CN112417701B - Method and device for predicting residual life of numerical control machine tool and network side server - Google Patents
Method and device for predicting residual life of numerical control machine tool and network side server Download PDFInfo
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Abstract
The invention belongs to the field of workpiece life prediction, and provides a method, a device and a network side server for predicting the residual life of a numerical control machine tool, aiming at the problem of low precision in the residual life of a part in the prior art, wherein the method comprises the steps of obtaining a preset health prediction model; acquiring a target health index; calculating to obtain the residual life time according to the target health index and a preset health prediction model; the health prediction model is established by bringing the time sequence and the upper order limit into the ARIMA model, and the essence is that the time of the target health index of the part at a certain future moment is estimated according to the law of the change of the historical comprehensive health index with time.
Description
Technical Field
The invention belongs to the field of workpiece service life prediction, and particularly relates to a method and a device for predicting the residual service life of a numerical control machine tool and a network side server.
Background
In actual operation of a machine tool, the service lives of the parts are different from each other, and in order to ensure normal operation of the machine tool, the remaining life of each part is generally required to be known. In the prior art, the effective time of the part can be used from the point of view of the label purchased by the part, and the residual service life of the part can be obtained by calculating the difference between the effective time and the current service time by combining the current service time. Because the effective time is detected when the part leaves the factory, and the part leaves the factory and is transported and lost (such as the loss generated by friction between the machine tool and other parts), the actual residual time life is smaller than the calculated residual service life. That is, in the prior art, the remaining life of the part is calculated in a manner that is not accurate.
Disclosure of Invention
The invention provides a method and a device for predicting the residual life of a numerical control machine tool and a network side server, which solve the problem of low precision in calculating the residual life of a part in the prior art.
The basic scheme of the invention is as follows: the method for predicting the residual life of the numerical control machine tool comprises the following steps:
acquiring a preset health prediction model;
acquiring a target health index;
and calculating to obtain the residual life time according to the target health index and a preset health prediction model.
Further, the debugging of the health prediction model includes:
acquiring a comprehensive health index and a time associated with the comprehensive health index;
correlating the normalized comprehensive health index with time, and integrating the normalized comprehensive health index into a time sequence;
calculating sample variance of time series
The time health sequence is brought into an ARIMA model, and the autoregressive order P and the moving average order Q are subjected to order determination through a minimum Bayesian information criterion.
Further, the ARIMA model is:
wherein HI is t,d Is a stable time sequence value obtained after d times of difference, P is an autoregressive order, Q is a moving average order, mu p Is an autoregressive coefficient, θ q Is the running average coefficient, ε t Representing a zero mean white noise sequence.
Further, the autoregressive order P and the moving average order Q are fixed, including:
acquiring a preset order upper limit L;
the autoregressive order P and the moving average order Q are scaled,
make it meet
BIC(P′,Q′)＝min 0≤P,Q≤L BIC(P,Q)。
Further, after the autoregressive order P and the moving average order Q are subjected to order determination, parameters in the ARIMA model are adjusted by means of moment estimation.
Further, after the autoregressive order P and the moving average order Q are subjected to order determination, the autoregressive order P and the moving average order Q with good order determination are brought into the ARIMA model, and a prediction model is obtained and output.
Further, the calculating to obtain the remaining life time according to the target health index and the preset health prediction model includes:
calculating the maximum life time according to the target health index and the health prediction model;
acquiring the current time;
and calculating a difference value between the maximum life time and the current time, wherein the difference value is the residual life time.
The invention also provides a device for predicting the residual life of the numerical control machine tool based on the autoregressive sum moving average model, which comprises the following steps:
the receiving module is used for receiving the correlated comprehensive health index and time sent by the requesting party and the target health index and the upper order limit;
the health prediction model building module is used for calculating a health prediction model through an ARIMA model according to the correlated comprehensive health index and time and the upper order limit;
the storage module is used for storing the health prediction model;
the health prediction module is used for calculating residual life time according to the target health index and the health prediction model in the storage module;
and the output module is used for outputting the residual life time.
A computer-readable storage medium storing a computer program which, when executed by a processor, implements the method for predicting remaining life of a numerical control machine tool according to one of the above.
A network side server, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of predicting remaining life of a numerically controlled machine tool as set forth in any one of the preceding claims.
In the scheme, a health prediction model is adopted to perform prediction calculation for reaching the target health index, so that the time length required for reaching the target health index is obtained, and the time length is taken as the residual life time. Compared with the prior art, the method and the device have the advantages that the label is read, the health prediction model introduced by the scheme is used for calculation, and accuracy is higher.
In addition, the health prediction model in the present embodiment is obtained by ARIMA model according to a time sequence and a preset upper order limit, wherein the time sequence is a comprehensive health index and time, that is, the time sequence represents a change of the comprehensive health index with time, that is, a change of the comprehensive health index of the part under a history time. The establishment of the health prediction model is essentially to summarize the comprehensive health index change of the part in the historical time during working, and summarize the change of the health index of the part along with the change of time.
In addition, in the normalized comprehensive health index in the scheme, 0 represents that the workpiece cannot work normally, and 1 represents that the workpiece is good. Therefore, in the scheme, the maximum life time is calculated by presetting the target health index and the health prediction model and combining with the current time, and the residual life time of the part is calculated again, wherein the situation that the target health index appears in the part at a certain moment in the future is estimated substantially according to the law of the change of the historical comprehensive health index of the part along with time. The future change is presumed according to the historical change of the comprehensive health index, and compared with the prior art which does not consider the situation that the part is worn after being put into operation, the residual service life is calculated directly according to the factory label and the current moment, and the method is more accurate.
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One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings.
FIG. 1 is a flowchart of a method for predicting the residual life of a numerical control machine tool according to a first embodiment of the present invention;
FIG. 2 is a flow chart of the preset health prediction model of FIG. 1;
FIG. 3 is a flow chart of the calculation of remaining life in FIG. 1;
FIG. 4 is a schematic block diagram of a device for predicting residual life of a numerical control machine tool based on an autoregressive and moving average model according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a network side server according to a third embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings. However, those of ordinary skill in the art will understand that in various embodiments of the present invention, numerous technical details have been set forth in order to provide a better understanding of the present application. However, the technical solutions claimed in the present application can be implemented without these technical details and with various changes and modifications based on the following embodiments.
A first embodiment of the present invention relates to a method for predicting the remaining life of a numerically controlled machine tool, an autoregressive and additive moving average model (Autoregressive Integrated Moving Average model, ARIMA) algorithm, abbreviated as ARIMA algorithm, i.e., a steady ARIMA model. The ARIMA model is as follows:
wherein HI is t,d Is a stable time sequence value obtained after d times of difference, P is an autoregressive order, Q is a moving average order, mu p Is an autoregressive coefficient, θ q Is the running average coefficient, ε t Representing a zero mean white noise sequence.
As shown in fig. 1, the method for predicting the remaining life of the numerical control machine tool includes:
s1, acquiring a preset health prediction model;
s2, obtaining a target health index;
and S3, calculating to obtain the residual life time according to the target health index and a preset health prediction model.
The health prediction model is established by adopting the method shown in fig. 2:
s1-1, acquiring a comprehensive health index and time which are related to each other, and normalizing the comprehensive health index;
s1-2, correlating the normalized comprehensive health index with time, and integrating the normalized comprehensive health index into a time sequence;
s1-3, calculating sample variance of the time sequence
S1-4, bringing the time health sequence into an ARIMA model, and carrying out order determination on an autoregressive order P and a moving average order Q through a minimum Bayesian information criterion;
s1-5, bringing the autoregressive order P and the moving average order Q with good order into the ARIMA model, adjusting parameters in the ARIMA model by adopting moment estimation, obtaining a prediction model and outputting the prediction model.
Specifically, the comprehensive health index represents the overall health condition of the current test part, and in the normalized comprehensive health index, 0 represents that the workpiece cannot work normally, and 1 represents that the workpiece is good. The health prediction model in the present solution is obtained by ARIMA model according to a time sequence and a preset upper order limit, wherein the time sequence is a comprehensive health index and time, that is, the time sequence represents the change of the comprehensive health index with time, that is, the change of the comprehensive health index of the part under the history time. The establishment of the health prediction model is essentially to summarize the comprehensive health index change of the part in the historical time during working, and summarize the change of the health index of the part along with the change of time.
Specifically, in S1-4, the specific process of performing the order determination of the autoregressive order P and the moving average order Q is as follows:
s1-4-1, obtaining a preset order upper limit L;
s1-4-2, the autoregressive order P and the moving average order Q are subjected to fixed order,
make it meet
BIC(P′,Q′)＝min 0≤P,Q≤L BIC(P,Q)。
Specifically, in S2, the target health index is set by the user. For example, when the workpiece is not available, the user again performs replacement of the workpiece, and at this time, the user wants to know when the part is completely scrapped, and the input target health index is 0. For another example, factory building regulations require that the workpiece be replaced in advance to avoid serious workpiece wear and ensure product quality, and at this time, the user may set the target workpiece to 0.001 (or other value close to 0 wirelessly).
Specifically, in S3, as shown in fig. 3, according to the target health index and the preset health prediction model, the remaining life time is calculated, including:
s3-1, calculating the maximum life time according to the target health index and the health prediction model;
s3-2, obtaining the current time;
s3-3, calculating a difference value between the maximum life time and the current time, wherein the difference value is the residual life time.
According to the scheme, through the preset target health index and health prediction model, the maximum life time is calculated and combined with the current time, and the residual life time of the part is calculated again, wherein the fact is that the target health index of the part appears at a certain moment in the future (the target health index is usually set to be 0) is estimated according to the rule of the historical comprehensive health index of the part changing along with time. The future change is presumed according to the historical change of the comprehensive health index, and compared with the prior art which does not consider the situation that the part is worn after being put into operation, the residual service life is calculated directly according to the factory label and the current moment, and the method is more accurate.
The above steps of the methods are divided, for clarity of description, and may be combined into one step or split into multiple steps when implemented, so long as they include the same logic relationship, and they are all within the protection scope of this patent; it is within the scope of this patent to add insignificant modifications to the algorithm or flow or introduce insignificant designs, but not to alter the core design of its algorithm and flow.
A second embodiment of the present invention relates to a numerical control machine tool remaining life prediction apparatus based on an autoregressive sum moving average model. As shown in fig. 4, the device for predicting the residual life of the numerical control machine tool based on the autoregressive sum moving average model comprises a receiving module 401, a health prediction model establishing module 402, a storage module 403, a health prediction module 404 and an output module 405.
Specifically, the receiving module 401 is configured to receive the integrated health index and time associated with each other and the target health index and the upper order limit sent by the requesting party; the health prediction model building module 402 is configured to calculate a health prediction model according to the correlated comprehensive health index and time, and the upper order limit through the ARIMA model; a storage module 403, configured to store a health prediction model; the health prediction module 404 is configured to calculate a remaining lifetime according to the target health index and the health prediction model in the storage module; and an output module 405 for outputting the remaining lifetime.
It is to be noted that this embodiment is a system example corresponding to the first embodiment, and can be implemented in cooperation with the first embodiment. The related technical details mentioned in the first embodiment are still valid in this embodiment, and in order to reduce repetition, a detailed description is omitted here. Accordingly, the related art details mentioned in the present embodiment can also be applied to the first embodiment.
Compared with the prior art that the residual life of the part is calculated according to the service life marked on the factory label and the current time, the embodiment provides the numerical control machine tool residual life prediction device based on the autoregressive sum-of-motion average model, wherein a health prediction model is obtained in advance according to a time sequence and a preset upper order limit through an ARIMA model, namely, the comprehensive health index change under the historical time of the part during working is summarized, and the change of the health index of the part under the time change is summarized; and the maximum life time is calculated by presetting a target health index and a health prediction model and combined with the current time, the residual life time of the part is calculated again, the future change is estimated according to the historical change of the comprehensive health index, and the accuracy is higher.
It should be noted that each module in this embodiment is a logic module, and in practical application, one logic unit may be one physical unit, or may be a part of one physical unit, or may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, units that are not so close to solving the technical problem presented by the present invention are not introduced in the present embodiment, but this does not indicate that other units are not present in the present embodiment.
The third embodiment of the present invention relates to a network server, as shown in fig. 5, including at least one processor 501; and a memory 502 communicatively coupled to the at least one processor 501; the memory 502 stores instructions executable by the at least one processor 501, and the instructions are executed by the at least one processor 501, so that the at least one processor 501 can execute the method for predicting the remaining life of the numerically-controlled machine tool.
The memory 502 and the processor 501 are connected by a bus, which may include any number of interconnected buses and bridges, which connect together the various circuits of the one or more processors 501 and the memory 502. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 501 is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor 501.
The processor 501 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 502 may be used to store data used by processor 501 in performing operations.
A fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program, when executed by the processor, implements the method for predicting the remaining life of the numerical control machine tool according to the first embodiment.
That is, it will be understood by those skilled in the art that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, where the program includes several instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps in the methods of the embodiments described herein. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely an embodiment of the present invention, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application day or before the priority date of the present invention, and can know all the prior art in the field, and have the capability of applying the conventional experimental means before the date, so that a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (6)
1. The method for predicting the residual life of the numerical control machine tool is characterized by comprising the following steps:
acquiring a preset health prediction model;
acquiring a target health index;
calculating to obtain the residual life time according to the target health index and a preset health prediction model;
debugging of the health prediction model, comprising:
acquiring a comprehensive health index and a time associated with the comprehensive health index;
correlating the normalized comprehensive health index with time, and integrating the normalized comprehensive health index into a time sequence;
calculating sample variance of time series
Bringing the time health sequence into an ARIMA model, and carrying out order determination on the autoregressive order P and the moving average order Q through a minimum Bayesian information criterion;
the ARIMA model is as follows:
wherein HI is t,d Is a stable time sequence value obtained after d times of difference, P is an autoregressive order, Q is a moving average order, mu p Is an autoregressive coefficient, θ q Is the running average coefficient, ε t Representing a zero-mean white noise sequence;
the autoregressive order P and the moving average order Q are subjected to order determination, and the method comprises the following steps:
acquiring a preset order upper limit L;
the autoregressive order P and the moving average order Q are scaled,
make it meet
BIC(P′,Q′)＝min 0≤P,Q≤L BIC(P,Q)；
After the autoregressive order P and the moving average order Q are fixed,
and carrying the autoregressive order P and the moving average order Q with good order into the ARIMA model to obtain a prediction model and outputting the prediction model.
2. The method for predicting remaining life of a numerical control machine tool according to claim 1, wherein: and after the autoregressive order P and the moving average order Q are subjected to order determination, adjusting parameters in the ARIMA model by adopting moment estimation.
3. The method for predicting the remaining life of a numerically-controlled machine tool according to claim 1, wherein the calculating the remaining life time according to the target health index and the preset health prediction model includes:
calculating the maximum life time according to the target health index and the health prediction model;
acquiring the current time;
and calculating a difference value between the maximum life time and the current time, wherein the difference value is the residual life time.
4. Numerical control machine tool residual life prediction device based on autoregressive sum moving average model, which is characterized by comprising:
the receiving module is used for receiving the correlated comprehensive health index and time sent by the requesting party and the target health index and the upper order limit;
the health prediction model building module is used for calculating a health prediction model through an ARIMA model according to the correlated comprehensive health index and time and the upper order limit;
the storage module is used for storing the health prediction model;
the health prediction module is used for calculating residual life time according to the target health index and the health prediction model in the storage module;
the output module is used for outputting the residual life time;
wherein, the debugging of the health prediction model comprises:
acquiring a comprehensive health index and a time associated with the comprehensive health index;
correlating the normalized comprehensive health index with time, and integrating the normalized comprehensive health index into a time sequence;
calculating sample variance of time series
Bringing the time health sequence into an ARIMA model, and carrying out order determination on the autoregressive order P and the moving average order Q through a minimum Bayesian information criterion;
the ARIMA model is as follows:
wherein HI is t,d Is the stationary time obtained after d times of differentiationSequence value, P is autoregressive order, Q is moving average order, μ p Is an autoregressive coefficient, θ q Is the running average coefficient, ε t Representing a zero-mean white noise sequence;
the autoregressive order P and the moving average order Q are subjected to order determination, and the method comprises the following steps:
acquiring a preset order upper limit L;
the autoregressive order P and the moving average order Q are scaled,
make it meet
BIC(P′,Q′)＝min 0≤P,Q≤L BIC(P,Q)；
After the autoregressive order P and the moving average order Q are fixed,
and carrying the autoregressive order P and the moving average order Q with good order into the ARIMA model to obtain a prediction model and outputting the prediction model.
5. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements the numerical control machine tool remaining life prediction method of any one of claims 1 to 3.
6. A network side server, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the numerical control machine tool remaining life prediction method of any one of claims 1 to 3.
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