CN110516612B - Fault weak signal detection method and system based on variable-scale convex peak method - Google Patents
Fault weak signal detection method and system based on variable-scale convex peak method Download PDFInfo
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Abstract
The invention discloses a fault weak signal detection method and system based on a variable-scale convex peak method, wherein the detection method comprises the following steps: acquiring an input signal; determining a signal to be detected according to the input signal; constructing a nonlinear variable-scale duffin system according to the signal to be detected; constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system; judging whether the nonlinear variable-scale duffin detection system has state transition or not; and when the nonlinear variable-scale duffing detection system does not generate state transition, judging whether the parameters in the nonlinear variable-scale duffing detection system exceed a set range or not, and further realizing the identification of the fault weak signal. The fault weak signal detection method based on the variable-scale convex peak method can quickly identify the fault weak signal with a certain frequency, and improves the efficiency and accuracy of fault weak signal detection.
Description
Technical Field
The invention relates to the field of fault weak signal detection, in particular to a fault weak signal detection method and system based on a variable-scale convex peak method.
Background
The weak signal is a useful signal with a small amplitude which is emitted by engineering equipment in the operation process and is easily submerged by noise, and the weak signal detection technology is widely applied to the engineering field because the weak signal often carries some important useful information. The common means for detecting weak signals are mostly to suppress or filter noise, but in doing so, useful weak signals are often damaged, distortion of different degrees is caused, the weak signals cannot be effectively detected, and therefore faults of equipment cannot be accurately detected.
In the research of a new method for identifying weak signal frequency, a part of scholars pay attention to research on detecting the weak signal frequency by utilizing the chaotic burst phenomenon. The detection performance of the chaos and the traditional detection method is compared by Liuhai waves and the like, and the frequency difference detection is carried out by adopting the burst chaos; based on the frequency locking principle of the forced duffin oscillator periodic intermittent chaos characteristic, Wangcuan and the like discuss a method for identifying weak signals by using 79 chaotic oscillators, and then a plurality of scholars begin to detect the frequency of the weak signals by using the intermittent chaos principle; some scholars pay attention to the change of system parameters, and then the weak signal frequency is detected by using system phase change; shiitake wisdom et al introduces a variable scale coefficient into a duffin detection system, discusses the relationship between time scale transformation and weak signal frequency, and sets different variable scale coefficients to identify the frequency of a weak signal; there have also been a number of studies in the meantime beginning to use system phase changes to detect weak signals. The two new methods indeed improve the success rate of weak signal frequency identification, but still have certain disadvantages; the detection model constructed by the former is complex, the operation difficulty is high, the accurate frequency identification degree is not high, and the influence of noise on the detection is enlarged to a certain extent; the latter introduces a large amount of simulation parameters, and needs a large amount of tests to find parameters conforming to the phenomenon, and the detection efficiency is too low.
Disclosure of Invention
The invention aims to provide a fault weak signal detection method and system based on a variable-scale convex peak method, and solves the problems of low efficiency and low accuracy of frequency detection of fault weak signals.
In order to achieve the purpose, the invention provides the following scheme:
a fault weak signal detection method based on a variable-scale convex peak method comprises the following steps:
acquiring an input signal; the input signals comprise original fault weak signals, noise signals and other signals sent out when the equipment works;
determining a signal to be detected according to the input signal, wherein the signal to be detected comprises a fault weak signal and a noise signal;
constructing a nonlinear variable-scale duffin system according to the signal to be detected;
constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
judging whether the nonlinear variable-scale duffin detection system has state transition or not, and determining a first judgment result; the state transition is that the nonlinear variable-scale duffing detection system changes from a chaotic state to a periodic state and then to the chaotic state; the state transitions are determined by a hump phenomenon in a bifurcation diagram;
if the first judgment result indicates that the nonlinear variable-scale duffing detection system has state transition, detecting a weak fault signal, determining that the equipment has a fault, and sending an alarm signal;
if the first judgment result indicates that the nonlinear variable-scale duffin detection system does not have state transition, judging whether the parameter in the nonlinear variable-scale duffin detection system exceeds a set range or not to obtain a second judgment result;
if the second judgment result shows that the parameters in the nonlinear variable-scale duffin detection system do not exceed the set range, optimizing the nonlinear variable-scale duffin detection system, and returning to the step of constructing the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
and if the second judgment result shows that the parameters in the nonlinear variable-scale duffing detection system exceed the set range, determining that no fault weak signal is detected, and determining that the equipment has no fault.
Optionally, the determining a signal to be detected according to the input signal specifically includes:
carrying out filtering pretreatment on the input signal to obtain a pretreated input signal; the filtering pretreatment is to adopt a filtering technology to carry out preliminary filtering and eliminate high-frequency noise signals sent out by equipment during working in the input signals;
and determining the signal to be detected according to the preprocessed input signal.
Optionally, the constructing a nonlinear variable-scale duffin system according to the signal to be detected specifically includes:
determining the damping ratio and the driving force amplitude of a nonlinear variable-scale duffin system according to the signal to be detected;
determining the nonlinear variable-scale duffing system according to the damping ratio and the driving force amplitude of the nonlinear variable-scale duffing system;
the nonlinear variable-scale duffin system comprises the following components:
wherein, mu*To the damping ratio, f*cos ω t is the driving force term, f*Driving force amplitude is adopted, and omega is driving force frequency; x is displacement and y is velocity.
Optionally, the constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system specifically includes:
constructing the nonlinear variable-scale duffin detection system:
wherein σ is the intensity of white Gaussian noise σ n (t), and the power spectral density of white Gaussian noise is
rcosω1t is the weak signal of fault, r is the amplitude of the weak signal of fault, omega1For faulty weak signal frequencies, rcos omega1t + σ n (t) is a signal to be detected.
Optionally, after the nonlinear variable-scale duffing detection system is constructed according to the signal to be detected and the nonlinear variable-scale duffing system, the method further includes:
determining a phase diagram and a bifurcation diagram according to the nonlinear variable-scale duffing detection system;
determining a chaos threshold value of a nonlinear variable-scale duffin detection system by using a random Mel-Ni-kov method;
and optimizing the nonlinear variable-scale duffin detection system by using the chaos threshold.
Optionally, the determining the chaos threshold of the nonlinear variable-scale duffin detection system by using the random mellnikov method specifically includes:
using formulas
Determining a chaos threshold; wherein mu is a damping ratio, f is a driving force amplitude, omega is a driving force frequency, sigma is the intensity of Gaussian white noise sigma n (t), K is the power spectral density of the Gaussian white noise,
r is the amplitude of the weak signal of the fault, omega1For a faulty weak signal frequency, t0Is the current time.
Optionally, the inputting the signal to be detected into the nonlinear variable-scale duffin detection system, judging whether a state transition occurs in the system, and before determining a judgment result, further including:
and visualizing the state of the nonlinear variable-scale duffing detection system by using MATLAB, Simulink, Multisim, LabVIEW or C language, namely constructing a bifurcation diagram.
Optionally, after determining that the device fails and sending an alarm signal, the method further includes:
determining the frequency of the fault weak signal according to a peak phenomenon of a bifurcation diagram under the change of the driving force frequency;
determining the fault position of the equipment according to the frequency of the fault weak signal; and repairing the fault position of the equipment.
A fault weak signal detection system based on a variable-scale convex peak method comprises:
the input signal acquisition module is used for acquiring an input signal; the input signals comprise original fault weak signals, noise signals and other signals sent out when the equipment works;
the to-be-detected signal determining module is used for determining to-be-detected signals according to the input signals, wherein the to-be-detected signals comprise fault weak signals and noise signals;
the nonlinear variable-scale duffin system construction module is used for constructing a nonlinear variable-scale duffin system according to the signal to be detected;
the nonlinear variable-scale duffin detection system construction module is used for constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
the first judgment module is used for judging whether the nonlinear variable-scale duffin detection system has state transition or not and determining a first judgment result; the state transition is that the nonlinear variable-scale duffing detection system changes from a chaotic state to a periodic state and then to the chaotic state; the state transitions are determined by a hump phenomenon in a bifurcation diagram;
the alarm module is used for detecting a weak fault signal if the first judgment result indicates that the nonlinear variable-scale duffin detection system is in state transition, determining that the equipment is in fault and sending an alarm signal;
the second judgment module is used for judging whether the parameter in the nonlinear variable-scale duffin detection system exceeds a set range or not if the first judgment result shows that the nonlinear variable-scale duffin detection system does not have state transition, so as to obtain a second judgment result;
the optimization module is used for optimizing the nonlinear variable-scale duffin detection system and returning to the step of constructing the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system if the second judgment result shows that the parameter in the nonlinear variable-scale duffin detection system does not exceed the set range;
and the device failure-free determining module is used for determining that a failure weak signal is not detected and determining that the device fails if the second judgment result indicates that the parameters in the nonlinear variable-scale duffing detection system exceed the set range.
Optionally, the determining the module according to the to-be-detected signal specifically includes:
the preprocessing unit is used for carrying out filtering preprocessing on the input signal to obtain a preprocessed input signal; the filtering pretreatment is to adopt a filtering technology to carry out preliminary filtering and eliminate high-frequency noise signals sent out by equipment during working in the input signals;
and the to-be-detected signal determining unit is used for determining the to-be-detected signal according to the preprocessed input signal.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention provides a fault weak signal detection method and system based on a variable-scale convex peak method. The method is simple to operate, does not need to construct other detection models and does not refer to excessive parameters, so that the fault weak signal can be rapidly identified, and the efficiency and the accuracy of fault weak signal detection are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a method for detecting a weak fault signal based on a variable-scale peak method provided by the invention.
Fig. 2 is a structural diagram of a fault weak signal detection system based on a variable scale convex peak method provided by the invention.
FIG. 3 shows a fault micro-algorithm based on a variable-scale convex peak method provided by the present inventionThe weak signal detection method is characterized in that a bifurcation diagram of the nonlinear variable-scale duffin detection system is obtained when r is 0.02 and sigma is 0.01; FIG. 3(a) is ω1A bifurcation diagram of the nonlinear variable-scale duffin detection system at 0.8; FIG. 3(b) is ω1A bifurcation diagram of the non-linear, variable-scale duffin detection system at 0.835; FIG. 3(c) is ω1A bifurcation diagram of the nonlinear variable-scale duffin detection system at 0.875; FIG. 3(d) is ω1A bifurcation diagram of the nonlinear variable-scale duffin detection system at 0.9.
Fig. 4 is a bifurcation diagram of the nonlinear variable-scale duffin detection system when r is 0.03 and σ is 0.01 in the variable-scale convex peak method-based weak fault signal detection method provided by the present invention; FIG. 4(a) is ω1A bifurcation diagram of the nonlinear variable-scale duffin detection system at 0.8; FIG. 4(b) is ω1A bifurcation diagram of the non-linear, variable-scale duffin detection system at 0.835; FIG. 4(c) is ω1A bifurcation diagram of the nonlinear variable-scale duffin detection system at 0.875; FIG. 4(d) is ω1A bifurcation diagram of the nonlinear variable-scale duffin detection system at 0.9.
Fig. 5 shows a method for detecting a weak fault signal based on a variable-scale peak method, where r is 0.02, ω is1A bifurcation diagram of the nonlinear variable-scale duffin detection system when 0.8; fig. 5(a) is a bifurcation diagram of the non-linear variable-scale duffin detection system when σ is 0.0001; fig. 5(b) is a bifurcation diagram of the non-linear scale-varying duffin detection system when σ is 0.001; fig. 5(c) is a bifurcation diagram of the non-linear scale-varying duffin detection system when σ is 0.01; fig. 5(d) is a bifurcation diagram of the nonlinear variable-scale duffin detection system when σ is 0.1.
Description of the drawings: 201-an input signal acquisition module, 202-a to-be-detected signal determination module, 203-a nonlinear variable-scale duffin system construction module, 204-a nonlinear variable-scale duffin detection system construction module, 205-a first judgment module, 206-an alarm module, 207-a second judgment module, 208-an optimization module and 209-a device fault-free determination module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a fault weak signal detection method and system based on a variable-scale convex peak method, and aims to solve the problems of low efficiency and low accuracy of fault weak signal detection.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a method for detecting a weak fault signal based on a variable scale peak method, as shown in fig. 1, the method for detecting a weak fault signal based on a variable scale peak method includes:
s101, acquiring an input signal; the input signals comprise original weak fault signals, noise signals and other signals sent by equipment during operation.
And S102, determining a signal to be detected according to the input signal, wherein the signal to be detected comprises a fault weak signal and a noise signal.
S103, constructing a nonlinear variable-scale duffin system according to the signal to be detected.
And S104, constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system.
S105, judging whether the nonlinear variable-scale duffin detection system has state transition or not, and determining a first judgment result; the state transition is that the nonlinear variable-scale duffing detection system changes from a chaotic state to a periodic state and then to the chaotic state; the state transitions are determined by the convex peak phenomenon in the bifurcation diagram.
S106, if the first judgment result shows that the nonlinear variable-scale duffin detection system is in state transition, detecting a weak fault signal, determining that the equipment is in fault, and sending an alarm signal.
S107, if the first judgment result shows that the nonlinear variable-scale duffin detection system does not have state transition, judging whether the parameter in the nonlinear variable-scale duffin detection system exceeds a set range, and obtaining a second judgment result.
And S108, if the second judgment result shows that the parameters in the nonlinear variable-scale duffing detection system do not exceed the set range, optimizing the nonlinear variable-scale duffing detection system, and returning to S104.
And S109, if the second judgment result shows that the parameters in the nonlinear variable-scale duffing detection system exceed the set range, determining that no fault weak signal is detected, and determining that the equipment has no fault.
The input signals comprise all signals sent out when the equipment works, namely original fault weak signals, noise signals and other signals sent out when the equipment works;
the determining a signal to be detected according to the input signal specifically includes:
carrying out filtering pretreatment on the input signal to obtain a pretreated input signal; the filtering pretreatment is to adopt a filtering technology to carry out preliminary filtering and eliminate high-frequency noise signals sent out by equipment during working in the input signals;
and determining the signal to be detected according to the preprocessed input signal.
The filtering technology is software and hardware filtering technology, and can eliminate interference signals to the greatest extent and improve the effectiveness of the signals to be detected.
The constructing of the nonlinear variable-scale duffin system according to the signal to be detected specifically comprises the following steps:
determining the damping ratio and the driving force amplitude of a nonlinear variable-scale duffin system according to the signal to be detected;
determining the nonlinear variable-scale duffing system according to the damping ratio and the driving force amplitude of the nonlinear variable-scale duffing system;
the nonlinear variable-scale duffin system comprises the following components:
wherein, mu*To the damping ratio, f*cos ω t is the driving force term, f*Driving force amplitude is adopted, and omega is driving force frequency; x is displacement and y is velocity.
In a specific embodiment, after the signal to be detected is determined, the damping ratio and the driving force amplitude of the nonlinear variable-scale duffin system are determined according to the digital characteristics of the signal to be detected, and then the nonlinear variable-scale duffin system is constructed according to the damping ratio and the driving force amplitude of the nonlinear variable-scale duffin system.
The digital characteristics of the signal to be detected are the amplitude, the frequency, the noise intensity and the like of the fault weak signal.
In practical application, determining the digital characteristics of a signal to be detected; then according to the digital characteristics of the signal to be detected, the damping ratio mu of the nonlinear variable-scale duffin system is determined by utilizing a random Mel-Nikov method and a continuous function mesopic principle*And driving force amplitude f*And constructing the nonlinear variable-scale duffin system:
constructing the nonlinear variable-scale duffin detection system:
wherein σ is the intensity of white Gaussian noise σ n (t), and the power spectral density of white Gaussian noise is
rcosω1t is the weak signal of fault, r is the amplitude of the weak signal of fault, omega1For faulty weak signal frequencies, rcos omega1t + σ n (t) is a signal to be detected.
After the nonlinear variable-scale duffin detection system is constructed according to the signal to be detected and the nonlinear variable-scale duffin system, the method further comprises the following steps:
determining a phase diagram and a bifurcation diagram according to the nonlinear variable-scale duffing detection system;
determining a chaos threshold value of a nonlinear variable-scale duffin detection system by using a random Mel-Ni-kov method;
and optimizing the nonlinear variable-scale duffin detection system by using the chaos threshold.
In practical application, a random Merrill Nikov method is utilized to determine the chaos threshold of the nonlinear variable-scale duffin detection system.
Drawing a bifurcation diagram by using software, and identifying the frequency of a signal to be detected if a peak phenomenon occurs; if the convex peak does not appear and the parameters of the detection system do not exceed the preset range, the parameters of the nonlinear variable-scale duffin detection system are further optimized by utilizing the random Mel-Ni-kov method and the mesopic principle of the continuous function again according to the result of the bifurcation diagram.
The formula for the random melnicov method is as follows:
using formulas
Determining a chaos threshold; mu is damping ratio, f is driving force amplitude, omega is driving force frequency, sigma is intensity of Gaussian white noise sigma n (t), K is power spectrum density of Gaussian white noise,
r is the amplitude of the weak signal of the fault, omega1For a faulty weak signal frequency, t0Is the current time.
The step of specifically determining the threshold comprises:
(1) let omega be1When the measured value is omega, the chaos threshold value of the nonlinear variable-scale duffin detection system is as follows:
(2) let omega be1When not equal to omega, let sin omega1t0＝sin(ω+Δω)t0. From said non-linear variable-scale duffing test
The chaos threshold of the measuring system should satisfy:
wherein
Wherein the content of the first and second substances,
it is clear that,
in summary, when ω ≠ ω1And then, the chaos threshold value of the nonlinear variable-scale duffin detection system is as follows:
in practical application, the specific steps of optimizing the nonlinear variable-scale duffin detection system by using the chaos threshold value are as follows:
when omega > 0, let
Obviously, f (ω) ∈ C [ ω'01,ω″01]，
0＜ω′01＜ω1＜ω″01. And choosing the appropriate combination of parameters f (ω) is not a constant function. That is, | fm-f0|＞0,
Therefore, according to [ omega ]0′1,ω0″1]The theorem of mesology of arbitrary and continuous functions of (1) is that ω 'is always present'1∈[ω′01,ω″01]So that
fm＜f*＝f(ω′1)＜f0Or f0＜f*＝f(ω′1)＜fm.
(1) When f (ω) ∈ C [ ω'01,ω″01]When not a monotonic function: take f to f*If f ism＜f*＝f(ω′1)＜f0The nonlinear variable-scale duffing detection system enters chaotic motion (omega epsilon [ omega'01,ω′1-δ]) To periodic motion (ω ∈ [ ω'1-δ,ω1)∪ω1∪(ω1,ω′1+ delta)) to chaos (ω ∈ [ ω'1+δ,ω″01]) Moving; if f0＜f*＝f(ω′1)＜fmThe nonlinear variable-scale duffing detection system enters a period (omega epsilon [ omega'01,ω′1-δ]) Motion to chaotic motion (ω ∈ [ ω'1-δ,ω1)∪ω1∪(ω1,ω′1+ δ)) to periodic motion (ω ∈ [ ω'1+δ,ω″01]). Can utilize the nonlinear variable-scale duffin detection systemThe transition of the movement identifies the frequency of the faulty weak signal.
(2) When f (ω) ∈ C [ ω'01,ω″01]When it is a monotonic function: take f*＝f(ω′1)＝f0Or f*＝f(ω′1)＝fmThe nonlinear variable-scale duffing detection system enters into chaotic-periodic motion or periodic-chaotic motion. The frequency of the fault weak signal can still be identified by the transition of the nonlinear variable-scale duffing detection system motion.
In fact, in engineering practice, noise is unavoidable, the noise immunity of the nonlinear variable-scale duffin detection system is good, and the chaos state of the nonlinear variable-scale duffin detection system is changed to the periodic state and then to the chaos state, so that the actual working condition is facilitated. Furthermore, according to the necessary insufficiency of the chaos threshold, in practical application, numerical simulation can be used to find out the proper delta f > 0 and delta f' > 0, so that f is greater than 0m＜f*＝f(ω′1)＜f0+ Δ f or fm+Δf′＜f*＝f(ω′1)＜f0+ delta f, the nonlinear variable-scale duffing detection system still has a chaotic state, a periodic state and a chaotic state, and f is determined*Range of (1) and μ*The value of (a).
And determining the optimal range of the driving force amplitude, and accurately determining the state transition of the nonlinear variable-scale duffin detection system.
In practical application, the inputting the signal to be detected into the nonlinear variable-scale duffin detection system, judging whether the system has a state transition, and before determining a judgment result, further comprising:
constructing a bifurcation diagram under the change of the driving force frequency by using MATLAB, Simulink, Multisim, LabVIEW or C language;
after determining that the equipment has a fault and sending an alarm signal, the method further comprises the following steps:
and determining the frequency of the fault weak signal according to the peak phenomenon of the bifurcation diagram under the change of the driving force frequency.
Determining the fault position of the equipment according to the frequency of the fault weak signal; and repairing the fault position of the equipment.
The fault weak signal detection method based on the variable-scale convex peak method can accurately identify the fault weak signal under the background of strong noise. And the method has the advantages of visibility, easier identification, higher reliability and wider application range, and provides a better and feasible new method for practical engineering.
Fig. 2 is a structural diagram of a fault weak signal detection system based on a variable scale convex peak method provided by the invention. As shown in fig. 2, a system for detecting a weak fault signal based on a variable scale peak method includes: the device comprises an input signal acquisition module 201, a to-be-detected signal determination module 202, a nonlinear variable-scale duffin system construction module 203, a nonlinear variable-scale duffin detection system construction module 204, a first judgment module 205, an alarm module 206, a second judgment module 207, an optimization module 208 and a device failure-free determination module 209.
The input signal acquiring module 201 is configured to acquire an input signal; the input signals comprise original fault weak signals, noise signals and other signals sent out when the equipment works;
the to-be-detected signal determining module 202 is configured to determine a to-be-detected signal according to the input signal, where the to-be-detected signal includes a weak fault signal and a noise signal;
the nonlinear variable-scale duffin system construction module 203 is used for constructing a nonlinear variable-scale duffin system according to the signal to be detected;
the nonlinear variable-scale duffin detection system construction module 204 is used for constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
the first judging module 205 is configured to judge whether the nonlinear variable-scale duffin detection system undergoes state transition, and determine a first judgment result; the state transition is that the nonlinear variable-scale duffing detection system changes from a chaotic state to a periodic state and then to the chaotic state; the state transitions are determined by a hump phenomenon in a bifurcation diagram;
the alarm module 206 is configured to detect a weak fault signal if the first determination result indicates that the nonlinear variable-scale duffin detection system is in a state transition state, determine that the device is in a fault, and send an alarm signal;
the second judging module 207 is configured to judge whether the parameter in the nonlinear variable-scale duffin detection system exceeds a set range if the first judging result indicates that the nonlinear variable-scale duffin detection system does not have a state transition, and obtain a second judging result;
the optimization module 208 is configured to optimize the nonlinear variable-scale duffin detection system if the second determination result indicates that the parameter in the nonlinear variable-scale duffin detection system does not exceed the set range, and return to the step of constructing the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
the device failure-free determining module 209 is configured to determine that a failure weak signal is not detected and determine that the device fails if the second determination result indicates that the parameter in the nonlinear variable-scale duffing detection system exceeds a set range.
The determining module 202 according to the to-be-detected signal specifically includes: the device comprises a preprocessing unit and a to-be-detected signal determining unit.
The preprocessing unit is used for carrying out filtering preprocessing on the input signal to obtain a preprocessed input signal; the filtering pretreatment is to adopt a filtering technology to carry out preliminary filtering and eliminate high-frequency noise signals sent out by equipment during working in the input signals;
and the to-be-detected signal determining unit is used for determining the to-be-detected signal according to the preprocessed input signal.
In a specific application, the signal to be detected is input into the nonlinear variable-scale duffin detection system, a bifurcation diagram is constructed by software, and whether a convex peak phenomenon appears in the bifurcation diagram is observed. If the peak phenomenon occurs, identifying the frequency of the signal to be detected, judging the fault position of the equipment, and calling a fault alarm output module to output a fault prompt; if the peak phenomenon does not occur and the detection system parameters do not exceed the preset range, calling a parameter optimization module according to the result of the bifurcation diagram, and further optimizing the parameters of the nonlinear variable-scale duffin detection system by utilizing the random Mel-Ni-kov method and the mesopic principle of the continuous function again. And inputting the signal to be detected into the optimized nonlinear variable-scale duffing detection system again, and drawing a bifurcation diagram to observe whether the peak phenomenon occurs. The above steps can be repeated a plurality of times. In the period, if the peak phenomenon occurs, the frequency of the signal to be detected is identified, so that the fault position of the equipment can be judged, and a fault alarm output module is called to output a fault prompt; if the peak phenomenon does not occur and the detection system parameter exceeds the preset range, determining that the equipment is possible to have no fault, calling a fault-free confirmation output module, and outputting a fault-free prompt.
In one implementation, let σ be 0.01 in a non-linear, variable-scale duffing detection system,
μ＝0.5，ω1when the value is 0.8, f0＝f(0.8)＝0.3362。
Let r be 0.02. After multiple numerical simulations, if Δ f is selected to be 0.49, f is0+ Δ f is 0.8262. Let omega equal to 0.2 to obtain fm＝0.7434。
In order to realize the state transition of the nonlinear variable-scale duffin detection system, the amplitude of the reference signal needs to satisfy fm＜f＜f0+ Δ f; so, f can be equal to 0.815, and the bifurcation diagram is shown in fig. 3 (a); it is clear that ω is ω ═ ω1There is a peak at 0.8. The hump phenomenon indicates that the number of maximum values of x is limited, i.e. at ω ═ ω1When the value is equal to 0.8, the nonlinear variable-scale duffin detection system of the system is in periodic motion; when omega is not equal to omega1When the number of the maximum values of x is 0.8, the number of the maximum values of x is infinite, namely the nonlinear variable-scale duffin detection system is in a chaotic state; therefore, the frequency omega of the harmonic signal is transferred from the period of the nonlinear scale-variable duffing detection system to the chaotic state10.8 is detected.
ω1∈(0.8,0.9]Bifurcation diagram of time-nonlinear scale-variable duffin detection systemSee fig. 3(b) - (d).
When r is 0.03, the bifurcation diagram of the nonlinear scale-variable duffin detection system is shown in fig. 4(a) - (d).
When sigma is equal to [0.0001,0.1 ]]The time-varying scale convex peak method is still effective, and the convex peak phenomenon can be used for detecting the frequency omega1See fig. 5(a) - (d).
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A fault weak signal detection method based on a variable-scale convex peak method is characterized by comprising the following steps:
acquiring an input signal; the input signals comprise original fault weak signals, noise signals and other signals sent out when the equipment works;
determining a signal to be detected according to the input signal; the signals to be detected comprise fault weak signals and noise signals;
constructing a nonlinear variable-scale duffin system according to the signal to be detected;
constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
judging whether the nonlinear variable-scale duffin detection system has state transition or not, and determining a first judgment result; the state transition is that the nonlinear variable-scale duffing detection system changes from a chaotic state to a periodic state and then to the chaotic state; the state transitions are determined by a hump phenomenon in a bifurcation diagram;
if the first judgment result indicates that the nonlinear variable-scale duffing detection system has state transition, detecting a weak fault signal, determining that the equipment has a fault, and sending an alarm signal;
if the first judgment result indicates that the nonlinear variable-scale duffin detection system does not have state transition, judging whether the parameter in the nonlinear variable-scale duffin detection system exceeds a set range or not to obtain a second judgment result;
if the second judgment result shows that the parameters in the nonlinear variable-scale duffin detection system do not exceed the set range, optimizing the nonlinear variable-scale duffin detection system, and returning to the step of constructing the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
if the second judgment result shows that the parameters in the nonlinear variable-scale duffing detection system exceed the set range, determining that no fault weak signal is detected, and determining that the equipment has no fault;
the constructing of the nonlinear variable-scale duffin system according to the signal to be detected specifically comprises:
determining the damping ratio and the driving force amplitude of the nonlinear variable-scale duffin system according to the signal to be detected;
determining the nonlinear variable-scale duffing system according to the damping ratio and the driving force amplitude of the nonlinear variable-scale duffing system;
the nonlinear variable-scale duffin system comprises the following components:
wherein, mu*To the damping ratio, f*cos ω t is the driving force term, f*Driving force amplitude is adopted, and omega is driving force frequency; x is displacement and y is velocity;
the constructing of the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system specifically comprises:
constructing the nonlinear variable-scale duffin detection system:
wherein σ is the intensity of white Gaussian noise σ n (t), and the power spectral density of white Gaussian noise is
rcosω1t is the weak signal of fault, r is the amplitude of the weak signal of fault, omega1For faulty weak signal frequencies, rcos omega1t + σ n (t) is a signal to be detected.
2. The method for detecting the weak fault signal based on the variable-scale peak method according to claim 1, wherein the determining the signal to be detected according to the input signal specifically includes:
carrying out filtering pretreatment on the input signal to obtain a pretreated input signal; the filtering pretreatment is to adopt a filtering technology to carry out preliminary filtering and eliminate high-frequency noise signals sent out by equipment during working in the input signals;
and determining the signal to be detected according to the preprocessed input signal.
3. The method for detecting the fault weak signal based on the variable-scale convex peak method according to claim 1, wherein after the nonlinear variable-scale duffing detection system is constructed according to the signal to be detected and the nonlinear variable-scale duffing system, the method further comprises the following steps:
determining a phase diagram and a bifurcation diagram according to the nonlinear variable-scale duffing detection system;
determining a chaos threshold value of a nonlinear variable-scale duffin detection system by using a random Mel-Ni-kov method;
and optimizing the nonlinear variable-scale duffin detection system by using the chaos threshold.
4. The method for detecting the fault weak signal based on the variable-scale convex peak method according to claim 3, wherein the determining the chaos threshold of the nonlinear variable-scale duffin detection system by using the random mellnikov method specifically comprises:
using formulas
Determining a chaos threshold; wherein mu is a damping ratio, f is a driving force amplitude, omega is a driving force frequency, sigma is the intensity of Gaussian white noise sigma n (t), K is the power spectral density of the Gaussian white noise,
r is the amplitude of the weak signal of the fault, omega1For a faulty weak signal frequency, t0Is the current time.
5. The method for detecting the weak fault signal based on the variable-scale convex peak method according to claim 1, wherein before determining the first determination result, the method for determining whether the nonlinear variable-scale duffin detection system has a state transition further comprises:
and visualizing the state of the nonlinear variable-scale duffing detection system by using MATLAB, Simulink, Multisim, LabVIEW or C language, namely constructing a bifurcation diagram.
6. The method according to claim 1, wherein if the first determination result indicates that the nonlinear variable-scale duffing detection system is in a state transition state, a weak fault signal is detected, the device is determined to be in a fault, and an alarm signal is sent out, further comprising:
determining the frequency of the fault weak signal according to a peak phenomenon of a bifurcation diagram under the change of the driving force frequency;
determining the fault position of the equipment according to the frequency of the fault weak signal; and repairing the fault position of the equipment.
7. A fault weak signal detection system based on a variable-scale convex peak method is characterized by comprising the following steps:
the input signal acquisition module is used for acquiring an input signal; the input signals comprise original fault weak signals, noise signals and other signals sent out when the equipment works;
the to-be-detected signal determining module is used for determining to-be-detected signals according to the input signals, wherein the to-be-detected signals comprise fault weak signals and noise signals;
the nonlinear variable-scale duffin system construction module is used for constructing a nonlinear variable-scale duffin system according to the signal to be detected;
the nonlinear variable-scale duffin detection system construction module is used for constructing a nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system;
the first judgment module is used for judging whether the nonlinear variable-scale duffin detection system has state transition or not and determining a first judgment result; the state transition is that the nonlinear variable-scale duffing detection system changes from a chaotic state to a periodic state and then to the chaotic state; the state transitions are determined by a hump phenomenon in a bifurcation diagram;
the alarm module is used for detecting a weak fault signal if the first judgment result indicates that the nonlinear variable-scale duffin detection system is in state transition, determining that the equipment is in fault and sending an alarm signal;
the second judgment module is used for judging whether the parameter in the nonlinear variable-scale duffin detection system exceeds a set range or not if the first judgment result shows that the nonlinear variable-scale duffin detection system does not have state transition, so as to obtain a second judgment result;
the optimization module is used for optimizing the nonlinear variable-scale duffin detection system and returning to the step of constructing the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system if the second judgment result shows that the parameter in the nonlinear variable-scale duffin detection system does not exceed the set range;
the device failure-free determining module is used for determining that a failure weak signal is not detected and determining that the device fails if the second judgment result indicates that the parameters in the nonlinear variable-scale duffin detection system exceed a set range;
the constructing of the nonlinear variable-scale duffin system according to the signal to be detected specifically comprises:
determining the damping ratio and the driving force amplitude of the nonlinear variable-scale duffin system according to the signal to be detected;
determining the nonlinear variable-scale duffing system according to the damping ratio and the driving force amplitude of the nonlinear variable-scale duffing system;
the nonlinear variable-scale duffin system comprises the following components:
wherein, mu*To the damping ratio, f*cos ω t is the driving force term, f*Driving force amplitude is adopted, and omega is driving force frequency; x is displacement and y is velocity;
the constructing of the nonlinear variable-scale duffin detection system according to the signal to be detected and the nonlinear variable-scale duffin system specifically comprises:
constructing the nonlinear variable-scale duffin detection system:
wherein σ is the intensity of white Gaussian noise σ n (t),power spectral density of white gaussian noise of
r cosω1t is the weak signal of fault, r is the amplitude of the weak signal of fault, omega1For a faulty weak signal frequency, rcos omega1t + σ n (t) is a signal to be detected.
8. The system according to claim 7, wherein the module is determined according to the signal to be detected, and specifically includes:
the preprocessing unit is used for carrying out filtering preprocessing on the input signal to obtain a preprocessed input signal; the filtering pretreatment is to adopt a filtering technology to carry out preliminary filtering and eliminate high-frequency noise signals sent out by equipment during working in the input signals;
and the to-be-detected signal determining unit is used for determining the to-be-detected signal according to the preprocessed input signal.
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