CN111680389A - Equipment life quantifying method and device, computer equipment and storage medium - Google Patents

Equipment life quantifying method and device, computer equipment and storage medium Download PDF

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CN111680389A
CN111680389A CN202010326972.5A CN202010326972A CN111680389A CN 111680389 A CN111680389 A CN 111680389A CN 202010326972 A CN202010326972 A CN 202010326972A CN 111680389 A CN111680389 A CN 111680389A
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time data
vibration
failure time
fatigue failure
equipment
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CN111680389B (en
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汪凯蔚
沈峥嵘
何宗科
胡湘洪
吴栋
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China Electronic Product Reliability and Environmental Testing Research Institute
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China Electronic Product Reliability and Environmental Testing Research Institute
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    • 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
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2115/00—Details relating to the type of the circuit
    • G06F2115/04—Micro electro-mechanical systems [MEMS]
    • 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
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
    • 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

Abstract

The application relates to a method and a device for quantifying the service life of equipment, computer equipment and a storage medium. The method comprises the following steps: the computer equipment respectively carries out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment, and carries out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device; then carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device; and finally, carrying out fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density function of the equipment. In the method, the computer equipment can obtain the service life quantification result of the equipment according to the service life probability density function of the equipment, so that the interconnection failure distribution condition and the reliability of the equipment are determined.

Description

Equipment life quantifying method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of reliability assessment technologies, and in particular, to a method and an apparatus for quantifying device lifetime, a computer device, and a storage medium.
Background
With the development of microelectronic technology, microelectronic devices have been widely used in various industries, and in order to improve the efficient operation of microelectronic devices, reliability evaluation of microelectronic devices has received attention, and among them, reliability evaluation of circuit board products in microelectronic devices is particularly prominent.
The reliability evaluation of the circuit board product is generally performed on the interconnection failure of the circuit board product, wherein the interconnection failure refers to the circuit board failure caused by failure modes such as pin fracture, solder joint crack, through hole failure and the like of devices on the circuit board due to thermal cycle fatigue and vibration fatigue. In the prior art, CalcePWA software is usually used to perform simulation analysis on the interconnection failure distribution of a circuit board product to obtain a simulated value of the failure life of a welding spot of each component on the circuit board.
However, the overall reliability of the entire circuit board cannot be accurately reflected by the simulated value of the solder joint failure life of each component.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a device lifetime quantifying method, apparatus, computer device and storage medium for solving the above technical problems.
In a first aspect, a method for quantifying device lifetime is provided, the method comprising:
respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
carrying out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
performing fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
performing fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In one embodiment, the method further comprises: and acquiring thermal fatigue failure time data of each device of the equipment under a plurality of temperature sections and vibration fatigue failure time data under a plurality of vibration quantities.
In one embodiment, the analyzing the accumulated damage of the thermal fatigue failure time data of each device in the equipment to obtain the accumulated thermal fatigue failure time data includes:
calculating the temperature time proportion of each temperature section in all the temperature sections aiming at the ith device of the equipment; each temperature section comprises Q thermal fatigue failure time data, and different thermal fatigue failure time data correspond to different identifications;
multiplying each thermal fatigue failure time data of each temperature section by the corresponding temperature time proportion to obtain Q target thermal fatigue failure time data of each temperature section;
accumulating the target thermal fatigue failure time data of each temperature section corresponding to the same identification to obtain Q accumulated thermal fatigue failure time data.
In one embodiment, the analyzing the accumulated damage of the vibration fatigue failure time data of each device in the equipment to obtain the accumulated vibration fatigue failure time data includes:
calculating the vibration time proportion of each vibration quantity in all vibration quantities aiming at the ith device of the equipment; each vibration quantity comprises Q pieces of vibration fatigue failure time data, and different vibration fatigue failure time data correspond to different identifications;
multiplying each vibration fatigue failure time data of each vibration quantity by the corresponding vibration time proportion to obtain Q target vibration fatigue failure time data of each vibration quantity;
and accumulating the target vibration fatigue failure time data of each vibration quantity corresponding to the same identification to obtain Q accumulated vibration fatigue failure time data.
In one embodiment, the performing fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device includes:
performing distribution fitting on the accumulated thermal fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a thermal fatigue life distribution function of each device in the equipment;
and performing distribution fitting on the accumulated vibration fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a vibration fatigue life distribution function of each device in the equipment.
In one embodiment, the performing fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain the life probability density function of each device includes:
performing a sampling operation for each device of the apparatus;
wherein the sampling operation comprises: obtaining a random number by using a Monte Carlo random sampling method, and substituting the random number into a thermal fatigue life distribution function and a vibration fatigue life distribution function of the device respectively to obtain two random values; acquiring the minimum value of the two random values, and inputting the minimum value into a first sampling data set;
repeatedly executing the sampling operation until the number of elements in the first sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the device according to the first sampling data set.
In one embodiment, the lifetime probability density functions of all devices in the equipment are subjected to fault fusion to obtain the lifetime probability density functions of the equipment; the life probability density function of the equipment is used for obtaining the life quantification result of the equipment, and comprises the following steps:
performing a sampling operation for all devices in the apparatus;
wherein the sampling operation comprises: acquiring a random number by using a Monte Carlo random sampling method, and respectively substituting the random number into service life probability density functions of K devices of equipment to obtain K random values; acquiring the minimum value of the K random values, and inputting the minimum value into a second sampling data set;
repeatedly executing the sampling operation until the number of elements in the second sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the equipment according to the second sampling data set.
In a second aspect, an apparatus for quantifying device lifetime is provided, the apparatus comprising:
the accumulated damage analysis module is used for respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
the fault distribution fitting module is used for carrying out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
the device fault fusion module is used for carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
the equipment fault fusion module is used for carrying out fault fusion on the service life probability density functions of all devices in the equipment to obtain the service life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In a third aspect, there is provided a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the device lifetime quantifying method according to any one of the first aspect when executing the computer program.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for quantifying device lifetime as defined in any of the first aspects above.
According to the equipment life quantification method and device, the computer equipment and the storage medium, the computer equipment respectively carries out accumulated damage analysis on thermal fatigue failure time data and vibration fatigue failure time data of each device in the equipment, and then carries out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device; then carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device; and finally, carrying out fault fusion on the service life probability density functions of all devices in the equipment to obtain the service life probability density functions of the equipment, so as to obtain a service life quantification result of the equipment according to the service life probability density functions. In the method, the computer equipment can obtain the life probability density function corresponding to the equipment by calculating and processing the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment, so that the expected life value of the equipment, namely the life quantification result of the equipment can be obtained according to the life probability density function of the equipment, and the life quantification result of the equipment is obtained through the failure data of each device in the equipment, so that the interconnection failure distribution condition of the equipment and the reliability condition of the equipment can be accurately and practically reflected.
Drawings
FIG. 1 is a diagram illustrating an exemplary embodiment of a method for quantifying device lifetime;
FIG. 2 is a flow diagram illustrating a method for quantifying device lifetime in one embodiment;
FIG. 3 is a flow chart illustrating a method for quantifying device lifetime in another embodiment;
FIG. 4 is a flow chart illustrating a method for quantifying device lifetime in another embodiment;
FIG. 5 is a flow chart illustrating a method for quantifying device lifetime in another embodiment;
FIG. 6 is a flow chart illustrating a method for quantifying device lifetime in another embodiment;
FIG. 7 is a flowchart illustrating an apparatus life quantifying method according to another embodiment;
FIG. 8 is a flow chart illustrating a method for quantifying device lifetime in another embodiment;
FIG. 9 is a block diagram showing the structure of an apparatus life quantifying means in one embodiment;
FIG. 10 is a block diagram showing the structure of an apparatus life quantifying means in another embodiment;
FIG. 11 is a block diagram showing the structure of an apparatus life quantifying means in another embodiment;
FIG. 12 is a block diagram showing the structure of an apparatus life quantifying means in another embodiment;
fig. 13 is a block diagram of a device lifetime quantifying apparatus in another embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The device life quantifying method provided by the application can be applied to the application environment shown in fig. 1. Fig. 1 provides a computer device, which may be a server or a terminal, and its internal structure diagram may be as shown in fig. 1. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of device lifetime quantification. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In the prior art, printed assembly circuit boards are usually completed by CalcePWA software for reliability simulation evaluation and fault prediction. CalcePWA software is reliability simulation software jointly developed by American Computer Aided Life cycle engineering (Calce) electronic products and a system center in combination with Maryland university, and comprises three main links of thermal analysis, vibration analysis and fault prediction.
The CalcePWA software operation process is roughly divided into the following steps of firstly establishing a circuit board model, establishing a component library according to the parameters of the circuit board such as the size, the number of layers, the material and the thickness, and a component list of the circuit board, recording the information of basic information, a packaging mode, thermal resistance and the like of the components, and placing the components in the component library to corresponding positions of the model according to the coordinates of the components on the circuit board. And determining the thermal analysis and vibration analysis boundary conditions required by calcePWA fault prediction according to the results of the thermal simulation analysis and the vibration simulation analysis, and finally obtaining the thermal analysis and vibration analysis results required by the fault prediction. According to the reliability profile of the product, the profile of the reliability simulation is determined, and generally comprises a plurality of high-temperature and low-temperature circulation and vibration processes. And the high-low temperature circulation and vibration process needs the results of the thermal analysis and the vibration analysis in the front, and finally, the circuit board model is operated under a reliability simulation section. Through stress damage analysis and accumulated damage analysis algorithms provided by software, a failure time predicted value of each potential failure point in a product can be obtained.
The predicted value is determined, and in reality, the failure occurrence time of each failure point presents certain randomness due to the influence of factors such as stress, materials, structures, processes and the like. In order to consider the randomness, a Monte Carlo simulation method needs to be introduced into the determined stress analysis, the random sampling and stress analysis calculation of the large sample size are carried out on the structure parameters, the material parameters, the process parameters and the like of the product, the fault time data of the product with the large sample size are obtained, after the Monte Carlo simulation is carried out, 1000 Monte Carlo simulation results are obtained for each interconnection fault mechanism of each component, and the result is 1000 failure times of the device under the fault mode.
In the step of establishing the model of the circuit board, besides the circuit board simulation model, a fault physical model of the circuit board needs to be established. Establishing a fault physical model refers to establishing a mathematical function model which quantitatively reflects the relation between fault occurrence time and materials, structures, stress and the like on the basis of basic physical, chemical and other principle formulas and/or analytical regression formulas aiming at a specific fault mechanism. The failure mechanism of a welding point of the circuit board under the thermal cycle condition is thermal fatigue generally; under the vibration condition, the failure mechanism of a welding spot is usually vibration fatigue, and the interconnection failure distribution of each key component of a board-level product is generally obtained by adopting a coffee-Manson model and a Steinberg model of random vibration fatigue. Two solder joint thermal fatigue life models and their application ranges are shown in table 1.
TABLE 1
Figure BDA0002463572120000071
When the fault physical model is constructed, the determination method of each material parameter can be realized by the following method.
The material parameters of the components are determined according to the specifications of the electronic components, the production technical data of electronic products and the engineering technical requirements, and then the materials are obtained by referring to a material manual; the material property parameters of the circuit board and the interconnection part are obtained by referring to a material manual according to the material grade provided by a process designer. For the material marks which cannot be found in the material handbook, reference can be made to the published documents at home and abroad or the material marks can be obtained by measuring by using a material performance testing instrument. For example: for the coffee-Manson model, the material attribute parameters aref，αcAnd αs. The components of model CYD09SV18 were soldered to FR4 circuit board with Sn63Pb37 solder. The fatigue ductility coefficient of the solder can be found by referring to the handbook of materialsf0.325, linear thermal expansion coefficient of the device αc12m/K, linear thermal expansion coefficient α of PCB boardsIs 20 m/K.
When the fault physics simulation software PWA is used for carrying out fault simulation of the circuit board, the following information needs to be input: the geometric structure parameters of the components comprise the detailed structure sizes of welding spots and pins of the components and the position information on the printed circuit board; relevant geometric structure information, geometric structure parameter distribution information and fluctuation range inside the important component; material attribute parameters including thermal expansion coefficients of the packaging materials of the components, the printed circuit board materials, welding spots and pin materials and other material attributes related to board-level interconnection stress damage analysis; relevant material properties of an analysis object inside the important component; distribution information and fluctuation range of material attribute parameters; stress parameters including steady state temperature, temperature cycle, vibration stress and other parameters; the local stress of the potential fault point is determined through simulation analysis according to the environmental conditions and the working stress of the product, namely, the temperature and the vibration response near the potential fault point are extracted by using the results of the temperature analysis and the vibration analysis.
Stress parameters in the Coffin-Manson modelIncluding Delta Tc，ΔTs，TSJAnd td. Wherein, Delta TcAnd Δ TsIs the local temperature variation value of the component, and can be obtained by simulation analysisSJAnd tdIs a parameter related to the temperature cycling profile, determined from the temperature cycling profile to which the product is subjected.
After the information is obtained, for a circuit board with K components, CalcePWA software can obtain N interconnection failure mechanisms of each component, and each failure mechanism corresponds to 1000 estimated failure times. The CalcePWA software simulation results are shown in table 2.
TABLE 2
Failure point Mechanism 1 Mechanism 2 Mechanism i Mechanism N
Failure point 1 t11(1～1000) t21(1～1000) ti1(1～1000) tN1(1～1000)
Failure point 2 t12(1～1000) t22(1～1000) ti2(1～1000) tN2(1～1000)
... ... ... ... ...
Failure point j T1j(1～1000) t2j(1～1000) tij(1～1000) tNj(1～1000)
... ... ... ... ...
Failure point K t1K(1～1000) t2K(1～1000) tiK(1～1000) tNK(1～1000)
The calibration method is characterized in that simulation analysis is carried out on interconnection failure distribution of a circuit board product through CalcePWA software, so that Monte Carlo simulation values of the solder joint failure lives of all components on the circuit board can be obtained, namely, simulation analysis is carried out on the interconnection failure distribution of the circuit board product through CalcePWA software, and only fault time data of each fault point on each component of the circuit board product can be given, as shown in the table 2.
However, the interconnection failure distribution of the whole circuit board is more concerned by the equipment user, and the interconnection failure distribution of the circuit board level product cannot be directly evaluated through the CalcePWA software.
Therefore, the present embodiment provides an apparatus life quantifying method for determining interconnection failure distribution of a circuit board by using a fault physics simulation result.
The following describes in detail the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems by embodiments and with reference to the drawings. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. It should be noted that, in the method for quantifying device lifetime provided in the embodiments of fig. 2 to fig. 8 of the present application, an execution subject may be a computer device, and may also be a device for quantifying device lifetime, and the device for quantifying device lifetime may be a part or all of the computer device by software, hardware, or a combination of software and hardware. In the following method embodiments, the execution subject is a computer device as an example.
In an embodiment, as shown in fig. 2, a method for quantifying the lifetime of a device is provided, which relates to a specific process in which a computer device performs accumulated damage analysis, data fitting, and data fusion on thermal fatigue failure time data and vibration fatigue failure time data of each device in the device, so as to obtain a lifetime probability density function corresponding to the device, and includes the following steps:
s201, performing accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment respectively to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data.
The accumulated damage analysis refers to a method for overlapping damages caused by different value ranges of single stress. The thermal fatigue failure time data refers to time data or time data of cracks and fractures caused by cyclic strain of each device of the equipment generated by external temperature change in CalcePWA simulation software under the condition of no external mechanical stress; the vibration fatigue failure time data refers to time data or time data of cracks and fractures caused by cyclic strain generated by each device of equipment due to external vibration change under the condition of no external mechanical stress in CalcePWA software simulation.
In this embodiment, the thermal fatigue failure time data and the vibration fatigue failure time data are both obtained according to a simulation result of CalcePWA simulation software, and after the thermal fatigue failure time data and the vibration fatigue failure time data are obtained by the computer device, cumulative damage analysis needs to be performed on the thermal fatigue failure time data and the vibration fatigue failure time data, that is, cumulative damage calculation is performed by using a cumulative damage rule for a single stress fault mechanism, where the damage cumulative model is of many types, such as a linear cumulative model, a bilinear cumulative model, a nonlinear cumulative model, and the like. For example, the computer device may superimpose Q thermal fatigue failure time data of a plurality of different temperature segments under the temperature stress to obtain Q fatigue failure time data under the accumulated temperature stress, which is not limited in this embodiment.
S202, performing fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device.
In the embodiment, the computer equipment performs fault distribution fitting according to the accumulated thermal fatigue failure time data to obtain a thermal fatigue life distribution function of each device; and the computer equipment performs fault distribution fitting according to the accumulated vibration fatigue failure moment data to obtain a vibration fatigue life distribution function of each device. Illustratively, the accumulated 1000 thermal fatigue failure time data of the ith device of the equipment is si1、si2、…、si1000The computer equipment can perform distribution fitting on the 1000 thermal fatigue failure moment data through any distribution fitting mode such as exponential distribution fitting, normal distribution fitting, Gaussian distribution fitting and the like to obtain a thermal fatigue life distribution function f of the ith device of the equipmenti(x) (ii) a Similarly, the accumulated 1000 vibration fatigue failure time data of the ith device of the equipment is ti1、ti2、…、ti1000The computer equipment can perform distribution fitting on the 1000 vibration fatigue failure moment data in any distribution fitting mode to obtain a vibration fatigue life distribution function g of the ith device of the equipmenti(x) This embodiment is not limited to this.
S203, carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device.
The failure fusion refers to the fusion of failure time data under various stresses into failure time data under comprehensive stresses.
In this embodiment, the computer device may perform the fault fusion operation on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device through any one of a monte carlo sampling method, a normal distribution random sampling algorithm, a lognormal distribution random sampling algorithm, a weibull distribution random sampling algorithm, and the like. For example, the computer device may perform a sampling operation on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device through a monte carlo sampling algorithm to obtain two random values, and preferably, the computer device may further perform a processing on the random values through a competition algorithm to finally obtain a plurality of random values equal to a preset threshold, and obtain a life probability density function of each device according to a plurality of random values through fitting, which is not limited in this embodiment.
S204, carrying out fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In this embodiment, when the computer device performs fault fusion on the lifetime probability density functions of all devices in the device, by using any one sampling algorithm, such as a monte carlo sampling method, a normal distribution random sampling algorithm, a lognormal distribution random sampling algorithm, a weibull distribution random sampling algorithm, and the like, the fault fusion operation on the lifetime probability density functions of all devices can be performed, similar to the specific implementation manner of the embodiment of step S203. For example, the computer device may perform a sampling operation on the lifetime probability density functions of K devices in the device through a monte carlo sampling algorithm to obtain K random values, then process the K random values through a competition algorithm to finally obtain a plurality of random values equal to a preset threshold, and fit the obtained lifetime probability density functions of the device according to the plurality of random values, which is not limited in this embodiment.
In the method for quantifying the service life of the equipment, the computer equipment respectively carries out accumulated damage analysis on thermal fatigue failure time data and vibration fatigue failure time data of each device in the equipment, and then carries out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device; then carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device; and finally, carrying out fault fusion on the service life probability density functions of all devices in the equipment to obtain the service life probability density functions of the equipment, so as to obtain a service life quantification result of the equipment according to the service life probability density functions. In this embodiment, the computer device may obtain the life probability density function corresponding to the device by performing calculation processing on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the device, so that the expected life value of the device, that is, the life quantization result, may be obtained according to the life probability density function of the device.
Before the computer device performs cumulative damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the step 201, in an embodiment, the method for quantifying the service life of the device further includes: and acquiring thermal fatigue failure time data of each device of the equipment under a plurality of temperature sections and vibration fatigue failure time data under a plurality of vibration quantities.
In this embodiment, the computer device obtains, according to the simulation result of the CalcePWA simulation software, thermal fatigue failure time data of each device of the device in a plurality of temperature ranges and vibration fatigue failure time data of each device in a plurality of vibration quantities, for example, the computer device may obtain thermal fatigue failure time data of each device of the device in M temperature ranges, where each temperature range includes Q pieces of thermal fatigue failure time data; the computer device may obtain vibration fatigue failure time data of each device of the device under N vibration quantities, where each vibration quantity includes Q vibration fatigue failure time data, and this embodiment does not limit this.
In this embodiment, the computer device obtains thermal fatigue failure time data and vibration fatigue failure time data through a simulation result of calceppa simulation software, and the calceppa simulation software adopts multiple fault analysis models, so that the thermal fatigue failure time data and the vibration fatigue failure time data obtained through the calceppa simulation software are more accurate.
For the interconnection failure analysis of each device in the device under the temperature stress, the computer device may perform cumulative damage analysis according to the obtained thermal fatigue failure time data of each device in the device, specifically, in an embodiment, as shown in fig. 3, the performing cumulative damage analysis on the thermal fatigue failure time data of each device in the device respectively to obtain the accumulated thermal fatigue failure time data includes:
s301, calculating the temperature time proportion of each temperature section in all the temperature sections aiming at the ith device of the equipment; each temperature section comprises Q thermal fatigue failure time data, and different thermal fatigue failure time data correspond to different identifications.
In this embodiment, the ith device in the apparatus may include M temperature segments, each of which includes Q thermal fatigue failure time data, each of which has a unique and different serial number identifier. For each temperature section in a computer apparatusBefore the thermal fatigue failure time data are accumulated and superposed, the time proportion of each temperature section in all the temperature sections can be calculated to give corresponding weight to each temperature section data, and for example, the computer device can divide the sum of the thermal fatigue failure time data of the mth temperature section by the sum of the thermal fatigue failure time data of all the temperature sections to obtain the time proportion of the mth temperature section in all the temperature sections, and M is usedmThis is not limited in this embodiment.
And S302, multiplying each thermal fatigue failure time data of each temperature section by the corresponding temperature time proportion to obtain Q target thermal fatigue failure time data of each temperature section.
In the present embodiment, the thermal fatigue failure time data included in each temperature segment is: sim1、sim2、…、simQGenerally, the number of the thermal fatigue failure time data of each temperature section obtained by the CalcePWA simulation software is 1000, that is, Q is 1000, and Q is a certain value. The time proportion of the mth temperature section in all the temperature sections calculated by the computer equipment is MmBefore the computer device multiplies each thermal fatigue failure time data of each temperature section by the corresponding temperature time proportion, the computer device can preferably invert each thermal fatigue failure time data of each temperature section, namely the qth target thermal fatigue failure time data of the mth temperature section of the ith device in the device can be expressed as
Figure BDA0002463572120000131
This embodiment is not limited to this.
And S303, accumulating the target thermal fatigue failure time data of each temperature section corresponding to the same identifier to obtain Q accumulated thermal fatigue failure time data.
In this embodiment, the computer device accumulates the target thermal fatigue failure time data of all temperature segments corresponding to the same identifier of the ith device in the device, and preferably, the computer device may invert the accumulation result to obtain the accumulated thermal fatigue failure time data under the same identifier, which may be denoted as si1、si2、…、siq. For example, the accumulated thermal fatigue failure time data for the ith device identified as 1 may be expressed as
Figure BDA0002463572120000132
The accumulated thermal fatigue failure time data for the ith device, denoted as q, may be expressed as
Figure BDA0002463572120000133
Then, the computer device may obtain Q accumulated thermal fatigue failure time data according to the expression, which is not limited in this embodiment.
In this embodiment, the computer device performs accumulated damage analysis on the thermal fatigue failure time data of the ith device at each temperature segment, so as to obtain the thermal fatigue failure time data accumulated at the integrated temperature segment of the ith device, where the thermal fatigue failure time data can accurately reflect the failure condition of the ith device under a single temperature stress.
For the interconnection failure analysis of each device in the equipment under the vibration stress, the computer equipment may perform cumulative damage analysis according to the obtained vibration fatigue failure time data of each device in the equipment, specifically, in an embodiment, as shown in fig. 4, the performing cumulative damage analysis on the vibration fatigue failure time data of each device in the equipment respectively to obtain the accumulated vibration fatigue failure time data includes:
s401, calculating the vibration time proportion of each vibration quantity in all vibration quantities aiming at the ith device of the equipment; each vibration quantity comprises Q pieces of vibration fatigue failure time data, and different vibration fatigue failure time data correspond to different identifications.
In this embodiment, the ith device in the apparatus may include N vibration quantities, each vibration quantity includes Q pieces of vibration fatigue failure time data, and each piece of data has a unique and different serial number identifier. Before the computer device cumulatively adds the vibration fatigue failure time data of each vibration quantity, the vibration quantity can account for all the vibration quantities by calculatingFor example, the computer device may divide the sum of the vibration fatigue failure time data of the nth vibration amount by the sum of the vibration fatigue failure time data of all vibration amounts to obtain the time ratio of the nth vibration amount to all vibration amounts, using MnThis is not limited in this embodiment.
And S402, multiplying each vibration fatigue failure time data of each vibration quantity by the corresponding vibration time proportion to obtain Q pieces of target vibration fatigue failure time data of each vibration quantity.
In the present embodiment, the vibration fatigue failure time data included in each vibration amount is: t is tin1、tin2、…、tinqIn general, the number of vibration fatigue failure time data for each vibration quantity obtained by CalcePWA simulation software is 1000, that is, Q is 1000, and Q is a certain value. The time proportion of the nth vibration quantity to all the vibration quantities calculated by the computer equipment is MnBefore the computer device multiplies each vibration fatigue failure time data of each vibration quantity by the corresponding vibration time proportion, preferably, the computer device may invert each vibration fatigue failure time data of each vibration quantity, that is, the q-th target vibration fatigue failure time data of the nth vibration quantity of the ith device in the device may be expressed as
Figure BDA0002463572120000141
This embodiment is not limited to this.
And S403, accumulating the target vibration fatigue failure time data of each vibration quantity corresponding to the same identification to obtain Q accumulated vibration fatigue failure time data.
In this embodiment, the computer device accumulates the target vibration fatigue failure time data of all vibration quantities corresponding to the same identifier of the ith device in the device, and preferably, the computer device may invert the accumulation result to obtain the accumulated vibration fatigue failure time data under the same identifier, which may be represented as ti1、ti2、…、tiq. Exemplarily, the ithThe accumulated vibration fatigue failure time data for individual device number 1 can be expressed as
Figure BDA0002463572120000142
The accumulated vibration fatigue failure time data for the ith device, denoted as q, may be expressed as
Figure BDA0002463572120000151
Then, the computer device may obtain Q accumulated vibration fatigue failure time data according to the expression, which is not limited in this embodiment.
In this embodiment, the computer device performs accumulated damage analysis on the vibration fatigue failure time data of the ith device under each vibration quantity, so as to obtain the vibration fatigue failure time data accumulated under the integrated vibration quantity of the ith device, and the vibration fatigue failure time data can accurately reflect the failure condition of the ith device under a single vibration stress.
The computer device may perform fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device by any one of exponential distribution fitting, normal distribution fitting, gaussian distribution fitting, and the like, and preferably, in an embodiment, as shown in fig. 5, the performing fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device includes:
s501, performing distribution fitting on the accumulated thermal fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a thermal fatigue life distribution function of each device in the equipment.
In this embodiment, the computer device performs distribution fitting on the accumulated thermal fatigue failure time data, and may perform distribution fitting according to an exponential distribution algorithm as follows.
The accumulated thermal fatigue failure time data of the current device can be expressed as si1、si2、…、siqBy the following formula (1), the computer device can calculate a point estimate of the mathematical expectation θ of the thermal fatigue life distribution function of the device from the accumulated thermal fatigue failure time data
Figure BDA0002463572120000152
Figure BDA0002463572120000153
Wherein, t(i)The data is the ith thermal fatigue failure moment; q is the number of thermal fatigue failure time data.
Thus, the computer device can calculate a point estimate of the mathematical expectation θ in the thermal fatigue life distribution function calculated according to equation (1)
Figure BDA0002463572120000154
The thermal fatigue life distribution function of the device is calculated according to equation (2).
F(t)＝1-e-λt(2)
Wherein, the lambda is the failure rate,
Figure BDA0002463572120000161
s502, performing distribution fitting on the accumulated vibration fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a vibration fatigue life distribution function of each device in the equipment.
Similar to the way the distribution of the thermal fatigue life distribution function is fitted in the above described embodiment. In this embodiment, the computer device performs distribution fitting on the accumulated vibration fatigue failure time data, and may also perform distribution fitting according to the following exponential distribution algorithm.
The accumulated vibration fatigue failure time data of the current device can be represented as ti1、ti2、…、tiqWill ti1、ti2、…、tiqAs t in the above formula (1)(i)The medium mathematical expectation of the vibration fatigue life distribution function of the device is obtained by calculationPoint estimation of theta
Figure BDA0002463572120000162
Thus, the computer device can estimate the point of the mathematical expectation θ in the vibration fatigue life distribution function calculated according to equation (1)
Figure BDA0002463572120000163
According to equation (2), the vibration fatigue life distribution function of the device is calculated.
In this embodiment, the computer device performs distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device, and the reliability of the device under temperature stress and the reliability of the device under vibration stress can be obtained through the thermal fatigue life distribution function and the vibration fatigue life distribution function of the device.
The computer device may perform a fault fusion operation on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device through any one sampling algorithm, such as a monte carlo sampling method, a normal distribution random sampling algorithm, a log normal distribution random sampling algorithm, a weibull distribution random sampling algorithm, and the like, and preferably, in an embodiment, as shown in fig. 6, the performing fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device includes:
s601, aiming at each device of the equipment, sampling operation is executed.
Wherein the sampling operation comprises: obtaining a random number by using a Monte Carlo random sampling method, and substituting the random number into a thermal fatigue life distribution function and a vibration fatigue life distribution function of the device respectively to obtain two random values; the minimum value of the two random values is obtained and input into the first sample data set.
In this embodiment, the computer apparatus may implement the sampling operation of each device of the apparatus by the following steps. In particular toFor step 1, according to the distribution function f of the thermal fatigue life of the ith devicei(x) And vibration fatigue life distribution function gi(x) Sampling once by using a Monte Carlo method, randomly generating a random number x which is uniformly distributed in a (0, Q) interval, and substituting x into fi(x) And gi(x) 2 random numbers (t) are obtained by the calculationi1,ti2) (ii) a Step 2, the 2 random numbers (t) are added11,t12) Arranged from small to large, and the minimum value is taken as t1min. Will t1minAnd outputting the data to the first sampling data set.
S602, repeatedly executing sampling operation until the number of elements in the first sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the device according to the first sampling data set.
Wherein the preset threshold refers to a preset number of elements in the first sampling data set.
In this embodiment, the computer device repeatedly performs the sampling operation in the above embodiment, specifically, when repeating for the second time, obtains 2 new random numbers (t)21,t22) Taking the minimum of the two as t2minWill t2minOutput into the first sampled data set until the number of elements in the first sampled data set reaches a preset threshold, which may be represented as (t) when the preset threshold is 500, for example1min,t2min,...,t500min) And carrying out distribution goodness-of-fit inspection on the first sampling data set to obtain a service life probability density function h of the devicei(t)。
In this embodiment, the computer device may obtain a lifetime probability density function of each device according to the thermal fatigue lifetime distribution function and the vibration fatigue lifetime distribution function of each device in the apparatus, and may determine the reliability and the expected lifetime value of the device according to the lifetime probability density function of the device, so as to provide a reliable basis for determining the lifetime probability density function of the apparatus according to the lifetime probability density function of the device.
The computer device may perform fault fusion on the lifetime probability density functions of all devices in the device through any one of sampling algorithms such as a monte carlo sampling method, a normal distribution random sampling algorithm, a log-normal distribution random sampling algorithm, a weibull distribution random sampling algorithm, and the like, and preferably, in an embodiment, as shown in fig. 7, the fault fusion is performed on the lifetime probability density functions of all devices in the device to obtain the lifetime probability density functions of the device; the life probability density function of the equipment is used for obtaining the life quantification result of the equipment, and comprises the following steps:
and S701, executing sampling operation aiming at all devices in the equipment.
Acquiring a random number by using a Monte Carlo random sampling method, and respectively substituting the random number into service life probability density functions of K devices of equipment to obtain K random values; and acquiring the minimum value of the K random values, and inputting the minimum value into the second sampling data set.
In this embodiment, the computer apparatus may implement the sampling operation of each device of the apparatus by the following steps. Specifically, step 1, according to the life probability density function h of each device in K devices in the equipmenti(t) sampling once by using a Monte Carlo method, randomly generating random numbers x obeying uniform distribution in a (0, Q) interval, and substituting x into a service life probability density function h of K devicesi(t) to obtain K random numbers (t)11,t12,...,t1K) (ii) a Step 2, the K random numbers (t) are added11,t12,...,t1K) Arranged from small to large, and the minimum value is taken as t1min. Will t1minAnd outputting to the second sample data set.
S702, repeatedly executing the sampling operation until the number of elements in the second sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the equipment according to the second sampling data set.
In this embodiment, the computer device repeatedly performs the sampling operation in the above embodiment, specifically, when repeating for the second time, K new random numbers (t) are obtained21,t22,...,t2K) Taking the minimum of the two as t2minWill t2minIs output toThe first sampled data set may be represented as (t) until the number of elements in the second sampled data set reaches a preset threshold, illustratively 5001min,t2min,...,t50min) And obtaining a life probability density function f (t) of the device by performing distribution goodness-of-fit test on the second sampling data set, and preferably, according to the life probability density function f (t) of the device, the computer device may calculate and obtain a life evaluation value of the device according to the following formula:
Figure BDA0002463572120000181
in this embodiment, the computer device may obtain the lifetime probability density function of the device through fault fusion according to the lifetime probability density functions of all devices in the device, so as to obtain a lifetime evaluation value of the device according to the lifetime probability density function of the device, and may determine the reliability of the device according to the lifetime evaluation value.
To better explain the above method, as shown in fig. 8, the present embodiment provides a method for quantifying the lifetime of a device, which specifically includes:
s801, acquiring thermal fatigue failure time data of each device of equipment at a plurality of temperature sections;
s802, acquiring vibration fatigue failure moment data of each device of the equipment under a plurality of vibration quantities;
s803, calculating the temperature time proportion of each temperature section in all temperature sections aiming at the ith device of the equipment;
s804, multiplying each thermal fatigue failure time data of each temperature section by the corresponding temperature time proportion to obtain Q target thermal fatigue failure time data of each temperature section;
s805, accumulating the target thermal fatigue failure time data of each temperature section corresponding to the same identifier to obtain m accumulated thermal fatigue failure time data;
s806, calculating the vibration time proportion of each vibration quantity in all vibration quantities aiming at the ith device of the equipment;
s807, multiplying each vibration fatigue failure time data of each vibration quantity by the corresponding vibration time proportion to obtain Q target vibration fatigue failure time data of each vibration quantity;
s808, accumulating the target vibration fatigue failure time data of each vibration quantity corresponding to the same identification to obtain m accumulated vibration fatigue failure time data;
s809, performing distribution fitting on the accumulated thermal fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a thermal fatigue life distribution function of each device in the equipment;
s810, performing distribution fitting on the accumulated vibration fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a vibration fatigue life distribution function of each device in the equipment;
s811, carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
s812, performing fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In this embodiment, the computer device performs cumulative damage analysis, data fitting, and data fusion processing on thermal fatigue failure time data and vibration fatigue failure time data of each device in the device to obtain a lifetime probability density function corresponding to the device, and obtains a lifetime quantization result of the device according to the lifetime probability density function of the device, thereby determining an interconnection failure distribution condition and reliability of the device.
The implementation principle and technical effect of the method for quantifying the service life of the device provided by the above embodiment are similar to those of the method embodiment, and are not described herein again.
It should be understood that although the various steps in the flow charts of fig. 2-8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-8 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 9, there is provided an apparatus for quantifying lifetime of a device, including: accumulated damage analysis module 01, fault distribution fitting module 02, device fault fusion module 03 and equipment fault fusion module 04, wherein:
the accumulated damage analysis module 01 is used for respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
the fault distribution fitting module 02 is used for performing fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
the device fault fusion module 03 is configured to perform fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
the equipment fault fusion module 04 is used for performing fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density function of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In one embodiment, as shown in fig. 10, the device lifetime quantifying apparatus further comprises an obtaining module 05;
the acquiring module 05 is used for acquiring thermal fatigue failure time data of each device of the equipment under a plurality of temperature sections and vibration fatigue failure time data under a plurality of vibration quantities.
In one embodiment, as shown in fig. 11, the cumulative damage analysis module 01 includes a thermal fatigue failure cumulative analysis unit 011;
the thermal fatigue failure accumulation analysis unit 011 is used for calculating the temperature-time proportion of each temperature section in all the temperature sections aiming at the ith device of the equipment; each temperature section comprises Q thermal fatigue failure time data, and different thermal fatigue failure time data correspond to different identifications; the system is also used for multiplying each thermal fatigue failure time data of each temperature section by the corresponding temperature time proportion to obtain Q target thermal fatigue failure time data of each temperature section; and accumulating the target thermal fatigue failure time data of each temperature section corresponding to the same identifier to obtain Q accumulated thermal fatigue failure time data.
In one embodiment, as shown in fig. 12, the cumulative damage analysis module 01 includes a vibration fatigue failure cumulative analysis unit 012;
a vibration fatigue failure accumulation analysis unit 012 configured to calculate a vibration time ratio of each vibration amount to all vibration amounts for an i-th device of the apparatus; each vibration quantity comprises Q pieces of vibration fatigue failure time data, and different vibration fatigue failure time data correspond to different identifications; the vibration fatigue failure time data of each vibration quantity are multiplied by the corresponding vibration time proportion to obtain Q pieces of target vibration fatigue failure time data of each vibration quantity; and accumulating the target vibration fatigue failure time data of each vibration quantity corresponding to the same identification to obtain Q accumulated vibration fatigue failure time data.
In one embodiment, as shown in fig. 13, the fault distribution fitting module 02 includes a thermal fatigue failure fitting unit 021 and a vibration fatigue failure fitting unit 022, wherein:
the thermal fatigue failure fitting unit 021 is used for performing distribution fitting on the accumulated thermal fatigue failure time data of each device by adopting an exponential distribution fitting algorithm to obtain a thermal fatigue life distribution function of each device in the equipment;
and a vibration fatigue failure fitting unit 022, configured to perform distribution fitting on the accumulated vibration fatigue failure time data of each device by using an exponential distribution fitting algorithm, so as to obtain a vibration fatigue life distribution function of each device in the equipment.
In an embodiment, the device failure fusion module 03 is specifically configured to perform a sampling operation on each device of the apparatus; wherein the sampling operation comprises: obtaining a random number by using a Monte Carlo random sampling method, and substituting the random number into a thermal fatigue life distribution function and a vibration fatigue life distribution function of the device respectively to obtain two random values; acquiring the minimum value of the two random values, and inputting the minimum value into a first sampling data set; repeatedly executing the sampling operation until the number of elements in the first sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the device according to the first sampling data set.
In an embodiment, the device failure fusion module 04 is specifically configured to perform a sampling operation on all devices in the device; wherein the sampling operation comprises: acquiring a random number by using a Monte Carlo random sampling method, and respectively substituting the random number into service life probability density functions of K devices of equipment to obtain K random values; acquiring the minimum value of the K random values, and inputting the minimum value into a second sampling data set; repeatedly executing the sampling operation until the number of elements in the second sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the equipment according to the second sampling data set.
For specific limitations of the device lifetime quantifying apparatus, see the above limitations on the device lifetime quantifying method, which are not described herein again. The respective modules in the above-described apparatus life-time quantifying apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
carrying out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
performing fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
performing fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In one embodiment, the method further comprises: and acquiring thermal fatigue failure time data of each device of the equipment under a plurality of temperature sections and vibration fatigue failure time data under a plurality of vibration quantities.
The implementation principle and technical effect of the computer device provided by the above embodiment are similar to those of the above method embodiment, and are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
carrying out fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
performing fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
performing fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
In one embodiment, the method further comprises: and acquiring thermal fatigue failure time data of each device of the equipment under a plurality of temperature sections and vibration fatigue failure time data under a plurality of vibration quantities.
The implementation principle and technical effect of the computer-readable storage medium provided by the above embodiments are similar to those of the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for quantifying device lifetime, the method comprising:
respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
performing fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
performing fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
performing fault fusion on the life probability density functions of all devices in the equipment to obtain the life probability density function of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
2. The method of claim 1, further comprising:
and acquiring thermal fatigue failure time data of each device of the equipment under a plurality of temperature sections and vibration fatigue failure time data under a plurality of vibration quantities.
3. The method according to claim 2, wherein the step of performing cumulative damage analysis on the thermal fatigue failure time data of each device in the equipment to obtain the cumulative thermal fatigue failure time data comprises:
calculating the temperature time proportion of each temperature section in all the temperature sections aiming at the ith device of the equipment; each temperature section comprises Q thermal fatigue failure time data, and different thermal fatigue failure time data correspond to different identifications;
multiplying each thermal fatigue failure time data of each temperature section by a corresponding temperature time proportion to obtain Q target thermal fatigue failure time data of each temperature section;
accumulating the target thermal fatigue failure time data of each temperature section corresponding to the same identification to obtain Q accumulated thermal fatigue failure time data.
4. The method according to claim 2, wherein the step of performing accumulated damage analysis on the vibration fatigue failure time data of each device in the equipment to obtain accumulated vibration fatigue failure time data comprises:
calculating the vibration time proportion of each vibration quantity in all vibration quantities aiming at the ith device of the equipment; each vibration quantity comprises Q pieces of vibration fatigue failure time data, and different vibration fatigue failure time data correspond to different identifications;
multiplying each vibration fatigue failure time data of each vibration quantity by a corresponding vibration time proportion to obtain Q target vibration fatigue failure time data of each vibration quantity;
accumulating the target vibration fatigue failure time data of each vibration quantity corresponding to the same identification to obtain Q accumulated vibration fatigue failure time data.
5. The method according to any one of claims 1 to 4, wherein the performing a fault distribution fit on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device comprises:
performing distribution fitting on the accumulated thermal fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a thermal fatigue life distribution function of each device in the equipment;
and performing distribution fitting on the accumulated vibration fatigue failure moment data of each device by adopting an exponential distribution fitting algorithm to obtain a vibration fatigue life distribution function of each device in the equipment.
6. The method according to any one of claims 1 to 4, wherein the fault fusing the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device comprises:
performing a sampling operation for each device of the apparatus;
wherein the sampling operation comprises: obtaining a random number by using a Monte Carlo random sampling method, and respectively substituting the random number into a thermal fatigue life distribution function and a vibration fatigue life distribution function of the device to obtain two random values; acquiring the minimum value of the two random values, and inputting the minimum value into a first sampling data set;
repeatedly executing the sampling operation until the number of elements in the first sampling data set reaches a preset threshold value; and obtaining a lifetime probability density function of the device according to the first sampling data set.
7. The method according to any one of claims 1 to 4, wherein the fault fusion is performed on the lifetime probability density functions of all devices in the equipment to obtain the lifetime probability density function of the equipment; the life probability density function of the equipment is used for obtaining the life quantification result of the equipment, and comprises the following steps:
performing a sampling operation for all devices in the apparatus;
wherein the sampling operation comprises: acquiring a random number by using a Monte Carlo random sampling method, and respectively substituting the random number into service life probability density functions of K devices of the equipment to obtain K random values; acquiring the minimum value of the K random values, and inputting the minimum value into a second sampling data set;
repeatedly performing the sampling operation until the number of elements in the second sampled data set reaches a preset threshold; and obtaining a lifetime probability density function of the equipment according to the second sampling data set.
8. An apparatus for quantifying device lifetime, the apparatus comprising:
the accumulated damage analysis module is used for respectively carrying out accumulated damage analysis on the thermal fatigue failure time data and the vibration fatigue failure time data of each device in the equipment to obtain accumulated thermal fatigue failure time data and accumulated vibration fatigue failure time data;
the fault distribution fitting module is used for performing fault distribution fitting on the accumulated thermal fatigue failure time data and the accumulated vibration fatigue failure time data of each device to obtain a thermal fatigue life distribution function and a vibration fatigue life distribution function of each device;
the device fault fusion module is used for carrying out fault fusion on the thermal fatigue life distribution function and the vibration fatigue life distribution function of each device to obtain a life probability density function of each device;
the equipment fault fusion module is used for carrying out fault fusion on the service life probability density functions of all devices in the equipment to obtain the service life probability density functions of the equipment; and the service life probability density function of the equipment is used for obtaining a service life quantification result of the equipment.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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CN113447875B (en) * 2021-05-27 2022-09-20 国网山东省电力公司营销服务中心(计量中心) Method and system for evaluating residual life of disassembled intelligent electric energy meter
CN113343382A (en) * 2021-06-01 2021-09-03 上海时驾科技有限公司 Valve body protection method based on fatigue failure

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