CN111144267A - Equipment operation state detection method and device, storage medium and computer equipment - Google Patents
Equipment operation state detection method and device, storage medium and computer equipment Download PDFInfo
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Abstract
On one hand, the method and the device are based on all original signal values of an original signal sequence of target equipment to obtain a signal matrix, and then based on the corresponding relation between singular values and an operation state, the corresponding operation state is determined according to the singular values obtained by the signal matrix, so that more comprehensive equipment operation state information can be obtained; on the other hand, the random number is added on the basis of the original signal value, so that the generated signal matrix can be prevented from being highly correlated, a sick equation set is prevented from appearing in the singular value decomposition process, the obtained singular value is not close to 0, namely the obtained singular value has reference significance, and when the corresponding operation state is determined according to the obtained singular value, the accuracy of the operation state determination result can be effectively improved.
Description
Technical Field
The present application relates to the field of signal processing technologies, and in particular, to a method and an apparatus for detecting an operating state of a device, a storage medium, and a computer device.
Background
With the development of scientific technology, the types of the equipment are more and more, and the method has important significance for detecting the running state of the equipment.
In the prior art, the state of the device is usually detected according to a signal generated during the operation of the device, and a more common detection means is to determine whether the device has an operation fault by extracting time domain features (such as a mean value, a root mean square value, a peak value, and a kurtosis value) of the signal, frequency domain features (such as a mean square frequency, a root mean square frequency, and a frequency variance of a frequency domain), time-frequency domain features (such as a wavelet packet energy of the time-frequency domain), and the like. However, the above detection means only relies on the extracted partial features to detect the device state, and the accuracy of the detection result is low.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a storage medium, and a computer device for detecting an operating state of a device, which are advantageous for improving the accuracy of detecting the operating state of the device.
An equipment running state detection method comprises the following steps:
acquiring an original signal sequence generated by target equipment to be detected in the operation process, wherein the original signal sequence comprises a plurality of original signal values;
generating a signal matrix based on the original signal sequence, wherein each element in the signal matrix comprises the sum of a single original signal value and a random number within a preset range;
performing singular value decomposition on the signal matrix to obtain a plurality of singular values;
and carrying out state classification on the plurality of singular values based on the corresponding relation between the singular values and the operation state to obtain the operation state information of the target equipment.
An apparatus operation state detection device comprising:
the device comprises a signal acquisition module, a signal processing module and a signal processing module, wherein the signal acquisition module is used for acquiring an original signal sequence generated by target equipment to be detected in the operation process, and the original signal sequence comprises a plurality of original signal values;
a matrix generation module, configured to generate a signal matrix based on the original signal sequence, where each element in the signal matrix is a sum of a single original signal value and a random number within a preset range;
the singular value decomposition module is used for carrying out singular value decomposition on the signal matrix to obtain a plurality of singular values;
and the state classification module is used for performing state classification on the plurality of singular values based on the corresponding relation between the singular values and the running state to obtain the running state information of the target equipment.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
On one hand, in the method, the device, the storage medium and the computer device for detecting the running state of the equipment, in the embodiment of the application, a signal matrix is obtained based on all original signal values of an original signal sequence of target equipment, and then the corresponding running state is determined according to singular values obtained by the signal matrix based on the corresponding relation between the singular values and the running state, namely, the running state of the equipment is determined by considering all characteristics in the running process of the equipment, so that more comprehensive running state information of the equipment can be obtained; on the other hand, the random number is added on the basis of the original signal value, so that the generated signal matrix can be prevented from being highly correlated, a sick equation set is prevented from appearing in the singular value decomposition process, the obtained singular value is not close to 0, namely the obtained singular value has reference significance, and when the corresponding operation state is determined according to the obtained singular value, the accuracy of the operation state determination result can be effectively improved.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for detecting an operating state of a device according to an embodiment;
FIG. 2(a) is a waveform diagram of an original signal sequence of length 2000;
fig. 2(b) is a first row data diagram in a signal matrix of size 1001 x 1000 (1001 rows, 1000 columns) generated from the original signal sequence in fig. 2 (a);
fig. 2(c) is a data plot of the second row in the signal matrix generated to a size of 1001 x 1000 (1001 rows, 1000 columns) from the original signal sequence in fig. 2 (a);
FIG. 2(d) is a graph of singular values obtained from a signal matrix;
FIG. 3 is a schematic flow chart of generating a signal matrix based on an original signal sequence according to an embodiment;
FIG. 4 is a schematic diagram of a process for generating a signal matrix based on an original signal sequence according to another embodiment;
FIG. 5 is a flowchart illustrating a process of performing state classification on a plurality of singular values based on a correspondence between the singular values and operating states to obtain operating state information of a device under test in one embodiment;
FIG. 6 is a flowchart illustrating a method for detecting an operation status of a device according to another embodiment;
FIG. 7 is a schematic structural diagram of an apparatus operation state detection device according to an embodiment;
FIG. 8 is a schematic structural view of an apparatus operation state detection device according to another embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In an embodiment, as shown in fig. 1, a method for detecting an operating state of a device is provided, which is explained by taking an example that the method is applied to a processor capable of detecting an operating state of a device, and the method mainly includes the following steps:
step S100, an original signal sequence corresponding to a target device to be detected is obtained, wherein the original signal sequence comprises a plurality of original signal values.
The original signal sequence is a signal sequence acquired by a signal acquisition device (specifically, various sensors, such as a speed signal sensor, etc.) when the target device is in an operating state, and the original signal sequence includes original signal values generated by a plurality of targets in an operating process.
When the processor detects the running state of the equipment, the real-time state detection can be carried out, namely, the original signal sequence of the target equipment during running is collected in real time through the signal collecting device and is sent to the processor in real time, and the processor receives the original signal sequence sent by the signal collecting device in real time and carries out subsequent processing, so that real-time monitoring is realized. Of course, the original signal sequence may also be collected in advance and stored in the memory, and when the processor needs to detect the operation state of the target device, the original signal sequence corresponding to the target device is directly read from the memory. Of course, the processor may also obtain the original signal sequence from an external device. For example, an original signal sequence of the target device is stored in the cloud, and when the processor needs to detect the running state of the target device, the original signal sequence corresponding to the target device is directly obtained from the cloud. In addition, the external device may also be an external storage medium, and the embodiment does not limit the manner in which the processor acquires the original signal sequence corresponding to the target device.
Step S200, generating a signal matrix based on the original signal sequence, wherein each element in the signal matrix includes a sum of a single original signal value and a random number within a preset range.
The processor, after obtaining the original signal sequence, generates a signal matrix based on the original signal sequence. In the prior art, when a signal matrix is generated based on an original signal sequence, original signal values in the original signal sequence are generally directly used as elements in the signal matrix, however, only one element is different between every two lines of the signal matrix generated by the prior art, so that the signal matrix is likely to be highly correlated, and thus a problem of a sick equation set occurs when singular values are solved, the sick equation set can cause partial singular values to approach to 0, the partial singular values approaching to 0 do not belong to singular values with reference significance, and thus partial useful information can be lost, and detection results are inaccurate.
Specifically, as shown in fig. 2(a) to 2(d), a specific example of obtaining singular values by the related art is shown. Wherein, fig. 2(a) is a waveform diagram of an original signal sequence with a length of 2000; fig. 2(b) and fig. 2(c) are data diagrams of a first row and a second row, respectively, in a signal matrix of 1001 × 1000 size (1001 rows and 1000 columns) generated from the original signal sequence; fig. 2(d) is a graph of singular values obtained from a signal matrix.
Referring to fig. 2(b) and 2(c), it can be seen that the data of the first row and the second row are staggered by only one data point, and in fact, two adjacent rows in all 1001 rows are different by only one data point, which causes the problem of solving the ill-conditioned equation set when the singular value decomposition is performed on the 1001 x 1000 matrix, and as a result, a large part of the singular values are very close to 0, that is, the singular value curve shown in fig. 2(d) appears.
The present application differs from the prior art in that each element in the signal matrix generated by the present application is not a single original signal value itself, but a sum of the single original signal value and a random number within a preset range is taken as an element in the signal matrix. The purpose of adding the random number is to add a tiny disturbance on the basis of an original signal value, so that the situation that the signal matrix is highly correlated is prevented, namely the problem that only one element is different between every two lines of the signal matrix, so that a sick equation set is caused to occur is prevented (the sick equation set can cause partial singular values to approach to 0), and the singular values obtained by solving are singular values with reference significance.
In addition, when the random number is added on the basis of the original signal value, the added random number is in a smaller preset range so as to ensure that the added random number does not influence the overall trend of the original signal value, namely, the added random number cannot submerge the original signal value. For example, the added random number may be 1/2 that is less than the original signal amplitude. In addition, the added random numbers are different for each original signal value (unless the generated random numbers are the same due to coincidence), thereby preventing the problem of the ill-conditioned system of equations occurring during the singular value decomposition.
Step S300, singular value decomposition is carried out on the signal matrix to obtain a plurality of singular values.
After the processor obtains the signal matrix, singular value decomposition is carried out, and a plurality of singular values corresponding to the signal matrix can be obtained. In this step, the singular value decomposition process performed by the processor may be implemented by an existing singular value decomposition method, which is not limited herein.
And step S400, performing state classification on the plurality of singular values based on the corresponding relation between the singular values and the operation state to obtain the operation state information of the target equipment.
After obtaining the plurality of singular values, the processor may perform state classification on the plurality of singular values corresponding to the signal matrix of the target object according to a pre-established correspondence between the singular values and the operating state, to obtain operating state information of the target device, where the operating state information may include normal operation, abnormal operation, and the like.
In addition, the pre-established correspondence between singular values and operating states may include different correspondence between abnormal states and singular values, so that when the processor determines that the target device is abnormal in operation, the processor may further determine what kind of abnormality the target device specifically belongs to, thereby enabling the operating state detection result of the target device to be more detailed and comprehensive. For example, the features of the singular values corresponding to different abnormal types may be extracted according to the pre-established correspondence between the singular values and the operating state, and the correspondence between the abnormal types and the singular value features may be established. After the singular value corresponding to the target equipment is obtained, feature extraction is carried out on the obtained singular value, and then the specific abnormal type corresponding to the singular value corresponding to the target equipment is determined according to the corresponding relation between the abnormal type and the singular value feature.
On one hand, the present application obtains a signal matrix based on all original signal values of an original signal sequence of a target device, and then determines a corresponding operating state according to a singular value obtained from the signal matrix based on a correspondence between the singular value and the operating state, that is, the present application determines an operating state of the device in consideration of all features in an operating process of the device, so as to obtain more comprehensive device operating state information; on the other hand, the random number is added on the basis of the original signal value, so that the generated signal matrix can be prevented from being highly correlated, a sick equation set is prevented from appearing in the singular value decomposition process, the obtained singular value is not close to 0, namely the obtained singular value has reference significance, and when the corresponding operation state is determined according to the obtained singular value, the accuracy of the operation state determination result can be effectively improved.
In one embodiment, the original signal values corresponding to the elements on each of the inverse diagonals in the signal Matrix are equal, and the signal Matrix may be a Hankel Matrix (Hankel Matrix).
Specifically, if the original signal sequence x (t) is a one-dimensional time sequence with a length N, i.e., x (t) { x ═ x }1,x2,…xNH, a Hankel matrix H of order p × q can be constructed as shown belowp*q：
Wherein epsiloni,jFor the added random numbers, then for matrix Hp*qAnd (3) carrying out singular value decomposition to obtain min { p, q } singular values larger than zero as the characteristics of the original signal sequence x (t).
Alternatively, in order to obtain sufficient features, the values of p and q can be determined by the following formula:
q＝N+1-p
for example, when the signal length N is 2000, p is 1000 and q is 1001, so that 1000 singular values greater than 0 can be obtained as the features of the original signal sequence.
In this embodiment, a Hankel matrix is constructed based on a one-dimensional original signal sequence, then singular value decomposition is performed on the constructed matrix, and the obtained singular value is used as a feature of the original signal sequence.
In one embodiment, as shown in fig. 3, step S200 generates a signal matrix based on the original signal sequence, including steps S212 to S214.
Step S212, using each original signal value in the original signal sequence as an initial element to generate an initial matrix;
and step S214, taking the sum of each initial element and the random number as a new element, and replacing the initial element at the corresponding position in the initial matrix with the new element to obtain a signal matrix.
Specifically, the process of generating the signal matrix by the processor is explained by taking the example that the original signal sequence contains 2000 original signal values. The original signal sequence obtained by the processor has a signal length N of 2000, i.e. includes (x)1,x2,…x2000) For a total of 2000 original signal values. The processor then takes these 2000 raw signal values as initial elements, generating the following initial matrix:
then, each initial element xmAnd a random number epsiloni,jAnd xm+εi,jAs a new element, replacing the initial element of the corresponding position, the following signal matrix is obtained:
in the embodiment, the random number is added on the basis of the original signal value, so that the generated signal matrix is prevented from being highly correlated, a sick equation set is prevented from occurring in the singular value decomposition process, the obtained singular value is ensured not to approach 0, namely, the obtained singular value has reference significance, and when the corresponding operation state is determined according to the obtained singular value, the accuracy of the operation state determination result can be effectively improved.
In one embodiment, as shown in fig. 4, step S200 generates a signal matrix based on the original signal sequence, including steps S222 to S224.
Step S222, taking the sum of each original signal value and the random number as a new signal value, and replacing the original signal value at the corresponding position in the original signal sequence with the new signal value to obtain a new signal sequence;
in step S224, a signal matrix is generated using each new signal value in the new signal sequence as an element.
Specifically, the process of generating the signal matrix by the processor is explained by taking the example that the original signal sequence contains 2000 original signal values. The original signal sequence obtained by the processor has a signal length N of 2000, i.e. includes (x)1,x2,…x2000) For a total of 2000 original signal values. The processor then compares each initial element xmAnd a random number epsiloni,jAnd xm+εi,jAs new signal value, and replacing the original signal value of corresponding position to obtain new signal sequence (x)1+ε1，1,x2+ε1，2,…x2000+ε1000,1001) The processor then takes each new signal value in the new signal sequence as an element to generate the following signal matrix:
in the embodiment, the random number is added on the basis of the original signal value, so that the generated signal matrix is prevented from being highly correlated, a sick equation set is prevented from occurring in the singular value decomposition process, the obtained singular value is ensured not to approach 0, namely, the obtained singular value has reference significance, and when the corresponding operation state is determined according to the obtained singular value, the accuracy of the operation state determination result can be effectively improved.
In one embodiment, when constructing the signal matrix based on the original signal sequence, the original signal sequence may be sampled at intervals, and then the signal matrix may be generated.
Specifically, for the explanation, the interval is taken as one original signal value, and the signal length N of the original signal sequence obtained by the processor is 2000, that is, (x) is included1,x2,…x2000) For a total of 2000 original signal values. The processor firstly samples the original signal sequence at intervals of 1 to obtain a signal sequence (x) containing 1000 original signal values1,x3,…x1999) Then according to the signal sequence (x)1,x3,…x1999) The following signal matrix is generated:
in this embodiment, after the processor obtains the original signal sequence, the processor performs interval sampling to obtain new sequences, the number of which is less than that of the original signal sequence, and then generates a signal matrix based on the new sequences, so that the data amount can be effectively reduced, and the data processing efficiency is improved.
In one embodiment, a specific example of singular value decomposition based on a signal matrix is provided.
The signal length N of the original signal sequence obtained by the processor is 9, which includes a total of 9 original signal values (1,2,3, … 8, 9). Under the condition that no random number is added, a Hankel matrix obtained according to an original signal sequence is as follows:
after singular value decomposition is performed on the matrix A, 5 singular values are obtained, and the singular values are arranged in the order from large to small as follows: 26.9,1.86, 8.37e-16,5.83e-16,4.43 e-17. It can be seen that the last 3 singular values all approach very close to 0, so that only the first two singular values belong to the valid singular values with reference significance.
In the application, on the basis of the matrix A, a random number in a preset range is added to each element in the matrix A to serve as noise to resist the high correlation of the matrix, and then singular value decomposition is carried out on the matrix subjected to noise addition. For example, taking the preset range of [ -1,1] as an example, after adding a random number distributed between [ -1,1] to each element on the basis of the matrix a, a singular value decomposition result is obtained (due to uncertainty of the random number, the specifically added random number is not shown here) as follows: 27.590,1.713, 1.514,0.502, 0.103 (leaving 3 decimal places). It can be seen that the singular values obtained after adding the random numbers all belong to the valid singular values with reference significance.
Therefore, compared with a signal matrix without random numbers, the method and the device have the advantages that the random numbers in the preset range are added to each element in the signal matrix, and on the premise that the main characteristics of the original signal sequence are not changed, more effective singular values with reference meanings can be extracted.
In one embodiment, as shown in fig. 5, the step S400 performs state classification on a plurality of singular values based on the correspondence between the singular values and the operating states, and obtaining the operating state information of the device to be detected includes steps S420 to S460.
Step S420, singular value sample data and corresponding state categories are obtained, wherein the singular value sample data comprises singular values corresponding to different state categories;
step S440, performing model training based on singular value sample data and corresponding state classes to obtain a trained state classification model;
and step S460, carrying out state classification on the plurality of singular values through a state classification model to obtain the running state information of the equipment to be detected.
Specifically, signal sequences with sufficient length and different operation states (normal state and abnormal state) can be collected through the sensor, and for the signal sequence in each operation state, at least more than 2 periods of data length is intercepted as a sample signal and a corresponding state class label is set. Then, according to the method flow of the previous embodiment of the present application, a corresponding signal matrix is generated based on the sample signal, singular value decomposition is performed on the generated signal matrix to obtain a singular value corresponding to the sample signal, and the state class label corresponding to the sample signal is used as the state class label of the singular value obtained according to the sample data.
After the singular value sample data and the corresponding state class labels are obtained, training the initial model, specifically, inputting the singular value sample data into the initial model, then calculating a loss value according to a state prediction result output by the initial model and the corresponding state class labels, and then performing optimization training on the initial model according to the loss value, thereby obtaining a trained state classification model. The state classification model includes Neural Networks, such as DNN (Deep Neural Networks), CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and the like. After the trained state classification model is obtained, state classification can be automatically carried out on the singular values through the state classification model.
It can be understood that the state classification model used in this embodiment may also be trained in advance, and when the state classification of the singular value is required, the trained state classification model is directly called, that is, the step of performing model training in this application is not necessary.
In the embodiment, the state classification is performed on the singular values through the trained state classification model, so that the classification efficiency and the accuracy of the classification result can be improved.
In one embodiment, as shown in fig. 6, the method for detecting the operation state of the device further includes: step S500, when the running state information of the target device contains abnormal running state, outputting corresponding prompt information.
Specifically, when detecting that there is an abnormality in the operating status of the target device, the processor may display a corresponding prompt message to the user through the display interface, for example, "XX device has a XX fault, please note |)! "etc., thereby facilitating the user to understand the relevant abnormal situation and solve the problem.
In addition, the processor can also send an alarm instruction to the alarm device, so that the alarm device sends corresponding alarm information after receiving the alarm instruction sent by the processor, and the alarm device can be particularly an audible and visual alarm device and the like, so that a user can be immediately reminded when the abnormal operation of the device is detected.
In one embodiment, the method for detecting the operating state of the device provided by the application can be particularly applied to devices such as a CT bulb, a bearing, a gear box, a motor, an engine, a fan, a pump, a drill bit and a lathe, so that the accuracy of the operating state detection result of the devices can be improved.
It should be understood that, under reasonable circumstances, although the steps in the flowcharts referred to in the foregoing embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in each flowchart may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided an apparatus for detecting an operation state of a device, the apparatus mainly includes the following modules:
the signal acquiring module 100 is configured to acquire an original signal sequence generated by a target device to be detected in an operation process, where the original signal sequence includes a plurality of original signal values;
a matrix generation module 200, configured to generate a signal matrix based on an original signal sequence, where each element in the signal matrix is a sum of a single original signal value and a random number within a preset range;
a singular value decomposition module 300, configured to perform singular value decomposition on the signal matrix to obtain a plurality of singular values;
the state classification module 400 is configured to perform state classification on the plurality of singular values based on a correspondence between the singular values and the operating states, so as to obtain operating state information of the target device.
On one hand, the present application obtains a signal matrix based on all original signal values of an original signal sequence of a target device, and then determines a corresponding operating state according to a singular value obtained from the signal matrix based on a correspondence between the singular value and the operating state, that is, the present application determines an operating state of the device in consideration of all features of the device in an operating process, so as to obtain more comprehensive device operating state information; on the other hand, the random number is added on the basis of the original signal value, so that the generated signal matrix can be prevented from being highly correlated, a sick equation set is prevented from appearing in the singular value decomposition process, the obtained singular value is not close to 0, namely the obtained singular value has reference significance, and when the corresponding operation state is determined according to the obtained singular value, the accuracy of the operation state determination result can be effectively improved.
In one embodiment, the matrix generation module 200 is further configured to: using each original signal value in the original signal sequence as an initial element to generate an initial matrix; and taking the sum of each initial element and the random number as a new element, and replacing the initial element at the corresponding position in the initial matrix with the new element to obtain the signal matrix.
In one embodiment, the matrix generation module 200 is further configured to: taking the sum of each original signal value and the random number as a new signal value, and replacing the original signal value at the corresponding position in the original signal sequence with the new signal value to obtain a new signal sequence; and generating a signal matrix by taking each new signal value in the new signal sequence as an element.
In one embodiment, the state classification module 400 is further configured to: acquiring singular value sample data and corresponding state categories, wherein the singular value sample data comprises singular values corresponding to different state categories; performing model training based on the singular value sample data and the corresponding state class to obtain a trained state classification model; and carrying out state classification on the plurality of singular values through a state classification model to obtain the running state information of the equipment to be detected.
In one embodiment, as shown in fig. 8, the apparatus operation state detection device further includes: the abnormal prompting module 500 is configured to output corresponding prompting information when the running state information of the target device includes an abnormal running state.
For the specific limitation of the device operation state detection apparatus, reference may be made to the above limitation on the device operation state detection method, which is not described herein again. The modules in the device operation state detection device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring an original signal sequence corresponding to target equipment to be detected, wherein the original signal sequence comprises a plurality of original signal values; generating a signal matrix based on the original signal sequence, wherein each element in the signal matrix comprises the sum of a single original signal value and a random number in a preset range; performing singular value decomposition on the signal matrix to obtain a plurality of singular values; and carrying out state classification on the plurality of singular values based on the corresponding relation between the singular values and the operation state to obtain the operation state information of the target equipment.
In one embodiment, the processor, when executing the computer program, further performs the steps of: using each original signal value in the original signal sequence as an initial element to generate an initial matrix; and taking the sum of each initial element and the random number as a new element, and replacing the initial element at the corresponding position in the initial matrix with the new element to obtain the signal matrix.
In one embodiment, the processor, when executing the computer program, further performs the steps of: taking the sum of each original signal value and the random number as a new signal value, and replacing the original signal value at the corresponding position in the original signal sequence with the new signal value to obtain a new signal sequence; and generating a signal matrix by taking each new signal value in the new signal sequence as an element.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring singular value sample data and corresponding state categories, wherein the singular value sample data comprises singular values corresponding to different state categories; performing model training based on the singular value sample data and the corresponding state class to obtain a trained state classification model; and carrying out state classification on the plurality of singular values through a state classification model to obtain the running state information of the equipment to be detected.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and when the running state information of the target equipment contains the abnormal running state, outputting corresponding prompt information.
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment. The computer device may specifically be a terminal (or server). As shown in fig. 9, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program which, when executed by the processor, causes the processor to implement the device operating state detection method. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the method for detecting the operational status of the device. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring an original signal sequence corresponding to target equipment to be detected, wherein the original signal sequence comprises a plurality of original signal values; generating a signal matrix based on the original signal sequence, wherein each element in the signal matrix comprises the sum of a single original signal value and a random number in a preset range; performing singular value decomposition on the signal matrix to obtain a plurality of singular values; and carrying out state classification on the plurality of singular values based on the corresponding relation between the singular values and the operation state to obtain the operation state information of the target equipment.
In one embodiment, the computer program when executed by the processor further performs the steps of: using each original signal value in the original signal sequence as an initial element to generate an initial matrix; and taking the sum of each initial element and the random number as a new element, and replacing the initial element at the corresponding position in the initial matrix with the new element to obtain the signal matrix.
In one embodiment, the computer program when executed by the processor further performs the steps of: taking the sum of each original signal value and the random number as a new signal value, and replacing the original signal value at the corresponding position in the original signal sequence with the new signal value to obtain a new signal sequence; and generating a signal matrix by taking each new signal value in the new signal sequence as an element.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring singular value sample data and corresponding state categories, wherein the singular value sample data comprises singular values corresponding to different state categories; performing model training based on the singular value sample data and the corresponding state class to obtain a trained state classification model; and carrying out state classification on the plurality of singular values through a state classification model to obtain the running state information of the equipment to be detected.
In one embodiment, the computer program when executed by the processor further performs the steps of: and when the running state information of the target equipment contains the abnormal running state, outputting corresponding prompt information.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, the computer program can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An equipment operation state detection method is characterized by comprising the following steps:
acquiring an original signal sequence corresponding to target equipment to be detected, wherein the original signal sequence comprises a plurality of original signal values;
generating a signal matrix based on the original signal sequence, wherein each element in the signal matrix comprises the sum of a single original signal value and a random number within a preset range;
performing singular value decomposition on the signal matrix to obtain a plurality of singular values;
and carrying out state classification on the plurality of singular values based on the corresponding relation between the singular values and the operation state to obtain the operation state information of the target equipment.
2. The method of claim 1, wherein generating a signal matrix based on the original signal sequence comprises:
using each original signal value in the original signal sequence as an initial element to generate an initial matrix;
and taking the sum of each initial element and a random number as a new element, and replacing the initial element at the corresponding position in the initial matrix with the new element to obtain the signal matrix.
3. The method of claim 1, wherein generating a signal matrix based on the original signal sequence comprises:
taking the sum of each original signal value and a random number as a new signal value, and replacing the original signal value at the corresponding position in the original signal sequence with the new signal value to obtain a new signal sequence;
and generating the signal matrix by taking each new signal value in the new signal sequence as an element.
4. The method of claim 1, wherein the original signal values corresponding to the elements on each of the inverse diagonals in the signal matrix are equal.
5. The method according to claim 1, wherein the state classification of the plurality of singular values based on the correspondence between singular values and operating states to obtain the operating state information of the device to be tested comprises:
acquiring singular value sample data and corresponding state categories, wherein the singular value sample data comprises singular values corresponding to different state categories;
performing model training based on the singular value sample data and the corresponding state class to obtain a trained state classification model;
and carrying out state classification on the plurality of singular values through the state classification model to obtain the running state information of the equipment to be detected.
6. The method of claim 5, wherein the state classification model comprises a neural network.
7. The method of claim 1, further comprising:
and outputting corresponding prompt information when the running state information of the target equipment contains an abnormal running state.
8. An apparatus operation state detection device, characterized by comprising:
the device comprises a signal acquisition module, a signal processing module and a signal processing module, wherein the signal acquisition module is used for acquiring an original signal sequence generated by target equipment to be detected in the operation process, and the original signal sequence comprises a plurality of original signal values;
a matrix generation module, configured to generate a signal matrix based on the original signal sequence, where each element in the signal matrix is a sum of a single original signal value and a random number within a preset range;
the singular value decomposition module is used for carrying out singular value decomposition on the signal matrix to obtain a plurality of singular values;
and the state classification module is used for performing state classification on the plurality of singular values based on the corresponding relation between the singular values and the running state to obtain the running state information of the target equipment.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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