CN117886241B - Tower crane self-checking system based on vibration analysis - Google Patents
Tower crane self-checking system based on vibration analysis Download PDFInfo
- Publication number
- CN117886241B CN117886241B CN202410287993.9A CN202410287993A CN117886241B CN 117886241 B CN117886241 B CN 117886241B CN 202410287993 A CN202410287993 A CN 202410287993A CN 117886241 B CN117886241 B CN 117886241B
- Authority
- CN
- China
- Prior art keywords
- time
- vibration
- representing
- frequency
- spectral peak
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 59
- 230000005856 abnormality Effects 0.000 claims abstract description 32
- 238000000605 extraction Methods 0.000 claims abstract description 29
- 230000004044 response Effects 0.000 claims abstract description 27
- 238000001514 detection method Methods 0.000 claims abstract description 19
- 238000003745 diagnosis Methods 0.000 claims abstract description 8
- 230000003595 spectral effect Effects 0.000 claims description 76
- 230000006870 function Effects 0.000 claims description 50
- 238000001228 spectrum Methods 0.000 claims description 49
- 239000013598 vector Substances 0.000 claims description 49
- 238000000034 method Methods 0.000 claims description 32
- 238000005312 nonlinear dynamic Methods 0.000 claims description 26
- 230000007704 transition Effects 0.000 claims description 16
- 238000004088 simulation Methods 0.000 claims description 15
- 238000012546 transfer Methods 0.000 claims description 11
- 239000000284 extract Substances 0.000 claims description 10
- 238000010183 spectrum analysis Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 230000004069 differentiation Effects 0.000 claims description 7
- 238000005316 response function Methods 0.000 claims description 4
- 230000006399 behavior Effects 0.000 description 21
- 230000008569 process Effects 0.000 description 11
- 230000008859 change Effects 0.000 description 7
- 230000000739 chaotic effect Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 238000012937 correction Methods 0.000 description 5
- 230000007774 longterm Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009022 nonlinear effect Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000005291 chaos (dynamical) Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Abstract
The invention discloses a self-checking system of a tower crane based on vibration analysis, and relates to the technical field of tower cranes; comprising the following steps: the system comprises a data acquisition part, a characteristic extraction part, a dynamics analysis part and an abnormality detection and early warning part; the data acquisition part is used for acquiring vibration data of the tower crane and representing the vibration data as time sequence signals; the feature extraction part is used for extracting nonlinear features of the time sequence signals; the dynamics analysis part is used for reconstructing the time sequence signal into a state space form and simulating the vibration response of the tower crane; the abnormality detection and warning unit compares the simulated vibration response with the time-series signal difference to detect and diagnose the abnormality, and if the detected vibration response exceeds a set threshold value, the abnormality warning unit generates an abnormality warning. The invention improves the diagnosis accuracy and reliability of vibration abnormality, improves the safety and stability of crane equipment, reduces the cost of manpower and time and improves the working efficiency.
Description
Technical Field
The invention relates to the technical field of tower cranes, in particular to a tower crane self-checking system based on vibration analysis.
Background
The tower crane is widely applied to loading, unloading and carrying of cargoes, vibration is a common phenomenon in the operation process of the tower crane, and how to timely discover and accurately evaluate the vibration abnormality of the tower crane is important for improving the safety of equipment and guaranteeing the production efficiency. In the prior art, the traditional vibration detection method of the tower crane mainly depends on manual inspection and periodic maintenance, and along with the development of emerging technologies, a self-checking system of the tower crane based on vibration analysis has been proposed and applied to actual production. These systems typically include a data acquisition section, a feature extraction section, a dynamics analysis section, and an anomaly detection and pre-warning section. The data acquisition part is responsible for acquiring vibration data of the tower crane, the characteristic extraction part extracts characteristic parameters of vibration signals through high-order spectrum analysis on the vibration data, the dynamics analysis part carries out nonlinear dynamics simulation based on the characteristic parameters, and finally monitoring and early warning of vibration anomalies are realized through the anomaly detection and early warning part.
However, there are still some problems and limitations in the prior art. Firstly, the traditional vibration analysis method only considers the linear characteristic of a vibration signal, and is difficult to accurately capture the nonlinear behavior of the vibration of the tower crane, so that the diagnosis of vibration abnormality is not accurate enough; secondly, the accuracy and instantaneity of the existing method for anomaly detection and early warning are still to be improved, and the conditions of missing report and false report are often existed; moreover, the prior art has complex processing and analysis processes of vibration data of the tower crane, needs professional operation and interpretation, has higher cost and is not easy to popularize and apply.
Therefore, in view of the problems and limitations existing in the prior art, further research and development of a new vibration analysis method and a self-checking system are needed, and a more powerful guarantee is provided for the safe operation and production efficiency of tower crane equipment.
Disclosure of Invention
The invention aims to provide a self-checking system of a tower crane based on vibration analysis, which improves the diagnosis accuracy and reliability of vibration abnormality, improves the safety and stability of crane equipment, reduces the manpower and time cost, improves the working efficiency and the like, and has important significance for improving the operation management and maintenance of the crane equipment.
In order to solve the technical problems, the invention provides a tower crane self-checking system based on vibration analysis, which comprises: the system comprises a data acquisition part, a characteristic extraction part, a dynamics analysis part and an abnormality detection and early warning part; the data acquisition part is used for acquiring vibration data of the tower crane and representing the vibration data as time sequence signals; the characteristic extraction part is used for carrying out high-order spectrum analysis on the time sequence signal so as to capture the nonlinear characteristic of the time sequence signal, obtain a high-order spectrum, extract characteristic parameters from the high-order spectrum and extract nonlinear characteristics of the time sequence signal based on a nonlinear dynamics theory; the dynamics analysis part is used for reconstructing the time sequence signal into a state space form, setting a state vector and a state transition equation, setting a boundary condition, carrying out nonlinear dynamics simulation based on the characteristic parameter and the nonlinear characteristic, and simulating the vibration response of the tower crane according to the boundary condition and the initial state of the system; the abnormality detection and warning unit performs abnormality detection and diagnosis by comparing the difference between the simulated vibration response and the time-series signal, uses an abnormality metric to represent the degree of difference between the simulated vibration response and the time-series signal, sets a threshold value, compares the difference metric with the threshold value, and if the difference metric exceeds the set threshold value, sets an abnormality alarm.
Further, the characteristic parameters extracted from the high-order spectrum include: spectral peak frequency, spectral peak amplitude and spectral peak quality factor.
Further, the data acquisition unit includes: a sensor section and a data acquisition section; the sensor part comprises a plurality of sensors which are respectively arranged on a crane boom, a bracket, a landing leg, an antenna and a crane hook of the tower crane, and each sensor acquires vibration data in real time; the data acquisition section represents the acquired vibration data as a time-series signal, which is represented using the following formula:
；
Wherein, Representing time series signals,/>Time is; /(I)Is/>Amplitude of each vibration component; /(I)Is the firstThe frequency of the individual vibration components; /(I)Is/>The phase of each vibrating component; /(I)Is the number of frequency components contained in the time-series signal; /(I)Representing a noise error term.
Further, when the feature extraction part performs high-order spectrum analysis on the time-series signal to capture nonlinear characteristics of the time-series signal and obtain a high-order spectrum, the high-order spectrum density of the time-series signal is calculated by the following formula:
the expression is used as follows:
；
Wherein, Representing time series signals/>/>The order Gao Jiepu density reflects the time series signal over the frequency range/>To/>Correlation between frequency components within; /(I)Representing a time delay; /(I)Is a time period representing the observed duration of the time-series signal; /(I)Is the impulse response function of the filter; /(I)Representing/>, time-series signalSub-time differentiation; /(I)Representing a convolution operation; /(I)Is a complex exponential function, representing a frequency ofFor projecting the time-series signal into the frequency domain; and then calculating to obtain a high-order spectrum based on the calculated high-order spectrum density.
Further, the feature extraction unit calculates a higher order spectrum according to the following formula：
；
Wherein,Is selected/>Frequency components; in Gao Jiepu, the spectral peak, i.e. the frequency component with the highest amplitude, is found; obtaining a spectral peak frequency from a spectral peak, corresponding to a frequency component of a highest amplitude in the high-order spectrum, and obtaining a spectral peak amplitude from the spectral peak, corresponding to the highest amplitude in the high-order spectrum; definition of spectral peak quality factor is defined as the ratio of the full width at half maximum of the high order spectrum at the spectral peak frequency to the spectral peak frequency.
Further, the feature extraction unit extracts a nonlinear feature of the time-series signal based on a nonlinear dynamics theory, and the method includes: reconstructing the time sequence signal into tracks in a phase space by a delay coordinate method, and calculating an evolution function of Euclidean distance between adjacent tracks in the phase space along with time; obtaining Lyapunov indexes as nonlinear characteristics of time sequence signals by carrying out exponential fitting of primary items, secondary items and positive items on an evolution function in a phase space; time series signalIs reconstructed as a high-dimensional vector sequence:
，
Wherein the method comprises the steps of Is a delay parameter,/>Is the embedding dimension; evolution function/>The expression is used as follows:
；
Wherein, The time interval is a value of 1.
Further, the Lyapunov index is obtained by performing exponential fitting of the primary term, the secondary term and the positive option to the evolution function in the phase space using the following formula:
；
Wherein, Is Lyapunov index; /(I)The Euclidean distance is the initial moment; /(I)The value range is 0.3 to 0.6 for the quadratic coefficient; /(I)The positive option coefficient is a value ranging from 0.4 to 0.7.
Further, the dynamics analysis unit reconstructs a time-series signal into a state space form, and the established state vector is a high-dimensional vector sequence:
；
Then, a state transition equation is established by the following formula:
；
Wherein, For the state transfer function, the following formula is used for the representation:
；
Wherein, Representing state vector, sign/>Representing element-by-element multiplication; /(I)Representing taking a sine for each element in the state vector; /(I)Each element representing a state vector is multiplied separately, which is an element-by-element square; /(I)Representing the inverse of each element of the state vector; /(I)Representing state vectors and exponential functions/>The derivative of the element-wise product with respect to time.
Further, when the dynamics analysis unit performs nonlinear dynamics simulation based on the characteristic parameters and the nonlinear characteristics, the initial state is set as:
；
At the boundary condition of ; Wherein/>For the lower frequency of vibration,/>Is the upper limit frequency of vibration; nonlinear dynamics simulation is performed by using the following formula, and vibration response/>, of the tower crane is simulated：
；
Wherein,Is the spectral peak frequency; /(I)Is the peak amplitude of the spectrum; /(I)Is a spectral peak quality factor; /(I)Representation calculationIs a2 nd order Frobenius norm.
The self-checking system of the tower crane based on vibration analysis has the following beneficial effects: firstly, the invention introduces nonlinear dynamics theory, and more accurate and comprehensive analysis is carried out on the crane vibration signal. Compared with the traditional linear vibration analysis method, the nonlinear dynamics theory can better capture the nonlinear characteristics of vibration signals, so that the diagnosis accuracy and reliability of vibration abnormality are improved. The vibration signal is reconstructed into the track in the phase space, the evolution function of Euclidean distance between adjacent tracks in the phase space along with time is calculated, and the Lyapunov index is obtained through exponential fitting and is used as the nonlinear characteristic of the time sequence signal, so that the system can analyze the vibration signal more comprehensively and deeply, and the occurrence of vibration abnormality can be better identified and predicted. Secondly, the invention utilizes advanced data processing technology to analyze vibration signals more carefully and comprehensively. The feature extraction part captures nonlinear characteristics of the vibration signal through Gao Jiepu analysis, and extracts characteristic parameters such as spectral peak frequency, spectral peak amplitude, spectral peak quality factor and the like, so that frequency distribution and amplitude of the vibration signal are more accurately described, and important parameter support is provided for subsequent dynamic analysis. In addition, the dynamics analysis part reconstructs a time sequence signal by using a state space form, establishes a state transition equation, further analyzes the dynamic characteristics of the vibration signal by performing operations such as element-by-element multiplication, sine function operation, exponential function operation and the like on the state vector, and provides a more reliable basis for abnormality detection and early warning. And thirdly, the invention realizes real-time monitoring and early warning of vibration abnormality and improves the safety and stability of crane equipment. The abnormality detection and early warning part compares the simulated vibration response with the time series signal, uses the abnormality measurement to represent the degree of difference between the simulated vibration response and the time series signal, sets a threshold value, compares the difference measurement with the threshold value, and once the difference measurement exceeds the set threshold value, triggers an abnormality alarm, and timely finds and processes vibration abnormality, thereby effectively avoiding the risk of equipment damage and personnel injury.
Drawings
Fig. 1 is a schematic system structure diagram of a tower crane self-checking system based on vibration analysis according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention.
Example 1: referring to fig. 1, a tower crane self-test system based on vibration analysis, the system comprising: the system comprises a data acquisition part, a characteristic extraction part, a dynamics analysis part and an abnormality detection and early warning part; the data acquisition part is used for acquiring vibration data of the tower crane and representing the vibration data as time sequence signals; the characteristic extraction part is used for carrying out high-order spectrum analysis on the time sequence signal so as to capture the nonlinear characteristic of the time sequence signal, obtain a high-order spectrum, extract characteristic parameters from the high-order spectrum and extract nonlinear characteristics of the time sequence signal based on a nonlinear dynamics theory; the dynamics analysis part is used for reconstructing the time sequence signal into a state space form, setting a state vector and a state transition equation, setting a boundary condition, carrying out nonlinear dynamics simulation based on the characteristic parameter and the nonlinear characteristic, and simulating the vibration response of the tower crane according to the boundary condition and the initial state of the system; the abnormality detection and warning unit performs abnormality detection and diagnosis by comparing the difference between the simulated vibration response and the time-series signal, uses an abnormality metric to represent the degree of difference between the simulated vibration response and the time-series signal, sets a threshold value, compares the difference metric with the threshold value, and if the difference metric exceeds the set threshold value, sets an abnormality alarm.
Specifically, the data acquisition part acquires vibration data of the tower crane in real time through a sensor or other vibration monitoring equipment. The vibration data are vibration signals which are perceived by the sensor and generated in the running process of the tower crane, and the signals reflect the motion states and the working conditions of all parts of the tower crane. The vibration signal is typically recorded in time series, i.e. the vibration signal is sampled at successive points in time, forming a series of data points. The main function of the data acquisition part is to acquire vibration information of the tower crane and provide data support for subsequent feature extraction, dynamics analysis and anomaly detection. Specifically, the functions of the data acquisition unit include: by collecting vibration data, the vibration condition of each component of the tower crane can be known in real time, and the vibration condition comprises information such as amplitude, frequency and phase of vibration. The method is helpful for judging the running state and the working load of the tower crane, and timely finding out abnormal vibration. The collected vibration data may be recorded to form a historical data record. The data can be used for analyzing the long-term operation trend of the tower crane, finding potential fault signs and providing basis for equipment maintenance and optimization. The vibration data acquired by the data acquisition part is the basis of subsequent analysis, and particularly for the feature extraction part and the dynamics analysis part, the vibration data is the original data for performing feature analysis and simulation, and has important significance for monitoring and predicting the system.
The feature extraction section processes the vibration data using a high-order spectral analysis technique and a nonlinear dynamics theory to capture nonlinear characteristics and important features of the vibration signal. In this step, the vibration signal is first converted into a frequency domain representation, and then higher order spectral information of the vibration signal is obtained by a higher order spectral analysis technique. Gao Jiepu can provide more information than the traditional spectrum, and is particularly suitable for nonlinear systems. Then, based on nonlinear dynamics theory, the vibration signal is analyzed, and nonlinear characteristics, such as a phase space track, a nonlinear vibration mode and the like, of the vibration signal are extracted. The feature extraction part is used for extracting feature parameters with representativeness and distinguishing degree from complex vibration signals and describing vibration characteristics and running states of the tower crane. Through high-order spectrum analysis and nonlinear dynamics theory, the characteristic extraction part can more comprehensively analyze nonlinear characteristics of vibration signals, such as periodicity, chaos and the like, so that the vibration behavior of the tower crane can be more accurately described. During the feature extraction process, the system will extract a series of key feature parameters, such as resonance frequency, nonlinear amplitude, phase difference, etc., from Gao Jiepu and nonlinear characteristics. The characteristic parameters can reflect important information of the running state of the tower crane, and provide a data basis for subsequent dynamics analysis and anomaly detection. The vibration signal often contains a large amount of information, and the original vibration signal can be converted into a feature vector with lower dimensionality through the feature extraction part, so that the subsequent processing steps are simplified, and the analysis efficiency and accuracy are improved.
The principle of the dynamics analysis part is based on nonlinear dynamics theory and state space method. In this section, the vibration signal is converted into a state space form in which the state of the system is represented by a set of variables and the evolution of the system is described by a state transition equation. State space representation is a commonly used mathematical model describing the behavior of a dynamic system, which can more accurately reflect the dynamic characteristics of the system, and is particularly suitable for nonlinear systems. Specifically, the dynamics analysis section converts the time-series signal into a state vector series by performing state reconstruction on the vibration signal, and establishes a state transition equation. In establishing state transition equations, nonlinear characteristics of the system are typically considered, such as by describing the behavior of the system using nonlinear functions or nonlinear dynamic equations. In addition, boundary conditions are set to determine the evolution track of the system. The main function of the dynamics analysis part is to simulate the vibration response of the tower crane so as to understand the dynamic behavior and evaluate the running state of the tower crane. By establishing a state space model and a state transfer equation, the dynamic analysis part can dynamically simulate the vibration response of the tower crane. The simulation can simulate the vibration condition of the tower crane under different working conditions, including the change of the lifting load, the change of the working environment and the like, so that the operation characteristics of the tower crane are more comprehensively known. In the dynamics analysis process, the initial state and boundary conditions of the system need to be set, and the conditions can be determined according to the actual situation and the characteristics of the vibration signals. The setting of the boundary conditions has an important influence on the accuracy and reliability of dynamic simulation, so that reasonable selection is required according to actual conditions. By simulating the vibration response of the tower crane, the dynamics analysis part can analyze the evolution process of the system state, including the change rule of the system state, the characteristics of dynamic behaviors and the like. This helps to find potential problems and anomalies, providing basis for subsequent fault diagnosis and prediction.
Example 2: the characteristic parameters extracted from the high-order spectrum include: spectral peak frequency, spectral peak amplitude and spectral peak quality factor.
In particular, the spectral peak frequency is the frequency component in the higher order spectrum having the greatest energy or amplitude. In vibration analysis, the spectral peak frequency corresponds to the resonant frequency or main vibration frequency of the system, and is the frequency component of the system which is most obvious in vibration under a specific working condition. The extraction of the spectral peak frequency can help to determine the main frequency component of the vibration of the tower crane, so that the vibration characteristic of the tower crane can be known more accurately. The peak amplitude represents the amplitude or energy magnitude at the corresponding peak frequency in Gao Jiepu. It reflects the intensity or amplitude of the vibration signal at the spectral peak frequency, typically in relation to the vibration amplitude of the system. The amplitude of the spectrum peak can be extracted to help evaluate the intensity and energy distribution of the vibration of the tower crane, so as to analyze the vibration characteristics and the working state of the system. The spectral peak quality factor is a parameter describing the relationship between the spectral peak frequency and the frequency bandwidth of the spectral peak. It is the ratio of the frequency of the spectral peak to the half-width of the spectral peak, reflecting the sharpness and concentration of the spectral peak. In general, the larger the spectral peak quality factor, the sharper the spectral peak, the higher the frequency resolution, and the better the certainty of the system vibration frequency. Extracting the spectral peak quality factor helps to evaluate the frequency resolution of the vibration signal and the sharpness of the spectral peak, thereby analyzing the vibration characteristics of the system more accurately.
Example 3: the data acquisition unit includes: a sensor section and a data acquisition section; the sensor part comprises a plurality of sensors which are respectively arranged on a crane boom, a bracket, a landing leg, an antenna and a crane hook of the tower crane, and each sensor acquires vibration data in real time; the data acquisition section represents the acquired vibration data as a time-series signal, which is represented using the following formula:
；
Wherein, Representing time series signals,/>Time is; /(I)Is/>Amplitude of each vibration component; /(I)Is the firstThe frequency of the individual vibration components; /(I)Is/>The phase of each vibrating component; /(I)Is the number of frequency components contained in the time-series signal; /(I)Representing a noise error term.
Example 4: when the feature extraction part performs high-order spectrum analysis on the time series signal to capture nonlinear characteristics of the time series signal and obtain a high-order spectrum, the high-order spectrum density of the time series signal is calculated according to the following formula:
the expression is used as follows:
；
Wherein, Representing time series signals/>/>The order Gao Jiepu density reflects the time series signal over the frequency range/>To/>Correlation between frequency components within; /(I)Representing a time delay; /(I)Is a time period representing the observed duration of the time-series signal; /(I)Is the impulse response function of the filter; /(I)Representing/>, time-series signalSub-time differentiation; /(I)Representing a convolution operation; /(I)Is a complex exponential function, representing a frequency ofFor projecting the time-series signal into the frequency domain; and then calculating to obtain a high-order spectrum based on the calculated high-order spectrum density.
In particular, the method comprises the steps of,Representing time series signals/>/>Higher order spectral density. The Gao Jiepu density is a deeper analysis of the spectral distribution of the signal, and can reflect the correlation between the frequency components of the signal. By calculating Gao Jiepu density, it can be understood that the vibration signal is in the frequency range/>To/>The interaction between the frequency components in the signal, thereby revealing the nonlinear characteristics of the signal. /(I)Is a time delay parameter for controlling the length of the analysis window. By adjusting the time delay, the size of the analysis window can be changed, thereby affecting the accuracy of analysis of the frequency content of the signal. /(I)For a time period, the observed duration of the time series signal is indicated. /(I)The setting of (2) determines the length of the observation time window and thus affects the time range and resolution of the analysis. /(I)Is the impulse response function of the filter. The filter serves to pre-process the time series signal to remove noise or unwanted frequency components that may be present, thereby improving the accuracy of subsequent analysis.This part represents the/>, of the filtered signalA secondary time differentiation operation. The time differentiation can highlight the high-order dynamic characteristic of the signal, and the nonlinear characteristic of the signal with nonlinear characteristic can be better captured after a plurality of time differentiation. /(I)Is a complex exponential function, and is used for performing frequency domain modulation on signals. By frequency domain modulation, the signal can be converted from the time domain to the frequency domain, thereby realizing the analysis of the correlation between the frequency components of the signal.
Example 5: the feature extraction part calculates the higher order spectrum by the following formula：
；
Wherein,Is selected/>Frequency components; in Gao Jiepu, the spectral peak, i.e. the frequency component with the highest amplitude, is found; obtaining a spectral peak frequency from a spectral peak, corresponding to a frequency component of a highest amplitude in the high-order spectrum, and obtaining a spectral peak amplitude from the spectral peak, corresponding to the highest amplitude in the high-order spectrum; definition of spectral peak quality factor is defined as the ratio of the full width at half maximum of the high order spectrum at the spectral peak frequency to the spectral peak frequency.
Specifically, the core of the formula is the computation of a higher order spectrum that reflects the signal at the selected pointFrequency componentsCorrelation between the frequency components below. The specific calculation process involves operations such as integration, complex exponential function, differentiation, etc. First, by integrating the signal, gao Jiepu densities over a time range are obtained, representing the correlation of the signal over the frequency range. Then, the Gao Jiepu densities are projected into the frequency domain using complex exponential functions for frequency analysis. Next, the projected higher order spectral density is/>A sub-time differential operation to extract the higher order dynamics of the signal. And finally, carrying out integral solution on the obtained result to obtain a final high-order spectrum. This series of operations enables the higher order spectrum to more fully reflect the frequency characteristics and nonlinear characteristics of the signal. After obtaining the higher order spectrum, the peak, i.e. the frequency component with the highest amplitude, needs to be found. This is achieved by searching for and comparing magnitudes in the higher order spectrum. The spectral peaks correspond to the dominant frequency components in the signal, so finding the spectral peaks helps to determine the dominant frequency characteristics of the signal. Once the spectral peak is found, the spectral peak frequency and amplitude can be obtained therefrom. The spectral peak frequency is the frequency value corresponding to the frequency component with the highest amplitude in the higher order spectrum, typically corresponding to the dominant frequency component of the signal. The peak amplitude is the maximum amplitude of the higher order spectrum at the peak of the spectrum, reflecting the energy or intensity of the signal at that frequency. The spectral peak quality factor is the ratio of the full width at half maximum of the high order spectrum at the spectral peak frequency to the spectral peak frequency. It is an important parameter for measuring the shape of the spectrum peak, and can reflect the concentration degree and frequency resolution of the frequency components of the signal. The larger the spectral peak quality factor, the sharper and more concentrated the spectral peak, and the higher the frequency resolution.
Example 6: the method for extracting the nonlinear characteristics of the time series signal by the characteristic extraction part based on the nonlinear dynamics theory comprises the following steps: reconstructing the time sequence signal into tracks in a phase space by a delay coordinate method, and calculating an evolution function of Euclidean distance between adjacent tracks in the phase space along with time; obtaining Lyapunov indexes as nonlinear characteristics of time sequence signals by carrying out exponential fitting of primary items, secondary items and positive items on an evolution function in a phase space; time series signalIs reconstructed as a high-dimensional vector sequence:
，
Wherein the method comprises the steps of Is a delay parameter,/>Is the embedding dimension; evolution function/>The expression is used as follows:
；
Wherein, The time interval is a value of 1.
Specifically, the time-series signal is reconstructed into a trajectory in the phase space by the delay coordinate method. This method maps the time series signal onto a track in Gao Weixiang space based on concepts in chaos theory. The purpose of this is to convert the original one-dimensional time series signal into a multi-dimensional data series in order to better capture the dynamic behavior and non-linear characteristics of the signal. In the phase space, the evolution function of Euclidean distance between adjacent tracks along with time is calculated. This evolution function describes the relative movement between the trajectories in phase space, i.e. the change in distance between the trajectories over time. Such an evolution function can reflect the nonlinear dynamics of the system. And performing exponential fitting of the primary term, the secondary term and the positive term on the evolution function in the phase space to obtain a Lyapunov exponent. The Lyapunov index is one of the features used to describe a chaotic system and reflects the local exponential growth rate of the system. The larger the Lyapunov index, the more chaotic the system, i.e. the more unpredictable and complex its local dynamics evolve over time. Evolution functionDynamic changes in phase space are described by computing euclidean distances between adjacent tracks in phase space. Specifically, each term in the formula represents the delay parameter/>In the case of a change in time of two adjacent tracks. By calculating the euclidean distance, the distance between tracks in the phase space can be quantified, thereby revealing the nonlinear characteristics of the system. Time series signal/>Is reconstructed as a high-dimensional vector sequence/>Wherein delay parameter/>A plurality of observations within. The purpose of this is to reconstruct the trajectory of the system in phase space for subsequent analysis and feature extraction.
Example 7: the Lyapunov index is obtained by performing an exponential fit of the primary term, the secondary term and the positive term to the evolution function in the phase space using the following formula:
；
Wherein, Is Lyapunov index; /(I)The Euclidean distance is the initial moment; /(I)The value range is 0.3 to 0.6 for the quadratic coefficient; /(I)The positive option coefficient is a value ranging from 0.4 to 0.7.
Specifically, the Lyapunov index is one of important indexes for evaluating chaotic properties of a system. By calculating the Lyapunov index, the growth rate of the micro-disturbance in the system can be known. The larger the Lyapunov index, the more turbulent the system, i.e. the trajectory in the system is highly sensitive to the initial conditions, and it becomes difficult to predict the long-term behavior of the system. The Lyapunov index may also be used to determine the stability of the system. When the Lyapunov index is negative, it indicates that the system tends to stabilize, i.e., the micro-disturbance decays over time, while when the Lyapunov index is positive, it indicates that the system is in an unstable state, the micro-disturbance grows. By calculating the Lyapunov index, the dynamic behavior of the system can be predicted. The higher Lyapunov index indicates that the system has complex dynamic behavior and can possibly show chaos phenomenon, so that long-term prediction becomes difficult; the lower Lyapunov index indicates that the dynamic behavior of the system is relatively stable, and long-term prediction is easier to carry out. The Lyapunov index calculation can be used for deeply analyzing the nonlinear characteristics of a complex system. The method reflects the local exponential growth rate of the track of the system in the phase space, and can help understand the evolution rule, dynamic characteristics and chaotic behavior of the system. The Lyapunov index is an index for describing the local index growth rate in a power system and is commonly used for judging the chaotic nature and the stability of the system. It reflects the exponential diffusion rate between the trajectories in phase space, i.e. the rate of increase of the micro-disturbances in the system. The larger the Lyapunov index, the more chaotic the system. Evolution functionRepresenting the evolution over time of the euclidean distance between adjacent tracks in phase space. In the calculation of the Lyapunov exponent,Is used to measure the distance change between the tracks and thereby evaluate the nonlinear characteristics and dynamic behavior of the system. Pair evolution function/>, in the formulaExponential fits of the primary, secondary and positive options were performed. Such a fit may help find the most suitable functional form to describe the evolution function over time. Fitting coefficient/>And/>The coefficients representing the quadratic and positive options, respectively, whose range of values affects the fit result. The Lyapunov exponent calculation formula includes the initial time/>, of the evolution functionAnd infinity time/>Natural logarithm of the ratio of euclidean distance at, and exponential fitting results of the primary, secondary, and positive options. The local exponential growth rate of the system, namely Lyapunov exponent, is obtained by taking the logarithm and averaging. Parameter in the formula/>And/>There is a specific range of values. These ranges are set according to the characteristics of the actual system and the computational requirements. The selection of parameters affects the calculation result of Lyapunov indexes, so that reasonable adjustment and selection are needed.
Example 8: a dynamics analysis unit which reconstructs a time-series signal into a state space format, and sets up a state vector as a high-dimensional vector sequence:
；
Then, a state transition equation is established by the following formula:
；
Wherein, For the state transfer function, the following formula is used for the representation:
；
Wherein, Representing state vector, sign/>Representing element-by-element multiplication; /(I)Representing taking a sine for each element in the state vector; /(I)Each element representing a state vector is multiplied separately, which is an element-by-element square; /(I)Representing the inverse of each element of the state vector; /(I)Representing state vectors and exponential functions/>The derivative of the element-wise product with respect to time.
Specifically, first, a state transfer function is a mathematical expression describing how the state of the system changes over time. In the formula in embodiment 8, the state transfer functionIs a complex function comprising a state vector/>And time/>Two variables so it can describe the evolution of the system state over time. First part of the equationRepresenting taking a sine for each element in the state vector. The sine function is a periodic nonlinear function that introduces nonlinear relationships between the state vector elements. This part reflects nonlinear dynamic effects that may be present in the system, such as nonlinear vibrations, nonlinear coupling, etc. Next, the second part/>, in the equationEach element representing a state vector is multiplied separately, realizing an element-by-element square of the state vector. The effect of this term is to increase the cross term between the states of the system, further introducing nonlinear effects of the system. By this element-wise squaring operation, more complex interactions between system states can be captured. Third part/>Representing the inverse of each element of the state vector. This term introduces a division operation between state vector elements, possibly reflecting the effects of reverse coupling or attenuation in the system. The reciprocal operation causes each element of the state vector to be affected in the opposite direction, thereby affecting the dynamic evolution process of the system. Finally, the fourth part/>, in the formulaRepresenting state vectors and exponential functions/>The derivative of the element-wise product with respect to time. This term describes the rate of change of the state vector over time and introduces a time dependency. The introduction of an exponential function may reflect the effects of attenuation or growth in the system, and the derivative operation describes the trend of the state vector over time.
First, the dynamics analysis section constructs a state vectorThe time-series signal is converted into a state space form. The state vector contains data for a plurality of observation instants, each element representing a certain characteristic or state variable of the system, by introducing a delay parameter/>The construction mode of the state vector can be flexibly adjusted, and the method is suitable for dynamic characteristics of different systems. Then, the dynamics analysis part establishes a state transition equation, describing the evolution rule of the system in the state space. This equation is passed through the state transfer function/>To show, the function comprehensively considers the influence of each element and time of the state vector, and describes the dynamic evolution rule of the system. By resolving the state transition equation, the future state of the system in the state space can be predicted, revealing the dynamic behavior and properties of the system. The dynamic behavior of the system can be analyzed in depth by the state transition equation. The state transfer function contains various nonlinear effects and interactions, describing the way the system evolves in the state space. By analyzing the characteristics of the state transfer function, the dynamic behaviors such as stability, periodicity, chaos and the like of the system can be known. The formula in embodiment 8 introduces a number of non-linear terms, such as sine functions, element multiplications, inverse of elements, etc., which reflect the non-linear characteristics of the system. By analyzing the roles of these terms in the state transfer function, nonlinear dynamic behavior of the system, including nonlinear vibration, mutual coupling, etc., can be revealed. By analyzing the state transition equation, the evolution process of the system in the state space can be simulated, and the future state of the system can be predicted. This is important for understanding the dynamic behavior of the system, predicting the response of the system, and designing control strategies. The state transition equation provides an effective method for describing the dynamic characteristics of the system through a mathematical model, and provides a theoretical basis for the simulation and prediction of the system. By analyzing the dynamic behavior and state transition equations of the system, guidance can be provided for the design of the control system. By adjusting parameters in the state transfer function or introducing a control strategy, the evolution track of the system in the state space can be influenced, so that the control and regulation of the system behavior are realized.
Example 9: the dynamics analysis unit performs nonlinear dynamics simulation based on the characteristic parameters and nonlinear characteristics, and sets an initial state as:
；
At the boundary condition of ; Wherein/>For the lower frequency of vibration,/>Is the upper limit frequency of vibration; nonlinear dynamics simulation is performed by using the following formula, and vibration response/>, of the tower crane is simulated：
；
Wherein,Is the spectral peak frequency; /(I)Is the peak amplitude of the spectrum; /(I)Is a spectral peak quality factor; /(I)Representation calculationIs a2 nd order Frobenius norm.
Specifically, first, in simulating the vibration response of the tower crane, it is necessary to consider the dynamic characteristics of the tower crane structure and the influence of the external environment on the vibration thereof. These vibrations may originate from a variety of factors such as tower crane movements, load changes, wind forces, earthquakes, etc., and thus the simulation of the vibration response requires a comprehensive consideration of the effects of these factors. Second, in the formula, the vibration responseIs divided into two parts. The first part is an integral term for the external force, representing the effect of the external force on the tower crane over a given period of time. These external forces may come from load variations, wind forces, mechanical vibrations, etc., which exert forces of different directions and magnitudes on the tower crane structure, causing a vibrational response. The integral term reflects the cumulative effect of these forces on the tower crane vibration behavior, revealing the dynamic response of the system over time. The second part is a correction term that is corrected by taking into account the characteristic parameters and nonlinear characteristics of the vibration signal. The correction term includes parameters such as spectral peak frequency, spectral peak amplitude, and spectral peak quality factor for adjusting the influence of forces in the integral term. Specifically, the spectral peak frequency reflects the main frequency component of the vibration signal, the spectral peak amplitude reflects the amplitude of the vibration signal, and the spectral peak quality factor reflects the frequency resolution of the vibration signal. These parameters can be determined based on the characteristics of the actual vibration signal to more accurately simulate the vibration response of the tower crane. In the correction term, the Frobenius norm is also used to measure the size of the state vector. The Frobenius norm is a matrix norm used to measure the size and shape of the matrix, here to evaluate the size of the state vector, and thus affect the calculation of the correction term. By considering the size of the state vector, the influence of the correction term on the vibration response can be better regulated, so that the simulation result is more in line with the actual situation.
The present invention has been described in detail above. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Claims (1)
1. Tower crane self-checking system based on vibration analysis, characterized in that it includes: the system comprises a data acquisition part, a characteristic extraction part, a dynamics analysis part and an abnormality detection and early warning part; the data acquisition part is used for acquiring vibration data of the tower crane and representing the vibration data as time sequence signals; the characteristic extraction part is used for carrying out high-order spectrum analysis on the time sequence signal so as to capture the nonlinear characteristic of the time sequence signal, obtain a high-order spectrum, extract characteristic parameters from the high-order spectrum and extract nonlinear characteristics of the time sequence signal based on a nonlinear dynamics theory; the dynamics analysis part is used for reconstructing the time sequence signal into a state space form, setting a state vector and a state transition equation, setting a boundary condition, carrying out nonlinear dynamics simulation based on the characteristic parameter and the nonlinear characteristic, and simulating the vibration response of the tower crane according to the boundary condition and the initial state of the system; the abnormality detection and early warning unit performs abnormality detection and diagnosis by comparing the difference between the simulated vibration response and the time-series signal, uses an abnormality metric to represent the degree of difference between the simulated vibration response and the time-series signal, sets a threshold value, compares the difference metric with the threshold value, and if the difference metric exceeds the set threshold value, sets an abnormality alarm; the characteristic parameters extracted from the high-order spectrum include: spectral peak frequency, spectral peak amplitude and spectral peak quality factor; the data acquisition unit includes: a sensor section and a data acquisition section; the sensor part comprises a plurality of sensors which are respectively arranged on a crane boom, a bracket, a landing leg, an antenna and a crane hook of the tower crane, and each sensor acquires vibration data in real time; the data acquisition section represents the acquired vibration data as a time-series signal, which is represented using the following formula:
；
Wherein, Representing time series signals,/>Time is; /(I)Is/>Amplitude of each vibration component; /(I)Is/>The frequency of the individual vibration components; /(I)Is/>The phase of each vibrating component; /(I)Is the number of frequency components contained in the time-series signal; representing a noise error term; when the feature extraction part performs high-order spectrum analysis on the time series signal to capture nonlinear characteristics of the time series signal and obtain a high-order spectrum, the high-order spectrum density of the time series signal is calculated according to the following formula:
the expression is used as follows:
；
Wherein, Representing time series signals/>/>The order Gao Jiepu density reflects the time series signal over the frequency range/>To/>Correlation between frequency components within; /(I)Representing a time delay; /(I)Is a time period representing the observed duration of the time-series signal; /(I)Is the impulse response function of the filter; /(I)Representing/>, time-series signalSub-time differentiation; * Representing a convolution operation; /(I)Is a complex exponential function, representing a frequency of/>For projecting the time-series signal into the frequency domain; then, calculating to obtain a high-order spectrum based on the calculated high-order spectrum density; the feature extraction unit calculates the higher-order spectrum/>, by the following formula：
；
Wherein,Is selected/>Frequency components; in Gao Jiepu, the spectral peak, i.e. the frequency component with the highest amplitude, is found; obtaining a spectral peak frequency from a spectral peak, corresponding to a frequency component of a highest amplitude in the high-order spectrum, and obtaining a spectral peak amplitude from the spectral peak, corresponding to the highest amplitude in the high-order spectrum; defining a spectral peak quality factor to be defined as the ratio of the full width at half maximum of the high order spectrum at the spectral peak frequency to the spectral peak frequency; the method for extracting the nonlinear characteristics of the time series signal by the characteristic extraction part based on the nonlinear dynamics theory comprises the following steps: reconstructing the time sequence signal into tracks in a phase space by a delay coordinate method, and calculating an evolution function of Euclidean distance between adjacent tracks in the phase space along with time; obtaining Lyapunov indexes as nonlinear characteristics of time sequence signals by carrying out exponential fitting of primary items, secondary items and positive items on an evolution function in a phase space; set time series signal/>Is reconstructed as a high-dimensional vector sequence:
，
Wherein the method comprises the steps of Is a delay parameter,/>Is the embedding dimension; evolution function/>The expression is used as follows:
；
Wherein, The value is 1 for the time interval; the Lyapunov index is obtained by performing an exponential fit of the primary term, the secondary term and the positive term to the evolution function in the phase space using the following formula:
；
Wherein, Is Lyapunov index; /(I)The Euclidean distance is the initial moment; /(I)The value range is 0.3 to 0.6 for the quadratic coefficient; /(I)The value range is 0.4 to 0.7 for positive option coefficients; a dynamics analysis unit which reconstructs a time-series signal into a state space format, and sets up a state vector as a high-dimensional vector sequence:
；
Then, a state transition equation is established by the following formula:
；
Wherein, For the state transfer function, the following formula is used for the representation:
；
Wherein, Representing state vector, sign/>Representing element-by-element multiplication; /(I)Representing taking a sine for each element in the state vector; /(I)Each element representing a state vector is multiplied separately, which is an element-by-element square; /(I)Representing the inverse of each element of the state vector; /(I)Representing state vectors and exponential functions/>A derivative of the element-wise product with respect to time; the dynamics analysis unit performs nonlinear dynamics simulation based on the characteristic parameters and nonlinear characteristics, and sets an initial state as:
；
At the boundary condition of ; Wherein/>For the lower frequency of vibration,/>Is the upper limit frequency of vibration; nonlinear dynamics simulation is performed by using the following formula, and vibration response/>, of the tower crane is simulated：
；
Wherein,Is the spectral peak frequency; /(I)Is the peak amplitude of the spectrum; /(I)Is a spectral peak quality factor; /(I)Representing the calculation/>Is a2 nd order Frobenius norm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410287993.9A CN117886241B (en) | 2024-03-14 | Tower crane self-checking system based on vibration analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410287993.9A CN117886241B (en) | 2024-03-14 | Tower crane self-checking system based on vibration analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117886241A CN117886241A (en) | 2024-04-16 |
| CN117886241B true CN117886241B (en) | 2024-06-04 |
Family
ID=
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102323476A (en) * | 2011-06-08 | 2012-01-18 | 山东电力研究院 | Method for measuring harmonic waves and interharmonic waves in electric power system by adopting spectrum estimation and chaology |
| CN105651376A (en) * | 2014-11-10 | 2016-06-08 | 上海宝钢工业技术服务有限公司 | Mechanical equipment offline detection system vibration signal spectrum analysis and alarm method |
| CN112327149A (en) * | 2020-10-19 | 2021-02-05 | 西安工程大学 | SSD-SRAE-based high-voltage circuit breaker fault diagnosis method |
| CN112525749A (en) * | 2020-11-19 | 2021-03-19 | 扬州大学 | Tribology state online identification method based on friction signal recursion characteristic |
| CN112651078A (en) * | 2020-12-02 | 2021-04-13 | 西安航天动力技术研究所 | Rocket body structure state anomaly detection method based on symbolic dynamics |
| CN113916302A (en) * | 2021-10-28 | 2022-01-11 | 承德睿民科技有限公司 | Overhead traveling crane online detection and diagnosis system and method based on artificial intelligence technology |
| CN115353017A (en) * | 2022-08-11 | 2022-11-18 | 东南大学 | Tower crane vibration intelligent early warning and control system suitable for multidirectional wind effect |
| CN117392587A (en) * | 2023-11-02 | 2024-01-12 | 华能澜沧江水电股份有限公司景洪水电厂 | Special equipment safety monitoring system based on Internet of things |
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102323476A (en) * | 2011-06-08 | 2012-01-18 | 山东电力研究院 | Method for measuring harmonic waves and interharmonic waves in electric power system by adopting spectrum estimation and chaology |
| CN105651376A (en) * | 2014-11-10 | 2016-06-08 | 上海宝钢工业技术服务有限公司 | Mechanical equipment offline detection system vibration signal spectrum analysis and alarm method |
| CN112327149A (en) * | 2020-10-19 | 2021-02-05 | 西安工程大学 | SSD-SRAE-based high-voltage circuit breaker fault diagnosis method |
| CN112525749A (en) * | 2020-11-19 | 2021-03-19 | 扬州大学 | Tribology state online identification method based on friction signal recursion characteristic |
| CN112651078A (en) * | 2020-12-02 | 2021-04-13 | 西安航天动力技术研究所 | Rocket body structure state anomaly detection method based on symbolic dynamics |
| CN113916302A (en) * | 2021-10-28 | 2022-01-11 | 承德睿民科技有限公司 | Overhead traveling crane online detection and diagnosis system and method based on artificial intelligence technology |
| CN115353017A (en) * | 2022-08-11 | 2022-11-18 | 东南大学 | Tower crane vibration intelligent early warning and control system suitable for multidirectional wind effect |
| CN117392587A (en) * | 2023-11-02 | 2024-01-12 | 华能澜沧江水电股份有限公司景洪水电厂 | Special equipment safety monitoring system based on Internet of things |
Non-Patent Citations (2)
| Title |
|---|
| 陈进,姜鸣.高阶循环统计量理论在机械故障诊断中的应用.振动工程学报.2001,(第02期), * |
| 高阶循环统计量理论在机械故障诊断中的应用;陈进, 姜鸣;振动工程学报;20010730(第02期) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lv et al. | High-order synchroextracting transform for characterizing signals with strong AM-FM features and its application in mechanical fault diagnosis | |
| Perez-Sanjines et al. | Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence | |
| CN111209934A (en) | Fan fault prediction and alarm method and system | |
| CN112559598B (en) | Telemetry time series data abnormity detection method and system based on graph neural network | |
| CN111881594B (en) | Non-stationary signal state monitoring method and system for nuclear power equipment | |
| Jaber et al. | A simulation of non-stationary signal analysis using wavelet transform based on LabVIEW and Matlab | |
| CN117609836A (en) | Electromagnetic sensitivity prediction and health management method for integrated module | |
| CN117401578B (en) | Intelligent management system for lifting weight weighing signals | |
| CN111291918A (en) | Rotating machine degradation trend prediction method based on stationary subspace exogenous vector autoregression | |
| CN114048879A (en) | Fault detection method, device, system, computer equipment and readable storage medium | |
| CN117886241B (en) | Tower crane self-checking system based on vibration analysis | |
| CN108507607A (en) | A kind of method for detecting weak signals based on kernel function | |
| CN117630797A (en) | Ammeter health state detection method, system and storage medium based on working current | |
| CN117171657A (en) | Wind power generation equipment fault diagnosis method and device, electronic equipment and storage medium | |
| CN117886241A (en) | Tower crane self-checking system based on vibration analysis | |
| CN110994802A (en) | Method and device for monitoring running state of power transmission conductor | |
| CN114112390B (en) | Nonlinear complex system early fault diagnosis method | |
| CN114993671A (en) | Vibration fault diagnosis method and system based on Q factor wavelet transform | |
| Montalvo et al. | Improving the in situ measurement of RTD response times through discrete wavelet transform in NPP | |
| Deng et al. | A fractional hilbert transform order optimization algorithm based DE for bearing health monitoring | |
| CN107506779B (en) | Estimation method and system for water content of plant stems | |
| CN117668751B (en) | High-low voltage power system fault diagnosis method and device | |
| CN117723782B (en) | Sensor fault identification positioning method and system for bridge structure health monitoring | |
| CN117970864B (en) | Petroleum and natural gas PLC control monitoring system and method based on electric signal analysis | |
| CN112836583B (en) | Wind turbine fault diagnosis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |