CN115618195A - Sensor circuit fault diagnosis method, system, medium, and apparatus - Google Patents
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Abstract
The invention belongs to the field of sensor circuit fault diagnosis, and discloses a method, a system, a medium and a device for diagnosing sensor circuit faults, which comprise the steps of acquiring a time domain continuous voltage signal output by a sensor circuit; performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data; and calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result. The method has the advantages that the combined processing of the feature extraction and the time sequence signal is realized, the correlation between the time domain continuous voltage signal and the sensor circuit fault is considered, the time domain and the frequency domain dual feature extraction is carried out on the time domain continuous voltage signal by using a feature engineering method, the data are jointly input into a sensor circuit fault diagnosis model for fusion, the fault diagnosis result of the sensor circuit is obtained, and the accuracy is greatly improved.
Description
Technical Field
The invention belongs to the field of sensor circuit fault diagnosis, and relates to a method, a system, a medium and a device for diagnosing sensor circuit faults.
Background
With the rapid development of the internet of things technology and the electronic circuit industry, sensor circuits comprising various components are widely applied to various fields of industrial production and daily life, and people have higher and higher requirements on the operational reliability of the sensor circuits. Most faults in the sensor circuit result from a component failure of the sensor circuit. Technological deviation in the actual production process, poor contact in the welding process and various non-ideal factors in the external environment can cause partial component faults of the sensor circuit, further cause the sensor circuit faults, influence the operation of equipment, and can cause great economic loss and even derive dangerous accidents in severe cases. Along with the improvement of the complexity of the sensor circuit components, the traditional troubleshooting mode is difficult to meet the existing diagnosis requirement, and how to rapidly position the positions of the sensor circuit fault components gradually becomes a research hotspot in academia and industrial circles.
The fault diagnosis of hardware circuits such as sensor circuits is to accurately locate the fault occurrence point of the circuit by processing and analyzing the output signal of the circuit. At present, two methods mainly exist in fault diagnosis methods of hardware circuits such as sensor circuits and the like, wherein one method is to perform feature extraction on circuit output signals to obtain a small number of features related to circuit faults and then perform fault classification by using machine learning methods such as SVM and the like; the other method is to directly utilize a time sequence signal or a frequency domain signal output by the circuit, input the signal into a neural network for high-dimensional data processing and output classification information, and further finish the diagnosis of the circuit fault. However, both methods have low diagnostic accuracy.
Disclosure of Invention
It is an object of the present invention to overcome the above-mentioned disadvantages of the prior art and to provide a method, system, medium, and apparatus for diagnosing a fault in a sensor circuit.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
in a first aspect of the present invention, a method for diagnosing a fault of a sensor circuit is provided, including:
acquiring a time domain continuous voltage signal output by a sensor circuit; performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data; and calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result.
Optionally, the performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data includes: acquiring one or more of the maximum value, the minimum value, the average value, the standard deviation, the kurtosis and the skewness of the time domain continuous voltage signal to obtain time domain characteristic data; and performing time-frequency conversion on the time domain continuous voltage signal to obtain a frequency domain continuous voltage signal, and acquiring one or more of the bandwidth and the central frequency of the frequency domain continuous voltage signal to obtain frequency domain characteristic data.
Optionally, the calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data, and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result includes: combining the time domain characteristic data and the frequency domain characteristic data to obtain time-frequency mixed data; normalizing the time domain continuous voltage signal and the time frequency mixed data to obtain normalized time frequency voltage data and normalized time frequency mixed data; and inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result.
Optionally, the preset sensor circuit fault diagnosis model includes a DNN neural network, an LSTM neural network, a full connection layer network, and a SoftMax layer network; the DNN neural network is used for inputting normalized time-frequency mixed data and outputting DNN neural network output data to the full-connection layer network; the LSTM neural network is used for inputting normalized time-frequency voltage data and outputting LSTM neural network output data to the full-connection layer network; the full connection layer network is used for performing full connection processing on DNN neural network output data and LSTM neural network output data to obtain full connection layer network output data and outputting the full connection layer network output data to a SoftMax layer network; and the SoftMax layer network is used for outputting data according to the input full-connection layer network to obtain and output a fault diagnosis result of the sensor circuit.
Optionally, the SoftMax function of the SoftMax layer network is:
the method comprises the steps that a first group of time domain continuous voltage signals are acquired, wherein fc _ out (m) i is the ith data of all-connection layer network output data of the mth group of time domain continuous voltage signals, fc _ out (m) j is the jth data of all-connection layer network output data of the mth group of time domain continuous voltage signals, m is the acquisition group number of the time domain continuous voltage signals, and K is the fault type number of a sensor circuit; s (fc _ out (m) [ i ]) is the i-th data of the sensor circuit failure diagnosis result, and indicates the probability of failure of the sensor circuit element i when i > 0, and indicates the probability of failure of the sensor circuit element i when i = 0.
In a second aspect of the present invention, there is provided a sensor circuit fault diagnosis system, including:
the data acquisition module is used for acquiring a time domain continuous voltage signal output by the sensor circuit;
the characteristic extraction module is used for carrying out time domain characteristic extraction and frequency domain characteristic extraction on the time domain continuous voltage signal to obtain time domain characteristic data and frequency domain characteristic data;
and the diagnosis module is used for calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal to obtain a sensor circuit fault diagnosis result.
Optionally, the diagnostic module is specifically configured to: combining the time domain characteristic data and the frequency domain characteristic data to obtain time-frequency mixed data; normalizing the time domain continuous voltage signal and the time frequency mixed data to obtain normalized time frequency voltage data and normalized time frequency mixed data; and inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result.
Optionally, the preset sensor circuit fault diagnosis model includes a DNN neural network, an LSTM neural network, a full connection layer network, and a SoftMax layer network; the DNN neural network is used for inputting normalized time-frequency mixed data and outputting DNN neural network output data to the full-connection layer network; the LSTM neural network is used for inputting normalized time-frequency voltage data and outputting LSTM neural network output data to the full-connection layer network; the full-connection layer network is used for performing full-connection processing on the output data of the DNN neural network and the output data of the LSTM neural network to obtain output data of the full-connection layer network and outputting the output data of the full-connection layer network to a SoftMax layer network; and the SoftMax layer network is used for outputting data according to the input full-connection layer network to obtain and output a fault diagnosis result of the sensor circuit.
In a third aspect of the present invention, there is provided a sensor circuit fault diagnosis apparatus, including a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the processor implements the steps of the sensor circuit fault diagnosis method when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described sensor circuit failure diagnosis method.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to a sensor circuit fault diagnosis method, which comprises the steps of carrying out time domain characteristic extraction and frequency domain characteristic extraction on a time domain continuous voltage signal to obtain time domain characteristic data and frequency domain characteristic data, calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result, realizing the joint processing of the characteristic extraction and a time sequence signal when carrying out the sensor circuit fault diagnosis, not only considering the relevance of the time domain continuous voltage signal and the sensor circuit fault, but also utilizing a characteristic engineering method to carry out time domain and frequency domain dual characteristic extraction on the time domain continuous voltage signal, and finally inputting the data into the preset sensor circuit fault diagnosis model together for fusion to finally obtain the fault diagnosis result of a sensor circuit, so that the accuracy of the sensor circuit fault diagnosis result is greatly improved.
Drawings
FIG. 1 is a flow chart of a method for diagnosing a fault in a sensor circuit according to an embodiment of the present invention;
FIG. 2 is a block diagram of a sensor circuit fault diagnosis model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an LSTM neural network according to an embodiment of the present invention;
fig. 4 is a block diagram of a sensor circuit fault diagnosis system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As introduced in the background art, two methods mainly exist in the current fault diagnosis method of the sensor circuit, one method is to extract the characteristics of the circuit output signal to obtain a small amount of characteristics related to the circuit fault, and then to classify the fault by using machine learning methods such as SVM and the like; the other method is to directly utilize a time sequence signal or a frequency domain signal output by the circuit, input the signal into a neural network for high data processing and output classification information, and further finish the diagnosis of the circuit fault. However, both of these methods have a problem of low diagnostic accuracy.
In order to improve the above problem, an embodiment of the present invention provides a sensor circuit fault diagnosis method, including: acquiring a time domain continuous voltage signal output by a sensor circuit; performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data; and calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result. The method realizes the combined processing of the feature extraction and the time sequence signal, not only considers the correlation between the time domain continuous voltage signal and the sensor circuit fault, but also utilizes the method of feature engineering to extract the time domain and frequency domain features of the time domain continuous voltage signal, and finally inputs the data into the sensor circuit fault diagnosis model together for fusion, so as to obtain the fault diagnosis result of the sensor circuit finally, thereby greatly improving the accuracy. The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, in an embodiment of the present invention, a sensor circuit fault diagnosis method is provided, which proposes a concept of mutually fusing feature extraction and time sequence feature processing, so as to improve accuracy of fault diagnosis of a sensor circuit, and finally, accurately classify and locate fault types of the sensor circuit.
Specifically, the sensor circuit fault diagnosis method comprises the following steps:
s1: and acquiring a time domain continuous voltage signal output by the sensor circuit.
S2: and performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data.
S3: and calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result.
Optionally, in S1, when the time-domain continuous voltage signal output by the sensor circuit is obtained, the target sensor circuit may be simulated to obtain the target analog circuit, and the output end of the target analog circuit is used as the test point (i.e., the data acquisition point) to obtain the time-domain continuous voltage signalVoltage signals are sampled at time intervals delta T, and N voltage value points are collected in total and are represented as V (m) = [ V = [ V ] ] 1 ,v 2 ,...,v N ]Wherein M represents the number of acquisition groups of the time domain continuous voltage signal, the total number of acquisition groups of the time domain continuous voltage signal is M, and the fault type of each group of time domain continuous voltage signal can be represented as { F } 0 ,F 1 ,...,F K In which F i I ∈ {1, 2.., K } indicates that element i of the sensor circuit is faulty, F 0 Indicating that the sensor circuit is not faulty.
Optionally, a preset sensor circuit fault diagnosis model may be constructed through a neural network, and according to different characteristics of the time-domain continuous voltage signal, the time-domain feature data, and the frequency-domain feature data, high-dimensional features are extracted, processed, and fused, so as to finally obtain positioning and classification prediction of the sensor circuit fault as a diagnosis result.
In summary, the sensor circuit fault diagnosis method of the present invention obtains time domain feature data and frequency domain feature data by performing time domain feature extraction and frequency domain feature extraction on a time domain continuous voltage signal, calls a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain feature data and the frequency domain feature data to obtain a sensor circuit fault diagnosis result, and realizes the combined processing of feature extraction and a time sequence signal when performing sensor circuit fault diagnosis.
In a possible implementation manner, the performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data includes: acquiring one or more of the maximum value, the minimum value, the average value, the standard deviation, the kurtosis and the skewness of the time domain continuous voltage signal to obtain time domain characteristic data; and performing time-frequency conversion on the time domain continuous voltage signal to obtain a frequency domain continuous voltage signal, and acquiring one or more of the bandwidth and the central frequency of the frequency domain continuous voltage signal to obtain frequency domain characteristic data.
Specifically, regarding the time domain feature extraction of the time domain continuous voltage signal, in the present embodiment, the time domain feature extraction is performed on the time domain continuous voltage signal by using the maximum value, the minimum value, the average value, the standard deviation, the kurtosis and the skewness as time domain feature indexes, that is, the maximum value, the minimum value, the average value, the standard deviation, the kurtosis and the skewness of the time domain continuous voltage signal are obtained, and the time domain feature data may be represented as td (m):
td(m)＝[v max (m),v min (m),v avg (m),v std (m),v peak (m),v ske (m)]
wherein v is max (m) = max (V (m)) is the maximum value of the time domain continuous voltage signal, V min (m) = min (V (m)) is the minimum value of the time domain continuous voltage signal,
is the average value of the time domain continuous voltage signal,
is the standard deviation of the time domain continuous voltage signal,
is the kurtosis of the time domain continuous voltage signal and is used for representing the peak value height of the probability density distribution curve at the average value,
the deviation degree of the time domain continuous voltage signal is used for representing the asymmetry degree of the probability distribution density curve relative to the average value.
Regarding the frequency domain feature extraction of the time domain continuous voltage signal, firstly, time-frequency conversion is performed to obtain a frequency domain continuous voltage signal, and then, feature extraction is performed on the frequency domain continuous voltage signal, in this embodiment, a bandwidth and a center frequency are used as frequency domain feature indexes to obtain a bandwidth and a center frequency of the frequency domain continuous voltage signal to obtain frequency domain feature data, and the frequency domain feature data can be represented as fd (m):
fd(m)＝[band(m),freq(m)]
wherein, band (m) is the bandwidth of the frequency domain continuous voltage signal, and freq (m) is the center frequency of the frequency domain continuous voltage signal.
In a possible implementation manner, the calling a preset sensor circuit fault diagnosis model according to the time-domain continuous voltage signal, the time-domain feature data, and the frequency-domain feature data to obtain a sensor circuit fault diagnosis result includes: combining the time domain characteristic data and the frequency domain characteristic data to obtain time-frequency mixed data; normalizing the time domain continuous voltage signal and the time frequency mixed data to obtain normalized time frequency voltage data and normalized time frequency mixed data; and inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result.
Specifically, firstly, time domain feature data and frequency domain feature data are combined to form time-frequency mixed data, which is represented as tf (m): tf (m) = [ fd (m), td (m) ].
Then, each group of time-frequency mixed data is normalized to obtain corresponding normalized time-frequency mixed data norm _ tf (m), and each group of time-domain continuous voltage signals is normalized to obtain corresponding normalized time-frequency voltage data norm _ V (m).
Optionally, when performing normalization processing on each group of time-frequency mixed data, a min-max normalization method is adopted, and normalization of each group of time-frequency mixed data is realized through the following formula:
wherein, tf i (m) represents the ith data, tf, in the mth group of time-frequency mixed data i * (m) represents tf i (m) normalized value of, tf min (m) represents the minimum value of tf (m), tf max (m) represents the maximum value of tf (m).
Optionally, each group of time domain continuous voltage signals is normalized, or a min-max normalization method may be used, where the normalization of each group of time domain continuous voltage signals is implemented by the following formula:
wherein v is i (m) represents the ith data in the mth set of time-domain continuous voltage signals,
denotes v i (m) normalized magnitude of the value.
In one possible implementation, referring to fig. 2, the sensor circuit fault diagnosis model includes a DNN neural network, an LSTM neural network, a full connectivity layer network, and a SoftMax (classification network) layer network.
Fully connected layers (FC) act as "classifiers" throughout the convolutional neural network. If we say that operations such as convolutional layers, pooling layers, and activation function layers map raw data to the hidden feature space, the fully-connected layer serves to map the learned distributed feature representation to the sample label space. In actual use the fully connected layer may be implemented by a convolution operation. The Softmax layer maps several (- ∞, + ∞) real numbers to the same number of (0, 1) real numbers (representable probabilities) while ensuring that their sum is 1.
The DNN neural network is used for inputting normalized time-frequency mixed data and outputting DNN neural network output data to a full-connection layer network; the LSTM neural network is used for inputting normalized time-frequency voltage data and outputting LSTM neural network output data to the full-connection layer network; the full connection layer network is used for performing full connection processing on DNN neural network output data and LSTM neural network output data to obtain full connection layer network output data and outputting the full connection layer network output data to a SoftMax layer network; and the SoftMax layer network is used for outputting data according to the input full-connection layer network to obtain and output a fault diagnosis result of the sensor circuit.
The specific process of inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result is as follows:
step 1: and inputting the normalized time-frequency mixed data norm _ tf (m) into the DNN neural network to obtain output data d _ out (m) of the DNN neural network.
Step 2: and inputting the normalized time-frequency voltage data norm _ V (m) into the LSTM neural network to obtain the output data l _ out (m) of the LSTM neural network.
And step 3: combining the DNN neural network output data d _ out (m) in the step 1 and the LSTM neural network output data l _ out (m) in the step 2, inputting the data into a full-connection layer network, and acquiring full-connection layer network output data fc _ out (m), wherein fc _ out (m) is a K + 1-dimensional vector, K is the fault type number of the sensor circuit, the fault type number of the sensor circuit is related to the number of elements of the sensor circuit, and the sensor circuit shares the sameKThe failure type of each element is { element 1 failure, \ 8230;, element K failure }.
And 4, step 4: and (3) taking the output data fc _ out (m) of the full-connection layer network as the input of the SoftMax layer network, and obtaining a vector with the dimension of K +1 through calculation of the SoftMax layer network, namely a fault diagnosis result of the sensor circuit.
Specifically, in the embodiment, according to different characteristics of the time-frequency mixed data and the time-frequency voltage data, a DNN (Deep Neural Networks) decision Neural network and an LSTM (Long Short Term Memory) Neural network are respectively used for feature extraction, and finally, the two network data are combined to obtain a final predicted value, so as to improve the prediction capability of the sensor circuit fault diagnosis model.
Optionally, the DNN neural network is a fully-connected neural network including an input layer, an output layer, and two hidden layers. Wherein, the forward propagation function of the hidden layer is:
wherein, y i For the ith output of the hidden layer, x j For the jth input of the hidden layer, w i,j Weight of the jth input corresponding to the ith output, b i Corresponding to the bias of the ith input. Note that the forward propagation formula of the full-connection layer network according to the present embodiment corresponds to the above formula.
Alternatively, referring to fig. 3, each cell of the LSTM neural network is forward propagated through an input gate, a forgetting gate, and an output gate.
Wherein, the update of the forgetting gate can be expressed as:
f t ＝σ·(W f h t-1 +U f x t +b f )
wherein sigma is sigmoid activation function, W f ，U f And b f To forget the coefficient and bias of the door, both are trainable parameters, h t-1 Hidden output state for t-1 th cell, x t The t-th input of the sequence corresponds to the value f of the t-th element of the normalized time-frequency voltage data norm _ V (m) input in the round t The updated state of the t-th forgetting gate.
The update of the input gate can be expressed as:
i t ＝σ·(W i h t-1 +U i x t +b i )
C t ＝f t ·C t-1 +i t ·C t
wherein, W i ，U i ，b i ，W c ，U c And b c For input gate coefficients and biases, both trainable parameters, C t-1 For a long-term state at the last moment, C t For the tth input gateNew state, representing long-term state at current time, i t And
are all intermediate state quantities of the input gate,
indicating the current memory state, i t Pair of representations
The forgetting ability of (c).
The update of the output gate can be expressed as:
o t ＝σ·(W o h t-1 +U o x t +b o )
h t ＝o t ·tanh(C t )
wherein, W o ，U o And b o For output gate coefficients and offsets, both trainable parameters, h t Hidden output state for the t-th cell, O t Indicating the ability to forget the long-term state at the current time.
To this end, one cell of the LSTM neural network achieves complete forward propagation.
In one possible implementation, the SoftMax function of the SoftMax network layer is:
the method comprises the steps that a first group of time domain continuous voltage signals are acquired, wherein fc _ out (m) i is the ith data of all-connection layer network output data of the mth group of time domain continuous voltage signals, fc _ out (m) j is the jth data of all-connection layer network output data of the mth group of time domain continuous voltage signals, m is the acquisition group number of the time domain continuous voltage signals, and K is the fault type number of a sensor circuit; s (fc _ out (m) [ i ]) is the ith data of the sensor circuit failure diagnosis result, and indicates the probability that the sensor circuit element i is failed when i > 0, and indicates the probability that the sensor circuit is not failed when i = 0.
Finally, s (m) = [ s (fc _ out (m) 0 ]), s (fc _ out (m) 1 ]), s (fc _ out (m) K ]) is output as a sensor circuit failure diagnosis result, thereby representing the probability of failure of the sensor circuit and each internal element.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details not disclosed in the device embodiments, reference is made to the method embodiments of the invention.
Referring to fig. 4, in a further embodiment of the present invention, a sensor circuit fault diagnosis system is provided, which can be used to implement the above sensor circuit fault diagnosis system method.
The data acquisition module is used for acquiring a time domain continuous voltage signal output by the sensor circuit; the characteristic extraction module is used for carrying out time domain characteristic extraction and frequency domain characteristic extraction on the time domain continuous voltage signal to obtain time domain characteristic data and frequency domain characteristic data; and the diagnosis module is used for calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal to obtain a sensor circuit fault diagnosis result.
In a possible embodiment, the diagnostic module is specifically configured to: combining the time domain characteristic data and the frequency domain characteristic data to obtain time-frequency mixed data; carrying out normalization processing on the time domain continuous voltage signal and the time frequency mixed data to obtain normalized time frequency voltage data and normalized time frequency mixed data; and inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result.
In one possible embodiment, the sensor circuit fault diagnosis model includes a DNN neural network, an LSTM neural network, a full connectivity layer network, and a SoftMax layer network; the DNN neural network is used for inputting normalized time-frequency mixed data and outputting DNN neural network output data to the full-connection layer network; the LSTM neural network is used for inputting normalized time-frequency voltage data and outputting LSTM neural network output data to the full-connection layer network; the full-connection layer network is used for performing full-connection processing on the output data of the DNN neural network and the output data of the LSTM neural network to obtain output data of the full-connection layer network and outputting the output data of the full-connection layer network to a SoftMax layer network; and the SoftMax layer network is used for outputting data according to the input full-connection layer network to obtain and output a fault diagnosis result of the sensor circuit.
In a possible implementation, the feature extraction module is specifically configured to: acquiring one or more of the maximum value, the minimum value, the average value, the standard deviation, the kurtosis and the skewness of the time domain continuous voltage signal to obtain time domain characteristic data; and performing time-frequency conversion on the time domain continuous voltage signal to obtain a frequency domain continuous voltage signal, and acquiring one or more of the bandwidth and the central frequency of the frequency domain continuous voltage signal to obtain frequency domain characteristic data.
In one possible embodiment, the SoftMax function of the SoftMax tier network is:
wherein fc _ out (m) i is the ith data of the full-connection layer network output data of the mth group of time domain continuous voltage signals, fc _ out (m) j is the jth data of the full-connection layer network output data of the mth group of time domain continuous voltage signals, m is the acquisition group number of the time domain continuous voltage signals, and K is the fault type number of the sensor circuit; s (fc _ out (m) [ i ]) is the ith data of the sensor circuit failure diagnosis result, and indicates the probability that the sensor circuit element i is failed when i > 0, and indicates the probability that the sensor circuit is not failed when i = 0.
All relevant contents of each step related to the embodiment of the sensor circuit fault diagnosis method may be introduced to the functional description of the functional module corresponding to the sensor circuit fault diagnosis system in the embodiment of the present invention, and are not described herein again.
The division of the modules in the embodiments of the present invention is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in the form of a sensor or a software functional module.
In yet another embodiment of the present invention, a sensor circuit fault diagnosis apparatus is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor of the embodiment of the invention can be used for the operation of the sensor circuit fault diagnosis method.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, the memory space stores one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the sensor circuit fault diagnosis method in the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method of diagnosing a fault in a sensor circuit, comprising:
acquiring a time domain continuous voltage signal output by a sensor circuit;
performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data;
and calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal, the time domain characteristic data and the frequency domain characteristic data to obtain a sensor circuit fault diagnosis result.
2. The method for diagnosing the circuit fault of the sensor according to claim 1, wherein the performing time domain feature extraction and frequency domain feature extraction on the time domain continuous voltage signal to obtain time domain feature data and frequency domain feature data comprises:
acquiring one or more of the maximum value, the minimum value, the average value, the standard deviation, the kurtosis and the skewness of the time domain continuous voltage signal to obtain time domain characteristic data;
and performing time-frequency conversion on the time domain continuous voltage signal to obtain a frequency domain continuous voltage signal, and acquiring one or more of the bandwidth and the central frequency of the frequency domain continuous voltage signal to obtain frequency domain characteristic data.
3. The sensor circuit fault diagnosis method according to claim 1, wherein the obtaining of the sensor circuit fault diagnosis result by calling a preset sensor circuit fault diagnosis model according to the time-domain continuous voltage signal, the time-domain characteristic data and the frequency-domain characteristic data comprises:
combining the time domain characteristic data and the frequency domain characteristic data to obtain time-frequency mixed data;
normalizing the time domain continuous voltage signal and the time frequency mixed data to obtain normalized time frequency voltage data and normalized time frequency mixed data;
and inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result.
4. The sensor circuit failure diagnosis method according to claim 3, wherein the preset sensor circuit failure diagnosis model includes a DNN neural network, an LSTM neural network, a full connection layer network, and a SoftMax layer network;
the DNN neural network is used for inputting normalized time-frequency mixed data and outputting DNN neural network output data to the full-connection layer network;
the LSTM neural network is used for inputting normalized time-frequency voltage data and outputting LSTM neural network output data to the full-connection layer network;
the full connection layer network is used for performing full connection processing on DNN neural network output data and LSTM neural network output data to obtain full connection layer network output data and outputting the full connection layer network output data to a SoftMax layer network;
and the SoftMax layer network is used for outputting data according to the input full-connection layer network to obtain and output a fault diagnosis result of the sensor circuit.
5. The sensor circuit fault diagnostic method of claim 4, wherein the SoftMax function of the SoftMax tier network is:
the method comprises the steps that a first group of time domain continuous voltage signals are acquired, wherein fc _ out (m) i is the ith data of all-connection layer network output data of the mth group of time domain continuous voltage signals, fc _ out (m) j is the jth data of all-connection layer network output data of the mth group of time domain continuous voltage signals, m is the acquisition group number of the time domain continuous voltage signals, and K is the fault type number of a sensor circuit; s (fc _ out (m) [ i ]) is the i-th data of the sensor circuit failure diagnosis result, and indicates the probability of failure of the sensor circuit element i when i > 0, and indicates the probability of failure of the sensor circuit element i when i = 0.
6. A sensor circuit fault diagnostic system, comprising:
the data acquisition module is used for acquiring a time domain continuous voltage signal output by the sensor circuit;
the characteristic extraction module is used for carrying out time domain characteristic extraction and frequency domain characteristic extraction on the time domain continuous voltage signal to obtain time domain characteristic data and frequency domain characteristic data;
and the diagnosis module is used for calling a preset sensor circuit fault diagnosis model according to the time domain continuous voltage signal to obtain a sensor circuit fault diagnosis result.
7. The sensor circuit fault diagnostic system of claim 6, wherein the diagnostic module is specifically configured to:
combining the time domain characteristic data and the frequency domain characteristic data to obtain time-frequency mixed data;
carrying out normalization processing on the time domain continuous voltage signal and the time frequency mixed data to obtain normalized time frequency voltage data and normalized time frequency mixed data;
and inputting the normalized time-frequency voltage data and the normalized time-frequency mixed data into a preset sensor circuit fault diagnosis model to obtain a sensor circuit fault diagnosis result.
8. The sensor circuit fault diagnosis system according to claim 7, wherein the preset sensor circuit fault diagnosis model includes a DNN neural network, an LSTM neural network, a full connectivity layer network, and a SoftMax layer network;
the DNN neural network is used for inputting the normalized time-frequency mixed data and outputting the output data of the DNN neural network to a full-connection layer network;
the LSTM neural network is used for inputting normalized time-frequency voltage data and outputting LSTM neural network output data to the full-connection layer network;
the full connection layer network is used for performing full connection processing on DNN neural network output data and LSTM neural network output data to obtain full connection layer network output data and outputting the full connection layer network output data to a SoftMax layer network;
and the SoftMax layer network is used for outputting data according to the input full-connection layer network to obtain and output a fault diagnosis result of the sensor circuit.
9. A sensor circuit failure diagnosis apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the sensor circuit failure diagnosis method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the sensor circuit failure diagnosis method according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116484263A (en) * | 2023-05-10 | 2023-07-25 | 江苏圣骏智能科技有限公司 | Intelligent self-service machine fault detection system and method |
| CN117200203A (en) * | 2023-09-07 | 2023-12-08 | 航电所(成都)科技有限公司 | Operation optimization method and system applied to power system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116484263A (en) * | 2023-05-10 | 2023-07-25 | 江苏圣骏智能科技有限公司 | Intelligent self-service machine fault detection system and method |
| CN116484263B (en) * | 2023-05-10 | 2024-01-05 | 江苏圣骏智能科技有限公司 | Intelligent self-service machine fault detection system and method |
| CN117200203A (en) * | 2023-09-07 | 2023-12-08 | 航电所(成都)科技有限公司 | Operation optimization method and system applied to power system |
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