CN110703743A - Equipment failure prediction and detection system and method - Google Patents
Equipment failure prediction and detection system and method Download PDFInfo
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- CN110703743A CN110703743A CN201911104021.7A CN201911104021A CN110703743A CN 110703743 A CN110703743 A CN 110703743A CN 201911104021 A CN201911104021 A CN 201911104021A CN 110703743 A CN110703743 A CN 110703743A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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Abstract
The invention provides a system and a method for predicting and detecting equipment faults, which relate to the technical field of spare fault detection and comprise a data acquisition device, a data processing device and a data processing device, wherein the data acquisition device is used for acquiring operation data and environment data of a plurality of pieces of equipment distributed in different areas; a data storage device for storing the operation data and the environment data of a plurality of devices in a database; a machine learning means for acquiring latest operation data and environment data from the database at a first cycle to perform failure prediction on the plurality of devices, and performing optimization based on the database at a second cycle; and the visualization device is used for displaying the prediction results of the machine learning device for fault presetting of the equipment, and detecting the equipment faults more accurately by utilizing big data and AI technology, so that the fault rate is reduced.
Description
Technical Field
The invention relates to the technical field of equipment fault detection, in particular to an equipment fault prediction and detection system and method.
Background
The current troubleshooting of hardware equipment problems is mainly based on the regular inspection of personnel and the autonomous reporting of events. The problem causes need to be manually checked one by one, the checking is time-consuming and difficult, and the problem causes have hysteresis and cannot be predicted in advance. In addition, the reasons for equipment failure are various, and troubleshooting is time-consuming and difficult due to network, operator, over-temperature of equipment, short circuit of lines and the like.
Disclosure of Invention
In view of the above, the present invention provides a system and a method for predicting and detecting an equipment fault, which can detect the equipment fault more accurately by using big data and AI techniques, and reduce the fault rate.
In a first aspect, an embodiment of the present invention provides an apparatus failure prediction and detection system, including:
the data acquisition device is used for acquiring the operation data and the environment data of a plurality of devices distributed in different regions;
a data storage device for storing the operation data and the environment data of the plurality of devices in a database;
a machine learning means for acquiring latest operation data and environment data from the database at a first cycle to perform failure prediction on the plurality of devices, and performing optimization based on the database at a second cycle;
and the visualization device is used for displaying the prediction results of the machine learning device for fault presetting of the plurality of equipment.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the environment data includes geographic location information of a device.
With reference to the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the visualization device is further configured to perform fault analysis and fault early warning according to the prediction result.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the machine learning apparatus includes:
the machine learning model is used for carrying out fault prediction based on the operation data and the environment data, wherein different environment data can correspond to different weights;
an optimization strategy model for optimizing the machine learning model based on a second period;
and the cache module is used for storing the prediction result of the machine learning model.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the data acquisition device is obtained by integrating real-time acquisition and data ETL; the data storage device is implemented based on an HDFS distributed file system.
In a second aspect, an embodiment of the present invention further provides an apparatus failure prediction and detection method, including:
collecting operation data and environment data of a plurality of devices distributed in different regions;
storing operational data and environmental data of the plurality of devices in a database;
acquiring latest operating data and environment data from the database at a first period, and predicting faults of the plurality of devices through a machine learning model, wherein the machine learning model is optimized based on the database at a second period;
and displaying the prediction result of the machine learning device for fault presetting of the plurality of equipment.
With reference to the second aspect, an embodiment of the present invention provides a first possible implementation manner of the second aspect, where the environment data includes information of a geographic location where the device is located.
With reference to the second aspect, an embodiment of the present invention provides a second possible implementation manner of the second aspect, where the method further includes:
and carrying out fault analysis and fault early warning according to the prediction result.
With reference to the second aspect, an embodiment of the present invention provides a third possible implementation manner of the second aspect, where the obtaining, at a first cycle, latest operating data and environment data from the database to perform fault prediction on the multiple devices, and performing, at a second cycle, optimization based on the database includes:
performing fault prediction based on the operation data and the environment data, wherein different environment data can correspond to different weights;
optimizing the machine learning model based on a second period;
and storing the prediction result of the machine learning model.
With reference to the second aspect, an embodiment of the present invention provides a fourth possible implementation manner of the second aspect, where the data acquisition device is obtained by integrating real-time acquisition and data ETL; the data storage device is realized based on an HDFS distributed file system.
The embodiment of the invention provides a system and a method for predicting and detecting equipment faults, wherein a data acquisition device is used for acquiring running data and environment data of each equipment and storing the running data and the environment data through a data storage device, and a machine learning device is used for predicting the equipment faults through the equipment running data and the environment data stored in a database according to a first period and optimizing the database according to a second period, so that the aim of predicting the equipment faults more accurately is fulfilled.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a functional structure diagram of an apparatus failure prediction and detection system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of another structure for predicting and detecting a failure of a device according to a first embodiment of the present invention;
fig. 3 is a flowchart of an apparatus failure prediction and detection method according to a second embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, the feedback of equipment faults has hysteresis, the running condition of the equipment cannot be predicted in advance, the equipment cannot be repaired until the equipment is damaged, and the experience is poor. And the equipment aging period is based on laboratory sampling, the sampling amount is small, the environment is single, various scenes in the real environment cannot be simulated, and the hardware optimization and improvement are not facilitated.
Based on this, the system and the method for predicting and detecting the equipment fault provided by the embodiment of the invention can more accurately detect the equipment fault by using big data and AI technology, thereby reducing the fault rate.
To facilitate understanding of the present embodiment, a detailed description will be first given of an apparatus failure prediction and detection system disclosed in the present embodiment.
The first embodiment is as follows:
fig. 1 is a functional structure diagram of an apparatus failure prediction and detection system according to an embodiment of the present invention.
Referring to fig. 1, an embodiment of the present invention provides an apparatus failure prediction and detection system, including:
the data acquisition device is used for acquiring the operation data and the environment data of a plurality of devices distributed in different regions;
a data storage device for storing the operation data and the environment data of a plurality of devices in a database;
a machine learning means for acquiring latest operation data and environment data from the database at a first cycle to perform failure prediction on the plurality of devices, and performing optimization based on the database at a second cycle;
and the visualization device is used for displaying the prediction results of the machine learning device for fault presetting of the plurality of equipment.
In a preferred embodiment of practical application, the data acquisition device is used for acquiring the operation data and the environment data of each device and storing the operation data and the environment data by the data storage device, and the machine learning device is used for predicting the faults of the devices according to the first period and the equipment operation data and the environment data stored in the database and optimizing the database according to the second period, so that the purpose of predicting the faults of the devices more accurately is achieved.
In some possible embodiments, the environment data includes, among other things, geographic location information where the device is located. The embodiment of the invention can analyze the equipment aging period of each cell in each city, predict the equipment failure, check and replace in advance and analyze the reason of the equipment failure.
In some possible embodiments, the visualization device is further configured to perform fault analysis and fault pre-warning according to the prediction result. And (3) continuously optimizing artificial intelligence and a machine learning layer, predicting the aging period of equipment, predicting the failure of the equipment, examining and replacing in advance, analyzing the failure reason of the equipment and the like, and outputting the failure reason to a visual diagnosis and early warning platform DataV.
In some possible embodiments, the machine learning apparatus includes:
the machine learning model is used for carrying out fault prediction based on the operation data and the environment data, wherein different environment data can correspond to different weights;
the optimization strategy model is used for optimizing the machine learning model based on the second period;
specifically, the optimization is carried out by modeling through continuously pulling data in a large data warehouse and calculating the mean square error.
Wherein MSE is mean square error, observed represents the actual observed equipment value, subscript t represents the equipment individual representation, predict represents the average expected value, N is the equipment sample number, and finally the average of the square of the difference between the actual value and the expected value is obtained.
Namely, the preset result is compared with the actual equipment running state, the error is calculated, the error is recorded into the system, and the weight is adjusted.
And the cache module is used for storing the prediction result of the machine learning model.
In some possible embodiments, the data collection means is obtained by integrating the real-time collection with the data ETL, wherein the metadata collection is mainly performed by integrating the real-time collection with the data ETL. The real-time data acquisition mode mainly comprises equipment heartbeat reporting, equipment fault autonomous reporting and the like. The integration of the data ETL mainly comprises the step of collecting data maintenance records, equipment basic data and the like from a platform through an ETL technology.
Further, the data storage device is implemented based on an HDFS distributed file system. And storing the collected data in an HDFS distributed file system.
The bottom layer in the embodiment of the invention extracts, transposes and loads a large amount of metadata to a large data warehouse through data real-time acquisition and data ETL integration. And training and continuously strengthening learning through a neural network of artificial intelligence and machine learning, and finally performing equipment aging prediction, equipment failure prediction, equipment diagnosis and analysis and the like. And presenting the prognosis and diagnosis results to a visualization platform.
Fig. 2 is a schematic diagram of another device failure prediction and detection structure according to an embodiment of the present invention.
As shown in fig. 2, metadata collection is realized through data real-time collection and data ETL integration, collected data is stored in a big data warehouse, data in the big data warehouse is optimized and equipment faults are detected through artificial intelligence machine learning, weighting, policy management service, neural network deep learning service, cache cluster service, models, algorithms and the like, and detection results are displayed on a visual diagnosis and early warning platform so as to be convenient for a user to check. Specifically, the internet of things module is installed at the device end to be tested, and the running data can be monitored and reported. For example, the information is reported to the server when the lamp box is short-circuited. And according to the property report, the information fed back by the equipment, the information collected by the inspection engineering personnel and the like, proper maintenance, adjustment and the like are carried out.
Here, the model refers to a model established according to different scenes and weights set in the system. For example, the drop rate of this model is 10% weighted and the workload is 30% weighted. The above algorithm refers to the mean square error calculation formula explained in the above embodiments.
The embodiment of the invention obtains the high-accuracy reference value, saves time and is simple in the aspect of fault detection of equipment, reduces the labor investment, predicts the equipment aging and the fault in advance, analyzes the big data, is favorable for optimizing hardware and reducing the fault rate, and realizes the integrated utilization of the big data, the AI technology and the visual platform.
Example two:
fig. 3 is a flowchart of an apparatus failure prediction and detection method according to a second embodiment of the present invention.
Referring to fig. 3, the method for predicting and detecting the device failure can be applied to intelligent devices such as a server and a PC, and specifically includes the following steps:
step S202, collecting operation data and environment data of a plurality of devices distributed in different areas;
step S204, storing the operation data and the environment data of a plurality of devices in a database;
step S206, acquiring the latest running data and environment data from the database in a first period, and predicting the faults of the equipment through a machine learning model, wherein the machine learning model is optimized based on the database in a second period;
and step S208, displaying the prediction results of the machine learning device for fault presetting of the plurality of equipment.
In some possible embodiments, the environmental data includes geographic location information where the device is located.
In some possible embodiments, the method in the above embodiments further comprises:
and carrying out fault analysis and fault early warning according to the prediction result.
In some possible embodiments, step S206 further includes the following steps:
1) performing fault prediction based on the operation data and the environment data, wherein different environment data can correspond to different weights;
2) optimizing the machine learning model based on the second period;
3) and storing the prediction result of the machine learning model.
In some possible embodiments, the data acquisition device is obtained by integrating real-time acquisition with the data ETL; the data storage device is realized based on an HDFS distributed file system.
The embodiment of the invention can obtain the high-accuracy reference value, saves time and is simple in the aspect of fault detection of equipment, reduces the labor input, and predicts the equipment aging and fault in advance, wherein the big data analysis is favorable for optimizing hardware and reducing the fault rate, and the integration and utilization of big data, AI technology and a visual platform are realized.
The equipment failure prediction method provided by the embodiment of the invention has the same technical characteristics as the equipment failure prediction system provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
The computer program product of the device failure prediction system and method provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and will not be described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the device failure prediction and detection method provided in the above embodiment are implemented.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the device failure prediction and detection method in the above embodiment are executed.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. An equipment failure prediction and detection system, comprising:
the data acquisition device is used for acquiring the operation data and the environment data of a plurality of devices distributed in different regions;
a data storage device for storing the operation data and the environment data of the plurality of devices in a database;
a machine learning means for acquiring latest operation data and environment data from the database at a first cycle to perform failure prediction on the plurality of devices, and performing optimization based on the database at a second cycle;
and the visualization device is used for displaying the prediction results of the machine learning device for fault presetting of the plurality of equipment.
2. The system of claim 1, wherein the environmental data comprises geographic location information of the device.
3. The system of claim 1, wherein the visualization device is further configured to perform fault analysis and fault pre-warning according to the prediction result.
4. The system of claim 1, wherein the machine learning device comprises:
the machine learning model is used for carrying out fault prediction based on the operation data and the environment data, wherein different environment data correspond to different weights;
an optimization strategy model for optimizing the machine learning model based on a second period;
and the cache module is used for storing the prediction result of the machine learning model.
5. The system of claim 1, wherein the data acquisition device is obtained by integrating real-time acquisition with data ETL; the data storage device is implemented based on an HDFS distributed file system.
6. An apparatus failure prediction and detection method, comprising:
collecting operation data and environment data of a plurality of devices distributed in different regions;
storing operational data and environmental data of the plurality of devices in a database;
acquiring latest operating data and environment data from the database at a first period, and predicting faults of the plurality of devices through a machine learning model, wherein the machine learning model is optimized based on the database at a second period;
and displaying the prediction result of the machine learning device for fault presetting of the plurality of equipment.
7. The method of claim 6, wherein the environmental data comprises geographic location information of the device.
8. The method of claim 6, further comprising:
and carrying out fault analysis and fault early warning according to the prediction result.
9. The method of claim 6, wherein said obtaining the most recent operational data and environmental data from the database for failure prediction of the plurality of devices at a first cycle and optimizing based on the database at a second cycle comprises:
performing fault prediction based on the operation data and the environment data, wherein different environment data correspond to different weights;
optimizing the machine learning model based on a second period;
and storing the prediction result of the machine learning model.
10. The method according to claim 6, characterized in that the data acquisition means are obtained by integrating real-time acquisition with the data ETL; the data storage device is realized based on an HDFS distributed file system.
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