CN115077628B - Crown block conductive sliding block abrasion monitoring system and monitoring method - Google Patents
Crown block conductive sliding block abrasion monitoring system and monitoring method Download PDFInfo
- Publication number
- CN115077628B CN115077628B CN202211002560.1A CN202211002560A CN115077628B CN 115077628 B CN115077628 B CN 115077628B CN 202211002560 A CN202211002560 A CN 202211002560A CN 115077628 B CN115077628 B CN 115077628B
- Authority
- CN
- China
- Prior art keywords
- working
- periods
- dust
- degree
- adjacent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005299 abrasion Methods 0.000 title claims abstract description 40
- 238000012544 monitoring process Methods 0.000 title claims abstract description 35
- 238000000034 method Methods 0.000 title claims abstract description 31
- 239000000428 dust Substances 0.000 claims abstract description 103
- 238000009792 diffusion process Methods 0.000 claims description 14
- 230000002159 abnormal effect Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 5
- 238000013480 data collection Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 12
- 229910052799 carbon Inorganic materials 0.000 description 12
- 230000008859 change Effects 0.000 description 7
- 238000007605 air drying Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 230000005611 electricity Effects 0.000 description 2
- 239000012634 fragment Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009414 blockwork Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
- B66C15/06—Arrangements or use of warning devices
- B66C15/065—Arrangements or use of warning devices electrical
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Control And Safety Of Cranes (AREA)
Abstract
The application relates to a crown block conductive sliding block abrasion monitoring system and a monitoring method, which are used for determining dust influence indexes of all working periods according to dust concentration sequences and humidity data sequences at different moments in all working periods; calculating the association degree of two adjacent working time periods based on the abrasion quality sequence of each working time period; correcting the association degree of the two adjacent working periods by using dust influence indexes of the two adjacent working periods to obtain corrected association; marking different working periods, classifying the same marked working periods into one type, and respectively obtaining independent working conditions and a plurality of grouping working conditions comprising at least two working periods; and calculating the similarity degree of the independent working conditions and each grouping working condition, analyzing the independent working conditions according to the similarity degree, and giving an alarm. The abrasion degree of the conductive sliding block during the operation of the crown block is corrected by introducing the dust influence index, so that the working condition of the crown block can be more comprehensively analyzed, and the crown block is convenient to overhaul in time.
Description
Technical Field
The application relates to the field of crown block equipment monitoring, in particular to a crown block conductive sliding block abrasion monitoring system and a crown block conductive sliding block abrasion monitoring method.
Background
The crown block is an important device in industrial production, and the crown block is used for transferring materials with high efficiency and convenience. The crown block is driven by a motor to run along the track, and the crown block is provided with a conductive sliding block which slides on the sliding wire to perform electricity taking operation, so that normal contact electrification between the conductive sliding block and the sliding wire is an important condition for normal running of the crown block.
However, due to continuous use of the crown block, abrasion of the conductive sliding block occurs, and poor contact between the conductive sliding block and the sliding wire is easily caused, so that intermittent power failure is caused; the abrasion of the conductive sliding block is generally judged according to experience, and the conductive sliding block is replaced again, wherein the replacement time is generally about three days, and the abrasion condition of the conductive sliding block is not deeply analyzed, so that the problems of low efficiency caused by frequent replacement of the conductive sliding block and loss caused by untimely replacement of the conductive sliding block exist.
Disclosure of Invention
In order to solve the technical problems, the application aims to provide a crown block conductive sliding block abrasion monitoring system and a crown block conductive sliding block abrasion monitoring method, and the adopted technical scheme is as follows:
the application provides a wear monitoring system of a conductive sliding block of an overhead travelling crane, which comprises a controller, and a chip collecting device and a dust concentration monitor which are connected with the controller in a signal manner, wherein the chip collecting device is arranged on the conductive sliding block and is used for collecting chips corresponding to different moments in different working time periods so as to obtain the wear quality of the chips; the dust concentration monitor is used for monitoring dust concentration near the conductive sliding block; the humidity sensor is used for monitoring humidity data of the environment; the controller acquires a wear quality sequence, a dust concentration sequence and a humidity data sequence corresponding to each working period;
determining dust influence indexes of each working period according to dust concentration sequences and humidity data sequences at different moments in each working period;
calculating the association degree of two adjacent working time periods based on the abrasion quality sequence of each working time period;
correcting the association degree of the two adjacent working periods by using dust influence indexes of the two adjacent working periods to obtain corrected association;
marking different working periods based on each relevance, classifying the same marked working periods into one type, and respectively obtaining independent working conditions and a plurality of grouping working conditions comprising at least two conductive sliding block working conditions;
and calculating the similarity degree of the independent working conditions and each grouping working condition, analyzing the independent working conditions according to the similarity degree, and giving an alarm.
Further, the dust influence index is determined according to the ratio of the product of the average value and the standard deviation of the dust concentration in two adjacent working periods to the humidity range data.
Further, the correction process is as follows:
dividing dust concentration and humidity data in each working period respectively, and calculating each dust influence index to obtain a dust influence index sequence corresponding to each working period; and determining the relative difference value of each working period based on the dust influence index sequence corresponding to each working period, further obtaining the difference value of the relative difference values of two adjacent working periods, obtaining the dust diffusion degree, and correcting the association degree by using the dust diffusion degree.
Further, according to the similarity, analyzing the independent working conditions includes that when each similarity is in a set threshold value interval, the working conditions are abnormal working conditions, and alarming is conducted; otherwise, the data acquisition is carried out again, and the judgment of the working condition is carried out.
Further, the degree of similarity is
Wherein Q is an adjustment coefficient,，/>is the average value of the sum of the correlation degrees of all adjacent two working periods in the j-th grouping working condition, +.>Setting the correlation degree sum of all adjacent two working periods in the j-th grouping working condition, +.>And the association degree of the s and s+1 two adjacent working periods corresponding to the kth independent working condition is obtained.
Further, before the crown block does not work, the method further comprises the steps of carrying out voltage and current testing on the conductive sliding blocks, comparing the obtained voltage and current curves of the conductive sliding blocks, and if the voltage and current curves of the conductive sliding blocks have the same fluctuation trend, enabling the conductive sliding blocks to work normally, and enabling the crown block to work; if the voltage current curve fluctuation trend of one conductive slide block is different from that of other conductive slide blocks, the conductive slide block is abnormal, and an alarm is directly given.
The application also provides a method for monitoring the abrasion of the conductive sliding block of the crown block, which comprises the following steps:
acquiring a wear quality sequence, a dust concentration sequence and a humidity data sequence corresponding to each working period;
determining dust influence indexes of each working period according to dust concentration sequences and humidity data sequences at different moments in each working period;
calculating the association degree of two adjacent working time periods based on the abrasion quality sequence of each working time period;
correcting the association degree of the two adjacent working periods by using dust influence indexes of the two adjacent working periods to obtain corrected association;
marking different working periods based on each relevance, classifying the same marked working periods into one type, and respectively obtaining independent working conditions and a plurality of grouping working conditions comprising at least two conductive sliding block working conditions;
and calculating the similarity degree of the independent working conditions and each grouping working condition, analyzing the independent working conditions according to the similarity degree, and giving an alarm.
Further, the correction process is as follows:
dividing dust concentration and humidity data in each working period respectively, and calculating each dust influence index to obtain a dust influence index sequence corresponding to each working period; and determining the relative difference value of each working period based on the dust influence index sequence corresponding to each working period, further obtaining the difference value of the relative difference values of two adjacent working periods, obtaining the dust diffusion degree, and correcting the association degree by using the dust diffusion degree.
Further, according to the similarity, the analysis of the independent working conditions includes: when the similarity degrees are all in the set threshold value interval, the abnormal working condition is adopted, and an alarm is given; otherwise, the data acquisition is carried out again, and the judgment of the working condition is carried out.
Further, the degree of similarity is
Wherein Q is an adjustment coefficient,，/>is the average value of the sum of the correlation degrees of all adjacent two working periods in the j-th grouping working condition, +.>Setting the correlation degree sum of all adjacent two working periods in the j-th grouping working condition, +.>And the association degree of the s and s+1 two adjacent working periods corresponding to the kth independent working condition is obtained.
The application has the following beneficial effects:
according to the scheme, the abrasion quality of the conductive sliding block is monitored, dust influence indexes are introduced, the abrasion quality of each working period is corrected, the association degree of two adjacent working periods is obtained, and the method is used for evaluating the collected working condition conditions of the two adjacent working periods, namely monitoring whether the working conditions of the crown block are along with the working of the crown block or not, and abnormal working conditions possibly occur; due to the fact that the factor of dust influence index is introduced, the conductive sliding block of the crown block can be more comprehensively and accurately estimated; meanwhile, according to the application, each working period is divided according to the time sequence of acquisition, the abrupt working period is extracted, and whether the independent working condition is an abnormal working condition or not is further confirmed by calculating the similarity degree of the independent working condition and other grouping working conditions, or the division is inaccurate due to errors of equipment and the like.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of an overhead travelling crane conductive slider wear monitoring system of the present application;
fig. 2 is a flow chart of a method for monitoring the abrasion of the conductive sliding block of the crown block.
Detailed Description
In order to further describe the technical means and effects adopted by the present application for achieving the preset purpose, the following detailed description of the specific embodiments, structures, features and effects thereof according to the present application is given with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
The application provides a crown block conductive sliding block abrasion monitoring system and a monitoring method.
The crown block comprises a crown block body and a wear monitoring system of a crown block conductive sliding block.
Wherein, the overhead traveling crane body is conventional overhead traveling crane equipment. In this embodiment, the crown block body includes a sliding wire guide rail and a sliding portion, where the sliding portion includes a conductive slider for taking electricity on the sliding wire guide rail. Because the crown block device is common crown block device, the structural composition, the working principle and the working process are not repeated.
As shown in fig. 1, the wear monitoring system of the conductive sliding block of the crown block comprises a controller, and a chip collecting device and a dust concentration monitor which are connected with the controller in a signal manner, wherein the chip collecting device is arranged on the conductive sliding block and is used for collecting chips corresponding to different moments in different working time periods so as to obtain the wear quality of the chips; the dust concentration monitor is used for monitoring dust concentration near the conductive sliding block; the humidity sensor is used for monitoring humidity data of the environment; the controller acquires abrasion quality, dust concentration and humidity data, analyzes and processes the quality data, the dust concentration and the humidity data, and determines the working condition of the conductive sliding block.
The wear monitoring system of the crown block conductive sliding block also comprises an alarm device which is connected with the controller through signals; the alarm device is an indicator lamp or an alarm and is used for timely alarming under abnormal working conditions.
The abrasion quality, dust concentration and humidity data acquired in the above steps are acquired according to a time sequence, the acquired working time period can be set to be 1 hour, the acquired working time period is used as a working condition state of the crown block, and the data acquisition of the next working time period is sequentially carried out according to a set interval time, so that the abrasion quality sequence, the dust concentration sequence and the humidity data sequence of each working time period can be respectively acquired; of course, the data acquisition of a plurality of working periods can be performed by setting time according to intervals for every three hours as one working period, wherein the sampling interval in each working period needs to be set according to actual conditions. The set interval time may be set to half a day or one day, and the sampling interval may be set to 10s.
For example, the air drying degree D and the dust concentration C in the air in the current working process are obtained through a monitoring system, recording is carried out every 10 seconds, 60 minutes is taken as sampling time of one state, and 360 dust concentrations C and air drying degrees D are recorded in total; and collecting the next state according to the set interval time.
The dust concentration monitor can be arranged on the sliding wire guide rail or the sliding part, and the specific position of the dust concentration monitor can be set according to actual conditions as long as the dust concentration monitor can monitor the dust concentration around the conductive sliding block.
The above mentioned collection of the wear mass is wear debris generated between the conductive slider and the rail friction during operation of the conductive slider. The method comprises the steps that a piece collecting device is arranged on each conductive sliding block, the abrasion mass of each day is obtained through measurement, and the carbon brush working condition and the weight change rate of each day are obtained through a counting table; the chip collecting device provided by the application has small collecting equipment with certain tightness and is convenient to detach, so that daily data collection is facilitated.
According to the application, the fact that dust possibly enters into the conductive sliding block to influence the final daily weight record is considered because the dust collecting device cannot be completely sealed on the conductive sliding block, so that the influence of the dust on a recording result can be corrected by combining the environmental dryness.
Fig. 2 is a flowchart of the method for analyzing and processing the obtained abrasion quality sequence, dust concentration sequence and humidity sequence, which specifically includes the following steps:
firstly, determining dust influence indexes of each working period according to dust concentration sequences and humidity data sequences at different moments in each working period; calculating the association degree of two adjacent working time periods based on the abrasion quality sequence of each working time period; correcting the association degree of the two adjacent working periods by using dust influence indexes of the two adjacent working periods to obtain corrected association;
the correction process comprises the following steps:
dividing a dust concentration sequence and a humidity data sequence in each working period respectively, and calculating dust influence indexes of each segment to obtain a dust influence index sequence corresponding to each working period; based on the dust influence index sequences, determining the relative difference value of each working period, further obtaining the difference value of the relative difference values of two adjacent working periods, obtaining the dust diffusion degree, and correcting the association degree by using the dust diffusion degree.
Specifically, the corrected association degree is:
wherein,represents->The maximum value in the dust influence degree sequence of each working period;represents->The minimum value in the dust influence degree sequence of each working period; />Represents the firstAn average value of the dust influence degree sequences of the individual working periods; />Represents->Carbon brush mass corresponding to each working period +.>Represents->The maximum value in the dust influence degree sequence of each working period; />Represents->The minimum value in the dust influence degree sequence of each working period; />Represents->An average value of the dust influence degree sequences of the individual working periods; />Represents->Carbon brush quality corresponding to each working period.
Wherein,a difference representing the relative difference in the dust impact levels of two adjacent operating periods; the meaning is that the difference of the dust diffusion degree in the two working conditions is larger, and the larger the difference represents the larger the difference of the two working conditions. When the difference value of the relative difference values of different dust diffusion degrees under two working conditions is fixed, the mass of the carbon brush under the two working conditions is different>,/>The greater the distance measure of (2), the greater the actual situation gap for the two conditions R (i, i+1). Otherwise, the smaller the actual situation difference of the two working conditions R (i, i+1).
The association degree can also be obtained by adopting cosine similarity.
The dust influence index is the ratio of the product of the average value of the dust concentration and the standard deviation to the humidity range data.
Wherein the humidity range data is the difference between the maximum value and the minimum value of the humidity sequence. The product of the average value and the standard deviation of the dust concentration represents the activity degree of dust in the air, and the larger the activity degree is, the larger the diffusion degree is, and the conductive sliding block (carbon brush) is easy to wear.
The difference between the maximum and minimum values of the humidity sequence represents a differential change in the air drying degree during the day, and the dust concentration C is proportional to the dust influence degree U since the change in the air drying degree during the day does not float much.
When the air drying degree is regarded as a constant value, the larger the dust concentration is, the larger the influence degree is, and the probability of falling onto the carbon brush in the diffusion process is increased, so that the loss of the carbon brush is increased when the environment is dried; thus, a relationship between the degree of air drying and the dust concentration can be obtained.
As other embodiments, the dust impact index of each working period can be directly performed in the present application, and each working period does not need to be divided.
In this embodiment, the correlation degree of the abrasion quality of two different working conditions of the same carbon brush is calculated based on the abrasion quality sequences of any two adjacent working periods, so that the difference between the abrasion states of the working conditions corresponding to the two adjacent working periods can be judged. It should be noted that any two adjacent working periods in the application are working periods according to the sequence of collection, and are not any two randomly selected working periods.
Secondly, marking different working periods based on each relevance, classifying the same marked working periods into one type, and respectively obtaining independent working conditions and a plurality of grouping working conditions comprising at least two working periods;
the labels in this embodiment may be classified by a clustering method, where the clustering method may be: DBSCAN algorithm, mean Shift algorithm, K-Means algorithm, etc.
In this embodiment, a DBSCAN algorithm is taken as an example, and clustering is performed to obtain independent working conditions and a plurality of grouping working conditions including at least two working periods. Wherein the independent working conditions are independent working periods, and the plurality of grouping working conditions respectively comprise a plurality of working periods.
Due to the characteristics of DBSCAN, if two groups of working conditions with less obvious differences appear in the clustering calculation process, the numerical value of r can be properly adjusted at the moment, so that the working condition clustering under different conditions is more obvious, the analysis is more specific, the obtained groups are more refined, and whether the crown block has accident risk in working can be accurately judged. Thus, in the refinement of the grouping, some instances may occur where the sample distance is far from most grouping cases.
And then, calculating the similarity degree of the independent working condition and each grouping working condition, analyzing the independent working condition according to the similarity degree, and giving an alarm.
The analysis of the independent working condition in the steps comprises the following steps: when the similarity degrees are all in the set threshold value interval, the abnormal working condition is adopted, and an alarm is given; otherwise, the data acquisition is carried out again, and the judgment of the working condition is carried out. Of course, as another embodiment, each similarity is compared with the set threshold interval, and when the number of the similarity in the set threshold interval is more than 60%, the abnormal working condition is detected, and an alarm is given.
Specifically, the degree of similarity is
Wherein Q is an adjustment coefficient,is the average value of the sum of the correlation degrees of all adjacent two working periods in the j-th grouping working condition, +.>And the association degree of the s and s+1 two adjacent working time periods corresponding to the working condition of the kth independent conductive sliding block.
The regulating coefficient Q isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Setting the sum of the association degrees of all adjacent two working periods in the j-th grouping working condition; the sum of the set association degrees can be obtained through empirical data, namely, according to the historical positive of the crown blockAnd clustering the normal data to obtain the association degree of the normal work corresponding group, and further obtaining the sum of the association degrees.
Wherein, the closer Q is to 1, the more practical working condition is illustratedIs +.>The closer the representative clustering algorithm is, the more accurate. Conversely, the less accurate. However, in the grouping process, cases occur when the converted distance of some working condition difference degrees is larger than most working condition difference degrees, and at the moment, the cases represent that the working condition of the period of time has a problem, and early warning measures should be carried out.
Of course, as other embodiments, straight line fitting can be performed according to known historical normal data of the crown block or the association degree of each working time period in the grouping working condition, so as to obtain a fitted straight line, and the fitted association degree is obtained and is used as a set association degree, so that the sum of the set association degrees is obtained.
In the case of straight line fitting, the ideal state is that the slope of the straight line in each grouping condition is kI.e. when k->And when the association degrees in the grouping working conditions are basically equal, the grouping working conditions are considered to be normal working conditions. Of course, the slope is not necessarily equal to 0, due to the machine itself and environmental factors.
In the above, the slope formula of the fitted straight line is:
where x=，y=/>Wherein n is the number of the working period in the j-th grouping working condition, +.>The nth numbered operating period in the j-th grouping condition.
It should be noted that, the number n in the foregoing description is a renumbering performed by all working periods in each grouping working condition, and the number is random, and of course, the number may also be in order of magnitude of association degree, etc., which is used to perform straight line fitting on the scattered points, and obtain the value of the ideal working condition corresponding to the actual working condition, and has no great relation with the positive and negative of the k value.
Is converted from the difference degree R (i, i+1) of different working conditions, and is a distance measure for representing the quality of the fragments worn by the carbon brush. The fragments due to abrasion of the carbon brush are gradually increased, but appear to be approximately linear as a whole. The abrasion loss of the carbon brush can be regarded as k +.>An approximately horizontal straight line.
The linear fitting considers that the abrasion of the conductive sliding blocks is more serious along with the operation of the crown block and the operation time of the crown block, but if the crown block works normally, the abrasion loss of the crown block is not changed much each time, and the abrasion loss of the crown block is equal due to the influence of environmental factors, so that the abrasion loss is corrected by introducing the environmental factors, and the association degree of any two adjacent operation periods is equal to the association degree of the two adjacent operation periods next or last; therefore, the straight line to which it is fitted should theoretically be of slope kIs a straight line of (2); and further, the division of each working period in the grouping working condition can be proved to be accurate and normal working conditions.
The adjusting coefficient is used for adjusting the actual working conditions corresponding to the grouping working conditions, so that the actual working conditions can approach to the ideal working conditions (normal working conditions), and further, the difference between the independent working conditions and the grouping working conditions can be accurately obtained, and the working state corresponding to the independent working conditions can be judged.
The set threshold interval is (0, 1), and when the similarity is in the interval, the difference between the independent working condition and the normal working condition is considered to be large, and early warning measures should be carried out. Conversely, as in [1, +) When the independent working condition is similar to the grouping working condition; however, the DBSCAN cluster does not belong to the same situation, data acquisition needs to be continued, and equipment working condition judgment is performed.
Since there are four conductive sliders, anomalies of the conductive sliders are determined more quickly and efficiently; further, before the crown block does not work, the method further comprises the steps of carrying out voltage and current testing on the conductive sliding blocks, comparing the obtained voltage and current curves of the conductive sliding blocks, and if the voltage and current curves of the conductive sliding blocks have the same fluctuation trend, enabling the conductive sliding blocks to work normally, and enabling the crown block to work; if the voltage current curve fluctuation trend of one conductive slide block is different from that of other conductive slide blocks, the conductive slide block is abnormal, and an alarm is directly given; otherwise, if the operation is normal, the operation can be continued.
The voltage-current curve is obtained by taking down the newly purchased carbon brush and applying voltage to combine with a universal meter. And repeating the above operation four times to obtain the U-I change curve graphs of the four groups of conductive sliding blocks. The four groups of change curves are roughly compared, and if the change curvature of any curve is found to be larger than the change curvature of other curves, accident early warning analysis is directly carried out. If the four groups of curves fluctuate within a certain range, the method continues to be used. And finally, evaluating whether the overhead travelling crane has indexes of accident hidden danger or not according to the result, reducing the occurrence of the accident, reducing the potential safety hazard and improving the working efficiency.
The application also provides a method for monitoring the abrasion of the conductive sliding block of the crown block; because the related monitoring method has been described in the above crown block conductive slider wear monitoring system, redundant description is omitted here.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
Claims (4)
1. The wear monitoring system is characterized by comprising a controller, a humidity sensor, a chip collecting device and a dust concentration monitor, wherein the chip collecting device and the dust concentration monitor are in signal connection with the controller, and the chip collecting device is arranged on the conductive slider and is used for collecting chips corresponding to different moments in different working time periods so as to obtain the wear quality of the chips; the dust concentration monitor is used for monitoring dust concentration near the conductive sliding block; the humidity sensor is used for monitoring humidity data of the environment; the controller acquires a wear quality sequence, a dust concentration sequence and a humidity data sequence corresponding to each working period; the abrasion quality, dust concentration and humidity data are collected according to a time sequence, data collection of a plurality of working time periods is carried out according to a set interval time, and data collection of a plurality of moments is carried out in each working time period according to a set sampling interval; determining dust influence indexes of each working period according to dust concentration sequences and humidity data sequences at different moments in each working period; calculating the association degree of two adjacent working time periods based on the abrasion quality sequence of each working time period; correcting the association degree of the two adjacent working periods by using dust influence indexes of the two adjacent working periods to obtain corrected association;
marking different working periods based on each relevance, classifying the same marked working periods into one type, and respectively obtaining independent working conditions and a plurality of grouping working conditions comprising at least two working periods;
calculating the similarity degree of the independent working conditions and each grouping working condition, analyzing the independent working conditions according to the similarity degree, and giving an alarm;
the correction process comprises the following steps: dividing a dust concentration sequence and a humidity data sequence in each working period respectively, and calculating dust influence indexes of each segment to obtain a dust influence index sequence corresponding to each working period; determining the relative difference value of each working period based on each dust influence index sequence, further obtaining the difference value of the relative difference values of two adjacent working periods, obtaining the dust diffusion degree, and correcting the association degree by using the dust diffusion degree;
according to the similarity, the analysis of the independent working conditions comprises the following steps: when the similarity degrees are all in the set threshold value interval, the abnormal working condition is adopted, and an alarm is given; otherwise, data acquisition is carried out again, and judgment of working conditions is carried out;
the degree of similarity is
Wherein Q is an adjustment coefficient,，/>is the average value of the sum of the correlation degrees of all adjacent two working periods in the j-th grouping working condition, +.>Setting the correlation degree sum of all adjacent two working periods in the j-th grouping working condition, +.>And for the association degree of the s and s+1 two adjacent working periods corresponding to the kth conductive sliding block working condition, mj is the sum of the association degrees of all the two adjacent working periods in the jth grouping working condition.
2. The overhead travelling crane conductive slide wear monitoring system of claim 1 wherein the dust impact indicator is a ratio of a product of an average value of dust concentration and a standard deviation to humidity range data.
3. The crown block conductive slide wear monitoring system of claim 1, further comprising voltage and current testing the conductive slide before the crown block is not operated, and comparing the obtained voltage and current curves of the conductive slide, wherein if the voltage and current curves of the conductive slide have the same fluctuation trend, the conductive slide is normal, and the crown block is operated; if the voltage current curve fluctuation trend of one conductive slide block is different from that of other conductive slide blocks, the conductive slide block is abnormal, and an alarm is directly given.
4. The crown block conductive sliding block abrasion monitoring method is characterized by comprising the following steps of:
acquiring a wear quality sequence, a dust concentration sequence and a humidity data sequence corresponding to each working period; the abrasion quality, dust concentration and humidity data are collected according to a time sequence, data collection of a plurality of working time periods is carried out according to a set interval time, and data collection of a plurality of moments is carried out in each working time period according to a set sampling interval;
determining dust influence indexes of each working period according to dust concentration sequences and humidity data sequences at different moments in each working period;
calculating the association degree of two adjacent working time periods based on the abrasion quality sequence of each working time period;
correcting the association degree of the two adjacent working periods by using dust influence indexes of the two adjacent working periods to obtain corrected association;
marking different working periods based on each relevance, classifying the same marked working periods into one type, and respectively obtaining independent working conditions and a plurality of grouping working conditions comprising at least two working periods;
calculating the similarity degree of the independent working conditions and each grouping working condition, analyzing the independent working conditions according to the similarity degree, and giving an alarm;
the correction process comprises the following steps: dividing a dust concentration sequence and a humidity data sequence in each working period respectively, and calculating dust influence indexes of each segment to obtain a dust influence index sequence corresponding to each working period; determining the relative difference value of each working period based on each dust influence index sequence, further obtaining the difference value of the relative difference values of two adjacent working periods, obtaining the dust diffusion degree, and correcting the association degree by using the dust diffusion degree;
according to the similarity, the analysis of the independent working conditions comprises the following steps: when the similarity degrees are all in the set threshold value interval, the abnormal working condition is adopted, and an alarm is given; otherwise, data acquisition is carried out again, and judgment of working conditions is carried out;
the degree of similarity is
Wherein Q is an adjustment coefficient,，/>is the average value of the sum of the correlation degrees of all adjacent two working periods in the j-th grouping working condition, +.>Setting the correlation degree sum of all adjacent two working periods in the j-th grouping working condition, +.>And for the association degree of the s and s+1 two adjacent working periods corresponding to the kth independent working condition, mj is the sum of the association degrees of all the two adjacent working periods in the jth grouping working condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211002560.1A CN115077628B (en) | 2022-08-22 | 2022-08-22 | Crown block conductive sliding block abrasion monitoring system and monitoring method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211002560.1A CN115077628B (en) | 2022-08-22 | 2022-08-22 | Crown block conductive sliding block abrasion monitoring system and monitoring method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115077628A CN115077628A (en) | 2022-09-20 |
| CN115077628B true CN115077628B (en) | 2023-11-24 |
Family
ID=83245157
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211002560.1A Active CN115077628B (en) | 2022-08-22 | 2022-08-22 | Crown block conductive sliding block abrasion monitoring system and monitoring method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115077628B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2343443C1 (en) * | 2007-06-28 | 2009-01-10 | Открытое акционерное общество "Научно-исследовательский и конструкторско-технологический институт подвижного состава" | Method for lubricated items wear monitoring in internal combustion engines |
| CN101354332A (en) * | 2008-09-26 | 2009-01-28 | 北京绿林创新数码科技有限公司 | Laser powder dust detector with humidity continuous self-correcting function and detecting method thereof |
| CN106596308A (en) * | 2016-10-20 | 2017-04-26 | 云南电网有限责任公司电力科学研究院 | Method and device for predicting wear degree of power transmission line hardware |
| CN207231935U (en) * | 2017-07-31 | 2018-04-13 | 哈尔滨电机厂有限责任公司 | Hydrogenerator carbon brush abrasion on-Line Monitor Device |
| CN110126878A (en) * | 2018-02-09 | 2019-08-16 | 比亚迪股份有限公司 | The wheel wear appraisal procedure of rail traffic vehicles, apparatus and system |
| CN110308065A (en) * | 2019-06-05 | 2019-10-08 | 西北工业大学 | Rotary engine wear detector and detection method |
| CN212609112U (en) * | 2020-05-20 | 2021-02-26 | 中天钢铁集团有限公司 | Service life indicating device for conductive sliding block of overhead travelling crane |
| CN114739850A (en) * | 2022-04-01 | 2022-07-12 | 宁波迪卡数控科技有限公司 | Grinding wheel service life detection method and system, storage medium and intelligent terminal |
-
2022
- 2022-08-22 CN CN202211002560.1A patent/CN115077628B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2343443C1 (en) * | 2007-06-28 | 2009-01-10 | Открытое акционерное общество "Научно-исследовательский и конструкторско-технологический институт подвижного состава" | Method for lubricated items wear monitoring in internal combustion engines |
| CN101354332A (en) * | 2008-09-26 | 2009-01-28 | 北京绿林创新数码科技有限公司 | Laser powder dust detector with humidity continuous self-correcting function and detecting method thereof |
| CN106596308A (en) * | 2016-10-20 | 2017-04-26 | 云南电网有限责任公司电力科学研究院 | Method and device for predicting wear degree of power transmission line hardware |
| CN207231935U (en) * | 2017-07-31 | 2018-04-13 | 哈尔滨电机厂有限责任公司 | Hydrogenerator carbon brush abrasion on-Line Monitor Device |
| CN110126878A (en) * | 2018-02-09 | 2019-08-16 | 比亚迪股份有限公司 | The wheel wear appraisal procedure of rail traffic vehicles, apparatus and system |
| CN110308065A (en) * | 2019-06-05 | 2019-10-08 | 西北工业大学 | Rotary engine wear detector and detection method |
| CN212609112U (en) * | 2020-05-20 | 2021-02-26 | 中天钢铁集团有限公司 | Service life indicating device for conductive sliding block of overhead travelling crane |
| CN114739850A (en) * | 2022-04-01 | 2022-07-12 | 宁波迪卡数控科技有限公司 | Grinding wheel service life detection method and system, storage medium and intelligent terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115077628A (en) | 2022-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108490880B (en) | Method for monitoring wear state of cutting tool of numerical control machine tool in real time | |
| CN109186813A (en) | A kind of temperature sensor self-checking unit and method | |
| CN110533229B (en) | Track maintenance time prediction method and device | |
| CN112393906B (en) | Method for diagnosing, classifying and evaluating health of weak signal fault of bogie bearing of metro vehicle | |
| CN108662983B (en) | Method for detecting and calculating correlation coefficient of steel rail abrasion | |
| CN113284143B (en) | Die casting deburring precision detection system based on image data processing | |
| CN113050018B (en) | Voltage transformer state evaluation method and system based on data driving evaluation result change trend | |
| CN113212180B (en) | Maglev train, suspension control system and vertical damping signal calculation method | |
| CN116400126B (en) | Low-voltage power box with data processing system | |
| CN115876258B (en) | Livestock and poultry breeding environment abnormity monitoring and alarming system based on multi-source data | |
| CN107844067A (en) | A kind of gate of hydropower station on-line condition monitoring control method and monitoring system | |
| US11597418B2 (en) | Method for determining an element characteristic of a railroad element | |
| CN115077628B (en) | Crown block conductive sliding block abrasion monitoring system and monitoring method | |
| CN104503361B (en) | Gear Processing process tool change decision method based on multi-pattern Fusion | |
| WO2017107790A1 (en) | Method and apparatus for predicting road conditions using big data | |
| CN114664063A (en) | Equipment grading alarm method based on general index trend analysis | |
| CN114001887B (en) | Bridge damage assessment method based on deflection monitoring | |
| CN113408383B (en) | Audible noise invalid data judgment method based on bounded beta (g, h) distribution and MWKPCA | |
| CN115535026A (en) | Railway track flatness detection method and system | |
| CN112228042B (en) | Method for judging working condition similarity of pumping well based on cloud edge cooperative computing | |
| CN114896900B (en) | Target tracking system | |
| CN116612820B (en) | Dairy product production intelligent management platform based on data analysis | |
| CN115017961B (en) | Intelligent control method of lubricating equipment based on neural network data set augmentation | |
| Qin et al. | Performance degradation assessment of train rolling bearings based on SVM and segmented vote method | |
| CN116304759A (en) | Vehicle derailment early warning method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231025 Address after: 075700 No.1, Jingyuan Road, Yuxian Economic Development Zone, Zhangjiakou City, Hebei Province Applicant after: Hebei Yuzhou Energy Comprehensive Development Co.,Ltd. Address before: Qingnian Road, Taijiao Industrial Park, Huilong Town, Qidong City, Nantong City, Jiangsu Province 226000 Applicant before: Qidong Datong Electric Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |