Electric chain saw chain device and operating safety detection method

Abstract

The present invention relates to the technical field of vibration data detection, and specifically relates to an electric chain saw chain device and an operating safety detection method. The method comprises: obtaining a time-series initial noise factor of vibration data at each moment on the basis of the difference between local vibration data in a vibration signal data sequence and distribution of the differences between all adjacent vibration data within a local range, and obtaining a time-series noise factor of the vibration data at each moment by means of distribution of time intervals between all adjacent extreme points in a local vibration data curve; on the basis of the difference between the energy corresponding to a fundamental frequency and the energy corresponding to harmonic frequencies and the degree of disorder in energy distribution of the harmonic frequencies, obtaining a frequency domain noise factor of the vibration data at each moment, and filtering the vibration data to obtain a filtered vibration signal data sequence; and then performing operating safety detection of the electric chain saw chain device. The present invention reduces the interference of environmental noise and improves the accuracy of the operating safety detection of the chain device.

Description

Electric chain saw chain device and working safety detection method TECHNICAL FIELD

[0001] The present application relates to the technical field of vibration data detection, and particularly relates to an electric chain saw chain device and a working safety detection method. BACKGROUND

[0002] An electric chain saw is a commonly used tool. The chain working safety is detected through a chain tightness structure and a vibration sensor, and the tightness structure is used for adjustment. During use, the loosening degree of the chain will change with the increase of the use time. If the electric chain saw chain is loosened too much, obvious shaking will occur during work, which not only affects the work efficiency, but also easily causes the chain to fall off or causes a safety accident. In addition, the chain that is loosened too much will also cause the chain and the sprocket to be worn out, thereby shortening the service life of the electric chain saw.

[0003] When detecting the electric chain saw chain device, a vibration sensor is usually installed on the electric chain saw, and the vibration data of the chain is collected by using the vibration sensor to analyze the abnormality of the electric chain saw chain device to ensure the safety of work. When detecting the vibration data of the electric chain saw chain, due to the interference of noise in the external environment, the collected vibration data cannot accurately detect the normal and abnormal conditions of the electric chain saw chain device, thereby reducing the accuracy of the working safety detection of the chain device. SUMMARY

[0004] The present application provides an electric chain saw chain device and a working safety detection method to solve the existing problems.

[0005] The electric chain saw chain device and the working safety detection method provided by the present application adopt the following technical scheme:

[0006] An embodiment of the present application provides an electric chain saw chain device and a working safety detection method. The method comprises the following steps:

[0007] Obtain the vibration signal data sequence of the electric chain saw chain in time sequence;

[0008] According to the difference between each time and the vibration data in the local range in the vibration signal data sequence, and the distribution of the difference between all adjacent vibration data in the local range of each time, obtain the time sequence initial noise factor of the vibration data of each time. Obtain the local vibration data sequence of each time, perform curve fitting on the local vibration data sequence of each time, obtain the local vibration data curve of each time, and correct the time sequence initial noise factor by the distribution of the time interval between all adjacent extreme points in the local vibration data curve of each time, to obtain the time sequence noise factor of the vibration data of each time.

[0009] Obtain the frequency spectrum corresponding to each time point of the local vibration data sequence, and obtain the main frequency and harmonic frequency in the frequency spectrum; obtain the frequency domain noise factor of the vibration data at each time point according to the difference between the energy corresponding to the main frequency and the harmonic frequency, and the degree of confusion of the energy of the harmonic frequency; obtain the reliability factor of the vibration data at each time point according to the time sequence noise factor and the frequency domain noise factor; filter the vibration signal data sequence through the reliability factor to obtain the filtered vibration signal data sequence;

[0010] Detect the chain saw chain equipment and work safety through the filtered vibration signal data sequence.

[0011] Further, the time sequence initial noise factor of the vibration data at each time point is obtained according to the difference between the vibration data at each time point and the local range, and the distribution of the difference between all adjacent vibration data in the local range at each time point, including the following specific steps:

[0012] The average of the difference between the vibration data at each time point and all vibration data in the corresponding local range is recorded as the first vibration difference at each time point;

[0013] The standard deviation of the difference between all adjacent vibration data in the local range at each time point is recorded as the smooth vibration difference degree at each time point; the time sequence initial noise factor of the vibration data at each time point is obtained through the first vibration difference and the smooth vibration difference degree at each time point;

[0014] The first vibration difference and the time sequence initial noise factor are negatively correlated, and the smooth vibration difference degree and the time sequence initial noise factor are positively correlated.

[0015] Further, the local vibration data curve at each time point is obtained by curve fitting the local vibration data sequence at each time point, including the following specific steps:

[0016] The local vibration data curve at each time point is obtained by curve fitting the local vibration data sequence at each time point through the least square method.

[0017] Further, the time sequence noise factor of the vibration data at each time point is obtained by correcting the time sequence initial noise factor according to the distribution of the time interval between all adjacent extreme points in the local vibration data curve at each time point, including the following specific steps:

[0018] The time sequence noise factor of the vibration data at each time point is obtained by correcting the time sequence initial noise factor according to the standard deviation of the time interval between all adjacent extreme points in the local vibration data curve at each time point;

[0019] The standard deviation of the time interval between all adjacent extreme points in the local vibration data curve of each time point is positively correlated with the time sequence noise factor.

[0020] Further, the specific steps of obtaining the main frequency and harmonic frequency in the frequency spectrum include the following:

[0021] The main frequency in the frequency spectrum is obtained by a peak search algorithm, and the frequencies other than the main frequency are harmonic frequencies.

[0022] Further, the specific steps of obtaining the frequency domain noise factor of the vibration data at each time point according to the difference between the corresponding energy of the main frequency and the harmonic frequency, and the chaotic degree of the energy of the harmonic frequency include the following:

[0023] The difference between the energy mean of all main frequencies in the frequency spectrum at each time point and the energy mean of all harmonic frequencies is recorded as the frequency spectrum energy difference at each time point.

[0024] The information entropy of the energy of all harmonic frequencies in the frequency spectrum at each time point is obtained by the chaotic degree of the energy of the harmonic frequency; and the frequency domain noise factor of the vibration data at each time point is obtained by the information entropy and the frequency spectrum energy difference at each time point.

[0025] The information entropy is positively correlated with the frequency domain noise factor, and the frequency spectrum energy difference at each time point is negatively correlated with the frequency domain noise factor.

[0026] Further, the specific steps of obtaining the reliability factor of the vibration data at each time point according to the time sequence noise factor and the frequency domain noise factor include the following:

[0027] The value of the product of the time sequence noise factor and the frequency domain noise factor after negative correlation mapping is recorded as the reliability factor of the vibration data at each time point.

[0028] Further, the specific steps of filtering the vibration signal data sequence by the reliability factor to obtain a filtered vibration signal data sequence include the following:

[0029] Each time point in the vibration signal data sequence is taken as the center point of the filtering window, and a second preset parameter is taken as the size of the filtering window to obtain the filtering window of the vibration data at each time point.

[0030] The filtering weight of the vibration data at each time point in the filtering window at each time point is obtained by the proportion of the reliability factor of the vibration data at each time point in the filtering window at each time point.

[0031] All the vibration data at each time point in the vibration signal data sequence is filtered by the filtering weight to obtain a filtered vibration signal data sequence.

[0032] Further, the filtering of the vibration data at all times in the vibration signal data sequence by the filtering weight comprises the following specific steps:

[0033] The vibration data at all times in the vibration signal data sequence of the electric chain saw chain device is filtered by weighting and averaging all vibration data in each time filtering window with the corresponding filtering weight to obtain the filtered vibration data at each time, and the filtered vibration signal data sequence is obtained.

[0034] Further, the electric chain saw chain device and working safety detection by the filtered vibration signal data sequence comprises the following specific steps:

[0035] The vibration data at all times in the subsequent one hour is obtained by ARIMA model prediction according to the filtered vibration signal data sequence, the abnormal time is obtained by anomaly detection according to the vibration data at all times in the subsequent one hour by the isolation forest algorithm, the time except the abnormal time is the normal time, and the working safety detection of the electric chain saw chain device is completed by the normal time and the abnormal time.

[0036] The beneficial effects of the technical scheme of the present application are: according to the difference between the vibration data at each time and the local range in the vibration signal data sequence, the distribution of the difference between all adjacent vibration data in the local range at each time, and the distribution of the time interval between all adjacent extreme points in the local vibration data curve at each time, the time series noise factor of the vibration data at each time is obtained, which improves the accuracy of the analysis of the influence of environmental noise on vibration data in time series; according to the difference between the energy corresponding to the main frequency and the harmonic frequency, and the chaotic degree of the energy of the harmonic frequency, the frequency domain noise factor of the vibration data at each time is obtained, which improves the accuracy of the analysis of the influence of environmental noise on vibration data in frequency domain; according to the time series noise factor and the frequency domain noise factor, the reliability factor of the vibration data at each time is obtained, which improves the precision of the analysis of the influence of environmental noise on the vibration data at each time; the vibration signal data sequence is filtered by the reliability factor to obtain the filtered vibration signal data sequence, which reduces the interference of environmental noise; the electric chain saw chain device and working safety detection are performed by the filtered vibration signal data sequence, which improves the accuracy of the working safety detection of the chain device. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0038] Fig. 1 is a step flow chart of an electric chain saw chain device and working safety detection method according to an embodiment of the present application;

[0039] Fig. 2 is a flow chart of an electric chain saw chain device and working safety detection method according to an embodiment of the present application;

[0040] Fig. 3 is a vibration data change graph when there is no any abnormality;

[0041] Fig. 4 is a vibration data change graph when the chain is loose and worn;

[0042] Fig. 5 is a vibration data change graph when there is noise interference. DETAILED DESCRIPTION

[0043] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purpose, the specific implementation, structure, features and effects of an electric chain saw chain device and working safety detection method according to the present application will be described in detail below in combination with the drawings and preferred embodiments. Different "one embodiment" or "another embodiment" in the following description do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art of the technology to which the present application belongs.

[0044] The specific scheme of an electric chain saw chain device and working safety detection method according to the present application will be described in detail below in combination with the drawings.

[0045] Please refer to Fig. 1, which shows a step flow chart of an electric chain saw chain device and working safety detection method according to an embodiment of the present application. The method includes the following steps:

[0046] Step S001: Collecting vibration data of the electric chain saw chain.

[0047] It should be noted that due to the wear of the electric chain saw chain caused by long-term use, the safety of the workers cannot be guaranteed when the chain is worn during the use process. Therefore, the vibration of the chain needs to be analyzed to determine whether the electric chain saw chain is abnormal. Therefore, the vibration data of the electric chain saw chain needs to be collected.

[0048] Specifically, a vibration sensor is installed on the shell of the electric chain saw chain to collect vibration data of the chain at all times within a two-hour work period at a sampling interval of 1 second, all the vibration data of the chain at all times are sorted in chronological order to form a sequence, which is recorded as the vibration signal data sequence of the electric chain saw chain. The sampling interval and the sampling duration are not specifically limited and can be determined by the implementer according to the specific circumstances.

[0049] Thus, the vibration signal data sequence of the electric chain saw chain is obtained.

[0050] Step S002: According to the difference between the vibration data at each time and in the local range, the distribution of the difference between all adjacent vibration data in the local range at each time, the time sequence initial noise factor of the vibration data at each time is obtained; the local vibration data sequence at each time is obtained, the local vibration data curve at each time is obtained by curve fitting of the local vibration data sequence at each time, and the time sequence noise factor of the vibration data at each time is obtained by correcting the time sequence initial noise factor through the distribution of the time interval between all adjacent extreme points in the local vibration data curve at each time.

[0051] It should be noted that the chain of the electric chain saw is operated and worked by the electric motor. Since the sawtooth on the chain will generate inertial force and inertial vibration when rotating at high speed in the process of normal work and without any interference, that is, the inertial vibration of the sawtooth is stable when sawing wood normally; and the vibration of the electric motor is also stable when the chain is normally operated by the electric motor, therefore, the corresponding vibration data of the electric chain saw chain is stable when it is not disturbed by the environment and abnormal interference, and the corresponding vibration data change is shown in FIG. 3. When the chain of the electric chain saw is too loose, it will cause the relative movement of the chain to increase in the transmission process, thereby increasing the vibration amplitude of the chain. This is because the loose chain has a larger vibration space when subjected to force, so its vibration amplitude will be relatively large; long-term loose state may cause the chain to wear out faster, thereby intensifying the instability and amplitude increase of vibration, and even causing damage to other parts of the transmission system; therefore, the looseness and wear of the chain will increase the vibration amplitude of the chain, so that the vibration data of the chain is more volatile, and the corresponding vibration data change is shown in FIG. 4. Since noise usually exhibits as continuous random disturbance, the vibration signal data is analyzed by the fluctuation change. The corresponding vibration data change under noise interference is shown in FIG. 5.

[0052] It is further needed to be explained that, since the looseness and wear of the chain will introduce unstable force and vibration in the transmission process, resulting in the instantaneous amplitude of the vibration signal changing sharply; and this change is a regular periodic disturbance. The influence of noise is relatively stable and continuous random disturbance, so the time sequence abnormal factor of the chain vibration data can be analyzed by the disturbance degree and distribution of the chain vibration data.

[0053] Since the influence of noise on the chain vibration data is smaller than the looseness and wear of the chain, the fluctuation factor of the vibration data at each time can be analyzed according to the difference between the vibration data at each time and the local vibration data; because the interference of noise is a random disturbance, and the looseness and wear of the chain is a regular disturbance, the difference between adjacent vibration data in the regular disturbance is equal, and the difference between adjacent vibration data in the random disturbance is not equal, so the regularity of the vibration data at each time can be analyzed by the difference between all adjacent vibration data in the local range at each time. Therefore, according to the difference between the vibration data at each time and the local vibration data in the vibration signal data sequence, the distribution of the difference between all adjacent vibration data in the local range at each time, the time sequence initial noise factor of the vibration data at each time is obtained.

[0054] Preferably, taking the vibration data at each time in the vibration signal data sequence as a center point, the adjacent vibration data on the left side of the center point and the adjacent vibration data on the right side of the center point are recorded as the vibration data in the local range at each time; wherein the vibration data in the local range includes the center point. In this embodiment, is a first preset parameter, wherein , but is not specifically limited, and the implementer can be determined according to the specific situation. Further, as an embodiment, the specific calculation method of the time sequence initial noise factor of the vibration data at each time is:

[0055] The average of the difference between the vibration data at each time and all vibration data in the corresponding local range is recorded as the first vibration difference at each time;

[0056] The standard deviation of the difference between all adjacent vibration data in the local range at each time is recorded as the stable vibration difference degree at each time; the time sequence initial noise factor of the vibration data at each time is obtained by the first vibration difference and the stable vibration difference degree at each time;

[0057] Wherein, the first vibration difference and the time sequence initial noise factor are in a negative correlation, and the stable vibration difference degree and the time sequence initial noise factor are in a positive correlation.

[0058] In one embodiment of the present invention, the specific formula is as follows:

[0059]

[0060] In the formula, Indicates the first The standard deviation of the difference between all adjacent vibration data within a local area at a given time. The data sequence representing the vibration signal of the electric chainsaw chain. Vibration data at each moment, Indicates the first The local range of the nth time moment Vibration data, Indicates the first The number of all vibration data points within a local area at a given time. Indicates the first The initial temporal noise factor of the vibration data at each time point This represents an exponential function with the natural constant as its base. The symbol represents the absolute value. Specifically, the smaller the standard deviation of the difference between all adjacent vibration data within a local range at each moment, the stronger the regularity of the vibration data variation, and the lower the probability of noise disturbance, i.e., the smaller the initial noise factor of the time series; conversely, the larger the standard deviation, the weaker the regularity of the vibration data variation, and the higher the probability of noise disturbance, i.e., the larger the initial noise factor of the time series. This represents the difference between the vibration data at each moment and the vibration data within the corresponding local area. The larger the difference, the more likely the vibration data is abnormal due to a loose chain or wear. The smaller the difference, the more likely it is caused by noise.

[0061] Thus, the initial temporal noise factor of the vibration data at each moment is obtained.

[0062] It should be noted that the fluctuation of vibration data is small when there is no chain looseness, wear, or noise interference. However, fluctuation factor analysis may show similarities between the cases with and without noise interference, thus requiring further differentiation. Since noise interference is random, vibration data does not exhibit periodicity when noise is present. Vibration data without chain looseness, wear, or noise interference is regular and periodic, while data with chain looseness or wear generally also exhibits periodicity. Therefore, the degree of noise interference in the vibration data at each moment is corrected by periodicity. Thus, the initial time-series noise factor is corrected by analyzing the distribution of time intervals between all adjacent extreme points in the local vibration data curve at each moment, obtaining the time-series noise factor of the vibration data at each moment.

[0063] Preferably, all the vibration data in the local range of each time point is sorted in time sequence to form a sequence, denoted as a local vibration data sequence of each time point. The local vibration data sequence of each time point is curve-fitted by using a quintic polynomial through a least square method to obtain a local vibration data curve of each time point. All extreme points in the local vibration data curve of each time point are obtained, wherein the extreme points include maximum points and minimum points. The least square method is a known technology and will not be described in detail herein; and in the embodiment, a quintic polynomial is used for curve-fitting, but no specific limitation is made and the implementer can determine according to the specific situation.

[0064] Further, as an embodiment, the specific calculation method of the time sequence noise factor of each time point vibration data is as follows:

[0065] The time sequence initial noise factor is corrected according to the standard deviation of the time interval between all adjacent extreme points in the local vibration data curve of each time point to obtain the time sequence noise factor of each time point vibration data.

[0066] The standard deviation of the time interval between all adjacent extreme points in the local vibration data curve of each time point and the time sequence noise factor are in a positive correlation relationship.

[0067] In an embodiment of the present application, the specific formula is as follows:

[0068]

[0069] In the formula, σt represents the standard deviation of the time interval between all adjacent extreme points in the local vibration data curve of the t th time point, σt0 represents the time sequence initial noise factor of the t th time point vibration data, and σt represents the time sequence noise factor of the t th time point vibration data.

[0070] When the standard deviation of the time interval between all adjacent extreme points in the local vibration data curve of each time point is smaller, it means that the periodicity is stronger and the time point is less affected by noise; when the standard deviation is larger, it means that the periodicity is weaker and the time point is more affected by noise.

[0071] Thus, the time sequence noise factor of each time point vibration data is obtained.

[0072] ​​​​​​Step S003: obtaining a frequency spectrum corresponding to each time point of the local vibration data sequence, obtaining a main frequency and a harmonic frequency in the frequency spectrum; obtaining a frequency domain noise factor of the vibration data at each time point according to a difference between the energy corresponding to the main frequency and the harmonic frequency, and a degree of confusion of the energy of the harmonic frequency; obtaining a reliability factor of the vibration data at each time point according to the time sequence noise factor and the frequency domain noise factor; filtering the vibration signal data sequence through the reliability factor to obtain a filtered vibration signal data sequence.

[0073] It should be noted that, when the chain is loose and worn, the vibration radiation of the vibration data of the chain will increase, so the vibration signal data sequence of the electric chain saw chain corresponds to a larger amplitude of the main frequency in the frequency spectrum, which makes the difference between the energy corresponding to the main frequency and the harmonic frequency larger. As for the influence of noise on the vibration data, due to the existence of noise, the originally clear vibration signal frequency component may become blurred or unclear on the frequency spectrum, which makes the difference between the energy corresponding to the main frequency and the harmonic frequency smaller. Moreover, the influence of noise is random disturbance, so the energy distribution of the harmonic frequency in the frequency spectrum is more chaotic. Therefore, the frequency domain noise factor of the vibration data at each time point is obtained according to the difference between the energy corresponding to the main frequency and the harmonic frequency, and the degree of confusion of the energy of the harmonic frequency.

[0074] Preferably, the corresponding frequency spectrum of each time point of the local vibration data sequence is obtained through Fourier transform; wherein the Fourier transform is a known technology, which will not be described in detail here. The main frequency in the frequency spectrum is obtained through a peak search algorithm, wherein the frequencies other than the main frequency are harmonic frequencies. The peak search algorithm is a known technology, which will not be described in detail here. The horizontal axis of the frequency spectrum is the frequency, and the vertical axis is the energy corresponding to the frequency.

[0075] Further, as an embodiment, the specific calculation method of the frequency domain noise factor of the vibration data at each time point is as follows:

[0076] The difference between the average energy of all main frequencies in the frequency spectrum at each time point and the average energy of all harmonic frequencies is recorded as the frequency spectrum energy difference at each time point.

[0077] The information entropy of the energy corresponding to all harmonic frequencies in the frequency spectrum at each time point is obtained through the degree of confusion of the energy of the harmonic frequency; the frequency domain noise factor of the vibration data at each time point is obtained through the information entropy and the frequency spectrum energy difference at each time point.

[0078] The information entropy and the frequency domain noise factor are in a positive correlation, and the frequency spectrum energy difference at each time point and the frequency domain noise factor are in a negative correlation.

[0079] In an embodiment of the present application, the formula is specifically expressed as:

[0080]

[0081] In the formula, Indicates the first The information entropy of the energy corresponding to all harmonic frequencies in the spectrum at a given time. Indicates the first The mean energy corresponding to all dominant frequencies in the spectrum at a given time. Indicates the first The mean energy corresponding to all harmonic frequencies in the spectrum at a given time. Indicates the first Frequency domain noise factor of vibration data at each time point This represents an exponential function with the natural constant as its base. The symbol represents the absolute value. The acquisition of the information entropy of the energy corresponding to all harmonic frequencies in the spectrum at each moment is a well-known technique and will not be elaborated upon here.

[0082] in, This represents the difference in energy between the dominant frequency and harmonic frequencies in the spectrum at each moment. A smaller difference indicates a greater degree of noise disturbance, i.e., a larger frequency domain noise factor; conversely, a larger difference indicates a smaller degree of noise disturbance, i.e., a smaller frequency domain noise factor. A higher information entropy of the energy corresponding to all harmonic frequencies in the spectrum at each moment indicates a greater likelihood of noise, i.e., a larger frequency domain noise factor; conversely, a lower information entropy indicates a lower likelihood of noise, i.e., a smaller frequency domain noise factor.

[0083] It should be noted that the larger the temporal noise factor and frequency domain noise factor of the vibration data at each moment, the greater the degree of interference from external environmental noise. This means the vibration data collected by the vibration sensor at that moment is less reliable, and the vibration data at that moment cannot be used to determine whether the chain is abnormal. Therefore, the reliability of each vibration data point is analyzed using the temporal noise factor and frequency domain noise factor. Thus, the reliability factor of the vibration data at each moment is obtained based on the aforementioned temporal noise factor and frequency domain noise factor.

[0084] Preferably, as an embodiment, the specific calculation method for the reliability factor of vibration data at each moment is as follows:

[0085] The value obtained by negatively mapping the product of the time-series noise factor and the frequency-domain noise factor is denoted as the reliability factor of the vibration data at each moment.

[0086] In one embodiment of the present invention, the specific formula is as follows:

[0087]

[0088] In the formula, represents the time sequence noise factor of the vibration data at the i th moment, represents the frequency domain noise factor of the vibration data at the i th moment, represents the reliability factor of the vibration data at the i th moment, represents the exponential function with the natural constant as the base. When the time sequence noise factor and the frequency domain noise factor of each moment of vibration data are larger, it indicates that the vibration data at the moment is less reliable due to the interference of noise, and then the reliability factor of each moment of vibration data is smaller; on the contrary, the reliability factor of each moment of vibration data is larger. At this point, the reliability factor of each moment of vibration data is obtained.

[0089] It should be noted that since the collected vibration data is affected by the external environmental noise, it is necessary to filter and correct the vibration data at each moment according to the reliability of the surrounding vibration data at each moment. Therefore, the vibration signal data sequence is filtered by the reliability factor to obtain a filtered vibration signal data sequence.

[0090] Preferably, each moment of the vibration signal data sequence is taken as the center point of the filter window, and the filter window size is , so as to obtain the filter window of each moment of vibration data. In the filter window, the left and right numbers are equal, and both are

[0091] . In this embodiment, is a second preset parameter, wherein , but is not specifically limited and can be determined according to specific conditions.

[0092] Further, as an embodiment, the filtering weight of each moment of vibration data in each filter window at each moment is specifically calculated as follows:

[0093] The filtering weight of each moment of vibration data in each filter window at each moment is obtained by the proportion of the reliability factor of each moment of vibration data in each filter window of each moment of vibration data.

[0094] In an embodiment of the present application, it is specifically expressed by the formula:

[0095]

[0096] In the formula, represents the i th moment of the vibration data in the filter window at the i th moment, represents the i th moment of the vibration data in the filter window at the i th moment, ​​Reliability factor of vibration data at each moment Show the first Within the filtering window at time n, the first Reliability factor of vibration data at each moment This represents the total number of vibration data points corresponding to all times within each time window. Indicates the first Within the filtering window at time n, the first The filtering weights for vibration data at each time point.

[0097] Specifically, the higher the reliability factor of the vibration data at each moment, the greater the corresponding filter weight; conversely, the lower the reliability factor, the smaller the corresponding filter weight.

[0098] Thus, the filtering weights of the vibration data at each time step within each time step filter window are obtained.

[0099] The filtered vibration data at each time moment is obtained by weighting all vibration data within the filtering window with the corresponding filtering weight; the vibration data at all times in the vibration signal data sequence of the electric chainsaw chain are filtered to obtain the filtered vibration signal data sequence.

[0100] Thus, the filtered vibration signal data sequence is obtained.

[0101] Step S004: Perform safety testing on the electric chainsaw chain equipment and its operation using the filtered vibration signal data sequence.

[0102] It should be noted that by filtering out noise in the environment, the filtered data can be analyzed to ensure work safety.

[0103] Specifically, based on the filtered vibration signal data sequence, the vibration data for all moments within the following hour is predicted using the ARIMA model. Anomaly detection is then performed using the Isolation Forest algorithm based on this vibration data to identify abnormal moments. Moments other than abnormal moments are considered normal moments. The operational safety inspection of the electric chainsaw chain equipment is completed by comparing the normal and abnormal moments. This concludes the operational safety inspection of the electric chainsaw chain equipment. The flowchart of the electric chainsaw chain equipment and its operational safety inspection is shown in Figure 2.

[0104] The ARIMA model and the isolated forest algorithm are well-known technologies and will not be elaborated upon here.

[0105] It should be noted that the embodiments used in this example The model is only used to represent negative correlations and the results of the constraint model output are in Within this range, in specific implementations, other models with the same purpose can be substituted; this embodiment is merely an example. The model is described by way of example and is not limited thereto, wherein denotes an input to the model.

[0106] Thus far, the present embodiment is completed.

[0107] The above description is merely that of the preferred embodiments of the application, and is not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the principles of the application should be included in the protection scope of the application.

Claims

1. An electric chain saw chain device and work safety detection method, characterized by, The method comprises the following steps: Obtain the vibration signal data sequence of the electric chain saw chain in time sequence; Obtain the time sequence initial noise factor of the vibration data at each time point according to the difference between the vibration data at each time point and the vibration data in the local range, and the distribution of the difference between all adjacent vibration data in the local range at each time point; obtain the local vibration data sequence at each time point, perform curve fitting on the local vibration data sequence at each time point to obtain the local vibration data curve at each time point, correct the time sequence initial noise factor according to the distribution of the time interval between all adjacent extreme points in the local vibration data curve at each time point, and obtain the time sequence noise factor of the vibration data at each time point; Obtain the frequency spectrum corresponding to the local vibration data sequence at each time point, obtain the main frequency and the harmonic frequency in the frequency spectrum, obtain the frequency domain noise factor of the vibration data at each time point according to the difference between the energy corresponding to the main frequency and the harmonic frequency and the chaotic degree of the energy of the harmonic frequency, obtain the reliability factor of the vibration data at each time point according to the time sequence noise factor and the frequency domain noise factor, filter the vibration signal data sequence through the reliability factor, and obtain the filtered vibration signal data sequence; Perform electric chain saw chain equipment and working safety detection through the filtered vibration signal data sequence.

2. The electric chain saw chain device and work safety detection method according to claim 1, characterized in that, The specific steps of obtaining the time sequence initial noise factor of the vibration data at each time point according to the difference between the vibration data at each time point and the vibration data in the local range, and the distribution of the difference between all adjacent vibration data in the local range at each time point include the following steps: The difference between the vibration data at each time point and all vibration data in the corresponding local range is denoted as the first vibration difference at each time point; The standard deviation of the difference between all adjacent vibration data in the local range at each time point is denoted as the stable vibration difference degree at each time point; the time sequence initial noise factor of the vibration data at each time point is obtained through the first vibration difference and the stable vibration difference degree at each time point. The first vibration difference is negatively correlated with the time sequence initial noise factor, and the stable vibration difference degree is positively correlated with the time sequence initial noise factor.

3. The electric chain saw chain device and work safety detection method according to claim 1, characterized in that, The specific steps of performing curve fitting on the local vibration data sequence at each time point to obtain the local vibration data curve at each time point include the following steps: The local vibration data curve at each time point is obtained by performing curve fitting on the local vibration data sequence at each time point through the least square method.

4. The electric chain saw chain device and work safety detection method according to claim 1, characterized in that, The specific steps of correcting the time sequence initial noise factor according to the standard deviation of the time interval between all adjacent extreme points in the local vibration data curve at each time point to obtain the time sequence noise factor of the vibration data at each time point include the following steps: The time sequence initial noise factor is corrected according to the standard deviation of the time interval between all adjacent extreme points in the local vibration data curve at each time point to obtain the time sequence noise factor of the vibration data at each time point; The standard deviation of the time interval between all adjacent extreme points in the local vibration data curve at each time point is positively correlated with the time sequence noise factor.

5. The electric chain saw chain device and work safety detection method according to claim 1, characterized in that, The specific steps of acquiring the main frequency and harmonic frequency in the frequency spectrum include the following: The main frequency in the frequency spectrum is acquired through a peak search algorithm, wherein the frequencies other than the main frequency are harmonic frequencies.

6. The electric chain saw chain device and work safety detection method according to claim 5, wherein The specific steps of obtaining the frequency domain noise factor of the vibration data at each time point according to the difference between the corresponding energy of the main frequency and the harmonic frequency, and the chaotic degree of the energy of the harmonic frequency include the following: The difference between the average energy of all main frequencies in the frequency spectrum at each time point and the average energy of all harmonic frequencies is denoted as the frequency spectrum energy difference at each time point. The information entropy of the energy corresponding to all harmonic frequencies in the frequency spectrum at each time point is acquired through the chaotic degree of the energy of the harmonic frequency; the frequency domain noise factor of the vibration data at each time point is obtained through the information entropy and the frequency spectrum energy difference at each time point. The information entropy and the frequency domain noise factor are in a positive correlation, and the frequency spectrum energy difference at each time point and the frequency domain noise factor are in a negative correlation.

7. The electric chain saw chain device and work safety detection method according to claim 1, characterized by, The specific steps of obtaining the reliability factor of the vibration data at each time point according to the time sequence noise factor and the frequency domain noise factor include the following: The value obtained by negatively correlating the product of the time sequence noise factor and the frequency domain noise factor is denoted as the reliability factor of the vibration data at each time point.

8. The electric chain saw chain device and work safety detection method according to claim 1, characterized by, The specific steps of filtering the vibration signal data sequence through the reliability factor to obtain a filtered vibration signal data sequence include the following: Each time point in the vibration signal data sequence is taken as the center point of a filtering window, and a second preset parameter is taken as the size of the filtering window to obtain a filtering window of the vibration data at each time point. The filtering weight of the vibration data at each time point in the filtering window is obtained through the proportion of the reliability factor of the vibration data at each time point in the filtering window of the vibration data at each time point. All the vibration data at each time point in the vibration signal data sequence is filtered through the filtering weight to obtain a filtered vibration signal data sequence.

9. The electric chain saw chain device and work safety detection method according to claim 8, characterized by, The specific steps of filtering all the vibration data at each time point in the vibration signal data sequence through the filtering weight to obtain a filtered vibration signal data sequence include the following: All the vibration data in each filtering window and the corresponding filtering weight are weighted and averaged to obtain the filtered vibration data at each time point, and all the vibration data at each time point in the vibration signal data sequence of the electric chain saw chain is filtered to obtain a filtered vibration signal data sequence.

10. The electric chain saw chain device and work safety detection method according to claim 1, characterized in that, The specific steps of performing electric chain saw chain equipment and working safety detection through the filtered vibration signal data sequence include the following: The vibration data at all the time points in the subsequent one hour is obtained through ARIMA model prediction according to the filtered vibration signal data sequence, the vibration data at all the time points in the subsequent one hour is used for anomaly detection through an isolation forest algorithm to obtain abnormal time points, the time points other than the abnormal time points are normal time points, and the working safety detection of the electric chain saw chain equipment is completed through the normal time points and the abnormal time points.

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