CN108645619B - Intelligent monitoring system for vibration of mechanical bearing - Google Patents
Intelligent monitoring system for vibration of mechanical bearing Download PDFInfo
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- CN108645619B CN108645619B CN201810827045.4A CN201810827045A CN108645619B CN 108645619 B CN108645619 B CN 108645619B CN 201810827045 A CN201810827045 A CN 201810827045A CN 108645619 B CN108645619 B CN 108645619B
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
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Abstract
The invention provides an intelligent monitoring system for mechanical bearing vibration, which comprises a sensing subsystem configured to collect mechanical bearing vibration data, a data storage device configured to store the mechanical bearing vibration data and a visualization device configured to display the mechanical bearing vibration data; the sensing subsystem and the visualization device are connected with the data storage device; the sensor subsystem comprises a sink node and a plurality of sensor nodes configured to collect vibration data of the mechanical bearing, the sink node and the sensor nodes construct a wireless sensor network with a clustering structure in a self-organizing manner, and a cluster head is selected from the sensor nodes and clustered when network topology is constructed; the cluster head is configured to collect and send mechanical bearing vibration data collected by the sensor nodes in the cluster to the sink node; the sink node is configured to send the aggregated mechanical bearing vibration data for each cluster head to a data storage device.
Description
Technical Field
The invention relates to the technical field of equipment monitoring, in particular to an intelligent mechanical bearing vibration monitoring system.
Background
The mechanical bearing is used as a key part of the rotating machine, and the working state of the mechanical bearing directly influences the working state of the whole mechanical equipment. Rotating machine bearing failure is one of the major causes of failure of rotating machinery, and in severe cases can even result in significant property damage. Therefore, in order to avoid mechanical failure of the bearings by the rotating machinery and reduce economic losses, it is necessary to monitor the condition of the bearings to ensure their proper operation.
Disclosure of Invention
Aiming at the problems, the invention provides an intelligent monitoring system for the vibration of a mechanical bearing.
The purpose of the invention is realized by adopting the following technical scheme:
an intelligent monitoring system for mechanical bearing vibration is provided, the system comprising a sensing subsystem configured to collect mechanical bearing vibration data, a data storage device configured to store mechanical bearing vibration data, and a visualization device configured to display the mechanical bearing vibration data; the sensing subsystem and the visualization device are connected with the data storage device; the sensor subsystem comprises a sink node and a plurality of sensor nodes configured to collect vibration data of the mechanical bearing, the sink node and the sensor nodes construct a wireless sensor network with a clustering structure in a self-organizing manner, and a cluster head is selected from the sensor nodes and clustered when network topology is constructed; the cluster head is configured to collect and send mechanical bearing vibration data collected by the sensor nodes in the cluster to the sink node; the sink node is configured to send the aggregated mechanical bearing vibration data for each cluster head to a data storage device.
And the mechanical bearing vibration data comprises vibration acceleration signals of the mechanical bearing to be detected in the vertical direction under different working states.
Preferably, the sensor node comprises a sensor and a signal adapter configured to convert a sensor signal into corresponding mechanical bearing vibration data, the signal adapter being connected to the sensor; also included is a controller configured to control the acquisition frequency, the controller being connected to the sensor.
Further, the visualization apparatus is further configured to: and before displaying the mechanical bearing vibration data, preprocessing the mechanical bearing vibration data, including removing data abnormal points and carrying out data normalization processing.
The invention has the beneficial effects that: the invention can intelligently acquire the vibration data of the mechanical bearing in real time, is convenient for monitoring personnel to know the vibration information of the mechanical bearing in time, is convenient for the monitoring personnel to further analyze the state of the mechanical bearing according to the vibration information of the mechanical bearing, and timely checks the mechanical bearing which is possibly in fault, thereby reducing the loss caused by the fault of the mechanical bearing.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram illustrating the structure of an intelligent monitoring system for vibration of a mechanical bearing according to an exemplary embodiment of the present invention;
fig. 2 is a block diagram schematically illustrating a structure of a sensor node according to an exemplary embodiment of the present invention.
Reference numerals:
a sensing subsystem 1, a data storage device 2, a visualization device 3, a sensor 10, a signal adapter 20, and a controller 30.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, an embodiment of the present invention provides an intelligent monitoring system for mechanical bearing vibration, which includes a sensing subsystem 1 configured to collect mechanical bearing vibration data, a data storage device 2 configured to store mechanical bearing vibration data, and a visualization device 3 configured to display the mechanical bearing vibration data; the sensing subsystem 1 and the visualization device 3 are both connected with the data storage device 2.
In an alternative, the visualization apparatus is further configured to: and before displaying the mechanical bearing vibration data, preprocessing the mechanical bearing vibration data, including removing data abnormal points and carrying out data normalization processing.
In an implementation mode, the sensing subsystem 1 comprises a sink node and a plurality of sensor nodes configured to acquire vibration data of a mechanical bearing, the sink node and the sensor nodes construct a wireless sensor network with a clustering structure in a self-organizing manner, and a cluster head is selected from the sensor nodes and clustered when a network topology is constructed; the cluster head is configured to collect and send mechanical bearing vibration data collected by the sensor nodes in the cluster to the sink node; the sink node is configured to send the mechanical bearing vibration data summarized by each cluster head to the data storage 2.
And the mechanical bearing vibration data comprises vibration acceleration signals of the mechanical bearing to be detected in the vertical direction under different working states.
Wherein, as shown in fig. 2, the sensor node comprises a sensor 10 and a signal adapter 20 configured to convert a signal of the sensor 10 into corresponding mechanical bearing vibration data, the signal adapter 20 being connected with the sensor 10; further comprising a controller 30 configured to control the acquisition frequency, said controller 30 being connected to the sensor 10.
According to the embodiment of the invention, the vibration data of the mechanical bearing can be intelligently acquired in real time, so that monitoring personnel can conveniently know the vibration information of the mechanical bearing in time, the monitoring personnel can conveniently further analyze the state of the mechanical bearing according to the vibration information of the mechanical bearing, the mechanical bearing which is possibly in fault can be timely checked, and the loss caused by the fault of the mechanical bearing is reduced.
In one embodiment, if the distance between each sensor node in the cluster and the corresponding cluster head does not exceed the set communication distance threshold value LσDirectly communicating with the corresponding cluster head; if the distance between the cluster head and the corresponding cluster head exceeds the set communication distance threshold value LσThe sensor node selects a neighbor sensor node which is close to the corresponding cluster head for communication; and the neighbor sensor nodes of the sensor node i are other sensor nodes positioned in the communication range of the sensor node i.
In the communication routing mechanism, whether the sensor node is in direct communication with the cluster head or not is determined through the distance, and the mechanical bearing vibration data collected by the sensor node can be reliably and stably sent to the cluster head.
In an optional manner, the selecting, by the sensor node, a neighbor sensor node closer to the corresponding cluster head for communication includes:
(1) selecting neighbor sensor nodes meeting the path conditions from neighbor sensor nodes closer to the corresponding cluster head as alternative connection nodes, and classifying the alternative connection nodes into an alternative connection node set;
(2) the sensor node selects a neighbor sensor node with the largest current residual energy from the candidate connection node set for communication;
wherein the path condition is:
in the formula, OiRepresenting a corresponding cluster head of the sensor node i, and ij representing a jth neighbor sensor node in neighbor sensor nodes of the sensor node i, which are closer to the cluster head;
for the jth neighbor sensor node and cluster head OiDistance of (L)σFor the set communication distance threshold, niAnd the number of neighbor sensor nodes of the sensor node i, which are closer to the cluster head.
The embodiment innovatively sets the path condition, so that when the sensor node with the distance from the cluster head exceeding the set communication distance threshold selects the next hop, the sensor node can avoid selecting the next hop far away from the cluster head, thereby effectively reducing the time delay of mechanical bearing vibration data transmission and simultaneously reducing the occurrence rate of the phenomenon of mechanical bearing vibration data packet loss caused by data stream interference during multi-hop transmission.
In one embodiment, the sensor node periodically calculates and updates the congestion degree of the buffer space of the sensor node during the transmission process of the vibration data of the mechanical bearing, and simultaneously sends the updated congestion degree of the buffer space to the sensor node of the previous hop; the sensor node monitors the neighbor sensor node communicated with the sensor node in real time, and when the congestion degree of the cache space of the neighbor sensor node is found to exceed the set change degree threshold value, the sensor node reselects the neighbor sensor node for communication.
In an optional mode, the sensor node reselects the neighbor sensor node with the largest current residual energy from other neighbor sensor nodes in the own candidate connection node set for communication.
The congestion degree of the cache space at the initial time is set to be 0, and the calculation formula of the congestion degree of the cache space is as follows:
in the formula, Si(q) represents the calculated congestion degree of the cache space of the sensor node i at the moment of the current period q, di(q) is the length of a mechanical bearing vibration data packet queue in a self buffer space of the sensor node i at the moment of the current period q, di(q-delta q) the length of a mechanical bearing vibration data packet queue in a self buffer space of a sensor node i at the moment of a last period q, delta q is period interval time, di，maxThe initial cache size of the sensor node i;u is a cache space congestion degree influence factor, when the cache space congestion degree of a sensor node of the next hop of the sensor node i exceeds a set change degree threshold value, u is 0.1, otherwise, u is 0;
in the formula (I), the compound is shown in the specification,
distance, L, between a next hop sensor node of a sensor node i and the corresponding cluster headσSetting a communication distance threshold value for the communication distance; z [ d ]i(q)-di(q-Δq)]For the set judgment value function, when di(q)-diWhen (q- Δ q) is not less than 0, Z [ d ]i(q)-di(q-Δq)]When d is equal to 1i(q)-diWhen (q- Δ q) < 0, Z [ d ]i(q)-di(q-Δq)]＝-0.4。
In the embodiment, a path maintenance mechanism from the sensor node to the cluster head is set, wherein a calculation formula of the congestion degree of the cache space is set, and the congestion degree of the cache space can better reflect the congestion degree of the path from the sensor node of the next hop to the cluster head.
When the residual buffer space of one sensor node is too small or a large number of mechanical bearing vibration data packets arrive, the pressure of the sensor node for processing the mechanical bearing vibration data packets is increased sharply, and the mechanical bearing vibration data packets are accumulated in the buffer space of the sensor node, so that certain congestion is caused.
Based on the phenomenon, the congestion degree of the buffer space serves as an index for measuring the severity of the phenomenon, when the sensor node finds that the congestion degree of the buffer space of the neighbor sensor node communicated with the sensor node exceeds a set change degree threshold value, the next hop node is reselected, the processing pressure of the sensor node with the excessive buffer quantity of the mechanical bearing vibration data packet can be effectively reduced, the load of each sensor node is balanced, the probability of packet loss caused by the excessive buffer quantity of the mechanical bearing vibration data packet is effectively reduced, the reliability of mechanical bearing vibration data transmission is further improved, and a relatively comprehensive data basis is provided for follow-up fault monitoring of the mechanical bearing.
In one embodiment, when the distance between the cluster head and the sink node does not exceed the preset lower distance limit, the cluster head directly communicates with the sink node; when the distance between the cluster head and the sink node exceeds a preset distance lower limit, the cluster head sends the mechanical bearing vibration data to the sink node in a multi-hop routing mode.
In an optional mode, when a next hop is selected, the cluster head calculates the preferred value of each neighbor cluster head closer to the aggregation node, and selects the neighbor cluster head with the largest preferred value as the next hop;
the calculation formula for setting the optimal value is as follows:
in the formula, CabIs the preferred value of the b-th neighbor cluster head of the cluster head a closer to the sink node, STFor said set threshold value of degree of change, Sb(q) is the congestion degree of the current cache space of the neighbor cluster head closer to the sink node b, and L (b, o) is the distance from the neighbor cluster head closer to the sink node b to the sink node, LminFor the set lower distance limit, L (a, o) is the distance from the cluster head a to the sink node; w is a1、w2Is the set weight coefficient.
The embodiment creatively sets a calculation formula of an optimal value based on the congestion degree and the distance factor of the cache space, and correspondingly provides a communication routing mechanism from the cluster head to the sink node.
Based on the communication routing mechanism, in the embodiment, when the distance between the cluster head and the sink node exceeds the preset lower distance limit, the cluster head selects the neighbor cluster head with the largest preferred value as the next hop, which is favorable for improving the reliability of the vibration data transmission of the mechanical bearing, and can shorten the total length of the vibration data transmission path of the mechanical bearing as much as possible, save the cost of the vibration data transmission of the mechanical bearing, and further save the monitoring cost of the intelligent vibration monitoring system of the mechanical bearing on the whole.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. The intelligent monitoring system for the vibration of the mechanical bearing is characterized by comprising a sensing subsystem, a data storage device and a visualization device, wherein the sensing subsystem is configured to collect vibration data of the mechanical bearing, the data storage device is configured to store the vibration data of the mechanical bearing, and the visualization device is configured to display the vibration data of the mechanical bearing; the sensing subsystem and the visualization device are connected with the data storage device; the sensor subsystem comprises a sink node and a plurality of sensor nodes configured to collect vibration data of the mechanical bearing, the sink node and the sensor nodes construct a wireless sensor network with a clustering structure in a self-organizing manner, and a cluster head is selected from the sensor nodes and clustered when network topology is constructed; the cluster head is configured to collect and send mechanical bearing vibration data collected by the sensor nodes in the cluster to the sink node; the aggregation node is configured to send the mechanical bearing vibration data summarized by each cluster head to the data storage device; the mechanical bearing vibration data comprises vibration acceleration signals of the mechanical bearing to be detected in the vertical direction under different working states; if the distance between each sensor node in the cluster and the corresponding cluster head does not exceed the set communication distance threshold value LσDirectly communicating with the corresponding cluster head; if the distance between the cluster head and the corresponding cluster head exceeds the set communication distance threshold value LσThe sensor node selects a neighbor sensor node which is close to the corresponding cluster head for communication; the neighbor sensor nodes of the sensor node i are other sensor nodes positioned in the communication range of the sensor node i; the sensor node periodically calculates and updates the congestion degree of the buffer space of the sensor node in the vibration data transmission process of the mechanical bearing, and simultaneously sends the updated congestion degree of the buffer space to the sensor node of the previous hop; the sensor node monitors the neighbor sensor node communicated with the sensor node in real time, and when the sensor node finds the buffer of the neighbor sensor nodeWhen the congestion degree of the storage space exceeds a set change degree threshold value, the sensor node reselects a neighbor sensor node for communication;
the congestion degree of the cache space at the initial time is set to be 0, and the calculation formula of the congestion degree of the cache space is as follows:
in the formula, Si(q) represents the calculated congestion degree of the cache space of the sensor node i at the moment of the current period q, di(q) is the length of a mechanical bearing vibration data packet queue in a self buffer space of the sensor node i at the moment of the current period q, di(q-delta q) the length of a mechanical bearing vibration data packet queue in a self buffer space of a sensor node i at the moment of a last period q, delta q is period interval time, di,maxThe initial cache size of the sensor node i; u is a cache space congestion degree influence factor, when the cache space congestion degree of a sensor node of the next hop of the sensor node i exceeds a set change degree threshold value, u is 0.1, otherwise, u is 0;
in the formula (I), the compound is shown in the specification,
distance, L, between a next hop sensor node of a sensor node i and the corresponding cluster headσSetting a communication distance threshold value for the communication distance; z [ d ]i(q)-di(q-Δq)]For the set judgment value function, when di(q)-diWhen (q- Δ q) is not less than 0, Z [ d ]i(q)-di(q-Δq)]When d is equal to 1i(q)-di(q-Δq)<At 0, Z [ d ]i(q)-di(q-Δq)]＝-0.4。
2. The intelligent mechanical bearing vibration monitoring system as recited in claim 1, wherein the sensor node comprises a sensor and a signal adapter configured to convert a sensor signal into corresponding mechanical bearing vibration data, the signal adapter being connected to the sensor.
3. The intelligent mechanical bearing vibration monitoring system as recited in claim 2, wherein the sensor node further comprises a controller configured to control a frequency of acquisition, the controller coupled to the sensor.
4. The intelligent mechanical bearing vibration monitoring system of claim 2, wherein the visualization device is further configured to: and before displaying the mechanical bearing vibration data, preprocessing the mechanical bearing vibration data, including removing data abnormal points and carrying out data normalization processing.
5. The intelligent mechanical bearing vibration monitoring system as claimed in claim 1, wherein the sensor node selects a neighbor sensor node closer to the corresponding cluster head for communication, and comprises:
(1) selecting neighbor sensor nodes meeting the path conditions from neighbor sensor nodes closer to the corresponding cluster head as alternative connection nodes, and classifying the alternative connection nodes into an alternative connection node set;
(2) the sensor node selects a neighbor sensor node with the largest current residual energy from the candidate connection node set for communication;
wherein the path condition is:
in the formula, OiRepresenting a corresponding cluster head of the sensor node i, and ij representing a jth neighbor sensor node in neighbor sensor nodes of the sensor node i, which are closer to the cluster head;
for the jth neighbor sensor node and cluster head OiDistance of (L)σFor the set communication distance threshold, niAnd the number of neighbor sensor nodes of the sensor node i, which are closer to the cluster head.
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| CN109347952A (en) * | 2018-10-17 | 2019-02-15 | 深圳众宝城贸易有限公司 | Rotary equipment bearings abnormal vibration intelligent prompt system |
| CN109115498B (en) * | 2018-10-17 | 2020-11-13 | 徐州丰禾回转支承制造股份有限公司 | Real-time acquisition and analysis system for vibration data of machine bearing |
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| CN109115499A (en) * | 2018-10-19 | 2019-01-01 | 广州益牛科技有限公司 | Equipment bearing vibration data intelligence is capable of real-time acquisition and analysis system |
| CN109120832A (en) * | 2018-10-22 | 2019-01-01 | 泉州兴哲商贸有限公司 | Equipment bearing vibration data acquires in real time and analysis system |
| CN109211569A (en) * | 2018-10-25 | 2019-01-15 | 广州益牛科技有限公司 | Machine bearing vibrates intelligent monitor system |
| CN109238716B (en) * | 2018-11-08 | 2020-04-28 | 乔崴进(安徽)科技有限公司 | Real-time intelligent mechanical bearing abnormity detection system |
| CN109443770B (en) * | 2018-12-03 | 2019-11-26 | 慈溪市由由轴承有限公司 | A kind of mechanical bearing abnormal vibration detection system of real-time intelligent |
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