CN114353869B - Online monitoring method and system for mobile equipment and readable storage medium - Google Patents
Online monitoring method and system for mobile equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a dynamic equipment on-line monitoring method, a system and a readable storage medium, which relate to the technical field of equipment monitoring and comprise the following steps: establishing and storing a data monitoring model based on the operation characteristics of each device to be tested; establishing a first association model for reflecting theoretical association relations among all monitoring data; setting a plurality of monitoring data acquisition points on equipment to be tested to acquire monitoring data; and receiving each monitoring data, judging the reliability of the data according to the first association model, and outputting the running state data of the equipment to be tested based on the reliability data and combining the data monitoring model. Through the technical scheme, the obtained monitoring data can be effectively checked, so that the accuracy of the monitoring data is improved, and meanwhile, fault sites in equipment to be tested or a data transmission link can be identified in the monitoring data checking process, so that the reliability of a monitoring system is remarkably improved.
Description
Technical Field
The invention relates to the technical field of equipment monitoring, in particular to a method and a system for online monitoring of mobile equipment and a readable storage medium.
Background
In industrial parks such as refining, petroleum, steel and the like, stable and safe operation of each movable device (namely, a rotating device driven by a driving machine, such as a pump, a compressor, a fan and the like) is related to the efficiency and the safety of the whole production. The operation stability of the equipment is guaranteed, the hidden trouble of the equipment is eliminated in time, the production efficiency and the safety can be effectively improved, the comprehensive management cost can be reduced, and the service life of the equipment is prolonged.
However, because of the wide distribution of various devices in the production environment such as petroleum refining, the problems occurring in the operation process of the devices cannot be well found by means of real-time inspection by personnel. At present, remote monitoring of equipment can be realized by arranging monitoring sensors (such as vibration sensors, temperature sensors and the like) on site of the equipment, but the inventor finds that the reasons for abnormal data of a single equipment are often caused by various factors in practice, and besides the faults of the equipment, the abnormal operation of other related equipment, the monitoring faults of the monitoring sensors or the interference of a data signal transmission network are possibly caused.
How to ensure the accuracy of the monitoring data and simultaneously improve the reliability of the whole monitoring system is a key for improving the management efficiency of the movable equipment in the whole park and ensuring the safe operation of the movable equipment.
Disclosure of Invention
Aiming at the problem that the data acquired by a mobile equipment monitoring system are easy to be interfered in actual application so as to lead to inaccurate monitoring results, the application aims at providing an online mobile equipment monitoring method which aims at each equipment to be monitored to establish a data model, clean and screen the monitored data, ensure the effectiveness of the data and further improve the reliability of the whole monitoring system. And secondly, the on-line monitoring system of the mobile equipment is provided. The third objective is to propose and protect a computer readable storage medium loaded with a method for realizing the above-mentioned on-line monitoring of mobile equipment, and the specific scheme is as follows:
an on-line monitoring method for a mobile device, comprising:
establishing and storing a data monitoring model based on the operation characteristics of each device to be tested;
establishing a first association model for reflecting theoretical association relations among all monitoring data;
setting a plurality of monitoring data acquisition points on equipment to be tested to acquire monitoring data;
and receiving each monitoring data, judging the reliability of the data according to the first association model, and outputting the running state data of the equipment to be tested based on the reliability data and combining the data monitoring model.
According to the technical scheme, the acquired monitoring data can be examined by using the first association model, and if the monitoring data does not meet the first association model, the monitoring data is judged to be invalid or unreliable, so that the accuracy of the monitoring data is improved, and the reliability of a monitoring system is remarkably improved.
Further, the establishing a first association model for reflecting the theoretical association relation between the monitoring data includes:
setting a plurality of monitoring data acquisition points on the same equipment to be tested and configuring a monitoring sensor;
acquiring and storing standard data acquired by each monitoring sensor when the equipment to be tested normally operates;
and fitting and generating association relations among corresponding data of the equipment to be detected on each monitoring data acquisition point based on the standard data, and generating the first association model.
In practical application, each monitoring data acquisition point on the same equipment to be detected generally has relevance, through the technical scheme, the accuracy of each monitoring data is judged by utilizing the relevance among different monitoring data acquisition points of the same equipment to be detected, invalid interference data is eliminated, and the monitoring accuracy of the equipment is improved.
Further, the establishing a first association model for reflecting the theoretical association relationship between the monitoring data further includes:
acquiring an association relationship between the devices to be tested based on a device network formed between the devices to be tested;
setting monitoring data acquisition points on a plurality of devices to be tested and configuring monitoring sensors;
acquiring association relations among corresponding monitoring data of each device to be tested when the device network normally operates;
and generating the first association model based on the association relation.
The equipment to be detected in the same equipment network has the association in the control relation, so that the running state of the equipment to be detected also has the association.
Further, the method further comprises:
storing the standard fluctuation interval of the monitoring data corresponding to each device to be tested;
generating a change trend of monitoring data of each device to be tested based on the historical monitoring data fitting;
and calculating the time required by the monitoring data to exceed the standard fluctuation interval according to the change trend and the current moment, and outputting an alarm.
Through the technical scheme, the change trend of the monitoring data can be prejudged in advance, so that the alarm can be given out in time before the equipment to be tested breaks down, and the occurrence of shutdown or accidents is reduced.
Further, the method further comprises:
collecting monitoring data corresponding to each mobile device and comparing the monitoring data with the monitoring data collected in the adjacent time period;
if the fluctuation amplitude of the monitoring data exceeds a set threshold value, searching the monitoring data related to the monitoring data based on the first association model and judging whether the fluctuation amplitude exceeds the set threshold value;
if the fluctuation range of the related monitoring data exceeds a set threshold value, outputting a fault alarm of the equipment;
if the fluctuation range of the related monitoring data does not exceed the set threshold value, calculating and generating a theoretical value corresponding to the monitoring data with the fluctuation range exceeding the set threshold value based on the first related model and combining the related monitoring data, and outputting a monitoring sensor fault alarm.
Through the technical scheme, the reason of the data abnormality can be judged by monitoring the data, so that maintenance personnel can conveniently maintain related equipment.
Further, the receiving each monitoring data and determining the reliability of the data according to the first association model includes:
establishing a cross verification relation among the corresponding monitoring data of the plurality of monitoring data acquisition points to form a plurality of cross verification models, wherein one monitoring data is respectively associated with a plurality of associated monitoring data to form a plurality of corresponding cross verification models;
inputting the collected monitoring data into a corresponding cross verification model, and obtaining a plurality of verification data corresponding to the monitoring data collection points based on different cross verification models;
if the verification data are consistent, the monitoring data are judged to be reliable;
and if the verification data are inconsistent, selecting a cross verification model corresponding to the verification data, acquiring other monitoring data related to the cross verification model, and judging and outputting the reliability of the other monitoring data based on the other monitoring data and the corresponding cross verification model.
Through the technical scheme, abnormal data can be found out and eliminated through the association relation among the data, and the accuracy of the monitored data is ensured.
Further, the method further comprises:
establishing and storing fault diagnosis data of each device to be tested;
establishing a second relation model between the monitoring data and fault diagnosis data based on the historical monitoring data of each device to be tested;
and acquiring monitoring data of the current equipment to be tested, and generating fault diagnosis data of the equipment to be tested based on the second relation model.
Through the technical scheme, the equipment fault information can be deduced through the monitoring data, so that the equipment maintenance efficiency is improved.
Based on the above method, the application further provides a computer readable storage medium loaded with a computer program algorithm module for implementing the dynamic device online monitoring method.
Further, the application also provides an online monitoring system of the mobile device, which comprises:
the data acquisition end comprises a monitoring sensor arranged on the equipment to be detected and is used for acquiring and outputting monitoring data of the equipment to be detected;
the server end is in data connection with the data acquisition end and comprises a data processing unit, a data storage unit and a data output unit, wherein the data storage unit is provided with the computer readable storage medium, the data processing unit receives the monitoring data output by the data acquisition end, processes the monitoring data based on a computer program algorithm module stored in the data storage unit and outputs the processed monitoring data to the data output unit, and the data output unit receives the monitoring data and outputs the processed monitoring data after format processing;
the monitoring terminal is in data connection with the data output unit of the server end, receives the monitoring data and displays the monitoring data;
the communication terminal is configured to be used for communication connection among the data acquisition terminal, the server terminal and the monitoring terminal.
Further, the server side is configured with a local server and a cloud server, and the monitoring terminal is respectively connected with the local server and the cloud server through the communication side in a data mode.
Compared with the prior art, the invention has the following beneficial effects:
through establishing the associated data model among all the monitoring data, the acquired monitoring data can be subjected to validity check, so that the accuracy of the monitoring data is improved, and meanwhile, fault sites in equipment to be tested or a data transmission link can be identified in the monitoring data check process, so that the reliability of a monitoring system is remarkably improved.
Drawings
FIG. 1 is a schematic overall flow diagram of the method of the present invention;
FIG. 2 is a diagram illustrating a data reliability determination process according to the present invention;
fig. 3 is a schematic diagram of the framework of the system of the present invention.
Reference numerals: 1. a data acquisition end; 2. a server side; 3. a monitoring terminal; 4. a communication end; 31. A cloud server; 32. and a local server.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.
An online monitoring method for a mobile device, as shown in fig. 1, mainly comprises the following steps:
s100, establishing and storing a data monitoring model based on the operation characteristics of each device to be tested;
s200, establishing a first association model for reflecting theoretical association relations among all monitoring data;
s300, setting a plurality of monitoring data acquisition points on equipment to be tested to acquire monitoring data;
s400, receiving all monitoring data, judging the reliability of the data according to the first association model, and outputting the running state data of the equipment to be tested based on the reliability data and combining the data monitoring model.
It should be noted that the above steps may be sequentially performed in the above order, or may be performed in a permuted order as needed, such as steps S100 and S200 described above. The order in the embodiments of the present application is merely a preferred implementation.
In the step S100, the data monitoring model is established and stored based on the operation characteristics of each device to be tested, that is, the association relationship between the temperature data and the vibration data, for example, the functional relationship between the data, is established according to the operation data, such as the temperature data and the vibration data, of the device to be tested when the device to be tested is in the normal operation state. Because the collected data types are different and the types of the equipment to be detected are different, the data monitoring models corresponding to the equipment to be detected are different, and when the data is monitored, the data monitoring models are used as standard references, so that the running state of the equipment to be detected can be accurately and reasonably reflected.
In practical applications, each monitoring data acquisition point on the same device to be tested usually has a correlation, for example, data acquired by temperature sensors located at different positions on a motor housing often has a direct relationship with the running state of the motor, so that the monitoring data acquired from each monitoring data acquisition point also has a correlation. For this purpose, the step S200 includes:
s210, setting a plurality of monitoring data acquisition points on the same equipment to be tested and configuring a monitoring sensor;
s211, acquiring and storing standard data acquired by each monitoring sensor when the equipment to be tested normally operates;
s212, based on the standard data, fitting and generating association relations among corresponding data of the equipment to be tested on each monitoring data acquisition point, and generating the first association model.
And the accuracy of each monitoring data is judged by utilizing the relevance among different monitoring data acquisition points of the same equipment to be detected, so that invalid interference data can be eliminated, and the monitoring accuracy of the equipment is improved. For example, a plurality of vibration sensors are arranged on a shell of a motor to be tested, if the vibration amplitude acquired by one vibration sensor is 0, and the vibration amplitude acquired by the vibration sensors which are arranged in a related manner is X, and X is a standard reference value, then the fault or abnormal monitoring data of the one vibration sensor can be judged, the monitoring data are further removed, and the accuracy of the monitoring data is improved.
Similar to setting a plurality of monitoring data acquisition points on a single device under test, the above step S200 may further employ the following steps:
s220, acquiring an association relationship between the devices to be tested based on a device network formed between the devices to be tested;
s221, setting monitoring data acquisition points on a plurality of devices to be tested and configuring monitoring sensors;
s222, acquiring association relations among corresponding monitoring data of devices to be tested in normal operation of the device network, and generating the first association model based on the association relations.
The significance of the above steps is that: the device to be detected in the same device network has the correlation in control or material transmission, so that the running state of the device to be detected can also have the correlation, by the technical scheme, the correlation between the device to be detected and the device to be detected can be utilized to establish a data correlation model, namely a first correlation model, so that collected monitoring data can be verified, interference is eliminated, monitoring accuracy is improved, for example, the device A and the device B which have the correlation relation, the relation of the monitoring data is y=f (x), x is a certain running monitoring data of the device A, y is a certain running monitoring data of the device B, and after the device A fails, the data can be reflected through the monitoring data of the device B, so that verification among the monitoring data is realized.
In the step S300, the monitoring data collection point on the device to be tested may be selected according to the type of the device or the type of the data to be monitored, and the monitoring sensor, such as a temperature sensor and a vibration sensor, may be disposed at the corresponding position after the selection.
In the step S400, receiving each monitoring data and determining the reliability of the data according to the first correlation model, as shown in fig. 2, including:
s410, establishing a cross verification relation among the monitoring data corresponding to the plurality of monitoring data acquisition points to form a plurality of cross verification models, wherein one monitoring data is respectively associated with a plurality of associated monitoring data to form a plurality of corresponding cross verification models. Taking vibration monitoring on a motor shell as an example, setting A, B, C to total 3 monitoring data acquisition points, wherein the association relationship between the acquisition point A and the acquisition point B is f1, namely the cross verification model, the association relationship between the acquisition point A and the acquisition point C is f2, and the like, so that 3 cross verification models can be established.
S411, inputting the collected monitoring data into the corresponding cross verification model, and obtaining a plurality of verification data corresponding to the monitoring data collection points based on different cross verification models. For example, the monitoring data acquired by the acquisition point a is input to f1 and f2, respectively, so that the monitoring data which should theoretically correspond to the acquisition point B and the acquisition point C can be obtained, and otherwise, the monitoring data corresponding to the plurality of acquisition points a can be obtained.
Referring to fig. 2, if the verification data is identical, it is determined that the monitoring data is reliable;
and if the verification data are inconsistent, selecting a cross verification model corresponding to the verification data, acquiring other monitoring data related to the cross verification model, and judging and outputting the reliability of the other monitoring data based on the other monitoring data and the corresponding cross verification model. For example, when the monitored data corresponding to the collection point a is abnormal, the reason for the abnormality may be the collection point a itself, or may be the abnormality of the monitored data of other related collection points, for example, the abnormality of the monitored data of the collection point C may cause an abnormality in one of the verification data, and the reliability of the collection point C is verified based on the collection point C, so that the source of the abnormal monitored data is finally determined and excluded, thereby ensuring the accuracy of the monitored data.
In an embodiment of the present application, the method further includes:
s500, storing the standard fluctuation interval of the monitoring data corresponding to each device to be tested;
s501, generating a change trend of monitoring data of each device to be tested based on historical monitoring data fitting;
s502, calculating the time required by the monitoring data to exceed the standard fluctuation interval according to the change trend and the current moment, and outputting an alarm.
The technical scheme can pre-judge the change trend of the monitoring data in advance, and then can give an alarm in time before the equipment to be tested breaks down, so that the occurrence of shutdown or accidents is reduced.
Further, the method further comprises:
s600, collecting monitoring data corresponding to each mobile device and comparing the monitoring data with the monitoring data collected in the adjacent time period;
if the fluctuation amplitude of the monitoring data exceeds a set threshold value, searching the monitoring data related to the monitoring data based on the first association model and judging whether the fluctuation amplitude exceeds the set threshold value;
s601, if the fluctuation range of the related monitoring data exceeds a set threshold value, outputting equipment fault warning;
s602, if the fluctuation range of the associated monitoring data does not exceed the set threshold, calculating a theoretical value corresponding to the monitoring data with the fluctuation range exceeding the set threshold based on the first association model and combining the associated monitoring data, and outputting a monitoring sensor fault alarm.
Because of the association relation among the monitoring data, when one of the monitoring data is abnormal, the source of the abnormal data can be judged, namely, the equipment to be tested is in fault, and if only one of the monitoring data is abnormal, the fault of the monitoring sensor can be judged, and the alarm is output in a classified mode, so that the maintenance is convenient, and the monitoring effect is improved.
In an embodiment of the present application, the method further includes:
s700, establishing and storing fault diagnosis data of each device to be tested;
s701, establishing a second relation model between monitoring data and fault diagnosis data based on historical monitoring data of each device to be tested;
s702, acquiring monitoring data of the current equipment to be tested, and generating fault diagnosis data of the equipment to be tested based on the second relation model.
According to the technical scheme, the equipment fault information is deduced through the monitoring data, so that the equipment maintenance efficiency can be effectively improved, meanwhile, the service life or the fault period of the equipment can be predicted according to the change of the monitoring data, and the maintenance preparation work is convenient to be done in advance.
In the application, based on the method, a computer readable storage medium is also provided, and a computer program algorithm module for realizing the online monitoring method of the mobile device is loaded and stored. The above computer readable storage medium may be a data hard disk, a data optical disk, an SD card, or the like.
As shown in fig. 3, the present application further provides an online monitoring system for a mobile device, which mainly includes: the system comprises a data acquisition end 1, a server end 2, a monitoring terminal 3 and a communication end 4.
The data acquisition end 1 comprises a monitoring sensor arranged on the device to be detected and is used for acquiring and outputting monitoring data of the device to be detected, such as a temperature sensor, a vibration sensor, a rotation speed sensor, a displacement sensor and the like, and respectively outputting corresponding monitoring data. It should be noted that the sensors described in the present application refer to sensor modules having acquisition and output functions, such as a temperature sensor module configured with a wireless signal transmission function, a vibration sensor module, and the like.
The server 2 is in data connection with the data acquisition 1 through a communication end 4, and the communication end 4 is configured as a 4G/5G communication module or other intelligent wireless communication device for realizing data communication between the server 2 and the data acquisition 1.
The server side 2 is configured with a data processing unit, a data storage unit, and a data output unit. The data storage unit is configured with the computer readable storage medium, such as a computer hard disk loaded with a related program algorithm, and the data processing unit receives the monitoring data output by the data acquisition end 1, processes the monitoring data based on the computer program algorithm module stored in the data storage unit, and outputs the monitoring data to the data output unit. The data output unit receives the monitoring data, formats the monitoring data and outputs the monitoring data. The format processing includes outputting the monitoring data in the form of a set chart or in the form of a set text description. In this embodiment of the present application, the server includes a local server 32 and a cloud server 31, so as to facilitate uploading and accessing of monitoring data, and realize remote monitoring of devices.
In this embodiment of the present application, the monitoring terminal 3 may be configured as an intelligent display screen, an intelligent mobile phone, a tablet computer, etc., and is in data connection with the data output unit of the server 2, and receives and displays the monitoring data after being processed by the format.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (7)
1. An on-line monitoring method for a mobile device, comprising:
establishing and storing a data monitoring model based on the operation characteristics of each device to be tested;
establishing a first association model for reflecting theoretical association relations among all monitoring data;
setting a plurality of monitoring data acquisition points on equipment to be tested to acquire monitoring data;
receiving all monitoring data, judging the reliability of the data according to the first association model, and outputting the running state data of the equipment to be tested based on the reliability data and combining the data monitoring model;
the establishing a first association model for reflecting the theoretical association relation between the monitoring data comprises the following steps:
setting a plurality of monitoring data acquisition points on the same equipment to be tested and configuring a monitoring sensor;
acquiring and storing standard data acquired by each monitoring sensor when the equipment to be tested normally operates;
fitting and generating association relations among corresponding data of the equipment to be detected on each monitoring data acquisition point based on the standard data, and generating the first association model;
the establishing a first association model for reflecting the theoretical association relation between the monitoring data further comprises:
acquiring an association relationship between the devices to be tested based on a device network formed between the devices to be tested;
setting monitoring data acquisition points on a plurality of devices to be tested and configuring monitoring sensors;
acquiring association relations among corresponding monitoring data of each device to be tested when the device network normally operates;
generating the first association model based on the association relation;
the receiving each monitoring data and judging the data reliability according to the first association model comprises the following steps:
establishing a cross verification relation among the corresponding monitoring data of the plurality of monitoring data acquisition points to form a plurality of cross verification models, wherein one monitoring data is respectively associated with a plurality of associated monitoring data to form a plurality of corresponding cross verification models;
inputting the collected monitoring data into a corresponding cross verification model, and obtaining a plurality of verification data corresponding to the monitoring data collection points based on different cross verification models;
if the verification data are consistent, the monitoring data are judged to be reliable;
and if the verification data are inconsistent, selecting a cross verification model corresponding to the verification data, acquiring other monitoring data related to the cross verification model, and judging and outputting the reliability of the other monitoring data based on the other monitoring data and the corresponding cross verification model.
2. The method as recited in claim 1, further comprising:
storing the standard fluctuation interval of the monitoring data corresponding to each device to be tested;
generating a change trend of monitoring data of each device to be tested based on the historical monitoring data fitting;
and calculating the time required by the monitoring data to exceed the standard fluctuation interval according to the change trend and the current moment, and outputting an alarm.
3. The method as recited in claim 2, further comprising:
collecting monitoring data corresponding to each mobile device and comparing the monitoring data with the monitoring data collected in the adjacent time period;
if the fluctuation amplitude of the monitoring data exceeds a set threshold value, searching the monitoring data related to the monitoring data based on the first association model and judging whether the fluctuation amplitude exceeds the set threshold value;
if the fluctuation range of the related monitoring data exceeds a set threshold value, outputting a fault alarm of the equipment;
if the fluctuation range of the related monitoring data does not exceed the set threshold value, calculating and generating a theoretical value corresponding to the monitoring data with the fluctuation range exceeding the set threshold value based on the first related model and combining the related monitoring data, and outputting a monitoring sensor fault alarm.
4. The method as recited in claim 1, further comprising:
establishing and storing fault diagnosis data of each device to be tested;
establishing a second relation model between the monitoring data and fault diagnosis data based on the historical monitoring data of each device to be tested;
and acquiring monitoring data of the current equipment to be tested, and generating fault diagnosis data of the equipment to be tested based on the second relation model.
5. A computer readable storage medium loaded with computer program algorithm modules for implementing the method of on-line monitoring of a mobile device according to any of claims 1-4.
6. An on-line monitoring system for a mobile device, comprising:
the data acquisition end (1) comprises a monitoring sensor arranged on the equipment to be detected and is used for acquiring and outputting monitoring data of the equipment to be detected;
the server end (2) is in data connection with the data acquisition end (1) and comprises a data processing unit, a data storage unit and a data output unit, wherein the computer readable storage medium as claimed in claim 5 is configured in the data storage unit, the data processing unit receives the monitoring data output by the data acquisition end (1), processes the monitoring data based on a computer program algorithm module stored in the data storage unit and outputs the monitoring data to the data output unit, and the data output unit receives the monitoring data and performs format processing and then outputs the monitoring data;
the monitoring terminal (3) is in data connection with the data output unit of the server (2), receives the monitoring data and displays the monitoring data;
the communication terminal (4) is configured to be used for communication connection among the data acquisition terminal (1), the server terminal (2) and the monitoring terminal (3).
7. The system according to claim 6, wherein the server side (2) configures a local server (32) and a cloud server (31), and the monitoring terminal (3) is respectively in data connection with the local server (32) and the cloud server (31) through the communication side (4).
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