CN113279812B - Method and system for state monitoring and residual life prediction of mine main drainage equipment - Google Patents
Method and system for state monitoring and residual life prediction of mine main drainage equipment Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000012544 monitoring process Methods 0.000 title claims abstract description 32
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000012423 maintenance Methods 0.000 claims description 25
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 19
- 230000008569 process Effects 0.000 claims description 17
- 230000036541 health Effects 0.000 claims description 16
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 230000006378 damage Effects 0.000 claims description 7
- 238000013461 design Methods 0.000 claims description 6
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- 230000005540 biological transmission Effects 0.000 claims description 4
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- 238000012854 evaluation process Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 3
- 239000007788 liquid Substances 0.000 claims description 3
- 208000027418 Wounds and injury Diseases 0.000 claims 4
- 208000014674 injury Diseases 0.000 claims 4
- 241000555745 Sciuridae Species 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000003245 coal Substances 0.000 description 1
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- 238000011031 large-scale manufacturing process Methods 0.000 description 1
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- 230000008439 repair process Effects 0.000 description 1
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Classifications
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F16/00—Drainage
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
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Abstract
The invention discloses a method for monitoring the state of main drainage equipment of a mine and predicting the residual life, wherein the main drainage equipment of the mine comprises a plurality of centrifugal pumps, motors and valves which are connected through pipelines, and the method comprises the following steps: acquiring real-time low-frequency data and real-time high-frequency data of main mine drainage equipment; establishing an expert decision database module, and carrying out processing analysis on the real-time low-frequency data and the real-time high-frequency data; performing running state evaluation on the processed low-frequency data through the expert decision database module, and performing residual life prediction on the processed high-frequency data; the invention realizes the real-time monitoring, analysis and judgment of the running state of the mine main drainage system.
Description
Technical Field
The invention relates to the technical field of unit health management, in particular to a method and a system for monitoring the state of main drainage equipment of a mine and predicting the residual life.
Background
Currently, a mine main drainage system is used as one of four large-scale production systems of a coal mine and is used for carrying an important task of draining underground water burst. Once the mine main drainage system fails, the mine safety production is affected, the mine is even submerged, and the lives of workers are endangered.
However, the prior art does not consider the function of building perfect and reasonable health state monitoring and life prediction of the main drainage system of the mine, and cannot meet the functional requirement of implementing preventive maintenance on the unit.
Therefore, how to provide a method for monitoring the state of main drainage equipment and predicting the residual life of a mine, which can solve the above problems, is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method and a system for monitoring the state of main drainage equipment of a mine and predicting the residual life, which realize the real-time monitoring, analysis and judgment of the running state of the main drainage system of the mine, achieve the purpose of replacing planned maintenance with state maintenance and improve the production efficiency of the mine.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the method for monitoring the state of the mine main drainage equipment and predicting the residual life comprises the following steps of connecting a plurality of centrifugal pumps, motors and valves through pipelines, and specifically further comprises the following steps:
acquiring real-time low-frequency data and real-time high-frequency data of main mine drainage equipment;
establishing an expert decision database module, and carrying out processing analysis on the real-time low-frequency data and the real-time high-frequency data;
and performing running state evaluation on the processed low-frequency data through the expert decision database module, and performing residual life prediction on the processed high-frequency data.
Preferably, the process of performing the operation state evaluation on the processed low frequency data through the expert decision database module includes:
an alarm threshold value is arranged in the expert decision database module;
and when the processed low frequency data is higher than the alarm threshold value, starting an alarm.
Preferably, the process of performing the running state evaluation by using the low frequency data further includes:
and the expert decision database module also pre-starts the centrifugal pump with the least working days to work according to the working days of a plurality of centrifugal pumps contained in the mine main drainage equipment.
Preferably, the specific process of residual life prediction includes:
decomposing the real-time high-frequency data to obtain a characteristic value;
and inputting the characteristic value into a neural network for training, and outputting the running state parameters of the mine main drainage equipment.
Preferably, the specific process of remaining life prediction further includes:
and taking the remaining life of the mine main drainage equipment into consideration, and taking the health condition of a part connected with the mine main drainage equipment into consideration, and evaluating the remaining life, the health coefficient and the next maintenance time.
Preferably, the low-frequency data is any one or more of liquid level, negative pressure, positive pressure, working days and accumulated flow of the mine main drainage equipment, and the high-frequency data is a vibration parameter of the mine main drainage equipment.
Further, the invention also provides a system for monitoring the state of main drainage equipment of a mine and predicting the residual life, which comprises the following steps:
the low-frequency signal acquisition module is used for acquiring real-time low-frequency data of the mine main drainage equipment;
the high-frequency signal acquisition module is used for acquiring real-time high-frequency data of the mine main drainage equipment;
the processor is connected with the low-frequency signal acquisition module and the high-frequency signal acquisition module and is used for receiving and processing the real-time low-frequency data and the real-time high-frequency data;
and the expert decision database module is connected with the processor and is used for carrying out running state evaluation and residual life prediction according to the processed real-time low-frequency data and real-time high-frequency data.
Preferably, the expert decision database module includes:
the state monitoring unit is used for judging the running state of the equipment according to the processed real-time low-frequency data;
and the residual life prediction unit is used for predicting the residual life according to the processed real-time high frequency data.
Preferably, the method further comprises: and the wireless communication module is connected with the processor and is used for realizing wireless transmission of the real-time low-frequency data and the real-time high-frequency data.
Compared with the prior art, the invention discloses a method and a system for monitoring the state of main drainage equipment of a mine and predicting the residual life, which realize real-time effective transmission of multi-sensor information, realize on-line monitoring of working condition parameters and equipment states, realize real-time monitoring, analysis and judgment of the running state of the main drainage equipment of the mine, and achieve the aims of system fault diagnosis, life assessment and transition from planned maintenance to state maintenance.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for monitoring the state of main drainage equipment of a mine and predicting the residual life of the main drainage equipment of the mine;
fig. 2 is a schematic block diagram of a state monitoring and residual life predicting system for mine main drainage equipment provided by the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the embodiment of the invention discloses a method for monitoring the state of main drainage equipment of a mine and predicting the residual life, wherein the main drainage equipment of the mine comprises a plurality of centrifugal pumps, motors and valves which are connected through pipelines, and the method specifically comprises the following steps:
acquiring real-time low-frequency data and real-time high-frequency data of main mine drainage equipment;
in a specific embodiment, the low-frequency data is any one or more of liquid level, negative pressure, positive pressure, working days and accumulated flow of the mine main drainage equipment, and the high-frequency data is vibration parameters of the mine main drainage equipment.
Establishing an expert decision database module, and processing and analyzing the real-time low-frequency data and the real-time high-frequency data;
and performing running state evaluation on the processed low-frequency data through an expert decision database, and performing residual life prediction on the processed high-frequency data.
In a specific embodiment, the process of performing the operational state assessment on the processed low frequency data by the expert decision database module includes:
an alarm threshold value is set in the expert decision database;
when the processed low frequency data is higher than the alarm threshold value, an alarm is started.
In a specific embodiment, the process of performing the operation state evaluation by using the low frequency data further includes: the expert decision database module also pre-starts the centrifugal pump with the least working days to work according to the working days of a plurality of centrifugal pumps contained in the mine main drainage equipment.
In one particular embodiment, the specific process of residual life prediction includes:
decomposing the real-time high-frequency data to obtain a characteristic value;
and inputting the characteristic values into a neural network for training, and outputting the running state parameters of the mine main drainage equipment.
Specifically, the specific process of residual life prediction may be:
(1) Decomposing the real-time high-frequency data through wavelet packets to obtain energy values of different frequency bands, wherein the energy values are used as characteristic values;
(2) Residual life prediction using a radial basis (RBF) neural network, radial Basis (RBF) neural network comprising three layers of input layer, intermediate layer and output layer, only one hidden layer, hidden units being basis functions phi (x, x) i ) The basis function can be a Gaussian function, and the specific expression is:
output of RBF:
wherein x is i And sigma and omega are the center, the function width and the weight of the hidden unit respectively.
Training of the radial basis function neural network comprises training of the weight omega of the output unit and the center x of the hidden unit i And the function width sigma, at the moment, the embodiment of the invention can select S of the radial vibration signal 8-layer wavelet packet 0 (0Hz-19Hz)、S 1 (19Hz-39Hz)、S 4 (78Hz-98Hz)、S 5 (98Hz-118Hz)、S 6 (118Hz-138Hz)、S 10 (196Hz-215Hz)、S 11 (215Hz-235Hz)、S 12 (235Hz-255Hz)、S 13 (255Hz-275Hz)、S 14 (275Hz-294Hz)、S 15 (294Hz-313Hz)、S 16 (313Hz-333Hz)、S 17 (333Hz-353Hz)、S 18 (353Hz-373Hz)、S 19 (373Hz-393Hz)、S 20 (393Hz-412Hz)、S 21 (412Hz-431Hz)、S 22 (431Hz-451Hz)、S 30 (588Hz-608Hz)、S 31 (608Hz-627Hz)、S 32 (627Hz-647Hz)、S 33 (647Hz-666Hz)、S 34 (666Hz-686Hz)、S 35 (686Hz-706Hz)、S 36 (706Hz-725Hz)、S 37 (725Hz-745Hz)、S 38 (745Hz-764Hz)、S 40 (784 Hz-804 Hz) the 28 frequency band energy values are taken as fault characteristic values, and S of a 9-layer wavelet packet of a stator line current signal of the centrifugal pump motor is selected 3 (29Hz-39Hz)、S 4 (39Hz-49Hz)、S 5 (49Hz-59Hz)、S 6 (59Hz-68Hz)、S 7 (68Hz-78Hz)、S 8 (78Hz-88Hz)、S 9 (88Hz-98Hz)、S 10 (98Hz-108Hz)、S 11 (108Hz-117Hz)、S 15 (147Hz-156Hz)、S 16 (156Hz-166Hz)、S 17 (166Hz-176Hz)、S 18 (176Hz-186Hz)、S 19 (186Hz-195Hz)、S 25 (245Hz-254Hz)、S 35 (342Hz-352Hz)、S 56 (548Hz-558Hz)、S 66 The energy values of 18 frequency bands (646 Hz-656 Hz) are taken as fault characteristic values, 46 characteristic values are taken as total, and faults such as water pump motor bearing, squirrel cage, stator and air gap eccentricity, water pump impeller mechanism faults, centrifugal pump failure, water pump cavitation, foundation looseness and the like are evaluated. These 46 characteristic signals are taken as input X (X 1 ，x 2 ，……，x 45 ，x 46 ) The normal operation fault-free value is taken as the center X of the RBF i (x i1 ，x i2 ，……，x i45 ，x i46 ) The output unit weight ω (ω 1 ，ω 2 ，……，ω 45 ，ω 46 ) The training of the system is directly calculated by a least square method, thus a radial basis neural network with 46 inputs, 1 output and 46 hidden units is obtained, and the output is the running state of the water pump, including the states of normal state, water pump motor bearing ball fault, inner ring fault, outer ring fault, squirrel cage broken bar fault, air gap eccentric fault, water pump impeller mechanism fault, centrifugal pump water failure, water pump cavitation fault, foundation looseness fault and the like.
In a specific embodiment, the specific process of remaining life prediction further comprises:
the mine main drainage system can comprise a centrifugal pump, a high-voltage motor, a valve, a pipeline and the like, and because of the connection relation among the components, the states of the components not only influence the residual life of the components, but also influence the life of other components, so that the health condition of the components connected with the mine main drainage device is considered while the residual life of the mine main drainage device is considered, and the evaluation is carried out through the residual days, health coefficients and the next maintenance time, wherein the specific evaluation process is as follows:
1. the remaining number of days a of the component=component design lifetime (number of days folded) S-component operation number M-component planned maintenance period sx [ (Σhealthcondition tij×mutual damage degree coefficient Eij (self damage degree coefficient when i=j)) + Σdailymaintenance life influence coefficient N ].
The health condition Tij is a real-time monitoring result, namely a water pump running state output by a radial basis neural network, and comprises a normal state, a water pump motor bearing ball fault, an inner ring fault, an outer ring fault, a squirrel cage broken bar fault, an air gap eccentric fault, a water pump impeller mechanism fault, a centrifugal pump water-free fault, a water pump cavitation fault, a foundation loosening fault and the like, wherein the normal state is 0, the water pump motor bearing ball fault, the inner ring fault and the outer ring fault are respectively 1, 1.4 and 1.8, the motor squirrel cage broken bar fault is 2.0-2.6, the air gap eccentric fault is 3, the centrifugal pump impeller mechanism fault is 4, the centrifugal pump water-free fault is 5, the centrifugal pump cavitation fault is 6 and the foundation loosening fault is 7 according to different broken bar numbers.
The mutual damage degree coefficient Eij is an influence coefficient indicating that the jth component has on the ith component, and is an influence coefficient itself when i=j, that is, an influence coefficient itself for its lifetime when itself fails. In the embodiment of the present invention, when i=j, eij=0.5, eij=0.1 for the bearing and the rotor, and other eij=0.05, which is determined according to the structural correlation of the respective components.
The life influence coefficient of daily maintenance is a daily maintenance problem found in the inspection process, and if maintenance is not carried out according to the system requirement, one item is added with 0.1.
2. Next repair time U = date in machine) +number of days a remaining for the component.
3. The health coefficient K of the component = S x 100% of the number of days remaining of the component a/component design lifetime (number of days folded).
4. Water pump remaining days l=min (remaining days a of the part).
Further, referring to fig. 2, an embodiment of the present invention further provides a system for monitoring a state of a mine main drainage device and predicting a remaining life, including:
the low-frequency signal acquisition module 1 is used for acquiring real-time low-frequency data of the mine main drainage equipment;
the high-frequency signal acquisition module 2 is used for acquiring real-time high-frequency data of the mine main drainage equipment;
the processor 3 is connected with the low-frequency signal acquisition module 1 and the high-frequency signal acquisition module 2 and is used for receiving and processing real-time low-frequency data and real-time high-frequency data;
and the expert decision database module 4 is connected with the processor 3, and is used for carrying out running state evaluation and residual life prediction according to the processed real-time low-frequency data and real-time high-frequency data.
In a specific embodiment, the expert decision database module 4 comprises:
a state monitoring unit 41 for judging the operation state of the apparatus based on the processed real-time low frequency data;
a remaining life prediction unit 42 for predicting a remaining life based on the processed real-time high frequency data.
In a specific embodiment, the method further comprises: the wireless communication module 5 is connected with the processor 3 and is used for realizing wireless transmission of the real-time low-frequency data and the real-time high-frequency data.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for monitoring the state of a mine main drainage device and predicting the residual life, wherein the mine main drainage device comprises a plurality of centrifugal pumps, motors and valves which are connected through pipelines, and the method is characterized by comprising the following steps:
acquiring real-time low-frequency data and real-time high-frequency data of main mine drainage equipment;
establishing an expert decision database module, and carrying out processing analysis on the real-time low-frequency data and the real-time high-frequency data;
performing running state evaluation on the processed low-frequency data through the expert decision database module, and performing residual life prediction on the processed high-frequency data;
the specific process of residual life prediction also comprises:
the method is characterized in that the remaining life of the mine main drainage equipment is considered, the health condition of a part connected with the mine main drainage equipment is considered, and evaluation is carried out through the remaining days, health coefficients and next maintenance time, wherein the specific evaluation process is as follows:
component remaining days a = component design life S-component running days M-component planned maintenance period sx [ (Σhealthstatus T) ij Coefficient of degree of mutual injury E ij ) A + Σdaily maintenance life influence coefficient N]；
In the health condition T ij For real-time monitoring result, namely the running state of the water pump output by the radial basis function neural network, the coefficient E of the mutual injury degree ij The influence coefficient of the jth component on the ith component is shown, the daily maintenance life influence coefficient is a daily maintenance problem found in the inspection process, and if maintenance is not carried out according to the requirement, one item is added with 0.1;
next maintenance time u=in-machine date+number of days remaining of component a, component health coefficient k=number of days remaining of component a/component design lifetime s×100%, water pump number of days remaining L is the minimum number of days remaining of component a.
2. The method for monitoring the state of main drainage equipment of a mine and predicting the residual life according to claim 1, wherein the process of performing the operation state evaluation on the processed low frequency data through the expert decision database module comprises:
an alarm threshold value is arranged in the expert decision database module;
and when the processed low frequency data is higher than the alarm threshold value, starting an alarm.
3. The method for monitoring the state of a mine main drainage apparatus and predicting the remaining life as claimed in claim 2, wherein the process of performing the operation state assessment using the low frequency data further comprises:
and the expert decision database module also pre-starts the centrifugal pump with the least working days to work according to the working days of a plurality of centrifugal pumps contained in the mine main drainage equipment.
4. The method for monitoring the state of main drainage equipment of a mine and predicting the residual life according to claim 1, wherein the specific process of predicting the residual life comprises the following steps:
decomposing the real-time high-frequency data to obtain a characteristic value;
and inputting the characteristic value into a neural network for training, and outputting the running state parameters of the mine main drainage equipment.
5. The method for monitoring the state of main drainage equipment of a mine and predicting the residual life of the main drainage equipment of the mine according to any one of claims 1-4, wherein the low-frequency data is any one or more of liquid level, negative pressure, positive pressure, working days and accumulated flow of the main drainage equipment of the mine, and the high-frequency data is vibration parameters of the main drainage equipment of the mine.
6. A mine primary drainage condition monitoring and remaining life prediction system, comprising:
the low-frequency signal acquisition module (1) is used for acquiring real-time low-frequency data of the mine main drainage equipment;
the high-frequency signal acquisition module (2) is used for acquiring real-time high-frequency data of the mine main drainage equipment;
the processor (3) is connected with the low-frequency signal acquisition module (1) and the high-frequency signal acquisition module (2) and is used for receiving and processing the real-time low-frequency data and the real-time high-frequency data;
the expert decision database module (4) is connected with the processor (3) and is used for carrying out running state evaluation and residual life prediction according to the processed real-time low-frequency data and real-time high-frequency data;
the specific process of residual life prediction also comprises:
the method is characterized in that the remaining life of the mine main drainage equipment is considered, the health condition of a part connected with the mine main drainage equipment is considered, and evaluation is carried out through the remaining days, health coefficients and next maintenance time, wherein the specific evaluation process is as follows:
component remaining days a = component design life S-component running days M-component planned maintenance period sx [ (Σhealthstatus T) ij Coefficient of degree of mutual injury E ij ) A + Σdaily maintenance life influence coefficient N]；
In the health condition T ij For real-time monitoring result, namely the running state of the water pump output by the radial basis function neural network, the coefficient E of the mutual injury degree ij The influence coefficient of the jth component on the ith component is shown, the daily maintenance life influence coefficient is a daily maintenance problem found in the inspection process, and if maintenance is not carried out according to the requirement, one item is added with 0.1;
next maintenance time u=in-machine date+number of days remaining of component a, component health coefficient k=number of days remaining of component a/component design lifetime s×100%, water pump number of days remaining L is the minimum number of days remaining of component a.
7. A mine primary drainage status monitoring and remaining life prediction system according to claim 6, wherein said expert decision database module (4) comprises:
a state monitoring unit (41) for judging the operation state of the device according to the processed real-time low frequency data;
a remaining life prediction unit (42) for predicting a remaining life from the processed real-time high frequency data.
8. The mine main drain condition monitoring and remaining life prediction system of claim 6, further comprising: and the wireless communication module (5) is connected with the processor (3) and is used for realizing wireless transmission of the real-time low-frequency data and the real-time high-frequency data.
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| 液压支架健康评估与寿命预测模型研究;马旭东等;《煤炭科学技术》;第141-148 页 * |
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