CN113723978A - Bearing bush demand prediction method and system - Google Patents
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Abstract
The present disclosure provides a bearing bush demand prediction method and system. The method for predicting the bearing bush demand comprises the following steps: obtaining historical data of scanned tolerances of a crankcase and a crankshaft of an engine; determining bearing shell classes and historical data corresponding to bearing shell requirements for each bearing shell class based on tolerances of the crankcase and the crankshaft; establishing at least one time series prediction model for the historical data of the bearing bush demand according to each bearing bush type; receiving input from a user regarding a predicted time period and a particular bearing shell class; and predicting a bearing shell demand during the received prediction time period using the at least one time series prediction model for the particular bearing shell class.
Description
Technical Field
The disclosure relates to the field of time series prediction, and in particular to a method and a system for predicting bearing demand by using a time series prediction model.
Background
Bearing shells are widely found in automotive engines. The bearing bush is a bush-shaped component located between a crankcase and a crankshaft of the engine, and plays a role in fixing and lubricating. Since the crankcase and crankshaft of each engine have different tolerances, the type and number of bearing shells required may vary. The bearing shell comprises a normal bearing shell and a thrust bearing shell. Each bearing shell comprises a plurality of types, for example there may be 8 thrust bearing shells, 16 normal bearing shells.
These bearing shells are usually imported from foreign countries, such as germany. Transportation time takes about three weeks or so, and engine manufacturers typically place orders four weeks in advance. It is therefore necessary to accurately predict the type and number of bearing shells to be purchased. Because one to two changes to the engine can affect the type and number of bearing shells required, if too many bearing shells are purchased, the engine or some of the bearing shells are not used to produce the engine, and the engine or some of the bearing shells are left unused, which results in wasted resources and economic losses for the manufacturing plant. However, if the number of the purchased bearing bushes is insufficient, but one bearing bush is worn out quickly, the cost for purchasing and transporting the bearing bush separately is high. Currently, it is often determined by a human being empirically by considering inventory data on hand and engine data, etc. However, the manual judgment method has a large error, and the bearing bush demand of the future weeks cannot be objectively and accurately predicted.
Accordingly, there is a need for a method and system for objectively and accurately predicting future bearing shell requirements.
Disclosure of Invention
The present disclosure provides a novel bearing shell demand prediction method and system.
According to a first aspect of the present disclosure, there is provided a bearing bush demand prediction method including: obtaining historical data of scanned tolerances of a crankcase and a crankshaft of an engine; determining bearing shell classes and historical data corresponding to bearing shell requirements for each bearing shell class based on tolerances of the crankcase and the crankshaft; establishing at least one time series prediction model for the historical data of the bearing bush demand according to each bearing bush type; receiving input from a user regarding a predicted time period and a particular bearing shell class; and predicting a bearing shell demand during the received prediction time period using the at least one time series prediction model for the particular bearing shell class.
When the at least one time series prediction model is a plurality of time series prediction models, the method further comprises: predicting the bearing bush demand of a specific time period before the prediction time period by respectively utilizing the plurality of time series prediction models; comparing the bearing bush demand quantity in the specific time period predicted by each time series prediction model with the real demand quantity respectively to obtain each error; determining the time series prediction model corresponding to the minimum error as an optimal time series prediction model; and predicting a bearing shell demand during the received prediction time period for the particular bearing shell class using the optimal time series prediction model.
The specific time period is the previous week of the predicted time period.
The plurality of time series prediction models includes an integrated moving average autoregressive (ARIMA) model, a Bayesian Analysis Time Series (BATS) model, a trend error seasonal (ETS) model, and a Holt-Winters model.
The method further comprises the following steps: acquiring bearing bush inventory data; calculating a minimum safety inventory value based on the predicted bearing shell demand and the bearing shell inventory data during the predicted prediction time period; and issuing a warning message when the difference between the current bearing bush inventory data and the minimum safety inventory value is within a predetermined threshold.
Before establishing at least one time series prediction model, the method further comprises preprocessing the historical data, wherein the preprocessing comprises excluding weekdays and holidays and data corresponding to the weekdays and holidays. The preprocessing further includes excluding data in the historical data that is outside of a confidence interval with a confidence level of 95%.
The ARIMA model is expressed as ARIMA (p, d, q), wherein p is the number of autoregressive terms, q is the number of moving average terms, and d is the number of differences made to make the time series of the historical data a stationary sequence, and establishing the ARIMA model comprises: determining whether the time series of the historical data is a stationary series by adopting a unit root inspection method; if the sequence is a non-stationary sequence, carrying out differential processing on the time sequence, and checking again until the sequence becomes a stationary sequence, wherein the number of times of carrying out differential processing is determined as the value of d; determining the values of q and p through a time series identification rule; and training and testing the ARIMA model using 70% of the historical data as training data and the remaining 30% of the historical data as test data to obtain a final ARIMA model.
The input of the predicted time period is received through a slider on a user interface representing the time period. The prediction period is in days or weeks.
The user interface further comprises an input for selecting a particular bearing bush family from one or more bearing bush families, each of the one or more bearing bush families comprising one or more bearing bush categories, the method further comprising: in response to a user selection of the particular bearing shell family, utilizing the at least one time series prediction model to predict a bearing shell demand during the received prediction time period for each bearing shell class in the particular bearing shell family; displaying on the user interface historical data of bearing shell demand for each bearing shell class of the particular bearing shell family and a plot of predicted bearing shell demand as a function of time.
According to a second aspect of the present disclosure, there is provided a non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform the method of any one of claims 1-11.
According to a third aspect of the present disclosure, there is provided a computer system comprising: at least one processor; and at least one non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of any one of claims 1-11.
Other features of the present invention and advantages thereof will become more apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
fig. 1 shows a flow chart of a bearing shell demand prediction method according to an exemplary embodiment of the present invention.
Fig. 2 shows a flow chart of a bearing shell demand prediction method according to another exemplary embodiment of the present invention.
Fig. 3 shows a flow chart for establishing an ARIMA model according to an exemplary embodiment of the present invention.
FIG. 4 shows a schematic view of a user interface according to an exemplary embodiment of the present invention.
FIG. 5 illustrates an exemplary configuration of a computing device in which embodiments in accordance with the invention may be implemented.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Details and functions not essential to the present invention are omitted so as not to obscure the understanding of the present invention.
Note that like reference numerals and letters refer to like items in the figures, and thus once an item is defined in one figure, it need not be discussed in subsequent figures.
In this disclosure, the terms "first," "second," and the like are used merely to distinguish between elements or steps, and are not intended to indicate temporal order, priority, or importance.
According to the method, time series modeling is carried out on historical data of various types of bearing bush requirements, so that the future bearing bush requirements are predicted. Furthermore, the invention can establish different time series models for the historical data and automatically select the optimal time series model for prediction.
Fig. 1 shows a flow chart of a bearing shell demand prediction method according to an exemplary embodiment of the present invention. The method may be performed by a computer system.
As shown in fig. 1, at step S101, the computer system obtains historical data of scanned tolerances of the crankcase and crankshaft of the engine from a database. The tolerances of the crankcase and crankshaft are typically on the order of microns, difficult to identify to the naked eye, and are thus obtained by scanning the grooves of the engine with an optical scanning device. The optical scanning device scans each engine on the production line and stores the scanned tolerance data for the respective crankcase and crankshaft in a database.
In step S102, the computer system determines bearing shell classes and bearing shell requirements corresponding to each bearing shell class based on the tolerances of the crankcase and the crankshaft. The crankcase and crankshaft of each engine have different respective tolerances, requiring correspondingly different types and numbers of bearing shells. The bearing shells typically comprise normal bearing shells and thrust bearing shells. Each bearing shell is divided into several types, for example, there may be 8 thrust bearing shells and 16 normal bearing shells. The computer system calculates the consumption of each bearing bush required each day according to the engine data scanned each day.
In step S103, the computer system builds at least one time series model for the history of the bearing shell demand for each bearing shell class.
The time series refers to a group of numerical sequences arranged in time sequence, and the time series analysis is to use the group of numerical sequences to process by using a mathematical statistics method so as to predict the development of future things. With the development of machine learning in recent years, time series prediction has been widely used in various fields. A variety of different time series prediction models have been developed, including an integrated moving average autoregressive (ARIMA) model, a Bayesian Analysis Time Series (BATS) model, a trend error seasonal (ETS) model, and a Holt-Winters model, among others.
The ARIMA model is one of the time series predictive analysis methods, generally denoted ARIMA (p, d, q), where AR is "autoregressive" and p is the number of autoregressive terms; MA is "moving average", q is the number of terms of the moving average, and d is the number of differences (order) made to make the time series to be analyzed a stationary series.
The BATS model is a prediction using Bayesian statistics. Bayesian statistics is different from general statistical methods, which not only make use of model information and data information, but also make full use of prior information.
The ETS model applies a seasonal time series in the smoothing method calculation.
Holt-Winters is a cubic exponential smoothing algorithm that can make predictions of time series well.
The above 4 models are the most common algorithms for time series, but each is slightly different. The ARIMA model is more suitable when the similarity of each period of the data trend is high by paying more attention to the period. Holt-Winters focuses on averaging. The BATS is more accurate in prediction by using a common mean discount model for data which fluctuates randomly and changes relatively stably. ETS can be unmanageable or exceptionally slow for long-cycle sequences due to the extreme number of parameters to be estimated.
For each bearing shell class, the computer system builds one or more of the above time series models for historical data of bearing shell demand. The process of establishing the time series model will be described in detail later with reference to fig. 3.
It will be appreciated by those skilled in the art that the time series model listed above is non-exhaustive and is shown merely as an example for the purpose of illustrating the inventive concepts of the present invention. The inventive concept of the present invention is not limited to these models but can be applied to other models of time series, such as hw and/or nnetar.
Optionally, prior to this step, the historical data may be pre-processed. For example, weekdays and holidays and their corresponding data may be excluded from the historical data. Since the weekday is a non-production day, even if there is data, the production trial is performed, and such data samples affect the prediction accuracy, and are therefore excluded. In addition, discrete values may be excluded, for example, taking only data within a confidence interval of 95% confidence level in the historical data.
In step S104, the computer system receives input from a user regarding the predicted time period and the particular bushing type.
The prediction period may be in units of days or weeks. For example, the user may choose to predict the bearing shell requirement for the next 20 days, or around the future, etc. The user may specify the time period to be predicted by sliding a slider bar on the display interface representing the time period.
The type of bearing shell to be predicted can be input individually or by specifying the bearing shell series to which it belongs. For the latter case, the bearing shell species may be grouped in series. For example, some bearing shells are classified into a certain series according to their properties. For example, the bush series A00001 comprises three types of bush A00001-01, bush A00001-02 and bush A00001-03. The input of the bearing shell type can be performed by a user specifying the bearing shell series to which the bearing shell type belongs. For example, the user may select a particular bushing series from a drop-down menu of bushing series. In this case, the computer system will make a prediction for each bearing shell type of the selected bearing shell series. The prediction process will be described at step S105.
In step S105, the computer system predicts a bearing shell demand during the prediction time period using the at least one time-series prediction model established in step S103 for the particular bearing shell class selected by the user. The computer system can display historical data and predicted bearing bush demand on a user interface, and a user can conveniently and visually check the historical data and the predicted bearing bush demand.
In case the user specifies a bearing shell family instead of a single bearing shell class, the computer system will utilize the at least one time series prediction model for each bearing shell class in the selected bearing shell family to predict the bearing shell demand during the received prediction time period. Subsequently, the computer system displays on the user interface both historical data of the bearing shell demand for each bearing shell type of the selected specific bearing shell family and a plot of the predicted bearing shell demand as a function of time. A diagram of a user interface will be described below with reference to fig. 4.
Those skilled in the art will appreciate that the steps of the foregoing methods need not be performed in the foregoing order, but rather they may be performed simultaneously, in a different order, or in an overlapping manner. For example, step S104 may precede step S101.
As previously described, the computer system may build one or more time series models for each bearing shell class. When a plurality of time series models are established in step S103, the present invention may preferably automatically select an optimal time series model for future prediction. As described in detail below in conjunction with fig. 2.
As shown in fig. 2, the method may begin at the location indicated by reference character a in fig. 1. In step S201, the computer system predicts the bearing bush demand of a specific time period before the future time period to be predicted by using a plurality of established time series prediction models, such as ARIMA model, bat model, ETS model and/or Holt-Winters model, respectively. The particular time period may be the week preceding the future time period to be predicted, i.e., the week preceding the current time. Those skilled in the art will appreciate that the particular time period is not limited to a week and can be any time specified by the user, such as ten days, etc.
In step S202, the computer system compares each of the axle bush demand amounts in the specific time period predicted by each of the time-series prediction models with the real demand amount, respectively, to obtain each error. Various ways of calculating the error, such as sample variance, sample standard deviation, total standard deviation, etc., may be used by those skilled in the art.
In step S202, the computer system determines the time-series prediction model corresponding to the minimum error as the optimal time-series prediction model. For example, if the ARIMA model predicts the least error between the bearing bush demand and the real consumption in the last week, that is, the predicted value is closest to the real value, the ARIMA model is determined as the optimal time series prediction model for future prediction.
In step S202, the computer system predicts a bearing shell demand during a future time period for a particular bearing shell class selected by a user using a determined optimal time series prediction model, such as an ARIMA model.
Furthermore, as previously mentioned, the user may specify a series of bearing shells rather than a single bearing shell species. The computer system will utilize the determined optimal time series prediction model for each bearing shell class in the selected bearing shell family to predict the bearing shell demand during a future prediction time period.
The process of building the time series model is described below in conjunction with fig. 3. The ARIMA model is described below as an example.
The ARIMA model is generally described as being in ARIMA (p, d, q), where p is the number of autoregressive terms, q is the number of moving average terms, and d is such that the history isThe time series of data becomes the number of differences made by the stationary sequence. The time series predicted using the ARIMA model is generally denoted Xt＝(α1Xt-1+α2Xt-2+…+αp Xt-p)+(β1εt-1+β2εt-2+…+βqεt-q). Establishing ARIMA is therefore to determine d, p, q and the respective coefficients a and β. Specifically, establishing the ARIMA model generally includes steps S301-S304.
In step S301, the computer system determines whether the time series of the history data is a stationary series using a unit root checking method.
The unit root test is to test whether a unit root exists in the sequence, and if the unit root exists, the unit root is a non-stationary time sequence. In statistics, the Dickey-Fuller test (Dickey-Fullertest) can test whether an autoregressive model has a unit root. A simple AR (1) model is yt＝ρyt-1+ut。ytIs the variable to be examined, t is time, p is the coefficient, utIs an error term. If | ρ | ≧ 1, the unit root is declared present. The regression model can be written as Δ yt＝(ρ-1)yt-1+ut＝δyt-1+utAnd Δ is the first order difference. Testing whether a unit root is present is equivalent to testing whether δ is 0. Since the diky-fowler test tests residual terms, not raw data, the standard t statistic cannot be used. We need to use the diky-fowler statistic.
In step S302, if the test result is a non-stationary sequence, the time series is subjected to difference processing and the check is performed again until the sequence becomes a stationary sequence, wherein the number of times the difference is performed is determined as the value of d.
In step S303, the values of q and p are determined by the time-series identification rule. This step is commonly referred to as model scaling. The values of p and q may be determined by one skilled in the art through a variety of identification rules, such as Autocorrelation Coefficient (ACF) and Partial Autocorrelation Coefficient (PACF) maps, erythropool information criterion (AIC), Bayesian Information Criterion (BIC), thermodynamic diagrams, and the like.
For example, the parameters p and q can be found using ACF and PACF graphs. The ACF is a complete autocorrelation function that can provide any sequence of autocorrelation values with lag values. Briefly, it describes the degree of correlation between the current value of the sequence and its past values. The time series may contain components such as trends, seasonality, periodicity, and residuals. ACF considers all of these components in finding correlations. PACF is a partial autocorrelation function or partial autocorrelation function. Basically, instead of finding the correlation of a lag like ACF to the current, it finds the correlation of the residual to the next lag value. Therefore, we may get a good correlation if there is any hidden information in the residual that can be modeled by the next lag, and we will characterize the next lag when modeling. For example, if the ACF diagram shows a truncated property from after 1 st order and the PACF diagram shows a tailing property from after 1 st order, the MA (1) model, i.e., ARIMA (1,0,1), is used for human judgment.
If ACF and PACF are gradually decreased, this indicates that time series smoothing is required and d parameters are introduced.
According to another embodiment, AIC or BIC information may be used. And calculating the AIC or BIC information quantity of all combinations when both p and q are less than or equal to 3, and taking the model order in which the AIC or BIC information quantity reaches the minimum.
After determining d, p, q of the ARIMA model in step S303, we now go to step S304 of training and testing the model. In step S304, the computer system trains and tests the ARIMA model using 70% of the historical data as training data and the remaining 30% as test data to obtain a final ARIMA model.
In one embodiment, the ARIMA model building process described above may be implemented using the R language. By calling relevant functions in the R language, ACF, PACF, AIC, BIC calculation, model training and testing and the like are realized. It should be understood by those skilled in the art that the inventive concept is not limited to the R language platform, but may be implemented in other languages known in the art and developed in the future, such as python and the like.
A user interface and corresponding user operations according to an exemplary embodiment of the present invention are described below in conjunction with fig. 4.
As shown in fig. 4, the user interface may comprise a time input portion 401 for a future time period to be predicted and a bearing shell input portion 402 of a bearing shell series to be predicted. The time input portion 401 may be in the form of a slider. The user enters the time by sliding the slider bar. Time may be in units of days or weeks. It should be understood by those skilled in the art that the time input section 401 is not limited to a slider bar, but may be in the form of other input times.
The pad input portion 402 may be in the form of a drop down menu. The drop-down menu may list all the bearing types for the user to select, or may list only the bearing series. In the latter case, when the user selects a particular bearing shell family from a drop-down menu of bearing shell families, the computer system will make a prediction for each bearing shell type of the selected bearing shell family.
The user interface also includes a data diagram display portion 403. In the example shown in figure 4, the data diagram display 403 shows data diagrams 406, 407 and 408 for the three types of bearing shells included in the selected bearing shell family. Preferably, different colors are used to represent the data maps 406, 407, and 408 for each bearing shell type. Each data graph 406, 407, and 408 includes two portions, a historical data portion 404 and a predictive data portion 405, respectively. The historical data portion 404 refers to a graph presented by data before the selected current time, and the predicted data portion 405 refers to a predicted data graph of a future time period predicted by using a model. Different colors or shading may be used to distinguish the historical data portion 404 from the predictive data portion 405.
In addition, the user can calculate the safety stock according to the bearing bush demand predicted by the computer system. For example, the computer system may obtain current inventory data from a database and combine the predicted bearing shell demand over a future period of time to calculate a minimum safety inventory value. A warning message is issued when the real-time inventory differs from the minimum safety inventory value by within a predetermined threshold. Therefore, the inventory warning message can be sent to the user in advance, and the rationalization of the inventory arrangement is realized.
The method predicts the future bearing bush demand by using the time series model, and has higher prediction accuracy than the conventional method using empirical judgment. The user can make more reasonable transmission plan and inventory plan accordingly, thereby reducing bearing backlog and economic loss brought by technical change.
FIG. 5 illustrates an exemplary configuration of a computer system in which embodiments in accordance with the present invention may be implemented. Computer system 500 is an example of a hardware device in which the above-described aspects of the invention may be applied. Computer system 500 may be any machine configured to perform processing and/or computing. Computer system 500 may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a Personal Data Assistant (PDA), a smart phone, an in-vehicle computer, or a combination thereof.
As shown in FIG. 5, computer system 500 may include one or more elements connected to or in communication with bus 502, possibly via one or more interfaces. Bus 502 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA (eisa) bus, a Video Electronics Standards Association (VESA) local bus, a Peripheral Component Interconnect (PCI) bus, and the like. Computer system 500 may include, for example, one or more processors 504, one or more input devices 506, and one or more output devices 508. The one or more processors 504 may be any kind of processor and may include, but are not limited to, one or more general-purpose processors or special-purpose processors (such as special-purpose processing chips). Input device 506 may be any type of input device capable of inputting information to a computing device and may include, but is not limited to, a mouse, a keyboard, a touch screen, a microphone, and/or a remote controller. Output device 508 can be any type of device capable of presenting information and can include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer.
The computer system 500 may also include or be connected to a non-transitory storage device 514, the non-transitory storage device 514 may be any non-transitory storage device that may enable the storage of data and may include, but is not limited to, disk drives, optical storage devices, solid-state memory, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic medium, compact disks, or any other optical medium, cache memory, and/or any other memory chip or module, and/or any other medium from which a computer may read data, instructions, and/or code. The computer system 500 may also include Random Access Memory (RAM)510 and Read Only Memory (ROM) 512. The ROM 512 may store programs, utilities or processes to be executed in a nonvolatile manner. RAM 510 may provide volatile data storage and store instructions related to the operation of computer system 500. Computer system 500 may also include a network/bus interface 516 coupled to a data link 518. The network/bus interface 516 may be any kind of device or system capable of enabling communication with external devices and/or networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication device, and/or a chipset (such as bluetooth)TMDevices, 1302.11 devices, WiFi devices, WiMax devices, cellular communications facilities, etc.).
Various aspects, embodiments, implementations, or features of the foregoing embodiments may be used alone or in any combination. Various aspects of the foregoing embodiments may be implemented by software, hardware, or a combination of hardware and software.
For example, the foregoing embodiments may be embodied as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data which can thereafter be read by a computer system. Examples of a computer readable medium include read-only memory, random-access memory, CD-ROMs, DVDs, magnetic tape, hard drives, solid state drives, and optical data storage devices. The computer readable medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.
For example, the foregoing embodiments may take the form of hardware circuitry. Hardware circuitry may include any combination of combinational logic circuitry, clocked storage devices (such as floppy disks, flip-flops, latches, etc.), finite state machines, memories such as static random access memories or embedded dynamic random access memories, custom designed circuits, programmable logic arrays, etc.
In one embodiment, a hardware circuit according to the present disclosure may be implemented by encoding a circuit description in a Hardware Description Language (HDL) such as Verilog or VHDL. HDL descriptions can be synthesized for a library of cells designed for a given integrated circuit fabrication technology and can be modified for timing, power, and other reasons to obtain a final design database, which can be transferred to a factory for the production of integrated circuits by a semiconductor manufacturing system. Semiconductor manufacturing systems may produce integrated circuits by depositing semiconductor material (e.g., on a wafer that may include a mask), removing material, changing the shape of the deposited material, modifying the material (e.g., by doping the material or modifying the dielectric constant with ultraviolet processing), and so forth. The integrated circuit may include transistors and may also include other circuit elements (e.g., passive elements such as capacitors, resistors, inductors, etc.) and interconnections between the transistors and the circuit elements. Some embodiments may implement multiple integrated circuits coupled together to implement a hardware circuit, and/or may use discrete elements in some embodiments.
While some specific embodiments of the present invention have been shown in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are intended to be illustrative only and are not intended to limit the scope of the invention. It should be appreciated that some of the steps of the foregoing methods need not be performed in the order illustrated, but rather they may be performed simultaneously, in a different order, or in an overlapping manner. In addition, one skilled in the art may add some steps or omit some steps as desired. Some of the components in the foregoing systems need not be arranged as shown, and those skilled in the art may add or omit some components as desired. It will be appreciated by those skilled in the art that the above-described embodiments may be modified without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (13)
1. A bearing bush demand prediction method includes:
obtaining historical data of scanned tolerances of a crankcase and a crankshaft of an engine;
determining bearing shell classes and historical data corresponding to bearing shell requirements for each bearing shell class based on tolerances of the crankcase and the crankshaft;
establishing at least one time series prediction model for the historical data of the bearing bush demand according to each bearing bush type;
receiving input from a user regarding a predicted time period and a particular bearing shell class; and
predicting a bearing shell demand during the received prediction time period using the at least one time series prediction model for the particular bearing shell class.
2. The method of claim 1, wherein when the at least one time series prediction model is a plurality of time series prediction models, the method further comprises:
predicting the bearing bush demand of a specific time period before the prediction time period by respectively utilizing the plurality of time series prediction models;
comparing the bearing bush demand quantity in the specific time period predicted by each time series prediction model with the real demand quantity respectively to obtain each error;
determining the time series prediction model corresponding to the minimum error as an optimal time series prediction model; and
predicting, using the optimal time series prediction model, a bearing shell demand during the received prediction time period for the particular bearing shell class.
3. The method of claim 2, wherein the particular time period is the previous week of the predicted time period.
4. The method of claim 2, wherein the plurality of time series prediction models comprises an integrated moving average autoregressive (ARIMA) model, a Bayesian Analysis Time Series (BATS) model, a trend error seasonal (ETS) model, and a Holt-Winters model.
5. The method of claim 1, further comprising:
acquiring bearing bush inventory data;
calculating a minimum safety inventory value based on the predicted bearing shell demand and the bearing shell inventory data during the predicted prediction time period; and
issuing a warning message when the difference between the current bearing pad inventory data and the minimum safety inventory value is within a predetermined threshold.
6. The method of claim 1, wherein prior to establishing at least one time series prediction model, the method further comprises preprocessing the historical data, the preprocessing including excluding weekdays and holidays and their corresponding data.
7. The method of claim 6, wherein the preprocessing further comprises excluding data in the historical data that is outside of a confidence interval with a confidence level of 95%.
8. The method of claim 4, wherein the ARIMA model is represented as ARIMA (p, d, q), where p is the number of autoregressive terms, q is the number of sliding average terms, and d is the number of differences made to smooth the time series of the historical data, and wherein building the ARIMA model comprises:
determining whether the time series of the historical data is a stationary series by adopting a unit root inspection method;
if the sequence is a non-stationary sequence, carrying out differential processing on the time sequence, and checking again until the sequence becomes a stationary sequence, wherein the number of times of carrying out differential processing is determined as the value of d;
determining the values of q and p through a time series identification rule; and
training and testing the ARIMA model with 70% of the historical data as training data and the remaining 30% of the historical data as testing data to obtain a final ARIMA model.
9. The method of claim 1, wherein the input of the predicted time period is received through a slider on a user interface representing the time period.
10. The method of claim 9, wherein the predicted time period is in units of days or weeks.
11. The method of claim 9, wherein the user interface further comprises an input for selecting a particular bearing bush family from one or more bearing bush families, each of the one or more bearing bush families comprising one or more bearing bush categories, the method further comprising:
in response to a user selection of the particular bearing shell family, utilizing the at least one time series prediction model to predict a bearing shell demand during the received prediction time period for each bearing shell class in the particular bearing shell family;
displaying on the user interface historical data of bearing shell demand for each bearing shell class of the particular bearing shell family and a plot of predicted bearing shell demand as a function of time.
12. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform the method of any of claims 1-11.
13. A computer system, the computer system comprising:
at least one processor; and
at least one non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of any one of claims 1-11.
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