CN117741315A - Equipment online monitoring method and system based on equipment report data - Google Patents
Equipment online monitoring method and system based on equipment report data Download PDFInfo
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- CN117741315A CN117741315A CN202311795436.XA CN202311795436A CN117741315A CN 117741315 A CN117741315 A CN 117741315A CN 202311795436 A CN202311795436 A CN 202311795436A CN 117741315 A CN117741315 A CN 117741315A
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 238000011156 evaluation Methods 0.000 claims abstract description 50
- 230000036541 health Effects 0.000 claims abstract description 31
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 238000012423 maintenance Methods 0.000 claims abstract description 17
- 238000013210 evaluation model Methods 0.000 claims abstract description 9
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
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Abstract
The invention provides an equipment online monitoring method and system based on equipment report data. The method comprises the following steps: at least one fault detection is carried out on the target equipment in the running state of the target equipment; each of the fault detections includes: acquiring equipment report data of a preset target measuring point in target equipment, wherein the equipment report data is used for representing the running state of the target equipment; extracting feature data of preset dimensions from the device report data; inputting the characteristic data of the preset dimension into a device state evaluation model to obtain a current health evaluation index of the target device; and determining a fault detection result of the target equipment based on the health evaluation index and a preset evaluation condition. According to the invention, the fault condition of the equipment can be timely monitored by monitoring and analyzing the equipment report data in the running process of the equipment, so that maintenance personnel can take timely action to avoid sudden faults.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an equipment online monitoring method and system based on equipment report data.
Background
The intelligent device may malfunction or be abnormal during long-term use, resulting in performance degradation and even stop. Therefore, monitoring of the status of the device and fault diagnosis become vital. At present, a conventional equipment monitoring mode is based on emergency maintenance when regular maintenance or faults occur, wherein the regular maintenance mode needs to interrupt the working state of equipment, and the emergency maintenance is performed when the faults occur, and the faults often have larger influence on the equipment or the currently executed task of the equipment, so that the maintenance cost is higher.
Disclosure of Invention
The invention aims to: the invention aims to provide an equipment on-line monitoring method and system based on equipment report data, which can detect equipment states in real time in the running process of equipment and discover fault conditions in time based on the equipment states.
The invention comprises the following steps: in order to achieve the above purpose, the present invention proposes the following technical solutions:
in a first aspect, an online device monitoring method based on device report data is provided, where in the method, at least one fault detection is performed on a target device in a target device operation state; each of the fault detections includes:
acquiring equipment report data of a preset target measuring point in target equipment, wherein the equipment report data is used for representing the running state of the target equipment;
extracting feature data of preset dimensions from the device report data;
inputting the characteristic data of the preset dimension into a device state evaluation model to obtain a current health evaluation index of the target device;
and determining a fault detection result of the target equipment based on the health evaluation index and a preset evaluation condition.
As an optional implementation manner of the method of the first aspect, after acquiring device report data of a target measurement point preset in the target device, the method further includes:
preprocessing the equipment report data, wherein the preprocessing comprises processing the equipment report data by adopting at least one mode of data cleaning, denoising and outlier processing.
As an optional implementation manner of the method of the first aspect, the device state evaluation model performs weighted summation on the feature data based on a preset weight of the feature data of each dimension, so as to obtain a current health evaluation index of the target device.
Specifically, the device state assessment model calculates the current health assessment indicator for the target device using the following formula:
wherein F represents the current health evaluation index of the target equipment, and F i Feature data representing dimension i, w i Represents f i N represents the total number of dimensions of the feature data and b represents the bias term.
As an optional implementation of the method according to the first aspect, the evaluation condition is set according to a fault type; based on the health evaluation index and a preset evaluation condition, determining a fault detection result of the target device specifically comprises:
and judging whether the health evaluation index meets the evaluation condition, and if so, determining the fault type of the target equipment.
As an optional implementation of the method according to the first aspect, the method further comprises:
and when determining that the target equipment has faults, determining a maintenance strategy based on the fault type.
In particular, the maintenance policy is pre-stored and the maintenance policy is stored in association with the fault type.
In a second aspect, an online device monitoring system based on device report data is provided, where the system includes:
the first data acquisition module is used for acquiring equipment report data of a target measuring point preset in target equipment in the running state of the target equipment; the device report data is used for representing the running state of the target device;
the feature extraction module is used for extracting feature data with preset dimensions from the equipment report data;
the evaluation module is used for inputting the characteristic data of the preset dimension into a device state evaluation model to obtain the current health evaluation index of the target device;
and the determining module is used for determining a fault detection result of the target equipment based on the health evaluation index and a preset evaluation condition.
In a third aspect, a computer storage medium is provided, on which a computer program is stored, the computer program, when executed by a processor, implementing the device online monitoring method based on device report data.
In a fourth aspect, there is provided an electronic device comprising:
one or more processors; and
and a memory associated with the one or more processors, the memory configured to store program instructions that, when read and executed by the one or more processors, perform the device-based on-line monitoring method of device-report data.
The beneficial effects are that: according to the invention, the device report data in the running process of the device is acquired based on the data acquisition module integrated in the device, and the device report data is monitored and analyzed through the data analysis technology, so that the fault condition of the device can be monitored in time, and maintenance personnel can take timely action to avoid sudden faults. By timely maintenance and precautions, the lifetime of the equipment can be effectively extended, which will reduce the cost of equipment replacement and purchase. On the other hand, the invention adopts an on-line monitoring scheme, does not need to operate in the equipment shutdown state, and reduces the equipment shutdown time.
Drawings
Fig. 1 is a flow chart of an on-line monitoring method of a device based on device report data according to an embodiment;
fig. 2 is a block diagram of an on-line monitoring system for devices based on device report data according to an embodiment.
Detailed Description
The invention will be further described with reference to the drawings and the specific examples. It is to be understood that the invention may be embodied in various forms and that the exemplary and non-limiting embodiments shown in the drawings and described below are not intended to limit the invention to the specific embodiments described.
It is to be understood that the technical features listed above for the different embodiments may be combined with each other where technically feasible to form further embodiments within the scope of the invention. Furthermore, the particular examples and embodiments described herein are not limiting and corresponding modifications may be made to the structures, steps, and sequences set forth above without departing from the scope of the invention.
The embodiment aims at providing an equipment on-line monitoring method and system based on equipment report data acquisition and data analysis technology.
Referring to fig. 1, fig. 1 schematically shows a flow chart of an on-line monitoring method for a device based on data reported by the device, where the method performs at least one fault detection on a target device in an operating state of the target device; each of the fault detections includes steps S101 to S104:
s101, acquiring device report data of a preset target measuring point in target devices.
It should be noted that, the device report data output by the target measurement point can reflect the operation state of the target device, and the selection of the target measurement point can be adaptively selected according to the device type and the fault type to be monitored, which is not limited in this embodiment.
One or more target measuring points can be selected, that is, the number of the target measuring points can also be adaptively selected according to the type of the equipment and the type of the fault to be monitored, which is not limited in this embodiment.
The above-mentioned device report data may be various data generated during the operation of the device, so long as the device can reflect the operation state of the device, for example, sensor data inside/outside the target device, operation state data of one or more modules/components in the target device, operation parameters of the target device, and the like may be included.
The device report data can be obtained through a data acquisition module, and the data acquisition module is in accordance with a data transmission protocol specified by a data transmission interface of the target device, so that the data acquisition module can acquire the device data in real time through interaction with the data transmission interface of the device.
In some implementations, the collected device report data may be stored and preprocessed. The preprocessing mode can be adaptively selected according to the type of the data submitted by the equipment and the subsequent analysis requirement, for example, the preprocessing mode can comprise one or more modes of data cleaning, denoising, outlier processing and the like. Through preprocessing, the accuracy and consistency of the data reported by the equipment can be ensured, so that a reliable data basis is provided for subsequent equipment diagnosis analysis.
S102, extracting feature data with preset dimensions from the device report data.
It should be noted that, the feature data of the preset dimension refers to data useful for subsequent data analysis, where the dimension and the number of dimensions may be adaptively set according to the analysis requirement, which is not limited in this embodiment. For example, feature data of dimensions such as frequency domain features, time domain features, statistical features, etc. may be extracted from the device report data for subsequent device health status diagnostic analysis.
In some implementations, feature extraction may be achieved by signal processing, data analysis, and the like.
S103, inputting the characteristic data of the preset dimension into a device state evaluation model to obtain the current health evaluation index of the target device.
Specifically, the device state evaluation model may perform weighted summation on the input feature data based on the preset weights of the feature data of each dimension, so as to obtain the current health evaluation index of the target device.
In some implementations, the device state assessment model may calculate the current health assessment indicator for the target device using the following formula:
wherein F represents the current health evaluation index of the target equipment and is used for reflecting the running state and the equipment condition of the target equipment, and F i Feature data representing dimension i, controlling the importance of each feature in health assessment, w i Represents f i N represents the total number of dimensions of the feature data and b represents the bias term.
S104, determining a fault detection result of the target equipment based on the health evaluation index and a preset evaluation condition.
And (5) performing on-line health state diagnosis on the target equipment by using the health evaluation index.
It should be noted that, the evaluation condition is set according to the fault type, that is, whether the target device has the fault type corresponding to the evaluation condition can be determined by determining whether the health evaluation index evaluates the condition. When a certain evaluation condition is met, then it can be determined that the target device is currently malfunctioning and the type of malfunction is determined.
The evaluation conditions may be adaptively set according to specific requirements of fault diagnosis, and only one evaluation condition may be set, or a plurality of evaluation conditions may be set. Whether or not there is a certain failure in the target device may be determined by a single evaluation condition, or may be determined by a plurality of evaluation conditions. This embodiment is not limited thereto.
In some implementations, the evaluation condition may be a trigger threshold for sensor data, an observation within a time window, or a particular event occurrence, or the like.
In some alternative embodiments, corresponding fault handling and maintenance policies may also be formulated based on the fault detection results.
These maintenance policies may be pre-stored and stored in association with the fault type so that after the fault type is determined, the corresponding maintenance policy may be retrieved from the fault type.
The embodiment also provides an equipment online monitoring system based on the equipment report data, which is used for realizing the equipment online monitoring method based on the equipment report data. Referring to fig. 2, fig. 2 schematically shows a structure of an on-line monitoring system for devices based on data reported by the devices, the system includes:
the first data obtaining module 201 is configured to obtain, in an operation state of the target device, device report data of a target measurement point preset in the target device. The device report data here is used to characterize the operational status of the target device.
The feature extraction module 202 is configured to extract feature data of a preset dimension from device report data.
The evaluation module 203 is configured to input feature data of a preset dimension into a device state evaluation model to obtain a current health evaluation index of the target device.
The determining module 204 is configured to determine a fault detection result of the target device based on the health evaluation index and a preset evaluation condition.
The embodiment also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the above-mentioned device online monitoring method based on device report data.
The embodiment also provides an electronic device, which includes:
one or more processors; and
and a memory associated with the one or more processors, the memory configured to store program instructions that, when read and executed by the one or more processors, perform the device online monitoring method based on device report data described above.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The equipment online monitoring method based on the equipment report data is characterized in that at least one fault detection is carried out on target equipment in the running state of the target equipment; each of the fault detections includes:
acquiring equipment report data of a preset target measuring point in target equipment, wherein the equipment report data is used for representing the running state of the target equipment;
extracting feature data of preset dimensions from the device report data;
inputting the characteristic data of the preset dimension into a device state evaluation model to obtain a current health evaluation index of the target device;
and determining a fault detection result of the target equipment based on the health evaluation index and a preset evaluation condition.
2. The method of claim 1, further comprising, after acquiring the device report data of the target measurement point preset in the target device:
preprocessing the equipment report data, wherein the preprocessing comprises processing the equipment report data by adopting at least one mode of data cleaning, denoising and outlier processing.
3. The method of claim 1, wherein the device state assessment model performs weighted summation on the feature data based on a preset weight of the feature data of each dimension to obtain a current health assessment index of the target device.
4. The method of claim 3, wherein the device state assessment model calculates the current health assessment indicator for the target device using the following formula:
wherein F represents the current health evaluation index of the target equipment, and F i Feature data representing dimension i, w i Represents f i N represents the total number of dimensions of the feature data and b represents the bias term.
5. The method of claim 1, wherein the evaluation condition is set according to a fault type; based on the health evaluation index and a preset evaluation condition, determining a fault detection result of the target device specifically comprises:
and judging whether the health evaluation index meets the evaluation condition, and if so, determining the fault type of the target equipment.
6. The method of claim 1, wherein the method further comprises:
and when determining that the target equipment has faults, determining a maintenance strategy based on the fault type.
7. The method of claim 6, wherein the maintenance policy is pre-stored and the maintenance policy is stored in association with the fault type.
8. Device on-line monitoring system based on data is reported to equipment, characterized by comprising:
the first data acquisition module is used for acquiring equipment report data of a target measuring point preset in target equipment in the running state of the target equipment; the device report data is used for representing the running state of the target device;
the feature extraction module is used for extracting feature data with preset dimensions from the equipment report data;
the evaluation module is used for inputting the characteristic data of the preset dimension into a device state evaluation model to obtain the current health evaluation index of the target device;
and the determining module is used for determining a fault detection result of the target equipment based on the health evaluation index and a preset evaluation condition.
9. A computer storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1 to 7.
10. An electronic device, comprising:
one or more processors; and
a memory associated with the one or more processors for storing program instructions that, when read for execution by the one or more processors, perform the method of any of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311795436.XA CN117741315A (en) | 2023-12-25 | 2023-12-25 | Equipment online monitoring method and system based on equipment report data |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311795436.XA CN117741315A (en) | 2023-12-25 | 2023-12-25 | Equipment online monitoring method and system based on equipment report data |
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| CN117741315A true CN117741315A (en) | 2024-03-22 |
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| CN202311795436.XA Pending CN117741315A (en) | 2023-12-25 | 2023-12-25 | Equipment online monitoring method and system based on equipment report data |
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