CN110597889A - Machine tool fault prediction method based on improved Apriori algorithm - Google Patents

Machine tool fault prediction method based on improved Apriori algorithm Download PDF

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CN110597889A
CN110597889A CN201910950225.6A CN201910950225A CN110597889A CN 110597889 A CN110597889 A CN 110597889A CN 201910950225 A CN201910950225 A CN 201910950225A CN 110597889 A CN110597889 A CN 110597889A
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杜佼玲
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Sichuan Changhong Electric Co Ltd
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Abstract

The invention discloses a machine tool fault prediction method based on an improved Apriori algorithm, which comprises the following steps: A. building a Hadoop system, and storing the acquired mass machine tool data into a distributed file system (HDFS); B. data cleaning is carried out on data in the HDFS, preprocessed machine tool data are obtained, and a corresponding candidate 1 item set is generated; C. setting the minimum support degree and the minimum confidence degree of an Apriori algorithm, scanning HBase, and obtaining a frequent item set with different item numbers according to the improved Apriori algorithm; D. finding out association rules among machine tool data and corresponding confidence degrees through a frequent item set; E. and C, comparing with the minimum confidence coefficient in the step C to obtain a final strong association rule between the machine tool data, namely a machine tool fault prediction result. The method of the invention can effectively predict the machine tool fault, can reduce the manpower consumed by technicians to check the machine tool condition regularly or irregularly, and can increase the efficiency of checking the machine tool.

Description

Machine tool fault prediction method based on improved Apriori algorithm
Technical Field
The invention relates to the technical field of machine tool fault diagnosis, in particular to a machine tool fault prediction method based on an improved Apriori algorithm.
Background
With the advent of the 4.0 era of industry, the technological level of modern industry is continuously improved, and various electromechanical devices are gradually developed towards automation, centralization, continuity, high speed and the like, so that the failure rate of the electromechanical devices is increased and the diagnosis difficulty coefficient is increased. In particular, if the machine tool is not checked timely, the whole system may fail, and a certain economic loss of industrial production may be caused. Therefore, it is very important to analyze and predict the failure of the machine tool.
By the predictive analysis of the machine tool faults, the economic loss in industrial production caused by machine tool damage can be reduced or avoided to a certain extent. The existing prevention technology comprises two main categories, namely a traditional method and an online monitoring method, wherein the traditional technology is that technicians judge state factors such as noise, temperature, rotating speed and the like of a machine tool regularly or irregularly and presume the health state of the machine tool; the online detection method mainly monitors the machine tool states such as main shaft current, torque, vibration, sound, temperature and the like in real time through related systems, and analyzes and judges acquired data so as to infer the health degree of the machine tool. The traditional method has subjective factors of professional ability of technicians, accuracy cannot be guaranteed, reliability is poor, and efficiency is low; the online monitoring method has high real-time requirement, and with the continuous increase of machine tool data, the traditional centralized database technology can bring certain pressure to network flow and a server.
Disclosure of Invention
The invention aims to overcome the defects in the background technology and provide a machine tool fault prediction method based on an improved Apriori algorithm.
In order to achieve the technical effects, the invention adopts the following technical scheme:
a machine tool fault prediction method based on an improved Apriori algorithm comprises the following steps:
A. setting up a Hadoop system, wherein the Hadoop system comprises: the method comprises the steps that a distributed file system HDFS and a distributed non-relational database HBase (the distributed file system HDFS is used for storing machine tool data, and the distributed non-relational database HBase is used for accessing and reading and writing the machine tool data in a processing file system) are used, and collected mass machine tool data are stored in the distributed file system HDFS;
B. data in the distributed file system HDFS are cleaned, preprocessed machine tool data are obtained, and a corresponding candidate 1 item set is generated;
C. setting a minimum support degree min _ sup and a minimum confidence degree min _ conf of an Apriori algorithm, scanning a database HBase, and obtaining a frequent item set with different item numbers according to the improved Apriori algorithm;
D. finding out association rules among machine tool data and corresponding confidence degrees through a frequent item set;
E. and D, comparing the result obtained in the step D with the minimum confidence coefficient in the step C to obtain a final strong association rule between the machine tool data, namely a machine tool fault prediction result.
Further, the Hadoop system in step a further includes: the efficient parallel computing MapReduce framework is used for parallelly computing frequent item sets of different item numbers, association rules and corresponding confidence degrees of an Apriori algorithm.
Further, the data cleaning in the step B is to process unreasonable data in the acquisition process to obtain a unified data format, and abstract the corresponding candidate 1 item set through mapping table modeling.
Further, the unreasonable data are scrambled data and data containing units.
Further, the support degree of the Apriori algorithm in step C is the probability of the item set X in the transaction set D, and the frequent item set is the item set with the support degree not less than the minimum support degree min _ sup.
Further, the Apriori algorithm improved in the step C is a connection mode in which a frequent item set is improved, and only a frequent (k-1) item set needs to be connected with the frequent 1 item set to generate a frequent k item set, so that the algorithm greatly reduces the connection times, avoids generating a huge candidate item set, and transplants the improved Apriori algorithm to a Hadoop platform in a parallel processing mode to compute the frequent item set in parallel, thereby reducing the computation time.
Further, the obtaining of the frequent item sets with different numbers of items according to the improved Apriori algorithm in step C is specifically to compare the support degrees corresponding to the candidate items with different numbers of items with the minimum support degree, so as to obtain the frequent item sets with corresponding numbers of items.
Further, the method specifically comprises the following steps when obtaining the frequent item sets with different item numbers according to the improved Apriori algorithm:
s1, taking each state value (such as a rotating speed value, a noise value, a temperature value, a humidity value, a rotating speed value, a current value and the like) of the machine tool as a candidate 1 item set c1，c1Is C1And when c is1When the support degree of (c) is not less than min _ sup, c1Become frequent 1 item set l1，l1Is L1；
S2, judging LkIf the number of the middle item sets is larger than k, entering S3, otherwise obtaining a final frequent k item set;
s3, when k is more than or equal to 2, frequently (k-1) item set Lk-1And frequent 1 item set L1Direct join to obtain candidate k item set CkThen each c is judgedkWhether the support degree of (C) is greater than or equal to min _ sup, from the candidate k item set CkScreening out a frequent k item set L with the support degree of more than or equal to min _ supk；
S4, repeating S2 and S3 until no more frequent item sets are generated.
Further, in the step D, the confidence is a confidence that the association rule "X ═ Y" is called if the transaction set C including the item set X also includes the item set Y (the "═ symbol is a specific expression symbol of the association rule between X and Y); where C is a subset of the transaction set D, and the association rule is to discover the possible associations or connections between things behind the data.
Further, the strong association rule in step E refers to a rule corresponding to that the confidence between each non-empty subset generated by the frequent item set obtained in step D and its complement is greater than or equal to the minimum confidence min _ conf, that is, the association rule "X ═ X" (D-X) "; the final strong association rule can obtain the necessary relation among the machine tool data, so that the fault condition of the machine tool is predicted.
Compared with the prior art, the invention has the following beneficial effects:
the machine tool fault prediction method based on the improved Apriori algorithm is based on a big data technology and an associated data mining technology, and a Hadoop distributed system is used for storing mass machine tool data; carrying out associated data mining on mass machine tool data by using an improved Apriori algorithm to obtain inevitable relation among the machine tool data, thereby predicting the fault condition of the machine tool and reminding technicians to check the condition of the machine tool in time and overhaul in time; it is possible to achieve a reduction in the labor consumed by a technician to check the condition of the machine tool on a regular or irregular basis and an increase in the efficiency of checking the machine tool.
Drawings
Fig. 1 is a schematic diagram of the machine tool fault prediction method based on the modified Apriori algorithm of the present invention.
Fig. 2 is a schematic flow diagram of an Apriori algorithm modified in the method of the present invention.
Detailed Description
The invention will be further elucidated and described with reference to the embodiments of the invention described hereinafter.
Example (b):
the first embodiment is as follows:
a machine tool fault prediction method based on an improved Apriori algorithm is based on a big data technology and an associated data mining technology, and a Hadoop distributed system is used for storing mass machine tool data; and (3) performing associated data mining on mass machine tool data by using an improved Apriori algorithm to obtain inevitable relation among the machine tool data, thereby predicting the fault condition of the machine tool and reminding technicians to check the condition of the machine tool in time and overhaul in time.
As shown in fig. 1, the method for predicting a machine tool fault based on the modified Apriori algorithm of the present embodiment specifically includes the following steps:
step one, a Hadoop system is built, collected mass machine tool data are stored in a distributed file system (HDFS), a transaction database D corresponding to original machine tool data is horizontally divided into a plurality of data blocks with the same size, and the data blocks are used as data sources of a distributed computing framework MapReduce.
Specifically, in this embodiment, the Hadoop system includes: the system comprises a distributed file system (HDFS), a distributed non-relational database (HBase) and an efficient parallel computing MapReduce framework, wherein the distributed file system (HDFS) is used for storing machine tool data, the distributed non-relational database (HBase) is used for accessing, reading and writing the machine tool data in the file system, and the efficient parallel computing MapReduce framework is used for parallelly computing frequent item sets, association rules and corresponding confidence degrees of different item numbers of an Apriori algorithm.
Step two, cleaning data of the machine tool in the HDFS file system, and processing unreasonable data in the acquisition process, namely messy code data and data containing units to obtain a unified data format, wherein each row of data of the file is respectively a 'timestamp machine tool number temperature value, frequency value, noise value, rotating speed value, current value', such as '2012-9-14 +09:40:26 A160.56570.11300.2'; creating a mapping table and abstracting a corresponding candidate 1 item set c through mapping table modeling1: by key-value pairs<key,value>Representing the key value as a set of data items and the value as the corresponding support of the set of data items, e.g.<[43]，1>The state value of the machine tool, which represents the rotating speed of 130-139, is one in the database. The mapping table created in this embodiment is specifically as follows:
and step three, setting the minimum support degree min _ sup ═ 2 and the minimum confidence degree min _ conf ═ 60 of the Apriori algorithm, scanning the HBase database, and obtaining frequent item sets with different item numbers according to the improved Apriori algorithm.
Specifically, as shown in fig. 2, in this embodiment, obtaining the frequent item sets with different item numbers according to the improved Apriori algorithm specifically includes the following steps:
step1. set c of all candidate 1 items from all candidate 1 items using MapReduce framework1Screening out frequent 1 item set L with support degree greater than or equal to 21Such as<[31],4>、<[6],3>、<[22],3>Etc.;
when step2.k is 1, the number of the frequent k item sets is judged to be more than k, so that the frequent 2 item set operation can be carried out;
step3.k 2, set L of frequent 1 terms1And frequent 1 item set L1Directly connecting to obtain all candidate 2 item sets C2And screening out a frequent 2 item set L with the support degree of more than or equal to 22Such as<[31，6],3>、<[31，22],2>、<[6，22],2>Etc.;
step4. repeat step2 and step3 until no more frequent item sets are generated, such as < [31, 6, 22],2 >.
Finding out association rules among machine tool data and corresponding confidence degrees through the frequent item set, specifically: and calculating the support degrees of all non-empty subsets of the last frequent k item set, and then obtaining an association rule and a confidence degree between each non-empty subset and a complementary set thereof, such as the frequent 3 item set obtained by step4 in step three, wherein the non-empty subsets are < [31],4>, [6],3>, [22],3>, [31, 6],1>, [31, 22],2>, [6, 22], and 2>, one of the association rules is '31 ═ 6, 22' and the confidence degree thereof is equal to the support degree corresponding to the frequent 3 item set [31, 6, 22] and is more than the support degree corresponding to the frequent 1 item set [31 ].
Step five, comparing with the minimum confidence coefficient in the step three, screening out a strong association rule with the confidence coefficient larger than the minimum confidence coefficient, and obtaining the inevitable relation between the machine tool state values according to the mapping table in the step two, so as to obtain a machine tool fault prediction result, such as: for example, the confidence of the strong association rule "2, 16,20,30 ═ 45" is 70.4%, which indicates that the probability of the rotation speed being in the range of 150 to 159r/min is 70.4% when the machine temperature is in the range of 20.00 to 29.99 ℃, the machine noise is in the range of 60.00 to 69.00dB, the machine current is in the range of 0.00 to 0.09A, and the machine frequency is in the range of 0 to 49Hz (when 4 conditions are satisfied at the same time). According to the associated data mining result, when the temperature of the machine tool is not within the range of 20.00-29.99 ℃, the noise of the machine tool is not within the range of 60.00-69.00 dB, the current of the machine tool is not within the range of 0.00-0.09A or the frequency of the machine tool is within the range of 0-49 Hz, the rotating speed of the machine tool is measured within the range of 150-159 r/min, the probability that the machine tool possibly breaks down is higher, at the moment, a worker is timely reminded to overhaul the machine tool, the failure reason can be quickly analyzed according to the obtained result, and the production quality of the machine tool is prevented from being influenced.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.

Claims (10)

1. A machine tool fault prediction method based on an improved Apriori algorithm is characterized by comprising the following steps:
A. setting up a Hadoop system, wherein the Hadoop system comprises: the method comprises the steps that a distributed file system HDFS and a distributed non-relational database HBase store collected mass machine tool data into the distributed file system HDFS;
B. data in the distributed file system HDFS are cleaned, preprocessed machine tool data are obtained, and a corresponding candidate 1 item set is generated;
C. setting a minimum support degree min _ sup and a minimum confidence degree min _ conf of an Apriori algorithm, scanning a database HBase, and obtaining a frequent item set with different item numbers according to the improved Apriori algorithm;
D. finding out association rules among machine tool data and corresponding confidence degrees through a frequent item set;
E. and D, comparing the result obtained in the step D with the minimum confidence coefficient in the step C to obtain a final strong association rule between the machine tool data, namely a machine tool fault prediction result.
2. The method for predicting the machine tool fault based on the modified Apriori algorithm according to claim 1, wherein the Hadoop system in the step a further comprises: the efficient parallel computing MapReduce framework is used for parallelly computing frequent item sets of different item numbers, association rules and corresponding confidence degrees of an Apriori algorithm.
3. The method according to claim 1, wherein the step B of cleaning the data specifically comprises processing unreasonable data in an acquisition process to obtain a unified data format, and abstracting a corresponding candidate 1 item set through mapping table modeling.
4. The method for predicting machine tool failure based on modified Apriori algorithm as claimed in claim 3, wherein the unreasonable data are scrambled data and unit-containing data.
5. The method according to claim 1, wherein the degree of support of Apriori algorithm in step C is the probability of item set X in transaction set D, and the frequent item set is the item set with the degree of support not less than the minimum degree of support min _ sup.
6. The method according to claim 5, wherein the modified Apriori algorithm in step C is a connection method that improves a frequent item set, and the frequent item set can be generated by connecting the frequent (k-1) item set and the frequent 1 item set.
7. The method according to claim 6, wherein the step C of obtaining the frequent item sets with different numbers of terms according to the modified Apriori algorithm is to compare the support degrees corresponding to the candidates with the minimum support degrees to obtain the frequent item sets with corresponding numbers of terms.
8. The method for predicting machine tool faults based on the improved Apriori algorithm according to claim 7, wherein the step of obtaining the frequent item sets with different item numbers according to the improved Apriori algorithm specifically comprises the following steps:
s1, taking each state value of the machine tool as a candidate 1 item set c1，c1Is C1And when c is1When the support degree of (c) is not less than min _ sup, c1Become frequent 1 item set l1，l1Is L1；
S2, judging LkIf the number of the middle item sets is larger than k, entering S3, otherwise obtaining a final frequent k item set;
s3, when k is more than or equal to 2, frequently (k-1) item set Lk-1And frequent 1 item set L1Direct join to obtain candidate k item set CkThen each c is judgedkWhether the support degree of (C) is greater than or equal to min _ sup, from the candidate k item set CkScreening out a frequent k item set L with the support degree of more than or equal to min _ supk；
S4, repeating S2 and S3 until no more frequent item sets are generated.
9. The method according to claim 5, wherein the confidence in step D is a confidence in an association rule "X ═ Y" if the transaction set C containing the item set X also contains the item set Y; where C is a subset of the transaction set D, and the association rule is to discover the possible associations or connections between things behind the data.
10. The method for predicting machine tool faults based on the modified Apriori algorithm according to claim 9, wherein the strong association rule in step E is a rule corresponding to that the confidence between each non-empty subset generated by the frequent item set obtained in step D and its complement is greater than or equal to a minimum confidence min _ conf, that is, an association rule "X > (D-X)"; the final strong association rule can obtain the necessary relation among the machine tool data, so that the fault condition of the machine tool is predicted.
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