CN116663722A - Coal preparation electromechanical device fault prediction system based on data analysis - Google Patents
Coal preparation electromechanical device fault prediction system based on data analysis Download PDFInfo
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Abstract
The invention discloses a fault prediction system of coal dressing electromechanical equipment based on data analysis, and relates to the technical field of fault prediction; the system is used for solving the problems that the working environment of the coal dressing electromechanical equipment is complex, the working condition changes greatly, faults are easy to occur and the production efficiency of a factory is affected, and comprises a data acquisition module, a data analysis module, a fault prediction module and an early warning execution module; according to the invention, through analyzing the operation information and the environment information of the equipment, the corresponding early warning value can be obtained, the obtained early warning value is compared with the preset range of values to generate the corresponding signal, and further the generated signal is analyzed and matched to obtain the corresponding prevention scheme, so that the operation state of the coal preparation electromechanical equipment can be accurately predicted and monitored, the occurrence of equipment faults and production accidents is prevented, and the stability and the safety of the production process are ensured.
Description
Technical Field
The invention relates to a fault prediction technology, in particular to a fault prediction system of coal dressing electromechanical equipment based on data analysis.
Background
Along with the progress of automation technology, coal preparation plants are continuously developed to large-scale and intelligent, and raw coal is inevitably mixed with impurities in the processes of generation, exploitation and transportation; and the quality of raw coal is poorer and poorer as the mining is deeper. In order to reduce impurities in raw coal, coal resources are effectively and reasonably utilized, and meanwhile, coal is divided into various products according to quality and specification, and the coal is subjected to mechanical processing, and the mechanical processing is commonly called coal washing equipment.
Because the working environment of the coal dressing electromechanical equipment is complex, the working condition changes greatly, faults are easy to occur, the production efficiency of a factory is influenced, and a series of potential safety hazards are also caused, such as:
1. the temperature of the factory building or the dust concentration is too high; high temperatures may cause the equipment to overheat or fail, while high concentrations of dust may clog the equipment or reduce its efficiency;
2. the screen effect of the screen cloth can be influenced by overlong service time of the equipment, and the screen cloth is blocked.
In order to solve the problems, a fault prediction system of coal dressing electromechanical equipment based on data analysis is provided.
Disclosure of Invention
The invention aims to solve the problems that the working environment of coal dressing electromechanical equipment is complex, the working condition changes greatly, faults are easy to occur and the production efficiency of a factory is affected, and provides a coal dressing electromechanical equipment fault prediction system based on data analysis.
The aim of the invention can be achieved by the following technical scheme: the system comprises a data acquisition module, a data analysis module, a fault prediction module and an early warning execution module;
the data acquisition module is used for mainly acquiring operation information and environment information of the coal separator equipment in a working state;
the data analysis module analyzes and processes the operation information and the environment information, and the specific process is as follows:
processing the operation information and the environment information to obtain parameter values, wherein the parameter values comprise early warning use values, early warning state values, early warning operation values, early warning bearing values and ring abnormal values;
comparing the parameter value with a corresponding preset machine value range, and generating corresponding signals when the parameter value belongs to the corresponding preset machine value range, wherein the signals comprise a use signal, a state signal, an operation signal, a bearing signal and an annular signal;
transmitting the generated signal to a fault prediction module;
the fault prediction module is used for receiving the generated signals, selecting a fault analysis end with the highest efficiency value for diagnosis, analyzing the signals to obtain a device data set, analyzing the device data set through scheme matching to obtain a diagnosis result and a prevention scheme of the device, and sending the obtained prevention scheme to the early warning execution module;
the early warning execution module is used for receiving the prevention scheme and executing corresponding operation; the method comprises the following steps:
when a maintenance scheme is received, the service time and the maintenance times of the equipment are obtained, and the equipment is maintained or overhauled and replaced;
when a cleaning scheme is received, acquiring a coal ball state of a screen of the equipment, and controlling a cleaning mechanism to clean the screen;
when an inspection scheme is received, acquiring a power state of equipment operation, and inspecting the power state of the equipment and a cable connection part;
when the bearing treatment scheme is received, acquiring the temperature, sound and vibration times of the bearing during running, and replacing or adjusting and resetting the bearing;
when receiving the regulation scheme, acquire dust concentration in the factory building, dust concentration around the equipment and factory building temperature, improve temperature and dust in the factory building through dust catcher and air conditioner control.
As a preferred embodiment of the invention, the specific analysis process of the data analysis module is as follows:
s1: the equipment using time, maintenance times and cleaning times are processed, and the method specifically comprises the following steps: marking the initial operation time of the equipment as the use time, calculating the use time length value of the equipment by using a difference method between the real time and the use time, and normalizing the use time length value, the maintenance times and the cleaning times to obtain an early warning use value;
s2: the method is characterized by comprising the following steps of treating the rolling speed of coal on a screen and the granularity of the coal: obtaining a movement track of the coal balls on the screen, processing the movement track to obtain the rolling speed of the coal balls on the screen, randomly obtaining a batch of speed values of the coal balls from the movement track, adding the obtained speed values and taking an average value to obtain a rolling speed value, obtaining the particle size of the coal balls, randomly screening a batch from the obtained speed values, adding the screened particle values of the coal balls and taking the average value to obtain a particle value, and normalizing the obtained rolling speed value and the particle value to obtain an early warning state value;
s3: the power supply voltage and the impact times of the equipment are processed, specifically: the method comprises the steps of drawing a numerical point corresponding to a line graph, connecting two adjacent sets of numerical points to obtain abnormal numerical lines, calculating the slope of each abnormal numerical line and the included angle between each abnormal numerical line and a horizontal line, marking the slope of each abnormal numerical line as a first slope when the included angle between each abnormal numerical line and the horizontal line is an acute angle, marking the abnormal numerical line as a second slope when the included angle is an obtuse angle, summing all the first slopes to obtain a first total value, summing all the second slopes to obtain a second total value, connecting the numerical point with the forefront numerical point and the numerical point with the last numerical point in the line graph to obtain a line segment, marking the line segment as a constant line, calculating the slope of the initial line and the included angle between the initial line and the horizontal line, marking the slope of the initial line as a third slope and representing the third slope by a symbol K1 when the included angle between the initial line and the horizontal line is an acute angle, marking the slope of the initial line as a fourth slope and representing the fourth slope by a symbol K2 when the included angle between the initial line and the horizontal line is an obtuse angle, calculating the vertical distance between the highest numerical point and the lowest numerical point to obtain a distance value, normalizing the first total value, the second total value and the distance value to obtain a voltage change value, and normalizing the voltage change value and the impact value to obtain an early warning running value;
s4: the sound change, temperature and vibration times of the equipment bearing are processed, and the method specifically comprises the following steps: obtaining sound shell values of different moments in a period of time when the bearing operates, removing the maximum value and the minimum value, adding the rest sound shell values and taking the average value to obtain an average sound shell value, simultaneously obtaining bearing temperature values of different moments in the period of time, adding the obtained bearing temperature values after removing abnormal values and taking the average value to obtain an average temperature value, and carrying out normalization processing on the obtained average sound shell value, the average temperature value and vibration times to obtain an early warning bearing value;
s5: the temperature and dust concentration around the equipment in operation are treated, specifically: the method comprises the steps of acquiring the temperature of the surrounding environment of equipment in real time, extracting temperature data acquired in a period of time, adding residual values after removing abnormal values, taking an average value to obtain an average ambient temperature value, simultaneously acquiring the dust concentration of the surrounding environment of the equipment and the dust concentration generated during the operation of the equipment, processing the dust concentration and the dust concentration to obtain dust values, and normalizing the obtained dust values and the average Zhou Wenzhi to obtain a ring abnormal value.
As a preferred implementation mode of the invention, the specific process of selecting the fault analysis end by the fault prediction module is as follows:
transmitting a test data packet and a position feedback instruction to a fault analysis end to obtain analysis parameters and positions of the fault analysis end, marking the time for transmitting the test data packet as transmission time, marking response time of the fault analysis end as feedback time, obtaining a diagnosis speed base value of the fault analysis end by a difference method between the feedback time and the transmission time, wherein the analysis parameters comprise a downloading rate of the fault analysis end and an uploading rate of the uploading test data packet, processing the downloading rate and the uploading rate of the fault analysis end to obtain an efficiency value, obtaining a transmission distance of the test data packet by performing distance difference calculation on the position of the fault analysis end and a position corresponding to a fault prediction module, normalizing the obtained diagnosis speed base value, the efficiency value and the transmission distance to obtain a diagnosis speed base value of the fault analysis end, selecting the fault analysis end with the maximum diagnosis speed base value, and establishing communication connection with the fault prediction module.
As a preferred implementation manner of the invention, the specific processing procedure of the early warning execution module is as follows:
s1: when the early warning use value is in a preset machine value range, generating a use signal and sending the use signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated maintenance scheme to an early warning execution module, wherein the early warning execution module carries out maintenance or overhaul replacement on equipment through the acquired maintenance scheme;
s2: when the early warning state value is in a preset machine value range, a state signal is generated and sent to a fault prediction module, the signal is analyzed through the fault prediction module, the generated cleaning scheme is sent to an early warning execution module, and the early warning execution module controls a cleaning mechanism to clean a screen of the equipment through the acquired cleaning scheme;
s3: when the early warning operation value is in a preset machine value range, generating an operation signal and sending the operation signal to a fault prediction module, analyzing the signal through the fault prediction module and sending a generated inspection scheme to an early warning execution module, wherein the early warning execution module inspects a power supply, a cable junction and equipment parts through the acquired inspection scheme;
s4: when the early warning bearing value is in a preset machine value range, generating a bearing signal and sending the bearing signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated bearing processing scheme to an early warning execution module, and overhauling the abrasion degree and the position of the bearing through the acquired bearing processing scheme by the early warning execution module;
s5: when the early warning annular value is in a preset machine value range, generating an annular signal and sending the annular signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated adjustment scheme to an early warning execution module, wherein the early warning execution module improves the environment in a factory building through the acquired adjustment scheme, and S1: when the early warning use value is in a preset machine value range, a use signal is generated and sent to a fault prediction module, the fault prediction module analyzes the signal and sends the generated maintenance scheme to an early warning execution module, and the early warning execution module carries out maintenance or overhaul replacement on equipment through the acquired maintenance scheme.
As a preferred implementation mode of the invention, the early warning use value, the early warning state value, the early warning running value, the early warning bearing value and the abnormal value are normalized to obtain the fault value of the equipment, the obtained fault value is compared with a preset machine value range, when the fault value is in the preset machine value range, a fault early warning signal is generated and sent to a fault prediction module, and the fault prediction module receives the generated signal and analyzes the generated signal to generate a corresponding solution.
As a preferred implementation mode of the invention, the cleaning mechanism comprises a driving motor, the driving motor is arranged on a coal dressing machine body, the driving motor is in transmission connection with a transmission rod through a plurality of first transmission wheels and a first transmission belt, a first reciprocating screw is rotatably connected to the rear inner wall of the coal dressing machine body, the transmission rod is fixedly connected with a second transmission wheel with the front end of the first reciprocating screw, a sliding rod is slidably connected to the front side wall of the coal dressing machine body through a sliding chute, a third transmission wheel is rotatably connected to the front end of the sliding rod, a second transmission belt is in transmission connection between the third transmission wheel and two groups of second transmission wheels, an electric push rod is arranged on the front side wall of the coal dressing machine body, the extending end of the electric push rod is fixedly connected to the outer surface of the sliding rod, a cleaning brush is in threaded connection with the outer surface of the first reciprocating screw, a screen is arranged on the coal dressing machine body, the top of the cleaning brush is abutted to the bottom of the screen, a mounting frame is fixedly connected to the front side wall of the coal dressing machine body, a rotating rod is rotatably connected to the front side wall, a fourth transmission plate is fixedly connected to the outer surface of the second transmission rod, and the outer surface of the fourth transmission plate is fixedly connected to the outer surface of the fourth transmission screw, and the outer surface of the fourth transmission plate is in threaded connection with the outer surface of the fourth transmission plate.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, through analyzing the operation information and the environment information of the equipment, the corresponding early warning value can be obtained, the obtained early warning value is compared with the preset range of values to generate the corresponding signal, and further the generated signal is analyzed and matched to obtain the corresponding prevention scheme, so that the operation state of the coal preparation electromechanical equipment can be accurately predicted and monitored, the occurrence of equipment faults and production accidents is prevented, and the stability and the safety of the production process are ensured.
2. According to the invention, the electric push rod is started to drive the slide bar to slide downwards, so that the third driving wheel moves downwards to be in contact with the second driving belt and enable the second driving belt to be in a tight state, at the moment, the two groups of second driving wheels and the third driving wheel are driven by the second driving belt, so that the driving rod drives the first reciprocating screw to rotate while rotating, at the moment, the cleaning brush starts to move on the first reciprocating screw to clean the bottom of the screen, the first reciprocating screw drives the rotating rod to rotate through the two groups of fourth driving wheels and the fourth driving belt, at the moment, the rotating rod drives the second reciprocating screw to rotate through the two groups of gears, the adjusting plate moves downwards to clean the screen holes of the screen, and the cleaning mechanism is controlled to clean the screen in a preventive manner, so that the screen is prevented from being blocked due to overlong work and the influence on the efficiency of coal selection.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic block diagram of the present invention;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a voltage variation line graph of the present invention;
FIG. 4 is a block diagram of the present invention;
FIG. 5 is a partial view of FIG. 4 in accordance with the present invention;
fig. 6 is a partial view of fig. 5 in accordance with the present invention.
1. A coal separator body; 111. a driving motor; 112. a transmission rod; 113. a first reciprocating screw; 114. a second driving wheel; 115. a second belt; 116. a slide bar; 117. a third driving wheel; 118. an electric push rod; 119. cleaning brushes; 120. a screen; 121. a mounting frame; 122. a rotating lever; 123. a fourth driving wheel; 124. a fourth belt; 125. a second reciprocating screw rod; 126. a gear; 127. and an adjusting plate.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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. 1-6, a fault prediction system of coal dressing electromechanical equipment based on data analysis comprises a data acquisition module, a data analysis module, a fault prediction module and an early warning execution module;
the data acquisition module is used for mainly acquiring operation information and environment information of the coal separator equipment in a working state; the data acquisition module comprises a sensor unit and a camera unit, and the acquired data mainly comprise the service time of equipment, the speed of a coal ball on a screen, the power supply voltage of the equipment, the temperature and sound of a bearing of the equipment and the temperature and dust concentration of a factory building;
the data analysis module analyzes and processes the operation information and the environment information;
the equipment using time, maintenance times and cleaning times are processed, and the method specifically comprises the following steps: marking the initial operation time of the equipment as a use time T1, calculating a use time length value T3 of the equipment by using a difference method T2-T1 between the real time T2 and the use time T1, normalizing the use time length value T3, the maintenance times T4 and the cleaning times T5, and substituting the normalized values into a formulaObtaining early warning use value T, wherein lambda 1 、λ 2 And lambda (lambda) 3 The early warning use value T is compared with a corresponding preset machine value range, and when the early warning use value T is in the preset machine value range, a use signal is generated and fed back to the fault prediction module;
the method is characterized by comprising the following steps of treating the rolling speed of coal on a screen and the granularity of the coal: acquiring a movement track S of the coal ball on the screen, processing the movement track S, and passingObtaining the rolling speed V of the coal balls on the screen, wherein N is the rolling time period, randomly obtaining a batch of speed values V of the coal balls from the rolling time period, adding the obtained speed values V and taking an average value to obtain a rolling speed value V1, obtaining the particle size of the coal balls and randomly screening a batch from the obtained speed values, adding the screened coal ball particle values D and taking an average value to obtain a coal particle value D1, normalizing the obtained rolling speed value V1 and the coal particle value D1, and substituting the obtained rolling speed value V1 and the coal particle value D1 into a formulaObtaining an early warning state value E, wherein alpha 1 And alpha 2 The early warning state value E is compared with a corresponding preset machine value range, and when the early warning state value E is in the preset machine value range, a state signal is generated and fed back to the fault prediction module;
electric power to the deviceThe source voltage and the impact times are processed, specifically: the method comprises the steps of drawing a numerical point corresponding to a line graph in a moment point, connecting two adjacent groups of numerical points to obtain abnormal numerical lines, calculating the slope of each abnormal numerical line and the included angle between each abnormal numerical line and a horizontal line, marking the slope of each abnormal numerical line as a first slope when the included angle between each abnormal numerical line and the horizontal line is an acute angle, marking the abnormal numerical line as a second slope when the included angle is an obtuse angle, summing all the first slopes to obtain a first total value A1, summing all the second slopes to obtain a second total value A2, connecting the numerical point with the forefront numerical point and the numerical point with the last numerical point in the line graph, marking the line segment as an all-line, calculating the slope of the initial line and the included angle between the initial line and the horizontal line, marking the slope of the initial line as a third slope and representing the slope by a symbol K1 when the included angle between the initial line and the horizontal line is an acute angle, marking the slope of the initial line as a fourth slope and representing the slope by a symbol K2 when the included angle between the initial line and the horizontal line is an obtuse angle, calculating the vertical distance between the highest numerical point and the lowest numerical point to obtain a distance value A3, normalizing the first total value, the second total value and the distance value and substituting the distance value into a formula A= (A1/A2) x beta 1 +K*×β 2 +A3×β 3 Obtaining a voltage change value A, wherein the value is 1 or 2, beta 1 、β 2 Beta 3 Are all preset weight factors, and are substituted into a formula through a voltage change value A and an impact value BObtaining an early warning operation value C, wherein ASD1 and ASD2 are preset weight factors, comparing the obtained early warning operation value C with a corresponding preset machine value range, and generating an operation signal to feed back to a fault prediction module when the early warning operation value C is in the preset machine value range;
the sound change, temperature and vibration times of the equipment bearing are processed, and the method specifically comprises the following steps: obtaining the acoustic shell values of the bearings at different moments in a period of time, removing the maximum value and the minimum value, adding the rest acoustic shell values and taking the average value to obtain an average acoustic shell value S, simultaneously obtaining the temperature values of the bearings at different moments in the period of time, and removing the abnormal values to obtainAdding the obtained bearing temperature values, taking an average value to obtain an average temperature value W, normalizing the obtained average sound shell value S, the average temperature value W and the vibration times Q, and substituting the normalized values into a formulaObtaining an early warning bearing value ED, wherein TSQ1, TSQ2 and TSQ3 are preset weight factors, comparing the obtained early warning bearing value ED with a corresponding preset machine value range, and when the early warning bearing value ED is in the preset machine value range, generating a bearing signal and feeding back the bearing signal to a fault prediction module;
the temperature and dust concentration around the equipment in operation are treated, specifically: acquiring the temperature of the surrounding environment when the equipment operates in real time, extracting temperature data acquired in a period of time, adding residual values after removing abnormal values, taking an average value to obtain an average ambient temperature value M, simultaneously acquiring the dust concentration U1 of the surrounding environment of the equipment and the dust concentration U2 generated when the equipment operates, processing and substituting the dust concentration U1 and the dust concentration U2 into a formula U=U1xPQ1+U2xPQ2 to obtain a dust value U, wherein PQ1 and PQ2 are preset weight factors, normalizing the obtained dust value U and the average Zhou Wenzhi M, and substituting the dust value U and the average value into a formula H=Uxmu 1 +|M-20|×μ 2 Obtaining the ring hetero value H, wherein mu 1 Sum mu 2 The ring-shaped abnormal value H is compared with a corresponding preset machine value range, and when the ring-shaped abnormal value H is in the preset machine value range, a ring-shaped signal is generated and fed back to the early warning execution module;
it should be noted that, when the difference between M and 20 is larger, H increases correspondingly, and when M is equal to 20, i.e., |m—20| is 0, so H will be the minimum value, and the absolute sign ensures that M is always non-negative. The fault prediction module receives the generated signals, selects a fault analysis end with the highest efficiency value for diagnosis, analyzes the signals to obtain a device data set, analyzes the device data set through scheme matching to obtain a diagnosis result and a prevention scheme of the device, and sends the obtained prevention scheme to the early warning execution module;
the generated prevention scheme is mainly divided into a maintenance scheme, a cleaning scheme, an inspection scheme, a bearing treatment scheme and an adjustment scheme;
the early warning execution module is used for receiving the prevention scheme and executing corresponding operation; the method comprises the following steps:
when the early warning use value is within a preset machine value range, generating a use signal and sending the use signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated maintenance scheme to an early warning execution module, wherein the early warning execution module carries out maintenance on equipment through the acquired maintenance scheme, and meanwhile checks the fittings in the equipment to see whether the fittings are aged or slightly damaged, and if so, repairing or replacing the fittings;
when the early warning state value is within a preset machine value range, a state signal is generated and sent to a fault prediction module, the signal is analyzed through the fault prediction module, the generated cleaning scheme is sent to an early warning execution module, the early warning execution module controls through an acquired cleaning scheme cleaning mechanism, a feed inlet of the coal preparation machine body 1 is closed, an electric push rod 118 starts to drive a slide rod 116 to slide downwards, a third driving wheel 117 moves downwards to be in contact with a second driving belt 115 and enables the second driving belt 115 to be in a tight state, at the moment, two groups of second driving wheels 114 and the third driving wheel 117 are driven through the second driving belt 115, a driving rod 112 drives a first reciprocating screw 113 to rotate while rotating, at the moment, a cleaning brush 119 starts to move on the first reciprocating screw 113 to clean the bottom of a screen, at the moment, the rotating rod 122 is driven to rotate through two groups of fourth driving wheels 123 and the fourth driving belt 124 through the first reciprocating screw 113, at the moment, the rotating rod 122 drives the second reciprocating screw 125 to rotate through two groups of gears 126, and the adjusting plate 127 moves downwards to clean the screen holes of the screen 120, and the screen mesh of the equipment is cleaned;
when the early warning operation value is in a preset machine value range, generating an operation signal and sending the operation signal to a fault prediction module, analyzing the signal through the fault prediction module and sending a generated checking scheme to an early warning execution module, adjusting the voltage of a power supply by the early warning execution module through the acquired checking scheme, checking whether the poor contact condition occurs at the connection part of a cable, and finally checking all parts of the equipment;
when the early warning bearing value is in a preset machine value range, generating a bearing signal and sending the bearing signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated bearing processing scheme to an early warning execution module, checking the abrasion degree of the bearing through the acquired bearing processing scheme by the early warning execution module, and then checking whether the position of the bearing is slightly deviated or not, and resetting the bearing if the position of the bearing is slightly deviated;
when the early warning annular value is within a preset machine value range, generating an annular signal and sending the annular signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated regulation scheme to an early warning execution module, wherein the early warning execution module regulates the temperature of a factory building through the acquired regulation scheme, particularly regulates the air-conditioning temperature of the factory building, and simultaneously cleans the factory building through maintenance personnel to reduce dust accumulation or open a dust collector in the factory building to clean dust generated by equipment operation;
normalizing the early warning use value T, the early warning state value E, the early warning operation value C, the early warning bearing value ED and the ring difference value and substituting the normalized values into a formulaObtaining an early warning fault value GK of the equipment, wherein I 1 、I 2 、I 3 、I 4 I 5 And the early warning fault value GK is compared with a preset machine value range, when the early warning fault value is in the preset machine value range, a fault early warning signal is generated and sent to a fault prediction module, and the fault prediction module receives the generated signal and analyzes the generated signal to generate a corresponding solution.
The specific process of selecting the fault analysis end by the fault prediction module is as follows:
transmitting a test data packet and a position feedback instruction to a fault analysis end to obtain analysis parameters and positions of the fault analysis end, marking the time for transmitting the test data packet as the transmission time, and marking the response time of the fault analysis endObtaining a diagnosis speed basic value P1 of a fault analysis end by a difference method between the feedback time and the sending time, wherein analysis parameters comprise a downloading rate of the fault analysis end and an uploading rate of an uploading test data packet, performing addition calculation on the downloading rate and the uploading rate of the fault analysis end to obtain an efficiency value, performing distance difference calculation on the position of the fault analysis end and the position corresponding to a fault prediction module to obtain a transmission distance P3 of the test data packet, normalizing the obtained diagnosis speed basic value P1, the efficiency value P2 and the transmission distance P3, and substituting the normalized values into a formulaAnd obtaining a solution optimal value P of the fault analysis end, selecting the fault analysis end with the maximum solution optimal value, and establishing communication connection with the fault prediction module.
Further, the cleaning mechanism comprises a driving motor 111, the driving motor 111 is arranged on the coal separator body 1, the driving motor 111 is in transmission connection with a transmission rod 112 through a plurality of first transmission wheels and a first transmission belt, a first reciprocating screw 113 is rotationally connected on the rear inner wall of the coal separator body 1, the transmission rod 112 and the front end of the first reciprocating screw 113 are fixedly connected with a second transmission wheel 114, the front side wall of the coal separator body 1 is in sliding connection with a sliding rod 116 through a sliding chute, the front end of the sliding rod 116 is rotationally connected with a third transmission wheel 117, a second transmission belt 115 is in transmission connection between the third transmission wheel 117 and the two groups of second transmission wheels 114, an electric push rod 118 is arranged on the front side wall of the coal separator body 1, the extending end of the electric push rod 118 is fixedly connected on the outer surface of the sliding rod 116, the outer surface of the first reciprocating screw 113 is in threaded connection with a cleaning brush 119, the coal preparation machine body 1 is provided with a screen 120, the top of a cleaning brush 119 is abutted against the bottom of the screen 120, a limit rod penetrates through the cleaning brush 119, a mounting frame 121 is arranged above the screen 120, the front side wall of the mounting frame 121 is rotationally connected with a rotating rod 122, the outer surface of a first reciprocating screw 113 and the front end of the rotating rod 122 are fixedly connected with fourth driving wheels 123, a fourth driving belt 124 is in transmission connection between the two groups of fourth driving wheels 123, the upper inner wall of the mounting frame 121 is rotationally connected with a second reciprocating screw 125, the rear end of the rotating rod 122 and the outer surface of the second reciprocating screw 125 are fixedly connected with gears 126, the two groups of gears 126 are respectively provided with mutually meshed conical gears, the outer surface of the second reciprocating screw 125 is in threaded connection with an adjusting plate 127, the adjusting plate 127 is in sliding connection on the inner side wall of the mounting frame 121 through the limit block, and the bottom of the adjustment plate 127 is provided with a cleaning insert that mates with the screen 120.
It should be noted that, first, the electric push rod 118 is started to drive the slide rod 116 to slide downward, so that the third driving wheel 117 moves downward to contact with the second driving belt 115 and make the second driving belt 115 in a tight state, and the driving motor 111 drives the driving rod 112 to rotate through a plurality of first driving wheels and first driving wheels. At this time, the two sets of second driving wheels 114 and the third driving wheels 117 are driven by the second driving belt 115, so that the driving rod 112 drives the first reciprocating screw 113 to rotate while rotating, the cleaning brush 119 starts to move on the first reciprocating screw 113 to clean the bottom of the screen, the first reciprocating screw 113 drives the rotating rod 122 to rotate by the two sets of fourth driving wheels 123 and the fourth driving belt 124, and the rotating rod 122 drives the second reciprocating screw 125 to rotate by the two sets of gears 126, so that the adjusting plate 127 moves downwards to clean the screen holes of the screen 120, and cleaning of the screen is completed.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The utility model provides a coal dressing electromechanical device fault prediction system based on data analysis, includes data acquisition module, data analysis module, fault prediction module and early warning execution module, its characterized in that:
the data acquisition module acquires operation information and environment information of the coal separator equipment in a working state;
the data analysis module analyzes and processes the operation information and the environment information, and the specific process is as follows:
processing the operation information and the environment information to obtain parameter values, wherein the parameter values comprise early warning use values, early warning state values, early warning operation values, early warning bearing values and ring abnormal values;
comparing the parameter value with a corresponding preset machine value range, and generating a corresponding signal and sending the signal to a fault prediction module when the parameter value belongs to the corresponding preset machine value range; wherein the signals include a usage signal, a status signal, a running signal, a bearing signal, and a ring signal;
the fault prediction module is used for receiving the generated signals, selecting a fault analysis end with the highest efficiency value for diagnosis, analyzing the signals to obtain a device data set, analyzing the device data set through scheme matching to obtain a diagnosis result and a prevention scheme of the device, and sending the obtained prevention scheme to the early warning execution module;
the early warning execution module is used for receiving the prevention scheme and executing corresponding operation; the method comprises the following steps:
when a maintenance scheme is received, the service time and the maintenance times of the equipment are obtained, and the equipment is maintained or overhauled and replaced; when a cleaning scheme is received, acquiring a coal ball state of a screen of the equipment, and controlling a cleaning mechanism to clean the screen; when an inspection scheme is received, acquiring a power state of equipment operation, and inspecting the power state of the equipment and a cable connection part; when the bearing treatment scheme is received, acquiring the temperature, sound and vibration times of the bearing during running, and replacing or adjusting and resetting the bearing; when receiving the regulation scheme, acquire dust concentration in the factory building, dust concentration around the equipment and factory building temperature, improve temperature and dust in the factory building through dust catcher and air conditioner control.
2. The coal preparation electromechanical equipment fault prediction system based on data analysis according to claim 1, wherein the specific analysis process of the data analysis module is as follows:
s1: the equipment using time, maintenance times and cleaning times are processed, and the method specifically comprises the following steps: marking the initial operation time of the equipment as the use time, calculating the use time length value of the equipment by using a difference method between the real time and the use time, and normalizing the use time length value, the maintenance times and the cleaning times to obtain an early warning use value;
s2: the method is characterized by comprising the following steps of treating the rolling speed of coal on a screen and the granularity of the coal: obtaining a movement track of the coal balls on the screen, processing the movement track to obtain the rolling speed of the coal balls on the screen, randomly obtaining a batch of speed values of the coal balls from the movement track, adding the obtained speed values and taking an average value to obtain a rolling speed value, obtaining the particle size of the coal balls, randomly screening a batch from the obtained speed values, adding the screened particle values of the coal balls and taking the average value to obtain a particle value, and normalizing the obtained rolling speed value and the particle value to obtain an early warning state value;
s3: the power supply voltage and the impact times of the equipment are processed, specifically: the method comprises the steps of drawing a numerical point corresponding to a line graph, connecting two adjacent sets of numerical points to obtain abnormal numerical lines, calculating the slope of each abnormal numerical line and the included angle between each abnormal numerical line and a horizontal line, marking the slope of each abnormal numerical line as a first slope when the included angle between each abnormal numerical line and the horizontal line is an acute angle, marking the abnormal numerical line as a second slope when the included angle is an obtuse angle, summing all the first slopes to obtain a first total value, summing all the second slopes to obtain a second total value, connecting the numerical point with the forefront numerical point and the numerical point with the last numerical point in the line graph to obtain a line segment, marking the line segment as a constant line, calculating the slope of the initial line and the included angle between the initial line and the horizontal line, marking the slope of the initial line as a third slope and representing the third slope by a symbol K1 when the included angle between the initial line and the horizontal line is an acute angle, marking the slope of the initial line as a fourth slope and representing the fourth slope by a symbol K2 when the included angle between the initial line and the horizontal line is an obtuse angle, calculating the vertical distance between the highest numerical point and the lowest numerical point to obtain a distance value, normalizing the first total value, the second total value and the distance value to obtain a voltage change value, and normalizing the voltage change value and the impact value to obtain an early warning running value;
s4: the sound change, temperature and vibration times of the equipment bearing are processed, and the method specifically comprises the following steps: obtaining sound shell values of different moments in a period of time when the bearing operates, removing the maximum value and the minimum value, adding the rest sound shell values and taking the average value to obtain an average sound shell value, simultaneously obtaining bearing temperature values of different moments in the period of time, adding the obtained bearing temperature values after removing abnormal values and taking the average value to obtain an average temperature value, and carrying out normalization processing on the obtained average sound shell value, the average temperature value and vibration times to obtain an early warning bearing value;
s5: the temperature and dust concentration around the equipment in operation are treated, specifically: the method comprises the steps of acquiring the temperature of the surrounding environment of equipment in real time, extracting temperature data acquired in a period of time, adding residual values after removing abnormal values, taking an average value to obtain an average ambient temperature value, simultaneously acquiring the dust concentration of the surrounding environment of the equipment and the dust concentration generated during the operation of the equipment, processing the dust concentration and the dust concentration to obtain dust values, and normalizing the obtained dust values and the average Zhou Wenzhi to obtain a ring abnormal value.
3. The system for predicting faults of coal dressing electromechanical equipment based on data analysis as claimed in claim 2, wherein the specific process of selecting a fault analysis end by the fault prediction module is as follows:
transmitting a test data packet and a position feedback instruction to a fault analysis end to obtain analysis parameters and positions of the fault analysis end, marking the time for transmitting the test data packet as transmission time, marking response time of the fault analysis end as feedback time, obtaining a diagnosis speed base value of the fault analysis end by a difference method between the feedback time and the transmission time, wherein the analysis parameters comprise a downloading rate of the fault analysis end and an uploading rate of the uploading test data packet, processing the downloading rate and the uploading rate of the fault analysis end to obtain an efficiency value, obtaining a transmission distance of the test data packet by performing distance difference calculation on the position of the fault analysis end and a position corresponding to a fault prediction module, normalizing the obtained diagnosis speed base value, the efficiency value and the transmission distance to obtain a diagnosis speed base value of the fault analysis end, selecting the fault analysis end with the maximum diagnosis speed base value, and establishing communication connection with the fault prediction module.
4. The system for predicting faults of coal dressing electromechanical equipment based on data analysis as claimed in claim 3, wherein the specific processing procedure of the early warning execution module is as follows:
s1: when the early warning use value is in a preset machine value range, generating a use signal and sending the use signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated maintenance scheme to an early warning execution module, wherein the early warning execution module carries out maintenance or overhaul replacement on equipment through the acquired maintenance scheme;
s2: when the early warning state value is in a preset machine value range, a state signal is generated and sent to a fault prediction module, the signal is analyzed through the fault prediction module, the generated cleaning scheme is sent to an early warning execution module, and the early warning execution module controls a cleaning mechanism to clean a screen of the equipment through the acquired cleaning scheme;
s3: when the early warning operation value is in a preset machine value range, generating an operation signal and sending the operation signal to a fault prediction module, analyzing the signal through the fault prediction module and sending a generated inspection scheme to an early warning execution module, wherein the early warning execution module inspects a power supply, a cable junction and equipment parts through the acquired inspection scheme;
s4: when the early warning bearing value is in a preset machine value range, generating a bearing signal and sending the bearing signal to a fault prediction module, analyzing the signal through the fault prediction module and sending the generated bearing processing scheme to an early warning execution module, and overhauling the abrasion degree and the position of the bearing through the acquired bearing processing scheme by the early warning execution module;
s5: when the early warning annular value is in a preset machine value range, an annular signal is generated and sent to a fault prediction module, the signal is analyzed through the fault prediction module, the generated adjustment scheme is sent to an early warning execution module, and the early warning execution module improves the environment in a factory building through the acquired adjustment scheme.
5. The system for predicting faults of coal dressing electromechanical equipment based on data analysis according to claim 4, wherein the fault values of the equipment are obtained by normalizing the early warning use value, the early warning state value, the early warning running value, the early warning bearing value and the ring difference value, the obtained fault values are compared with a preset machine value range, when the fault values are in the preset machine value range, a fault early warning signal is generated and sent to a fault prediction module, and the fault prediction module receives the generated signal and analyzes the generated signal to generate a corresponding solution.
6. The system for predicting the failure of the coal dressing machine according to claim 5, wherein the cleaning mechanism comprises a driving motor (111), the driving motor (111) is installed on the coal dressing machine body (1), the driving motor (111) is in transmission connection with a transmission rod (112) through a plurality of first transmission wheels and a first transmission belt, a first reciprocating screw (113) is rotatably connected to the rear inner wall of the coal dressing machine body (1), a second transmission wheel (114) is fixedly connected to the front ends of the transmission rod (112) and the first reciprocating screw (113), a sliding rod (116) is slidingly connected to the front side wall of the coal dressing machine body (1) through a sliding groove, a third transmission wheel (117) is rotatably connected to the front end of the sliding rod (116), a second transmission belt (115) is in transmission connection between the third transmission wheel (117) and the two groups of second transmission wheels (114), an electric push rod (118) is installed on the front side wall of the coal dressing machine body (1), an extension end of the electric push rod (118) is fixedly connected to the front side wall of the first reciprocating screw (113), a cleaning screen (120) is installed on the outer surface of the first reciprocating screw (119) and the screen (120) is in contact with the outer surface of the cleaning machine body (119), the utility model discloses a screen cloth, including screen cloth (120), mounting bracket (121), rotation is connected with dwang (122) on the preceding lateral wall of mounting bracket (121), the surface of first reciprocating screw (113) and the front end of dwang (122) are all fixedly connected with fourth drive wheel (123), and the transmission is connected with fourth drive belt (124) between two sets of fourth drive wheels (123), the last inner wall rotation of mounting bracket (121) is connected with second reciprocating screw (125), the equal fixedly connected with gear (126) of the rear end of dwang (122) and the surface of second reciprocating screw (125), and the equal fixedly connected with gear (126) of two sets of gears (126), and the surface threaded connection of second reciprocating screw (125) has regulating plate (127), and the bottom of regulating plate (127) is provided with the clearance inserted bar with screen cloth (120).
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| CN202310594111.9A CN116663722A (en) | 2023-05-25 | 2023-05-25 | Coal preparation electromechanical device fault prediction system based on data analysis |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116929459A (en) * | 2023-09-12 | 2023-10-24 | 常州满旺半导体科技有限公司 | Electronic equipment automatic test early warning system and method based on Internet of things |
| CN117077040A (en) * | 2023-09-04 | 2023-11-17 | 武汉蓝海科创技术有限公司 | Large-scale complex equipment fault diagnosis and prediction system based on machine learning |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117077040A (en) * | 2023-09-04 | 2023-11-17 | 武汉蓝海科创技术有限公司 | Large-scale complex equipment fault diagnosis and prediction system based on machine learning |
| CN117077040B (en) * | 2023-09-04 | 2024-02-23 | 武汉蓝海科创技术有限公司 | Large-scale complex equipment fault diagnosis and prediction system based on machine learning |
| CN116929459A (en) * | 2023-09-12 | 2023-10-24 | 常州满旺半导体科技有限公司 | Electronic equipment automatic test early warning system and method based on Internet of things |
| CN116929459B (en) * | 2023-09-12 | 2023-12-05 | 常州满旺半导体科技有限公司 | Electronic equipment automatic test early warning system and method based on Internet of things |
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