CN111324676A - Mechanical equipment lubricating oil on-line monitoring system based on fuzzy C-means clustering algorithm - Google Patents
Mechanical equipment lubricating oil on-line monitoring system based on fuzzy C-means clustering algorithm Download PDFInfo
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- CN111324676A CN111324676A CN202010099233.7A CN202010099233A CN111324676A CN 111324676 A CN111324676 A CN 111324676A CN 202010099233 A CN202010099233 A CN 202010099233A CN 111324676 A CN111324676 A CN 111324676A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
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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
- F16N29/02—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 for influencing the supply of lubricant
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/26—Oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
- G01N33/2888—Lubricating oil characteristics, e.g. deterioration
Abstract
The mechanical equipment lubricating oil on-line monitoring system based on the fuzzy C-means clustering algorithm comprises the following steps. Step 1, acquiring the current state parameters of the lubricating oil of the mechanical equipment through a multi-parameter sensor, and transmitting the state parameters to an industrial control mainboard through a CAN bus; step 2, uploading multi-parameter data acquired by a sensor to an upper computer program and a MYSQL database through a WIFI module on the mainboard; step 3, the upper computer calculates Euclidean distances from the current monitoring data to the predetermined clustering centers of all quality grades of lubricating oil by combining a fuzzy C-means clustering algorithm; step 4, calculating the maximum value from the sample in each cluster to the corresponding cluster center, and taking the maximum value as the threshold value of the cluster; step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category; and 6, uploading the judgment result to the mobile terminal through the WIFI by the upper computer. The invention effectively realizes the monitoring of the lubricating oil state of mechanical equipment, improves the production efficiency and ensures the production safety.
Description
Technical Field
The invention relates to the field of safety protection of a lubricating system of mechanical equipment, in particular to an online monitoring system for lubricating oil of the mechanical equipment based on a fuzzy C-means clustering algorithm.
Background
Lubricating oil is used in various fields such as electric power, metallurgy, chemical industry, and automobiles as one of several petroleum products. In key equipment such as gears and sliding bearings of rotating machines such as steam turbines, pumps and compressors, lubricating oil is used as a working medium and mainly plays roles of lubrication, auxiliary cooling, rust prevention, cleaning, sealing, buffering and the like. Due to the decomposition and oxidation of the lubricating oil, the mixing of external impurities and the like, parameters such as viscosity, acid value, moisture and the like of the lubricating oil deviate from initial values after the lubricating oil is used for a period of time, so that the performance of the oil is reduced. If the lubricant is replaced when the lubricant does not reach the replacement standard, the waste of the lubricant is inevitably caused; on the contrary, if the deteriorated lubricating oil is used all the time, some safety hazards are brought, such as the abrasion of the rotating shaft and other rotating parts is increased, mechanical equipment is sometimes failed, and even disastrous accidents are caused.
Currently, mechanical equipment companies typically sample and test lubricating oil according to a lubricating oil test cycle provided by a lubricating oil manufacturer, for example, testing the water content of lubricating oil every half month and testing the viscosity every month. Conventional periodic detection easily causes water and pollutants generated from the outside or inside to accumulate or suddenly increase in a short period of time, so that the lubricating oil is deteriorated in a detection period. Therefore, the lubricating system of the mechanical equipment is monitored in real time, and the reason for the change of the oil quality of the lubricating oil is found in time through data analysis, so that corresponding measures are taken to ensure that the oil for the mechanical equipment reaches the specified standard.
Aiming at the problem of state monitoring of a lubricating system of mechanical equipment, a patent related to a solution of the problem in China is 'a guide rail lubricating oil monitoring device applied to an active safety elevator' (201920300280.6), and a liquid level sensor, a temperature sensor and a viscosity sensor are used for monitoring the state parameters of lubricating oil, but the invention is only limited to conventional parameter monitoring and does not solve the problem of judging the quality of the lubricating oil of the elevator. The invention discloses an equipment lubricating oil on-line monitoring system and method (201910364018.2), which combines a least square method to obtain a lubricating oil theoretical baseline threshold value, and compares the lubricating oil theoretical baseline threshold value with a parameter in the current state, thereby judging the current oil grade, but for variable lubricating oil state parameters, the least square method may generate larger deviation when estimating the threshold value, thereby ensuring that the precision of a state monitoring system is not well ensured. Therefore, it is necessary to develop a system with real-time and high precision for monitoring the state of the lubrication system of the mechanical equipment.
Disclosure of Invention
In order to solve the problems, the invention provides an online monitoring system for lubricating oil of mechanical equipment based on a fuzzy C-means clustering algorithm on the basis of technologies such as a multi-parameter sensor, the fuzzy C-means clustering algorithm, a mobile terminal and the like. Firstly, acquiring data of different quality grades of lubricating oil by using a multi-parameter sensor, and then determining clustering centers and category thresholds of different quality grades one by using a fuzzy C-means clustering algorithm; finally, in an actual industrial field, the system realizes real-time online monitoring on a lubricating system of mechanical equipment, can accurately judge the current oil state grade, improves the production efficiency and ensures the production safety. In order to achieve the purpose, the invention provides an online monitoring system for lubricating oil of mechanical equipment based on a fuzzy C-means clustering algorithm, which comprises the following specific steps:
step 1, acquiring the current state parameters of the lubricating oil of the mechanical equipment through a multi-parameter sensor, and transmitting the state parameters to an industrial control mainboard through a CAN bus;
step 2, uploading multi-parameter data acquired by a sensor to an upper computer program and a MYSQL database through a WIFI module on the mainboard;
step 3, the upper computer calculates Euclidean distances from the current monitoring data to the predetermined clustering centers of all quality grades of lubricating oil by combining a fuzzy C-means clustering algorithm;
step 4, calculating the maximum value from the sample in each cluster to the corresponding cluster center, and taking the maximum value as the threshold value of the cluster;
step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, if the Euclidean distance is lower than the threshold value, judging the Euclidean distance to be the category, and if not, retraining the model and determining a corresponding clustering center and a category threshold value;
and 6, uploading the judgment result to the mobile terminal through the WIFI by the upper computer, and acquiring the current state of the lubricating oil through the mobile terminal by an operator so as to perform corresponding maintenance work and feed the maintenance work back to the program of the upper computer.
Further, the specific steps of acquiring the state parameters of the lubricating oil by using the multi-parameter sensor in the step 1 are as follows:
step 1.1, monitoring four parameters of viscosity, density, dielectric constant and temperature of the lubricating oil by adopting an FPS sensor produced by MEAS company. The density of the lubricating oil is in direct relation with the oxidation degree of the lubricating oil, the content of water and metal impurity particles in the lubricating oil, and the current performance of the lubricating oil can be reflected to a certain degree; the viscosity of the lubricating oil can reflect the fluidity of the lubricating oil well, and the larger the viscosity, the larger the internal resistance and the poorer the fluidity. Generally, the more stable the viscosity, the less affected by the external environment such as temperature and pressure;
and 1.2, detecting the moisture content in the lubricating oil by adopting a moisture sensor. Because the water content in the lubricating oil determines whether the lubricating oil is turbid or emulsified, when the water content in the lubricating performance is too high, the lubricating effect of a lubricating system can be reduced, so that the abrasion of mechanical equipment is further aggravated, and even disastrous accidents are caused;
and 1.3, measuring the content of the abrasive particles in the lubricating oil by using an abrasive particle sensor. As the abrasive particles in the lubricating oil are increased, a certain amount of granular substances are dissolved in the lubricating oil, so that the mechanical system can be subjected to a runaway accident;
and step 1.4, detecting the oil quality parameters of the lubricating oil by using an oil quality sensor. Because the oil quality parameter can directly reflect the current state of the oil liquid.
Further, the specific step of using the fuzzy C-means clustering algorithm to predetermine the clustering centers of the lubricating oil in each quality grade in the step 3 is as follows:
and 3.1, initializing parameters of the fuzzy C-means clustering algorithm. Let X be X ═ Xj1,2,., n, the total number of categories is c (2 ≦ c ≦ n), XiPhi (i-1, 2.., c), the number Num of initialization iterations is 1. The sample set can be divided into the following classes under the given classification number C: x1,X2,...,Xc；
Step 3.2, randomly selecting the clustering centers of c categories, and recording the clustering centers as Vk＝{v1(k),v2(k),...,vc(k) Determining a division membership index m, and simultaneously setting the maximum iteration times N and a threshold value epsilon for terminating the iteration;
and 3.3, calculating the Euclidean distance between all samples and each clustering center. For the s-dimensional sample, sample xjThe Euclidean distance from the class i center is:
step 3.4, updating and acquiring the corresponding fuzzy partition matrix:
wherein u isijRepresenting the degree of membership of the jth sample in the ith class, which satisfies:
and 3.5, updating the clustering centers of all categories. The update formula is as follows:
and 3.6, setting iteration termination conditions, namely: when | Vk+1-VkIf | ≦ ε or the iteration number reaches the set number N, stopping iteration, otherwise, Num ═ Num +1, and jumping to step 3.4 to continue iteration.
The invention discloses a mechanical equipment lubricating oil on-line monitoring system based on a fuzzy C-means clustering algorithm, which has the beneficial effects that: the invention has the technical effects that:
1. the invention utilizes the multi-parameter sensor to collect seven state parameters of the lubricating oil, such as viscosity, moisture and the like in real time, and can effectively reflect the state of the lubricating oil compared with the traditional state monitoring method with single parameter or less parameters;
2. the fuzzy C-means clustering algorithm is used for carrying out clustering analysis on the multi-state parameters of the lubricating oil, so that the clustering center and the category threshold of each quality grade of the lubricating oil can be accurately determined;
3. the lubricating oil state monitoring system of the invention supports the optimization and upgrade of the model, namely: when the data collected in real time do not belong to the known category, the current data are substituted into the model for retraining, so that each clustering center and a category threshold value are updated, and the generalization of the model is greatly enhanced by the strategy;
4. the invention supports the remote access of the mobile terminal, realizes targeted equipment maintenance, improves the production efficiency and simultaneously ensures the operation safety of mechanical equipment.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the present invention identifying cluster centers and class thresholds of different classes using fuzzy C-means clustering;
fig. 3 is a diagram of the information interaction rules among different modules of the whole system.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides an online monitoring system for lubricating oil of mechanical equipment based on a fuzzy C-means clustering algorithm, and aims to realize online monitoring of the lubricating system of the mechanical equipment, improve the production efficiency and guarantee the production safety.
FIG. 1 is a flow chart of the present invention. The steps of the present invention will be described in detail with reference to the flow chart.
Step 1, acquiring the current state parameters of the lubricating oil of the mechanical equipment through a multi-parameter sensor, and transmitting the state parameters to an industrial control mainboard through a CAN bus;
step 1.1, monitoring four parameters of viscosity, density, dielectric constant and temperature of the lubricating oil by adopting an FPS sensor produced by MEAS company. The density of the lubricating oil is in direct relation with the oxidation degree of the lubricating oil, the content of water and metal impurity particles in the lubricating oil, and the current performance of the lubricating oil can be reflected to a certain degree; the viscosity of the lubricating oil can reflect the fluidity of the lubricating oil well, and the larger the viscosity, the larger the internal resistance and the poorer the fluidity. Generally, the more stable the viscosity, the less affected by the external environment such as temperature and pressure;
and 1.2, detecting the moisture content in the lubricating oil by adopting a moisture sensor. Because the water content in the lubricating oil determines whether the lubricating oil is turbid or emulsified, when the water content in the lubricating performance is too high, the lubricating effect of a lubricating system can be reduced, so that the abrasion of mechanical equipment is further aggravated, and even disastrous accidents are caused;
and 1.3, measuring the content of the abrasive particles in the lubricating oil by using an abrasive particle sensor. As the abrasive particles in the lubricating oil are increased, a certain amount of granular substances are dissolved in the lubricating oil, so that the mechanical system can be subjected to a runaway accident;
and step 1.4, detecting the oil quality parameters of the lubricating oil by using an oil quality sensor. Because the oil quality parameter can directly reflect the current state of the oil liquid.
Step 2, uploading multi-parameter data acquired by a sensor to an upper computer program and a MYSQL database through a WIFI module on the mainboard;
step 3, the upper computer calculates Euclidean distances from the current monitoring data to the predetermined clustering centers of all quality grades of lubricating oil by combining a fuzzy C-means clustering algorithm;
and 3.1, initializing parameters of the fuzzy C-means clustering algorithm. Let X be X ═ Xj1,2,., n, the total number of categories is c (2 ≦ c ≦ n), XiPhi (i-1, 2.., c), the number Num of initialization iterations is 1. The sample set can be divided into the following classes under the given classification number C: x1,X2,...,Xc；
Step 3.2, randomly selecting the clustering centers of c categories, and recording the clustering centers as Vk＝{v1(k),v2(k),...,vc(k) Determining a division membership index m, and simultaneously setting the maximum iteration times N and a threshold value epsilon for terminating the iteration;
and 3.3, calculating the Euclidean distance between all samples and each clustering center. For the s-dimensional sample, sample xjThe Euclidean distance from the class i center is:
step 3.4, updating and acquiring the corresponding fuzzy partition matrix:
wherein u isijRepresenting the degree of membership of the jth sample in the ith class, which satisfies:
and 3.5, updating the clustering centers of all categories. The update formula is as follows:
and 3.6, setting iteration termination conditions, namely: when | Vk+1-VkIf | ≦ ε or the iteration number reaches the set number N, stopping iteration, otherwise, Num ═ Num +1, and jumping to step 3.4 to continue iteration.
Step 4, calculating the maximum value from the sample in each cluster to the corresponding cluster center, and taking the maximum value as the threshold value of the cluster;
step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, if the Euclidean distance is lower than the threshold value, judging the Euclidean distance to be the category, and if not, retraining the model and determining a corresponding clustering center and a category threshold value;
and 6, uploading the judgment result to the mobile terminal through the WIFI by the upper computer, and acquiring the current state of the lubricating oil through the mobile terminal by an operator so as to perform corresponding maintenance work and feed the maintenance work back to the program of the upper computer.
FIG. 2 is a schematic diagram of the present invention identifying cluster centers and class thresholds of different classes using fuzzy C-means clustering. As can be seen from the figure, the fuzzy C-means clustering algorithm can be used for simply and effectively determining the clustering centers and the boundaries of different categories, so as to obtain the corresponding category thresholds.
Fig. 3 is a schematic diagram of information interaction between different modules of the whole system. It can be clearly seen that, the lubricating oil monitoring system of the whole mechanical equipment utilizes multi-parameter sensors (FPS sensors, moisture sensors, abrasive particle sensors and oil quality sensors) to collect lubricating oil state parameters, uses WIFI as a carrier for information transmission to carry out information interaction between different modules, writes a fuzzy C mean value clustering algorithm in an upper computer program, accurately realizes judgment of the quality grade of the lubricating oil, and sends warning information through a mobile terminal, so that related operators complete related equipment maintenance work according to the warning information, production efficiency is improved, and equipment safety is guaranteed. In addition, a feedback mechanism is reasonably designed, so that the whole system is more intelligent.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (3)
1. The mechanical equipment lubricating oil on-line monitoring system based on the fuzzy C-means clustering algorithm specifically comprises the following steps:
step 1, acquiring the current state parameters of the lubricating oil of the mechanical equipment through a multi-parameter sensor, and transmitting the state parameters to an industrial control mainboard through a CAN bus;
step 2, uploading multi-parameter data acquired by a sensor to an upper computer program and a MYSQL database through a WIFI module on the mainboard;
step 3, the upper computer calculates Euclidean distances from the current monitoring data to the predetermined clustering centers of all quality grades of lubricating oil by combining a fuzzy C-means clustering algorithm;
step 4, calculating the maximum value from the sample in each cluster to the corresponding cluster center, and taking the maximum value as the threshold value of the cluster;
step 5, comparing each calculated Euclidean distance with a threshold value of a corresponding category, if the Euclidean distance is lower than the threshold value, judging the Euclidean distance to be the category, and if not, retraining the model and determining a corresponding clustering center and a category threshold value;
and 6, uploading the judgment result to the mobile terminal through the WIFI by the upper computer, and acquiring the current state of the lubricating oil through the mobile terminal by an operator so as to perform corresponding maintenance work and feed the maintenance work back to the program of the upper computer.
2. The on-line monitoring system for the lubricating oil of the mechanical equipment based on the fuzzy C-means clustering algorithm according to claim 1, characterized in that: the method for acquiring the state parameters of the lubricating oil by using the multi-parameter sensor in the step 1 comprises the following specific steps:
step 1.1, monitoring four parameters of viscosity, density, dielectric constant and temperature of the lubricating oil by adopting an FPS sensor produced by MEAS company. The density of the lubricating oil has direct relation with the oxidation degree of the lubricating oil, the content of water and metal impurity particles in the lubricating oil, and the current performance of the lubricating oil can be reflected to a certain degree; the viscosity of the lubricating oil can reflect the fluidity of the lubricating oil well, and the larger the viscosity, the larger the internal resistance and the poorer the fluidity. Generally, the more stable the viscosity, the less affected by the external environment such as temperature and pressure;
and 1.2, detecting the moisture content in the lubricating oil by adopting a moisture sensor. Because the water content in the lubricating oil determines whether the lubricating oil is turbid or emulsified, when the water content in the lubricating performance is too high, the lubricating effect of a lubricating system can be reduced, so that the abrasion of mechanical equipment is further aggravated, and even disastrous accidents are caused;
and 1.3, measuring the content of the abrasive particles in the lubricating oil by using an abrasive particle sensor. As the abrasive particles in the lubricating oil are increased, a certain amount of granular substances are dissolved in the lubricating oil, so that the mechanical system can be subjected to a runaway accident;
and step 1.4, detecting the oil quality parameters of the lubricating oil by using an oil quality sensor. Because the oil quality parameter can directly reflect the current state of the oil liquid.
3. The on-line monitoring system for the lubricating oil of the mechanical equipment based on the fuzzy C-means clustering algorithm according to claim 1, characterized in that: the specific steps of utilizing the fuzzy C-means clustering algorithm to predetermine the clustering centers of all the quality grades of the lubricating oil in the step 3 are as follows:
and 3.1, initializing parameters of the fuzzy C-means clustering algorithm. Let X be X ═ Xj1,2,., n, the total number of categories is c (2 ≦ c ≦ n), XiPhi (i-1, 2.., c), the number Num of initialization iterations is 1. The sample set can be divided into the following classes under the given classification number C: x1,X2,...,Xc；
Step 3.2, randomly selecting the clustering centers of c categories, and recording the clustering centers as Vk＝{v1(k),v2(k),...,vc(k) Determining a division membership index m, and simultaneously setting the maximum iteration times N and a threshold value epsilon for terminating the iteration;
and 3.3, calculating the Euclidean distance between all samples and each clustering center. For the s-dimensional sample, sample xjThe Euclidean distance from the class i center is:
step 3.4, updating and acquiring the corresponding fuzzy partition matrix:
wherein u isijRepresenting the degree of membership of the jth sample in the ith class, which satisfies:
and 3.5, updating the clustering centers of all categories. The update formula is as follows:
and 3.6, setting iteration termination conditions, namely: when | Vk+1-VkIf | ≦ ε or the iteration number reaches the set number N, stopping iteration, otherwise, Num ═ Num +1, and jumping to step 3.4 to continue iteration.
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