CN111609882A - Intelligent fault diagnosis system and method for large-sized combined dry separator - Google Patents
Intelligent fault diagnosis system and method for large-sized combined dry separator Download PDFInfo
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- CN111609882A CN111609882A CN202010412677.1A CN202010412677A CN111609882A CN 111609882 A CN111609882 A CN 111609882A CN 202010412677 A CN202010412677 A CN 202010412677A CN 111609882 A CN111609882 A CN 111609882A
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- G—PHYSICS
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- 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
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- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
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Abstract
The invention discloses a fault intelligent diagnosis system and method for a large-scale combined type dry separator, which relate to the field of ore dressing machinery, and comprise a detection module and an analysis and diagnosis module; the detection module is used for detecting the temperature of a bearing and a motor of the combined dry method separator, the amplitude and the frequency of the motor, the deformation of a beam and the noise decibel of the separator and transmitting detection data to the analysis and diagnosis module; the analysis and diagnosis module is used for analyzing the detection data, judging whether the composite dry-method separator has mechanical faults or not, storing fault waveforms, predicting and judging fault types, issuing a fault suggestion solution and sending alarm information. The intelligent fault diagnosis system and method for the combined dry-method separator can ensure the safe operation of equipment, prevent and reduce accidents, improve the production efficiency and reduce the economic loss of a coal preparation plant.
Description
Technical Field
The invention relates to the field of mineral processing machinery, in particular to a fault intelligent diagnosis system and method for a large-scale combined type dry separator.
Background
China has abundant coal resources, but due to the characteristic of rich coal and little water in western regions and the serious challenges of the huge consumption and yield of coal to the living environment and ecological safety of the nation, the quality of coal products needs to be improved and the pretreatment and quality improvement of the coal before combustion are needed. Coal dressing is the basis of clean utilization of coal, and application and popularization of an efficient dry coal dressing technology play an important role in deep processing of coal resources, so that a water-saving and environment-friendly coal dry sorting device needs to be popularized. The combined type dry method separator does not need water, has simple process, can be used for separating easily-argillized coal such as brown coal and the like, desulfurizing high-sulfur coal and pre-discharging gangue from coking coal, has larger volume and belongs to vibration equipment, is easy to cause structural fatigue of a mechanical structure, leads to the breakage of main parts such as beams and the like, easily leads to the large-area damage of the whole machine under the action of vibration if faults are not found and removed in time, and is a high-speed rotating part if a bearing, a motor and the like break down, and is easy to cause the larger-area damage if faults are not removed in time, thereby bringing loss to production. The state monitoring and fault diagnosis of the reinforced composite dry-method separator can ensure the safe operation of equipment, prevent and reduce accidents, improve the production efficiency and reduce the economic loss of a coal preparation plant. Therefore, the intelligent fault diagnosis method for the composite dry-method separator is the key for improving the separation efficiency of the composite dry-method separator.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a fault intelligent diagnosis system and method, aiming at the conditions that a large-sized composite separator is easy to generate structural fatigue and component faults, time and labor are consumed for shutdown and maintenance when faults occur, and fault sources cannot be determined.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: an intelligent fault diagnosis system and method for a large-scale combined type dry separator.
The system comprises a detection module and an analysis and diagnosis module; the detection module is used for detecting the temperature of a bearing and a motor of the combined dry method separator, the amplitude and frequency of the motor, the deformation of a beam and the noise decibel of the separator, and transmitting detection information to the analysis and diagnosis module; the analysis and diagnosis module is used for analyzing the detection information, judging whether the composite dry-method separator has mechanical faults or not, storing fault waveforms, predicting and judging fault types, giving a fault suggestion solution and sending alarm information.
The detection module comprises a temperature sensor, a strain sensor, a vibration sensor and a noise sensor. The temperature sensors are used for detecting the temperature conditions of the bearing and the vibration motor, are respectively integrated at the three-phase winding of the stator, the front shaft and the rear shaft in the motor, and are led out of sensor data ports through a connection box at the side part of the motor and used for monitoring the temperature of the motor and the bearing. The strain sensor is used for detecting the deformation condition of the separator beam, converting the strain change into the resistance change and installing the resistance change on the separator beam. The vibration sensor is used for detecting the vibration frequency and the vibration amplitude of the vibration motor, an electromechanical transducer which is made by utilizing the piezoelectric effect is arranged in the vibration sensor, the output end of the electromechanical transducer generates electric charge or voltage value which is in direct proportion to the borne acceleration, and the vibration sensor is respectively arranged on the driving end cover of the motor, the non-driving end cover and the vibration monitoring point in the middle of the machine side and used for monitoring the vibration condition of the motor. The noise sensor is used for detecting noise decibels sent by the sorting machine, converting acoustic signals into electric signals, and in order to weaken the influence of the vibration of the sorting machine on the noise sensor, the noise sensor is arranged on a fixed support at a certain distance from the sorting machine to detect the intensity of the noise signals.
The analysis and diagnosis module comprises a data acquisition unit, a data management server, a fault monitoring platform and an alarm. The data acquisition unit is used for converting analog quantity signals acquired by the temperature sensor, the strain sensor, the vibration sensor and the noise sensor into digital quantity signals and storing the digital quantity signals in the data management server. The data management server analyzes data through a fault intelligent diagnosis method, deeply excavates the abnormity in signals, sends monitoring waveforms and abnormal characteristic quantities to a fault monitoring platform, and simultaneously stores fault waveforms.
The fault monitoring platform is used for performing corresponding processing according to the monitoring waveform and the abnormal characteristic quantity, the fault monitoring platform sends the monitoring waveform and the abnormal characteristic quantity transmitted by the data management server to the cloud diagnosis subsystem, the cloud diagnosis subsystem is connected with the big database, and different fault types and fault suggestion solutions are stored in the big data. And processing the fault by searching the stored waveform, the abnormal characteristic quantity and the processing scheme in the large database, displaying the comparison result and judging the fault type. When the fault waveform can be retrieved from the big database, the cloud diagnosis subsystem can send the fault type and the processing scheme given in the big database back to the fault monitoring platform, and audible and visual alarm is carried out through the alarm. If the fault can not be searched in the large database, the fault is sent to an artificial expert, similar cases are searched, a fault suggestion solution method of the similar cases is sent to a fault monitoring platform to assist in solving the fault, sound and light alarm is carried out through an alarm, and the fault monitoring platform can transmit different information to the sound and light alarm according to the faults of different degrees.
The alarm is used for receiving signals from the fault monitoring platform and giving out alarms aiming at faults of different degrees, and the alarm is an audible and visual alarm. If no fault occurs, the alarm displays a green light. If the fault can be retrieved in the large database, only the temperature and the noise are abnormal, and no other fault occurs, the alarm only lights a yellow light; if the display beam and the bearing of the cloud diagnosis subsystem are broken and large-area damage is caused to the equipment, the alarm is lighted by a red light and gives an alarm sound. If the fault cannot be retrieved in the database, the alarm is lighted red and blinks.
The invention also provides an intelligent fault diagnosis method for the large-scale combined type dry separator, which comprises the following steps:
the method comprises the following steps: setting monitoring points, and acquiring the temperature of a bearing and a motor of the combined dry method separator, the amplitude and frequency of the motor, the deformation of a beam and the noise decibel data of the separator in real time;
step two: setting upper limit and lower limit thresholds of temperature, strain, vibration and noise and data abnormal tolerance time; carrying out time domain analysis on the acquired data, and extracting the characteristic quantities of temperature, strain, vibration and noise waveform at each moment;
step three: comparing the characteristic quantity with a threshold value one by one, and if the characteristic quantity exceeds an upper threshold value or is lower than a lower threshold value, judging that the numerical value of the characteristic quantity exceeds a limit and the data is abnormal; if the continuous existing time of the data abnormality exceeds the set data abnormality tolerance time, judging that the corresponding monitoring point has a fault;
step four: and sending the monitoring waveform and the abnormal characteristic quantity to a cloud end, searching a cloud end big database, comparing the cloud end big database with a fault waveform, preliminarily judging the fault type, and issuing a fault suggestion solution in the big database.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the invention can carry out real-time remote monitoring and advanced fault diagnosis on the abnormal condition of the composite dry-method separator and can find out the fault of the separator in time to provide a fault suggestion solution. And the corresponding treatment can be conveniently carried out by the staff in time. The defect that the traditional power failure maintenance is high in sensitivity, quick in response and good in reliability is overcome, major faults are avoided, and production loss is reduced.
Drawings
FIG. 1 is a structural diagram of a fault intelligent diagnosis system of a large-scale composite dry-method separator;
FIG. 2 is a flow chart of a fault intelligent diagnosis method for a large-scale composite dry separator.
Detailed Description
For a better understanding of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings and examples.
The invention relates to a fault intelligent diagnosis system of a large-scale combined type dry separator, which comprises a detection module and an analysis and diagnosis module; the detection module is used for detecting the temperature of a bearing and a motor of the combined dry method separator, the amplitude and frequency of the motor, the deformation of a beam and the noise decibel of the separator, and transmitting detection information to the analysis and diagnosis module; the analysis and diagnosis module is used for analyzing the detection information, judging whether the composite dry-method separator has mechanical faults or not, storing fault waveforms, predicting and judging fault types, giving a fault suggestion solution and sending alarm information.
As shown in fig. 1, the detection module includes a temperature sensor 101, a strain sensor 102, a vibration sensor 103, a noise sensor 104; the analysis and diagnosis module comprises a data collector 105, a data management server 106, a fault monitoring platform 107 and an alarm 108.
The temperature sensor 101 is used for detecting the temperature conditions of the vibration motor and the bearing, is respectively integrated at the three-phase winding of the stator, the front shaft and the rear shaft in the motor, and is led out of a sensor data port by a connection box at the side part of the motor and used for monitoring the temperature of the motor and the bearing.
The strain sensor 102 is used for detecting the strain condition of the separator beam, converting the bed layer strain change into resistance change and is arranged on the side beam and the bottom beam of the separator.
The vibration sensor 103 is used for detecting the frequency and amplitude of the vibration motor, an electromechanical transducer made by piezoelectric effect is arranged inside the vibration sensor, and the output end of the electromechanical transducer generates electric charge or voltage value which is in direct proportion to the borne acceleration. The vibration sensors are respectively arranged on the motor driving end cover, the non-driving end cover and the machine side middle vibration monitoring points and are used for monitoring the vibration condition of the motor.
The noise sensor 104 is used for detecting noise decibels emitted by the separator, converting an acoustic signal into an electric signal, and is installed on a fixed support at a certain distance from the separator to detect the intensity of the noise signal.
The data acquisition unit 105 converts analog quantity signals acquired by the temperature sensor 101, the strain sensor 102, the vibration sensor 103 and the noise sensor 104 into digital quantity signals and stores the digital quantity signals in the data management server 106, and the data management server 106 analyzes the digital quantity signals and sends the analyzed digital quantity signals to the fault monitoring platform 107.
The fault monitoring platform 107 performs corresponding processing in time according to the received signal. The fault monitoring platform 107 sends the monitoring waveform and the abnormal characteristic quantity transmitted by the data management server 106 to the cloud diagnosis subsystem, the cloud diagnosis subsystem is connected with the big database, different fault types and fault suggestion solutions are stored in the big data, faults are processed by retrieving the stored waveform, the abnormal characteristic quantity and the processing scheme in the big database, and the comparison result is displayed and the fault type is judged. When the fault monitoring platform 107 and the cloud diagnosis subsystem detect that a fault occurs, the alarm 108 gives an alarm, meanwhile, the cloud diagnosis subsystem gives a fault suggestion solution by searching the large database and sends the fault suggestion solution back to the fault monitoring platform 107, if fault information cannot be retrieved from the large database, similar cases are retrieved, and the similar cases and the fault suggestion solution are sent back to the fault monitoring platform.
The alarm 108 gives an alarm to the detection signal exceeding the threshold, and the alarm 108 is an audible and visual alarm. If no fault occurs, the alarm 108 displays a green light. If the fault can be retrieved in the big database, only temperature and noise are abnormal, and no other fault occurs, the alarm 108 only lights a yellow light, and sends a fault suggestion solution back to the fault monitoring platform; if the cloud diagnosis subsystem displays that the beam and the bearing are broken and large-area damage is caused to the equipment, the alarm 108 lights a red light and sends out an alarm sound, and after rapid auxiliary diagnosis is carried out through cloud diagnosis, a failure suggestion solution is sent back to the failure monitoring platform and is sent to the manual expert 109. If the fault cannot be retrieved from the database, the alarm 108 lights up a red light and flashes, meanwhile searches for a similar case, sends the similar case and a fault suggestion solution back to the fault monitoring platform, and sends the similar case and fault suggestion solution to the artificial expert 109 to assist in solving the fault.
The invention relates to a fault intelligent diagnosis method for a large-scale combined type dry separator, which has a flow shown in figure 2 and comprises the following steps:
s01, setting monitoring points, and collecting the temperature of a bearing and a motor of the composite dry-method separator, the amplitude and frequency of the motor, the deformation of a beam and the noise decibel data of the separator in real time;
s02, setting upper limit and lower limit thresholds of temperature, strain, vibration and noise and data abnormity tolerance time; carrying out time domain analysis on the acquired data, and extracting the characteristic quantities of temperature, strain, vibration and noise waveform at each moment;
s03, comparing the characteristic quantity with a threshold value one by one, if the characteristic quantity exceeds an upper threshold value or is lower than a lower threshold value, judging that the value of the characteristic quantity exceeds a limit, and judging that the data is abnormal; if the continuous existing time of the data abnormality exceeds the set data abnormality tolerance time, judging that the corresponding monitoring point has a fault;
and S04, sending the monitoring waveform and the abnormal characteristic quantity to the cloud, searching the cloud big database, comparing the cloud big database with the fault waveform, primarily judging the fault type, and issuing a fault suggestion solution in the big database.
The foregoing is a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (2)
1. The utility model provides a large-scale combined type dry process sorter trouble intelligent diagnosis system which characterized in that: comprises a detection module and an analysis and diagnosis module; the detection module comprises a temperature sensor, a strain sensor, a vibration sensor and a noise sensor; the analysis and diagnosis module comprises a data acquisition unit, a data management server, a fault monitoring platform and an alarm;
the temperature sensors are used for detecting the temperature of the bearing and the vibration motor, are respectively integrated at the three-phase winding of the stator, the front shaft and the rear shaft in the motor, and are led out of a sensor data port by a connection box at the side part of the motor; the strain sensor is used for detecting the deformation condition of the separator beam and is arranged on the separator beam; the vibration sensor is used for detecting the vibration frequency and the vibration amplitude of the vibration motor, is respectively arranged on the motor driving end cover, the non-driving end cover and the machine side middle vibration monitoring point and is used for monitoring the vibration condition of the motor; the noise sensor is used for detecting noise decibels emitted by the sorting machine and is arranged on a fixed support at a certain distance from the sorting machine;
the data acquisition unit is used for converting the analog quantity signal acquired by the detection module into a digital quantity signal and sending the digital quantity signal to the data management server for data analysis and storage; the data management server is used for analyzing the abnormity in the digital quantity signal transmitted by the data acquisition unit, sending the monitoring waveform and the abnormal characteristic quantity to the fault monitoring platform and storing the fault waveform;
the fault monitoring platform is used for comparing a real-time fault diagnosis result with a fault diagnosis result of the cloud diagnosis subsystem according to a monitoring waveform and abnormal characteristic quantity sent by the data management server and displaying a comparison result, pre-judging a fault type according to the comparison result, issuing a fault suggestion solution by combining the cloud diagnosis subsystem, sending the fault suggestion solution to an artificial expert and giving an alarm through an alarm; the alarm is used for giving an alarm to the detection signal exceeding the threshold value and giving different alarms aiming at faults of different degrees, and the alarm is an audible and visual alarm.
2. A fault intelligent diagnosis method for a large-scale combined type dry separator is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: setting monitoring points, and acquiring the temperature of a bearing and a motor of the combined dry method separator, the amplitude and frequency of the motor, the deformation of a beam and the noise decibel data of the separator in real time;
step two: setting upper limit and lower limit thresholds of temperature, strain, vibration and noise and data abnormal tolerance time; carrying out time domain analysis on the acquired data, and extracting the characteristic quantities of temperature, strain, vibration and noise waveform at each moment;
step three: comparing the characteristic quantity with a threshold value one by one, and if the characteristic quantity exceeds an upper threshold value or is lower than a lower threshold value, judging that the numerical value of the characteristic quantity exceeds a limit and the data is abnormal; if the continuous existing time of the data abnormality exceeds the set data abnormality tolerance time, judging that the corresponding monitoring point has a fault;
step four: and sending the monitoring waveform and the abnormal characteristic quantity to a cloud end, searching a cloud end big database, comparing the cloud end big database with a fault waveform, preliminarily judging the fault type, and issuing a fault suggestion solution in the big database.
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