CN117113104B - Intelligent management system and method applying data analysis technology - Google Patents
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Abstract
The invention relates to the field of intelligent management, in particular to an intelligent management system and method applying a data analysis technology, wherein the system comprises a data preprocessing module, a part precision analysis module, a fault risk judging module and a fault management module.
Description
Technical Field
The invention relates to the field of intelligent management, in particular to an intelligent management system and method applying a data analysis technology.
Background
Along with the continuous improvement of the automation, informatization and intellectualization levels of the equipment, the precision requirement on the machined parts is also continuously improved, because the machining equipment has certain error in precision due to the common influence of internal factors and external factors in the process of producing the parts, if the machining equipment is not calibrated in time, the machining quality of the workpiece is reduced, even the service life of the machining equipment is seriously damaged,
At present, analysis is carried out according to the equipment operation process, and then the corresponding equipment fault reasons are obtained, equipment parts are replaced according to the fault reasons, the fault points cannot be solved from the root, and because the operation duration of each machining equipment is inconsistent, a certain error exists in the precision of the parts produced by the machining equipment, and in order to more accurately reduce the fault influence degree generated during the combined operation between two associated parts, how to monitor the interaction influence between the two associated parts is still significant.
Disclosure of Invention
The invention aims to provide an intelligent management method applying a data analysis technology, so as to solve the problems in the background technology, and the invention provides the following technical scheme:
an intelligent management method applying data analysis technology, the method comprising the steps of:
s1, acquiring a device fault analysis report in a monitoring area through historical data, and preprocessing fault information by combining the device fault analysis report;
S2, analyzing the relation between the abrasion time of the part production equipment and the precision of the part, obtaining a corresponding part precision evaluation value by combining the analysis result, and further calculating the influence degree value of interference influence between adjacent parts on equipment faults according to the evaluation value;
s3, monitoring the performance index change condition of the current equipment in real time, and judging whether the current equipment has a fault risk or not by combining the performance index change condition;
And S4, further receiving an early warning signal according to the judgment result in the S3, combining and analyzing the current equipment performance index data detection value with the analysis result in the S2, and taking corresponding measures under the condition that the current index data detection value is abnormal.
Further, the method of S1 includes the following steps:
step 1001, recording the equipment fault analysis report in the monitoring area acquired in the step S1 as a set A,
A＝{A1,A2,A3,...,An}，
Wherein A n represents a fault set corresponding to the nth equipment, and n represents the total number of equipment fault analysis reports in the history monitoring area;
Step 1002, extracting a fault characteristic value corresponding to each fault in a fault set corresponding to an nth device, wherein the fault corresponds to a unique fault characteristic value, and the fault characteristic value is a database preset value;
Step 1003, repeating step 1002 to obtain corresponding fault characteristic values of the corresponding devices, and combining the fault characteristic values of the corresponding devices to obtain a fault part set of the corresponding devices, which is denoted as a set B,
B＝{G1(B),G2(B),G3(B),...,Gn(B)}，
Wherein G n (B) represents a part set in which the nth device has a failure.
According to the invention, the fault analysis report of the equipment in the monitoring area in the historical database is obtained, the fault characteristic value is extracted according to the fault analysis report, the fault characteristic value is further analyzed to obtain the part corresponding to the equipment fault, and the data reference is provided for the relationship between the precision of the subsequently analyzed part and the abrasion condition of the machining equipment.
Further, the method of S2 includes the following steps:
step 2001, obtaining a part set with faults of the nth equipment in step 1003, marking machining equipment corresponding to the faulty parts in the part set as a set C, wherein one faulty part corresponds to one part production equipment,
C＝(C1,C2,C3,...,Cm)，
C m represents machining equipment corresponding to an mth fault part of the nth equipment;
step 2002, the relation between the wear time of the machining equipment corresponding to the mth fault part of the nth equipment and the precision of the machined part is recorded as M m (t),
Mm(t)＝φ1*N(t)+φ2*Q(t)，
Wherein phi 1 and phi 2 are preset proportionality coefficients of a database, N (t) represents the change relation of the abrasion loss of the blade of the machining equipment along with time, Q (t) represents the change relation of heat energy along with time when the machining equipment is operated,
Wherein t is E [0, beta ],
When t epsilon [0, t 1 ] represents the earlier operation of the machining equipment, N (t) =a 1t2,a1 < 0,
When t epsilon [ t 1,t2 ] represents the middle operation period of the mechanical processing equipment, N (t) =t/a 2,a2 > 0,
When t epsilon [ t 2, beta ] represents the later operation period of the mechanical processing equipment, N (t) = (a 3t)2,a3 > 0,
Where a 1,a2,a3 is a database preset constant, t 1,t2, beta is a database preset constant,
Q (t) =μ×n×Δd (t), N representing the normal contact force between the blade and the machined part, μ representing the coefficient of friction, Δd (t) representing the average distance value between the initial position of the blade of the machining device and the machined part, the coefficient of friction being a database preset constant;
Step 2003, repeating step 2002, analyzing the relation between the abrasion time of the machining equipment corresponding to each fault part and the precision of the machined part when the operation time of the nth equipment is t h, and marking as a set D (t h),
D(th)＝{M1(th),M2(th),M3(th),...,Mm(th)},
Comparing the part precision values produced by the machining equipment corresponding to each fault part in different operation stages with corresponding standard part reference intervals when the operation time of the nth equipment is t h, wherein the standard part reference values are database preset constants,
If the precision of the corresponding processed part exceeds the reference interval of the standard part, marking abnormality, eliminating the corresponding part,
If the precision of the corresponding processed part does not exceed the reference interval of the standard part, marking is normal;
Step 2004, extracting the normal processed parts, carrying out combination analysis on adjacent parts in pairs according to the production flow, judging whether interference influence exists in the combination use between the adjacent parts,
Combining standard part reference intervals of corresponding machined parts in any group of combinations to obtain standard reference values of corresponding machined part precision, recording as (delta 1/2,δ2/2),δ1/2 represents the standard part reference value corresponding to the machined part of the first machining equipment in any acquired combination, delta 2/2 represents the standard part reference value corresponding to the machined part of the second machining equipment in any acquired combination,
Acquiring precision values of machined parts corresponding to different operation stages of corresponding machining equipment in the current combination, and recording the precision values as
{ (W b1,Wb2,Wb3),(Wc1,Wc2,Wc3) }, wherein W b1 represents a machined part precision value corresponding to a previous machining device operation earlier stage in the combination, W c2 represents a machined part precision value corresponding to a next machining device operation earlier stage in the combination, W b2 represents a machined part precision value corresponding to a previous machining device operation middle stage in the combination, W c2 represents a machined part precision value corresponding to a next machining device operation middle stage in the combination, W b3 represents a machined part precision value corresponding to a next machining device operation later stage in the combination, W c3 represents a machined part precision value corresponding to a next machining device operation later stage in the combination, and the machined part precision values corresponding to different stages are analyzed in pairs,
Inquiring the using time of the corresponding equipment under the corresponding combination condition through the historical data, calculating the fault influence degree value generated when the machined parts corresponding to different operation stages of the mechanical machining equipment are used in combination, recording as G b—c,
Gb—c＝-h1*T+h2，
The fault influence degree value formula reflects the relation between time and influence degree, namely, the longer the equipment is put into use, the higher the corresponding fault risk (direct relation) is, the precision of the corresponding processed parts of the equipment in different operation stages is different (indirect relation),
Where h 1 denotes a first coefficient, h 2 denotes a second coefficient, and T denotes a device usage duration.
According to the invention, through analyzing the relation between the abrasion conditions corresponding to the machining equipment in different operation stages and the precision of the machined parts, the machining equipment corresponding to the associated parts are combined in pairs, the use time of the corresponding equipment when the machining equipment is used for combining the parts produced in different stages is analyzed, and further the fault influence value of the machining equipment on the equipment when the parts produced in different stages are used in combination is further judged according to the equipment use time, so that data reference is provided for monitoring each performance index during subsequent real-time monitoring when the current equipment is used, and judging whether the current equipment has fault risk.
Further, the method of S3 includes the following steps:
step 3001, setting the current equipment performance index data detection period as η;
Step 3002, using an origin o as a reference point, using a line from west to east in a direction crossing the origin o as an x-axis, and using a line from south to north in a direction crossing the origin o as a y-axis, to construct a first plane rectangular coordinate system;
Step 3003, normalizing the data in each period detection report, labeling the normalized data in a first plane rectangular coordinate system to obtain corresponding coordinate points, and calculating the difference between adjacent coordinate points, wherein the normalization process is implemented by performing difference operation on the period detection report data and corresponding standard reference values, multiplying the period detection report data and corresponding scale coefficients, and summing,
If the difference value between the adjacent points exceeds the preset value, sending out an early warning signal to judge that the current equipment has fault risk,
If the difference value between the adjacent points does not exceed the preset value, continuing monitoring.
According to the invention, the first plane rectangular coordinate system is constructed, the performance index data of the current equipment are mapped into the first plane rectangular coordinate system after being normalized, the difference value between two adjacent data is further calculated, whether the current equipment has fault analysis or not is judged by combining the difference value, an early warning signal is sent out, and data reference is provided for tracing the fault part of the current equipment by combining the early warning signal subsequently.
Further, in S4, according to the determination result in S3, an early warning signal is further received and a corresponding measure is taken in combination with the performance index data detection value of the current device, if the early warning signal exists, a factory-returning maintenance request is sent to the current device, a fault position corresponding to the abnormality of the performance index data detection value of the current device is obtained, a processed part related to the fault position is extracted, whether the precision of the extracted processed part is not up to standard is determined, if the precision of a single processed part is not up to standard, an early warning signal is sent, the current device is determined, if the precision of the processed part related to the fault position is up to standard, interference influence among the processed parts related to the up to standard is further analyzed, the fault influence degree value generated when the corresponding processed parts are combined in the matching history data is compared with a preset value, when the fault influence degree value is determined to exceed the preset value, the fault risk exists in the current device, and when the fault influence degree value is determined not to exceed the preset value, the fault risk does not exist in the current device.
An intelligent management system applying data analysis techniques, the system comprising the following modules:
And a data preprocessing module: the data preprocessing module is used for analyzing equipment fault reports in the monitoring area in the historical data and further preprocessing the fault data by combining the equipment fault analysis results;
Part precision analysis module: the part precision analysis module is used for analyzing the relation between the abrasion degree of the mechanical processing equipment and the precision of the processed part, further evaluating the precision of the corresponding part by combining the analysis result, and analyzing the interference influence effect between two adjacent parts according to the evaluation result;
and a fault risk judging module: the fault risk judging module is used for monitoring the performance index change condition of the current equipment in real time and further judging whether the current equipment has fault risk according to the monitoring result;
And a fault management module: the fault management module is used for combining the early warning signals, further sending a factory return maintenance request to the equipment fault, and tracing the equipment fault reason according to the detection condition of the corresponding equipment performance index data.
Further, the data preprocessing module includes a data acquisition unit and a data processing unit:
The data acquisition unit is used for extracting equipment fault analysis reports in the monitoring area by combining the historical database;
the data processing unit is used for further analyzing the fault equipment by combining the data of the data acquisition unit and extracting the fault reason.
Further, the part precision analysis module comprises a precision analysis unit, a precision comparison unit and a fault influence analysis unit:
the precision analysis unit is used for further analyzing whether the precision value of the machined part has abnormal conditions or not according to the abrasion conditions of the machining equipment in different operation stages;
the precision comparison unit is used for combining the analysis result of the precision analysis module, removing abnormal values and further analyzing whether mutual interference influences exist when the related parts are used in combination;
The fault influence analysis unit is used for monitoring the corresponding service lives of the parts produced by the mechanical processing equipment in different operation stages in real time when the parts are combined and used, and further analyzing the influence degree value of the parts produced by the mechanical processing equipment in different operation stages on equipment faults when the parts are combined and used according to the service lives.
Further, the fault risk judging module comprises a device monitoring unit and an early warning unit:
The device monitoring unit is used for monitoring performance index data of the current device in real time;
the early warning unit is used for analyzing whether abnormal conditions exist in the difference value between the adjacent performance index data in the equipment monitoring module and sending an early warning signal in combination with the analysis result.
Further, the fault management module includes an early warning signal receiving unit and an equipment fault management unit:
The early warning signal receiving unit is used for monitoring the analysis result of the early warning unit in real time and receiving an early warning signal;
The equipment fault management unit is used for further sending a factory return maintenance request to the corresponding equipment according to the early warning signal, carrying out preliminary evaluation on the current equipment according to the service time of the current equipment and combining the fault influence analysis unit to obtain a corresponding fault condition, further tracing the corresponding fault part according to the fault condition, and taking corresponding measures on the fault part.
According to the invention, the faults of equipment in historical data are analyzed, specific parts are further traced, the relation between the abrasion condition of mechanical processing equipment corresponding to the parts and the precision of the processed parts is analyzed, the processed parts are subjected to preliminary screening, the influence of the interference generated when the processing equipment corresponding to the related parts is in different operation stages is analyzed when the processing equipment corresponding to the related parts is in different operation stages after abnormal values are removed, the influence degree value of the influence of the interference on equipment faults is obtained in combination with the use time of the equipment, further whether the current equipment has fault risks is judged according to the current equipment index data monitoring process, the fault reasons are further analyzed in combination with the fault risks, the parts corresponding to the faults are rapidly traced, and the equipment overhaul efficiency is improved.
Drawings
FIG. 1 is a flow chart of an intelligent management method using data analysis technology according to the present invention;
FIG. 2 is a schematic block diagram of an intelligent management system employing data analysis techniques according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, in this embodiment:
an intelligent management method applying a data analysis technology is realized, and the method comprises the following steps:
s1, acquiring a device fault analysis report in a monitoring area through historical data, and preprocessing fault information by combining the device fault analysis report;
the method of S1 comprises the following steps:
step 1001, recording the equipment fault analysis report in the monitoring area acquired in the step S1 as a set A,
A＝{A1,A2,A3,...,An}，
Wherein A n represents a fault set corresponding to the nth equipment, and n represents the total number of equipment fault analysis reports in the history monitoring area;
Step 1002, extracting a fault characteristic value corresponding to each fault in a fault set corresponding to an nth device, wherein the fault corresponds to a unique fault characteristic value, and the fault characteristic value is a database preset value;
Step 1003, repeating step 1002 to obtain corresponding fault characteristic values of the corresponding devices, and combining the fault characteristic values of the corresponding devices to obtain a fault part set of the corresponding devices, which is denoted as a set B,
B＝{G1(B),G2(B),G3(B),...,Gn(B)}，
Wherein G n (B) represents a part set in which the nth device has a failure.
S2, analyzing the relation between the abrasion time of the part production equipment and the precision of the part, obtaining a corresponding part precision evaluation value by combining the analysis result, and further calculating the influence degree value of interference influence between adjacent parts on equipment faults according to the evaluation value;
the method of S2 comprises the following steps:
Step 2001, obtaining a part set with faults of the nth equipment in step 1003, marking machining equipment corresponding to the faulty parts in the part set as a set C,
C＝(C1,C2,C3,...,Cm)，
C m represents machining equipment corresponding to an mth fault part of the nth equipment;
step 2002, the relation between the wear time of the machining equipment corresponding to the mth fault part of the nth equipment and the precision of the machined part is recorded as M m (t),
Mm(t)＝φ1*N(t)+φ2*Q(t)，
Wherein phi 1 and phi 2 are preset proportionality coefficients of a database, N (t) represents the change relation of the abrasion loss of the blade of the machining equipment along with time, Q (t) represents the change relation of heat energy along with time when the machining equipment is operated,
Wherein t is E [0, beta ],
When t epsilon [0, t 1 ] represents the earlier operation of the machining equipment, N (t) =a 1t2,a1 < 0,
When t epsilon [ t 1,t2 ] represents the middle operation period of the mechanical processing equipment, N (t) =t/a 2,a2 > 0,
When t epsilon [ t 2, beta ] represents the later operation period of the mechanical processing equipment, N (t) = (a 3t)2,a3 > 0,
Where a 1,a2,a3 is a database preset constant, t 1,t2, beta is a database preset constant,
Q (t) =μ×n×Δd (t), N representing the normal contact force between the blade and the machined part, μ representing the coefficient of friction, Δd (t) representing the average distance value between the initial position of the blade of the machining device and the machined part, the coefficient of friction being a database preset constant;
Step 2003, repeating step 2002, analyzing the relation between the abrasion time of the machining equipment corresponding to each fault part and the precision of the machined part when the operation time of the nth equipment is t h, and marking as a set D (t h),
D(th)＝{M1(th),M2(th),M3(th),...,Mm(th)},
Comparing the part precision values produced by the machining equipment corresponding to each fault part in different operation stages with corresponding standard part reference intervals when the operation time of the nth equipment is t h, wherein the standard part reference values are database preset constants,
If the precision of the corresponding processed part exceeds the reference interval of the standard part, marking abnormality, eliminating the corresponding part,
If the precision of the corresponding processed part does not exceed the reference interval of the standard part, marking is normal;
Step 2004, extracting the normal processed parts, carrying out combination analysis on adjacent parts in pairs according to the production flow, judging whether interference influence exists in the combination use between the adjacent parts,
Combining standard part reference intervals of corresponding machined parts in any group of combinations to obtain standard reference values of corresponding machined part precision, recording as (delta 1/2,δ2/2),δ1/2 represents the standard part reference value corresponding to the machined part of the first machining equipment in any acquired combination, delta 2/2 represents the standard part reference value corresponding to the machined part of the second machining equipment in any acquired combination,
Acquiring precision values of machined parts corresponding to different operation stages of corresponding machining equipment in the current combination, and recording the precision values as
{ (W b1,Wb2,Wb3),(Wc1,Wc2,Wc3) }, wherein W b1 represents a machined part precision value corresponding to a previous machining device operation earlier stage in the combination, W c2 represents a machined part precision value corresponding to a next machining device operation earlier stage in the combination, W b2 represents a machined part precision value corresponding to a previous machining device operation middle stage in the combination, W c2 represents a machined part precision value corresponding to a next machining device operation middle stage in the combination, W b3 represents a machined part precision value corresponding to a next machining device operation later stage in the combination, W c3 represents a machined part precision value corresponding to a next machining device operation later stage in the combination, and the machined part precision values corresponding to different stages are analyzed in pairs,
Inquiring the using time of the corresponding equipment under the corresponding combination condition through the historical data, calculating the fault influence degree value generated when the machined parts corresponding to different operation stages of the mechanical machining equipment are used in combination, recording as G b—c,
Gb—c＝-h1*T+h2，
Where h 1 denotes a first coefficient, h 2 denotes a second coefficient, and T denotes a device usage duration.
S3, monitoring the performance index change condition of the current equipment in real time, and judging whether the current equipment has a fault risk or not by combining the performance index change condition;
The method of S3 comprises the following steps:
step 3001, setting the current equipment performance index data detection period as η;
Step 3002, using an origin o as a reference point, using a line from west to east in a direction crossing the origin o as an x-axis, and using a line from south to north in a direction crossing the origin o as a y-axis, to construct a first plane rectangular coordinate system;
Step 3003, normalizing the data in each period detection report, labeling the normalized data in a first plane rectangular coordinate system to obtain corresponding coordinate points, and calculating the difference between adjacent coordinate points, wherein the normalization process is implemented by performing difference operation on the period detection report data and corresponding standard reference values, multiplying the period detection report data and corresponding scale coefficients, and summing,
If the difference value between the adjacent points exceeds the preset value, sending out an early warning signal to judge that the current equipment has fault risk,
If the difference value between the adjacent points does not exceed the preset value, continuing monitoring.
S4, further receiving an early warning signal according to the judgment result in the S3, combining and analyzing the current equipment performance index data detection value with the analysis result in the S2, and taking corresponding measures under the condition that the current index data detection value is abnormal;
And S4, further receiving an early warning signal and taking corresponding measures by combining the performance index data detection value of the current equipment, if the early warning signal exists, sending a factory-returning maintenance request to the current equipment, acquiring a fault position corresponding to the abnormality of the performance index data detection value of the current equipment, extracting a processed part related to the fault position, judging whether the precision of the extracted processed part is not up to standard, if the precision of a single processed part is not up to standard, sending an early warning signal, judging that the current equipment is at fault risk, if the precision of the processed part related to the fault position is up to standard, further analyzing interference influence among the processed parts related to the standard, comparing the fault influence degree value with a preset value through matching the fault influence degree value generated when the corresponding processed part is combined in the history data, judging that the current equipment is at fault risk when the fault influence degree value is judged to be higher than the preset value, and judging that the current equipment is not at fault risk when the fault influence degree value is judged not to be higher than the preset value.
In this embodiment:
an intelligent management system (as shown in fig. 2) applying data analysis techniques is disclosed for implementing the specific scheme content of the method.
Example 2: setting the precision standard reference interval of the current machining part as [ alpha, beta ], setting the running time of the current machining equipment in the interval of [ T 1,t2 ], setting the precision value of the part machined by the current machining equipment as M m (T),
Mm(T)＝φ1*t/a2+φ2*μ*N*△d(T),
Comparing the precision value of the part processed by the current machining equipment with a precision standard reference interval of the current machined part, marking abnormal if the precision of the part processed by the current machining equipment exceeds the standard part reference interval, removing the corresponding part, and marking normal if the precision of the part processed by the current machining equipment does not exceed the standard part reference interval.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (2)
1. An intelligent management method applying a data analysis technology, which is characterized by comprising the following steps:
s1, acquiring a device fault analysis report in a monitoring area through historical data, and preprocessing fault information by combining the device fault analysis report;
S2, analyzing the relation between the abrasion time of the part production equipment and the precision of the part, obtaining a corresponding part precision evaluation value by combining the analysis result, and further calculating the influence degree value of interference influence between adjacent parts on equipment faults according to the evaluation value;
s3, monitoring the performance index change condition of the current equipment in real time, and judging whether the current equipment has a fault risk or not by combining the performance index change condition;
s4, further receiving an early warning signal according to the judgment result in the S3, combining and analyzing the current equipment performance index data detection value with the analysis result in the S2, and taking corresponding measures under the condition that the current index data detection value is abnormal;
the method of S1 comprises the following steps:
step 1001, recording the equipment fault analysis report in the monitoring area acquired in the step S1 as a set A,
A＝{A1,A2,A3,...,An}，
Wherein A n represents a fault set corresponding to the nth equipment, and n represents the total number of equipment fault analysis reports in the history monitoring area;
Step 1002, extracting a fault characteristic value corresponding to each fault in a fault set corresponding to an nth device, wherein the fault corresponds to a unique fault characteristic value, and the fault characteristic value is a database preset value;
Step 1003, repeating step 1002 to obtain corresponding fault characteristic values of the corresponding devices, and combining the fault characteristic values of the corresponding devices to obtain a fault part set of the corresponding devices, which is denoted as a set B,
B＝{G1(B),G2(B),G3(B),...,Gn(B)}，
Wherein G n (B) represents a part set in which the nth device has a failure;
the method of S2 comprises the following steps:
Step 2001, obtaining a part set with faults of the nth equipment in step 1003, marking machining equipment corresponding to the faulty parts in the part set as a set C,
C＝(C1,C2,C3,...,Cm)，
C m represents machining equipment corresponding to an mth fault part of the nth equipment;
step 2002, the relation between the wear time of the machining equipment corresponding to the mth fault part of the nth equipment and the precision of the machined part is recorded as M m (t),
Mm(t)＝φ1*N(t)+φ2*Q(t)，
Wherein phi 1 and phi 2 are preset proportionality coefficients of a database, N (t) represents the change relation of the abrasion loss of the blade of the machining equipment along with time, Q (t) represents the change relation of heat energy along with time when the machining equipment is operated,
Wherein t is E [0, beta ], beta is a preset constant for the database,
When t epsilon [0, t 1 ] represents the earlier operation of the machining equipment, N (t) =a 1t2,a1 > 0,
When t epsilon [ t 1,t2 ] represents the middle operation period of the mechanical processing equipment, N (t) =t/a 2,a2 > 0,
When t epsilon [ t 2, beta ] represents the later operation period of the mechanical processing equipment, N (t) = (a 3t)2,a3 > 0,
Wherein a 1,a2,a3 is a database preset constant, Q (t) =μ×n×Δx (t), N represents an orthogonal contact force between the blade and the machined part, μ represents a friction coefficient, Δx (t) represents a relative displacement between the blade and the machined part in a unit time, and the friction coefficient is the database preset constant;
Step 2003, repeating step 2002, analyzing the relation between the wear time of the machining equipment corresponding to each faulty part and the precision of the machined part at the time of the nth equipment t h, and recording as a set D (t h),
D(th)＝{M1(th),M1(th),M1(th),...,Mm(th)},
Comparing the precision values of parts produced by the machining equipment corresponding to each fault part at the moment t h of the nth equipment in different operation stages with the reference intervals of the corresponding standard parts, wherein the reference values of the standard parts are database preset constants,
If the precision of the corresponding processed part exceeds the reference interval of the standard part, marking abnormality, eliminating the corresponding part,
If the precision of the corresponding processed part does not exceed the reference interval of the standard part, marking is normal;
Step 2004, extracting the normal processed parts, carrying out combination analysis on adjacent parts in pairs according to the production flow, judging whether interference influence exists in the combination use between the adjacent parts,
Combining standard part reference intervals of corresponding machined parts in any group of combinations to obtain standard reference values of corresponding machined part precision, recording as (delta 1/2,δ2/2),δ1/2 represents the standard part reference value corresponding to the machined part of the first machining equipment in any acquired combination, delta 2/2 represents the standard part reference value corresponding to the machined part of the second machining equipment in any acquired combination,
Obtaining precision values of machined parts corresponding to different operation stages of corresponding machining equipment in a current combination, and recording the precision values as { (W b1,Wb2,Wb3),(Wc1,Wc2,Wc3) }, wherein W b1 represents the precision value of the machined part corresponding to the operation front stage of the previous machining equipment in the combination, W c1 represents the precision value of the machined part corresponding to the operation front stage of the next machining equipment in the combination, W b2 represents the precision value of the machined part corresponding to the operation middle stage of the previous machining equipment in the combination, W c2 represents the precision value of the machined part corresponding to the operation middle stage of the next machining equipment in the combination, W b3 represents the precision value of the machined part corresponding to the operation back stage of the previous machining equipment in the combination, W c3 represents the precision value of the machined part corresponding to the operation back stage of the next machining equipment in the combination, and carrying out two-by-two combination analysis on the precision values of the machining equipment corresponding to the different stages,
Inquiring the using time of the corresponding equipment under the corresponding combination condition through the historical data, calculating the fault influence degree value generated when the machined parts corresponding to different operation stages of the mechanical machining equipment are used in combination, recording as G b—c,
Gb—c＝-h1*T+h2，
Where h 1 represents a first coefficient, h 2 represents a second coefficient, and T represents a device usage duration;
The method of S3 comprises the following steps:
step 3001, setting the current equipment performance index data detection period as η;
Step 3002, using an origin o as a reference point, using a line from west to east in a direction crossing the origin o as an x-axis, and using a line from south to north in a direction crossing the origin o as a y-axis, to construct a first plane rectangular coordinate system;
Step 3003, normalizing the data in each period detection report, labeling the normalized data in a first plane rectangular coordinate system to obtain corresponding coordinate points, calculating the difference between adjacent coordinate points,
If the difference value between the adjacent points exceeds the preset value, sending out an early warning signal to judge that the current equipment has fault risk,
If the difference value between the adjacent points does not exceed the preset value, continuing monitoring;
And S4, further receiving an early warning signal and taking corresponding measures by combining the performance index data detection value of the current equipment, if the early warning signal exists, sending a factory-returning maintenance request to the current equipment, acquiring a fault position corresponding to the abnormality of the performance index data detection value of the current equipment, extracting a processed part related to the fault position, judging whether the precision of the extracted processed part is not up to standard, if the precision of a single processed part is not up to standard, sending an early warning signal, judging that the current equipment is at fault risk, if the precision of the processed part related to the fault position is up to standard, further analyzing interference influence among the processed parts related to the standard, comparing the fault influence degree value with a preset value through matching the fault influence degree value generated when the corresponding processed part is combined in the history data, judging that the current equipment is at fault risk when the fault influence degree value is judged to be higher than the preset value, and judging that the current equipment is not at fault risk when the fault influence degree value is judged not to be higher than the preset value.
2. An intelligent management system applying data analysis technology, which is applied to an intelligent management method applying data analysis technology as set forth in claim 1, characterized in that the system comprises the following modules:
And a data preprocessing module: the data preprocessing module is used for analyzing equipment fault reports in the monitoring area in the historical data and further preprocessing the fault data by combining the equipment fault analysis results;
Part precision analysis module: the part precision analysis module is used for analyzing the relation between the abrasion degree of the mechanical processing equipment and the precision of the processed part, further evaluating the precision of the corresponding part by combining the analysis result, and analyzing the interference influence effect between two adjacent parts according to the evaluation result;
and a fault risk judging module: the fault risk judging module is used for monitoring the performance index change condition of the current equipment in real time and further judging whether the current equipment has fault risk according to the monitoring result;
And a fault management module: the fault management module is used for combining the early warning signals, further sending a factory return maintenance request to equipment faults, and tracing equipment fault reasons according to detection conditions of corresponding equipment performance index data;
the data preprocessing module comprises a data acquisition unit and a data processing unit:
The data acquisition unit is used for extracting equipment fault analysis reports in the monitoring area by combining the historical database;
the data processing unit is used for further analyzing the fault equipment by combining the data of the data acquisition unit and extracting the fault reason;
The part precision analysis module comprises a precision analysis unit, a precision comparison unit and a fault influence analysis unit:
the precision analysis unit is used for further analyzing whether the precision value of the machined part has abnormal conditions or not according to the abrasion conditions of the machining equipment in different operation stages;
the precision comparison unit is used for combining the analysis result of the precision analysis module, removing abnormal values and further analyzing whether mutual interference influences exist when the related parts are used in combination;
The fault influence analysis unit is used for monitoring the corresponding service lives of the parts produced by the mechanical processing equipment in different operation stages in real time, and further analyzing the degree of influence on equipment faults when the parts produced by the mechanical processing equipment in different operation stages are used in combination according to the service lives;
The fault risk judging module comprises an equipment monitoring unit and an early warning unit:
The device monitoring unit is used for monitoring performance index data of the current device in real time;
the early warning unit is used for analyzing whether abnormal conditions exist in the difference value between the adjacent performance index data in the equipment monitoring module and sending an early warning signal by combining the analysis result;
The fault management module comprises an early warning signal receiving unit and an equipment fault management unit:
The early warning signal receiving unit is used for monitoring the analysis result of the early warning unit in real time and receiving an early warning signal;
The equipment fault management unit is used for further sending a factory return maintenance request to the corresponding equipment according to the early warning signal, carrying out preliminary evaluation on the current equipment according to the service time of the current equipment and combining the fault influence analysis unit to obtain a corresponding fault condition, further tracing the corresponding fault part according to the fault condition, and taking corresponding measures on the fault part.
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