Port equipment vibration signal processing method

Abstract

The invention relates to the technical field of signal processing, and discloses a harbor equipment vibration signal processing method, which comprises the following steps: constructing a comprehensive function, and calculating a harbor equipment vibration signal based on the characteristics of a frequency scale, a second wavelet basis function and a threshold value; the concentration ratio of energy distribution, the calculation complexity of second wavelet basis functions under the frequency scale, the orthogonal value between the second wavelet basis functions under the frequency scale, the sparse value of second wavelet coefficients under the frequency scale and the signal-to-noise ratio of time domain signals reconstructed after threshold processing are comprehensively considered. The selection of the optimized frequency scale, the optimized second wavelet basis function and the optimized threshold is realized. The innovative method gets rid of the limitation of a single or optimized algorithm, fixed parameter setting or predefined rules in the prior art, and significantly improves the precision and robustness of low-frequency vibration signal processing.

Description

Technical Field

[0001] The invention relates to the technical field of signal processing, and in particular to a method for processing vibration signals of port equipment. Background Art

[0002] As a key hub in the international and domestic logistics chain, the safety and reliability of the port infrastructure are of vital importance. With the continuous expansion and increasing complexity of ports, there is a growing demand for health monitoring of port equipment (such as large cranes, dock structures, ships, etc.). Vibration signals are one of the important indicators for evaluating the operating status and structural integrity of these equipment. However, existing vibration sensors face many challenges in processing low-frequency vibration signals, especially in complex environments such as ports with multiple sea areas. Therefore, it is particularly urgent to develop a method that can effectively expand the measurement range of vibration sensors and accurately process low-frequency vibration signals.

[0003] At present, the commonly used magnetoelectric vibration sensors perform well in the high frequency band, but have limited measurement capabilities in the low frequency band. Moreover, port equipment usually involves large structures and heavy machinery with low vibration frequencies, especially when starting, stopping or changing loads, which will produce significant low-frequency vibrations. The environment is complex and is affected by various factors such as temperature, humidity, salt spray, wind and waves. These external conditions will further affect the collection and processing of low-frequency vibration signals. The existing technology is based on the processing of low-frequency signals. Most of them use filters to remove background noise to obtain denoised signals, and then select wavelet functions to perform wavelet processing on the denoised signals to obtain wavelet coefficients. The processed time domain signals are obtained through threshold processing, and finally the signal-to-noise ratio of the time domain signal is verified to be significantly improved. If the verification fails, the frequency, function and threshold need to be reselected and the signal processing needs to be re-performed. The existing selection of wavelet functions, frequency divisions, and thresholds are all achieved through a single or optimized algorithm or rely on fixed parameter settings or predefined rules. Although these methods can achieve good results in certain specific scenarios, most of them rely on a single or optimized algorithm, or select wavelet functions, frequency divisions, and thresholds based on fixed parameter settings and predefined rules. The limitation of this method is that, in some scenarios, signal processing cannot be completed quickly, and the frequency range, function, and threshold need to be reset, resulting in the inability to fully adapt to different types of low-frequency vibration signals and complex noise environments. In practical applications, it may not be possible to quickly obtain the optimal processing effect, and the signal processing efficiency is low. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for processing vibration signals of port equipment, comprising: Step 1, collecting original signals through sensors; Step 2, preprocessing the original signal; performing initial frequency scale division on the preprocessed original signal to obtain sub-band signals of different frequencies; setting a number of sampling points on the sub-band signals of different initial frequency scales, performing a first wavelet transform on each sampling point through a first wavelet basis function, and obtaining a first wavelet coefficient of each sampling point at a different initial frequency scale; calculating the energy distribution at each initial frequency scale according to the first wavelet coefficient of each sampling point at different initial frequency scales, and obtaining the concentration according to the energy distribution at each initial frequency scale; Step 3, remove the background noise of the subband signal at each initial frequency scale through a filter to obtain a denoised signal, and record the filter length at each initial frequency scale; obtain the second wavelet basis function, select the second wavelet basis function to perform the second wavelet transform on each denoised signal at each initial frequency scale, obtain the second wavelet coefficient, calculate the orthogonal value of the current second wavelet basis function and other second wavelet basis functions at each initial frequency scale, and calculate the computational complexity of each second wavelet basis function at each initial frequency scale; obtain the threshold, perform threshold processing on the second wavelet coefficient through the threshold; input the thresholded second wavelet coefficient into the inverse wavelet transform to reconstruct the time domain signal; calculate the signal-to-noise ratio based on the reconstructed time domain signal and the original signal; obtain the sparse value based on the second wavelet coefficient at each initial frequency scale; Step 4, constructing a comprehensive function through sparse value, signal-to-noise ratio, concentration, computational complexity, and orthogonal value, and selecting an optimized frequency scale from the initial frequency scale, selecting an optimized second wavelet basis function from the second wavelet basis function, and selecting an optimized threshold from the threshold according to the comprehensive function; Step 5, determine the optimized subband signal according to the optimized frequency scale, denoise the optimized subband signal through a filter to obtain an optimized denoised signal, perform a second wavelet transform on the optimized denoised signal by optimizing the second wavelet basis function to obtain an optimized second wavelet coefficient, perform threshold processing on the second wavelet coefficient according to the optimized threshold to obtain an optimized time domain signal.

[0005] Further, selecting an optimized frequency scale from the initial frequency scale according to the comprehensive function, selecting an optimized second wavelet basis function from the second wavelet basis function, and selecting an optimized threshold from the threshold include the following steps: Step 41, calculating the concentration of the subband signal energy distribution at each initial frequency scale; taking the initial frequency scale with the largest concentration as the initial optimized frequency scale; Step 42, presetting a candidate second wavelet basis function and a candidate threshold; Step 43, selecting a candidate second wavelet basis function to perform a second wavelet transform on each denoised signal at each initial optimized frequency scale to obtain a candidate second wavelet coefficient; Step 44, calculating the orthogonality value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimized frequency scale; Step 45, calculating the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale; Step 46, threshold processing is performed on each candidate second wavelet coefficient by using a candidate threshold; the candidate second wavelet coefficient after threshold processing is input into an inverse wavelet transform to reconstruct a candidate time domain signal; a candidate signal-to-noise ratio is calculated based on the reconstructed candidate time domain signal and the original signal; a candidate sparse value is obtained based on the candidate second wavelet coefficient at each initial optimized frequency scale; Step 47, calculating the comprehensive function value according to the orthogonal value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale, the computational complexity of each candidate second wavelet basis function at each initial optimization frequency scale, the candidate sparse value obtained from the candidate second wavelet coefficients at each initial optimization frequency scale, the candidate signal-to-noise ratio calculated between the reconstructed candidate time domain signal and the original signal, and the concentration of the subband signal energy distribution at each initial optimization frequency scale, taking the candidate second wavelet basis function with the maximum comprehensive function value as the optimized second wavelet basis function, and taking the candidate threshold value with the maximum comprehensive function value as the optimized threshold value; Step 48, based on optimizing the second wavelet basis function and optimizing the threshold, recalculate the comprehensive function value at each initial frequency scale, and use the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale.

[0006] Furthermore, the calculation process of the orthogonal value of each candidate wavelet function at the initial optimization frequency scale is as follows: calculate the correlation between each candidate second wavelet basis function and other candidate second wavelet basis functions at the initial optimization frequency scale to obtain a correlation matrix; calculate the maximum singular value and the minimum singular value of the correlation matrix, and obtain the condition number of the correlation matrix according to the maximum singular value and the minimum singular value, and use the condition number as the orthogonal value; construct an evaluation threshold for the orthogonal value, and divide the orthogonal value into a reward item and a penalty item according to the evaluation threshold.

[0007] Furthermore, the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale is: ; In the formula, represents the computational complexity of the g-th candidate second wavelet basis function under the initial optimization frequency scale c, represents the signal length of the initial optimized frequency scale c, represents the filter length at the initial optimized frequency scale c, and N is the length of the original signal.

[0008] Furthermore, the orthogonal value of each candidate wavelet function at the initial optimized frequency scale is: ; In the formula, represents the orthogonal value of the correlation matrix A between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function under the initial optimization frequency scale c, represents the maximum singular value of the correlation matrix between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function at the initial optimized frequency scale c, represents the minimum singular value of the correlation matrix between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function at the initial optimization frequency scale c, and Z represents the total number of candidate second wavelet basis functions at the initial optimization frequency scale c.

[0009] Furthermore, the comprehensive function is: ; In the formula, Represents a comprehensive consideration of the initial frequency scale d, the second wavelet basis function , Threshold The comprehensive function of represents the concentration of energy distribution on the i-th initial frequency scale d, represents the conversion function, represents the orthogonal value of the jth second wavelet basis function, represents the computational complexity of the jth second wavelet basis function, represents the signal-to-noise ratio of the time domain signal reconstructed after the kth threshold processing and the original signal, represents the sparse value of the optimized second wavelet coefficient after the kth threshold processing, , , Represent the adjustment coefficients, .

[0010] Furthermore, the conversion function is: ; In the formula, represents the conversion value of the orthogonal value of the jth second wavelet basis function, z represents a positive number, z represents the evaluation threshold, Represents the reward item, Represents a penalty term.

[0011] The embodiments of the present invention have the following technical effects: The present invention constructs a comprehensive function based on the characteristics of frequency scale, second wavelet basis function and threshold, comprehensively considers the concentration of energy distribution, the computational complexity of the second wavelet basis function at the frequency scale, the orthogonal value between the second wavelet basis functions at the frequency scale, the sparse value of the second wavelet coefficients at the frequency scale, and the signal-to-noise ratio of the time domain signal reconstructed after threshold processing, and realizes the selection of optimizing the frequency scale, optimizing the second wavelet basis function and optimizing the threshold. This innovative method gets rid of the limitations of the single or optimization algorithm, fixed parameter setting or predefined rules in the prior art, and significantly improves the accuracy and robustness of low-frequency vibration signal processing. First, through multi-dimensional comprehensive optimization, the present invention can more comprehensively capture the multi-scale characteristics of the signal, ensuring that the optimal denoising effect can be obtained in different application scenarios. Secondly, the highly adaptive optimization method enables the method to flexibly respond to signals of different types and complexities, especially in complex environments such as port equipment, under the influence of various external factors such as temperature, humidity, salt spray, wind and waves, it can still maintain stable denoising performance. In addition, the flexible selection of wavelet basis functions and dynamically adjusted frequency division enable this method to adapt to different types of low-frequency vibration signals, whether they are high-frequency signals, low-frequency signals or non-stationary signals, and the most suitable processing solution can be found. Furthermore, the search for the global optimal solution ensures that the best denoising effect can be maintained throughout the entire signal processing process, rather than just local optimization. Finally, by introducing computational complexity as one of the optimization objectives, the present invention can reasonably control the consumption of computing resources while ensuring the denoising effect, and is suitable for real-time processing and large-scale data processing scenarios. In summary, the present invention not only improves the processing accuracy and robustness of low-frequency vibration signals, but also has stronger adaptability and flexibility, and can achieve efficient and stable signal denoising in complex environments, providing strong technical support for health monitoring and fault prediction of port equipment.

[0012] The present invention significantly improves the accuracy and robustness of low-frequency vibration signal processing through a systematic multi-stage optimization strategy. First, the present invention uses the initial frequency scale with the largest concentration as the initial optimized frequency scale. This selection is based on the concentration of energy distribution, which can ensure that the initially selected frequency scale can best capture the main components of the signal, thereby providing a solid foundation for subsequent optimization. Then, at the initial optimized frequency scale, the computational complexity, orthogonal value, sparse value of the second wavelet basis function and the signal-to-noise ratio of the reconstructed time domain signal after threshold processing are calculated respectively. By constructing a comprehensive function and combining these multi-dimensional parameters, the second wavelet basis function corresponding to the maximum comprehensive function value is selected as the optimized second wavelet basis function, and the threshold corresponding to the maximum comprehensive function value is selected as the optimized threshold. This optimization method based on the comprehensive function not only takes into account the denoising effect, but also takes into account the computational complexity and orthogonality, ensuring the efficiency and stability of the algorithm in different application scenarios.

[0013] Subsequently, the present invention further recalculates the comprehensive function value at each initial frequency scale based on the optimized second wavelet basis function and the optimization threshold, and finally selects the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale. This process ensures that the best denoising effect can be obtained in the entire signal processing process through global optimization, rather than just a local optimal solution. This method avoids the limitations of the prior art that relies on a single or fixed parameter setting, and can flexibly respond to signals of different types and complexities in complex environments, especially in port equipment and other situations that are affected by multiple external factors such as temperature, humidity, salt spray, wind and waves, and can still maintain stable denoising performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The present invention provides a flow chart of a method for processing vibration signals of port equipment. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0017] Figure 1 1 is a flow chart of a method for processing vibration signals of port equipment provided by an embodiment of the present invention. Figure 1 , specifically including: Step 1: Collect the original signal through the sensor.

[0018] The sensor is preferably a magnetoelectric vibration sensor for low-frequency vibration measurement, such as a magnetoelectric vibration velocity sensor of model LDT05-06C. The sensor has the following features: Frequency range: 0.5 Hz to 500 Hz, covering the low-frequency vibration range common in port equipment.

[0019] Sensitivity: 40 mV / (mm / s), ensuring sufficient output voltage in the low frequency band.

[0020] Operating temperature range: -40°C to +125°C, adapting to temperature changes in port environments.

[0021] Protection level: IP67, with good waterproof and dustproof performance, suitable for the complex environment of the port.

[0022] Anti-interference ability: Built-in electromagnetic shielding to reduce the impact of external electromagnetic interference on the signal.

[0023] Sensor installation location: Select key locations to install sensors based on the specific structure of the port equipment. For example, install sensors on crane booms, bases, transmission shafts, and other locations that are prone to low-frequency vibrations. Ensure that the sensor is in close contact with the object being measured to avoid signal distortion due to looseness.

[0024] Step 2: preprocess the original signal.

[0025] Preprocessing includes: Remove DC components: Use a high-pass filter (such as a cutoff frequency of 0.1 Hz) to remove DC components from the signal to ensure the accuracy of subsequent processing.

[0026] Normalization processing: Normalize the signal amplitude to the interval [-1, 1] to facilitate subsequent wavelet transform and other signal processing operations.

[0027] Remove high-frequency noise: Use a low-pass filter (such as a cutoff frequency of 500 Hz) to remove high-frequency noise in the signal and retain the low-frequency vibration components.

[0028] The original signal after preprocessing is divided into initial frequency scales to obtain sub-band signals of different frequencies. Several sampling points are set for the sub-band signals of different initial frequency scales, and the first wavelet transform is performed on each sampling point through the first wavelet basis function (Db4 wavelet) to obtain the first wavelet coefficient of each sampling point at different initial frequency scales; according to the first wavelet coefficient of each sampling point at different initial frequency scales, the energy distribution at each initial frequency scale is calculated, and the concentration is obtained according to the energy distribution at each initial frequency scale.

[0029] The initial frequency scale is: , , ..., ,in, is the lowest frequency scale, is a virtual high frequency. Each frequency scale corresponds to a subband signal , a represents the frequency scale, and b represents the translation parameter.

[0030] The first wavelet coefficients: ; represents the first wavelet coefficient of the qth sampling point under the i-th initial frequency scale d, x represents the original signal, Represents the first wavelet basis function at the i-th initial frequency scale d.

[0031] The energy distribution at each initial frequency scale is: , represents the energy distribution at the i-th initial frequency scale d, and H is the total number of initial frequency scales.

[0032] Concentration indicates the concentration of energy distribution of a signal at a certain scale. Generally, the higher the energy concentration, the more concentrated the signal energy is on fewer wavelet coefficients, which is helpful for subsequent denoising and feature extraction.

[0033] ; represents the concentration of energy distribution on the i-th initial frequency scale d, represents the variance of the energy distribution on the i-th initial frequency scale d, Represents the mean of the energy distribution on the i-th initial frequency scale d.

[0034] The concentration obtained based on variance and mean can effectively reflect the distribution of signal energy and accurately identify the main components of the signal even in the case of large noise. Compared with other complex energy distribution indicators, the energy concentration based on variance is simple to calculate and easy to implement. It is suitable for multi-scale wavelet transform and can evaluate the energy concentration of the signal at different scales to help select the optimal optimization scale.

[0035] Step 3, remove the background noise of the subband signal at each initial frequency scale through a filter to obtain a denoised signal, and record the filter length at each initial frequency scale; obtain the second wavelet basis function, select the second wavelet basis function to perform the second wavelet transform on each denoised signal at each initial frequency scale, obtain the second wavelet coefficient, calculate the orthogonal value of the current second wavelet basis function and other second wavelet basis functions at each initial frequency scale, and calculate the computational complexity of each second wavelet basis function at each initial frequency scale; obtain the threshold, perform threshold processing on the second wavelet coefficient through the threshold; input the thresholded second wavelet coefficient into the inverse wavelet transform to reconstruct the time domain signal; calculate the signal-to-noise ratio based on the reconstructed time domain signal and the original signal; obtain the sparse value based on the second wavelet coefficient at each initial frequency scale.

[0036] The calculation process of the orthogonal value and the calculation complexity is the same as the calculation logic of step 47, and will not be described in detail in this embodiment. The signal-to-noise ratio is calculated by the prior art.

[0037] The sparse value is calculated by the ratio of the second wavelet coefficient at each initial frequency scale to the maximum second wavelet coefficient at each initial frequency scale.

[0038] In this embodiment, the calculation of the sparse value is mainly used to evaluate the sparsity of the second wavelet coefficients after threshold processing. The higher the sparsity, the more it means that after threshold processing, the main energy of the signal is concentrated on a few non-zero coefficients, which helps to remove noise and retain the key features of the signal. By introducing the sparse value as part of the comprehensive optimization function, the present invention can ensure that the sparse structure of the signal is better preserved while ensuring the denoising effect, thereby improving the quality of the reconstructed time domain signal. By calculating the sparse value, the processing flow of the low-frequency vibration signal is further optimized. Specifically, the introduction of the sparse value enables the algorithm to better retain the sparse structure of the signal, reduce the impact of noise on the signal, and ensure that the reconstructed time domain signal has higher clarity and reliability. Combined with other parameters such as energy concentration, computational complexity, orthogonal value and signal-to-noise ratio, the present invention constructs a multi-dimensional comprehensive optimization function, realizes the global optimal frequency scale, second wavelet basis function and threshold selection, and significantly improves the accuracy and robustness of signal processing.

[0039] Step 4, construct a comprehensive function through sparse value, signal-to-noise ratio, concentration, computational complexity, and orthogonal value, and select the optimized frequency scale from the initial frequency scale, select the optimized second wavelet basis function from the second wavelet basis function, and select the optimized threshold from the threshold according to the comprehensive function.

[0040] Based on the signal characteristics, this embodiment has made a number of innovative improvements, which significantly improved the accuracy, robustness and computational efficiency of low-frequency vibration signal processing. First, in the selection of frequency scale, the present invention introduces the concentration of energy distribution as a key evaluation index. By calculating the energy concentration at each initial frequency scale, the scale with the highest concentration is selected as the preliminary optimized frequency scale. This improvement ensures that the selected scale can most effectively capture the main components of the signal, especially in the low-frequency vibration signals commonly found in port equipment, and can more accurately identify key vibration features. Secondly, for the selection of the second wavelet basis function, the present invention not only considers its computational complexity at a specific frequency scale, but also introduces orthogonal values ​​as evaluation criteria. The introduction of computational complexity enables the algorithm to reasonably control the consumption of computing resources while ensuring the denoising effect, which is suitable for real-time processing and large-scale data processing scenarios; and the introduction of orthogonal values ​​ensures the independence between different wavelet basis functions, avoids the introduction of redundant information, and further improves the accuracy of signal processing. Third, for the selection of thresholds, the present invention comprehensively considers two key parameters: sparse value and signal-to-noise ratio. The introduction of sparse values ​​enables the algorithm to better retain the sparse structure of the signal and reduce the impact of noise on the signal; and the introduction of the signal-to-noise ratio ensures that the time domain signal reconstructed after threshold processing has higher clarity and reliability. Finally, by constructing a comprehensive function, the parameters of the above multiple dimensions (energy concentration, computational complexity, orthogonal value, sparse value and signal-to-noise ratio) are organically combined to achieve multi-objective global optimization. This comprehensive optimization strategy not only overcomes the limitations of the prior art that relies on a single or fixed parameter setting, but also can flexibly respond to signals of different types and complexities in complex environments, ensuring that the best denoising effect can be obtained in various application scenarios. In summary, this embodiment significantly improves the accuracy, robustness and computational efficiency of low-frequency vibration signal processing through multi-dimensional comprehensive optimization, and provides strong technical support for health monitoring and fault prediction of port equipment.

[0041] The process of optimizing the frequency scale, optimizing the second wavelet basis function and optimizing the threshold through comprehensive function selection is as follows: Step 41, calculating the concentration of the subband signal energy distribution at each initial frequency scale; taking the initial frequency scale with the largest concentration as the initial optimized frequency scale.

[0042] Step 42: preset candidate second wavelet basis functions and candidate thresholds.

[0043] Step 43 , selecting a candidate second wavelet basis function to perform a second wavelet transform on each denoised signal at each initial optimized frequency scale to obtain a candidate second wavelet coefficient.

[0044] Step 44, calculating the orthogonality value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimized frequency scale.

[0045] Step 45, calculating the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale.

[0046] Step 46, threshold processing is performed on each candidate second wavelet coefficient through a candidate threshold; the candidate second wavelet coefficient after threshold processing is input into an inverse wavelet transform to reconstruct a candidate time domain signal; the candidate signal-to-noise ratio is calculated based on the reconstructed candidate time domain signal and the original signal; and the candidate sparse value is obtained based on the candidate second wavelet coefficient at each initial optimized frequency scale.

[0047] Step 47, calculate the comprehensive function value according to the orthogonal value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale, the computational complexity of each candidate second wavelet basis function at each initial optimization frequency scale, the candidate sparse value obtained by the candidate second wavelet coefficient at each initial optimization frequency scale, the candidate signal-to-noise ratio calculated by reconstructing the candidate time domain signal and the original signal, and the concentration of the subband signal energy distribution at each initial optimization frequency scale, and use the candidate second wavelet basis function with the maximum comprehensive function value as the optimized second wavelet basis function, and use the candidate threshold of the maximum comprehensive function value as the optimization threshold.

[0048] Calculate the correlation between each candidate second wavelet basis function and other candidate second wavelet basis functions at the initial optimized frequency scale to obtain the correlation matrix; calculate the maximum singular value and the minimum singular value of the correlation matrix, and obtain the condition number of the correlation matrix according to the maximum singular value and the minimum singular value, and use the condition number as the orthogonal value; construct an evaluation threshold for the orthogonal value, and divide the orthogonal value into reward items and penalty items according to the evaluation threshold.

[0049] ; In the formula, represents the orthogonal value of the correlation matrix A between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function under the initial optimization frequency scale c, represents the maximum singular value of the correlation matrix between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function at the initial optimized frequency scale c, represents the minimum singular value of the correlation matrix between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function at the initial optimization frequency scale c, and Z represents the total number of candidate second wavelet basis functions at the initial optimization frequency scale c.

[0050] The signal length of the de-noised signal at the initial optimized frequency scale and the filter length at the initial optimized frequency scale are obtained, and the computational complexity of each candidate second wavelet basis function is calculated according to the signal length and the filter length.

[0051] ; In the formula, represents the computational complexity of the g-th candidate second wavelet basis function under the initial optimization frequency scale c, represents the signal length of the initial optimized frequency scale c, represents the filter length at the initial optimized frequency scale c, N is the length of the original signal, Represents the candidate second wavelet basis function.

[0052] ; In the formula, Represents a comprehensive consideration of the initial frequency scale d, the second wavelet basis function , Threshold The comprehensive function of represents the concentration of energy distribution on the i-th initial frequency scale d, represents the conversion function, represents the orthogonal value of the jth second wavelet basis function, represents the computational complexity of the jth second wavelet basis function, represents the signal-to-noise ratio of the time domain signal reconstructed after the kth threshold processing and the original signal, represents the sparse value of the optimized second wavelet coefficient after the kth threshold processing, , , Represent the adjustment coefficients, .

[0053] Further, ; In the formula, represents the conversion value of the orthogonal value of the jth second wavelet basis function, z represents a positive number, z>2, and Y represents the evaluation threshold. Represents the reward item, Represents a penalty term.

[0054] In signal processing, orthogonality is a key indicator that measures the independence and non-interference between different wavelet basis functions. The higher the orthogonality, the lower the correlation between the wavelet basis functions, which helps to improve the accuracy and robustness of signal processing. When constructing a comprehensive function, the consideration of orthogonality is divided into reward terms and penalty terms.

[0055] when , the reward is ,along with As increases, the reward term gradually decreases, but is always greater than 0. This indicates that wavelet basis functions with higher orthogonality will receive higher rewards and thus receive higher weights in the comprehensive function.

[0056] when , the penalty term is ,along with As decreases, the penalty term gradually increases, but is always less than 0. This indicates that wavelet basis functions with lower orthogonality will receive lower weights and may even be excluded.

[0057] Step 48, based on optimizing the second wavelet basis function and optimizing the threshold, recalculate the comprehensive function value at each initial frequency scale, and use the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale.

[0058] The optimized second wavelet basis function can better match the time-frequency characteristics of the signal, while the optimized threshold can retain the key features of the signal while removing noise. On this basis, the comprehensive function value at each initial frequency scale is recalculated, and the parameters of multiple dimensions such as energy concentration, computational complexity, orthogonal value, sparse value and signal-to-noise ratio are combined to ensure that the evaluation at each frequency scale is more comprehensive and accurate. Finally, the initial frequency scale with the maximum comprehensive function value is selected as the optimized frequency scale, which not only ensures the global optimality of signal processing, but also can flexibly respond to signals of different types and complexities in complex environments, ensuring the best denoising effect in various application scenarios.

[0059] Step 5, determine the optimized subband signal according to the optimized frequency scale, denoise the optimized subband signal through a filter to obtain an optimized denoised signal, perform a second wavelet transform on the optimized denoised signal by optimizing the second wavelet basis function to obtain an optimized second wavelet coefficient, perform threshold processing on the second wavelet coefficient according to the optimized threshold to obtain an optimized time domain signal.

[0060] The processing process of this step is the same as the principle of the prior art, except that the frequency scale is optimized, the denoising signal is optimized, and the threshold is optimized.

[0061] This processing method significantly improves the efficiency and reliability of low-frequency vibration signal processing, and completely gets rid of the problem of repeated verification after obtaining the time domain signal in the prior art. Traditional methods often rely on fixed parameter settings or empirical values ​​to determine the frequency scale, filter type, wavelet basis function and threshold, which leads to multiple trial and error and verification when processing different types of signals to ensure the accuracy of the final result. However, this application uses a global optimization strategy to dynamically adjust these key parameters during the processing process to ensure that each step is based on the optimal solution. Specifically, the selection of optimized frequency scale enables the main components of the signal to be captured more accurately, avoiding information loss caused by improper frequency selection; optimizing the denoised signal effectively removes noise through an adaptive filter while retaining the key features of the signal; the application of the optimized threshold ensures that in the coefficient processing after the second wavelet transform, the noise can be effectively suppressed without over-smoothing the signal. Therefore, the entire processing process is not only more efficient, but also a high-quality optimized time domain signal can be obtained at one time without the need for subsequent repeated verification, which greatly saves time and computing resources.

[0062] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0063] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing vibration signals of port equipment, characterized in that: include: Step 1, collecting original signals through sensors; Step 2, preprocessing the original signal; Performing initial frequency scale division on the preprocessed original signal to obtain sub-band signals of different frequencies; setting a number of sampling points on the sub-band signals of different initial frequency scales, performing the first wavelet transform on each sampling point through the first wavelet basis function, and obtaining the first wavelet coefficient of each sampling point at different initial frequency scales; calculating the energy distribution at each initial frequency scale according to the first wavelet coefficient of each sampling point at different initial frequency scales, and obtaining the concentration according to the energy distribution at each initial frequency scale; Step 3, removing the background noise of the subband signal at each initial frequency scale through a filter to obtain a denoised signal, and recording the filter length at each initial frequency scale; obtaining a second wavelet basis function, selecting a second wavelet basis function to perform a second wavelet transform on each denoised signal at each initial frequency scale to obtain a second wavelet coefficient, calculating the orthogonal value of the current second wavelet basis function and other second wavelet basis functions at each initial frequency scale, and calculating the computational complexity of each second wavelet basis function at each initial frequency scale; Obtain a threshold value, and perform threshold processing on the second wavelet coefficient by using the threshold value; The second wavelet coefficient after threshold processing is input into the inverse wavelet transform to reconstruct the time domain signal; the signal-to-noise ratio is calculated based on the reconstructed time domain signal and the original signal; the sparse value is obtained based on the second wavelet coefficient at each initial frequency scale; Step 4, constructing a comprehensive function through sparse value, signal-to-noise ratio, concentration, computational complexity, and orthogonal value, and selecting an optimized frequency scale from the initial frequency scale, selecting an optimized second wavelet basis function from the second wavelet basis function, and selecting an optimized threshold from the threshold according to the comprehensive function; Step 5, determine the optimized subband signal according to the optimized frequency scale, denoise the optimized subband signal through a filter to obtain an optimized denoised signal, perform a second wavelet transform on the optimized denoised signal by optimizing the second wavelet basis function to obtain an optimized second wavelet coefficient, perform threshold processing on the second wavelet coefficient according to the optimized threshold to obtain an optimized time domain signal.

2. A method for processing vibration signals of port equipment according to claim 1, characterized in that: The method comprises the following steps: selecting an optimized frequency scale from an initial frequency scale according to a comprehensive function, selecting an optimized second wavelet basis function from a second wavelet basis function, and selecting an optimized threshold value from a threshold value. Step 41, calculating the concentration of the subband signal energy distribution at each initial frequency scale; taking the initial frequency scale with the largest concentration as the initial optimized frequency scale; Step 42, presetting a candidate second wavelet basis function and a candidate threshold; Step 43, selecting a candidate second wavelet basis function to perform a second wavelet transform on each denoised signal at each initial optimized frequency scale to obtain a candidate second wavelet coefficient; Step 44, calculating the orthogonality value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimized frequency scale; Step 45, calculating the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale; Step 46, threshold processing is performed on each candidate second wavelet coefficient by using a candidate threshold; the candidate second wavelet coefficient after threshold processing is input into an inverse wavelet transform to reconstruct a candidate time domain signal; a candidate signal-to-noise ratio is calculated based on the reconstructed candidate time domain signal and the original signal; a candidate sparse value is obtained based on the candidate second wavelet coefficient at each initial optimized frequency scale; Step 47, calculating the comprehensive function value according to the orthogonal value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale, the computational complexity of each candidate second wavelet basis function at each initial optimization frequency scale, the candidate sparse value obtained from the candidate second wavelet coefficients at each initial optimization frequency scale, the candidate signal-to-noise ratio calculated between the reconstructed candidate time domain signal and the original signal, and the concentration of the subband signal energy distribution at each initial optimization frequency scale, taking the candidate second wavelet basis function with the maximum comprehensive function value as the optimized second wavelet basis function, and taking the candidate threshold value with the maximum comprehensive function value as the optimized threshold value; Step 48, based on optimizing the second wavelet basis function and optimizing the threshold, recalculate the comprehensive function value at each initial frequency scale, and use the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale.

3. A method for processing vibration signals of port equipment according to claim 2, characterized in that: The calculation process of the orthogonal value of each candidate wavelet function at the initial optimized frequency scale is as follows: calculating the correlation between each candidate second wavelet basis function and other candidate second wavelet basis functions at the initial optimized frequency scale to obtain a correlation matrix; Calculate the maximum singular value and the minimum singular value of the correlation matrix, obtain the condition number of the correlation matrix according to the maximum singular value and the minimum singular value, and use the condition number as the orthogonal value; An evaluation threshold is constructed for the orthogonal value, and the orthogonal value is divided into reward items and penalty items according to the evaluation threshold.

4. A method for processing vibration signals of port equipment according to claim 3, characterized in that: The computational complexity of each candidate second wavelet basis function at the initial optimization frequency scale includes: obtaining the signal length of the de-noised signal at the initial optimization frequency scale and the filter length at the initial optimization frequency scale, and calculating the computational complexity of each candidate second wavelet basis function according to the signal length and the filter length.

5. A method for processing vibration signals of port equipment according to claim 4, characterized in that: The computational complexity of each candidate wavelet basis function at each initial optimized frequency scale is: ； In the formula, represents the computational complexity of the g-th candidate second wavelet basis function under the initial optimization frequency scale c, represents the signal length of the initial optimized frequency scale c, represents the filter length at the initial optimized frequency scale c, and N is the length of the original signal.

6. A method for processing vibration signals of port equipment according to claim 3, characterized in that: The orthogonal value of each candidate wavelet function at the initial optimized frequency scale is: ； In the formula, represents the orthogonal value of the correlation matrix A between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function under the initial optimization frequency scale c, represents the maximum singular value of the correlation matrix between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function at the initial optimized frequency scale c, represents the minimum singular value of the correlation matrix between the hth candidate second wavelet basis function and the oth candidate second wavelet basis function at the initial optimization frequency scale c, and Z represents the total number of candidate second wavelet basis functions at the initial optimization frequency scale c.

7. A method for processing vibration signals of port equipment according to claim 1, characterized in that: The comprehensive function is: ； In the formula, Represents a comprehensive consideration of the initial frequency scale d, the second wavelet basis function , Threshold The comprehensive function of represents the concentration of energy distribution on the i-th initial frequency scale d, represents the conversion function, represents the orthogonal value of the jth second wavelet basis function, represents the computational complexity of the jth second wavelet basis function, represents the signal-to-noise ratio of the time domain signal reconstructed after the kth threshold processing and the original signal, represents the sparse value of the optimized second wavelet coefficient after the kth threshold processing, , , Represent the adjustment coefficients, .

8. A method for processing vibration signals of port equipment according to claim 7, characterized in that: The conversion function is: ； In the formula, represents the conversion value of the orthogonal value of the jth second wavelet basis function, z represents a positive number, z represents the evaluation threshold, Represents the reward item, Represents a penalty term.

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