CN114510833A - Method and device for estimating residual service life of equipment - Google Patents

Method and device for estimating residual service life of equipment Download PDF

Info

Publication number
CN114510833A
CN114510833A CN202210102053.9A CN202210102053A CN114510833A CN 114510833 A CN114510833 A CN 114510833A CN 202210102053 A CN202210102053 A CN 202210102053A CN 114510833 A CN114510833 A CN 114510833A
Authority
CN
China
Prior art keywords
equipment
distribution
degradation model
vibration signal
determining
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210102053.9A
Other languages
Chinese (zh)
Inventor
曹双华
王欣
张福海
昌正科
凌世情
陶佳林
喻松
彭瑞华
刘峰
刘双
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nuclear Power Operation Research Shanghai Co ltd
Original Assignee
Nuclear Power Operation Research Shanghai Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nuclear Power Operation Research Shanghai Co ltd filed Critical Nuclear Power Operation Research Shanghai Co ltd
Priority to CN202210102053.9A priority Critical patent/CN114510833A/en
Publication of CN114510833A publication Critical patent/CN114510833A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/12—Simultaneous equations, e.g. systems of linear equations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/10—Noise analysis or noise optimisation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Databases & Information Systems (AREA)
  • Algebra (AREA)
  • Operations Research (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The disclosure belongs to the technical field of nuclear power, and particularly relates to a method and a device for estimating the residual service life of equipment. The present disclosure analyzes a device vibration signal to obtain a device health indicator. The vibration signals contain abundant health state information of mechanical equipment, the health index is constructed by quantizing the vibration signals according to the scheme disclosed by the invention, so that the health state degradation trend of the equipment is represented, the degradation process of the equipment is modeled by adopting multiple degradation trends such as polynomial degradation and the like based on the extracted equipment health indexes, and finally the estimation of the residual usable life of the equipment is realized based on the concept of a soft threshold value. Therefore, the system and the equipment can realize real-time online monitoring, intelligent early warning, intelligent diagnosis and service life prediction, improve the reliability analysis and management capability of the equipment, and are beneficial to enterprise managers to optimize maintenance decisions.

Description

Method and device for estimating residual service life of equipment
Technical Field
The invention belongs to the technical field of nuclear power, and particularly relates to a method and a device for estimating the residual service life of equipment.
Background
In the industrial field, since the equipment is worn during use, the wear is continuously accumulated according to different running times, running conditions and the like, and the service life of the equipment is gradually reduced. How to more objectively and accurately monitor the service life of the equipment becomes a problem to be solved urgently.
Disclosure of Invention
In order to overcome the problems in the related art, a method and a device for estimating the residual service life of equipment are provided.
According to an aspect of an embodiment of the present disclosure, there is provided a method for estimating remaining service life of a device, the method including:
acquiring an original vibration signal of target equipment in a preset time period according to a preset sampling frequency;
determining a sensitive frequency band where a non-fault signal is located, and filtering the original vibration signal according to the sensitive frequency band to obtain a filtered vibration signal;
carrying out envelope demodulation on the filtered vibration signal to obtain an envelope signal;
determining a root mean square value of the envelope signal, and performing normalization processing on the root mean square value to obtain health degree data of the target equipment in a preset time period;
fitting according to the health degree data to obtain a degradation model shown as a formula I,
Xk＝b0+Akb + epsilon (t) formula one
Wherein, XkFor the target device's health data set at each time instant,
Figure BDA0003492763720000021
Akfor each set of time instants, the time instants are,
Figure BDA0003492763720000022
b is a degeneration model parameter set, b ═ b0,b1,b2)TWhere ε (t) is the error term and ε (t) is σ2Ikσ is a constant, IkIs a k-order identity matrix;
and aiming at the set failure threshold, determining the residual service life of the target equipment under the current failure threshold by adopting the degradation model.
In one possible implementation, the method further includes:
and updating parameters of the degradation model by adopting a Bayesian method according to health degree data newly acquired from the target equipment, so that the degradation model is adaptively updated.
In a possible implementation manner, updating parameters of the degradation model by using a bayesian method according to health degree data newly acquired from a target device, so that the degradation model is adaptively updated, includes:
according to the Bayes parameter, the posterior distribution is in direct proportion to the product of the prior distribution and the likelihood function, the conjugate prior assumption can ensure that the prior distribution and the posterior distribution have the same distribution form, and the prior distribution of b is the mean value mubVariance of ∑bThe posterior distribution of b is then:
Figure BDA0003492763720000023
wherein, it is provided with
Figure BDA0003492763720000031
The posterior distribution of the parameter b obeys N (C)kDk,Ck) Then XkThe posterior distribution of
Figure BDA0003492763720000032
p(Xk| b) is a likelihood function;
then at tk+lWhen, Xk+lHas a mean value of
Figure BDA0003492763720000033
Variance of
Figure BDA0003492763720000034
According to another aspect of the embodiments of the present disclosure, there is provided an apparatus for estimating remaining useful life of a device, the apparatus including:
the acquisition module is used for acquiring an original vibration signal of the target equipment in a preset time period according to a preset sampling frequency;
the filtering module is used for determining a sensitive frequency band where a non-fault signal is located, and filtering the original vibration signal according to the sensitive frequency band to obtain a filtered vibration signal;
the envelope module is used for carrying out envelope demodulation on the filtered vibration signal to obtain an envelope signal;
the normalizing module is used for determining a root mean square value of the envelope signal and carrying out normalization processing on the root mean square value to obtain health degree data of the target equipment in a preset time period;
a first determining module for fitting to obtain a degradation model shown as a formula I according to the health degree data,
Xk＝b0+Akb + epsilon (t) formula one
Wherein, XkFor the target device's health data set at each time instant,
Figure BDA0003492763720000035
Akfor each set of time instants, the time instants are,
Figure BDA0003492763720000036
b is a degeneration model parameter set, b ═ b0,b1,b2)TWhere ε (t) is the error term and ε (t) is σ2Ikσ is a constant, IkIs a k-order identity matrix;
and the second determining module is used for determining the residual service life of the target equipment under the current failure threshold value by adopting the degradation model aiming at the set failure threshold value.
In one possible implementation, the apparatus further includes:
and the updating module is used for updating parameters of the degradation model by adopting a Bayesian device according to the health degree data newly acquired from the target equipment, so that the degradation model is adaptively updated.
In one possible implementation, the update module includes:
an updating submodule for making the posterior distribution of the Bayes parameters proportional to the product of the prior distribution and the likelihood function, conjugate prior assumption ensuring the prior distribution and the posterior distribution to have the same distribution form, and b prior distribution being the mean value mubVariance of ∑bThe posterior distribution of b is then:
Figure BDA0003492763720000041
wherein, it is provided with
Figure BDA0003492763720000042
The posterior distribution of the parameter b obeys N (C)kDk,Ck) Then XkThe posterior distribution of
Figure BDA0003492763720000043
p(Xk| b) is likeBut the function;
then at tk+lWhen, Xk+lHas a mean value of
Figure BDA0003492763720000044
Variance of
Figure BDA0003492763720000045
According to another aspect of the embodiments of the present disclosure, there is provided an apparatus for estimating remaining useful life of a device, the apparatus including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method described above.
According to another aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
The beneficial effect of this disclosure lies in: the present disclosure analyzes a device vibration signal to obtain a device health indicator. The vibration signals contain abundant health state information of mechanical equipment, the health index is constructed by quantizing the vibration signals according to the scheme disclosed by the invention, so that the health state degradation trend of the equipment is represented, the degradation process of the equipment is modeled by adopting multiple degradation trends such as polynomial degradation and the like based on the extracted equipment health indexes, and finally the estimation of the residual usable life of the equipment is realized based on the concept of a soft threshold value. Therefore, the system and the equipment can realize real-time online monitoring, intelligent early warning, intelligent diagnosis and service life prediction, improve the reliability analysis and management capability of the equipment, and are beneficial to enterprise managers to optimize maintenance decisions.
Drawings
FIG. 1 is a flow chart illustrating a method for estimating remaining useful life of a device according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating an apparatus remaining useful life estimating apparatus 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 for estimating remaining useful life of a device according to an exemplary embodiment. The method may be executed by a terminal device, for example, the terminal device may be a server, a desktop computer, or the like, and the type of the terminal device is not limited in the embodiments of the present disclosure. As shown in fig. 1, the method may include:
step 100, acquiring an original vibration signal of a target device in a preset time period according to a preset sampling frequency;
step 101, determining a sensitive frequency band where a non-fault signal is located, and filtering the original vibration signal according to the sensitive frequency band to obtain a filtered vibration signal;
102, carrying out envelope demodulation on the filtered vibration signal to obtain an envelope signal;
103, determining a root mean square value of the envelope signal, and performing normalization processing on the root mean square value to obtain health degree data of the target equipment in a preset time period;
step 104, fitting according to the health degree data to obtain a degradation model shown as a formula I,
Xk＝b0+Akb + epsilon (t) formula one
Wherein, XkFor the target device's health data set at each time instant,
Figure BDA0003492763720000061
Akfor each set of time instants, the time instants are,
Figure BDA0003492763720000062
b is a degeneration model parameter set, b ═ b0,b1,b2)TWhere ε (t) is the error term and ε (t) is σ2Ikσ is a constant, IkIs a k-order identity matrix;
and 105, aiming at the set failure threshold, determining the residual service life of the target equipment under the current failure threshold by adopting the degradation model.
In one possible implementation, the method further includes:
and updating parameters of the degradation model by adopting a Bayesian method according to health degree data newly acquired from the target equipment, so that the degradation model is adaptively updated.
For example, parameters of the degradation model may be updated by a bayesian method according to health data newly acquired from the target device, so that the degradation model is adaptively updated, including:
according to the Bayes parameter, the posterior distribution is in direct proportion to the product of the prior distribution and the likelihood function, the conjugate prior assumption can ensure that the prior distribution and the posterior distribution have the same distribution form, and the prior distribution of b is the mean value mubVariance of ∑bThe posterior distribution of b is then:
Figure BDA0003492763720000071
wherein, it is provided with
Figure BDA0003492763720000072
The posterior distribution of the parameter b obeys N (C)kDk,Ck) Then XkThe posterior distribution of
Figure BDA0003492763720000073
p(Xk| b) is a likelihood function;
Figure BDA0003492763720000074
Figure BDA0003492763720000075
then at tk+lWhen, Xk+lHas a mean value of
Figure BDA0003492763720000076
Variance of
Figure BDA0003492763720000077
In one possible implementation, an apparatus for estimating remaining useful life of a device is provided, the apparatus including:
the acquisition module is used for acquiring an original vibration signal of the target equipment in a preset time period according to a preset sampling frequency;
the filtering module is used for determining a sensitive frequency band where a non-fault signal is located, and filtering the original vibration signal according to the sensitive frequency band to obtain a filtered vibration signal;
the envelope module is used for carrying out envelope demodulation on the filtered vibration signal to obtain an envelope signal;
the normalizing module is used for determining a root mean square value of the envelope signal and carrying out normalization processing on the root mean square value to obtain health degree data of the target equipment in a preset time period;
a first determining module for obtaining a degradation model shown in the formula I by fitting according to the health degree data,
Xk＝b0+Akb + epsilon (t) formula one
Wherein, XkFor the health data set of the target device at each time instant,
Figure BDA0003492763720000081
Akfor each set of time instants, the time instants are,
Figure BDA0003492763720000082
b is a degeneration model parameter set, b ═ b0,b1,b2)TWhere ε (t) is the error term and ε (t) is σ2Ikσ is a constant, IkIs a k-order identity matrix;
and the second determining module is used for determining the residual service life of the target equipment under the current failure threshold value by adopting the degradation model aiming at the set failure threshold value.
In one possible implementation, the apparatus further includes:
and the updating module is used for updating parameters of the degradation model by adopting a Bayesian device according to the health degree data newly acquired from the target equipment, so that the degradation model is adaptively updated.
In one possible implementation, the update module includes:
an updating submodule for making the posterior distribution of the Bayes parameters proportional to the product of the prior distribution and the likelihood function, conjugate prior assumption ensuring the prior distribution and the posterior distribution to have the same distribution form, and b prior distribution being the mean value mubVariance of ∑bThe posterior distribution of b is then:
Figure BDA0003492763720000091
wherein, it is provided with
Figure BDA0003492763720000092
The posterior distribution of the parameter b obeys N (C)kDk,Ck) Then XkA posterior distribution of
Figure BDA0003492763720000093
p(Xk| b) is a likelihood function;
then at tk+lWhen, Xk+lHas a mean value of
Figure BDA0003492763720000094
Variance of
Figure BDA0003492763720000095
The description of the above apparatus has been detailed in the description of the above method, and is not repeated here.
Fig. 2 is a block diagram illustrating an apparatus remaining useful life estimating apparatus according to an exemplary embodiment. For example, the apparatus 1900 may be provided as a server. Referring to fig. 2, 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 the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
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 encoding device, such as punch cards or in-groove raised structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
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, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
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.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. 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 terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (8)

1. A method for estimating remaining useful life of a device, the method comprising:
acquiring an original vibration signal of target equipment in a preset time period according to a preset sampling frequency;
determining a sensitive frequency band where a non-fault signal is located, and filtering the original vibration signal according to the sensitive frequency band to obtain a filtered vibration signal;
carrying out envelope demodulation on the filtered vibration signal to obtain an envelope signal;
determining a root mean square value of the envelope signal, and performing normalization processing on the root mean square value to obtain health degree data of the target equipment in a preset time period;
fitting according to the health degree data to obtain a degradation model shown as a formula I,
Xk＝b0+Akb + epsilon (t) formula one
Wherein, XkFor the health data set of the target device at each time instant,
Figure FDA0003492763710000011
Akfor each set of time instants, the time instants are,
Figure FDA0003492763710000012
b is a degeneration model parameter set, b ═ b0,b1,b2)TWhere ε (t) is the error term and ε (t) is σ2Ikσ is a constant, IkIs a k-order identity matrix;
and aiming at the set failure threshold, determining the residual service life of the target equipment under the current failure threshold by adopting the degradation model.
2. The method of claim 1, further comprising:
and updating parameters of the degradation model by adopting a Bayesian method according to health degree data newly acquired from the target equipment, so that the degradation model is adaptively updated.
3. The method according to claim 2, wherein updating parameters of the degradation model by using a Bayesian method according to health degree data newly acquired from a target device, so that the degradation model is updated adaptively, comprises:
the posterior distribution of the Bayes parameters is in direct proportion to the product of the prior distribution and the likelihood function, and the conjugate prior hypothesis can ensure that the prior distribution and the posterior distribution haveHaving the same distribution form, let b be the prior distribution with the mean value μbVariance of ∑bThe posterior distribution of b is then:
Figure FDA0003492763710000021
wherein, it is provided with
Figure FDA0003492763710000022
The posterior distribution of the parameter b obeys N (C)kDk,Ck) Then XkThe posterior distribution of
Figure FDA0003492763710000023
p(Xk| b) is a likelihood function;
then at tk+lWhen, Xk+lHas a mean value of
Figure FDA0003492763710000024
Variance of
Figure FDA0003492763710000025
4. An apparatus for estimating remaining useful life of a device, the apparatus comprising:
the acquisition module is used for acquiring an original vibration signal of the target equipment in a preset time period according to a preset sampling frequency;
the filtering module is used for determining a sensitive frequency band where a non-fault signal is located, and filtering the original vibration signal according to the sensitive frequency band to obtain a filtered vibration signal;
the envelope module is used for carrying out envelope demodulation on the filtered vibration signal to obtain an envelope signal;
the normalizing module is used for determining a root mean square value of the envelope signal and carrying out normalization processing on the root mean square value to obtain health degree data of the target equipment in a preset time period;
a first determining module for fitting to obtain a degradation model shown as a formula I according to the health degree data,
Xk＝b0+Akb + epsilon (t) formula one
Wherein, XkFor the target device's health data set at each time instant,
Figure FDA0003492763710000031
Akfor each set of time instants, the time instants are,
Figure FDA0003492763710000032
b is a degeneration model parameter set, b ═ b0,b1,b2)TWhere ε (t) is the error term and ε (t) is σ2Ikσ is a constant, IkIs a k-order identity matrix;
and the second determining module is used for determining the residual service life of the target equipment under the current failure threshold value by adopting the degradation model aiming at the set failure threshold value.
5. The apparatus of claim 1, further comprising:
and the updating module is used for updating parameters of the degradation model by adopting a Bayesian device according to the health degree data newly acquired from the target equipment, so that the degradation model is adaptively updated.
6. The apparatus of claim 2, wherein the update module comprises:
an updating submodule for making the posterior distribution of the Bayes parameters proportional to the product of the prior distribution and the likelihood function, conjugate prior assumption ensuring the prior distribution and the posterior distribution to have the same distribution form, and b prior distribution being the mean value mubVariance of ∑bThe posterior distribution of b is then:
Figure FDA0003492763710000033
wherein, it is provided with
Figure FDA0003492763710000034
The posterior distribution of the parameter b obeys N (C)kDk,Ck) Then XkThe posterior distribution of
Figure FDA0003492763710000035
p(Xk| b) is a likelihood function;
then at tk+lWhen, Xk+lHas a mean value of
Figure FDA0003492763710000041
Variance of
Figure FDA0003492763710000042
7. An apparatus for estimating remaining useful life of a device, the apparatus comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1 to 3.
8. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein the computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 3.
CN202210102053.9A 2022-01-27 2022-01-27 Method and device for estimating residual service life of equipment Pending CN114510833A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210102053.9A CN114510833A (en) 2022-01-27 2022-01-27 Method and device for estimating residual service life of equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210102053.9A CN114510833A (en) 2022-01-27 2022-01-27 Method and device for estimating residual service life of equipment

Publications (1)

Publication Number Publication Date
CN114510833A true CN114510833A (en) 2022-05-17

Family

ID=81549142

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210102053.9A Pending CN114510833A (en) 2022-01-27 2022-01-27 Method and device for estimating residual service life of equipment

Country Status (1)

Country Link
CN (1) CN114510833A (en)

Similar Documents

Publication Publication Date Title
US10361905B2 (en) Alert remediation automation
US9509710B1 (en) Analyzing real-time streams of time-series data
US8868993B1 (en) Data replacement policy
US20160202228A1 (en) Water quality monitoring and early event detection
US10162326B2 (en) Self-adjusting test time estimation
US20170109654A1 (en) Identifying intervals of unusual activity in information technology systems
US20170111378A1 (en) User configurable message anomaly scoring to identify unusual activity in information technology systems
CN111666187B (en) Method and apparatus for detecting abnormal response time
US20170075723A1 (en) Increasing the efficiency of scheduled and unscheduled computing tasks
US10001925B2 (en) Method to improve page out mechanism with compressed memory pools
US10572336B2 (en) Cognitive closed loop analytics for fault handling in information technology systems
CN114510833A (en) Method and device for estimating residual service life of equipment
CN110321243B (en) Method, system, and storage medium for system maintenance using unified cognitive root cause analysis for multiple domains
CN112685222A (en) Method, apparatus and computer program product for managing backup data
CN112799932B (en) Method, electronic device, and storage medium for predicting health level of application
CN114496299A (en) Epidemic prevention information processing method based on deep learning and epidemic prevention service system
CN116776137A (en) Data processing method and electronic equipment
US11087050B2 (en) Generation of event transition model from event records
CN111857558B (en) Method, apparatus and computer program product for storage management
US10990995B2 (en) System for cognitive assessment of transactions
CN116720847A (en) Nuclear power equipment maintenance task period determining method and device
CN112580773A (en) Method and apparatus for compressing deep learning models
US9672322B2 (en) Virtual positive slack in physical synthesis
CN114639496A (en) Redundancy association method and device for nuclear power technological equipment
US20230409421A1 (en) Anomaly detection in computer systems

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination