CN114941796A - Intelligent lubrication equipment fault monitoring method based on industrial big data - Google Patents
Intelligent lubrication equipment fault monitoring method based on industrial big data Download PDFInfo
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- CN114941796A CN114941796A CN202210881047.8A CN202210881047A CN114941796A CN 114941796 A CN114941796 A CN 114941796A CN 202210881047 A CN202210881047 A CN 202210881047A CN 114941796 A CN114941796 A CN 114941796A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N29/00—Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems
- F16N29/04—Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems enabling a warning to be given; enabling moving parts to be stopped
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N39/00—Arrangements for conditioning of lubricants in the lubricating system
- F16N39/06—Arrangements for conditioning of lubricants in the lubricating system by filtration
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N7/00—Arrangements for supplying oil or unspecified lubricant from a stationary reservoir or the equivalent in or on the machine or member to be lubricated
- F16N7/38—Arrangements for supplying oil or unspecified lubricant from a stationary reservoir or the equivalent in or on the machine or member to be lubricated with a separate pump; Central lubrication systems
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2260/00—Fail safe
- F16N2260/02—Indicating
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2260/00—Fail safe
- F16N2260/02—Indicating
- F16N2260/04—Oil level
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2260/00—Fail safe
- F16N2260/02—Indicating
- F16N2260/06—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2260/00—Fail safe
- F16N2260/02—Indicating
- F16N2260/08—Pressure
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to the technical field of fault detection, in particular to an intelligent lubricating equipment fault monitoring method based on industrial big data, which comprises the following steps: acquiring filter data, oil pump data and oil data of lubricating equipment to be detected in real time; acquiring the deposition degree of the filter through filter data, and acquiring the real-time quality index of the oil according to the temperature, the deposition degree and the cleanliness of the oil; acquiring the working state of the lubricating equipment through the change of the oil height, the quality index, the pressure data and the oil pump temperature in the preset time; the method comprises the steps of obtaining standard data and standard working states of standard equipment of the same model in a database within preset time, obtaining working differences between lubricating equipment to be detected and the standard equipment, and performing fault early warning when the working differences are larger than a difference threshold value. The embodiment of the invention can be applied to the intelligent manufacturing equipment industry, and can be used for monitoring the faults of the lubricating equipment in real time and early warning the faults in time.
Description
Technical Field
The invention relates to the technical field of fault detection, in particular to an intelligent lubricating equipment fault monitoring method based on industrial big data.
Background
Mechanical equipment needs to be lubricated regularly, and the main mode of the conventional lubrication is to perform manual lubrication after a certain maintenance period is reached according to the working condition of the equipment, for example, a grease gun is used as a manual lubrication device to lubricate the equipment. With the development of the intelligent manufacturing equipment industry, automatic lubricating equipment is more and more, and nowadays, lubricating equipment has two categories of manual lubrication and automatic lubrication according to working modes.
The hydraulic pump delivers hydraulic oil to the hydraulic cylinder by pressurizing it, and the hydraulic cylinder converts hydraulic energy into mechanical energy by reciprocating or oscillating motion. Due to the particularity of the hydraulic cylinder, lubricating oil needs to be added to the hydraulic cylinder through a lubricating device to reduce friction loss. If the lubricating equipment breaks down, can not lubricate the pneumatic cylinder in time, reduce the life of pneumatic cylinder, can make pneumatic cylinder pressure receive the influence even, the great harm appears. It is therefore necessary to detect a malfunction of the lubricating apparatus in a timely manner.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent lubricating equipment fault monitoring method based on industrial big data, and the adopted technical scheme is as follows:
one embodiment of the invention provides an intelligent lubrication equipment fault monitoring method based on industrial big data, which comprises the following steps:
acquiring filter data, oil pump data and oil data of lubricating equipment to be detected in real time; the oil pump data comprises oil pump temperature, oil pump torque and pressure data after the oil pump is pressurized, and the oil data comprises oil temperature, oil height and oil cleanliness;
acquiring the deposition degree of the filter according to the filter data, and acquiring the real-time quality index of the oil according to the oil temperature, the deposition degree and the cleanliness;
acquiring the working state of the lubricating equipment through the change of the oil height, the quality index, the pressure data and the oil pump temperature in the preset time; the quality index and the pressure data are in positive correlation with the working state; the change of the oil liquid height and the temperature of the oil pump are in negative correlation with the working state;
the method comprises the steps of obtaining a standard torque sequence and standard pressure data of standard equipment of the same type in a database within preset time, obtaining a standard working state, obtaining a working difference between the lubricating equipment to be detected and the standard equipment according to a first difference between the torque sequence and the standard torque sequence of the lubricating equipment to be detected within the preset time, a second difference between the pressure data and the standard pressure data, and a third difference between the working state and the standard working state, and performing fault early warning when the working difference is larger than a difference threshold value.
Preferably, the method for obtaining the deposition degree comprises the following steps:
the filter data includes the oil inlet pressure and the pressure of producing oil of filter, according to the oil inlet pressure with the pressure differential of the pressure of producing oil obtains the deposit degree, the pressure differential with the deposit degree is positive correlation.
Preferably, the method for acquiring the quality index comprises:
and taking the oil temperature as a negative index of the Euler number, multiplying the obtained exponential function by the cleanliness, and then quoting the obtained exponential function by the deposition degree to obtain the real-time quality index.
Preferably, the method for acquiring the change of the oil level comprises the following steps:
and in a preset time, making a difference between the oil heights at adjacent moments, and calculating an average value of all obtained difference values to serve as the change of the oil heights.
Preferably, the method for acquiring the first difference comprises:
acquiring the correlation between the moment sequence and a standard moment sequence, and acquiring the first difference based on the correlation, wherein the sum of the correlation and the first difference is a preset value; the correlation is obtained by calculating the Pearson correlation coefficient of the moment sequence and the standard moment sequence.
Preferably, the method for acquiring the second difference comprises:
wherein the content of the first and second substances,
the second difference is represented by a second difference,
indicating a sequence of pressure indicators of the lubricating apparatus to be tested,
a sequence of standard pressure indicators is represented,
representing the covariance of the pressure indicator sequence and the standard pressure indicator sequence of the lubricating apparatus to be tested,
the standard deviation of the pressure index sequence of the lubrication equipment to be detected is shown,
represents the standard deviation of the standard pressure index sequence,
representing the pearson correlation coefficient of the pressure indicator sequence with the standard pressure indicator sequence.
Preferably, the third difference is obtained by:
and acquiring a difference value between the working state of the lubricating equipment to be detected and the standard working state, and acquiring the third difference according to an arctan function of an absolute value of the difference value.
Preferably, the method further comprises the steps of:
and when fault early warning is carried out, judging the fault reason and positioning the fault position of the lubricating equipment to be detected.
Preferably, the determining the cause of the fault includes:
acquiring an oil pump fault index according to oil pump temperature and pressure data in preset time, wherein the oil pump temperature and the oil pump fault index are in a positive correlation relationship, and the pressure data and the oil pump fault index are in a negative correlation relationship; and when the oil pump fault index is larger than the fault threshold value, the fault reason is oil pump fault.
Preferably, the determining the cause of the fault further includes:
according to the degree is revealed to change and pressure data acquisition fluid of fluid height, the change of fluid height with the degree is revealed to fluid is positive correlation, pressure data with the degree is revealed to fluid is negative correlation, works as the degree is revealed to fluid is greater than when revealing the threshold value, and the fault reason is the oil leak trouble.
The embodiment of the invention at least has the following beneficial effects:
acquiring the working state of the lubricating equipment through the change of the oil height, the quality index, the pressure data and the oil pump temperature in the preset time; and acquiring the working difference between the lubricating equipment to be detected and standard equipment within preset time, and performing fault early warning when the working difference is greater than a difference threshold value. The embodiment of the invention can be applied to the intelligent manufacturing equipment industry, and can be used for monitoring the faults of the lubricating equipment in real time and early warning the faults in time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of steps of an intelligent lubrication equipment fault monitoring method based on industrial big data according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following describes the method for intelligently monitoring the faults of the lubrication equipment based on industrial big data according to the present invention, with reference to the accompanying drawings and preferred embodiments, and the detailed implementation, structure, features and effects thereof are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the intelligent lubrication equipment fault monitoring method based on industrial big data in detail with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of steps of an intelligent monitoring method for faults of lubrication equipment based on industrial big data according to an embodiment of the present invention is shown, the method includes the following steps:
s001, acquiring filter data, oil pump data and oil data of the lubricating equipment to be detected in real time; the oil pump data comprises oil pump temperature, oil pump torque and pressure data after oil pump pressurization, and the oil data comprises oil temperature, oil height and oil cleanliness.
The lubricating device comprises a filter and an oil pump, and the working process of the lubricating device is as follows: the oil filters impurities in the oil through the filter, the oil is pressurized through the oil pump, and the pressurized lubricating oil is sprayed to the hydraulic cylinder or other equipment needing lubrication through the nozzle, so that the oil is delivered to a lubricating point of the hydraulic pump, and the lubricating effect is achieved. The lubricating device with the structure belongs to the prior art, and the structure is not described in detail.
The method comprises the following specific steps:
1. and collecting the filter data of the lubricating equipment to be detected in real time.
The filter data comprises the oil inlet pressure and the oil outlet pressure of the filter, a pressure transmitter is adopted to collect the real-time oil inlet pressure and the real-time oil outlet pressure of the oil filter, the pressure transmitter collects pressure data once every 30s, and the oil inlet pressure data collected in 5min is
The collected oil outlet pressure data is
。
2. And acquiring oil pump data of the lubricating equipment to be detected in real time.
2.1 collecting the oil pump temperature.
Lubricating device caused by oil pump failureThe temperature sensor is arranged on the oil pump and used for measuring the real-time temperature of the oil pump, the temperature sensor collects data every 1s, and the temperature data collected within 5min is
。
And 2.2, collecting the torque of the oil pump.
The torque sensor is used for measuring the real-time torque of the oil pump main shaft. The torque of the pump reflects the pressure applied by the oil pump to the lubricating oil. The torque sensor collects data once every 1s, and the torque data collected within 5min is
The faster the pump speed, the smaller the torque.
And 2.3, collecting pressure data after the oil pump is pressurized.
The lubricating oil pressurized by the oil pump is used for spraying oil to a hydraulic cylinder or other equipment needing lubricating through a nozzle, so that the lubricating effect is achieved. The method is characterized in that pressure data of the lubricating oil pressurized by an oil pump is detected based on a pressure transmitter, the pressure data is collected once every 30s, and the data collected in 5min is
For different lubricating equipment, the adjustable range of outlet pressure adjustment is smaller according to the setting of an instrument manufacturer, and the outlet pressure can be adjusted in a small range through a pressure adjusting valve of a lubricating oil pump. If a higher pressure is required, the oil pump with a higher supply pressure needs to be replaced.
3. And acquiring oil data of the lubricating equipment to be detected in real time.
3.1 collecting the cleanliness of the oil.
The oil cleanliness is an important index, the operating condition of the lubricating equipment can be judged by detecting the oil cleanliness, and the oil generally needs to be additionally provided with a filter to filter impurities in the oil when entering the lubricating equipment. The unqualified oil cleanliness can cause the jamming of the slide valve and the aging of acceleration parts,
The content of solid particulate contaminants in a unit volume of oil is somewhat clean, which can be expressed in terms of weight or number of particles, respectively, with lower numbers yielding cleaner oil. In the embodiment of the invention, the cleanliness of the oil is detected in real time by the online oil detector. The installation method of the online oil cleanliness detector is vertical installation, and the specific installation position is determined according to the actual working condition of the lubricating equipment. The online oil cleanliness detector can detect the particle diameter of more than 4 mu m, and the optimal oil flow of the oil cleanliness detector is 0.2L/min and 0.5L/min at most.
The oil cleanliness detection sends oil cleanliness data once every 30s, and the oil cleanliness data measured by the online oil cleanliness detector in 5min is
The higher the cleanliness of the oil indicates the lower the number of impurities in the oil.
And 3.2, collecting the temperature of the oil.
The real-time temperature of the oil in the oil tank of the lubricating equipment is detected based on a temperature sensor, and the normal temperature range of the oil is 15 ℃ and 65 DEG C]The temperature sensor measures temperature data once every 10s, and the measured oil temperature data in 5min is
。
And 3.3, collecting the oil height.
The height of oil in the oil tank is measured based on the liquid level sensor, and whether the oil leaks or enters water can be judged by detecting the change of the liquid level. The liquid level sensor detects the oil level of the oil tank in real time, the liquid level sensor measures the oil level once every 10s, and the measured data in 5min are
。
And S002, acquiring the deposition degree of the filter through the filter data, and acquiring the real-time quality index of the oil according to the temperature, the deposition degree and the cleanliness of the oil.
The method comprises the following specific steps:
1. the filter data includes an oil inlet pressure and an oil outlet pressure of the filter, the deposition degree is obtained according to a pressure difference between the oil inlet pressure and the oil outlet pressure, and the pressure difference and the deposition degree are in positive correlation.
Along with the increase of the working time, impurities in the lubricating oil can block a filter element of the oil filter, the pressure difference of the oil filter is increased, and when the pressure difference reaches a pressure difference threshold value of the filter, the pressure difference flood generator can send a signal to alarm to remind workers to replace and clean the filter element. However, the differential pressure flood generator can only sense that an alarm signal is sent when the differential pressure of the filter reaches a threshold value, and cannot detect the real-time pressure of the oil filter in real time.
Calculating the pressure difference data of the oil filter in real time according to the oil inlet pressure and the oil outlet pressure collected in the step S001
The pressure difference data of the oil filter obtained within 5min is
。
The deposition condition of the filter impurities is reflected through the pressure difference, and the larger the pressure difference is, the more impurities are deposited on a filter element in the oil filter. The more the sedimentary impurity of the filter core of filter, filter element reduces the filtering action of fluid, when the impurity deposit degree of filter increases to certain threshold value, reminds the staff to change and wash the filter core, and the filter core that will replace washs and waits for next use.
Degree of deposition of filter impurities
Wherein, in the step (A),
denotes the nth pressure difference, n =1, 2, 3, 4. The degree of deposition of the impurities is determined by the pressure difference of the filter, and the larger the pressure difference of the filter is, the more the filter isThe more impurities accumulated in the filter element in the filter, the filtering action of the filter on the oil impurities is continuously weakened.
2. And taking the oil temperature as a negative index of the Euler number, multiplying the obtained exponential function by the cleanliness, and then quoting the obtained exponential function by the deposition degree to obtain a real-time quality index.
The quality requirement of fluid in the lubricating apparatus is very high, and piston round trip movement in the pneumatic cylinder converts the hydraulic pressure energy of pressurization in the hydraulic pump into mechanical energy, and the more messenger of impurity can the pneumatic cylinder wearing and tearing accelerate in the lubricating oil. In step S001, the cleanliness of the oil is detected, the deposition degree of impurities in the oil filter is evaluated in the steps, and the quality index of the oil in the lubricating equipment is evaluated based on the two indexes.
The real-time quality indexes of the oil liquid are as follows:
，…，
. Wherein, the first and the second end of the pipe are connected with each other,
and the temperature of the ith oil is shown.
Because the oil temperature is obtained once every 10 seconds, and the cleanliness and the deposition degree are obtained once every 30 seconds, each cleanliness and deposition degree correspond to 3 oil temperatures in a time sequence, and the uniformity of the time sequence is ensured by calculating a quality index by using the average value of the corresponding 3 oil temperatures.
The lower the oil cleanliness value is, the higher the oil cleanliness is, the lower the oil temperature is, the higher the oil viscosity is, and the lower the relative oil quality index is. The quality index of the oil obtained within 5min is
。
Step S003, acquiring the working state of the lubricating equipment through the change of the oil height, the quality index, the pressure data and the oil pump temperature in preset time; the quality index and the pressure data are in positive correlation with the working state; the change of the oil level and the temperature of the oil pump are in negative correlation with the working state.
The method comprises the following specific steps:
1. and in a preset time, making a difference between the oil heights at adjacent moments, and calculating the mean value of all obtained difference values to serve as the change of the oil heights.
The preset time in the embodiment of the invention is 5 minutes, and the oil height is 30 in 5 minutes.
The specific calculation formula is as follows:
wherein the content of the first and second substances,
indicating the change in the height of the oil,
the jth oil level is shown,
represents the j +1 th oil height.
2. And calculating the working state of the lubricating equipment within the preset time.
The specific calculation formula is as follows:
wherein R represents the working state within the preset time,
represents the average value of the quality index in the preset time, e represents the Euler number,
indicating pressure data within a predetermined timeIs determined by the average value of (a) of (b),
represents the average value of the oil pump temperature over a preset time.
In particular, the average value of the quality index
The higher the quality index of the oil liquid is, the lower the number of the oil liquid impurities of the lubricating equipment is, and the higher the working state index of the lubricating equipment is.
Mean value of pressure data
The smaller the pressure data after the oil pump is pressurized, the worse the operating state of the lubricating apparatus.
Average value of oil pump temperature
If the temperature of the oil pump is too high, abnormal heating of the lubricating equipment is caused, and the working state of the lubricating equipment is reduced.
The quantity of lubricating oil supplied to the mechanical equipment by the lubricating equipment is generally fixed, and the oil level is changed
The larger the pressure difference, the more likely the lubrication equipment may leak oil or the regulation increases the pressure of the oil pump and the operating state of the equipment decreases.
And normalizing the working state of the lubricating equipment to enable the value range to be [0,1 ]. In this way, the operating state factor of the lubricating device is obtained, and the larger the value, the better the operating state of the lubricating device is.
And step S004, a standard torque sequence and standard pressure data of standard equipment with the same model in a database within preset time are obtained, a standard working state is obtained, a working difference between the lubricating equipment to be detected and the standard equipment is obtained according to a first difference between the torque sequence and the standard torque sequence of the lubricating equipment to be detected within the preset time, a second difference between the pressure data and the standard pressure data, and a third difference between the working state and the standard working state, and when the working difference is larger than a difference threshold value, fault early warning is carried out.
The method comprises the following specific steps:
1. and establishing a large database of lubricating equipment of different models which normally work within 5min, wherein the database comprises standard data of each model, and calculating standard working states and storing the standard working states in the database. And the data is updated at intervals, so that the loss trend of the lubricating equipment is met.
2. Acquiring the correlation between the moment sequence and a standard moment sequence, acquiring a first difference based on the correlation, wherein the sum of the correlation and the first difference is a preset value; the correlation is obtained by calculating the Pearson correlation coefficient of the moment sequence and the standard moment sequence.
Taking the lubricating device X to be detected as an example, with the preset value of 1, calculating a first difference:
wherein the content of the first and second substances,
a first difference is represented in the form of a first difference,
representing the torque sequence of the lubrication device to be tested,
a standard sequence of moments is represented, which,
representing the covariance of the torque sequence of the lubricating apparatus to be tested and the standard torque sequence,
representing the standard deviation of the torque sequence of the lubricating apparatus to be tested,
the standard deviation of the standard moment sequence is indicated.
3. Calculating a second difference by the same method for calculating the first difference, specifically, obtaining the correlation between the pressurized pressure index sequence and the standard pressure index sequence, and obtaining the second difference based on the correlation, wherein the sum of the correlation and the second difference is a preset value; the correlation is obtained by calculating the Pearson correlation coefficient between the pressure index sequence and the standard pressure index sequence.
Also taking the lubricating device X to be detected as an example, with a preset value of 1, calculating a second difference:
wherein the content of the first and second substances,
a second difference is indicated in the form of a second difference,
indicating a sequence of pressure indicators of the lubricating apparatus to be tested,
a sequence of standard pressure indicators is represented,
representing the covariance of the pressure indicator sequence and the standard pressure indicator sequence of the lubricating apparatus to be tested,
indicating the standard deviation of the pressure index sequence of the lubricating apparatus to be tested,
represents the standard deviation of the standard pressure index sequence,
representing the pearson correlation coefficient of the pressure indicator sequence with the standard pressure indicator sequence.
3. And acquiring a difference value between the working state of the lubricating equipment to be detected and the standard working state, and acquiring a third difference according to an arctan function of an absolute value of the difference value.
Also taking the lubrication device X to be detected as an example, the calculation formula of the third difference is:
wherein the content of the first and second substances,
indicating the operating state of the lubricating apparatus X to be tested,
indicating a standard operating condition.
4. And acquiring the working difference between the lubricating equipment to be detected and standard equipment.
Wherein the content of the first and second substances,
indicating a difference in operation.
Since the value ranges of the first difference, the second difference and the third difference are all [0,1], the value range of the working difference D calculated by the above formula is [0,1 ].
Preferably, the method further comprises the steps of:
and when fault early warning is carried out, fault reason judgment and fault position positioning are carried out on the lubricating equipment to be detected.
Acquiring an oil pump fault index according to oil pump temperature and pressure data in preset time, wherein the oil pump temperature and the oil pump fault index are in a positive correlation relationship, and the pressure data and the oil pump fault index are in a negative correlation relationship; and when the oil pump fault index is larger than the fault threshold value, the fault reason is the oil pump fault.
The main phenomena of oil pump failure are that the temperature of a main shaft bearing is increased and the pressure of an oil outlet is insufficient. Therefore, an oil pump fault indicator is calculated from oil pump temperature and pressure data:
the higher the value, the higher the degree of failure of the oil pump. Normalizing the oil pump fault index to make the value range of 0,1]When the fault index of the oil pump
The oil pump of the lubricating apparatus is described as malfunctioning. In the embodiment of the invention, an arctan function is adopted for normalization.
Wherein the content of the first and second substances,
indicating a failure threshold, as an example, a failure threshold in an embodiment of the present invention
Is 0.8.
Calculating the degree of clogging of a lubricating apparatus
Normalizing the blockage degree to obtain the blockage degree
The larger the size, the more serious the clogging of the lubricating apparatus. When in use
When the lubrication equipment is blocked, the lubrication equipment is indicated to remind workers to check the filter and the oil pipe. In the embodiment of the invention, an arctan function is adopted for normalization processing.
Wherein the content of the first and second substances,
indicating an occlusion threshold, as an example, in an embodiment of the present invention
Is 0.8.
The oil leakage degree is acquired according to the change of the oil height and the pressure data, the change of the oil height and the oil leakage degree are in positive correlation, the pressure data and the oil leakage degree are in negative correlation, and when the oil leakage degree is larger than the leakage threshold value, the fault reason is oil leakage fault.
Calculating the oil leakage degree
And normalizing the oil leakage degree, and then, indicating that the lubricating equipment leaks oil. And at the moment, reminding workers to check the positions of the safety valve and the like which are easy to leak. In the embodiment of the invention, an arctan function is adopted for normalization processing.
Wherein the content of the first and second substances,
indicating a leakage threshold, as an example, the value of the leakage threshold in the embodiment of the present invention is 0.8.
Through the steps, the intelligent monitoring of the lubricating equipment fault is realized, the working personnel can observe the real-time working state of the lubricating equipment, and meanwhile, the intelligent monitoring system can also determine the fault reason and the approximate position of the fault of the lubricating equipment, so that the maintenance of the lubricating equipment by the working personnel is greatly facilitated, and the labor cost is saved.
In summary, the embodiment of the invention collects the filter data, the oil pump data and the oil data of the lubricating equipment to be detected in real time; the oil pump data comprises oil pump temperature, oil pump torque and pressure data after the oil pump is pressurized, and the oil data comprises oil temperature, oil height and oil cleanliness; acquiring the deposition degree of the filter through filter data, and acquiring the real-time quality index of the oil according to the temperature, the deposition degree and the cleanliness of the oil; acquiring the working state of the lubricating equipment through the change of the oil height, the quality index, the pressure data and the oil pump temperature in the preset time; the quality index and the pressure data are in positive correlation with the working state; the change of the oil liquid height and the temperature of the oil pump are in negative correlation with the working state; the method comprises the steps of obtaining a standard torque sequence and standard pressure data of standard equipment of the same type in a database within preset time, obtaining a standard working state, obtaining a working difference between the lubricating equipment to be detected and the standard equipment according to a first difference between the torque sequence and the standard torque sequence of the lubricating equipment to be detected within the preset time, a second difference between the pressure data and the standard pressure data, and a third difference between the working state and the standard working state, and performing fault early warning when the working difference is larger than a difference threshold value. The embodiment of the invention can monitor the faults of the lubricating equipment in real time and perform fault early warning in time.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.
Claims (9)
1. The intelligent monitoring method for the faults of the lubricating equipment based on the industrial big data is characterized in that the method comprises the following steps:
acquiring filter data, oil pump data and oil data of lubricating equipment to be detected in real time; the oil pump data comprises oil pump temperature, oil pump torque and pressure data after oil pump pressurization, and the oil data comprises oil temperature, oil height and oil cleanliness;
acquiring the deposition degree of the filter according to the filter data, and acquiring the real-time quality index of the oil according to the oil temperature, the deposition degree and the cleanliness;
acquiring the working state of the lubricating equipment through the change of the oil height, the quality index, the pressure data and the oil pump temperature in the preset time; the quality index and the pressure data are in positive correlation with the working state; the change of the oil liquid height and the temperature of the oil pump are in negative correlation with the working state;
acquiring a standard torque sequence and standard pressure data of standard equipment of the same type in a database within the preset time, acquiring a standard working state, acquiring a working difference between the lubricating equipment to be detected and the standard equipment according to a first difference between the torque sequence and the standard torque sequence of the lubricating equipment to be detected within the preset time, a second difference between the pressure data and the standard pressure data, and a third difference between the working state and the standard working state, and performing fault early warning when the working difference is greater than a difference threshold value;
the quality index obtaining method comprises the following steps:
and taking the oil temperature as a negative index of the Euler number, multiplying the obtained exponential function by the cleanliness, and then quoting the obtained exponential function by the deposition degree to obtain the real-time quality index.
2. The intelligent monitoring method for the faults of the lubrication equipment based on the industrial big data is characterized in that the deposit degree is obtained by the following method:
the filter data includes the oil inlet pressure and the pressure of producing oil of filter, according to the oil inlet pressure with the pressure differential of the pressure of producing oil obtains the deposit degree, the pressure differential with the deposit degree is positive correlation.
3. The intelligent monitoring method for the faults of the lubricating equipment based on the industrial big data is characterized in that the method for acquiring the changes of the oil height comprises the following steps:
and in a preset time, making a difference between the oil heights at adjacent moments, and calculating an average value of all obtained difference values to serve as the change of the oil heights.
4. The intelligent monitoring method for the faults of the lubrication equipment based on the industrial big data is characterized in that the first difference is obtained by the following steps:
acquiring the correlation between the moment sequence and a standard moment sequence, and acquiring the first difference based on the correlation, wherein the sum of the correlation and the first difference is a preset value; the correlation is obtained by calculating the Pearson correlation coefficient of the moment sequence and the standard moment sequence.
5. The intelligent monitoring method for the faults of the lubrication equipment based on the industrial big data is characterized in that the second difference is obtained by the following steps:
wherein the content of the first and second substances,
the second difference is represented by a second difference,
indicating a sequence of pressure indicators of the lubricating apparatus to be tested,
a sequence of standard pressure indicators is represented,
representing the covariance of the pressure indicator sequence and the standard pressure indicator sequence of the lubricating apparatus to be tested,
the standard deviation of the pressure index sequence of the lubrication equipment to be detected is shown,
the standard deviation of the standard pressure index sequence is shown, and the Pearson correlation coefficient of the pressure index sequence and the standard pressure index sequence is shown.
6. The intelligent industrial big data based lubricating equipment fault monitoring method according to claim 1, wherein the third difference is obtained by the following steps:
and acquiring a difference value between the working state of the lubricating equipment to be detected and the standard working state, and acquiring the third difference according to an arctan function of an absolute value of the difference value.
7. The intelligent monitoring method for the faults of the lubrication equipment based on the industrial big data is characterized by further comprising the following steps:
and when fault early warning is carried out, judging the fault reason and positioning the fault position of the lubricating equipment to be detected.
8. The intelligent monitoring method for the faults of the lubricating equipment based on the industrial big data is characterized in that the fault reason judgment comprises the following steps:
acquiring an oil pump fault index according to oil pump temperature and pressure data in preset time, wherein the oil pump temperature and the oil pump fault index are in a positive correlation relationship, and the pressure data and the oil pump fault index are in a negative correlation relationship; and when the oil pump fault index is larger than the fault threshold value, the fault reason is the oil pump fault.
9. The intelligent monitoring method for the faults of the lubrication equipment based on the industrial big data is characterized in that the fault reason judgment further comprises the following steps:
according to the degree is revealed to change and pressure data acquisition fluid of fluid height, the change of fluid height with the degree is revealed to fluid is positive correlation, pressure data with the degree is revealed to fluid is negative correlation, works as the degree is revealed to fluid is greater than when revealing the threshold value, and the fault reason is the oil leak trouble.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115575155A (en) * | 2022-11-17 | 2023-01-06 | 南通东杰食品科技有限公司 | Foodstuff grinder fault detection method and system |
| CN118032327A (en) * | 2024-04-15 | 2024-05-14 | 山东能源数智云科技有限公司 | Equipment intelligent lubrication monitoring method and device based on artificial intelligence |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205014013U (en) * | 2015-09-25 | 2016-02-03 | 北京东方乾海润滑设备有限公司 | Lubrication system |
| CN111922453A (en) * | 2020-08-14 | 2020-11-13 | 韶关市志通达信息科技有限公司 | Automatic synchronization device and lubrication method for lubrication tapping of big data connecting piece |
| CN112097079A (en) * | 2020-09-25 | 2020-12-18 | 杨娜 | Industrial monitoring device based on internet big data |
-
2022
- 2022-07-26 CN CN202210881047.8A patent/CN114941796B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205014013U (en) * | 2015-09-25 | 2016-02-03 | 北京东方乾海润滑设备有限公司 | Lubrication system |
| CN111922453A (en) * | 2020-08-14 | 2020-11-13 | 韶关市志通达信息科技有限公司 | Automatic synchronization device and lubrication method for lubrication tapping of big data connecting piece |
| CN112097079A (en) * | 2020-09-25 | 2020-12-18 | 杨娜 | Industrial monitoring device based on internet big data |
Non-Patent Citations (3)
| Title |
|---|
| 吉可明等: "大数据技术及其在化工领域的应用和展望", 《现代化工》 * |
| 李然等: "综采集成供液系统智能监测诊断技术现状与发展", 《煤炭科学技术》 * |
| 殷勇勤等: "智能润滑技术在船舶智能化发展中的应用", 《船舶工程》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115575155A (en) * | 2022-11-17 | 2023-01-06 | 南通东杰食品科技有限公司 | Foodstuff grinder fault detection method and system |
| CN115575155B (en) * | 2022-11-17 | 2024-01-16 | 南通东杰食品科技有限公司 | Method and system for detecting faults of foodstuff grinder |
| CN118032327A (en) * | 2024-04-15 | 2024-05-14 | 山东能源数智云科技有限公司 | Equipment intelligent lubrication monitoring method and device based on artificial intelligence |
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