CN113283292B - Method and device for diagnosing faults of underwater micro-propeller - Google Patents
Method and device for diagnosing faults of underwater micro-propeller Download PDFInfo
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Abstract
The invention provides a method and a device for fault diagnosis of an underwater micro-propeller, wherein the method comprises a model training stage, a plurality of groups of motor current history signals of the propeller under different fault types are collected to be used as a training sample data set of the model, improved Hilbert-Huang transform is adopted to extract feature vectors, and then the model for fault diagnosis of the propeller is obtained based on the extracted feature vectors and combined with the fault type of the underwater micro-propeller for training; in the fault diagnosis stage, collecting a motor current real-time signal of the propeller to extract a characteristic vector of the motor current real-time signal, inputting the characteristic vector into a trained fault diagnosis model, and judging the fault state of the propeller; the model optimizing stage is used for adding the acquired data information into a pre-established model training sample data set, and training by combining the updated propeller fault type to obtain an optimized propeller fault diagnosis model; the device comprises an underwater propulsion system, a data acquisition module and a data processing and judging module.
Description
Technical Field
The invention relates to the field of underwater robots, in particular to a method and a device for diagnosing faults of an underwater micro-propeller.
Background
Underwater robots are receiving more and more national attention as novel high and new technical equipment for ocean exploration. The underwater micro-propeller is used as a power component of the underwater robot, bears the most main working load of the underwater robot, has a complex structure and a severe working environment, and is one of main fault sources of the underwater robot. The propulsion system of the underwater robot consists of a direct current power supply, a propeller motor and a propeller, the number of revolutions of the propeller motor is controlled by a current value output by the direct current power supply, and the propeller is driven by the motor to generate thrust. The fault types of the general underwater micro-propeller comprise 5 fault types including propeller blade winding sundry faults, propeller dynamic and static friction faults, propeller blade breakage faults, propeller blade deformation faults, propeller blade cavitation faults and the like.
The fault diagnosis technology provides a new solution for improving the reliability of the underwater robot propulsion system. At present, for intelligent diagnosis of fault types of underwater micro-propellers, mainly based on hardware fault diagnosis, for example, patent document CN111275164a discloses a diagnosis method of faults of an underwater robot propulsion system, wherein sensors for monitoring signals such as voltage, current, output rotating speed and pull voltage are used for detecting the working state of the underwater robot propulsion system in real time, then normalization preprocessing is carried out on the obtained data, convolutional neural network CNN is adopted for training to obtain a deep learning fault diagnosis model, and finally the deep learning fault diagnosis model is used for carrying out fault diagnosis on the underwater robot propulsion system. Although the method is very direct and reliable, the complexity of the hardware system of the underwater robot is increased, so that the weight and the construction cost of the underwater robot are increased sharply; meanwhile, the residual error is analyzed by adopting a threshold value judging method, and the threshold value judging method usually causes discontinuous fault judgment due to the existence of the monitored parameter signals and model errors, so that misjudgment of a diagnosis model is caused.
In addition, when the underwater micro-propeller fails, the failure signal is a multi-component amplitude modulation-frequency modulation signal, and before demodulation, the failure signal needs to be decomposed into a plurality of single-component amplitude modulation-frequency modulation signals. The analysis of the fault signal by using the hilbert-yellow transform generally adopts empirical mode decomposition to decompose the fault signal, and then carries out the hilbert transform on an Intrinsic Mode Function (IMF) obtained by decomposition to obtain a time-frequency spectrogram. However, this conventional hilbert-yellow conversion method does not have adaptability to actual fault signals, is unfavorable for suppressing the modal aliasing problem of empirical mode decomposition, and cannot ensure whether the hilbert-yellow conversion is completely demodulated.
Disclosure of Invention
In view of the above, the invention aims to provide a simple and easy method and device for diagnosing faults of an underwater micro-propeller, which can diagnose the fault type of the underwater micro-propeller timely and accurately.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a method of fault diagnosis of an underwater micro-propeller, the method comprising:
a) Model training stage
S101) collecting a plurality of groups of motor current history signals of the target underwater micro-propeller under different fault types in advance, and taking the motor current history signals as a training sample data set of a model;
s102) extracting feature vectors of the training sample data set obtained in the step S101) by adopting improved Hilbert-Huang transform;
s103) training by combining the fault type of the underwater micro-propeller based on the feature vector extracted in the step S102) to obtain a fault diagnosis model of the underwater micro-propeller.
B) Fault diagnosis stage
S201), measuring and data acquisition are carried out on motor current real-time signals of the underwater micro-propulsion to be diagnosed;
s202) extracting a characteristic vector of the motor current real-time signal obtained in the step S201) by adopting improved Hilbert-Huang transform;
s203) inputting the feature vector extracted in the step S202) into the trained underwater micro-propeller fault diagnosis model in the step S103) for pattern recognition, and diagnosing the fault state of the propeller.
C) Model optimization stage
S301) when the actual fault occurs to the underwater micro-propeller to be diagnosed and the trained fault diagnosis model does not output the corresponding fault type of the underwater micro-propeller, acquiring the fault type of the actual fault and a motor current real-time signal of the underwater micro-propeller to be diagnosed when the actual fault occurs;
s302) adding the collected fault type of the actual fault and the motor current real-time signal of the underwater micro-propeller to be diagnosed when the actual fault occurs to the pre-collected model training sample data set to obtain an updated model training sample data set;
s303) extracting feature vectors from the updated training sample data set obtained in the step S302) by adopting improved Hilbert-Huang transform;
s304) training based on the feature vector extracted in the step S303) and combined with the updated fault type of the underwater micro-propeller to obtain an optimized fault diagnosis model of the underwater micro-propeller.
Further, the fault types in the step S101) include: a fault of the impeller blade winding sundries, a fault of dynamic and static friction of the impeller, a fault of the impeller blade breakage, a fault of the impeller blade deformation, a fault of the impeller blade cavitation, and the like.
Further, the step S102) includes the following steps:
s1021) carrying out N-layer wavelet packet decomposition and reconstruction on an input tested current signal x (t) by using Daubechies wavelet to obtain 2 N Narrowband current signals with different frequency bands;
s1022) performing integrated empirical mode decomposition on each narrow-band current signal obtained in the step S1021) to obtain IMF components of all the narrow-band current signals;
s1023) screening IMF components of all the narrow-band current signals to obtain final IMF components of the whole detected signal x (t);
s1024) respectively carrying out Hilbert transform on the final IMF components to obtain the instantaneous attribute of the detected current signal x (t);
s1025) extracting time-frequency statistical characteristics capable of reflecting time-frequency characteristics of the measured current signal x (t) according to the instantaneous attribute of the measured current signal x (t), comprising: the mean of the instantaneous amplitude of each IMF component, the bandwidth, peak and variance of the marginal spectra of all IMF components.
Further, the step S103) includes the following steps:
s1031) performing non-supervision machine learning on the failure experience pool of the underwater micro-propeller based on a support vector machine technology;
s1032) optimizing the penalty factor C and the parameter g of the radial basis function by adopting a cross validation (K-CV) and grid parameter searching method;
s1033) adopting a binary tree multi-classification algorithm to enable the support vector machine to identify and classify different running states of the underwater micro-propeller, and obtaining a trained fault diagnosis model.
The invention further aims to provide an underwater micro-propeller fault diagnosis device which can timely and accurately diagnose the type of propeller faults.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
an underwater micro-propeller fault diagnosis apparatus, the apparatus comprising: the device comprises an underwater propulsion system, a data acquisition module, a data processing and judging module;
the underwater propulsion system comprises a direct-current power supply and a propeller;
the direct current power supply is used for providing a power source for the propeller motor and controlling the thrust generated by the propeller by changing the output current value;
the data acquisition module is realized through a data acquisition program of an NI data acquisition card and a LabVIEW;
the data processing and judging module processes and diagnoses the fault of the acquired data through MATLAB data processing program;
the underwater micro-propeller fault diagnosis device also provides a computer for installing the LabVIEW data acquisition program and the MATLAB data processing program;
one end of the NI data acquisition card is connected with a computer with a built-in LabVIEW data acquisition program through the NI acquisition card interface, and the other end of the NI data acquisition card is connected to an outlet of the direct current motor and is used for measuring motor current real-time signals;
the underwater micro-propeller fault diagnosis device measures current real-time signals of the motor through the NI data acquisition card, saves the current signals into a computer through a LabVIEW data acquisition program, and then inputs acquired data into a trained underwater micro-propeller fault diagnosis model through a MATLAB data processing program to perform fault identification, so that the fault state of the propeller is diagnosed.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the running state of the underwater micro-propeller can be monitored by measuring and analyzing the current signal of the direct current motor of the propeller without approaching running equipment, and the installation and the use are convenient and flexible;
2. the device has very low price, and meanwhile, the operation characteristic information of the underwater micro-propeller can be reflected in a current signal in real time, so that the information integration level is high;
3. the invention adopts the improved Hilbert-Huang transformation-based current signal feature extraction method, namely the Hilbert-Huang transformation method based on wavelet packet decomposition and false IMF component elimination to improve the accuracy of time-frequency analysis of signals, can more efficiently extract the features reflecting the characteristics of the current signals, and improves the efficiency of data mining and pattern recognition of the acquired current signals.
4. According to the invention, the trained fault diagnosis model is continuously optimized according to the actual fault data of the underwater micro-propeller, so that the timeliness and accuracy of fault diagnosis can be further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of an embodiment of a method for diagnosing a fault of an underwater micro-propeller according to the present invention;
FIG. 2 is a program flow diagram of a model training phase in one embodiment of a method for diagnosing a failure of an underwater micro-propeller of the present invention;
FIG. 3 is a program flow diagram of a fault diagnosis stage in one embodiment of the method for diagnosing a fault in an underwater micro-propeller of the present invention;
FIG. 4 is a program flow diagram of a model optimization stage in one embodiment of a method for diagnosing a failure of an underwater micro-propeller of the present invention;
fig. 5 is a functional block diagram of an embodiment of the fault diagnosis apparatus for an underwater micro-propulsion device of the present invention.
In the figure, 1, a computer; 2. an NI data acquisition card; 3. a direct current power supply; 4. an underwater micro-propeller.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic may be included in at least one implementation of the invention. In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "top", "bottom", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of description and simplification of the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.
The fault diagnosis method of the underwater micro-propeller provided by the embodiment of the invention is shown in fig. 1 to 4, and comprises the following steps:
a) Model training stage
S101) collecting a plurality of groups of motor current history signals of the target underwater micro-propeller under different fault types in advance, and taking the motor current history signals as a training sample data set of a model;
s102) extracting feature vectors of the training sample data set obtained in the step S101) by adopting improved Hilbert-Huang transform;
s103) training by combining the fault type of the underwater micro-propeller based on the feature vector extracted in the step S102) to obtain a fault diagnosis model of the underwater micro-propeller.
B) Fault diagnosis stage
S201), measuring and data acquisition are carried out on motor current real-time signals of the underwater micro-propulsion to be diagnosed;
s202) extracting a characteristic vector of the motor current real-time signal obtained in the step S201) by adopting improved Hilbert-Huang transform;
s203) inputting the feature vector extracted in the step S202) into the trained underwater micro-propeller fault diagnosis model in the step S103) for pattern recognition, and diagnosing the fault state of the propeller.
C) Model optimization stage
S301) when the actual fault occurs to the underwater micro-propeller to be diagnosed and the trained fault diagnosis model does not output the corresponding fault type of the underwater micro-propeller, acquiring the fault type of the actual fault and a motor current real-time signal of the underwater micro-propeller to be diagnosed when the actual fault occurs;
s302) adding the collected fault type of the actual fault and the motor current real-time signal of the underwater micro-propeller to be diagnosed when the actual fault occurs to the pre-collected model training sample data set to obtain an updated model training sample data set;
s303) extracting feature vectors from the updated training sample data set obtained in the step S302) by adopting improved Hilbert-Huang transform;
s304) training based on the feature vector extracted in the step S303) and combined with the updated fault type of the underwater micro-propeller to obtain an optimized fault diagnosis model of the underwater micro-propeller.
Specifically, the types of faults in step S101) described in the present embodiment include: a fault of the impeller blade winding sundries, a fault of dynamic and static friction of the impeller, a fault of the impeller blade breakage, a fault of the impeller blade deformation, a fault of the impeller blade cavitation, and the like.
Specifically, the step S102) in the present embodiment includes the following processes:
s1021) carrying out N-layer wavelet packet decomposition and reconstruction on an input tested current signal x (t) by using Daubechies wavelet to obtain 2 N Narrowband current signals with different frequency bands;
s1022) performing integrated empirical mode decomposition on each narrow-band current signal obtained in the step S1021) to obtain IMF components of all the narrow-band current signals;
s1023) screening IMF components of all the narrow-band current signals to obtain final IMF components of the whole detected signal x (t);
s1024) respectively carrying out Hilbert transform on the final IMF components to obtain the instantaneous attribute of the detected current signal x (t);
s1025) extracting time-frequency statistical characteristics capable of reflecting time-frequency characteristics of the measured current signal x (t) according to the instantaneous attribute of the measured current signal x (t), comprising: the mean of the instantaneous amplitude of each IMF component, the bandwidth, peak and variance of the marginal spectra of all IMF components.
Specifically, the step S103) described in the present embodiment includes the following processes:
s1031) performing non-supervision machine learning on the failure experience pool of the underwater micro-propeller based on a support vector machine technology;
s1032) optimizing the penalty factor C and the parameter g of the radial basis function by adopting a cross validation (K-CV) and grid parameter searching method;
s1033) adopting a binary tree multi-classification algorithm to enable the support vector machine to identify and classify different running states of the underwater micro-propeller, and obtaining a trained fault diagnosis model.
The invention also provides a fault diagnosis device for the underwater micro-propeller, as shown in fig. 5, the device provided by the embodiment comprises: the device comprises an underwater propulsion system, a data acquisition module, a data processing and judging module;
the underwater propulsion system in the embodiment comprises a direct-current power supply and a propeller;
the direct current power supply is used for providing a power source for the motor of the propeller and controlling the thrust generated by the propeller by changing the output current value;
in the embodiment, the data acquisition module is realized through a data acquisition program of an NI data acquisition card and LabVIEW;
in this embodiment, the data processing and judging module processes and diagnoses the fault of the collected data through MATLAB data processing program;
the fault diagnosis device of the underwater micro-propeller in the embodiment further provides a computer, which is used for installing the data acquisition program of the LabVIEW and the MATLAB data processing program;
in this embodiment, one end of the NI data acquisition card is connected with a computer with a built-in LabVIEW data acquisition program through the NI acquisition card interface, and the other end of the NI data acquisition card is connected to an outlet of the dc motor for measuring a real-time signal of motor current;
in this embodiment, the fault diagnosis device of the underwater micro-propeller measures the current real-time signal of the motor through the NI data acquisition card, saves the current signal into the computer through the data acquisition program of LabVIEW, and then inputs the acquired data into the trained fault diagnosis model of the underwater micro-propeller through the MATLAB data processing program to perform fault identification, so as to diagnose the fault state of the propeller.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A method for diagnosing faults of an underwater micro-propeller, the method comprising:
a) Model training stage
S101) collecting a plurality of groups of motor current history signals of the target underwater micro-propeller under different fault types in advance, and taking the motor current history signals as a training sample data set of a model;
s102) extracting feature vectors of the training sample data set obtained in the step S101) by adopting improved Hilbert-Huang transform;
s103) training based on the feature vector extracted in the step S102) and combining with the fault type of the propeller to obtain a fault diagnosis model of the underwater micro-propeller;
b) Fault diagnosis stage
S201), measuring and data acquisition are carried out on motor current real-time signals of the underwater micro-propulsion to be diagnosed;
s202) extracting a characteristic vector of the motor current real-time signal obtained in the step S201) by adopting improved Hilbert-Huang transform;
s203) inputting the feature vector extracted in the step S202) into the trained underwater micro-propeller fault diagnosis model in the step S103) for pattern recognition, and diagnosing the fault state of the propeller;
c) Model optimization stage
S301) when the actual fault occurs to the underwater micro-propeller to be diagnosed and the trained fault diagnosis model does not output the corresponding fault type of the underwater micro-propeller, acquiring the fault type of the actual fault and a motor current real-time signal of the underwater micro-propeller to be diagnosed when the actual fault occurs;
s302) adding the collected fault type of the actual fault and the motor current real-time signal of the underwater micro-propeller to be diagnosed when the actual fault occurs to a model training sample data set collected in advance to obtain an updated model training sample data set;
s303) extracting feature vectors from the updated training sample data set obtained in the step S302) by adopting improved Hilbert-Huang transform;
s304) training based on the feature vector extracted in the step S303) and combined with the updated fault type of the underwater micro-propeller to obtain an optimized fault diagnosis model of the underwater micro-propeller.
2. The method for diagnosing a fault of an underwater micro-propeller according to claim 1, wherein the fault type in the step S101) includes: fault of propeller blade winding sundries, fault of dynamic and static friction of propeller, fault of propeller blade breakage, fault of propeller blade deformation and fault of propeller blade cavitation.
3. The method for diagnosing a fault of an underwater micro-propeller according to claim 1, wherein the step S102) comprises the steps of:
s1021) carrying out N-layer wavelet packet decomposition and reconstruction on an input tested current signal x (t) by using Daubechies wavelet to obtain 2 N Narrowband current signals with different frequency bands;
s1022) performing integrated empirical mode decomposition on each narrow-band current signal obtained in the step S1021) to obtain IMF components of all the narrow-band current signals;
s1023) screening IMF components of all the narrow-band current signals to obtain final IMF components of the whole detected signal x (t);
s1024) performing Hilbert-Huang transform on the final IMF components respectively to obtain the instantaneous attribute of the detected current signal x (t);
s1025) extracting time-frequency statistical characteristics capable of reflecting time-frequency characteristics of the measured current signal x (t) according to the instantaneous attribute of the measured current signal x (t), wherein the time-frequency statistical characteristics comprise: the mean of the instantaneous amplitude of each IMF component, the bandwidth, peak and variance of the marginal spectra of all IMF components.
4. The method for diagnosing a fault of an underwater micro-propeller according to claim 1, wherein the step S103) comprises the steps of:
s1031) performing non-supervision machine learning on the failure experience pool of the underwater micro-propeller based on a support vector machine technology;
s1032) optimizing the penalty factor C and the parameter g of the radial basis function by adopting a cross validation (K-CV) and grid parameter searching method;
s1033) adopting a binary tree multi-classification algorithm to enable the support vector machine to identify and classify different running states of the underwater micro-propeller, and obtaining a trained fault diagnosis model.
5. An underwater micro-propeller fault diagnosis device applying the method for diagnosing an underwater micro-propeller fault according to any one of claims 1 to 4, which is characterized by comprising an underwater propulsion system, a data acquisition module, a data processing and judging module;
the underwater propulsion system comprises a direct-current power supply and a propeller;
the data acquisition module is realized through a data acquisition program of an NI data acquisition card and a LabVIEW;
the data processing and judging module processes and diagnoses the fault of the acquired data through MATLAB data processing program;
the underwater micro-propeller fault diagnosis device also provides a computer for installing the LabVIEW data acquisition program and the MATLAB data processing program.
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