CN-122017027-A - Cage guide weak defect detection method, inspection system, storage medium and equipment

CN122017027ACN 122017027 ACN122017027 ACN 122017027ACN-122017027-A

Abstract

The invention discloses a method for detecting weak defects of a cage guide, a patrol system, a storage medium and equipment, belonging to the field of safety monitoring of a mine deep well lifting system; the method is used for constructing a second-order underdamped tristable stochastic resonance system model based on variable-scale frequency shift aiming at the physical problem that a high-frequency transient echo signal generated by active excitation is difficult to meet stochastic resonance adiabatic approximation conditions, performing time domain compression and frequency domain rescale on an original signal by introducing a frequency scale conversion factor, converting high-frequency impact characteristics to a low-frequency resonance region, simultaneously introducing a second-order inertia term and nonlinear damping, constructing a dynamics system with band-pass filtering characteristics, adopting an improved self-adaptive particle swarm algorithm to synchronously optimizing scale factors, damping coefficients and potential well parameters, and focusing broadband noise energy on defect characteristic frequencies by utilizing the variable-scale stochastic resonance effect, so that high-sensitivity detection of early defects such as microcracks, bolt looseness and the like is realized under extremely low signal-to-noise ratio.

Inventors

HUANG JIAHAI
HUANG CHENHUI
LI XIAODONG
ZHANG YIN
WANG ZHAOGUO
JIN HUAWEI

Assignees

安徽理工大学

Dates

Publication Date: 20260512
Application Date: 20260312

Claims (9)

1. The method for detecting the weak defect of the cage guide is characterized by comprising the following steps of: S1, actively and transiently knocking a cage guide, and collecting an original composite signal on the surface of the cage guide, wherein the original composite signal comprises a weak defect characteristic signal and underground strong background noise; S2, receiving an original composite signal, and constructing a second-order underdamping tristable stochastic resonance system model based on variable-scale frequency shift, wherein the model introduces a second-order inertia item and a frequency scale transformation mechanism and is used for matching a high-frequency transient echo signal generated by active knocking; S3, based on the characteristics of the current acquired signals, adopting an optimization algorithm to adaptively adjust system parameters and damping factors of the second-order underdamping model, inputting an original composite signal into the second-order model with optimized parameters, and utilizing an inertial stochastic resonance mechanism to enable the background noise energy and the defect impact signal to generate cooperative resonance so as to output a signal with obviously enhanced signal-to-noise ratio; S4, extracting defect characteristics from the enhanced signals, and judging the existence and the position of the defect by combining the positioning information.
2. The method for detecting weak defects of a cage guide according to claim 1, wherein the dynamic equation of the variable-scale second-order tristable stochastic resonance system in the step S2 is as follows: Wherein, the For the transformed time scale, satisfy , Is a frequency scale conversion factor; is output by the system; as a non-linear damping term, As a result of the basic damping coefficient, Is a nonlinear regulatory factor; is a tristable potential well structural parameter; And The defect characteristic signal and the background noise after time scale compression are respectively obtained.
3. A weak defect detection method of cage guide according to claim 1 or 2, wherein the optimization algorithm is an adaptive particle swarm optimization algorithm, which uses the signal-to-noise ratio of the output signal of the tristable stochastic resonance system as a fitness function, and iteratively searches for a system parameter combination which maximizes the signal-to-noise ratio.
4. A method for detecting weak defects of a cage guide according to claim 3, wherein the execution of the adaptive particle swarm optimization algorithm comprises the following sub-steps: s3.1, setting the particle swarm size M and the maximum iteration number T, and randomly initializing a position vector Xi and a speed vector Vi of each particle, wherein the position vector represents a group of system control parameters Wherein As a function of the frequency-scale conversion factor, As a parameter of the damping it is possible to provide, Is a potential well parameter; S3.2 for each particle, substituting the parameter represented by the particle into a variable-scale second-order tristable stochastic resonance system, and introducing time-scale transformation Inputting the original mixed signal and calculating an output signal The weighted kurtosis of the particle is multiplied by the local signal to noise ratio and used as the fitness value of the particle; S3.3 recording the historic optimal position of each particle Historical optimal position of whole particle swarm ; S3.4, according to the current iteration times t, the inertia weight w (t), the individual learning factors c1 (t) and the social learning factors c2 (t) are adjusted in a nonlinear mode; s3.5, updating the speed and the position of each particle according to a formula; ; wherein r1, r2 is a random number in the [0,1] interval; s3.6, iterating and outputting, namely repeating the steps S3.2 to S3.5 until the termination condition is met, and outputting the global optimal position The corresponding parameters are used as the optimal parameter combination of the second-order tristable system.
5. A weak defect detection method of cage guide according to claim 4, wherein in the step S3.4, the inertia weight w (t) is decreased from the initial value to the final value by a nonlinear decreasing strategy with increasing iteration times, and the learning factor is satisfied by c1 (t) > c2 (t) in the initial stage of iteration to enhance global searching capability, and c1 (t) < c2 (t) in the later stage of iteration to accelerate convergence.
6. A weak defect detection method of a cage guide according to claim 1, wherein in the step S5, multi-source information fusion judgment is carried out, and when a characteristic peak exceeding a threshold value appears in the enhanced vibration signal, and abnormal audio frequency spectrum characteristics are synchronously detected by a sound sensor, and/or abnormal texture of the surface of the cage guide is captured by a visual sensor, the detection is comprehensively judged to be a confident defect.
7. A weak defect inspection system for a cage guide, which is characterized in that the inspection is performed by adopting the detection method as set forth in any one of claims 1 to 6, and the inspection system comprises; the inspection trolley unit is used for performing inspection movement along the cage guide; the active excitation unit is used for performing active transient knocking on the cage guide; and the positioning unit is used for measuring the distance of the inspection movement of the inspection trolley unit.
8. A computer readable storage medium storing a computer program, which when executed by a processor causes the processor to perform the steps of the method according to any one of claims 1 to 6.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the computer program, when executed by the processor, causes the processor to perform the steps of the method according to any of claims 1 to 6.

Description

Cage guide weak defect detection method, inspection system, storage medium and equipment Technical Field The invention relates to the technical field of safety monitoring of mine deep well lifting systems, in particular to a method for detecting weak defects of a cage guide, a patrol system, a storage medium and equipment. Background Along with the increase of deep resource exploitation intensity, the mine hoist is developed to high speed, heavy load and deep well, and the rigid cage guide is used as a life line of a hoisting system, so that early defects such as micro cracks, bolt looseness, abnormal gaps and the like are extremely easy to occur. The existing detection mode has obvious defects: Underground coal mine environments are severe, and cage operation is accompanied by strong pneumatic noise, steel wire rope vibration and mechanical friction (strong background noise). The traditional passive detection or simple active knocking method has extremely low energy of echo signals of early weak defects (such as 0.1 mm-level microcracks and deep bolt looseness) excited by the traditional passive detection or simple active knocking method, and is extremely easy to be submerged by background noise (low signal to noise ratio). The existing linear filtering denoising technology (such as wavelet transformation and low-pass filtering) usually filters weak defect characteristic signals overlapped with noise frequency bands together while filtering environmental noise, so that denoising, namely distortion, is caused, and early hidden danger is caused. Therefore, it is desirable to provide a method and a system for detecting weak defects of a cage guide, which are used for solving the above problems. Disclosure of Invention The invention mainly aims to provide a method for detecting weak defects of a cage guide, a patrol system, a storage medium and equipment, and aims to solve the existing technical problems. In order to achieve the above object, the present invention provides a method for detecting weak defects of a cage guide, comprising: S1, actively and transiently knocking a cage guide, and collecting an original composite signal on the surface of the cage guide, wherein the original composite signal comprises a weak defect characteristic signal and underground strong background noise; S2, receiving an original composite signal, and constructing a second-order underdamping tristable stochastic resonance system model based on variable-scale frequency shift, wherein the model introduces a second-order inertia item and a frequency scale transformation mechanism and is used for matching a high-frequency transient echo signal generated by active knocking; S3, based on the characteristics of the current acquired signals, adopting an optimization algorithm to adaptively adjust system parameters and damping factors of the second-order underdamping model, inputting an original composite signal into the second-order model with optimized parameters, and utilizing an inertial stochastic resonance mechanism to enable the background noise energy and the defect impact signal to generate cooperative resonance so as to output a signal with obviously enhanced signal-to-noise ratio; S4, extracting defect characteristics from the enhanced signals, and judging the existence and the position of the defect by combining the positioning information. Further, the dynamic equation of the variable-scale second-order tristable stochastic resonance system in the step S2 is as follows: Wherein, the For the transformed time scale, satisfy,Is a frequency scale conversion factor; is output by the system; as a non-linear damping term, As a result of the basic damping coefficient,Is a nonlinear regulatory factor; is a tristable potential well structural parameter; And The defect characteristic signal and the background noise after time scale compression are respectively obtained. Further, the optimization algorithm is an adaptive particle swarm optimization algorithm, which takes the signal-to-noise ratio of the output signal of the tristable stochastic resonance system as a fitness function, and iteratively searches for a system parameter combination which maximizes the signal-to-noise ratio. Further, the execution of the adaptive particle swarm optimization algorithm comprises the following sub-steps: s3.1, setting the particle swarm size M and the maximum iteration number T, and randomly initializing a position vector Xi and a speed vector Vi of each particle, wherein the position vector represents a group of system control parameters WhereinAs a function of the frequency-scale conversion factor,As a parameter of the damping it is possible to provide,Is a potential well parameter; S3.2 for each particle, substituting the parameter represented by the particle into a variable-scale second-order tristable stochastic resonance system, and introducing time-scale transformation Inputting the original mixed signal and calculating an output signa