CN113901862B - Electromagnetic repulsion mechanism fault monitoring method, system, equipment and readable storage medium - Google Patents
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Abstract
The invention discloses a fault monitoring method, a system, equipment and a readable storage medium of an electromagnetic repulsion mechanism, which adopts a wavelet packet normalized energy method to extract signal characteristics of a vibration signal sample set, adopts the obtained signal characteristics to carry out model training to obtain a fault diagnosis model, sets a fault diagnosis threshold of the diagnosis model, takes the electromagnetic repulsion mechanism as a monitoring object, firstly realizes the on-line monitoring of the electromagnetic repulsion mechanism, can judge the running state of the electromagnetic repulsion mechanism in real time, thereby rapidly and accurately reducing the occurrence of faults, and further guides the parameter and the structure optimization of the electromagnetic repulsion mechanism.
Description
Technical Field
The invention belongs to the technical field of electricians, and particularly relates to a fault monitoring method, a fault monitoring system, a fault monitoring device and a fault monitoring program for an electromagnetic repulsion mechanism.
Background
With the development of economy and society, the safety problem of the power system is also faced with a more and more serious challenge, and the breaking of short-circuit current is an important link. However, the conventional mechanical circuit breaker cannot meet the open end capacity and speed of a part of places, so that a vacuum circuit breaker using an electromagnetic repulsion mechanism is applied. The electromagnetic repulsion mechanism has high switching-on and switching-off speed and short time, and can meet the switching-on and switching-off requirements of the current short-circuit current.
However, since the electromagnetic repulsion mechanism is fast in breaking speed, impact force on the electromagnetic repulsion mechanism and the circuit breaker is large, and therefore the electromagnetic repulsion mechanism is easy to break. The fault diagnosis of the existing electromagnetic repulsion mechanism still stays at the algorithm level, and a monitoring system which can be practically applied is rarely provided. Therefore, it is necessary to combine the algorithm and hardware to develop a fault monitoring system which can be practically applied to quickly and accurately detect the faults in the electromagnetic repulsion mechanism, so as to avoid the occurrence of economic losses.
Disclosure of Invention
The invention aims to provide a fault monitoring method, system and equipment for an electromagnetic repulsion mechanism and a readable storage medium, so as to overcome the defects in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the fault monitoring method of the electromagnetic repulsion mechanism is characterized by comprising the following steps of:
s1, acquiring a vibration signal sample set generated by a brake-separating action under a normal state of an electromagnetic repulsion mechanism, and extracting signal characteristics of the vibration signal sample set by adopting a wavelet packet normalization energy method;
s2, performing model training by using the acquired signal characteristics to obtain a fault diagnosis model, and setting a fault diagnosis threshold of the diagnosis model;
s3, acquiring a vibration signal to be detected generated by the opening action of the electromagnetic repulsion mechanism to be detected through an acceleration sensor, inputting the acquired vibration signal to be detected into a fault diagnosis model to obtain the signal characteristic of the vibration signal, and comparing the signal characteristic with a set threshold value, so that the state of the opening action of the electromagnetic repulsion mechanism to be detected can be obtained.
Further, wavelet packet decomposition is carried out on the acquired vibration signals in LabVIEW to realize signal feature extraction.
Further, wavelet packet decomposition is carried out on the original signal, and energy of each node after the wavelet packet decomposition is calculated: the signal energy is calculated by the following formula
Wherein x (k) is the signal of each node after the wavelet packet is decomposed, N is the number of signal points, and Q is the energy of the obtained signal;
normalizing the vibration signal: the calculation formula is that
Wherein E is the total energy of each node after the wavelet packet is decomposed, E i The energy is normalized for each node, j is the number of wavelet packet decomposition layers.
Further, the fault diagnosis model is based on a Support Vector Machine (SVM), and the classification principle thereof meets the following formula
Wherein { x i ,y i N i =1 represents a sample set, ζ i For the relaxation factor c is the penalty coefficient, b is the base coefficient,is an objective function, s.t. is a constraint condition, E i Representing normalized energy of each node after wavelet packet decomposition, y i Representing signal class, ω being the normal vector to the classification hyperplane, ω Τ Is the transpose of the hyperplane normal vector.
Further, a Support Vector Machine (SVM) adopts a two-classification algorithm, and an optimal classification hyperplane is searched for an input sample in an n-dimensional space, so that the two types of samples obtain the best classification effect under the hyperplane.
The utility model provides an electromagnetic repulsion mechanism fault monitoring system, including host computer and lower computer, the lower computer includes the data acquisition card, the data acquisition card is used for acquireing the vibration signal that the electromagnetic repulsion mechanism that awaits measuring brake action produced, and transmit the vibration signal who acquires to the host computer, the host computer includes signal acquisition memory cell, feature extraction unit and fault diagnosis unit, signal acquisition memory cell is used for receiving the vibration signal of data acquisition card transmission and stores, simultaneously transmit to feature extraction unit and draw the signal characteristic of vibration signal, transmit the signal characteristic who draws to fault diagnosis unit and carry out model training, fault diagnosis unit is used for storing the training model in advance, simultaneously with the signal characteristic that the vibration signal that awaits measuring obtains vibration signal to fault diagnosis model compares with the settlement threshold value, output diagnosis state.
Further, the data acquisition card acquires a vibration signal to be detected, which is generated by the opening action of the electromagnetic repulsion mechanism to be detected, through an acceleration sensor, the model of the acceleration sensor is LC0157T, the measurement range is 0-5000g, the frequency response range is 0-20kHz, and the LC0207 constant current source module is adopted for supplying power and conditioning signals.
Furthermore, the data acquisition card adopts NI USB-6346BNC, and has 8 analog signal input channels, and the highest single channel sampling rate is 500kHz.
A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the electromagnetic repulsion mechanism fault monitoring method described above when executing the computer program.
A computer readable storage medium storing a computer program which when executed by a processor implements the steps of the electromagnetic repulsion mechanism fault monitoring method described above.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention relates to a fault monitoring method of an electromagnetic repulsion mechanism, which adopts a wavelet packet normalized energy method to extract signal characteristics of a vibration signal sample set, adopts the obtained signal characteristics to carry out model training to obtain a fault diagnosis model, sets a fault diagnosis threshold of the diagnosis model, takes the electromagnetic repulsion mechanism as a monitoring object, firstly realizes the on-line monitoring of the electromagnetic repulsion mechanism, can judge the running state of the electromagnetic repulsion mechanism in real time, thereby quickly and accurately reducing the occurrence of faults, and further guides the parameter and the structure optimization of the electromagnetic repulsion mechanism.
Further, the electromagnetic repulsion mechanism fault monitoring system combines the sensor of the lower computer with the LabVIEW and Python software and hardware of the data acquisition card and the upper computer, realizes the on-line monitoring of the electromagnetic repulsion mechanism fault, has simple and convenient use and short running time, and can rapidly and accurately detect the fault in the electromagnetic repulsion mechanism.
Drawings
Fig. 1 is a structural diagram of an electromagnetic repulsion mechanism fault monitoring system in an embodiment of the invention.
Fig. 2 is a schematic structural diagram of an electromagnetic repulsion mechanism in an embodiment of the invention.
Fig. 3 is a circuit diagram of a constant current source power supply module of a acceleration sensor according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of classification of a support vector machine according to an embodiment of the present invention.
FIG. 5 is a diagram showing the detection result of the upper computer in the embodiment of the invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, a fault monitoring method for an electromagnetic repulsion mechanism includes the following steps:
s1, acquiring a vibration signal sample set generated by a brake-separating action under a normal state of an electromagnetic repulsion mechanism, and extracting signal characteristics of the vibration signal sample set by adopting a wavelet packet normalization energy method;
s2, performing model training by using the acquired signal characteristics to obtain a fault diagnosis model, and setting a fault diagnosis threshold of the diagnosis model;
s3, acquiring a vibration signal to be detected generated by the opening action of the electromagnetic repulsion mechanism to be detected through an acceleration sensor, inputting the acquired vibration signal to be detected into a fault diagnosis model to obtain the signal characteristic of the vibration signal, and comparing the signal characteristic with a set threshold value, so that the state of the opening action of the electromagnetic repulsion mechanism to be detected can be obtained.
Specifically, wavelet packet decomposition is carried out on the acquired vibration signals in LabVIEW to realize signal feature extraction. LabVIEW is used as LabVIEW 2018.
The method is to decompose the wavelet packet (Wavelet Packet Decomposition) of the original signal, and the main method is to decompose the low frequency sub-band and the high frequency sub-band further when decomposing each level of signal based on wavelet transformation, and finally calculate the optimal signal decomposition path by minimizing a cost function, and decompose the original signal by the decomposition path.
Specifically, energy of each node after wavelet packet decomposition is calculated: the signal energy is calculated by the following formula
Wherein x (k) is the signal of each node after the wavelet packet is decomposed, N is the number of signal points, and Q is the energy of the obtained signal;
normalizing the vibration signal: the calculation formula is that
Wherein E is the total energy of each node after the wavelet packet is decomposed, E i The energy is normalized for each node, j is the number of wavelet packet decomposition layers.
The fault diagnosis model is based on a Support Vector Machine (SVM), and the classification principle thereof meets the following formula:
wherein { x i ,y i N i =1 represents a sample set, ζ i For the relaxation factor c is the penalty coefficient, b is the base coefficient,is an objective function, s.t. is a constraint condition, E i Representing normalized energy of each node after wavelet packet decomposition, y i Representing signal class, ω being the normal vector to the classification hyperplane, ω Τ Is the transpose of the hyperplane normal vector;
the Support Vector Machine (SVM) adopts a two-classification algorithm, and searches an optimal classification hyperplane for the input samples in the n-dimensional space, so that the two types of samples can obtain the best classification effect under the hyperplane, as shown in fig. 4. F (E) i )＝ω T E i +b represents a classification hyperplane function, when f (E i ) When=0, E i Points that lie on the hyperplane; f (E) i )>At 0, correspond to y i Data points of=1, i.e. signals are classified as first class at this time; f (E) i )<At 0, correspond to y i Data points of = -1, i.e. the signals are classified into the second class at this time.
The utility model provides an electromagnetic repulsion mechanism fault monitoring system, including host computer and lower computer, the lower computer includes the data acquisition card, the data acquisition card is used for acquireing the vibration signal that the electromagnetic repulsion mechanism that awaits measuring brake action produced, and transmit the vibration signal who acquires to the host computer, the host computer includes signal acquisition memory cell, feature extraction unit and fault diagnosis unit, signal acquisition memory cell is used for receiving the vibration signal of data acquisition card transmission and stores, simultaneously transmit to feature extraction unit and draw the signal characteristic of vibration signal, transmit the signal characteristic who draws to fault diagnosis unit and carry out model training, fault diagnosis unit is used for storing the training model in advance, simultaneously with the signal characteristic that the vibration signal that awaits measuring obtains vibration signal to fault diagnosis model compares with the settlement threshold value, output diagnosis state.
Specifically, the data acquisition card acquires a vibration signal to be detected, which is generated by the opening action of the electromagnetic repulsion mechanism to be detected, through an acceleration sensor, the model of the acceleration sensor is LC0157T, the measurement range is 0-5000g, the frequency response range is 0-20kHz, and the LC0207 constant current source module is adopted to supply power and condition signals.
The data acquisition card adopts NI USB-6346BNC, totally 8 analog signal input channels, and the highest sampling rate of a single channel is 500kHz.
The electromagnetic repulsion mechanism comprises a metal disc, a brake separating coil and a brake closing coil, when the breaker is opened, the capacitor of the brake separating coil is precharged, then the thyristor is closed, annular current is generated in the brake separating coil, the annular current can induce a reverse vortex in the metal disc, and therefore the annular current and the vortex can generate a repulsion force, and the metal disc is pushed to move.
The signal characteristic extraction method used for the vibration signal is wavelet packet normalized energy, and the calculation formula is
Wherein x (k) is a signal of each node after the wavelet packet is decomposed, N is the number of signal points, Q is the energy of the obtained signal, and then normalization processing is carried out on the energy.
Referring to fig. 2 and 3, the electromagnetic repulsion mechanism of the invention comprises a metal disc, a brake-separating coil and a brake-closing coil, and when the breaker is opened, the brake-separating coil loop capacitor C 1 Precharging, then closing the thyristor S 1 An annular current is generated in the brake separating coil, and a reverse vortex is induced in the metal disc by the annular current, so that a repulsive force is generated by the annular current and the vortex, and the metal disc is pushed to move.
The acceleration sensor adopts a piezoelectric acceleration sensor, the model is LC0157T, the acquisition range is 0-5000g, the frequency response range is 0-20kHz, the acceleration sensor is powered and signal conditioned by adopting an LC0207 constant current source module, as shown in a second graph, the power supply voltage required by the constant current source module is 18-30V DC, the output current of the acceleration sensor is 4mA, and the acceleration signal is converted into a 0-5V analog voltage signal to be output.
In one embodiment of the present invention, there is provided a terminal device including a processor and a memory for storing a computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor adopts a Central Processing Unit (CPU), or adopts other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), ready-made programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and the like, which are a computation core and a control core of the terminal, and are suitable for realizing one or more instructions, in particular for loading and executing one or more instructions so as to realize corresponding method flows or corresponding functions; the processor provided by the embodiment of the invention can be used for the operation of the electromagnetic repulsion mechanism fault monitoring method.
The utility model provides an electromagnetic repulsion mechanism fault monitoring system, including host computer and lower computer, the lower computer includes the data acquisition card, the data acquisition card is used for acquireing the vibration signal that the electromagnetic repulsion mechanism that awaits measuring brake action produced, and transmit the vibration signal who acquires to the host computer, the host computer includes signal acquisition memory cell, feature extraction unit and fault diagnosis unit, signal acquisition memory cell is used for receiving the vibration signal of data acquisition card transmission and stores, simultaneously transmit to feature extraction unit and draw the signal characteristic of vibration signal, transmit the signal characteristic who draws to fault diagnosis unit and carry out model training, fault diagnosis unit is used for storing the training model in advance, simultaneously with the signal characteristic that the vibration signal that awaits measuring obtains vibration signal to fault diagnosis model compares with the settlement threshold value, output diagnosis state.
In still another embodiment of the present invention, a storage medium, specifically a computer readable storage medium (Memory), is a Memory device in a terminal device, for storing programs and data. The computer readable storage medium includes a built-in storage medium in the terminal device, provides a storage space, stores an operating system of the terminal, and may also include an extended storage medium supported by the terminal device. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium may be a high-speed RAM memory or a Non-volatile memory (Non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the electromagnetic repulsion mechanism fault monitoring method that may be used in the above-described embodiments.
Referring to fig. 5, the upper computer interface of the present invention comprises a data acquisition module, a feature extraction module and a fault diagnosis module, wherein the data acquisition module comprises sampling point number setting, sampling rate setting, data storage path setting and vibration signal waveform display; the characteristic quantity extraction module comprises wavelet energy distribution display; the fault diagnosis module comprises Python version selection and breaker state display.
The advantageous effects of the present invention will be further described with reference to fig. 5. The electromagnetic repulsion mechanism fault monitoring system is tested on an actual electromagnetic repulsion mechanism vacuum circuit breaker and comprises an electromagnetic repulsion mechanism, a constant current source module, a data acquisition card and an upper computer, wherein the electromagnetic repulsion mechanism and a sensor are arranged at the installation positions, and an acceleration sensor is arranged above a brake separating coil metal plate. The signal time-frequency analysis method adopts 7 layers of wavelet packet decomposition, the wavelet basis function is DB10, the characteristic extraction method selects wavelet normalized energy, and the fault diagnosis model selects a Support Vector Machine (SVM). The charging voltage of the opening capacitor is set to be 110% of the rated voltage, the electromagnetic repulsion mechanism is enabled to act (opening), after an opening vibration signal is acquired, the characteristic quantity and fault diagnosis are extracted from the signal, and as shown in a result in fig. 5, the state of the circuit breaker is shown as that the circuit voltage is higher and meets the set 110% of the rated voltage, so that the state of the circuit breaker can be rapidly and accurately judged through the opening vibration signal.
Claims (7)
1. The fault monitoring method of the electromagnetic repulsion mechanism is characterized by comprising the following steps of:
s1, acquiring a vibration signal sample set generated by a brake-separating action under a normal state of an electromagnetic repulsion mechanism, and extracting signal characteristics of the vibration signal sample set by adopting a wavelet packet normalization energy method; performing wavelet packet decomposition on an original signal, and calculating energy of each node after the wavelet packet decomposition: the signal energy is calculated by the following formula
Wherein x (k) is the signal of each node after the wavelet packet is decomposed, N is the number of signal points, and Q is the energy of the obtained signal;
normalizing the vibration signal: the calculation formula is that
Wherein E is the total energy of each node after the wavelet packet is decomposed, E i Normalizing energy for each node, wherein j is the number of wavelet packet decomposition layers;
s2, performing model training by using the acquired signal characteristics to obtain a fault diagnosis model, and setting a fault diagnosis threshold of the diagnosis model;
the fault diagnosis model is based on a Support Vector Machine (SVM), and the classification principle thereof meets the following formula
Wherein { x i ,y i Ni=1 generationTable sample set, ζ i For the relaxation factor c is the penalty coefficient, b is the base coefficient,is an objective function, s.t. is a constraint condition, E i Representing normalized energy of each node after wavelet packet decomposition, y i Representing signal class, ω being the normal vector to the classification hyperplane, ω T Is the transpose of the hyperplane normal vector;
a Support Vector Machine (SVM) adopts a two-classification algorithm, and an optimal classification hyperplane is searched for an input sample in an n-dimensional space, so that the two types of samples obtain the best classification effect under the hyperplane;
s3, acquiring a vibration signal to be detected generated by the opening action of the electromagnetic repulsion mechanism to be detected through an acceleration sensor, inputting the acquired vibration signal to be detected into a fault diagnosis model to obtain the signal characteristic of the vibration signal, and comparing the signal characteristic with a set threshold value, so that the state of the opening action of the electromagnetic repulsion mechanism to be detected can be obtained.
2. The fault monitoring method of the electromagnetic repulsion mechanism according to claim 1, wherein the signal feature extraction is achieved by carrying out wavelet packet decomposition on the collected vibration signals in LabVIEW.
3. The fault monitoring system for the electromagnetic repulsion mechanism is characterized by comprising an upper computer and a lower computer, wherein the lower computer comprises a data acquisition card, the data acquisition card is used for acquiring vibration signals generated by the opening action of the electromagnetic repulsion mechanism to be tested, transmitting the acquired vibration signals to the upper computer, the upper computer comprises a signal acquisition and storage unit, a feature extraction unit and a fault diagnosis unit, the signal acquisition and storage unit is used for receiving and storing the vibration signals transmitted by the data acquisition card, simultaneously transmitting the vibration signals to the feature extraction unit to extract the signal features of the vibration signals, transmitting the extracted signal features to the fault diagnosis unit to perform model training, and the fault diagnosis unit is used for storing a pre-training model, simultaneously inputting the acquired vibration signals to be tested to the fault diagnosis model to obtain the signal features of the vibration signals, comparing the signal features with a set threshold value and outputting a diagnosis state;
performing wavelet packet decomposition on an original signal, and calculating energy of each node after the wavelet packet decomposition: the signal energy is calculated by the following formula
Wherein x (k) is the signal of each node after the wavelet packet is decomposed, N is the number of signal points, and Q is the energy of the obtained signal;
normalizing the vibration signal: the calculation formula is that
Wherein E is the total energy of each node after the wavelet packet is decomposed, E i Normalizing energy for each node, wherein j is the number of wavelet packet decomposition layers;
the fault diagnosis model is based on a Support Vector Machine (SVM), and the classification principle thereof meets the following formula
Wherein { x i ,y i Ni=1 represents the sample set, ζ i For the relaxation factor c is the penalty coefficient, b is the base coefficient,is an objective function, s.t. is a constraint condition, E i Representing normalized energy of each node after wavelet packet decomposition, y i Representing signal class, ω being the normal vector to the classification hyperplane, ω T Is of the super typeA transpose of the planar normal vector;
the Support Vector Machine (SVM) adopts a two-classification algorithm, and searches an optimal classification hyperplane for an input sample in an n-dimensional space, so that the two types of samples obtain the best classification effect under the hyperplane.
4. The fault monitoring system of the electromagnetic repulsion mechanism according to claim 3, wherein the data acquisition card acquires a vibration signal to be detected generated by the opening action of the electromagnetic repulsion mechanism to be detected through an acceleration sensor, the model of the acceleration sensor is LC0157T, the measuring range is 0-5000g, the frequency response range is 0-20kHz, and the LC0207 constant current source module is adopted for power supply and signal conditioning.
5. A fault monitoring system for an electromagnetic repulsion mechanism according to claim 3, wherein the data acquisition card adopts NIUSB-6346BNC, and has 8 analog signal input channels, and the single channel highest sampling rate is 500kHz.
6. A terminal device 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 method according to any of claims 1 to 2 when the computer program is executed by the processor.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 2.
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