CN111273607B - Spark-based numerical control machine tool running state monitoring method - Google Patents
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Abstract
The invention relates to a Spark-based numerical control machine tool running state monitoring method.A system takes Apache Spark as a basic computing frame, thereby realizing the processing and analysis of mass running data, and simultaneously adding an acquisition module based on an OPC protocol, a Kafka distributed message queue and other components to provide a reliable data interface for the system. And discovering data distribution characteristics, namely different health states, in different time periods by adopting a density clustering-based DBSCAN algorithm on the historical data through Spark-MLib. Meanwhile, an SVM classification algorithm is adopted to establish a monitoring model, so that the real-time monitoring of the safe operation state of the numerical control machine tool is realized.
Description
Technical Field
The invention relates to the field of numerical control systems, digital workshops and intelligent manufacturing, in particular to a Spark-based numerical control machine running state monitoring system realized under a Windows platform.
Background
With the development of information technology, the traditional machine manufacturing industry starts to revolutionarily change by the characteristics of informatization and intellectualization, the machine manufacturing industry gradually enters the era of industrial big data, and Germany 'industry 4.0', american 'industrial internet' and 'Chinese manufacture 2025' all propose to utilize the internet and big data thinking to improve the intellectualization level of the manufacturing industry and comprehensively improve the overall efficiency of the manufacturing industry.
Meanwhile, many enterprises form numerically-controlled machine tool workshops of a considerable scale, data generated in the operation process of equipment is increased in a geometric progression, traditional numerically-controlled machine tool maintenance mainly adopts a manual regular inspection mode, the efficiency is extremely low, the traditional numerically-controlled machine tool maintenance completely depends on professional qualities of machine tool maintenance personnel, the maintenance personnel cannot be warned in advance, meanwhile, massive machining process data are regarded as garbage data to be discarded, and serious data resource waste is caused.
Disclosure of Invention
Aiming at the defects in the prior art, the technical problem to be solved by the invention is to provide a Spark-based numerical control machine running state monitoring system for monitoring and managing the real-time state of a numerical control machine by combining the Spark high-performance parallel computing engine quasi-real-time performance and high throughput.
The technical scheme adopted by the invention for realizing the purpose is as follows: a Spark-based numerical control machine tool running state monitoring system comprises the following steps:
step 1: collecting state data information of the numerical control machine tool;
and 2, step: then, carrying out normalization processing on the state data information to obtain state data;
and step 3: kafka accesses state data, different types of state data are integrated by adding a Topic field into the data, the data of the same Topic are partitioned into different servers according to a set algorithm, and a flow computing system spark streaming computes the data;
and 4, step 4: preprocessing the state data by using Spark streaming of Spark to obtain a state data sample;
and 5: taking a state data sample as input, dividing a monitored object into different safety states as output, and establishing a multi-classification prediction model; and predicting the monitoring data to be detected through a multi-classification prediction model.
The acquisition is realized in one of two ways; the first mode is through the development interface of the numerical control machine tool, and the other mode is through the OPC protocol acquisition.
The state data information comprises spindle motor temperature, spindle current, spindle motor load, spindle rotation error, X-axis motor temperature and feeding speed.
The preprocessing of the state data by using Spark streaming is specifically as follows:
in Spark Job of Spark, data of each Topic in Kafka is defined as a data stream DStream, and each DStream is internally represented by a set of consecutive RDDs.
The method for establishing the multi-classification prediction model by using the state data samples as input and the monitoring objects divided into different safety states as output comprises the following steps:
clustering data generated in different time periods in a data set into different clusters by training labeled state data samples and using a density-based clustering algorithm DBSCAN, thereby finding different safe operation states of a machine tool, and dividing a monitored object into different safe states according to a clustering result;
and establishing a multi-classification prediction model by adopting an SVM classification algorithm aiming at different safety states.
The clustering algorithm DBSCAN based on density is as follows:
1) Classifying core points, boundary points and noise points according to the density based on the center: if the number of points contained in the neighborhood with the epsilon as the radius exceeds a given threshold value MinPts, the point o is marked as a core point; points which are not core points but fall in the neighborhood of a certain core point are marked as boundary points; any point that is neither a core point nor a boundary point is a noise point;
2) Deleting all noise points;
3) Connecting two core points with the distance smaller than the neighborhood radius epsilon to form a new cluster;
4) The recorded data at each moment is given a cluster class label, namely corresponding to a safety state of the monitored object.
The method for establishing the multi-classification prediction model by adopting the SVM classification algorithm comprises the following steps:
and for the data corresponding to each class label, marking the points belonging to the class as positive examples, marking the points not belonging to the class as negative examples, and training a classifier for the class by the marked data through an SVM algorithm.
The prediction of the monitoring data to be detected through the multi-classification prediction model is as follows: classifying the newly generated monitoring data by using a classifier corresponding to each safety state respectively, integrating classification results of all the classifiers, and judging the safety state of the monitoring data to be the class when one and only one classifier divides the monitoring data into the corresponding states; otherwise, if there is no or more classifier to classify the monitored data into the corresponding state, the security state of the monitored data is determined as the outlier.
The invention has the following beneficial effects and advantages:
1. and (4) economy. Aiming at various different data processing occasions, the programming modes based on Spark are unified into the same processing mode, the Spark unifies a technical stack, and the research and development cost is reduced.
2. And (4) real-time performance. The invention is based on Spark framework, so that the invention has quasi-real-time performance and higher throughput.
3. And (4) intelligent early warning. The state of the component is preliminarily identified by comparing the monitoring state information and the characteristic parameters with normal values, and the health state of the component is predicted by establishing a health state model based on a time sequence to achieve the aim of early warning.
Drawings
FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a block diagram of a data acquisition and access module according to the present invention;
fig. 3 is a flow chart of Spark Streaming operation according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples.
As shown in fig. 1 to 2, a system for monitoring the running state of a numerically-controlled machine tool based on Spark comprises the following steps:
step 1: the method comprises the following steps of collecting state data information of the numerical control machine tool, wherein the first mode is a development interface provided by a numerical control manufacturer, and the other mode is to obtain the running state data of the numerical control machine tool in an OPC protocol mode;
step 2: then, carrying out normalization processing on the original sample parameters by adopting a z-score normalization method;
and 3, step 3: and the access to the state data is realized by introducing a distributed message queue Kafka. And the integrated transmission of different types of state data is realized by adding the Topic field into the data. I.e. messages within the same topoc are partitioned to different servers according to a certain algorithm. When issuing information, the stream data generation system distributes the data stream to the Kafka message subject as a producer of the Kafka message data, and the stream calculation system SparkStreaming consumes and calculates the data in real time.
And 4, step 4: preprocessing the implementation state monitoring data by using a Spark streaming real-time data calculation framework of Spark, namely defining continuous data sources of each Topic in Kafka as a data stream DStream in Spark Job, wherein each DStream is internally represented by a group of continuous RDDs, and then converting the operation of the DStream into the operation of the RDDs in Spark; as shown in fig. 3.
And 5: the method comprises the steps of establishing an operation state monitoring model by combining actual characteristics of machine tool parameters and using six parameters of spindle motor temperature, spindle current, spindle motor load, spindle rotation error, X-axis motor temperature and feed speed, clustering data generated in different time periods in a data set into different clusters by using a density-based clustering algorithm DBSCAN through training labeled parameter samples, finding different safe operation states of the machine tool, and dividing a monitored object into different safe states according to a clustering result. And establishing a multi-classification prediction model by adopting an SVM classification algorithm according to different safety states, and predicting the health state of monitoring data generated in the future.
The clustering algorithm DBSCAN based on density is as follows:
step 1: and classifying the core points, the boundary points and the noise points according to the density based on the center. If the number of points contained in the neighborhood with the epsilon as the radius of the point o exceeds a given threshold MinPts, the point o is marked as a core point; points which are not core points but fall in the neighborhood of a certain core point are marked as boundary points; any point that is neither a core point nor a boundary point is a noise point. Wherein the neighborhood radius ε and the density threshold MinPts are specified by the user;
step 2: deleting all noise points;
and 3, step 3: two core points having a distance smaller than the neighborhood radius epsilon are connected to form a new cluster.
And 4, step 4: the recorded data at each moment is given a cluster type label, namely, a safety state of the corresponding monitored object.
The method is characterized in that: the method for establishing the multi-classification prediction model by adopting the SVM classification algorithm comprises the following steps:
step 1: and aiming at the data corresponding to each class label, marking the points belonging to the class as positive examples, marking the points not belonging to the class as negative examples, and training a classifier aiming at the class by using the marked data through an SVM algorithm so as to distinguish whether the data belong to the class or not.
Step 2: classifying the newly generated monitoring data by using a classifier corresponding to each safety state respectively, integrating classification results of all the classifiers, and judging the safety state of the monitoring data to be the class when one and only one classifier divides the monitoring data into the corresponding states; otherwise, if there is no classifier or the classifiers classify the data into the corresponding states, the safety state of the monitored data is determined as the outlier.
Fig. 1 shows a system configuration of the present invention.
The method comprises the following steps:
step 1: acquiring running state data of the numerical control machine tool by adopting a development interface provided by a numerical control manufacturer or an OPC protocol mode, wherein the running state data comprises six parameters of spindle motor temperature, spindle current, spindle motor load, spindle rotation error, X-axis motor temperature and X-axis actual speed;
step 2: because the attribute value and the threshold value of each feature are different, the data set also needs to be subjected to data normalization processing, and a z-score normalization method is adopted as the standard of the data normalization;
and step 3: producing the theme by using Kafka to create topic, and sending the theme to Spark Streaming consumption;
and 4, step 4: preprocessing the implementation state monitoring data by using a Spark streaming real-time data calculation framework of Spark, namely defining continuous data sources of each Topic in Kafka as a data stream DStream in Spark Job, wherein each DStream is internally represented by a group of continuous RDDs, and then converting the operation of the DStream into the operation of the RDDs in Spark;
and 5: using a DBSCAN algorithm in Spark-MLib, setting a MinPts parameter and a neighborhood radius epsilon in the DBSCAN algorithm to be proper values, and clustering data generated in different time periods in a data set into different clusters through the DBSCAN algorithm;
step 6: dividing the data cluster into a training set and a test set according to the proportion of 2 in an equidistant sampling mode in a manner that the data cluster at the first two moments in every three adjacent moments is added into the training set, and the data cluster at the third moment is added into the test set. In the training process of the classifier corresponding to each state, regarding the data in the training set and the test set, the data belonging to the class is marked as a positive example, and the data not belonging to the class is marked as a negative example. And respectively calling a training algorithm provided in the Spark-MLib-SVM aiming at each safety state, obtaining a classifier corresponding to the state based on a training set, and sequentially verifying the accuracy of the generated classifier through a test set.
Claims (7)
1. A Spark-based method for monitoring the running state of a numerical control machine is characterized by comprising the following steps:
step 1: collecting state data information of the numerical control machine tool;
step 2: then, carrying out normalization processing on the state data information to obtain state data;
and 3, step 3: kafka accesses state data, different types of state data are integrated by adding a Topic field into the data, the data of the same Topic are partitioned into different servers according to a set algorithm, and a flow computing system spark streaming computes the data;
and 4, step 4: preprocessing the state data by Spark streaming to obtain a state data sample;
and 5: taking a state data sample as input, dividing a monitored object into different safety states as output, and establishing a multi-classification prediction model; forecasting the monitoring data to be measured through a multi-classification forecasting model;
the method for establishing the multi-classification prediction model by using the state data samples as input and the monitoring objects divided into different safety states as output comprises the following steps:
clustering data generated in different time periods in a data set into different clusters by training labeled state data samples and using a density-based clustering algorithm DBSCAN, thereby finding different safe operation states of a machine tool, and dividing a monitored object into different safe states according to a clustering result;
and establishing a multi-classification prediction model by adopting an SVM classification algorithm aiming at different safety states.
2. The Spark-based numerical control machine tool operating state monitoring method according to claim 1, wherein: the acquisition is realized in one of two ways; the first way is through the development interface of the numerical control machine tool, and the other way is through the OPC protocol acquisition.
3. The Spark-based numerical control machine tool operating state monitoring method according to claim 1, wherein: the state data information comprises spindle motor temperature, spindle current, spindle motor load, spindle rotation error, X-axis motor temperature and feeding speed.
4. The Spark-based numerical control machine tool operation state monitoring method according to claim 1, wherein: the preprocessing of the state data by using Spark streaming specifically comprises the following steps:
in Spark Job of Spark, data of each Topic in Kafka is defined as a data stream DStream, and each DStream is internally represented by a set of consecutive RDDs.
5. The Spark-based numerical control machine tool operation state monitoring method according to claim 1, wherein: the clustering algorithm DBSCAN based on density is as follows:
1) Classifying core points, boundary points and noise points according to the density based on the center: if the number of points contained in the neighborhood with the epsilon as the radius exceeds a given threshold value MinPts, the point o is marked as a core point; points which are not core points but fall in the neighborhood of a certain core point are marked as boundary points; any point that is neither a core point nor a boundary point is a noise point;
2) Deleting all noise points;
3) Connecting two core points with the distance smaller than the neighborhood radius epsilon to form a new cluster;
4) The recorded data at each moment is given a cluster class label, namely corresponding to a safety state of the monitored object.
6. The method for monitoring the running state of the numerically-controlled machine tool based on Spark according to claim 1, characterized in that: the multi-classification prediction model established by adopting the SVM classification algorithm is as follows:
and for the data corresponding to each class label, marking the points belonging to the class as positive examples, marking the points not belonging to the class as negative examples, and training a classifier for the class by the marked data through an SVM algorithm.
7. The Spark-based numerical control machine tool operation state monitoring method according to claim 6, wherein: the method for predicting the monitoring data to be detected through the multi-classification prediction model specifically comprises the following steps: classifying the newly generated monitoring data by using classifiers corresponding to each safety state respectively, integrating classification results of all the classifiers, and judging the safety state of the monitoring data as a category when one and only one classifier classifies the monitoring data into the corresponding state; otherwise, if there is no classifier or the classifiers classify the data into the corresponding states, the safety state of the monitored data is determined as the outlier.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844161A (en) * | 2017-02-20 | 2017-06-13 | 重庆邮电大学 | Abnormal monitoring and Forecasting Methodology and system in a kind of carrier state stream calculation system |
| CN106844585A (en) * | 2017-01-10 | 2017-06-13 | 广东精规划信息科技股份有限公司 | A kind of time-space relationship analysis system based on multi-source Internet of Things location aware |
| CN107846472A (en) * | 2017-11-24 | 2018-03-27 | 华北电力大学（保定） | The fleet anomaly detection method of extensive power transmission and transforming equipment Monitoring data flow |
| CN108803486A (en) * | 2018-08-16 | 2018-11-13 | 重庆理工大学 | Numerical control machining tool heat error prediction based on deep learning network in parallel and compensation method |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10417653B2 (en) * | 2013-01-04 | 2019-09-17 | PlaceIQ, Inc. | Inferring consumer affinities based on shopping behaviors with unsupervised machine learning models |
| CN105678398A (en) * | 2015-12-24 | 2016-06-15 | 国家电网公司 | Power load forecasting method based on big data technology, and research and application system based on method |
| US20170286838A1 (en) * | 2016-03-29 | 2017-10-05 | International Business Machines Corporation | Predicting solar power generation using semi-supervised learning |
| US10504038B2 (en) * | 2016-05-02 | 2019-12-10 | Cisco Technology, Inc. | Refined learning data representation for classifiers |
| CN108062555A (en) * | 2016-11-08 | 2018-05-22 | 南京理工大学 | Monitoring data early warning system based on Spark streamings cluster |
| CN106649809A (en) * | 2016-12-29 | 2017-05-10 | 苏碧云 | Internet of Things analysis method |
| CN107480699A (en) * | 2017-07-13 | 2017-12-15 | 电子科技大学 | A kind of intrusion detection method based on channel condition information and SVMs |
| CN108718345A (en) * | 2018-09-05 | 2018-10-30 | 电子科技大学 | A kind of digitlization workshop industrial data Network Transmitting system |
-
2018
- 2018-12-04 CN CN201811472641.1A patent/CN111273607B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844585A (en) * | 2017-01-10 | 2017-06-13 | 广东精规划信息科技股份有限公司 | A kind of time-space relationship analysis system based on multi-source Internet of Things location aware |
| CN106844161A (en) * | 2017-02-20 | 2017-06-13 | 重庆邮电大学 | Abnormal monitoring and Forecasting Methodology and system in a kind of carrier state stream calculation system |
| CN107846472A (en) * | 2017-11-24 | 2018-03-27 | 华北电力大学（保定） | The fleet anomaly detection method of extensive power transmission and transforming equipment Monitoring data flow |
| CN108803486A (en) * | 2018-08-16 | 2018-11-13 | 重庆理工大学 | Numerical control machining tool heat error prediction based on deep learning network in parallel and compensation method |
Non-Patent Citations (2)
| Title |
|---|
| 基于多维时间序列的数控机床状态预测方法研究;李海等;《工程科学与技术》;20180117;第50卷(第01期);187-195 * |
| 基于流计算的电力调度网络流量监测平台;吴奔等;《计算机系统应用》;20180715;第27卷(第07期);57-62 * |
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