A method for self-diagnosis of outrigger circuit failure of an aerial work platform

Abstract

The application relates to the technical field of fault diagnosis, in particular to a method for diagnosing faults of outrigger lines of an aerial work platform, which comprises the following steps: acquiring various types of electrical signals of each outrigger line of the aerial work platform in real time; decomposing the various types of electrical signals into a plurality of IMF components, and decomposing each IMF component into a plurality of wavelet signals; constructing a noise interference confidence degree based on the degree of noise interference on each type of electrical signal of each outrigger, the random fluctuation degree and the proportion of prominent sharp peaks of each layer of wavelet signals, and the correlation degree between each IMF component and the original electrical signal, so as to optimize the soft threshold function in the wavelet threshold denoising algorithm, and then obtain filtered various types of electrical signals, and then judge whether faults occur in each outrigger line in the current monitoring period. The soft threshold function in the wavelet threshold denoising algorithm is optimized, the denoising effect of the electrical signal is improved, and the accuracy of the self-diagnosis of the outrigger line fault is improved.

Description

Technical Field

[0001] This application relates to the field of fault diagnosis technology, specifically to a self-diagnosis method for outrigger wiring faults in aerial work platforms. Background Technology

[0002] As a typical aerial work platform, the safety and stability of its operation are paramount. Since aerial work platforms are typically used in scenarios requiring high-altitude operations, their outrigger wiring is responsible for transmitting outrigger status signals. Aging or poor contact in the outrigger wiring can lead to safety hazards during operations, and malfunctions could cause accidents. Therefore, self-diagnosis of faults in each outrigger wiring is extremely important. Existing resistance detection methods, which acquire the voltage and current of the four outrigger wirings and then calculate their equivalent resistance for fault analysis, are widely used due to their convenience.

[0003] When using resistance detection to self-diagnose outrigger circuit faults on aerial work platforms, the power lines surrounding the outrigger circuits generate power frequency electric and magnetic fields during normal operation. Simultaneously, interference sources within the aerial work platform itself, including the ignition system, solenoid valves in the hydraulic system, generators, and high-power motors, also generate significant electromagnetic interference. This results in electromagnetic interference in the voltage and current signals collected from each outrigger circuit. Therefore, filtering and denoising of the collected voltage and current signals is necessary. Traditional wavelet threshold denoising algorithms typically use a fixed threshold to filter the wavelet signal, failing to fully consider the varying noise content in different frequency bands of the electrical signals within each outrigger circuit. Using the same filtering strength leads to poor filtering results, potentially causing misdiagnosis during subsequent outrigger circuit fault self-diagnosis. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a self-diagnosis method for outrigger wiring faults in aerial work platforms, thereby resolving the existing issues.

[0005] The self-diagnosis method for outrigger wiring faults of aerial work platforms proposed in this application adopts the following technical solution: One embodiment of this application provides a self-diagnosis method for outrigger wiring faults in an aerial work platform, the method comprising the following steps: Real-time acquisition of various electrical signals from the outriggers of the aerial work platform; Based on the complexity and dispersion of amplitude energy distribution of various electrical signals of each leg in the frequency domain during the current monitoring period, as well as the degree of difference in signal fluctuation characteristics between various electrical signals of each leg and other legs, a noise interference factor is constructed to characterize the degree of noise interference on various electrical signals of each leg. The various electrical signals of each leg during the current monitoring period are decomposed into several IMF components, and each IMF component is decomposed into multi-layer wavelet signals to obtain prominent peaks in each layer of wavelet signals. Based on the proportion of prominent peaks in the detail coefficients of each layer of wavelet signals and the degree of random fluctuation of the detail coefficients, noise feature values ​​are constructed. Combined with the noise interference factor and the correlation between each IMF component and its original electrical signal before decomposition, a noise interference confidence score is constructed to characterize the degree of noise interference on each layer of wavelet signals. Based on the noise interference confidence level, the soft threshold function in the wavelet threshold denoising algorithm is optimized to obtain each IMF component after filtering. After the IMF components are reconstructed, various types of filtered electrical signals are obtained. Based on the various types of filtered electrical signals of each leg line in the current monitoring period, it is determined whether each leg line has a fault in the current monitoring period.

[0006] Preferably, the construction process of the noise interference factor is as follows: Obtain the amplitude sequence of various electrical signals of each leg in the frequency domain during the current monitoring period; Based on the fluctuation complexity of the obtained amplitude sequence and the dispersion of all its difference values, the signal fluctuation characteristic values ​​of various electrical signals of each leg are obtained. The mean absolute difference between the signal fluctuation characteristic values ​​of each outrigger and all other outriggers of the same type of electrical signal during the current monitoring period is calculated. During the current monitoring period, the noise interference factors of various electrical signals of each leg are positively correlated with the signal fluctuation characteristic value and negatively correlated with the mean value.

[0007] Preferably, the signal fluctuation characteristic value of each leg's various electrical signals refers to the product between the permutation entropy and standard deviation of the first-order difference sequence of the amplitude sequence of each leg's various electrical signals in the frequency domain.

[0008] Preferably, the method for obtaining prominent peaks in the wavelet signals of each layer is as follows: Arrange all the detail coefficients of each wavelet signal in chronological order, and denote them as the detail coefficient sequence of each wavelet signal. Obtain the peak values ​​in the detail coefficient sequence, and calculate the mean of the absolute differences between each peak value and its adjacent detail coefficients; The product between each peak value and its corresponding mean value is recorded as the characteristic value of each peak value. The peak values ​​in the detail coefficient sequence of each wavelet signal that are greater than the preset peak threshold are recorded as prominent peaks.

[0009] Preferably, the noise feature value refers to the product between the proportion of prominent peaks in all detail coefficients of each layer of wavelet signal and the normalized value of the sample entropy of all detail coefficients.

[0010] Preferably, the method for constructing the noise interference confidence level is as follows: The absolute values ​​of the Pearson correlation coefficients between each IMF component and its undecomposed electrical signal are calculated. The noise interference confidence of each wavelet signal layer is positively correlated with the noise interference factor and the noise feature value, and negatively correlated with the absolute value.

[0011] Preferably, the calculation formula for optimizing the soft threshold function in the wavelet thresholding denoising algorithm is as follows: In the formula, The u-th layer wavelet signal is the optimized IMF component of the current signal of the a-th leg during the current monitoring period. It is a symbolic function; This represents the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period; The adaptive optimized weight of the u-th layer wavelet signal in the k-th IMF component of the current signal of the a-th leg during the current monitoring period; The preset wavelet threshold is used.

[0012] Preferably, the adaptive optimization weights of each wavelet signal layer are negatively correlated with the noise interference confidence level of each wavelet signal layer.

[0013] Preferably, the method for obtaining the filtered IMF components is as follows: Each IMF component is used as the input to the wavelet threshold denoising algorithm, and the optimized soft threshold function is used as the denoising function to output the filtered IMF components.

[0014] Preferably, the specific process for determining whether each outrigger line has a fault during the current monitoring period is as follows: Calculate the resistance value of each outrigger line during the current monitoring period based on the filtered electrical signals of each outrigger line during the current monitoring period. If the resistance value of any leg circuit is equal to the resistance value of the detection resistor connected in series with the normally closed switch, or equal to the resistance value of the detection resistor connected in series with the normally open switch, then it is determined that there is no fault in that leg circuit during the current monitoring period. Conversely, if the signal is not received, it is determined that the leg circuit is faulty during the current monitoring period.

[0015] This application has at least the following beneficial effects: This application addresses the issue of poor filtering performance in the self-diagnosis of outrigger circuits on aerial work platforms. Traditional denoising algorithms fail to consider the varying degrees of electromagnetic interference (EMI) in different frequency bands of the electrical signals from different outriggers. By analyzing the energy distribution characteristics of the electrical signals in the frequency domain and the differences in signal fluctuations between outriggers, a noise interference factor is constructed, enabling a preliminary assessment of the noise interference level of each outrigger's electrical signals. Furthermore, by analyzing the correlation between the IMF components of various electrical signals and their original signals, as well as the noise significance in wavelet signals, a noise interference confidence level is constructed, allowing for an accurate assessment of the noise interference level of each wavelet signal. Finally, based on the noise interference confidence level, the soft threshold function in the wavelet threshold denoising algorithm is optimized, improving the filtering effect of each IMF component. This, in turn, enhances the filtering and denoising effect of the reconstructed electrical signal, thereby improving the accuracy of the self-diagnosis of outrigger circuit faults on aerial work platforms. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the steps of a self-diagnosis method for outrigger wiring faults in an aerial work platform provided in this application; Figure 2 A flowchart for obtaining the noise interference confidence level provided in this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a self-diagnosis method for outrigger wiring faults of an aerial work platform according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] The following description, in conjunction with the accompanying drawings, details a specific scheme for a self-diagnosis method for outrigger wiring faults in aerial work platforms provided in this application.

[0021] This application provides an embodiment of a self-diagnosis method for outrigger wiring faults in aerial work platforms. Specifically, it provides the following self-diagnosis method for outrigger wiring faults in aerial work platforms. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps: Step 1: Acquire various electrical signals from the outriggers of the aerial work platform in real time.

[0022] When performing fault self-diagnosis on the outrigger circuits of an aerial work platform, it is first necessary to install detection resistor boards at each outrigger. Specifically, a detection resistor of 1000 kilohms (w) is connected in series with the normally open switches in each of the four outrigger circuits, and a detection resistor of 1000 kilohms (b) is connected in series with the normally closed switches in each of the four outrigger circuits. The normally open and normally closed branches are then connected in parallel to the same detection port on the controller. In this embodiment, w and b are set to 2 and 5, respectively.

[0023] Next, analog signal acquisition equipment is used to collect various electrical signals of each outrigger circuit in real time; in this embodiment, the various electrical signals refer to voltage signals and current signals. Since fault self-diagnosis of the outrigger circuits of the aerial work platform is required, it is typically necessary to detect sudden faults such as instantaneous short circuits and open circuits in real time, requiring the system to respond extremely quickly. Therefore, in this embodiment, the sampling frequency of both voltage and current signals is set to 1kHz to ensure a response within a very short time after a fault occurs, reducing response delay. The acquired signals are uniformly time-calibrated using the UTC+8 time standard to avoid time errors affecting subsequent calculations.

[0024] Step 2: Based on the complexity and dispersion of the amplitude energy distribution of various electrical signals of each leg in the frequency domain during the current monitoring period, as well as the degree of difference in signal fluctuation characteristics between various electrical signals of each leg and other legs, a noise interference factor is constructed to characterize the degree of noise interference on various electrical signals of each leg.

[0025] Because aerial work platforms operate in scenarios requiring high-altitude work, the power transmission lines overhead generate power frequency electric and magnetic fields around them during normal operation. These fields can interfere with electronic equipment that collects electrical signals through inductive or capacitive coupling. Simultaneously, interference sources within the aerial work platform itself, such as the ignition system, the solenoid valves of the hydraulic system (especially the outrigger solenoid valves), the generator, and high-power motors, also generate significant electromagnetic interference. This results in varying degrees of electromagnetic interference affecting the voltage and current signals acquired from different outriggers during operation. Therefore, filtering of the acquired electrical signals is necessary. Traditional wavelet thresholding algorithms typically use a fixed threshold to filter wavelet signals, failing to fully consider the different noise levels in different frequency bands of the electrical signals from each outrigger line. Using the same filtering strength leads to poor filtering results, potentially causing misdiagnosis during subsequent fault diagnosis of each outrigger line.

[0026] This application aims to optimize the wavelet threshold denoising process. Specifically, when an aerial work platform is in operation, considering that the outrigger circuits are operated by a controller, weak arcs and jitter caused by aging or poor contact in the outrigger circuits may result in weak signal glitches. The overall change in the current and voltage signals of the four outrigger circuits is small and will not cause complex fluctuations. However, since weak arcs and jitter usually occur instantaneously in a single outrigger, the local signal characteristics of a single outrigger differ significantly from those of the other outriggers. Furthermore, the surrounding power lines, the operation of solenoid valves in the ignition system and hydraulic system (especially the outrigger solenoid valves), the generator, and high-power motors—all interference sources within the aerial work platform itself—generate significant electromagnetic noise interference. When the electrical signal is affected by noise, the energy of the electrical signal at different frequencies in the frequency domain will vary considerably due to the random frequency of the noise. Moreover, since noise typically causes common-mode interference between the four outriggers, the correlation between them is usually high.

[0027] Based on the above analysis, the entire data monitoring process is divided into multiple monitoring periods according to a preset duration. In this embodiment, the preset duration is 5 seconds. Taking the current signal of the a-th leg in the current monitoring period as an example, the current signal of the a-th leg in the current monitoring period is first subjected to a Fast Fourier Transform to obtain the amplitude spectrum corresponding to the current signal of the a-th leg in the current monitoring period. All amplitude values ​​in the obtained amplitude spectrum are arranged in descending order of their frequencies to obtain the amplitude sequence of the current signal of the a-th leg in the frequency domain. The permutation entropy of the first-order difference sequence of the amplitude sequence of the a-th leg in the frequency domain in the current monitoring period is calculated, where the embedding dimension is set to an empirical value of 3 and the time delay is set to an empirical value of 1. This is used to characterize the complexity of the energy distribution of the current signal of the a-th leg in the frequency domain. At the same time, the standard deviation of the first-order difference sequence of the amplitude sequence of the a-th leg in the frequency domain in the current monitoring period is calculated to evaluate the frequency domain energy dispersion intensity of the current signal of the a-th leg. The product of the permutation entropy and standard deviation of the first-order difference sequence of the amplitude sequence of the current signal of the a-th leg in the frequency domain during the current monitoring period is denoted as the signal fluctuation characteristic value of the current signal of the a-th leg during the current monitoring period. The larger the value, the more complex the fluctuation of the current signal of the a-th leg during the current monitoring period, and the greater the energy dispersion. In this case, the current signal is more likely to be affected by noise interference during the current monitoring period. The calculation of permutation entropy and standard deviation are well-known techniques, and the specific process will not be elaborated here.

[0028] As a preferred implementation, a noise interference factor is constructed based on the complexity and dispersion of the amplitude energy distribution of various electrical signals of each leg in the frequency domain during the current monitoring period, as well as the degree of difference in signal fluctuation characteristics between various electrical signals of each leg and those of other legs. This noise interference factor is used to characterize the possibility of noise interference affecting various electrical signals of each leg during the current monitoring period. The construction process of the noise interference factor is as follows: obtaining the signal fluctuation characteristic values ​​of various electrical signals of each leg; calculating the mean of the absolute differences between the signal fluctuation characteristic values ​​of the same type of electrical signals of each leg and those of all other legs during the current monitoring period; the noise interference factor of various electrical signals of each leg during the current monitoring period is positively correlated with the signal fluctuation characteristic values ​​and negatively correlated with the mean. The positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and the negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases).

[0029] Preferably, in this embodiment, the noise interference factor of the current signal of the a-th leg during the current monitoring period is denoted as... Its specific expression is: In the formula, The noise interference factor is the current signal of the a-th leg during the current monitoring period; The signal fluctuation characteristic value of the current signal of the a-th leg during the current monitoring period; This represents the average absolute difference between the signal fluctuation characteristic values ​​of the current signals of the a-th outrigger and those of all other outriggers during the current monitoring period. A preset minimum constant is used to prevent the denominator from being 0. Its value range is [0.001, 0.1]. In this embodiment, it is taken as 0.01. For the normalization function, this embodiment uses the minimum-maximum normalization method for normalization.

[0030] income The larger the value, the more complex the frequency domain energy distribution of the current signal of the a-th leg during the current monitoring period, the greater the energy dispersion, and the greater the difference in fluctuation between the current signals of this leg and other legs. In this case, the current signal of the a-th leg during the current monitoring period is more likely to be affected by noise interference.

[0031] Similarly, the noise interference factors of various electrical signals of all outriggers during the current monitoring period can be obtained through the above methods.

[0032] Step 3: Decompose the various electrical signals of each leg during the current monitoring period into several IMF components, and decompose each IMF component into multi-layer wavelet signals to obtain prominent peaks in each layer of wavelet signals; based on the proportion of prominent peaks in the detail coefficients of each layer of wavelet signals and the degree of random fluctuation of the detail coefficients, construct noise feature values, and combine the noise interference factor and the correlation between each IMF component and its original electrical signal before decomposition to construct a noise interference confidence level, which is used to characterize the degree of noise interference of each layer of wavelet signals.

[0033] Furthermore, considering that when collecting various electrical signals, the signals will exhibit different intensities of interference across different frequency ranges when subjected to electromagnetic interference from the environment, the frequency range most severely affected by noise will be dominated by noise, while the frequency range corresponding to the weak arc and jitter characteristics caused by aging or poor contact of the leg wiring will be dominated by the effective signal. Therefore, the electrical signal in the frequency range most severely affected by noise will differ significantly from the original signal in the time domain. To achieve preliminary separation of noise and effective signal and provide a more accurate target for subsequent wavelet thresholding, taking the current signal of the a-th leg in the current monitoring period as an example, the current signal of the a-th leg in the current monitoring period is first decomposed using the CEEMDAN (Fully Adaptive Empirical Mode Decomposition of Noise) algorithm, outputting several IMF components and residual terms of the current signal of the a-th leg in the current monitoring period, with each IMF component arranged in order from high frequency to low frequency. The specific implementation process of the CEEMDAN algorithm is well known to those skilled in the art and will not be elaborated further.

[0034] Considering that under noise conditions, the more severely noise-affected the IMF component, the more it contains the noise-interferenced components, and its time-domain characteristics differ significantly from those of the original signal. Therefore, the more noise-affected the IMF component, the lower its correlation with the original signal before decomposition. Conversely, the less noise-affected the IMF component, the more it contains the effective part of the signal, and the higher its correlation with the original signal before decomposition. Therefore, taking the k-th IMF component of the current signal of the a-th leg in the current monitoring period as an example, we calculate the absolute value of the Pearson correlation coefficient between this IMF component and its undecomposed current signal. This value characterizes the correlation between the k-th IMF component and the original signal. The smaller the value, the less correlated the k-th IMF component is with the original current signal, and thus, the more severely the IMF component is affected by noise.

[0035] Furthermore, each IMF component is decomposed into M layers of wavelet signals using wavelet transform. The wavelet basis db4 is used to decompose each IMF component into M layers of wavelet signals. The value of M is set by the operator according to the actual situation. In this embodiment, M is set to 7 to ensure detail denoising in subsequent processing. After decomposition, approximation coefficients and detail coefficients are obtained for each layer of wavelet signals. The approximation coefficients represent the low-frequency trend of the signal, and the detail coefficients correspond to the high-frequency components of different frequency bands.

[0036] Considering that under the condition of high-frequency electromagnetic interference, the detail coefficients in each layer of wavelet signals in each IMF component of the current signal of each leg will exhibit high-frequency random fluctuations and frequent prominent peaks when affected by large noise interference.

[0037] Therefore, it is necessary to analyze the distribution characteristics of the detail coefficients of each wavelet signal. Taking the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg line as an example, all the detail coefficients of the u-th layer wavelet signal are arranged in chronological order and denoted as the detail coefficient sequence of the u-th layer wavelet signal. When a detail coefficient is greater than its two adjacent detail coefficients, it is denoted as the peak value in the detail coefficient sequence. Considering that prominent peaks usually exhibit the characteristics of high peak value and significant variation trend on both sides, the mean of the absolute difference between each peak value and its two adjacent detail coefficients in the detail coefficient sequence is calculated and denoted as the trend change factor of each peak value. The product of each peak value and its trend change factor is denoted as the characteristic value of each peak value. The larger the obtained characteristic value, the more likely the corresponding peak value is a prominent peak. The eigenvalues ​​of all peaks in the detail coefficient sequence of the u-th layer wavelet signal are linearly mapped to a discrete interval of 0-255, and a frequency histogram is constructed. This frequency histogram is used as input to the Otsu thresholding method. The maximum inter-class variance is calculated on the mapped eigenvalues ​​of all peaks in the detail coefficient sequence of the u-th layer wavelet signal, and a segmentation threshold is output. This segmentation threshold is then inversely mapped back to the original eigenvalue data interval, and the resulting mapped value is recorded as the preset peak threshold. Peaks in the detail coefficient sequence whose eigenvalues ​​are greater than the preset peak threshold are recorded as prominent peaks. The ratio of the total number of prominent peaks to the total number of detail coefficients in the detail coefficient sequence of the u-th layer wavelet signal is calculated, and the product of this ratio and the normalized value of the sample entropy of the detail coefficient sequence of the u-th layer wavelet signal is recorded as the noise eigenvalue of the u-th layer wavelet signal. In the calculation of sample entropy, the embedding dimension is set to 3, and the tolerance threshold is set to 0.2 times the standard deviation of the original data. The calculation process of sample entropy is a well-known technique, and the specific process will not be elaborated further. The larger the noise eigenvalue of the u-th wavelet signal, the more random and unpredictable the fluctuation of the instantaneous energy amplitude distribution corresponding to the detail coefficients of that wavelet layer is, and the more prominent the spikes, the more severely the wavelet layer is affected by noise.

[0038] It should be noted that the specific process of normalizing the sample entropy of the detail coefficient sequence is as follows: Calculate the sample entropy of all layer wavelet signals of all IMF components in the current signals of all legs within the current monitoring period and the preset number of monitoring periods prior, count the maximum and minimum values, and then normalize all the sample entropies using the min-max normalization method. In this embodiment, the preset number is 20. The min-max normalization method is a well-known technique, and its specific process will not be elaborated further.

[0039] Based on the above analysis, as a preferred implementation, a noise interference confidence level is constructed based on the noise characteristic values ​​of each layer of wavelet signals, the noise interference factors of various electrical signals of each corresponding leg, and the correlation between each IMF component and its original electrical signal before decomposition. This confidence level is used to characterize the degree of noise interference on each layer of wavelet signals. The method for constructing the noise interference confidence level is as follows: the absolute value of the Pearson correlation coefficient between each IMF component and its original electrical signal before decomposition is calculated. The noise interference confidence level of each layer of wavelet signals is positively correlated with the noise interference factors of various electrical signals of each corresponding leg and the noise characteristic values ​​of each layer of wavelet signals, and negatively correlated with the absolute value. The flowchart for obtaining the noise interference confidence level in this application is shown below. Figure 2 As shown.

[0040] To eliminate the influence of dimensions on subsequent calculations, the noise interference factor of the current signal of all legs in the current monitoring period and the number of monitoring periods before it is calculated, and the minimum and maximum values ​​of all noise interference factors are counted. Then, the minimum-maximum normalization method is used to normalize the noise interference factor of the current signal of all legs in the current monitoring period.

[0041] Preferably, in this embodiment, the noise interference confidence level of the u-th layer wavelet signal of the k-th IMF component of the current signal of the a-th leg during the current monitoring period is denoted as... Its specific expression is: In the formula, The noise interference confidence level of the u-th layer wavelet signal of the k-th IMF component of the current signal of the a-th leg during the current monitoring period; This is the normalized value of the noise interference factor of the current signal of the a-th leg during the current monitoring period; The noise characteristic value of the u-th layer wavelet signal of the k-th IMF component of the current signal of the a-th leg during the current monitoring period; This represents the absolute value of the Pearson correlation coefficient between the k-th IMF component of the current signal of the a-th leg during the current monitoring period and the current signal before decomposition. This is a preset minimum constant used to prevent the denominator from being 0.

[0042] income The larger the value, the greater the degree of noise interference on the u-th layer wavelet signal of the k-th IMF component of the current signal of the a-th leg during the current monitoring period.

[0043] Step 4: Optimize the soft threshold function in the wavelet threshold denoising algorithm based on the noise interference confidence level to obtain each IMF component after filtering. After reconstructing the IMF components, obtain various types of filtered electrical signals. Based on the various types of filtered electrical signals of each leg line in the current monitoring period, determine whether each leg line has a fault in the current monitoring period.

[0044] Furthermore, when filtering noise in each IMF component of the electrical signal from different support lines, excessive filtering can lead to severe loss of detail in the originally noise-free wavelet signal; conversely, insufficient filtering can result in incomplete noise removal from the heavily noise-affected wavelet signal. Therefore, different denoising intensities should be set for wavelet signals with varying noise interference levels. Based on the above, this application optimizes the soft threshold function in wavelet threshold denoising based on the noise interference confidence level of each layer of wavelet signal, ensuring that each IMF component retains its detailed features while effectively filtering out noise.

[0045] Specifically, taking the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period as an example, the noise interference confidence of this layer wavelet signal is normalized using the minimum-maximum normalization method. The specific normalization process is as follows: calculate the noise interference confidence of all layer wavelet signals of all IMF components in the current signals of all legs during the current monitoring period and the preset number of monitoring periods before, count the maximum and minimum values, and then use the minimum-maximum normalization method to normalize the noise interference confidence of each noise interference during the current monitoring period.

[0046] Based on the normalized noise interference confidence of the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period, an adaptive optimization weight for the u-th layer wavelet signal is constructed: In the formula, The adaptive optimized weight of the u-th layer wavelet signal in the k-th IMF component of the current signal of the a-th leg during the current monitoring period; This is the normalized value of the noise interference confidence of the u-th layer wavelet signal of the k-th IMF component of the current signal of the a-th leg during the current monitoring period; using reduce The purpose is to ensure that the value of the obtained adaptive optimization weight is within the range of [0.5, 1.5], so that when performing soft thresholding denoising on each wavelet layer signal, the signal can be both amplified and reduced.

[0047] Among them, when The smaller the value, the more... The larger the value, the more severe the noise interference is on the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period. Therefore, when using the soft threshold function for denoising, the wavelet signal of this layer should be weakened so that the noise in this wavelet layer is reduced.

[0048] Furthermore, existing technologies typically use a fixed threshold to filter wavelet signals, but they fail to consider the varying degrees of noise impact on different outrigger lines due to their different distances from the noise source. This can lead to insufficient filtering or loss of detail during filtering. Therefore, this application fully considers the noise impact intensity of the electrical signals of different outrigger lines and the noise impact on different IMF components, and performs feature-weighted optimization on the expression of the soft threshold function. This allows for adaptive optimization of each wavelet signal in each IMF component based on the noise interference level, thereby improving the filtering effect on each IMF component, and ultimately improving the filtering effect of the current signal, thus enhancing the accuracy of subsequent self-diagnosis of outrigger line faults.

[0049] Specifically, the soft thresholding function in the wavelet thresholding denoising algorithm is optimized based on the adaptive optimization weights of each wavelet signal: In the formula, The u-th layer wavelet signal is the optimized IMF component of the current signal of the a-th leg during the current monitoring period. It is a symbolic function; This represents the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period; The adaptive optimized weight of the u-th layer wavelet signal in the k-th IMF component of the current signal of the a-th leg during the current monitoring period; To preset the wavelet threshold, this embodiment sets it to... , This is the ratio of the median absolute value of all detail coefficients of the first-level wavelet signal to 0.6745. This represents the length of the k-th IMF component in the current signal of the a-th leg during the current monitoring period.

[0050] When the wavelet layer is more severely affected by noise, a smaller adaptive optimization weight should be used to weaken the wavelet signal and effectively filter out the noise; when the wavelet layer is less affected by noise, a larger adaptive optimization weight should be used to amplify the features of the wavelet signal and thus preserve the effective detailed features.

[0051] Furthermore, each IMF component in the current signal of the a-th leg during the current monitoring period is used as the input of the wavelet threshold denoising algorithm. The optimized soft threshold function is used as the denoising function to output the filtered IMF components of the current signal of the a-th leg during the current monitoring period. All the filtered IMF components are merged to obtain the filtered current signal of the a-th leg during the current monitoring period.

[0052] Similarly, the filtered current and voltage signals of each leg are obtained during the current monitoring period.

[0053] Furthermore, based on the filtered voltage and current signals of each leg during the current monitoring period, Ohm's law is used to calculate the resistance value of each leg's circuit during the current monitoring period. The process of calculating the resistance value using Ohm's law is a well-known technique, and its specific steps will not be elaborated further.

[0054] Furthermore, the resistance values ​​of each leg circuit during the current monitoring period are used as input, and a resistance detection method is used to perform fault self-diagnosis for each leg circuit during the current monitoring period. Specifically, if the resistance value of any leg circuit is equal to the resistance value of the detection resistor connected in series with the normally closed switch, or equal to the resistance value of the detection resistor connected in series with the normally open switch, then it is determined that the leg circuit during the current monitoring period is fault-free; otherwise, it is determined that the leg circuit during the current monitoring period is faulty. When any leg circuit malfunctions, if the resistance value of the leg circuit approaches the parallel resistance value of the two detection resistors, it is determined that the leg circuit has a limit switch fault; if the resistance value of the leg circuit approaches infinity, it is determined that both sets of contacts of the leg circuit are simultaneously open or the detection wire is broken; if the resistance value of the leg circuit is close to 0, it is determined that the detection wire of the leg circuit is short-circuited.

[0055] Using the above fault diagnosis method, fault diagnosis is performed on the outrigger wiring during the current monitoring period. The fault diagnosis results are displayed in real time on a screen or remote terminal, and the fault diagnosis information is fed back to maintenance personnel to help them quickly locate the fault point. This completes the self-diagnosis method for the outrigger wiring of the aerial work platform vehicle.

[0056] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0057] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0058] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A self-diagnosis method for outrigger wiring faults in aerial work platforms, characterized in that, The method includes the following steps: Real-time acquisition of various electrical signals from the outriggers of the aerial work platform; Based on the complexity and dispersion of amplitude energy distribution of various electrical signals of each leg in the frequency domain during the current monitoring period, as well as the degree of difference in signal fluctuation characteristics between various electrical signals of each leg and other legs, a noise interference factor is constructed to characterize the degree of noise interference on various electrical signals of each leg. The various electrical signals of each leg during the current monitoring period are decomposed into several IMF components, and each IMF component is decomposed into multi-layer wavelet signals to obtain prominent peaks in each layer of wavelet signals. Based on the proportion of prominent peaks in the detail coefficients of each layer of wavelet signals and the degree of random fluctuation of the detail coefficients, noise feature values ​​are constructed. Combined with the noise interference factor and the correlation between each IMF component and its original electrical signal before decomposition, a noise interference confidence score is constructed to characterize the degree of noise interference on each layer of wavelet signals. Based on the noise interference confidence level, the soft threshold function in the wavelet threshold denoising algorithm is optimized to obtain each IMF component after filtering. After IMF reconstruction, various types of filtered electrical signals are obtained. Based on the various types of filtered electrical signals of each leg line in the current monitoring period, it is determined whether each leg line has a fault in the current monitoring period.

2. The self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The construction process of the noise interference factor is as follows: Obtain the amplitude sequence of various electrical signals of each leg in the frequency domain during the current monitoring period; Based on the fluctuation complexity of the obtained amplitude sequence and the dispersion of all its difference values, the signal fluctuation characteristic values ​​of various electrical signals of each leg are obtained. The mean absolute difference between the signal fluctuation characteristic values ​​of each outrigger and all other outriggers of the same type of electrical signal during the current monitoring period is calculated. During the current monitoring period, the noise interference factors of various electrical signals of each leg are positively correlated with the signal fluctuation characteristic value and negatively correlated with the mean value.

3. The self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 2, characterized in that, The signal fluctuation characteristic value of each leg refers to the product between the permutation entropy and standard deviation of the first-order difference sequence of the amplitude sequence of each leg's various electrical signals in the frequency domain.

4. The self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The method for obtaining the prominent spikes in the wavelet signals of each layer is as follows: Arrange all the detail coefficients of each wavelet signal in chronological order, and denote them as the detail coefficient sequence of each wavelet signal. Obtain the peak values ​​in the detail coefficient sequence, and calculate the mean of the absolute differences between each peak value and its adjacent detail coefficients; The product between each peak value and its corresponding mean value is recorded as the characteristic value of each peak value. The peak values ​​in the detail coefficient sequence of each wavelet signal that are greater than the preset peak threshold are recorded as prominent peaks.

5. A self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The noise eigenvalue refers to the product of the proportion of prominent spikes in all detail coefficients of each layer of wavelet signal and the normalized value of the sample entropy of all detail coefficients.

6. The self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The method for constructing the noise interference confidence level is as follows: The absolute values ​​of the Pearson correlation coefficients between each IMF component and its undecomposed electrical signal are calculated. The noise interference confidence of each wavelet signal layer is positively correlated with the noise interference factor and the noise feature value, and negatively correlated with the absolute value.

7. A self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The calculation formula for optimizing the soft threshold function in the wavelet threshold denoising algorithm is as follows: In the formula, The u-th layer wavelet signal is the optimized IMF component of the current signal of the a-th leg during the current monitoring period. It is a symbolic function; This represents the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period; The adaptive optimized weight of the u-th layer wavelet signal of the k-th IMF component in the current signal of the a-th leg during the current monitoring period; The preset wavelet threshold is used.

8. A self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 7, characterized in that, The adaptive optimization weights of wavelet signals at each layer are negatively correlated with the noise interference confidence of wavelet signals at each layer.

9. A self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The method for obtaining the filtered IMF components is as follows: Each IMF component is used as the input to the wavelet threshold denoising algorithm, and the optimized soft threshold function is used as the denoising function to output the filtered IMF components.

10. A self-diagnosis method for outrigger wiring faults in an aerial work platform as described in claim 1, characterized in that, The specific process for determining whether each support leg circuit has a fault during the current monitoring period is as follows: Calculate the resistance value of each outrigger line during the current monitoring period based on the filtered electrical signals of each outrigger line during the current monitoring period. If the resistance value of any leg circuit is equal to the resistance value of the detection resistor connected in series with the normally closed switch, or equal to the resistance value of the detection resistor connected in series with the normally open switch, then it is determined that there is no fault in that leg circuit during the current monitoring period. Conversely, if the signal is not received, it is determined that the leg circuit is faulty during the current monitoring period.

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