CN114117734A - Equipment life detection method and device, electronic equipment and storage medium - Google Patents
Equipment life detection method and device, electronic equipment and storage medium Download PDFInfo
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- CN114117734A CN114117734A CN202111235639.4A CN202111235639A CN114117734A CN 114117734 A CN114117734 A CN 114117734A CN 202111235639 A CN202111235639 A CN 202111235639A CN 114117734 A CN114117734 A CN 114117734A
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Abstract
The application provides a method, a device, equipment and a storage medium for detecting the service life of the equipment, wherein the method comprises the following steps: acquiring a vibration data sequence of equipment to be detected in a preset time period; performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions; respectively carrying out dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing; and determining the residual service life of the equipment according to the feature data sequence after the dimension reduction treatment, reducing the maintenance cost of the equipment, improving the safety and reliability of the equipment, and simultaneously improving the fault removal rate.
Description
Technical Field
The present disclosure relates to the field of device detection technologies, and in particular, to a method and an apparatus for detecting a device life, an electronic device, and a storage medium.
Background
At present, in a new energy system, a large amount of equipment may damage the health degree of the equipment due to long-term work, environmental changes, frequent start and stop and the like. Even when the scheduled maintenance time is not reached, the equipment fails, which may cause problems in the entire integrated energy system.
Disclosure of Invention
The object of the present application is to solve at least to some extent one of the above mentioned technical problems.
Therefore, the method for detecting the service life of the equipment is provided, the vibration data sequence of the equipment to be detected is obtained, the characteristic is extracted to obtain the characteristic data sequence, the characteristic data sequence is subjected to dimensionality reduction to obtain the characteristic data sequence subjected to dimensionality reduction, the remaining service life of the equipment is determined, the maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and meanwhile the fault removal rate is improved.
An embodiment of a first aspect of the present application provides an apparatus life detection method, including:
acquiring a vibration data sequence of equipment to be detected in a preset time period;
performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions;
respectively performing dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing;
and determining the residual service life of the equipment according to the feature data sequence after the dimension reduction treatment.
Optionally, before performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence, the method further includes:
determining whether the vibration parameters in the vibration data sequence meet corresponding fault conditions;
correspondingly, the performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence includes:
and when all the vibration parameters do not meet the corresponding fault conditions, performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence.
Optionally, the performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence includes:
determining the type of the equipment and vibration characteristics of multiple dimensions corresponding to the type;
performing feature extraction processing on vibration data corresponding to each time point in the vibration data sequence based on the vibration data and the vibration data in a preset sub-time period before the time point, and determining the vibration features of the multiple dimensions of the time point;
determining the characteristic data sequence based on the vibration characteristics of the plurality of dimensions at each time point.
Optionally, before performing dimension reduction processing on each of the plurality of feature data in the feature data sequence to obtain a feature data sequence after the dimension reduction processing, the method further includes:
determining a correlation between the vibration features of the plurality of dimensions based on the feature data sequence;
screening out a plurality of target dimensions from the plurality of dimensions based on the correlation, wherein the vibration characteristics on any two target dimensions in the plurality of target dimensions meet a preset correlation condition;
and filtering out the vibration characteristics on the non-target dimension in the characteristic data sequence to obtain an updated characteristic data sequence.
Optionally, the performing, respectively, a dimension reduction process on the plurality of feature data in the feature data sequence to obtain a feature data sequence after the dimension reduction process includes:
determining a matrix among the vibration characteristics in multiple dimensions based on the characteristic data sequence, wherein the matrix is a covariance matrix or a correlation coefficient matrix;
determining a projection feature coordinate system based on the matrix;
for each feature data, determining projected feature data of the feature data in the projected feature coordinate system;
and generating a feature data sequence after dimension reduction processing according to the projection feature data.
Optionally, the performing, respectively, a dimension reduction process on the plurality of feature data in the feature data sequence to obtain a feature data sequence after the dimension reduction process includes:
and inputting the characteristic data sequence into a preset dimension reduction characteristic model to obtain the characteristic data sequence output by the model after dimension reduction processing.
Optionally, the determining the remaining service life of the device according to the feature data sequence after the dimension reduction processing includes:
determining an eigenmode data sequence and a dimensionality reduced feature sequence of the dimensionality reduced features for each dimensionality reduced feature in the feature data sequence after the dimensionality reduction processing;
determining a trend sequence of the feature after dimension reduction based on the feature sequence after dimension reduction and the eigenmode data sequence;
determining a remaining service life based on the trend sequence of each of the dimensionality reduced features.
Optionally, the determining the remaining service life based on the trend sequence of each of the dimensionality reduction features includes:
for each dimensionality reduced feature, inputting trend data of each time point in the trend sequence of the dimensionality reduced feature into a pre-trained life prediction model, and determining the trend data of the dimensionality reduced feature at each time point in the future;
determining a target future time point at which the corresponding trend data reaches the fault trend threshold;
and determining the residual service life according to the target future time point of each dimensionality reduced feature and the preset time period.
According to the equipment service life detection method, a vibration data sequence of equipment to be detected in a preset time period is obtained; performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions; respectively carrying out dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing; and determining the residual service life of the equipment according to the characteristic data sequence after the dimension reduction treatment. The maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and meanwhile, the fault removal rate is improved.
An embodiment of a second aspect of the present application provides an apparatus life detection device, including:
the acquisition module is used for acquiring a vibration data sequence of the equipment to be detected within a preset time period;
the extraction module is used for carrying out feature extraction processing on the basis of the vibration data sequence so as to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions;
the dimension reduction module is used for respectively carrying out dimension reduction processing on the plurality of feature data in the feature data sequence to obtain a feature data sequence after the dimension reduction processing;
and the first determining module is used for determining the residual service life of the equipment according to the feature data sequence after the dimension reduction processing.
Optionally, the apparatus further comprises: a second determination module;
the second determining module is used for determining whether the vibration parameters in the vibration data sequence meet corresponding fault conditions;
correspondingly, the extraction module is specifically configured to, when all the vibration parameters do not meet the corresponding fault conditions, perform feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence.
Optionally, the extraction module is specifically configured to,
determining the type of the equipment and vibration characteristics of multiple dimensions corresponding to the type;
performing feature extraction processing on vibration data corresponding to each time point in the vibration data sequence based on the vibration data and the vibration data in a preset sub-time period before the time point, and determining the vibration features of the multiple dimensions of the time point;
determining the characteristic data sequence based on the vibration characteristics of the plurality of dimensions at each time point.
Optionally, the apparatus further comprises: the third determining module, the screening module and the filtering module;
the third determination module is used for determining correlation degrees among the vibration characteristics of the multiple dimensions based on the characteristic data sequence;
the screening module is used for screening a plurality of target dimensions from the plurality of dimensions based on the correlation, wherein the vibration characteristics of any two target dimensions in the plurality of target dimensions meet a preset correlation condition;
and the filtering module is used for filtering the vibration characteristics on the non-target dimension in the characteristic data sequence to obtain an updated characteristic data sequence.
Optionally, the dimension reduction module is specifically configured to,
determining a matrix among the vibration characteristics in multiple dimensions based on the characteristic data sequence, wherein the matrix is a covariance matrix or a correlation coefficient matrix;
determining a projection feature coordinate system based on the matrix;
for each feature data, determining projected feature data of the feature data in the projected feature coordinate system;
and generating a feature data sequence after dimension reduction processing according to the projection feature data.
Optionally, the dimension reduction module is specifically configured to,
and inputting the characteristic data sequence into a preset dimension reduction characteristic model to obtain the characteristic data sequence output by the model after dimension reduction processing.
Optionally, the first determining module is specifically configured to,
determining an eigenmode data sequence and a dimensionality reduced feature sequence of the dimensionality reduced features for each dimensionality reduced feature in the feature data sequence after the dimensionality reduction processing;
determining a trend sequence of the feature after dimension reduction based on the feature sequence after dimension reduction and the eigenmode data sequence;
determining a remaining service life based on the trend sequence of each of the dimensionality reduced features.
Optionally, the first determining module is specifically configured to,
for each dimensionality reduced feature, inputting trend data of each time point in the trend sequence of the dimensionality reduced feature into a pre-trained life prediction model, and determining the trend data of the dimensionality reduced feature at each time point in the future;
determining a target future time point at which the corresponding trend data reaches the fault trend threshold;
and determining the residual service life according to the target future time point of each dimensionality reduced feature and the preset time period.
The device life detection device of the embodiment of the application obtains a vibration data sequence of the device to be detected in a preset time period; performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions; respectively carrying out dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing; and determining the residual service life of the equipment according to the characteristic data sequence after the dimension reduction treatment. The maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and meanwhile, the fault removal rate is improved.
An embodiment of a third aspect of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of device lifetime detection as described in the first aspect when executing the program.
A fourth aspect of the present application is directed to a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the device lifetime detection method according to the first aspect.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of an apparatus life detection method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another method for detecting a lifetime of a device according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a joint training model for training;
FIG. 4 is a schematic structural diagram of an apparatus life detection device according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of another apparatus life detection device according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of another apparatus life detection device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes a device lifetime detection method, apparatus, electronic device, and storage medium according to embodiments of the present application with reference to the drawings.
The device lifetime detection method provided by the present application is described in detail below with reference to fig. 1.
Fig. 1 is a schematic flowchart of an apparatus life detection method according to an embodiment of the present disclosure.
The execution main body of the embodiment of the present application is the device life detection apparatus provided by the present application, and the device life detection apparatus may be configured in an electronic device, so that the electronic device may execute a device life detection function.
The electronic device may be any device having a computing capability, for example, a Personal Computer (PC), a mobile terminal, a server, and the like, and the mobile terminal may be a hardware device having various operating systems, touch screens, and/or display screens, such as an in-vehicle device, a mobile phone, a tablet Computer, a Personal digital assistant, and a wearable device.
As shown in fig. 1, the device life detection method includes the following steps:
step 101, acquiring a vibration data sequence of a device to be detected in a preset time period.
The vibration data sequence may include vibration data of the device to be detected at each time point within a preset time period. The vibration data may include at least one of the following: the operation age, historical maintenance data and current operation data of the equipment to be detected. Wherein the operational data may include at least one of the following vibration parameters: temperature, pressure, energy consumption data, etc.
In the embodiment of the present application, a vibration Data sequence of the device to be detected within a preset time period may be collected by using an SCADA (Supervisory Control And Data Acquisition, Data Acquisition And monitoring Control system).
102, performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions.
The characteristic data sequence may include characteristic data of the device to be detected at each time point within a preset time period.
In the embodiment of the application, before feature extraction processing is performed on the basis of the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence, whether the numerical value of the vibration parameter at each time point in the vibration data sequence meets the corresponding fault condition needs to be determined; and if the fault condition is that the numerical value of the vibration parameter exceeds a preset threshold value, determining that the equipment to be detected has a fault, and directly reporting and repairing.
And when all the vibration parameters do not meet the corresponding fault conditions, performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence.
In the embodiment of the present application, the process executed by the device life detection apparatus in step 102 may be, for example, determining a type of the device and vibration characteristics of multiple dimensions corresponding to the type; performing feature extraction processing on vibration data corresponding to each time point in the vibration data sequence based on the vibration data and the vibration data in a preset sub-time period before the time point, and determining vibration features of multiple dimensions of the time point; and determining a characteristic data sequence based on the vibration characteristics of multiple dimensions of each time point. And the preset sub-time period is less than the preset time period.
In the embodiment of the present application, in an example, taking vibration parameters of a device to be detected as an example, the vibration characteristics of multiple dimensions that can be extracted may include at least one of the following characteristics: peak-to-peak value, average upper peak value, average lower peak value, maximum peak value, mean square root, kurtosis and inclination on a single vibration parameter; entropy, mutual information, line integral and the like among the multiple vibration parameters can select different vibration characteristics according to different scenes. As shown in table 1.
TABLE 1
Wherein Peak to Peak is the Peak-to-Peak value, Mean (upper)pks) Mean (lower) being the average peak valuepks) For average lower peak, Maximum peak value is the Maximum peak value and Root mean square is the Root mean square deviation, where x1，x2，xnTypically, input sample data is used, and n is the number of input data. Kurtosis is Kurtosis, where x is the input sample data, E (x- μ) is the expectation, μ is the mean of the input sample data, and σ is the standard deviation of the input sample data。
And 103, respectively performing dimensionality reduction on the plurality of feature data in the feature data sequence to obtain the feature data sequence subjected to dimensionality reduction.
In the embodiment of the application, before performing dimension reduction processing on a plurality of feature data in a feature data sequence respectively, because the collected feature data sequences have different importance and are difficult to judge, an excessively high data amount may affect the operation time of the whole new energy system and the requirements of hardware, so that the feature data sequence needs to be selected, and the process of selecting the equipment life detection equipment may be, for example, determining the correlation among vibration features of a plurality of dimensions based on the feature data sequence; screening out a plurality of target dimensions from the plurality of dimensions based on the correlation, wherein the vibration characteristics on any two target dimensions in the plurality of target dimensions meet a preset correlation condition; and filtering out the vibration characteristics on the non-target dimension in the characteristic data sequence to obtain an updated characteristic data sequence.
In the embodiment of the present application, in one example, the process of selecting performed by the device life detection device may be implemented by formula (1),
wherein I (X, Y) is mutual information of random variables X and Y, H (X), H (Y) is information entropy, SU (X, Y) is correlation degree, and multiple target dimensions larger than the preset threshold are selected according to the preset threshold of the correlation degree.
In the embodiment of the present application, the device life detection apparatus may perform the process of step 103, for example, to determine a matrix between vibration characteristics in multiple dimensions based on the characteristic data sequence, where the matrix is a covariance matrix or a correlation coefficient matrix; determining a projection characteristic coordinate system based on the matrix; determining projection feature data of the feature data in a projection feature coordinate system for each feature data; and generating a feature data sequence after the dimension reduction processing according to the projection feature data.
In the embodiment of the present application, in an example, the feature data sequence is input into a preset dimension reduction feature model to obtain a dimension reduction processed feature data sequence output by the dimension reduction feature model.
And step 104, determining the residual service life of the equipment according to the feature data sequence after the dimension reduction processing.
According to the equipment service life detection method provided by the embodiment of the application, a vibration data sequence of equipment to be detected in a preset time period is obtained; performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions; respectively carrying out dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing; and determining the residual service life of the equipment according to the characteristic data sequence after the dimension reduction treatment. The maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and meanwhile, the fault removal rate is improved.
As a possible implementation manner of the embodiment of the present application, a device life detection method provided by the present application is further described with reference to fig. 2.
Fig. 2 is a schematic flowchart of another method for detecting a lifetime of a device according to an embodiment of the present disclosure. As shown in fig. 2, the specific steps are as follows:
step 201, for each feature after dimension reduction in the feature data sequence after dimension reduction, determining an eigenmode data sequence of the feature after dimension reduction and a feature sequence after dimension reduction.
The Intrinsic Mode data sequence is a data sequence obtained by calculating feature data subjected to dimensionality reduction by using an Intrinsic Mode Function (IMF), and the specific implementation process is as follows:
1) first, all local maxima and minima of the input signal (the feature data after the dimensionality reduction process) are found, and corresponding high and low envelope curves are calculated, for example, using a cubic sample method.
2) Then, calculating the mean value of the high and low envelope curves, and interpolating the result in 1).
3) Repeating the steps 1) and 2) until all the characteristic data are calculated.
The feature sequence after dimension reduction is to process each feature data in the feature data sequence including the vibration features of multiple dimensions to obtain the vibration features of specific dimensions in each feature data, and further obtain the feature sequence corresponding to the vibration features of specific dimensions.
And 202, determining a trend sequence of the feature after dimension reduction based on the feature sequence after dimension reduction and the eigenmode data sequence.
In the embodiment of the present application, for example, an EMD (empirical mode decomposition, empirical mode decomposition algorithm) may be used to determine the trend sequence of the feature after dimension reduction according to formula (2):
where X (t) is the data of the input signal at time t, imfi(t) is the ith value, r, of the eigenmode function corresponding to time tn(t) is the remainder of the n eigenmode function values aggregated and at time t.
Where time t is a continuous time sequence, rn(t) forming a trend curve, and further determining a trend sequence corresponding to the t moment.
And step 203, determining the remaining service life based on the trend sequence of the various reduced-dimension features.
In this embodiment, the process of the device life detection apparatus executing step 203 may be, for example, inputting trend data of each time point in the trend sequence of the reduced-dimension features into a life prediction model trained in advance for each reduced-dimension feature, and determining trend data of the reduced-dimension features of each time point in the future; determining a target future time point at which the corresponding trend data reaches the fault trend threshold; and determining the remaining service life according to the target future time point and the preset time period of each dimensionality-reduced feature. And the trend data is a residual value corresponding to the time t in the empirical mode decomposition algorithm.
Wherein, each dimension reduced feature determines a remaining useful life. And selecting the minimum residual service life as the final residual service life.
According to the equipment life detection method, for each feature after dimension reduction in the feature data sequence after dimension reduction, an eigenmode data sequence of the feature after dimension reduction and a feature sequence after dimension reduction are determined; determining a trend sequence of the features after dimension reduction based on the feature sequence after dimension reduction and the eigen module data sequence; based on the trend sequence of each reduced-dimension feature, the remaining service life is determined, the maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and meanwhile, the troubleshooting rate is improved.
In some embodiments, the process of the joint training of the dimension reduction feature model and the life prediction model is described by performing the joint training of the dimension reduction feature model and the life prediction model to ensure the information security when the local model is updated and adjusted, and an exemplary process is as follows: constructing an initial combined model, wherein the combined model comprises the following steps: the system comprises an initial dimension reduction feature model and a corresponding initial life prediction model, wherein the output of the initial dimension reduction feature model is the input of the initial life prediction model.
Based on the above example, an exemplary implementation of training the co-trained models may be that all data is distributed to different machines, each machine downloads the models from a server, then training the model using local data, then returning to the server the parameters that need to be updated, the server aggregating the returned parameters on each machine, updating the model, and then feeding back the latest model to each machine, as shown in fig. 3, which illustrates, as follows, that each of the participants downloads the latest model from server a, and the participators use the local data to train the model, encrypt the gradient and upload to the server A, the server A gathers the gradient of each user to update the model parameter, the server A returns the updated model to each participator, the more the number of the participants is, the more the samples of the models in the server are, the stronger the adaptability of the models is, and finally, each participant updates the respective model.
The application provides an equipment service life detection method, which comprises the steps of obtaining a vibration data sequence of equipment to be detected in a preset time period; performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions; respectively carrying out dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing; and determining the residual service life of the equipment according to the characteristic data sequence after the dimension reduction treatment. Therefore, the minimum remaining service life is selected as the final remaining service life based on the remaining service life of the equipment output by the joint training model, the maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and the fault removal rate is improved.
Corresponding to the method for detecting the service life of the device provided by the above embodiments, an embodiment of the present application further provides a device for detecting the service life of the device. Since the device life detection apparatus provided in the embodiments of the present application corresponds to the device life detection methods provided in the foregoing several embodiments, the implementation of the device life detection method is also applicable to the device life detection apparatus provided in the embodiments, and detailed description is not provided in this embodiment.
Fig. 4 is a schematic structural diagram of an apparatus life detection device according to an embodiment of the present application.
As shown in fig. 4, the device life detection apparatus 40 may include: an acquisition module 41, an extraction module 42, a dimension reduction module 43, and a first determination module 44.
The acquiring module 41 is configured to acquire a vibration data sequence of the device to be detected within a preset time period;
an extraction module 42, configured to perform feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions;
a dimension reduction module 43, configured to perform dimension reduction processing on the multiple feature data in the feature data sequence respectively to obtain a feature data sequence after the dimension reduction processing;
and the first determining module 44 is used for determining the remaining service life of the equipment according to the feature data sequence after the dimension reduction processing.
As a possible implementation manner of the embodiment of the present application, fig. 5 is a schematic structural diagram of another device life detection apparatus according to an embodiment of the present application, and as shown in fig. 5, on the basis of the embodiment shown in fig. 4, the device life detection apparatus 40 further includes a second determining module 45.
A second determining module 45, configured to determine whether the vibration parameters in the vibration data sequence satisfy corresponding fault conditions;
correspondingly, the extracting module 42 is specifically configured to, when all the vibration parameters do not meet the corresponding fault conditions, perform feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence.
As another possible implementation of the embodiment of the present application, the extraction module 42 is specifically configured to,
determining the type of the equipment and vibration characteristics of multiple dimensions corresponding to the type;
performing feature extraction processing on vibration data corresponding to each time point in the vibration data sequence based on the vibration data and the vibration data in a preset sub-time period before the time point, and determining vibration features of multiple dimensions of the time point;
and determining a characteristic data sequence based on the vibration characteristics of multiple dimensions of each time point.
As another possible implementation manner of the embodiment of the present application, fig. 6 is a schematic structural diagram of another device life detection apparatus according to an embodiment of the present application, and as shown in fig. 6, on the basis of the embodiment shown in fig. 4, the device life detection apparatus 40 further includes a third determining module 46, a screening module 47, and a filtering module 48.
A third determining module 46, configured to determine correlation degrees between the vibration features of multiple dimensions based on the feature data sequence;
the screening module 47 is configured to screen a plurality of target dimensions from the plurality of dimensions based on the correlation, where vibration characteristics of any two target dimensions in the plurality of target dimensions meet a preset correlation condition;
and the filtering module 48 is configured to filter out vibration features in the feature data sequence on the non-target dimension to obtain an updated feature data sequence.
As another possible implementation manner of the embodiment of the present application, the dimension reduction module 43 is specifically configured to,
determining a matrix among the vibration characteristics on multiple dimensions based on the characteristic data sequence, wherein the matrix is a covariance matrix or a correlation coefficient matrix;
determining a projection characteristic coordinate system based on the matrix;
determining projection feature data of the feature data in a projection feature coordinate system for each feature data;
and generating a feature data sequence after the dimension reduction processing according to the projection feature data.
As another possible implementation manner of the embodiment of the present application, the dimension reduction module 43 is specifically configured to,
and inputting the characteristic data sequence into a preset dimension reduction characteristic model to obtain the characteristic data sequence which is output by the model and subjected to dimension reduction processing.
As another possible implementation manner of the embodiment of the present application, the first determining module 44 is specifically configured to,
aiming at each feature after dimension reduction in the feature data sequence after dimension reduction, determining an eigenmode data sequence of the feature after dimension reduction and a feature sequence after dimension reduction;
determining a trend sequence of the features after dimension reduction based on the feature sequence after dimension reduction and the eigen module data sequence;
and determining the remaining service life based on the trend sequence of the various reduced-dimension features.
As another possible implementation manner of the embodiment of the present application, the first determining module 44 is specifically configured to,
for each feature after dimension reduction, inputting the trend data of each time point in the trend sequence of the feature after dimension reduction into a life prediction model trained in advance, and determining the trend data of the feature after dimension reduction of each time point in the future;
determining a target future time point at which the corresponding trend data reaches the fault trend threshold;
and determining the remaining service life according to the target future time point and the preset time period of each dimensionality-reduced feature.
The device life detection device provided by the embodiment of the application obtains a vibration data sequence of the device to be detected in a preset time period; performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions; respectively carrying out dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing; and determining the residual service life of the equipment according to the characteristic data sequence after the dimension reduction treatment. The maintenance cost of the equipment is reduced, the use safety and reliability of the equipment are improved, and meanwhile, the fault removal rate is improved.
In order to implement the foregoing embodiments, the present application further provides an electronic device, and fig. 7 is a schematic structural diagram of the electronic device provided in the embodiments of the present application. The electronic device includes:
The processor 1002, when executing the program, implements the device lifetime detection method provided in the above-described embodiment.
Further, the electronic device further includes:
a communication interface 1003 for communicating between the memory 1001 and the processor 1002.
A memory 1001 for storing computer programs that may be run on the processor 1002.
The processor 1002 is configured to implement the device life detection method according to the foregoing embodiment when executing the program.
If the memory 1001, the processor 1002, and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001, and the processor 1002 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 7, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 1001, the processor 1002, and the communication interface 1003 are integrated on one chip, the memory 1001, the processor 1002, and the communication interface 1003 may complete communication with each other through an internal interface.
The processor 1002 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
In order to implement the foregoing embodiments, the present application further proposes a non-transitory computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the device lifetime detection method provided in the foregoing embodiments.
In order to implement the foregoing embodiments, the present application further provides a computer program product, and when executed by an instruction processor in the computer program product, the method for detecting the lifetime of the device provided in the foregoing embodiments is implemented.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (18)
1. A method for detecting a lifetime of a device, comprising:
acquiring a vibration data sequence of equipment to be detected in a preset time period;
performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions;
respectively performing dimensionality reduction processing on a plurality of characteristic data in the characteristic data sequence to obtain a characteristic data sequence subjected to dimensionality reduction processing;
and determining the residual service life of the equipment according to the feature data sequence after the dimension reduction treatment.
2. The method according to claim 1, before performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence, further comprising:
determining whether the vibration parameters in the vibration data sequence meet corresponding fault conditions;
correspondingly, the performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence includes:
and when all the vibration parameters do not meet the corresponding fault conditions, performing feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence.
3. The method according to claim 1, wherein the performing a feature extraction process based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence comprises:
determining the type of the equipment and vibration characteristics of multiple dimensions corresponding to the type;
performing feature extraction processing on vibration data corresponding to each time point in the vibration data sequence based on the vibration data and the vibration data in a preset sub-time period before the time point, and determining the vibration features of the multiple dimensions of the time point;
determining the characteristic data sequence based on the vibration characteristics of the plurality of dimensions at each time point.
4. The method according to claim 1, wherein before performing dimension reduction processing on each of the plurality of feature data in the feature data sequence to obtain a dimension-reduced feature data sequence, the method further comprises:
determining a correlation between the vibration features of the plurality of dimensions based on the feature data sequence;
screening out a plurality of target dimensions from the plurality of dimensions based on the correlation, wherein the vibration characteristics on any two target dimensions in the plurality of target dimensions meet a preset correlation condition;
and filtering out the vibration characteristics on the non-target dimension in the characteristic data sequence to obtain an updated characteristic data sequence.
5. The method according to claim 1, wherein performing dimension reduction processing on each of the plurality of feature data in the feature data sequence to obtain a dimension-reduced feature data sequence includes:
determining a matrix among the vibration characteristics in multiple dimensions based on the characteristic data sequence, wherein the matrix is a covariance matrix or a correlation coefficient matrix;
determining a projection feature coordinate system based on the matrix;
for each feature data, determining projected feature data of the feature data in the projected feature coordinate system;
and generating a feature data sequence after dimension reduction processing according to the projection feature data.
6. The method according to claim 1, wherein performing dimension reduction processing on each of the plurality of feature data in the feature data sequence to obtain a dimension-reduced feature data sequence includes:
and inputting the characteristic data sequence into a preset dimension reduction characteristic model to obtain the characteristic data sequence output by the model after dimension reduction processing.
7. The method of claim 1, wherein determining the remaining useful life of the device according to the dimensionality reduction processed feature data sequence comprises:
determining an eigenmode data sequence and a dimensionality reduced feature sequence of the dimensionality reduced features for each dimensionality reduced feature in the feature data sequence after the dimensionality reduction processing;
determining a trend sequence of the feature after dimension reduction based on the feature sequence after dimension reduction and the eigenmode data sequence;
determining a remaining service life based on the trend sequence of each of the dimensionality reduced features.
8. The method of claim 7, wherein determining remaining useful life based on the trend series of each of the reduced-dimension features comprises:
for each dimensionality reduced feature, inputting trend data of each time point in the trend sequence of the dimensionality reduced feature into a pre-trained life prediction model, and determining the trend data of the dimensionality reduced feature at each time point in the future;
determining a target future time point at which the corresponding trend data reaches the fault trend threshold;
and determining the residual service life according to the target future time point of each dimensionality reduced feature and the preset time period.
9. An apparatus life detection device, comprising:
the acquisition module is used for acquiring a vibration data sequence of the equipment to be detected within a preset time period;
the extraction module is used for carrying out feature extraction processing on the basis of the vibration data sequence so as to obtain a feature data sequence corresponding to the vibration data sequence; wherein each feature data in the feature data sequence comprises vibration features in a plurality of dimensions;
the dimension reduction module is used for respectively carrying out dimension reduction processing on the plurality of feature data in the feature data sequence to obtain a feature data sequence after the dimension reduction processing;
and the first determining module is used for determining the residual service life of the equipment according to the feature data sequence after the dimension reduction processing.
10. The apparatus of claim 9, further comprising: a second determination module;
the second determining module is used for determining whether the vibration parameters in the vibration data sequence meet corresponding fault conditions;
correspondingly, the extraction module is specifically configured to, when all the vibration parameters do not meet the corresponding fault conditions, perform feature extraction processing based on the vibration data sequence to obtain a feature data sequence corresponding to the vibration data sequence.
11. The apparatus of claim 9, wherein the extraction module is specifically configured to,
determining the type of the equipment and vibration characteristics of multiple dimensions corresponding to the type;
performing feature extraction processing on vibration data corresponding to each time point in the vibration data sequence based on the vibration data and the vibration data in a preset sub-time period before the time point, and determining the vibration features of the multiple dimensions of the time point;
determining the characteristic data sequence based on the vibration characteristics of the plurality of dimensions at each time point.
12. The apparatus of claim 9, further comprising: the third determining module, the screening module and the filtering module;
the third determination module is used for determining correlation degrees among the vibration characteristics of the multiple dimensions based on the characteristic data sequence;
the screening module is used for screening a plurality of target dimensions from the plurality of dimensions based on the correlation, wherein the vibration characteristics of any two target dimensions in the plurality of target dimensions meet a preset correlation condition;
and the filtering module is used for filtering the vibration characteristics on the non-target dimension in the characteristic data sequence to obtain an updated characteristic data sequence.
13. The apparatus of claim 9, wherein the dimension reduction module is specifically configured to,
determining a matrix among the vibration characteristics in multiple dimensions based on the characteristic data sequence, wherein the matrix is a covariance matrix or a correlation coefficient matrix;
determining a projection feature coordinate system based on the matrix;
for each feature data, determining projected feature data of the feature data in the projected feature coordinate system;
and generating a feature data sequence after dimension reduction processing according to the projection feature data.
14. The apparatus of claim 9, wherein the dimension reduction module is specifically configured to,
and inputting the characteristic data sequence into a preset dimension reduction characteristic model to obtain the characteristic data sequence output by the model after dimension reduction processing.
15. The apparatus of claim 9, wherein the first determining module is specifically configured to,
determining an eigenmode data sequence and a dimensionality reduced feature sequence of the dimensionality reduced features for each dimensionality reduced feature in the feature data sequence after the dimensionality reduction processing;
determining a trend sequence of the feature after dimension reduction based on the feature sequence after dimension reduction and the eigenmode data sequence;
determining a remaining service life based on the trend sequence of each of the dimensionality reduced features.
16. The apparatus of claim 15, wherein the first determining module is specifically configured to,
for each dimensionality reduced feature, inputting trend data of each time point in the trend sequence of the dimensionality reduced feature into a pre-trained life prediction model, and determining the trend data of the dimensionality reduced feature at each time point in the future;
determining a target future time point at which the corresponding trend data reaches the fault trend threshold;
and determining the residual service life according to the target future time point of each dimensionality reduced feature and the preset time period.
17. An electronic device, comprising:
memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the device lifetime detection method according to any of claims 1-8 when executing the program.
18. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the device lifetime detection method according to any one of claims 1-8.
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