CN108009692A - Maintenance of equipment information processing method, device, computer equipment and storage medium - Google Patents
Maintenance of equipment information processing method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The present invention proposes a kind of maintenance of equipment information processing method, device, computer equipment and storage medium, wherein, method includes：According to the failure that each equipment has occurred in multiple equipment, obtain the mode input data of multiple equipment, the mode input data input prediction model of each equipment is trained, and predict to obtain the prediction failure-frequency and predictive maintenance expense in each observation period in equipment future using the prediction model after training, according to the prediction failure-frequency and predictive maintenance expense of each equipment, the total cost data of each equipment are determined.Pass through prediction model, failure-frequency in each equipment future observation period and maintenance cost are predicted, so that it is determined that total maintenance cost of each equipment, solve in the prior art, by the failure-frequency and maintenance cost of the pre- measurement equipment of experience or simple algorithm of people, the problem of causing forecasting inaccuracy true.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a method and an apparatus for processing device maintenance information, a computer device, and a storage medium.
Background
Large-scale machine rooms, factories, some infrastructures and the like are all inevitably subjected to equipment failure and maintenance. As the service life of the equipment increases, the failure rate changes to some extent, and maintenance costs may occur, and the company needs to estimate the maintenance cost for a period of time in the future, or estimate whether the equipment needs to be replaced with new equipment, so as to perform reporting or financial auditing and the like.
In the related art, a conventional method for estimating the maintenance cost of the equipment is generally used, that is, the maintenance cost required in the future of the equipment is estimated according to the frequency and cost of damage of the past equipment by a simple formula or human experience. However, the method has too coarse estimated maintenance cost and does not consider the change of the failure rate of the equipment per se along with the life cycle. Therefore, the maintenance cost estimation is not accurate enough.
Disclosure of Invention
The present invention is directed to solving, at least in part, one of the technical problems in the related art.
Therefore, a first object of the present invention is to provide an equipment maintenance information processing method, so as to implement a prediction model through SMC or NBD, and set the interval duration of equipment failures in the prediction model to be compliant with the weibull distribution of failure rate λ, so as to predict the failure frequency and maintenance cost of each equipment in the future observation period, thereby improving the accuracy of model prediction, and solving the problem in the prior art that the failure frequency and maintenance cost of equipment are predicted through human experience or simple algorithm, which results in inaccurate prediction.
A second object of the present invention is to provide an equipment maintenance information processing apparatus.
A third object of the invention is to propose a computer device.
A fourth object of the invention is to propose a non-transitory computer-readable storage medium.
A fifth object of the invention is to propose a computer program product.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides an apparatus maintenance information processing method, including:
obtaining model input data of a plurality of devices according to a fault of each device in the plurality of devices, wherein the model input data of each device comprises: the total number of cycles of the equipment with fault conditions, the number of cycles between the first fault cycle and the statistical time, the number of cycles between the first fault cycle and the last fault cycle, and the average maintenance cost data of the equipment fault; the period is obtained by dividing a statistical time interval before a statistical moment;
inputting the model input data of each device into a prediction model for training, and predicting by adopting the trained prediction model to obtain the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period; the prediction model is a sliding mode control SMC model or a negative binomial distribution NBD model;
determining total cost data for each device based on the predicted failure frequency and the predicted maintenance cost for each device.
According to the equipment maintenance information processing method, model input data of a plurality of pieces of equipment are obtained according to faults of each piece of equipment in the plurality of pieces of equipment, the model input data of each piece of equipment are input into a prediction model to be trained, the prediction fault frequency and the prediction maintenance cost of each piece of equipment in a future observation time period are obtained through prediction of the trained prediction model, and total cost data of each piece of equipment are determined according to the prediction fault frequency and the prediction maintenance cost of each piece of equipment. The failure frequency and the maintenance cost of each device in a future observation time period are predicted through an SMC or NBD prediction model, so that the total maintenance cost of each device is determined, and the problem of inaccurate prediction caused by prediction of the failure frequency and the maintenance cost of the device through human experience or simple algorithm in the prior art is solved.
To achieve the above object, a second embodiment of the present invention provides an equipment maintenance information processing apparatus, including:
a parameter obtaining module, configured to obtain a model input parameter of each of a plurality of devices according to a fault that has occurred in each of the plurality of devices, where the model parameter of each of the devices includes: the total number of cycles of the equipment with fault conditions, the number of cycles between the first fault cycle and the statistical time, the number of cycles between the first fault cycle and the last fault cycle, and the average maintenance cost data of the equipment fault; the period is obtained by dividing a statistical time interval before a statistical time;
the prediction module inputs model input data of each device into the prediction model for training, and predicts by adopting the trained prediction model to obtain the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period; the prediction model is a sliding mode control SMC model or a negative binomial distribution NBD model;
the determining module is used for determining total cost data of each device according to the predicted failure frequency and the predicted maintenance cost of each device;
in the device maintenance information processing apparatus according to the embodiment of the present invention, the parameter obtaining module obtains model input data of the plurality of devices according to a fault that has occurred in each of the plurality of devices, the prediction module is configured to input the model input data of each of the devices into the prediction model for training, and predict, using the trained prediction model, a predicted fault frequency and a predicted maintenance cost of each of the devices in a future observation time period, and the determining module is configured to determine total cost data of each of the devices according to the predicted fault frequency and the predicted maintenance cost of each of the devices. The failure frequency and the maintenance cost of each device in a future observation time period are predicted through an SMC or NBD prediction model, so that the total maintenance cost of each device is determined, and the problem of inaccurate prediction caused by prediction of the failure frequency and the maintenance cost of the device through human experience or simple algorithm in the prior art is solved.
In order to achieve the above object, a third embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the computer device implements the device repair information processing method according to the first aspect.
In order to achieve the above object, a fourth embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the processor executes the program, the processor implements the equipment servicing information processing method according to the first aspect.
In order to achieve the above object, an embodiment of a fifth aspect of the present invention provides a computer program product, where when instructions of the computer program product are executed by a processor, the method for processing equipment maintenance information according to the first aspect is implemented.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a method for processing equipment maintenance information according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating another method for processing equipment maintenance information according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an equipment maintenance information processing apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of another apparatus maintenance information processing device according to an embodiment of the present invention; and
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
An apparatus maintenance information processing method, an apparatus, a computer device, and a storage medium according to embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an apparatus maintenance information processing method according to an embodiment of the present invention.
As shown in fig. 1, the method includes:
step 101, obtaining model input data of a plurality of devices according to the fault of each device in the plurality of devices.
When the equipment runs, the running state of the equipment can be recorded in the equipment records, each equipment record corresponds to the running state of one equipment in a certain period, and for example, the running state can comprise normal state, fault state and shutdown maintenance.
For the sake of clarity of the device record form, a device will be taken as an example, and several device records are listed, see table 1, in table 1, the operation states of the device, and the costs generated by the sending fault maintenance are listed at different dates and times, and it should be noted that the device record form is only used as an illustrative description and does not constitute a limitation to the embodiment.
TABLE 1 Equipment records
Through the device records in table 1, the fault data of the device can be obtained according to the statistical time period, so as to determine the input data of the model.
Specifically, data of a failure that has occurred in each of a plurality of devices is acquired, a statistical time and a statistical period before the statistical time are determined, and the statistical period is divided into a plurality of cycles that are equal. Here, the length of the period is related to the frequency of the failures that have occurred in the plurality of devices, and the higher the frequency of the failures that have occurred, the shorter the period division, and the lower the failure frequency, the longer the period division.
And taking the period of the statistical moment as a reference, acquiring relevant fault data of each device in the period of the statistical time period as model input data, wherein the model input data corresponding to each device comprises: the total number of periods of the equipment with fault conditions, the period number between the period of the first fault and the counting moment, the period number between the period of the first fault and the period of the last fault, and the average maintenance cost data of the equipment fault.
It should be noted that, for each device, the total number of cycles of the failure condition of the device is the total number of times of failure of the device in the statistical period. The period of the first fault is away from the period number between the counting moments, namely the time interval from the first fault of the equipment to the end of the counting time period. The period of the first fault occurring is the number of periods between the period of the last fault, i.e. the time interval between the first fault and the last fault of the device.
And 102, inputting the model input data of each device into a prediction model for training, and predicting by using the trained prediction model to obtain the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period.
Specifically, before prediction is performed by using the prediction model, the prediction model needs to be trained according to the pre-acquired historical model input data of the device, so as to obtain the trained prediction model. And inputting the historical model input data of each device into the trained prediction model by adopting the trained prediction model to obtain the predicted failure frequency of each device in the future observation time period. The predicted maintenance cost may specifically be average maintenance cost data for which the corresponding equipment has failed.
The prediction model is a sliding mode control SMC model or a NEGATIVE BINOMIAL DISTRIBUTION (NBD) model, and the prediction of the prediction model is accurate for faults which occur randomly in the equipment. The NBD model is an optimized model of the SMC model, and the NBD model is exemplified in the present embodiment and the following embodiments, and the specific construction method of the model will be further explained in detail in the following embodiments.
And 103, determining total cost data of each device according to the predicted failure frequency of each device and the predicted maintenance cost.
Specifically, according to the prediction model, model input data of each device is input, the predicted failure frequency and the predicted maintenance cost of each device in a future period are obtained through prediction, and the total cost data of each device is obtained through summation calculation.
When the total cost data of each device is solved, the expected service life of the device can be inquired, wherein the expected service life can be obtained by averaging the service lives of the scrapped devices, and the predicted service life of the device is subtracted from the expected service life to obtain the available service life.
According to the following formula: total cost data = predicted failure frequency available duration predicted maintenance cost, total cost data for each device may be calculated.
In the equipment maintenance information processing method provided by the embodiment of the invention, model input data of a plurality of pieces of equipment are obtained according to faults of each piece of equipment in the plurality of pieces of equipment, the model input data of each piece of equipment are input into a prediction model for training, the prediction frequency and the prediction maintenance cost of each piece of equipment in a future observation time period are obtained by adopting the trained prediction model for prediction, and the total cost data of each piece of equipment is determined according to the prediction frequency and the prediction maintenance cost of each piece of equipment. The failure frequency and the maintenance cost of each device in the future observation time period are predicted through the SMC or NBD prediction model, so that the total maintenance cost of each device is determined, and the problem of inaccurate prediction caused by predicting the failure frequency and the maintenance cost of the device through human experience or simple algorithm in the prior art is solved.
On the basis of the previous embodiment, the present invention further provides a possible implementation manner of the equipment maintenance information processing method, which further clearly explains a construction strategy of the NBD prediction model, and fig. 2 is a schematic flow diagram of another equipment maintenance information processing method provided by the embodiment of the present invention, as shown in fig. 2, the method includes the following steps:
step 201, a prediction model is built according to a building strategy.
The NBD model comprises a B geometric binomial distribution BG-NBD model, the BG-NBD model is a probability distribution model, the model adopted in the embodiment is the BG-NBD model, the model can be constructed according to a construction strategy, and specifically, the model construction strategy comprises the following steps: the interval duration of equipment faults follows the Weibull distribution of the fault rate lambda; the probability density function of the non-uniformity of the fault rate lambda follows a gamma distribution; the probability P of scrapping the equipment after the jth fault obeys geometric distribution; the probability density function of the non-uniformity of the probability p of scrapping of an apparatus after a single failure obeys Beta distribution, that is, beta distribution. Hereinafter, each construction strategy will be further described.
Specifically, in the prediction model, the interval duration of the equipment fault is predicted by a failure time function and a risk function thereof, wherein the failure time function is represented by the failure time function constructed by a Weibull distribution of the fault rate lambda, the failure time function is used for indicating the probability that the equipment fault time interval is predicted to be delta t in the observation time period, the risk function is used for indicating the probability that the equipment fault occurs at the t moment in the future observation time period, and the risk function is used for indicating the probability that the equipment fault occurs at the t moment in the future observation time period. The failure time function constructed by the Weibull distribution is a Weibull failure probability density function of the failure rate lambda, so that the failure time function of the model can be expressed as follows:the risk function is derived as:wherein Δ t is a predicted failure time interval within an observation time period; k is a positive number and is determined according to the service life stage of the currently predicted equipment. Tong (Chinese character of 'tong')The Weibull distribution is used as the failure time function of the model because the risk rate function is a function of time t and is not a constant, so that the Weibull distribution can be used for predicting the failure rate of equipment in different time periods, and the accuracy of prediction is improved.
K and λ are 2 parameters of the weibull failure time function, wherein, when the failure rate λ is different, the width and height of the obtained weibull failure time function curve are different, that is, the values corresponding to the predicted equipment failure time interval Δ t are different, and the shape of the risk rate function is also determined by K and λ. The value of K is determined according to the service life stage of the currently predicted equipment, and the failure rate can be solved according to the K value, so that the setting mode accords with the actual change of the failure rate, and the value of K can be determined according to the following rules: if the equipment is in the initial stage of the service life stage, K is less than 1, namely the initial stage of the service life stage of the equipment, the fault rate is high due to trial operation, and the fault rate gradually decreases and tends to be stable along with the time; if the equipment is in the middle stage of the service life stage, K =1, the equipment is in a stable use stage, the accidental faults are more, and the fault rate is a constant and cannot be predicted; if the equipment is at the end of the life stage, K is greater than 1, the equipment belongs to the aging stage, and the failure rate increases along with time. And substituting the acquired failure rate of the equipment in the past period of time and the corresponding failure time interval into a formula of a Weibull distribution function, and determining the parameters of the Weibull distribution function in the model through calculation so as to determine the parameters of the risk rate function in the prediction model.
Probability density function of non-uniformity of failure rate lambda
Probability of scrapping after jth fault of equipment P = P (1-P) j-1 Where p is the probability of rejection after a single fault, and the probability density function of the non-uniformity of the probability of rejection p of the device after a single faultB (a, B) is the beta functionThe number, which can be expressed as a gamma function (gamma), is:
wherein a, b, r and alpha are parameters of the prediction model and are obtained by performing model training according to model input data. The process of calculating a, b, r and α will be described in detail in step 203, and will not be described herein.
According to the construction strategy of the model, a prediction model can be constructed, and the prediction model can output the predicted failure frequency and the predicted maintenance cost of each piece of equipment.
Step 202, obtaining model input data of the plurality of devices according to the fault of each device in the plurality of devices.
Specifically, model input data corresponding to a historical statistical time period is acquired according to a fault occurring in each of a plurality of devices. The input data includes: the total number of periods of the equipment with fault conditions, the period between the period distance statistics moments when the first fault occurs, the period when the last fault occurs and the average maintenance cost data of the equipment fault.
And 203, inputting the model input data of each device into a prediction model for training, and predicting by using the trained prediction model to obtain the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period.
Specifically, the model input data for each device is input to the predictive model, and the parameters a, b, r, and α of the model are determined. As a possible implementation, the parameters of the prediction model are estimated by a maximum likelihood method, which has the following formula: l (r, α, a, b | X = X, t x ,T)＝A 1 ·A 2 ·(A 3 +δ x＞0 A 4 ) Wherein, in the step (A), wherein X represents the 3 inputs of the modelData is input, namely the total number of cycles of the fault condition of the equipment and t X And counting the period number between the time corresponding to the last fault of the equipment and the time when the T is the period distance of the first fault.
After inputting data by a model of a known device, the input data is substituted into A 1 、A 2 、A 3 And A 4 Logarithm of (d):
A 1 logarithm of (c = ln [ r + x) ]]-ln[Γ(r)]+r ln(α)；
A 2 Logarithm of (c = ln [ Γ (a + b) ]]+ln[Γ(b+x)]-ln[Γ(b)]-ln[Γ(a+b+x)]；
A 3 Logarithm of = - (r + x) ln (α + T);
logarithm of A4 = ln (a) 1 )-ln(A 2 )+ln(exp(ln(A 3 ))+δ x＞0 A 4 )。
The above four equations are combined, and four parameters of a, b, r and alpha of the prediction model can be obtained through a maximum likelihood method formula.
Further, substituting four parameters, a, b, r and α, into each function of the prediction model includes:
1) Substituting r and alpha into the probability density function of the non-uniformity of the failure rate lambdaThereby obtaining the probability density distribution of the failure rate lambda, and the failure time function of the prediction model is
And under the condition, the failure rate lambda is used as a known quantity, the value of K is determined by combining the service life stage of the equipment, K is also used as the known quantity and is substituted into f (delta t; lambda, K), and the probability of the failure time interval delta t predicted by the equipment in the observation time period is obtained.
2) Substituting a and b into the probability density function of the non-uniformity of the probability p of rejection of the device after a single faultSo that the probability P = P (1-P) of rejection of the device after the jth fault can be determined j-1 。
Finally, after the predicted failure time interval delta t of the equipment in the observation time period and the probability of scrapping of the equipment after each failure are calculated, the average maintenance cost of the equipment with the failure can be used as the predicted maintenance cost, so that the predicted failure time interval delta t and the predicted maintenance cost of the equipment are obtained, and the predicted failure frequency of the equipment in the observation time period can be correspondingly obtained through the predicted failure time interval of the equipment.
And step 204, determining total cost data of each device according to the predicted failure frequency and the predicted maintenance cost of each device.
Optionally, after the failure time interval Δ t predicted by the device within the observation time period and the probability of scrapping of the device after each failure are calculated, when it is determined that the probability of scrapping after the failure is not higher than the threshold, the maximum device maintenance frequency of the device is obtained, and the available duration of the device is obtained by multiplying the maintenance frequency by the failure time interval Δ t. The predicted maintenance cost is obtained from the average maintenance cost for the equipment that has failed multiplied by the number of times the equipment has failed within the usable time period.
It should be noted that, if the probability of scrapping after each fault is higher than the threshold, the equipment needs to be scrapped, and the equipment can be scrapped directly without maintenance.
Step 205, determining whether to replace the device or generate a device record according to the total cost data of each device.
Specifically, as a possible implementation manner, whether a new device needs to be replaced or a device record needs to be generated according to the calculated predicted total cost data of each device, the predicted total cost data of each current device can be compared with the depreciation data of the device, if the predicted total cost data is larger than the depreciation data of the device, the device is replaced, otherwise, the device record of the device, namely the predicted maintenance cost record needed in the future of the device, is generated, so that a company can conveniently perform financial budget of future device maintenance.
According to the equipment maintenance information processing method, model input data of a plurality of pieces of equipment are obtained according to faults of each piece of equipment in the plurality of pieces of equipment, the model input data of each piece of equipment are input into a prediction model to be trained, the prediction fault frequency and the prediction maintenance cost of each piece of equipment in a future observation time period are obtained through prediction of the trained prediction model, total cost data of each piece of equipment are determined according to the prediction fault frequency and the prediction maintenance cost of each piece of equipment, and whether the equipment is replaced or not is determined or equipment records are generated according to the total cost data of each piece of equipment. By means of a prediction model of SMC or NBD, and setting interval duration of equipment failure in the prediction model to be subjected to Weibull distribution of failure rate lambda, a risk rate function for indicating the failure rate of the equipment is a function of time t and is not a constant which is constant, the risk rate function can be used for predicting the failure frequency and maintenance cost of each equipment in a future observation time period, accuracy of model prediction is improved, and the problem that prediction is inaccurate due to the fact that the failure frequency and maintenance cost of the equipment are predicted through human experience or a simple algorithm in the prior art is solved.
In order to implement the above embodiments, the present invention further provides an apparatus maintenance information processing device.
Fig. 3 is a schematic structural diagram of an equipment maintenance information processing apparatus according to an embodiment of the present invention.
As shown in fig. 3, the apparatus includes a parameter acquisition module 31, a prediction module 33, and a determination module 35.
A parameter obtaining module 31, configured to obtain model input parameters of multiple devices according to a fault that has occurred in each of the multiple devices, where the model parameters of each device include: the method comprises the steps of counting the total number of cycles of the equipment with fault conditions, counting the number of cycles between the first fault-occurring cycle and the last fault-occurring cycle, and averaging maintenance cost data of the equipment faults, wherein the cycles are obtained by dividing the counting time interval before the counting time.
And the prediction module 33 inputs the model input data of each device into the prediction model for training, and predicts and obtains the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period by using the trained prediction model, wherein the prediction model is a sliding mode control SMC model or a negative binomial distribution NBD model.
And a determining module 35, configured to determine total cost data of each device according to the predicted failure frequency and the predicted maintenance cost of each device.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and is not repeated herein.
In the device maintenance information processing apparatus according to the embodiment of the present invention, the parameter obtaining module obtains model input data of the plurality of devices according to a fault that has occurred in each of the plurality of devices, the prediction module is configured to input the model input data of each of the devices into the prediction model for training, and predict, using the trained prediction model, a predicted fault frequency and a predicted maintenance cost of each of the devices in a future observation period, and the determination module is configured to determine total cost data of each of the devices according to the predicted fault frequency and the predicted maintenance cost of each of the devices. The failure frequency and the maintenance cost of each device in the future observation time period are predicted through the prediction model, so that the total maintenance cost of each device is determined, and the problem of inaccurate prediction caused by prediction of the failure frequency and the maintenance cost of the device through human experience or simple algorithm in the prior art is solved.
Based on the foregoing embodiment, the present invention further provides a possible implementation manner of an equipment maintenance information processing apparatus, fig. 4 is a schematic structural diagram of another equipment maintenance information processing apparatus provided in the embodiment of the present invention, and as shown in fig. 4, the apparatus may further include: a processing module 37.
And a processing module 37 for determining whether to replace the device or generate a device record according to the total cost data of each device.
As a possible implementation, the prediction model in prediction module 33 is an NBD model, and the NBC model includes a beta geometric binomial distribution BG-NBD model. The model is constructed according to a construction strategy, wherein the construction strategy comprises the following steps:
the interval duration of equipment faults follows the Weibull distribution of the fault rate lambda;
the probability density function of the non-uniformity of the fault rate lambda follows a gamma distribution;
the probability P of scrapping the equipment after the jth fault obeys geometric distribution;
the probability density function of the non-uniformity of the probability p of scrapping of the equipment after a single failure obeys the beta distribution.
The prediction model includes: and constructing a failure time function and a risk function according to the interval duration of the equipment failure and the Weibull distribution of the failure rate lambda.
Wherein the failure time function of the prediction model is
The risk function of the predictive model is
The failure time function is used for indicating the probability that the predicted equipment failure time interval is delta t, and the risk rate function is used for indicating the probability that the equipment fails at the time t in the future observation time period; k is a positive number and is determined according to the service life stage of the currently predicted equipment.
As a possible implementation manner, the determination manner of K is: if the equipment is in the initial stage of the service life, K is less than 1; if the equipment is in the middle of the life stage, K =1; if the device is at the end of life, K >1.
The predictive model may further include: probability density function of non-uniformity of failure rate lambda
Probability of scrapping after jth fault of equipment P = P (1-P) j-1 Wherein p is the probability of scrapping after single fault;
probability density function of non-uniformity of probability p of rejection of equipment after single fault
Wherein a, b, r and alpha are parameters of the prediction model and are obtained by performing model training according to model input data.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and is not repeated herein.
In the device maintenance information processing apparatus according to the embodiment of the present invention, the parameter obtaining module obtains model input data of the plurality of devices according to a fault that has occurred in each of the plurality of devices, the prediction module is configured to input the model input data of each of the devices into the prediction model for training, and predict, using the trained prediction model, a predicted fault frequency and a predicted maintenance cost of each of the devices in a future observation period, and the determination module is configured to determine total cost data of each of the devices according to the predicted fault frequency and the predicted maintenance cost of each of the devices. The failure frequency and the maintenance cost of each device in the future observation time period are predicted through the prediction model, so that the total maintenance cost of each device is determined, and the problem of inaccurate prediction caused by prediction of the failure frequency and the maintenance cost of the device through human experience or simple algorithm in the prior art is solved.
In order to implement the foregoing embodiments, an embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the computer device implements the device maintenance information processing method according to the foregoing method embodiments.
In order to implement the foregoing embodiments, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the processor executes the computer program, the processor implements the equipment maintenance information processing method according to the foregoing method embodiment.
In order to implement the foregoing embodiments, an embodiment of the present invention further provides a computer program product, where instructions of the computer program product, when executed by a processor, implement the equipment servicing information processing method according to the foregoing method embodiment.
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use to implement embodiments of the present application. The computer device 12 shown in fig. 5 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.
As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, industry Standard Architecture (ISA) bus, micro Channel Architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
Computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with computer device 12, and/or with any devices (e.g., network card, modem, etc.) that enable computer device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. An equipment maintenance information processing method is characterized by comprising the following steps:
obtaining model input data of a plurality of devices according to a fault of each device in the plurality of devices, wherein the model input data of each device comprises: the total number of cycles of the equipment with fault conditions, the number of cycles between the first fault cycle and the statistical time, the number of cycles between the first fault cycle and the last fault cycle, and the average maintenance cost data of the equipment fault; the period is obtained by dividing a statistical time interval before a statistical time;
inputting the model input data of each device into a prediction model for training, and predicting by using the trained prediction model to obtain the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period; the prediction model is a sliding mode control SMC model or a negative binomial distribution NBD model;
determining total cost data for each device based on the predicted failure frequency and the predicted maintenance cost for each device.
2. The equipment servicing information processing method of claim 1, wherein the predictive model is an NBD model;
the prediction model is constructed according to a construction strategy;
wherein the construction strategy comprises:
the interval duration of the equipment fault follows the Weibull distribution of the fault rate lambda;
the probability density function of the non-uniformity of the fault rate lambda follows a gamma distribution;
the probability P of scrapping the equipment after the jth fault obeys geometric distribution;
the probability density function of the non-uniformity of the probability p of scrapping of the equipment after a single failure obeys the beta distribution.
3. The equipment servicing information processing method according to claim 2, wherein the prediction model includes a failure time function constructed in accordance with a weibull distribution of an interval duration of the equipment failure subject to a failure rate λ, the prediction model further including a risk rate function;
the prediction model has a failure time function of
The risk function of the prediction model is
The failure time function is used for indicating the probability that the predicted failure time interval of the equipment is delta t, and the risk rate function is used for indicating the probability that the equipment fails at the time t in the future observation time period; k is a positive number and is determined according to the service life stage of the currently predicted equipment.
4. The equipment servicing information processing method according to claim 3,
if the equipment is in the initial stage of the service life, K is less than 1;
if the equipment is in the middle of the service life stage, K =1;
if the device is at the end of its life, K >1.
5. The equipment servicing information processing method of claim 2, wherein the predictive model comprises:
probability density function of non-uniformity of failure rate lambda
Probability of scrapping after jth fault of equipment P = P (1-P) j-1 Wherein p is the probability of scrapping after single fault; probability density function of non-uniformity of probability p of equipment scrapping after single fault
And a, b, r and alpha are parameters of the prediction model and are obtained by performing model training according to model input data.
6. The equipment servicing information processing method of any one of claims 1 to 5, wherein, after determining the total cost data of each equipment according to the predicted failure frequency and the predicted servicing cost of each equipment, further comprising:
based on the total cost data for each device, a determination is made whether to replace the device, or a device record is generated.
7. An apparatus for processing equipment maintenance information, comprising:
a parameter obtaining module, configured to obtain model input parameters of multiple devices according to a fault that has occurred in each of the multiple devices, where the model parameters of each device include: the total number of cycles of the equipment with fault conditions, the number of cycles between the first fault cycle and the statistical time, the number of cycles between the first fault cycle and the last fault cycle, and the average maintenance cost data of the equipment fault; the period is obtained by dividing a statistical time interval before a statistical moment;
the prediction module inputs model input data of each device into the prediction model for training, and predicts and obtains the predicted failure frequency and the predicted maintenance cost of each device in the future observation time period by adopting the trained prediction model; the prediction model is a sliding mode control SMC model or a negative binomial distribution NBD model;
and the determining module is used for determining the total cost data of each device according to the predicted failure frequency of each device and the predicted maintenance cost.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the device servicing information processing method according to any one of claims 1 to 6 when executing the program.
9. A non-transitory computer-readable storage medium on which a computer program is stored, the program being characterized by implementing the device repair information processing method according to any one of claims 1 to 6 when executed by a processor.
10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor, perform the device servicing information processing method according to any one of claims 1-6.
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Application publication date: 20180508 |
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