removing abnormal values and smoothing an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, the target set comprises a plurality of target vibration data, different original vibration data correspond to different moments, and different target vibration data correspond to different moments;
the first determining module is used for determining a plurality of target subsets according to the target set, the duration of the analysis window and the duration of the sliding step length;
the second determining module is used for determining Pearson correlation coefficients of target vibration data change and time change in each target subset, and the time corresponding to each target vibration data in each target subset corresponds to the Pearson correlation coefficient of the target subset;
the third determining module is used for determining a fluctuation threshold value according to the plurality of determined Pearson correlation coefficients;
and the fourth determining module is used for taking the moment corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the moment of the abnormal vibration.
In one possible implementation, the processing module includes:
the first determination submodule is used for taking original vibration data with the value smaller than Q1-1.5IQR or larger than Q3+1.5IQR as abnormal data, wherein Q1 is the upper quartile of a box diagram of an original set, Q3 is the lower quartile of the box diagram of the original set, and IQR = Q3-Q1;
the second determining submodule is used for taking the original vibration data of which the time difference with the adjacent original vibration data is greater than the preset time length as abnormal data;
a third determining submodule, configured to remove the determined abnormal data from the original set, and form a set to be smoothed;
the fourth determining submodule is used for dividing the original vibration data in the set to be smoothed into a plurality of subsets to be smoothed, taking the average value of the original vibration data in each subset to be smoothed as target vibration data, and determining the time corresponding to the target vibration data according to the time corresponding to each original vibration data in the subset to be smoothed;
and the fifth determining submodule is used for forming the determined plurality of target vibration data into a target set.
In a possible implementation manner, the duration of the analysis window is the minimum duration between peaks of corresponding amplitude values of the original vibration data.
In one possible implementation, the step size of the sliding is determined according to the following equation:
s＝2×(1-α)T
wherein S is the sliding step length, T is the analysis window duration, and alpha is an adjustment factor.
In one possible implementation manner, the third determining module includes:
a sixth determining submodule, configured to determine, according to the plurality of pearson correlation coefficients, the number of pearson correlation coefficients of different values;
and the seventh determining submodule is used for determining the fluctuation threshold value according to the number of the Pearson correlation coefficients with different values.
It should be noted that the description of the above vibration abnormality positioning apparatus has already been set forth in detail in the description of the vibration abnormality positioning method, and is not repeated herein.
FIG. 4 is a block diagram illustrating a vibration anomaly locating device according to an exemplary embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 4, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communications component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 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.
A power supply component 806 provides power to the various components of the device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 800.
The multimedia component 808 includes a screen that provides an output interface between the device 800 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 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. 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 810 is configured to output and/or input audio signals. For example, audio component 810 includes a Microphone (MIC) configured to receive external audio signals when apparatus 800 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 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 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 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed state of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 814 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 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 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 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 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 apparatus 800 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 methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
FIG. 5 is a block diagram illustrating a vibration anomaly locating device according to an exemplary embodiment. For example, the apparatus 1900 may be provided as a server. Referring to FIG. 5, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as a memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the methods described above.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the disclosure are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A method of locating a vibration anomaly, the method comprising:
carrying out abnormal value removal processing and smoothing processing on an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, the target set comprises a plurality of target vibration data, different original vibration data correspond to different moments, and different target vibration data correspond to different moments;
determining a plurality of target subsets according to the target set, the time length of the analysis window and the time length of the sliding step length;
determining a Pearson correlation coefficient of target vibration data change in each target subset and change at a moment, wherein the moment corresponding to each target vibration data in each target subset corresponds to the Pearson correlation coefficient of the target subset;
determining a fluctuation threshold value according to the determined Pearson correlation coefficients;
taking the moment corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the moment of abnormal vibration;
carrying out abnormal value removal processing and smoothing processing on the original set to obtain a target set, wherein the abnormal value removal processing and the smoothing processing comprise the following steps:
taking original vibration data with the numerical value smaller than Q1-1.5IQR or larger than Q3+1.5IQR as abnormal data, wherein Q1 is the upper quartile of a box chart of an original set, Q3 is the lower quartile of the box chart of the original set, and IQR = Q3-Q1;
taking the original vibration data with the time difference from the adjacent original vibration data larger than the preset time as abnormal data;
removing the determined abnormal data from the original set to form a set to be smoothed;
dividing original vibration data in a set to be smoothed into a plurality of subsets to be smoothed, taking an average value of the original vibration data in each subset to be smoothed as target vibration data, and determining the time corresponding to the target vibration data according to the time corresponding to each original vibration data in the subset to be smoothed;
and forming the determined plurality of target vibration data into a target set.
2. The method of claim 1, wherein the duration of the analysis window is a minimum duration between corresponding amplitude value peaks of the raw vibration data.
3. The method of claim 1, wherein the step size of the sliding is determined according to the following equation:
s＝2×(1-α)T
wherein S is the sliding step length, T is the analysis window duration, and alpha is an adjustment factor.
4. The method of claim 1, wherein determining a fluctuation threshold based on the determined plurality of pearson correlation coefficients comprises:
determining the number of the Pearson correlation coefficients with different values according to the Pearson correlation coefficients;
and determining a fluctuation threshold value according to the number of the Pearson correlation coefficients with different values.
5. A vibration anomaly locating device, said device comprising:
the processing module is used for removing abnormal values and smoothing an original set to obtain a target set, wherein the original set comprises a plurality of original vibration data, the target set comprises a plurality of target vibration data, different original vibration data correspond to different moments, and different target vibration data correspond to different moments;
the first determining module is used for determining a plurality of target subsets according to the target set, the duration of the analysis window and the duration of the sliding step length;
the second determining module is used for determining the Pearson correlation coefficient of the change of the target vibration data in each target subset and the change of the time, and the time corresponding to each target vibration data in each target subset corresponds to the Pearson correlation coefficient of the target subset;
the third determining module is used for determining a fluctuation threshold value according to the determined Pearson correlation coefficients;
the fourth determining module is used for taking the moment corresponding to the Pearson correlation coefficient larger than the fluctuation threshold value as the moment of abnormal vibration;
the processing module comprises:
the first determining submodule is used for taking original vibration data with the value smaller than Q1-1.5IQR or larger than Q3+1.5IQR as abnormal data, wherein Q1 is the upper quartile of a box diagram of an original set, Q3 is the lower quartile of the box diagram of the original set, and IQR = Q3-Q1;
the second determining submodule is used for taking the original vibration data of which the time difference with the adjacent original vibration data is greater than the preset time length as abnormal data;
a third determining submodule, configured to remove the determined abnormal data from the original set, and form a set to be smoothed;
the fourth determining submodule is used for dividing the original vibration data in the set to be smoothed into a plurality of subsets to be smoothed, taking the average value of the original vibration data in each subset to be smoothed as target vibration data, and determining the time corresponding to the target vibration data according to the time corresponding to each original vibration data in the subset to be smoothed;
and the fifth determining submodule is used for forming the determined plurality of target vibration data into a target set.
6. The apparatus of claim 5, wherein the duration of the analysis window is a minimum duration between corresponding amplitude value peaks of the raw vibration data.
7. The apparatus of claim 5, wherein the step size of the sliding is determined according to the following equation:
s＝2×(1-α)T
wherein S is the sliding step length, T is the analysis window duration, and alpha is an adjustment factor.
8. The apparatus of claim 5, wherein the third determining module comprises:
a sixth determining submodule, configured to determine, according to the plurality of pearson correlation coefficients, the number of pearson correlation coefficients of different values;
and the seventh determining submodule is used for determining the fluctuation threshold value according to the number of the Pearson correlation coefficients with different values.
9. A vibration anomaly locating device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
-performing the method according to any of claims 1 to 4.
10. A non-transitory computer readable storage medium having instructions therein, which when executed by a processor, enable the processor to perform the method of any one of claims 1 to 4.
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