CN114781760B - Fault prediction method based on big data - Google Patents
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Abstract
The invention belongs to the technical field of weapon equipment fault prediction, and discloses a fault prediction method based on big data, which determines an abnormal data set by comparing a data set in a normal state with a data set in an abnormal state, but the abnormal data set has huge data volume and can not obviously reflect the data abnormity, so that the abnormal data set is converted into an abnormal data characteristic, a corresponding relation between the abnormal data characteristic and a fault event is found through a fault prediction model, the complex relation among sensing data does not need to be analyzed, and the fault prediction model is specific to the whole complex system, so that the fault prediction method has more accurate prediction precision compared with the fault prediction through a single model.
Description
Technical Field
The invention relates to the technical field of weapon equipment fault prediction, in particular to a fault prediction method based on big data.
Background
With the development of science and technology and industry, mechanical equipment is becoming large-sized, high-speed and complicated. Therefore, the existing weaponry is generally composed of a plurality of components, and the weaponry is multi-level in structure, complex in relation among different components and high in coupling performance. Weaponry systems, once malfunctioning, often endanger personnel's lives.
Therefore, in order to improve the reliability and safety of the weaponry system, it is necessary to perform equipment failure prediction, monitor the status data of weaponry at any time, and predict the future failure of the weaponry.
The existing fault estimation method based on principal component analysis is successfully used for fault prediction, but for data in a multi-working-condition process, the fault estimation method based on principal component analysis cannot accurately predict faults, when a complex system runs under multiple working conditions, data of a sensor changes according to the current working mode, the relation among the data of the whole system is very complex, and a single model is difficult to accurately predict the conditions of all the parties.
Disclosure of Invention
Aiming at the defects in the prior art, the fault prediction method based on big data provided by the invention solves the problem that the existing method for predicting equipment faults has inaccurate prediction on a complex system.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a big data-based fault prediction method comprises the following steps:
s1, extracting a data set based on the time sequence under the normal working state and a data set based on the time sequence under the abnormal state of the equipment from the big data;
s2, constructing an abnormal data set according to the data set based on the time sequence under the normal working state and the data set based on the time sequence under the abnormal state;
s3, extracting abnormal data characteristics from the abnormal data set, and constructing an event set;
s4, training the fault prediction model by adopting the event set to obtain a trained fault prediction model;
s5, acquiring equipment data through a sensor, and filtering the equipment data;
and S6, calculating and inputting the abnormal data characteristics of the filtered equipment data into the trained fault prediction model to obtain the fault type of the equipment to be generated.
Further, in S2, the time-series-based data set in the normal operating state is compared with the data points at the same time point in the time-series-based data set in the abnormal state, and abnormal data is extracted to construct an abnormal data set.
Further, the step S3 includes the following sub-steps:
s31, extracting abnormal data features of the abnormal data set;
s32, finding out corresponding fault events according to the abnormal data characteristics;
and S33, constructing the abnormal data characteristics and the fault events into event sets.
Further, the formula for extracting the abnormal data features in S31 is as follows:
wherein the content of the first and second substances,
in order to be a characteristic of the anomaly data,
the sample data size of the same type of fault event data,
is as follows
The number of the abnormal data is counted,
is the sample data size
The mean value of the data in (1),
is a first weight parameter that is a function of,
is a second weight parameter.
The beneficial effects of the above further scheme are: the abnormal condition of the data is characterized through the abnormal data characteristics, and compared with the original data, the abnormal condition of the data can be reflected, so that the prediction precision is higher.
Further, the fault prediction model in S4 includes: the system comprises a first feature extraction unit, a second feature extraction unit, a third feature extraction unit, a fourth feature extraction unit, a first attention fusion module, a second attention fusion module, a first splicing module and a full connection layer;
the input end of the first feature extraction unit is respectively connected with the input end of the second feature extraction unit, the input end of the third feature extraction unit and the input end of the fourth feature extraction unit and is used as the input end of the fault prediction model; the first input end of the first attention fusion module is connected with the output end of the first feature extraction unit, the second input end of the first attention fusion module is connected with the output end of the second feature extraction unit, and the output end of the first attention fusion module is connected with the first input end of the first splicing module; the first input end of the second attention fusion module is connected with the output end of the third feature extraction unit, the second input end of the second attention fusion module is connected with the output end of the fourth feature extraction unit, and the output end of the second attention fusion module is connected with the second input end of the first splicing module; and the input end of the full connection layer is connected with the output end of the first splicing module, and the output end of the full connection layer is used as the output end of the fault prediction model.
Furthermore, the convolution kernel size of the convolution layer of the first feature extraction unit is 1 × 1, the convolution kernel size of the convolution layer of the second feature extraction unit is 3 × 3, the convolution kernel size of the convolution layer of the third feature extraction unit is 5 × 5, and the convolution kernel size of the convolution layer of the fourth feature extraction unit is 7 × 7.
The beneficial effects of the above further scheme are: 4 kinds of convolution kernels with different sizes extract features from four dimensions, and an attention fusion module is introduced to pay attention to the feature of the focus.
Further, the first and second attention fusion modules include: the first convolution layer, the second convolution layer, the third convolution layer, the global average pooling layer, the weighted global average pooling layer, the global maximum pooling layer and the second splicing module;
the input end of the first convolution layer is respectively connected with the input end of the second convolution layer and the input end of the third convolution layer and is used as the input end of the first attention fusion module or the second attention fusion module; the input end of the global average pooling layer is connected with the output end of the first convolution layer, and the output end of the global average pooling layer is connected with the first input end of the second splicing module; the input end of the weighted global average pooling layer is connected with the output end of the second convolution layer, and the output end of the weighted global average pooling layer is connected with the second input end of the second splicing module; the input end of the global maximum pooling layer is connected with the output end of the third convolution layer, and the output end of the global maximum pooling layer is connected with the third input end of the second splicing module; and the output end of the second splicing module is used as the output end of the first attention fusion module or the second attention fusion module.
The beneficial effects of the above further scheme are: and features concerned by the global average pooling layer, the weighted global average pooling layer and the global maximum pooling layer are fused, so that the data features are abundant, and important features are prevented from being lost.
Further, the filtering formula in step S5 is as follows:
wherein the content of the first and second substances,
is as follows
The number of the filtered data is reduced to one,
is as follows
The number of the filtered data is reduced to one,
in order to determine the number of data,
is composed of
The mean value of the individual filtered data,
is a first
The data of the individual equipment is stored in a memory,
is composed of
The mean value of the data of the individual pieces of equipment,
is as follows
The data of the individual equipment is stored in a memory,
is as follows
And filtering the data.
In conclusion, the beneficial effects of the invention are as follows: according to the method, the abnormal data set is determined by comparing the data set in the normal state with the data set in the abnormal state, but the data volume of the abnormal data set is huge, and the abnormal data set cannot obviously show the data abnormality, so that the abnormal data set is converted into the abnormal data characteristic, the corresponding relation between the abnormal data characteristic and a fault event is found through the fault prediction model, the complex relation among all sensing data does not need to be analyzed, and the fault prediction model is specific to the whole complex system, so that compared with the fault prediction through a single model, the method has more accurate prediction precision.
Drawings
FIG. 1 is a flow chart of a big data based failure prediction method;
FIG. 2 is a schematic diagram of a fault prediction model;
fig. 3 is a schematic structural diagram of a first attention fusion module and a second attention fusion module.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a big data based failure prediction method includes the following steps:
s1, extracting a data set based on the time sequence under the normal working state and a data set based on the time sequence under the abnormal state of the equipment from the big data;
in the big data, data of temperature, pressure, current, voltage, load, heat and the like in a normal working state and an abnormal state of the equipment can be extracted.
S2, constructing an abnormal data set according to the data set based on the time sequence under the normal working state and the data set based on the time sequence under the abnormal state;
the time series are used for alignment, so the time series start point can be the weapon starting point, and can also be any time point of the two data sets which is the same.
In S2, the data set based on the time series in the normal operating state is compared with the data points at the same time point in the data set based on the time series in the abnormal state, and abnormal data is extracted to construct an abnormal data set.
S3, extracting abnormal data characteristics from the abnormal data set, and constructing an event set;
the step S3 includes the following sub-steps:
s31, extracting abnormal data features of the abnormal data set;
the formula for extracting the abnormal data features in S31 is as follows:
wherein the content of the first and second substances,
in order to be a characteristic of the anomaly data,
the sample data size of the same type of fault event data,
is as follows
The number of the abnormal data is counted,
is the sample data size
The mean value of the data in (1),
is a first weight parameter that is a function of,
is a second weight parameter.
S32, finding out corresponding fault events according to the abnormal data characteristics;
and S33, constructing the abnormal data characteristics and the fault events into event sets.
The abnormal condition of the data is characterized by the abnormal data characteristics, and compared with the original data, the abnormal condition of the data can be embodied, so that the prediction precision is higher.
S4, training the fault prediction model by adopting the event set to obtain a trained fault prediction model;
as shown in fig. 2, the fault prediction model in S4 includes: the system comprises a first feature extraction unit, a second feature extraction unit, a third feature extraction unit, a fourth feature extraction unit, a first attention fusion module, a second attention fusion module, a first splicing module and a full connection layer;
the input end of the first feature extraction unit is respectively connected with the input end of the second feature extraction unit, the input end of the third feature extraction unit and the input end of the fourth feature extraction unit and is used as the input end of the fault prediction model; the first input end of the first attention fusion module is connected with the output end of the first feature extraction unit, the second input end of the first attention fusion module is connected with the output end of the second feature extraction unit, and the output end of the first attention fusion module is connected with the first input end of the first splicing module; the first input end of the second attention fusion module is connected with the output end of the third feature extraction unit, the second input end of the second attention fusion module is connected with the output end of the fourth feature extraction unit, and the output end of the second attention fusion module is connected with the second input end of the first splicing module; and the input end of the full connection layer is connected with the output end of the first splicing module, and the output end of the full connection layer is used as the output end of the fault prediction model.
The convolution kernel size of the convolution layer of the first feature extraction unit is 1 x 1, the convolution kernel size of the convolution layer of the second feature extraction unit is 3 x 3, the convolution kernel size of the convolution layer of the third feature extraction unit is 5 x 5, and the convolution kernel size of the convolution layer of the fourth feature extraction unit is 7.
4 kinds of convolution kernels with different sizes extract features from four dimensions, and an attention fusion module is introduced to pay attention to the feature of the focus.
As shown in fig. 3, the first and second attention fusion modules include: the first convolution layer, the second convolution layer, the third convolution layer, the global average pooling layer, the weighted global average pooling layer, the global maximum pooling layer and the second splicing module;
the input end of the first convolution layer is respectively connected with the input end of the second convolution layer and the input end of the third convolution layer and serves as the input end of the first attention fusion module or the second attention fusion module; the input end of the global average pooling layer is connected with the output end of the first convolution layer, and the output end of the global average pooling layer is connected with the first input end of the second splicing module; the input end of the weighted global average pooling layer is connected with the output end of the second convolution layer, and the output end of the weighted global average pooling layer is connected with the second input end of the second splicing module; the input end of the global maximum pooling layer is connected with the output end of the third convolution layer, and the output end of the global maximum pooling layer is connected with the third input end of the second splicing module; and the output end of the second splicing module is used as the output end of the first attention fusion module or the second attention fusion module.
The characteristics concerned by the global average pooling layer, the weighted global average pooling layer and the global maximum pooling layer are fused, so that the data characteristics are abundant, the loss of important characteristics is prevented, and the data are spliced and then input to a follow-up module by the splicing module.
S5, acquiring equipment data through a sensor, and filtering the equipment data;
the filtering formula in step S5 is:
wherein the content of the first and second substances,
is as follows
The number of the filtered data is reduced to one,
is as follows
The number of the filtered data is set to be,
the number of the data is the number of the data,
is composed of
The mean value of the individual filtered data,
is as follows
The data of the individual equipment is stored in a memory,
is composed of
The mean value of the data of each piece of equipment,
is as follows
The data of the individual equipment is stored in a memory,
is as follows
And filtering the data.
And S6, calculating and inputting the abnormal data characteristics of the filtered equipment data into the trained fault prediction model to obtain the fault type of the equipment to be generated.
The formula of calculating the abnormal data characteristic of the filter-processed equipment data in step S6 is the same as that in step S31.
According to the method, the abnormal data set is determined by comparing the data set in the normal state with the data set in the abnormal state, but the data volume of the abnormal data set is huge, and the abnormal data set cannot obviously show the data abnormality, so that the abnormal data set is converted into the abnormal data characteristic, the corresponding relation between the abnormal data characteristic and a fault event is found through the fault prediction model, the complex relation among all sensing data does not need to be analyzed, and the fault prediction model is specific to the whole complex system, so that compared with the fault prediction through a single model, the method has more accurate prediction precision.
Claims (1)
1. A big data-based fault prediction method is characterized by comprising the following steps:
s1, extracting a data set based on the time sequence under the normal working state and a data set based on the time sequence under the abnormal state of the equipment from the big data;
s2, constructing an abnormal data set according to the data set based on the time sequence under the normal working state and the data set based on the time sequence under the abnormal state;
s3, extracting abnormal data characteristics from the abnormal data set, and constructing an event set;
s4, training the fault prediction model by adopting the event set to obtain a trained fault prediction model;
s5, acquiring equipment data through a sensor, and filtering the equipment data;
s6, calculating and inputting the abnormal data characteristics of the filtered equipment data into the trained fault prediction model to obtain the type of the fault of the equipment;
in S2, comparing the data set based on the time sequence in the normal operating state with the data points at the same time point in the data set based on the time sequence in the abnormal state, extracting abnormal data, and constructing an abnormal data set;
the S3 comprises the following substeps:
s31, extracting abnormal data features of the abnormal data set;
s32, finding out corresponding fault events according to the abnormal data characteristics;
s33, constructing the abnormal data characteristics and the fault events into event sets;
the formula for extracting the abnormal data features in S31 is as follows:
wherein R is abnormal data characteristic, I is sample data size of similar fault event data, x (I) is ith abnormal data,
the data mean value in the sample data volume I is shown, alpha is a first weight parameter, and beta is a second weight parameter;
the fault prediction model in S4 includes: the system comprises a first feature extraction unit, a second feature extraction unit, a third feature extraction unit, a fourth feature extraction unit, a first attention fusion module, a second attention fusion module, a first splicing module and a full connection layer;
the input end of the first feature extraction unit is respectively connected with the input end of the second feature extraction unit, the input end of the third feature extraction unit and the input end of the fourth feature extraction unit and is used as the input end of the fault prediction model; the first input end of the first attention fusion module is connected with the output end of the first feature extraction unit, the second input end of the first attention fusion module is connected with the output end of the second feature extraction unit, and the output end of the first attention fusion module is connected with the first input end of the first splicing module; the first input end of the second attention fusion module is connected with the output end of the third feature extraction unit, the second input end of the second attention fusion module is connected with the output end of the fourth feature extraction unit, and the output end of the second attention fusion module is connected with the second input end of the first splicing module; the input end of the full connection layer is connected with the output end of the first splicing module, and the output end of the full connection layer is used as the output end of the fault prediction model;
the convolution kernel size of the convolution layer of the first feature extraction unit is 1 x 1, the convolution kernel size of the convolution layer of the second feature extraction unit is 3 x 3, the convolution kernel size of the convolution layer of the third feature extraction unit is 5 x 5, and the convolution kernel size of the convolution layer of the fourth feature extraction unit is 7;
the first and second attention fusion modules comprise: the first convolution layer, the second convolution layer, the third convolution layer, the global average pooling layer, the weighted global average pooling layer, the global maximum pooling layer and the second splicing module;
the input end of the first convolution layer is respectively connected with the input end of the second convolution layer and the input end of the third convolution layer and is used as the input end of the first attention fusion module or the second attention fusion module; the input end of the global average pooling layer is connected with the output end of the first convolution layer, and the output end of the global average pooling layer is connected with the first input end of the second splicing module; the input end of the weighted global average pooling layer is connected with the output end of the second convolution layer, and the output end of the weighted global average pooling layer is connected with the second input end of the second splicing module; the input end of the global maximum pooling layer is connected with the output end of the third convolution layer, and the output end of the global maximum pooling layer is connected with the third input end of the second splicing module; the output end of the second splicing module is used as the output end of the first attention fusion module or the second attention fusion module;
the filtering formula in S5 is:
wherein yn is the nth filtered data, y (n-j) is the nth-j filtered data, n is the number of data,
is the average of n filtered data, x (n-j) is the n-j th equipment data,
is the average of the n pieces of equipment data, xn is the nth equipment data, and y (n-1) is the n-1 th filtered data.
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