CN110967182B - Cone crusher vibration data acquisition and preprocessing method - Google Patents

Cone crusher vibration data acquisition and preprocessing method Download PDF

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CN110967182B
CN110967182B CN201911104436.4A CN201911104436A CN110967182B CN 110967182 B CN110967182 B CN 110967182B CN 201911104436 A CN201911104436 A CN 201911104436A CN 110967182 B CN110967182 B CN 110967182B
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acceleration sensor
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CN110967182A (en
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马连成
张勇
段金利
董星宇
马振
王玉昆
孙福畅
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Ansteel Mining Co Ltd
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    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00Testing of machine parts
    • G01M13/02Gearings; Transmission mechanisms
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01HMEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00Testing of machine parts
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    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention belongs to the technical detection field, and particularly relates to a cone crusher vibration data acquisition and preprocessing method which is characterized in that a plurality of acceleration sensors are utilized to acquire vibration data of a cone crusher transmission shaft and a cone crusher base, double-channel vibration envelope acquisition cards are selected to be connected with double-sensor time-sharing synchronous sampling, the acquired data are subjected to sliding average processing with a priori term, and the data after primary sliding average processing are subjected to secondary sliding average processing to generate a priori term RnThe acceleration sensors which cannot work normally can be found by comparing the prior terms of the data, and abnormal data of the abnormal acceleration sensors are filtered out in advance. The invention effectively improves the utilization rate of information, optimizes and improves the quality and quantity of the acquired vibration signals, and provides a method for determining the abnormal working of the sensor.

Description

Cone crusher vibration data acquisition and preprocessing method
Technical Field
The invention belongs to the technical detection field, and particularly relates to a cone crusher vibration data acquisition and preprocessing method.
Background
The cone crusher is widely applied to a plurality of departments such as mines, smelting, building materials, roads, water conservancy and the like, and particularly plays a key role in the middle breaking and fine breaking of mine rocks. The cone crusher has high failure rate, and the failure diagnosis and early warning of the cone crusher are key problems to be solved urgently in mine equipment management. In the fault diagnosis of the cone crusher, the data acquisition and preprocessing determine the direction and success or failure of the whole fault diagnosis.
In the traditional data acquisition method, various data acquisition is mostly carried out in a mode that a single acquisition card is connected with a single sensor, and the data is sent to a data processing link after five-point three-time smoothing processing. Although the method can also achieve the purpose of data acquisition, the influence on the whole fault diagnosis system caused by the failure of the sensor is not considered; although the method has a good smoothing effect, the calculation amount is large, and the frequency of data acquisition is limited. The dependency of the whole system on the data processing algorithm is too strong. The moving average algorithm is small in calculation amount and has better smoothing effect on the vibration signal, but is greatly influenced by the transient interference pulse, so that the application of the moving average algorithm is limited. Double sensor time division synchronous sampling, while capable of determining transient interference pulses, does not provide an effective filtering method.
Disclosure of Invention
The invention aims to provide a method for acquiring and preprocessing vibration data of a cone crusher, which can effectively improve the utilization rate of the data by using acquired signals as much as possible, preprocess the acquired data in real time in a data acquisition stage by using a data fusion technology, provide high-quality data for fault diagnosis, reduce the algorithm complexity of the fault diagnosis and provide convenience for troubleshooting of the self fault of a sensor.
The purpose of the invention is realized by the following technical scheme:
the invention discloses a method for acquiring and preprocessing vibration data of a cone crusher, which is characterized in that a plurality of acceleration sensors are utilized to acquire the vibration data of a transmission shaft and a base of the cone crusher, a plurality of CPUs (central processing units) of a vibration envelope acquisition card are programmed to perform sliding average processing with a priori term on the acquired data, and the data after primary sliding average processing are subjected to secondary sliding average processing to generate a priori term RnWherein n is the serial number of the acceleration sensor, the acceleration sensor which can not work normally can be found by comparing the prior terms of the data, and the abnormal number of the abnormal acceleration sensor is filtered out in advanceAccording to the method, the following steps are included:
1) distribution and connection of acceleration sensors
According to the structure of the cone crusher, acceleration sensors are symmetrically arranged on the support of a transmission shaft of the cone crusher and the periphery of a base respectively to measure vibration signals so as to achieve the purpose of data acquisition, and the acceleration sensors are connected to a vibration envelope acquisition card;
2) vibration envelope acquisition card controls double sensors to perform time-sharing synchronous sampling
Two acceleration sensors are connected to a double-channel vibration envelope acquisition card and are uniformly controlled by the vibration envelope acquisition card, and the trigger pulse interval is 0.30 ms; the synchronous trigger pulse is used for controlling the gating sampling of the acceleration sensor, and the time-sharing sampling mode improves the frequency of the integral sampling; normal vibration signals acquired by the 1# acceleration sensor and the 2# acceleration sensor are directly added in the same time domain to obtain a sampling signal with higher frequency;
Figure BDA0002270851780000031
wherein f is1、f2Signals collected by 1# acceleration sensor and 2# acceleration sensor, F1Fitting signals of a 1# acceleration sensor and a 2# acceleration sensor; when one spike pulse is collected by one acceleration sensor, a priori term is used for replacing the current sampling, when another acceleration sensor which is synchronously sampled in a time-sharing mode also collects a spike pulse signal, the signal is possibly a fault signal and needs to be reserved, otherwise, the transient pulse signal is discarded;
3) carrying out prior moving average processing on data acquired by acceleration sensor
The acceleration signal in the continuous time domain is changed into an amplitude signal after two times of integral transformation, and actually, the sampling signal is a discrete signal and is calculated by the following formula,
Vx=Vx-1+ax×T (2)
when x is less than or equal to 1, V x-10 wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, axThe acceleration of the current sampling point is shown, T is the sampling period, and x is the number of the current sampling points; further, the vibration signal amplitude may be calculated,
Figure BDA0002270851780000032
wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, fxThe vibration amplitude of the current sampling point is shown, T is the sampling period, and x is the number of the current sampling points; the collected signals are preprocessed in real time to provide better data for subsequent fault diagnosis work;
the amplitude signal collected by the acceleration sensor is F, the amplitude signal F is processed into a relatively smooth signal F by the sliding average,
Figure BDA0002270851780000041
when x is<1 time, f x0, where, Fi and fxThe method comprises the steps of obtaining discrete amplitude signals after moving average processing and before processing, wherein x is the position of a current signal to be processed, and i is the position of the current signal after processing; in order to overcome the interference of random pulse signals, a method of adding a priori term is adopted to filter the random pulse interference; firstly, a prior term R is assumed, the prior term R is not manually set, and the prior term R is automatically generated by a vibration envelope acquisition card according to acquired data, wherein
Figure BDA0002270851780000042
i is the number of current sampling points, x is the current position of the signal to be processed, and delta is the critical value of the acceptable fluctuation range set artificially, and actually obtained
δ=Fmax-Fmin (6)
FmaxFor the last five FxMaximum value in the values, FminFor the last five FxMinimum value in the values; the value is stored in a vibration envelope acquisition card and is automatically updated; due to the adoption of double-sensor time-sharing control, instantaneous spike pulses are filtered, and continuous spike signals need to be reserved as important bases for subsequent fault diagnosis;
4) finding out and filtering signals acquired by abnormal acceleration sensor, and improving data validity
The prior terms of the signals of the acceleration sensors of all parts are compared with each other respectively, so that abnormal signals which deviate from the average prior terms of all parts and are larger can be found, the acceleration sensor which sends the abnormal signals can be found, the acceleration sensor can be replaced and repaired, and the reliability of the acquired data is guaranteed;
Figure BDA0002270851780000043
wherein,
Figure BDA0002270851780000044
is the average of the prior terms of the respective signals, RnThe prior terms of the signals are n, the serial numbers of the sensors of the measuring part are n, and m is the total number of the acceleration sensors of the measuring part; carrying out corresponding prior item comparison on the acquired signals, and finding out abnormal signals; for the acceleration sensor which can not work normally, the acceleration sensor can be detached for maintenance or replacement after the machine is stopped.
The acceleration sensors comprise 6 acceleration sensors for acquiring vibration data of a transmission shaft and a base of the cone crusher, the vibration envelope acquisition card comprises 3 vibration envelope acquisition cards, the 3 vibration envelope acquisition cards are connected with the 6 acceleration sensors and receive signals acquired by the acceleration sensors, and the 3 vibration envelope acquisition cards are connected with 1 data acquisition device.
The invention has the advantages that:
the cone crusher vibration data acquisition and preprocessing method effectively improves the utilization rate of information, optimizes and improves the quality and quantity of acquired vibration signals, provides a method for determining whether a sensor works abnormally and determining an abnormal sensor, and provides a more powerful data basis for subsequent work such as fault diagnosis.
Drawings
FIG. 1 is a block diagram of the present invention.
Fig. 2 is a schematic view showing the installation position of the acceleration sensor of the present invention on a cone crusher.
Fig. 3 is a block diagram of a connection structure of the vibration collecting device of the present invention.
Fig. 4 is a schematic diagram of the dual sensor time-sharing synchronous sampling of the present invention.
FIG. 5 is a schematic diagram of a dual sensor vibration signal fitting of the present invention.
FIG. 6 is a flow chart of amplitude signal preprocessing according to the present invention.
FIG. 7 is a schematic diagram of a sensor fault location method of the present invention.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
As shown in fig. 1-7, the method for acquiring and preprocessing vibration data of a cone crusher according to the present invention is characterized in that a plurality of acceleration sensors are used to acquire vibration data of a cone crusher transmission shaft 8 and a cone crusher base 9, a dual-channel vibration envelope acquisition card is selected to perform time-sharing synchronous sampling, the acquired data is subjected to sliding average processing with a priori term, the data after the primary sliding average processing is subjected to secondary sliding average processing to generate the priori term, the priori terms of the data are compared to find the acceleration sensor which cannot normally operate, and abnormal data of the abnormal acceleration sensor are filtered in advance, and the method comprises the following steps:
1) distribution and connection of acceleration sensors
According to the structure of the cone crusher, acceleration sensors are symmetrically arranged on the support 7 of a transmission shaft 8 of the cone crusher and the periphery of a base respectively to measure vibration signals so as to achieve the purpose of effective data acquisition, and the acceleration sensors are connected to a vibration envelope acquisition card;
2) vibration envelope acquisition card controls double sensors to perform time-sharing synchronous sampling
The biggest problem of the moving average algorithm is that the influence of instantaneous interference pulse cannot be eliminated, and the invention adopts a double-sensor time-sharing synchronous sampling method, thereby not only ensuring the effectiveness of the algorithm, but also eliminating the influence of the instantaneous interference pulse and providing reference for subsequent processing; according to the condition that the swing frequency of a moving cone of the cone crusher is 100-400r/min, the main shaft vibrates for about 100 times in each rotation, the running time of a tested preprocessing program is 0.00004s, the sampling frequency is 1.5KHZ, and the sampling period is 0.60 ms.
Connecting two acceleration sensors to a vibration envelope acquisition card, uniformly controlling the two acceleration sensors by a CPU of the vibration envelope acquisition card, and triggering pulse intervals of 0.30 ms; taking the 1# acceleration sensor 1 and the 2# acceleration sensor 2 as examples, the synchronous trigger pulse is used for controlling the gating sampling of the acceleration sensors, and the time-sharing sampling mode improves the frequency of the whole sampling; normal vibration signals collected by the 1# acceleration sensor 1 and the 2# acceleration sensor 2 are directly added in the same time domain to obtain a sampling signal with higher frequency;
Figure BDA0002270851780000071
wherein f is1、f2Signals collected by the 1# acceleration sensor 1 and the 2# acceleration sensor 2, F1Fitting signals of a 1# acceleration sensor 1 and a 2# acceleration sensor 2; for removing transient interference pulses, the most important is to judge whether the pulse signal is transient or not; when one acceleration sensor collects a spike pulse, a priori term is used for replacing the sampling, and when another acceleration sensor which is subjected to time-sharing synchronous sampling also collects the spike pulseCollecting the peak pulse signal, if the peak pulse signal is collected, said signal is probably fault sign signal, retaining it, otherwise discarding said transient pulse signal;
3) carrying out prior moving average processing on data acquired by acceleration sensor
The acceleration signal in the continuous time domain is changed into an amplitude signal after two times of integral transformation, and actually, the sampling signal is a discrete signal and is calculated by the following formula,
Vx=Vx-1+ax×T (2)
when x is less than or equal to 1, V x-10 wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, axThe acceleration of the current sampling point is shown, T is the sampling period, and x is the number of the current sampling points; further, the vibration signal amplitude may be calculated,
Figure BDA0002270851780000072
wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, fxThe vibration amplitude of the current sampling point is shown, T is the sampling period, and x is the number of the current sampling points; the collected signals are preprocessed in real time to provide better data for subsequent fault diagnosis work;
the amplitude signal collected by the acceleration sensor is F, the amplitude signal F is processed into a relatively smooth signal F by the sliding average,
Figure BDA0002270851780000081
when x is<1 time, f x0, wherein FiAnd fxThe method comprises the steps of obtaining discrete amplitude signals after moving average processing and before processing, wherein x is the position of a current signal to be processed, and i is the position of the current signal after processing; in order to overcome the interference of random pulse signals, a method of adding a priori term is adoptedFiltering random impulse interference; firstly, a prior term R is assumed, the prior term R is not manually set, and the prior term R is automatically generated by a vibration envelope acquisition card according to acquired data, wherein
Figure BDA0002270851780000082
i is the number of current sampling points, x is the current position of the signal to be processed, and delta is the critical value of the acceptable fluctuation range set artificially, and actually obtained
δ=Fmax-Fmin (6)
FmaxFor the last five FxMaximum value in the values, FminFor the last five FxMinimum value in the values; the value is stored in a vibration envelope acquisition card and is automatically updated; due to the adoption of double-sensor time-sharing control, instantaneous spike pulses are filtered, and continuous spike signals need to be reserved as important bases for subsequent fault diagnosis;
4) finding out and filtering signals acquired by abnormal acceleration sensor, and improving data validity
The prior terms of the acceleration sensor signals of all parts are compared with each other respectively, so that abnormal signals which deviate from the average prior terms of all parts and are larger can be found, the acceleration sensor which sends the abnormal signals can be found, the acceleration sensor can be replaced and repaired, and the reliability of the acquired data is guaranteed;
Figure BDA0002270851780000091
wherein,
Figure BDA0002270851780000092
is the average of the prior terms of the respective signals, RnThe prior terms of the signals are n, the number of each acceleration sensor of the measurement part is n, and m is the total number of the acceleration sensors of the measurement part; the acquired signals are compared with corresponding prior terms, and then the signals can be foundOutputting an abnormal signal; for the acceleration sensor which can not work normally, the acceleration sensor can be detached for maintenance or replacement after the machine is stopped.
The acceleration sensors comprise 6 acceleration sensors for acquiring vibration data of the periphery of a support 7 of a transmission shaft 8 of the cone crusher and the periphery of a base 9 of the cone crusher, the vibration envelope acquisition card comprises 5 vibration envelope acquisition cards, the 3 vibration envelope acquisition cards are connected with the 6 acceleration sensors and receive signals acquired by the acceleration sensors, the 3 vibration envelope acquisition cards are connected with 1 data acquisition device, and the data acquisition devices send acquired data to other links of fault diagnosis to continue analysis and processing.
The invention is further explained below with reference to the drawings and the specific examples of the description.
1) Distribution and connection of acceleration sensors
As shown in fig. 2, the 1# acceleration sensor 1 and the 2# acceleration sensor 2 are symmetrically arranged on the transmission shaft bracket 7 of the transmission shaft 8 of the cone crusher, and the difference between the two is 180 degrees; 3 # acceleration sensor 3, 4 # acceleration sensor 4, 5 # acceleration sensor 5, 6# acceleration sensor 6 are installed on the cone crusher base 9 evenly distributed, and each difference is 90 degrees. The 6 acceleration sensors are all connected to a vibration envelope acquisition card and are connected as shown in figure 3, a 1# acceleration sensor 1 and a 2# acceleration sensor 2 are connected to a vibration envelope acquisition card I, a 3# acceleration sensor 3 and a 4# acceleration sensor 4 are connected to a vibration envelope acquisition card II, and a 5# acceleration sensor 5 and a 6# acceleration sensor 6 are connected to a vibration envelope acquisition card III; all 3 vibration envelope acquisition cards are connected into a data acquisition unit.
2) Vibration envelope acquisition card controls double sensors to perform time-sharing synchronous sampling
According to the condition that the swing frequency of a moving cone of the cone crusher is 100-400r/min, the main shaft vibrates for about 100 times in each rotation, the running time of a tested preprocessing program is 0.00004s, the sampling frequency is 1.5KHZ, and the sampling period is 0.60 ms. Two acceleration sensors are connected to a vibration envelope acquisition card and are uniformly controlled by the vibration envelope acquisition card, and the trigger pulse interval is 0.30 ms. And programming a CPU (central processing unit) in the vibration envelope acquisition card to enable the vibration envelope acquisition card to have a time-sharing sampling function, and setting a time interval to be a half period for time-sharing sampling of the double sensors. The schematic diagram of the dual-sensor time-sharing synchronous sampling is shown in fig. 4, in the diagram, the time interval of the trigger pulse signals of the two acceleration sensors is half of a cycle, and the two acceleration sensors are alternately triggered in actual work and controlled by one trigger pulse signal. It can be seen from the schematic diagram of fig. 5 showing the fitting of the dual-sensor vibration signals that the normal signals synchronously acquired by the two acceleration sensors in a time-sharing manner are not overlapped, and the fitted signals are also more referential.
3) Carrying out prior moving average processing on data acquired by acceleration sensor
Calculated using the following formula, Vx=Vx-1+axX T, when x is less than or equal to 1, V x-10 wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, axThe acceleration of the current sampling point is shown, T is the sampling period, and x is the number of the current sampling points. Further, the vibration signal amplitude may be calculated,
Figure BDA0002270851780000101
wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, fxThe vibration amplitude of the current sampling point is T, the sampling period is T, and x is the number of the current sampling points. The collected signals are preprocessed in real time, so that data with higher quality can be provided for subsequent fault diagnosis work.
Assuming that the amplitude signal collected by the acceleration sensor is F, the moving average processing is performed on F to obtain a relatively smooth signal F,
Figure BDA0002270851780000102
when x is<1 time, f x0, wherein FiAnd fxIn order to perform discrete amplitude signals after the moving average processing and before the processing, x is the current signal position to be processed, and i is the current signal position after the processing. The method has good smoothing effect on the vibration signal, but cannot avoid random pulse signalsThe invention adopts a method of adding a priori term to filter random impulse interference in order to overcome the problem. A prior term R is assumed, and the prior term R is automatically generated by a vibration envelope acquisition card according to acquired data without adopting a method set by people. Wherein
Figure BDA0002270851780000111
i is the number of current sampling points, x is the current position of the signal to be processed, delta is a critical value of an acceptable fluctuation range set artificially, and delta is actually taken to be Fmax-Fmin. When x is<1 hour, F x0. This value is stored in the vibration envelope acquisition card and automatically updated. Due to the adoption of the dual-sensor time-sharing control, transient spike pulses are filtered, and continuous spike signals need to be reserved as important bases for subsequent fault diagnosis. The vibration signal preprocessing flow chart is shown in fig. 6, where F is an amplitude signal acquired by the acceleration sensor, F is the amplitude signal after preprocessing, δ is a set error threshold, and R is a prior term automatically generated by a two-stage moving average algorithm.
4) Finding out and filtering signals acquired by abnormal acceleration sensor, and improving data validity
The prior terms of the signals of the acceleration sensors of all parts are compared with each other, so that abnormal signals which deviate from the average prior terms of all parts and are larger can be found, the acceleration sensors which send the abnormal signals can be found, the acceleration sensors are replaced and repaired, and the reliability of data acquisition is guaranteed.
Figure BDA0002270851780000112
Wherein,
Figure BDA0002270851780000113
is the average of the prior terms of the respective signals, RnN is the number of each acceleration sensor of the measuring part, and m is the total number of the acceleration sensors of the measuring part. The schematic diagram of the fault location method of the acceleration sensor is shown in fig. 7, and the acquired signals are correspondingly processed firstlyAnd (5) comparing the test items to find out abnormal signals. Taking acceleration sensors distributed on a conical crusher base 9 as an example, assuming that a 4# acceleration sensor 4 has a fault and acquires an abnormal signal, comparing prior terms of 4 acceleration sensors acquiring signals on the conical crusher base 9 with an average prior term, and if the difference of the 4# acceleration sensor data compared with the average prior term is greater than Q, indicating that the signal acquired by the 4# acceleration sensor is abnormal. Q is as default
Figure BDA0002270851780000121
Figure BDA0002270851780000121
2 acceleration sensors installed on a transmission shaft 8 of the cone crusher directly carry out prior item comparison of 4 groups of signals when judging the faults of the acceleration sensors. For the acceleration sensor which can not work normally, the acceleration sensor can be detached for maintenance or replacement after the machine is stopped.
The cone crusher vibration data acquisition and preprocessing method effectively improves the utilization rate of information, optimizes and improves the quality and quantity of acquired vibration signals, provides a method for determining whether a sensor works abnormally and determining an abnormal sensor, and provides a more powerful data basis for subsequent work such as fault diagnosis.

Claims (2)

1. A vibration data acquisition and preprocessing method for a cone crusher is characterized in that a plurality of acceleration sensors are used for acquiring vibration data of a transmission shaft and a base of the cone crusher, a plurality of CPUs (central processing units) of a vibration envelope acquisition card are programmed, the acquired data are subjected to sliding average processing with a priori term, and the data subjected to the primary sliding average processing are subjected to secondary sliding average processing to generate a priori term RnWherein n is the serial number of the acceleration sensor, the acceleration sensor which can not work normally can be found by comparing the prior terms of the data, the abnormal data of the abnormal acceleration sensor is filtered out in advance, and the method comprises the following steps:
1) distribution and connection of acceleration sensors
According to the structure of the cone crusher, acceleration sensors are symmetrically arranged on the support of a transmission shaft of the cone crusher and the periphery of a base respectively to measure vibration signals so as to achieve the purpose of data acquisition, and the acceleration sensors are connected to a vibration envelope acquisition card;
2) vibration envelope acquisition card controls double sensors to perform time-sharing synchronous sampling
Two acceleration sensors are connected to a double-channel vibration envelope acquisition card and are uniformly controlled by the vibration envelope acquisition card, and the trigger pulse interval is 0.30 ms; the synchronous trigger pulse is used for controlling the gating sampling of the acceleration sensor, and the time-sharing sampling mode improves the frequency of the integral sampling; normal vibration signals acquired by the 1# acceleration sensor and the 2# acceleration sensor are directly added in the same time domain to obtain a sampling signal with higher frequency;
Figure FDA0003043092420000011
wherein f is1、f2Signals collected by 1# acceleration sensor and 2# acceleration sensor, F1Fitting signals of a 1# acceleration sensor and a 2# acceleration sensor; when one spike pulse is collected by one acceleration sensor, a priori term is used for replacing the current sampling, when another acceleration sensor which is synchronously sampled in a time-sharing mode also collects a spike pulse signal, the signal is possibly a fault signal and needs to be reserved, otherwise, the spike pulse signal is discarded;
3) carrying out prior moving average processing on data acquired by acceleration sensor
The acceleration signal in the continuous time domain is changed into an amplitude signal after two times of integral transformation, and actually, the sampling signal is a discrete signal and is calculated by the following formula,
Vx=Vx-1+ax×T (2)
when x is less than or equal to 1, Vx-10 wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, axIs the acceleration of the current sampling point, T is the samplingThe period x is the position of the current signal to be processed; further, the vibration signal amplitude may be calculated,
Figure FDA0003043092420000021
wherein, VxIs the velocity value of the current sampling point, Vx-1Is the velocity of the last sample point, fxThe vibration amplitude of the current sampling point is shown, T is the sampling period, and x is the current position of the signal to be processed; the collected signals are preprocessed in real time to provide better data for subsequent fault diagnosis work;
the amplitude signal collected by the acceleration sensor is F, the amplitude signal F is processed into a relatively smooth signal F by the sliding average,
Figure FDA0003043092420000022
when x is<1 time, fxWhere Fi is a discrete amplitude signal after the moving average processing, x is the current signal position to be processed, and fxThe vibration amplitude of the current sampling point is, and i is the number of the current sampling points; in order to overcome the interference of random pulse signals, a method of adding a priori term is adopted to filter the random pulse interference; firstly, a prior term R is assumed, the prior term R is not manually set, and the prior term R is automatically generated by a vibration envelope acquisition card according to acquired data, wherein
Figure FDA0003043092420000031
i is the number of current sampling points, x is the current position of the signal to be processed, and delta is the critical value of the acceptable fluctuation range set artificially, and actually obtained
δ=Fmax-Fmin (6)
FmaxFor the last five FxMaximum value in the values, FminFor the last five FxMinimum value in the values; the value is stored in a vibration envelope acquisition card and is automatically updated; due to the adoption of double-sensor time-sharing control, instantaneous spike pulses are filtered, and continuous spike signals need to be reserved as important bases for subsequent fault diagnosis;
4) finding out and filtering signals acquired by abnormal acceleration sensor, and improving data validity
The prior terms of the signals of the acceleration sensors of all parts are compared with each other respectively, so that abnormal signals which deviate from the average prior terms of all parts and are larger can be found, the acceleration sensor which sends the abnormal signals can be found, the acceleration sensor can be replaced and repaired, and the reliability of the acquired data is guaranteed;
Figure FDA0003043092420000032
wherein,
Figure FDA0003043092420000033
is the average of the prior terms of the respective signals, RnThe prior terms of the signals are n, the serial numbers of the sensors of the measuring part are n, and m is the total number of the acceleration sensors of the measuring part; carrying out corresponding prior item comparison on the acquired signals, and finding out abnormal signals; for the acceleration sensor which can not work normally, the acceleration sensor can be detached for maintenance or replacement after the machine is stopped.
2. The method as claimed in claim 1, wherein the plurality of acceleration sensors comprises 6 acceleration sensors for collecting vibration data of the transmission shaft and the base of the cone crusher, the vibration envelope acquisition card comprises 3 vibration envelope acquisition cards, the 3 vibration envelope acquisition cards are connected to the 6 acceleration sensors and receive signals collected by the acceleration sensors, and the 3 vibration envelope acquisition cards are connected to 1 data collector.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103909004A (en) * 2014-03-21 2014-07-09 鞍钢集团矿业公司 Intelligent protection device for iron passing prevention of cone crusher and protection method thereof
CN104121985A (en) * 2013-04-29 2014-10-29 艾默生电气(美国)控股公司(智利)有限公司 Selective decimation and analysis of oversampled data
CN107478423A (en) * 2017-08-16 2017-12-15 枣庄市瑞隆机械制造有限公司 A kind of detecting system for disintegrating machine vibratory sieve
CN107583722A (en) * 2017-09-22 2018-01-16 徐工集团工程机械有限公司 For detecting the method and disintegrating machine of crusher axis state
JP2018081012A (en) * 2016-11-17 2018-05-24 宇部興産機械株式会社 Abnormality detection device and method of bearing of crushing roller
CN108187896A (en) * 2017-12-28 2018-06-22 四川皇龙智能破碎技术股份有限公司 A kind of crusher remote data acquisition system
CN109562765A (en) * 2016-06-24 2019-04-02 久益环球地表采矿公司 Operation vibrating data collection system and method for Mars Miner

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104121985A (en) * 2013-04-29 2014-10-29 艾默生电气(美国)控股公司(智利)有限公司 Selective decimation and analysis of oversampled data
CN103909004A (en) * 2014-03-21 2014-07-09 鞍钢集团矿业公司 Intelligent protection device for iron passing prevention of cone crusher and protection method thereof
CN109562765A (en) * 2016-06-24 2019-04-02 久益环球地表采矿公司 Operation vibrating data collection system and method for Mars Miner
JP2018081012A (en) * 2016-11-17 2018-05-24 宇部興産機械株式会社 Abnormality detection device and method of bearing of crushing roller
CN107478423A (en) * 2017-08-16 2017-12-15 枣庄市瑞隆机械制造有限公司 A kind of detecting system for disintegrating machine vibratory sieve
CN107583722A (en) * 2017-09-22 2018-01-16 徐工集团工程机械有限公司 For detecting the method and disintegrating machine of crusher axis state
CN108187896A (en) * 2017-12-28 2018-06-22 四川皇龙智能破碎技术股份有限公司 A kind of crusher remote data acquisition system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于虚拟仪器的圆锥破碎机状态监控系统;肖成勇 等;《矿山机械》;20060131;第34卷(第1期);第65-66页 *

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