CN117347773A - Intelligent service method based on multi-equipment linkage - Google Patents
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
Abstract
The invention discloses an intelligent service method and system based on multi-equipment linkage, which are characterized in that after equipment signals to be detected are acquired, wavelet bispectral transformation is carried out, and wavelet bispectral entropy of the equipment signals to be detected is calculated; comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in the normal state, and if the value of the wavelet bispectral entropy is reduced, indicating that the secondary phase coupling of part of components is enhanced, and prompting that the equipment to be detected has faults; and carrying out instantaneous wavelet bispectrum analysis, detecting the occurrence frequency of secondary phase coupling, and confirming the fault position of equipment to be detected. The invention fully utilizes the phase relation among frequency components, detects the tiny amplitude values from a plurality of harmonic components of a current signal to obtain a modulation component, accurately extracts time-varying secondary phase coupling in the signal, quantitatively describes the distribution uniformity degree of signal secondary phase coupling components in a double-frequency domain by wavelet bispectral entropy, and is used as a diagnostic index for monitoring the change of the current signal of locomotive traction equipment and quantifying the working state of the traction equipment.
Description
Technical Field
The invention belongs to the field of equipment fault detection, and particularly relates to an intelligent service method based on multi-equipment linkage.
Background
Traction equipment is one of the core components of an electric locomotive transmission system, and stable operation of the traction equipment is related to running safety of the whole train. Because of the factors such as severe working environment, frequent load transformation, large power effect and the like, the traction equipment is easy to fail. The traction equipment working condition monitoring and fault diagnosis usually use a vibration signal analysis-based method, so that a good effect can be achieved. However, the alternating current transmission locomotive has compact structure, some vehicles have insufficient space for installing vibration sensors, in addition, vibration signals of traction equipment are easy to be interfered by vibration of a locomotive body and other parts, and practical use is limited.
Device current signature analysis is widely used to detect various types of device faults. Ac equipment is a highly symmetrical electromagnetic system and any equipment failure (or load change) can result in some degree of asymmetry. The asymmetry induces a rotating electromagnetic field change in the air gap, inducing a new frequency component in the stator current. Device current signature analysis is just a fault diagnosis by detecting changes in certain characteristic frequency components. Because of the requirements of control and equipment protection, two phases in the three-phase power supply current of the traction equipment are required to be continuously monitored during the running of the locomotive, and therefore, the fault detection method based on equipment current analysis does not need to additionally install a sensor, is equivalent to sensorless fault diagnosis, and has the outstanding advantages of easiness in implementation and economy.
At present, a high-resolution frequency spectrum analysis method is mostly adopted for current signal processing of equipment, and frequency components related to faults on a current signal power spectrum are identified. The effect is sometimes not ideal in industrial applications because the phase coupling characteristics of the fault signal are not fully utilized. Various types of equipment faults are known to cause secondary phase coupling of the equipment stator currents, producing amplitude modulation components. Analysis of secondary phase coupling closely related to faults is one of the effective methods to improve fault diagnosis accuracy.
The bispectrum is a powerful tool for analyzing the secondary phase coupling of signals, can quantitatively analyze the phase coupling degree, and is used for the successful case of equipment current analysis. But the bispectrum is only suitable for processing stationary signals. When the locomotive runs on an actual line, the power supply is interrupted for a short time through the phase separation (sub-section-phase of Catenary system), and the rotation speed of traction equipment is changed due to the change of line conditions (straight line, curves with different curvature radiuses, gradient change and the like). Therefore, the device current signal phase coupling under the condition of variable rotating speed is analyzed, the fault characteristic frequency is extracted, and the device current analysis diagnosis technology can be truly and effectively used in industrial sites.
The transient wavelet bispectrum is introduced into signal phase synchronous average, so that false components of the wavelet bispectrum can be effectively eliminated, and secondary phase coupling occurring at any moment can be accurately identified. Without time averaging, the amplitude of the transient wavelet bispectrum does not reflect the degree of frequency coupling. This is particularly important to detect changes in the distribution of the secondary phase coupling in the dual frequency domain. The invention provides a method for quantitatively describing the distribution form of nonlinear coupling components of signals in a double-frequency domain by using wavelet bispectral entropy. When the wavelet bispectrum amplitude is uniformly distributed, the transient wavelet bispectrum entropy is large; if the transient wavelet bispectral entropy is small, it is stated that the secondary phase coupling in the signal is concentrated in certain frequency components.
Disclosure of Invention
The invention provides a locomotive traction equipment fault diagnosis method by using wavelet bispectrum entropy and transient wavelet bispectrum detection equipment current signals. Firstly, carrying out wavelet bispectrum transformation on equipment signals, and calculating wavelet bispectrum entropy; and compared with the wavelet bispectrum entropy value in the normal state, if the wavelet bispectrum entropy value is reduced, the secondary phase coupling of certain components of the signal is enhanced, and the equipment is prompted to be in fault. And then, carrying out instantaneous wavelet bispectrum analysis on the signals, detecting the occurrence frequency of secondary phase coupling, and confirming the occurrence position of the fault. The locomotive line operation test proves that the method can accurately diagnose the faults of various locomotive traction equipment when the locomotive operates at variable speed.
According to a first aspect of the present invention, the present invention claims a smart service method based on multi-device linkage, which is characterized by comprising:
acquiring a device signal to be detected, carrying out wavelet bispectral transformation on the device signal to be detected, and calculating wavelet bispectral entropy of the device signal to be detected;
comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in a normal state, and if the value of the wavelet bispectral entropy of the equipment signal to be detected is reduced, indicating that the secondary phase coupling of part of components of the equipment signal to be detected is enhanced, and prompting that the equipment to be detected has faults;
and carrying out instantaneous wavelet bispectrum analysis on the signal of the equipment to be detected, detecting the occurrence frequency of secondary phase coupling, and confirming the position of the fault of the equipment to be detected.
Further, the secondary phase coupling of the partial components of the signal of the device to be detected specifically includes:
the secondary phase coupling calculation mode of the equipment current signal is as follows:
(1)；
wherein,a traction current signal representing the traction device in a normal state, < ->For the maximum value of the traction current,t represents traction equipment operation time for the basic power supply frequency;
amplitude of stator current when traction device failsWill be according to the main frequency of the fault->Modulating:
(2)；
wherein β represents the modulation depth;
performing cosine multiplication operation on the formula (2) to obtain:
(3)；
(3) The formula shows that the side bands around the power supply frequency exist in the current energy spectrum, the side band frequency is the characteristic of faults, and the phase relation of the side band frequency is secondary phase coupling;
the position of the equipment to be detected, which is faulty, comprises: rotor bar breakage failure, centrifugal failure, stator failure, bearing outer ring failure, bearing inner ring failure.
Further, the transient wavelet bispectrum analysis specifically includes:
(4)；
wherein, instantaneous wavelet bispectrumIs the frequencyf 1 , f 2 And time oftFunction of->Representing instantaneous phase randomization, R is a random distribution in +.>The variables in the inner part of the tube,E[·]representing ensemble averaging operator, ++>And->Wavelet bispectrum analysis representing corresponding frequency and time,/->Representing wavelet bispectrum complex conjugate analysis under corresponding frequency and time;
the instantaneous wavelet bispectrum is a complex sequence and can be used with instantaneous amplitudeAnd instantaneous biphases->The representation is:
(5)；
in the middle ofRepresenting the instantaneous biphasic, derived from the following equation:
(6)
in the middle ofThe instantaneous phase obtained for the continuous wavelet transform.
According to a second aspect of the present invention, the present invention claims an intelligent service system based on multi-device linkage, comprising:
the wavelet bispectrum conversion module is used for obtaining a to-be-detected equipment signal, carrying out wavelet bispectrum conversion on the to-be-detected equipment signal and calculating wavelet bispectrum entropy of the to-be-detected equipment signal;
the fault detection module is used for comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in a normal state, and prompting that the equipment signal to be detected has faults if the value of the wavelet bispectral entropy of the equipment signal to be detected is reduced, which indicates that the secondary phase coupling of part of components of the equipment signal to be detected is enhanced;
and the fault positioning module is used for carrying out instantaneous wavelet bispectrum analysis on the equipment signal to be detected, detecting the occurrence frequency of secondary phase coupling and confirming the position of the fault of the equipment to be detected.
The invention discloses an intelligent service method based on multi-equipment linkage, which comprises the steps of obtaining equipment signals to be detected, performing wavelet bispectral transformation, and calculating wavelet bispectral entropy of the equipment signals to be detected; comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in the normal state, and if the value of the wavelet bispectral entropy is reduced, indicating that the secondary phase coupling of part of components is enhanced, and prompting that the equipment to be detected has faults; and carrying out instantaneous wavelet bispectrum analysis, detecting the occurrence frequency of secondary phase coupling, and confirming the fault position of equipment to be detected. The invention fully utilizes the phase relation among frequency components, detects the tiny amplitude values from a plurality of harmonic components of a current signal to obtain a modulation component, accurately extracts time-varying secondary phase coupling in the signal, quantitatively describes the distribution uniformity degree of signal secondary phase coupling components in a double-frequency domain by wavelet bispectral entropy, and is used as a diagnostic index for monitoring the change of the current signal of locomotive traction equipment and quantifying the working state of the traction equipment.
Drawings
FIG. 1 is a workflow diagram of a smart service method based on multi-device linkage in accordance with the present invention;
FIG. 2 is a schematic diagram of waveforms and power spectrums of a first signal of a smart service method based on multi-device linkage according to the present invention;
FIG. 3 is a schematic diagram of a Fourier dual coherence spectrum of a first signal of a smart service method based on multi-device linkage according to the present invention;
FIG. 4 is a schematic diagram of a Fourier dual coherence spectrum of a second signal of a smart service method based on multi-device linkage according to the present invention;
FIG. 5 is a schematic diagram of wavelet dual coherence spectrum of a second signal of a smart service method based on multi-device linkage according to the present invention;
FIG. 6 is a time-frequency distribution diagram of a third signal of a smart service method based on multi-device linkage according to the present invention;
FIG. 7 is a schematic diagram of wavelet dual coherence spectrum of a third signal of a smart service method based on multi-device linkage according to the present invention;
FIG. 8 is a first temporal instantaneous wavelet bicoherence spectrum of a smart service method based on multi-device linkage according to the present invention;
FIG. 9 is a second temporal instantaneous wavelet bicoherence spectrum of a smart service approach based on multi-device linkage in accordance with the present invention;
FIG. 10 is a graph of current signal and spectrum of a traction device to be detected based on a multi-device linkage intelligent service method according to the present invention;
FIG. 11 is a diagram of the power of the device current signal of the redrawing of the traction device to be detected based on the intelligent service method of multi-device linkage according to the present invention;
FIG. 12 is a graph of current signal power of healthy traction equipment at the same speed based on a multi-equipment linkage intelligent service method according to the present invention;
FIG. 13 is a normalized instantaneous wavelet dual spectrum of a traction device current signal during locomotive acceleration based on a smart service approach of multi-device linkage in accordance with the present invention;
FIG. 14 is a normalized instantaneous wavelet bispectrum of a device current signal in a first state of a smart service approach based on multi-device linkage in accordance with the present invention;
FIG. 15 is a schematic diagram of a time domain waveform and power spectrum of stator current of a device during variable speed operation of a locomotive based on a smart service method for multi-device linkage in accordance with the present invention;
FIG. 16 is a schematic diagram of a wavelet dual coherence spectrum of a traction device current signal based on a smart service approach of multi-device linkage in accordance with the present invention;
FIG. 17 is a wavelet dual spectrum diagram of a first instantaneous speed of a smart service approach based on multi-device linkage in accordance with the present invention;
FIG. 18 is a wavelet bispectrum diagram of a second instantaneous speed of a smart service approach based on multi-device linkage in accordance with the present invention;
fig. 19 is a block diagram of a smart service system based on multi-device linkage according to the present invention.
Detailed Description
According to a first embodiment of the present invention, referring to fig. 1, the present invention claims an intelligent service method based on multi-device linkage, which is characterized by comprising:
acquiring a device signal to be detected, carrying out wavelet bispectral transformation on the device signal to be detected, and calculating wavelet bispectral entropy of the device signal to be detected;
comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in a normal state, and if the value of the wavelet bispectral entropy of the equipment signal to be detected is reduced, indicating that the secondary phase coupling of part of components of the equipment signal to be detected is enhanced, and prompting that the equipment to be detected has faults;
and carrying out instantaneous wavelet bispectrum analysis on the signal of the equipment to be detected, detecting the occurrence frequency of secondary phase coupling, and confirming the position of the fault of the equipment to be detected.
Further, the secondary phase coupling of the partial components of the signal of the device to be detected specifically includes:
the secondary phase coupling calculation mode of the equipment current signal is as follows:
(1)；
wherein,a traction current signal representing the traction device in a normal state, < ->For the maximum value of the traction current,t represents traction equipment operation time for the basic power supply frequency;
amplitude of stator current when traction device failsWill be according to the main frequency of the fault->Modulating:
(2)；
wherein β represents the modulation depth;
performing cosine multiplication operation on the formula (2) to obtain:
(3)；
(3) The formula shows that the side bands around the power supply frequency exist in the current energy spectrum, the side band frequency is the characteristic of faults, and the phase relation of the side band frequency is secondary phase coupling;
the position of the equipment to be detected, which is faulty, comprises: rotor bar breakage failure, centrifugal failure, stator failure, bearing outer ring failure, bearing inner ring failure.
Wherein in this example, table 1 shows the characteristic frequencies of amplitude modulation and sidebands in the stator current spectrum due to rotor bar breaks, eccentricities, stator and bearing faults, where k= ±1, ±2, ±3, …,is the main fault component of the series, +.>Is the rotational frequency of the rotor and s is the slip. />And->For the rolling bearing vibration characteristic frequency, the following formula can be used for calculation: />，/>Wherein->Is the number of bearing balls, +.>The rolling element ball diameter and pitch circle diameter are respectively the data processing, and beta is the ball contact angle. In industrial experiments, the characteristic frequency of the bearing vibration can also be estimated by using empirical formulas of simpler form, i.e. +.>，/>。
Table 1 device failure frequency correspondence table
High resolution spectral analysis is widely used to extract the characteristic frequencies given in table 1. Since the bispectrum and wavelet bispectrum describe the phase relationship of these frequencies, fault signatures can be identified at low signal-to-noise ratios in an industrial environment. However, classical MCSA is only suitable for diagnosis of steady state operation of the device. Under non-stationary conditions, in the expression of table 1,、/>and s varies with time, the spectrum of the stator current cannot show a characteristic peak that reveals the presence of a given fault.
Time frequency analysis has been used to track the fault harmonics given in table 1. However, the failure mode in the time-frequency plane is much more complex than a single frequency peak that occurs in the steady-state spectrum. To alleviate this problem, the phase relationship between these components under non-stationary conditions is analyzed.
When the locomotive is operating on-line, the power frequency, load, or its own frequency may vary over time for a given traction device. In an ideal, healthy machine, the stator current may be considered pure sinusoidal, the frequency of which depends on the variation of the power supply frequency:
(7)
in the case of time-varying conditions, a spectrogram can be used to identify trunk and fault components along time. Assuming that the signal remains quasi-stationary under an analysis window of length Δt, i.e. the time interval Δt is sufficiently small, the variation of the signal parameters during each time interval is negligible.
Using such small time periods to calculate the current, the signal can then be considered stationary for each small time interval, whereThe current can be modeled as
(8)
Wherein,；
wherein the method comprises the steps ofIs a specific time interval +.>Amplitude and frequency of the inner part +.>。
When the traction device fails, a new series of components will appear in the stator current due to amplitude modulation. Thus, within a time intervalThe fault current of (2) is given by:
(9)
wherein,；
is the amplitude of the fault component, +.>Is the modulation index, both at time +.>In addition, in the case of the optical fiber,。
using the elementary trigonometric relationship, the fault current (8) can be expressed as:
（9）
(9) The second and third terms of the formula are given timeIs a fault component of (a) which will be at a point in the spectrogramWhere high energy concentrations are generated.
According to the above formula, it is derived: the frequency of rotation and the frequency of failure of the traction device also change with time under variable speed conditions. Within a sufficiently small time interval, these frequency components and their phases still have a sum/difference relationship, i.e. a secondary phase coupling. Fourier bispectrum is only suitable for analyzing stationary signals, and cannot detect time-varying secondary phase coupling. The transient wavelet bispectrum is introduced into the ensemble average of transient phase randomization to eliminate stray bispectrum, and can effectively identify the time-varying secondary phase coupling.
Further, the transient wavelet bispectrum analysis specifically includes:
(4)；
wherein, instantaneous wavelet bispectrumIs the frequencyf 1 , f 2 And time oftFunction of->Representing instantaneous phase randomization, R is a random distribution in +.>The variables in the inner part of the tube,E[·]representing ensemble averaging operator, ++>And->Wavelet bispectrum analysis representing corresponding frequency and time,/->Representing wavelet bispectrum complex conjugate analysis under corresponding frequency and time;
the instantaneous wavelet bispectrum is a complex sequence and can be used with instantaneous amplitudeAnd instantaneous biphasingThe representation is:
(5)；
in the middle ofRepresenting the instantaneous biphasic, derived from the following equation:
(6)
in the middle ofThe instantaneous phase obtained for the continuous wavelet transform.
Wherein in this embodiment fourier bispectrum is required as a basis.
Given a signal x (t), the fourier transform is defined as:
（10）
fourier-based bispectrum is defined as:
（11）
wherein the method comprises the steps ofE[×]Is a statistical expectation operator; * Representing complex conjugates.
In practical applications, the fourier-based bispectrum is typically normalized by the power spectrum to fourier-based bispectrum (FB), i.e.:
（12）
since bicoherence provides a quantitative measure of the secondary phase coupling, they have been used in various applications to detect QPC. However, fourier-based bispectrum and bicoherence calculations require a large amount of data to average, and are not suitable for analysis of short-term nonlinear interactions. Therefore, it is proposed to detect the phase relationship of a short-time signal based on the bispectrum of the wavelet.
The Continuous Wavelet Transform (CWT) of a signal is defined as the convolution of x (t) with the scaled and normalized wavelet, written as:
（13）
where, represents complex conjugate, ψ (t) is the mother wavelet, s is the scale variable, t is the time shift variable, and f is the equivalent fourier frequency.
The wavelet bispectrum is as follows:
（14）
where T is the finite length of time of the signal, the frequency values f1, f2 and f3 satisfy the relationship f3=f1+f2. Wavelet Bicoherence (WB) is defined as
（15）
Where E [ ] represents the average operator operation.
Peaks in WB indicate phase coupling at dual frequencies (f 1, f 2) during time interval T, and this value has the same meaning as in FB. WB uses less data to describe QPC, but wavelet bispectrum in principle only analyzes signals stationary over time period T, failing to identify the secondary phase coupling of frequency over time.
The engineering actual signal wavelet double spectrum is complex, and parameters are required to be introduced to quantitatively describe the characteristics. A plurality of parameters can be used for describing the change of the double spectrum and the double frequency domain, and the second-order entropy expresses energy and can better reflect the overall change. The wavelet bispectral entropy is calculated as follows:
(1) For signalsx(t) Performing wavelet bispectrum transformation to obtain wavelet bispectrum；
(2) The wavelet bispectrum has symmetry, and in order to remove redundant information, the frequency domain of the wavelet bispectrum is divided into a triangle definition domain: (fs is the sample frequency) so that +.>;
(3) Calculating the energy probability of double frequency domain wavelet double spectrum:
（16）；
(4) Wavelet bispectral entropy is defined as:
(17)
similar to other information entropies, wavelet bispectral entropies also have non-negativity, symmetry, additivity, and symmetry. The more uniform the amplitude of the wavelet bispectrum is distributed in the bispectrum domain, the larger the value of the wavelet bispectrum entropy is, and the smaller the value is on the contrary. According to the embodiment, the simulation signal analysis shows that the wavelet bispectral entropy can effectively detect the characteristic change of the signal bispectral frequency domain.
Transient wavelet bispectrum describing signal secondary phase coupling in time-frequency space (in time-frequency space), its peak prompting signal in accurate time-frequency pointThere is a secondary phase coupling. The magnitude of the instantaneous wavelet bispectrum is between 0 and 1. But without time averaging, the magnitudes of the other instantaneous wavelet bispectrum, except for 0 and 1, do not accurately reflect the extent of the secondary coupling. Thus, the introduction of a quantitative index is particularly important in describing the distribution of the secondary coupling in the dual frequency domain.
The bispectral entropy can be used as a probability distribution of energy density in the f1 and f2 double frequencies. Under normal working conditions, the secondary phase coupling of the current signal of the equipment is not serious, and the distribution of the wavelet bispectrum amplitude value is relatively uniform. The value of the wavelet bispectral entropy is larger. After the fault occurs, the coupling degree of certain frequency secondary phases of the equipment signals is increased, the corresponding frequency wavelet bispectrum amplitude is increased, the distribution is not uniform, and the wavelet bispectrum entropy value is reduced. The more serious the fault, the deeper the secondary phase coupling degree of the signal, the more uneven the distribution, and the lower the wavelet bispectral entropy value. Therefore, the wavelet bispectral entropy value can be used as an index to monitor the working condition of the equipment. After the embodiment, the combination of wavelet bispectral entropy and instantaneous wavelet bispectrum is illustrated by simulation signals, so that fault characteristics can be effectively extracted under the variable speed condition.
Consider the following simulation signals:
(19)
wherein the method comprises the steps off 2 = 0.11×f s ，f 3 = 0.19×f s ，f 4 = f 2 + f 3 ，θ 2， θ 3 Andθ 4 is in [0, 2 pi ]]The random numbers are uniformly distributed in the inner part,e(t) Is-20 dB white noise. At the position off 4 =f 2 +f 3 Where half the energy comes fromf 2 And (3) withf 3 Coupling between other half is independent off 2 And (3) withf 3 The method comprises the steps of carrying out a first treatment on the surface of the And atf 1 = f 3 - f 2 Where a small amount of noise is removed, almost all the energy comes fromf 2 And (3) withf 3 Coupling between them. Sampling frequency acquisitionf s =1024 Hz, the experiment generates 64 samples, 256 points per sample. Signal signalx 1 (t) The waveform and power spectrum of (2) is shown in figure 2. Observing the signalx 1 Power spectrum, can be seenx 1 Comprising 4 frequency components, whereinf 1 Is half the energy of the other 3 frequency components. The power spectrum suppresses the signal phase from which we cannot obtain more information. FIG. 3 is a signalx 1 The Fourier dual-coherence spectrum of (C) can be seen in the frequency pair [ (]f 2 , f 3 ) And%f 1 , f 2 ) There are 2 distinct peaks. In frequency pair [ ]f 2 , f 3 ) The bicoherence spectrum amplitude is 0.48, which suggests that the phase coupling degree of 2 frequency components is 0.48, and nearly half of the energy comes from the phase coupling between the two frequencies. In frequency pair [ ]f 1 , f 2 ) The bicoherence spectrum amplitude is 0.98, which suggests that the 2 frequency components are phase-coupled to a degree of 0.98, and almost all the energy comes from the phase coupling between the two frequencies. Under the condition of enough data volume, the Fourier bispectrum can effectively detect the signal secondary phase coupling.
Consider another simulation signalx 11 (t)，x 11 (t) Constitution and front facex 1 (t) Just as much smaller, with only 4 samples. FIG. 4 is a diagram ofx 11 (t) From which no useful information can be found. At the data volumeIn small cases, fourier bispectrum cannot detect the second phase coupling of the signal.
Doing the signalx 11 (t) The results of the wavelet bicoherence spectrum analysis are shown in fig. 5. Except for the frequency pair on the spectrogramf 2 , f 3 ) And%f 1 , f 2 ) There are some "false peaks" at the boundaries in addition to the 2 distinct peaks. In the case of small data volumes, the wavelet bicoherence spectrum can barely give secondary phase coupling, sacrificing frequency resolution. In addition, since the average frequency is small, the amplitude of the wavelet bicoherence spectrum can not reflect the degree of the coupling of the secondary phase, and the frequency pair can be seenf 2 , f 3 ) And%f 1 , f 2 ) The peaks of the wavelet bicoherence spectrum are almost equal.
Wavelet bicoherence spectrum can be used for secondary phase coupling detection under the condition of short data, but has the defects of low frequency resolution and amplitude which cannot reflect the phase coupling degree. For some working conditions, such as equipment rotation speed change, the frequency of the secondary phase coupling is changed continuously, and at the moment, the wavelet bispectrum cannot identify the time-varying secondary phase coupling, so that a new mathematical tool needs to be introduced.
The following signals are considered to be a consideration,
(20)
it can be seen that the light source is,x 2 (t) Composition and front of (2)x 1 (t) Substantially identical, independent componentsf 2 And (3) withf 3 The components are frequency-coupled to produce 1 sum frequency component and 1 difference frequency component, and in additionf 4 There is also 1 independent component. And signalx 1 (t) In a different way, the difference is that,x 2 (t) The frequencies of the constituent components are time-varying. Generating 1024-point data by computer, sampling frequency 1024Hz, and taking time-varying factork=1. Thus doing sof 1 The frequency of the component takes on the value 82Hz,f 2 the frequency of the component is 112.6-113.6 Hz;f 3 the value of the component is 194.6-195.6 Hz;f 4 the value of the component is 307.2-309.2 Hz. The frequencies of the above components all vary linearly with time.
FIG. 6 is a signalx 2 (t) Can be seen from the time-frequency distribution map of (a)x 2 (t) The frequency of each component increases linearly with time. FIG. 7 is a signalx 2 (t) Wavelet bicoherence spectrum of (2) and some peaks exist on the spectrum. But due to the low frequency resolution, no reasonable explanation of these peaks can be made.
Fig. 8 and 9 are instantaneous wavelet bicoherence spectra at 0.2s and 0.7s, respectively. The instantaneous wavelet bispectrum has very high frequency resolution due to the introduction of random phase averaging. The instantaneous wavelet bispectrum of fig. 8 has sharp peaks at the frequency pairs (112.8 Hz, 194.8 Hz) and (194.8 Hz, 112.8 Hz) with amplitudes approaching 1, suggesting that there is deep secondary phase coupling of these two frequencies. While 112.8Hz and 194.8Hz are just time-varying frequency componentsf 2 ,f 3 Instantaneous value at 0.2 s. Calculated according to the formula (23)f 1 ~f 4 The instantaneous values of the four components at 0.2s are 82Hz, 112.8Hz, 194.8Hz and 307.6 Hz, respectively. The instantaneous wavelet bispectrum has clear spectral peaks at the frequency pairs (82 Hz, 112.8 Hz) and (112.8 Hz, 82 Hz), suggesting that there is also a secondary phase coupling of these two frequencies. (112.8 Hz, 194.8 Hz) as a componentf 2 ,f 3 The "sum" component of the interaction, (82 Hz, 112.8 Hz) is the corresponding "difference" component. In addition, the spectrogram is diagonally "pseudo-peaked" in some fashion, such as (82 Hz ), (112.8 Hz, 112.8 Hz), which is related to the introduction of random phase averaging by the instantaneous wavelet bispectrum calculation. The instantaneous wavelet bicoherence spectrogram is symmetrical about a diagonal line, so that only half spectrogram can be drawn when the instantaneous wavelet bicoherence spectrogram is actually drawn, and then the peak value of the boundary is filtered, so that the false peak can be effectively filtered.
FIG. 9 can find clear spectral peaks at the frequency pairs (113.3 Hz, 195.3 Hz) and (82 Hz, 113.3 Hz), which are frequency components at 0.7sf 2 ,f 3 The sum frequency and the difference frequency generated by the interaction are formed by the frequencyParts by weight. The instantaneous wavelet bicoherence spectrum has high frequency resolution, and can accurately identify the secondary phase coupling which is continuously changed along with time in the signal.
The instantaneous wavelet bicoherence spectrum is effective to detect the secondary phase coupling that occurs time-variant, but without time averaging, the amplitude is not indicative of the degree of secondary phase coupling. Therefore, the invention introduces wavelet bispectral entropy, quantitatively describes the distribution of the coupling of the secondary phase in the bispectrum domain, and monitors the working condition of equipment. As will be explained below by means of an analysis of the simulated signals, the following signals are considered,
(21)
signal signalx 3 (t) Composition of (3)x 2 (t) Is consistent, except that the parameters can be usedThe proportion of the components generated by the secondary phase coupling in the whole signal is adjusted.
The effectiveness of the equipment fault diagnosis method provided by the invention is further detected in an industrial field.
The experimental locomotive is an HXN3 type electric locomotive, and is provided with 6A 2938-5 squirrel-cage three-phase asynchronous traction motors, the rated power is 690KW, the rated rotating speed is 3 220 r/min and the equipment slip is 1.6%. The gearbox ratio was 85:16.
fig. 10 is a graph of the current signal and spectrum of a traction device near a maintenance period. The running speed of the sampling timing vehicle is 70km/h, which corresponds to the rotating speed 1950r/min of traction equipment and the power supply frequency is 65Hz. As can be seen from the figure, there are many harmonic components in the locomotive traction device current due to the device structure, grid harmonics and noise. The side bands on the power spectrogram are arranged on two sides of the device power supply frequency 65Hz, and are the most basic characteristics of the device current spectrum.
The acquired device current signal is resampled for viewing the sideband structure around the supply frequency. FIG. 11 shows the power spectrum of the current signal of the equipment, FIG. 12 shows the same running speed when the traction equipment is just put into operationAnd (5) lower collected power spectrum of the device current signal. As can be seen by comparison, the side band structures (63 Hz and 67 Hz) on the power spectrum on both sides of the supply frequency are much larger than when the device was just put into use, and this feature corresponds to a broken bar of the device rotor. (see Table 1 ，k=1,s=0.016,f s =65 Hz. ) Therefore, the traction equipment is primarily judged to have the rotor broken bar fault.
And selecting data of the locomotive in variable speed operation for further analysis. FIG. 13 is a normalized instantaneous wavelet bispectrum of the traction device current signal as the locomotive accelerates, with some smaller peaks filtered out for clarity of illustration. Locomotive instantaneous speed 62.8 km/h, traction device power frequency 59.2 Hz. There were distinct peaks in the spectral frequency pairs (59 Hz, 2 Hz) and (2 Hz, 59 Hz), suggesting a secondary phase coupling of these two frequencies. Wherein 59 Hz is close to the equipment supply frequency and 2Hz is the sideband component of the rotor bar fault. (see Table 1 ，k=1,s= 0.016,f s =59 Hz）。
FIG. 14 is a normalized instantaneous wavelet bispectrum of the plant current signal at a locomotive instantaneous speed of 63.9 km/h and a traction plant power frequency of 60.4 Hz. The peaks at frequency pairs (60 Hz, 2 Hz) and (2 Hz, 60 Hz) suggest that the two frequencies are secondarily phase coupled to coincide with the equipment rotor bar fault signature. The instantaneous wavelet bispectrum can analyze the instantaneous secondary phase coupling, has better frequency resolution and can meet the use requirement of industrial sites.
Through two different working conditions (constant-speed running and variable-speed running of the locomotive), the current analysis of the equipment under three speeds is carried out, and the fault characteristics point to the existence of broken bar faults of the traction equipment. And the traction equipment is disassembled when the locomotive is overhauled, and 3 broken bars of the equipment rotor are found, so that a diagnosis conclusion is verified. A diagnosis method for instantaneously occurring secondary phase coupling by instantaneous wavelet bispectrum analysis is adopted to obtain preliminary success.
The HXD3 locomotive is an electric locomotive with 6 AC transmission shafts, a total power 7200 kW and a transmission ratio 101, wherein the electric locomotive is an AC transmission line passenger and cargo general purpose electric locomotive: 21, the highest running speed is 120 km/h. The locomotive was equipped with 6 YJ85A1 traction devices rated at 1250 kW, 2150V rated at 390A rated current, 4 poles and 1.4% slip. The transmission end bearing is a NU330 cylindrical roller bearing, and the non-transmission end bearing is a NU320 cylindrical roller bearing and a QJ318 four-point contact ball bearing.
A fault monitoring system based on equipment current analysis is installed in a locomotive experiment, and traction equipment current signals are collected and analyzed when locomotive lines run. Continuous monitoring over a period of time found: the wavelet bispectrum entropy value of the current signal of the 3 rd axis traction equipment of a certain locomotive is continuously reduced, and the fault and the development are prompted.
FIG. 15 is a schematic view of the apparatus during variable speed operation of the locomotiveaPhase stator current time domain waveform and power spectrum, sampling frequency 2000 Hz. Locomotive running speed is 87-113 km/h, and corresponding equipment power supply frequency is 41-52 Hz. The traction equipment has a complex structure, and a plurality of frequency components exist on a power spectrogram. In addition, variable speed operation causes frequency ambiguity, characteristic frequency cannot be resolved, and equipment fault diagnosis at variable speeds is a challenging task.
The pulling device current signal wavelet bicoherence spectrum is shown in fig. 16. It can be seen that there are peaks on the spectrum, suggesting that there is a secondary phase coupling between certain frequency components. Because the frequency resolution of the wavelet bispectrum is low, reasonable interpretation of the peaks is difficult, and the position of the fault is judged.
The analysis of the stator current signal of the traction equipment at the moment of extracting the instantaneous running speed 88km/h and 112km/h of the locomotive is shown in the wavelet bispectrum of figures 17 and 18 respectively. As with previous drawing methods, some smaller spectral peaks are filtered out for clarity of illustration.
On fig. 17, 2 distinct spectral peaks can be observed, located at frequency pairs (43 Hz, 243 Hz) and (243 Hz, 43 Hz). Where 43Hz is close to the traction device power frequency and 243Hz is the traction device drive end bearing inner race failure frequency at that speed. The secondary phase coupling of the power supply frequency of the equipment and the fault characteristic frequency of the bearing inner ring is indicated, and the fault of the bearing inner ring is indicated. (see table 1.) the peak locations of the spectrum of fig. 18 are at frequency pairs (52 Hz, 315 Hz) and (315 Hz, 52 Hz), where 52 Hz is the traction device power supply frequency and 315 Hz is close to the traction device drive end bearing inner race failure frequency at that speed. The secondary phase coupling of the power supply frequency of the equipment and the characteristic frequency of the bearing inner ring is indicated, and the corresponding equipment inner ring fails.
At the same time, a locomotive bearing diagnostic system based on the vibration signal also indicates bearing failure. The locomotive engine service section overhauls, disassembles traction equipment, discovers the grinding loss of the inner ring of the bearing at the transmission end, and confirms the diagnosis conclusion. The equipment diagnosis method provided by the invention is successfully applied to the industrial field.
According to a second embodiment of the present invention, the present invention claims an intelligent service system based on multi-device linkage, which is characterized by comprising:
the wavelet bispectrum conversion module is used for obtaining a to-be-detected equipment signal, carrying out wavelet bispectrum conversion on the to-be-detected equipment signal and calculating wavelet bispectrum entropy of the to-be-detected equipment signal;
the fault detection module is used for comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in a normal state, and prompting that the equipment signal to be detected has faults if the value of the wavelet bispectral entropy of the equipment signal to be detected is reduced, which indicates that the secondary phase coupling of part of components of the equipment signal to be detected is enhanced;
and the fault positioning module is used for carrying out instantaneous wavelet bispectrum analysis on the equipment signal to be detected, detecting the occurrence frequency of secondary phase coupling and confirming the position of the fault of the equipment to be detected.
Those skilled in the art will appreciate that various modifications and improvements can be made to the disclosure. For example, the various devices or components described above may be implemented in hardware, or may be implemented in software, firmware, or a combination of some or all of the three.
A flowchart is used in this disclosure to describe the steps of a method according to an embodiment of the present disclosure. It should be understood that the steps that follow or before do not have to be performed in exact order. Rather, the various steps may be processed in reverse order or simultaneously. Also, other operations may be added to these processes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the methods described above may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, such as a read only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiment may be implemented in the form of hardware, or may be implemented in the form of a software functional module. The present disclosure is not limited to any specific form of combination of hardware and software.
Unless defined otherwise, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present disclosure and is not to be construed as limiting thereof. Although a few exemplary embodiments of this disclosure have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the claims. It is to be understood that the foregoing is illustrative of the present disclosure and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The disclosure is defined by the claims and their equivalents.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (4)
1. An intelligent service method based on multi-equipment linkage is characterized by comprising the following steps:
acquiring a device signal to be detected, carrying out wavelet bispectral transformation on the device signal to be detected, and calculating wavelet bispectral entropy of the device signal to be detected;
comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in a normal state, and if the value of the wavelet bispectral entropy of the equipment signal to be detected is reduced, indicating that the secondary phase coupling of part of components of the equipment signal to be detected is enhanced, and prompting that the equipment to be detected has faults;
and carrying out instantaneous wavelet bispectrum analysis on the signal of the equipment to be detected, detecting the occurrence frequency of secondary phase coupling, and confirming the position of the fault of the equipment to be detected.
2. The intelligent service method based on multi-device linkage according to claim 1, wherein the secondary phase coupling of the partial components of the device signal to be detected specifically comprises:
the secondary phase coupling calculation mode of the equipment current signal is as follows:
(1)；
wherein,a traction current signal representing the traction device in a normal state, < ->For maximum traction current, +.>T represents traction equipment operation time for the basic power supply frequency;
amplitude of stator current when traction device failsWill be according to the main frequency of the fault->Modulating:
(2)；
wherein β represents the modulation depth;
performing cosine multiplication operation on the formula (2) to obtain:
(3)；
(3) The formula shows that the side bands around the power supply frequency exist in the current energy spectrum, the side band frequency is the characteristic of faults, and the phase relation of the side band frequency is secondary phase coupling;
the position of the equipment to be detected, which is faulty, comprises: rotor bar breakage failure, centrifugal failure, stator failure, bearing outer ring failure, bearing inner ring failure.
3. The intelligent service method based on multi-equipment linkage as claimed in claim 1, wherein the instantaneous wavelet bispectrum analysis specifically comprises:
(4)；
wherein, instantaneous wavelet bispectrumIs the frequencyf 1 , f 2 And time oftFunction of->Representing instantaneous phase randomization, R is a random distribution in +.>The variables in the inner part of the tube,E[·]representing ensemble averaging operator, ++>Andwavelet bispectrum analysis representing corresponding frequency and time,/->Representing wavelet bispectrum complex conjugate analysis under corresponding frequency and time;
the instantaneous wavelet bispectrum is a complex sequence and can be used with instantaneous amplitudeAnd instantaneous biphases->The representation is:
(5)；
in the middle ofRepresenting the instantaneous biphasic, derived from the following equation:
(6)
in the middle ofThe instantaneous phase obtained for the continuous wavelet transform.
4. An intelligent service system based on multi-device linkage, comprising:
the wavelet bispectrum conversion module is used for obtaining a to-be-detected equipment signal, carrying out wavelet bispectrum conversion on the to-be-detected equipment signal and calculating wavelet bispectrum entropy of the to-be-detected equipment signal;
the fault detection module is used for comparing the wavelet bispectral entropy of the equipment signal to be detected with the wavelet bispectral entropy value in a normal state, and prompting that the equipment signal to be detected has faults if the value of the wavelet bispectral entropy of the equipment signal to be detected is reduced, which indicates that the secondary phase coupling of part of components of the equipment signal to be detected is enhanced;
and the fault positioning module is used for carrying out instantaneous wavelet bispectrum analysis on the equipment signal to be detected, detecting the occurrence frequency of secondary phase coupling and confirming the position of the fault of the equipment to be detected.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103198053A (en) * | 2013-03-21 | 2013-07-10 | 西安交通大学 | Instantaneous wavelet bicoherence method based on phase randomization |
| US20150208940A1 (en) * | 2014-01-28 | 2015-07-30 | Covidien Lp | Non-stationary feature relationship parameters for awareness monitoring |
| CN104820786A (en) * | 2015-05-13 | 2015-08-05 | 西安交通大学 | Method for analyzing instantly weighted synchronous extrusion wavelet bispectrum |
| CN106228116A (en) * | 2016-07-13 | 2016-12-14 | 华东理工大学 | A kind of grinding trembling recognition methods based on electric current instantaneous nonlinearity index |
| US20180049896A1 (en) * | 2016-08-17 | 2018-02-22 | Teledyne Scientific & Imaging, Llc | System and method for noninvasive identification of cognitive and behavioral goals |
-
2023
- 2023-12-05 CN CN202311648774.0A patent/CN117347773A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103198053A (en) * | 2013-03-21 | 2013-07-10 | 西安交通大学 | Instantaneous wavelet bicoherence method based on phase randomization |
| US20150208940A1 (en) * | 2014-01-28 | 2015-07-30 | Covidien Lp | Non-stationary feature relationship parameters for awareness monitoring |
| CN104820786A (en) * | 2015-05-13 | 2015-08-05 | 西安交通大学 | Method for analyzing instantly weighted synchronous extrusion wavelet bispectrum |
| CN106228116A (en) * | 2016-07-13 | 2016-12-14 | 华东理工大学 | A kind of grinding trembling recognition methods based on electric current instantaneous nonlinearity index |
| US20180049896A1 (en) * | 2016-08-17 | 2018-02-22 | Teledyne Scientific & Imaging, Llc | System and method for noninvasive identification of cognitive and behavioral goals |
Non-Patent Citations (2)
| Title |
|---|
| 冷军发等: "基于小波双谱的矿用齿轮箱故障诊断", 煤炭学报, vol. 35, no. 7, pages 1 - 3 * |
| 张明明: "基于牵引电机定子电流改进小波双谱分析的机车传动系统故障诊断技术研究", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, pages 17 - 91 * |
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