Method and system for monitoring running state of automatic water cloth winding and unwinding machine

Abstract

The invention relates to the field of water cloth wrapping and unwrapping machines, in particular to an automatic water cloth wrapping and unwrapping machine running state monitoring method and system. The method comprises the following steps: acquiring observation data at a target moment; constructing a window, constructing a coordinate system by taking the serial number of the sampling moment in the window as an abscissa and the rotating speed as an ordinate, and calculating the initial noise probability of the target moment according to the position of the sampling moment in the coordinate system; constructing the rotating speed at each sampling moment in the window into a rotating speed sequence, constructing the traction speed at each sampling moment in the window into a traction speed sequence, and calculating a correction factor according to the rotating speed sequence and the traction speed sequence for correcting the initial noise probability to obtain a final noise probability; and determining the filtering length of the observation data according to the final noise probability, and filtering the observation data by using Vondrak to complete state monitoring. According to the technical scheme, the accuracy of the monitoring result of the running state of the water cloth wrapping and unwrapping machine can be improved.

Description

Technical Field

[0001] The present invention relates to the field of water cloth untangling machines, and more specifically, to a method and system for monitoring the operating status of an automatic water cloth untangling machine. Background Art

[0002] The water cloth wrapping machine is a mechanical equipment used in the production of hoses. It is mainly used to wrap the hose with special water cloth before vulcanization to ensure uniform distribution of temperature and pressure during the vulcanization process. In order to ensure the stable operation of the equipment under high load, prevent potential failures, and ensure the smooth progress of the production process, it is crucial to monitor the speed of the water cloth wrapping machine in real time. The speed data is not only a key indicator for evaluating the quality of the wrapping, but also a basis for adjusting the equipment operating parameters and optimizing the production process. Through accurate monitoring of the speed, abnormal fluctuations can be discovered in time, and then the equipment speed can be adjusted to ensure the uniformity and stability of the winding process, thereby improving the quality and efficiency of hose production.

[0003] Titled "Research on Vondrak Filter Criteria and Applications", the author is Wu Yunyun, a master's thesis of Central South University in 2013, which disclosed that Vondrak filtering can effectively smooth observation data without knowing the variation law of observation data and its fitting function. Its basic idea is to control the degree of data smoothing by selecting different smoothing factors, and to select a compromise curve between absolute fitting and absolute smoothing of observation data, so as to retain useful signals and filter out noise signals. This method is applicable to both equally spaced observation data and unequally spaced observation data.

[0004] However, the speed data of the water-twisting cloth machine will change due to various factors such as equipment status and working environment. The traditional Vondrak filtering algorithm uses a fixed observation data sequence length and cannot accurately reflect the dynamic changes of the data, resulting in deviations in the filtering effect of the speed data, resulting in a reduction in the accuracy of the monitoring results of the operating status of the water-twisting cloth machine. Summary of the invention

[0005] In order to solve the problem of reduced accuracy of monitoring results of the operating status of a water-cloth untangling machine, the present invention proposes an automatic monitoring method and system for the operating status of a water-cloth untangling machine.

[0006] In a first aspect, the present invention discloses an operating state monitoring method for an automatic entanglement and untangling water cloth machine, comprising: obtaining observation data at a target time, the observation data including a rotation speed and a traction speed, and the target time is any sampling time; constructing a window, using the sequence number of the sampling time in the window as the horizontal coordinate and the rotation speed as the vertical coordinate to construct a coordinate system, and calculating the initial noise probability at the target time according to the position of the sampling time in the coordinate system; constructing the rotation speed at each sampling time in the window as a rotation speed sequence, constructing the traction speed at each sampling time in the window as a traction speed sequence, and calculating a correction factor according to the rotation speed sequence and the traction speed sequence, which is used to correct the initial noise probability and obtain the final noise probability, and the final noise probability satisfies the relationship: , Indicates the target time The final noise probability is Indicates the target time The initial noise probability is represents the speed sequence, represents the traction speed sequence, Indicates similarity, represents the exponential function, represents the normalization function; the filter length of the observation data is determined according to the final noise probability, and the observation data is filtered using Vondrak to complete the state monitoring.

[0007] The correction mechanism effectively suppresses unnecessary noise influence by introducing exponential function and normalization function, so that the noise probability can more reasonably reflect the actual operation status of the system. The appropriate filter length is determined by the final noise probability, and the Vondrak filter is applied to process the observed data, which not only improves the accuracy and reliability of the data, but also effectively filters out external interference and fluctuations, ensuring the stability and accuracy of system status monitoring.

[0008] Preferably, the window construction includes: constructing a window with the target moment as the center and in sampling time order, and the window size is a preset length; taking the sampling moments in the window except the target moment as reference moments, and in response to the unequal number of reference moments on the left side of the target moment and the right side of the target moment, discarding the data on the side with fewer reference moments.

[0009] When the sampling time is at the beginning or the end, the water distribution machine will be unstable, resulting in errors in the observation data. Discarding the observation data that is prone to abnormalities can improve the accuracy of the calculation results.

[0010] Preferably, the initial noise probability comprises: respectively calculating the distance between any two adjacent sampling moments in the window, and the distance satisfies the relationship: , Indicates the sampling time and sampling time The distance and Respectively represent the sampling time and sampling time The speed of the vehicle is obtained by traversing the distance between any two sampling moments, calculating the mean of all distances in the window, and taking the normalized mean as the initial noise probability.

[0011] By traversing all adjacent sampling moments in the window and calculating the distance, and then obtaining the mean, the stability of the speed change can be reflected. The normalized mean is used as the initial noise probability, which actually quantifies the stability or noise level of the speed signal.

[0012] Preferably, the initial noise probability further comprises: respectively calculating the distance between any two adjacent sampling moments in the window, and the distance satisfies the relationship: , Indicates the sampling time and sampling time The distance and Respectively represent the sampling time and sampling time The rotation speed, Represents the bi-norm; traverse to obtain the distance between any two sampling moments, take the mean of all distances as the first distance, take the sum of the two distances involved in the calculation at the target moment as the second distance, calculate the ratio of the second distance to the first distance, and take the normalized ratio as the initial noise probability.

[0013] The stability of the speed signal directly affects the accuracy of subsequent monitoring and analysis. Therefore, accurate identification of these sudden fluctuations is critical. By calculating the distance between adjacent sampling moments and normalizing it, the degree of interference of noise on the signal can be effectively quantified, especially in the presence of irregular fluctuations, and the degree of noise impact can be accurately evaluated. This calculation method further reveals whether the fluctuation at the target moment deviates significantly from the normal speed change law by calculating the distance ratio.

[0014] Preferably, the initial noise probability further comprises: respectively calculating the distance between any two adjacent sampling moments in the window to construct a distance sequence; respectively obtaining the relative angle at any sampling moment in the window, and calculating the sine value of each relative angle to construct a sine sequence; the initial noise probability satisfies the relationship: , Indicates the target time The initial noise probability is represents the variance of the distance series, represents the variance of the sine sequence, Represents the normalization function.

[0015] The variance of these two describes the discreteness and instability of signal changes respectively, and can provide key indicators of whether the signal has abnormal fluctuations or noise. By applying a normalization function to the variance, the impact of noise can be converted into a smooth, quantifiable noise probability, thereby more accurately assessing the noise risk of the observed data at each sampling moment.

[0016] Preferably, the relative angle includes: taking any sampling moment in the window as the marking moment, and taking the next sampling moment adjacent to the marking moment as the post-marking moment; in the coordinate system, taking the angle between the line connecting the marking moment and the post-marking moment and the horizontal axis as the relative angle of the marking moment, wherein counterclockwise is the positive direction.

[0017] Preferably, determining the filtering length of the observation data according to the final noise probability includes: traversing to obtain the final noise probability of multiple sampling moments, presetting the initial length of the observation data, and calculating the final noise probability mean of the observation data in the initial length; in response to the final noise probability mean being greater than an abnormal threshold, iteratively increasing the initial length until the final noise probability is not greater than the abnormal threshold or the initial length reaches a preset maximum value, stopping the iteration, and obtaining the length of the observation data.

[0018] In a second aspect, the present invention discloses an automatic water-cloth untangling machine operation status monitoring system, comprising: a processor; and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the system executes the above-mentioned automatic water-cloth untangling machine operation status monitoring method.

[0019] Beneficial effects of the present invention: The present invention can effectively identify abnormal fluctuations in the operating state of the equipment by dynamically monitoring key parameters such as the rotation speed and the traction speed, combined with the calculation of the noise probability and the correction factor.

[0020] By constructing a sampling time window, calculating the initial noise probability, and using the rotation speed and traction speed sequence for correction, the change of the state of the water cloth untangling machine can be accurately reflected, and potential faults or deviations can be discovered in time. In addition, the filtering process in the present invention helps to reduce noise interference and improve the reliability and accuracy of the monitoring results by adjusting the length of the observation data.

[0021] The present invention not only enhances the intelligence level of the monitoring system, but also can provide more efficient and stable operation status monitoring in practical applications, help prevent failures, extend the service life of equipment, and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a flow chart of a method for monitoring the operating status of an automatic water cloth untangling machine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The present invention provides a method for monitoring the operating status of an automatic water cloth untangling machine. Figure 1 As shown, the method for monitoring the operating status of the automatic water cloth untangling machine includes steps S1 to S4, which are described in detail below.

[0026] S1, obtaining observation data at a target time, the observation data including rotation speed and traction speed, and the target time is any sampling time.

[0027] In one embodiment, the speed sensor is precisely placed at the position of the water cloth unwrapping machine's water cloth unwrapping turntable to collect the speed data of this part, while the traction speed sensor is installed at the traction device position of the water cloth unwrapping machine to obtain the traction speed data of the traction device. The acquisition frequency of these sensors is set to 1Hz, that is, data is collected once per second, ensuring that the speed and traction speed changes of the water cloth unwrapping machine at different operating stages can be reflected in real time. Through continuous real-time data monitoring, abnormal conditions in equipment operation can be discovered in a timely manner and effective adjustments can be made to ensure the smooth progress of the production process and the stability of the hose quality.

[0028] S2, construct a window, construct a coordinate system with the sequence number of the sampling moment in the window as the horizontal coordinate and the rotation speed as the vertical coordinate, and calculate the initial noise probability at the target moment according to the position of the sampling moment in the coordinate system.

[0029] It should be noted that the water cloth unwrapping machine usually works in an environment full of vibration sources, such as the vibration of the workshop floor, the operation of adjacent mechanical equipment, etc. These external factors will interfere with the speed sensor, resulting in instability and fluctuations in the speed data collected by the sensor. Since the nature of the vibration source is usually periodic or random, these vibrations not only affect the accurate measurement of the sensor, but also cause the collected data to no longer present a smooth, continuous curve, but to show an obvious fluctuation trend. Specifically, these fluctuations can appear as periodic vibration interference or random sudden fluctuations, which in turn affect the accuracy and reliability of the data. In order to evaluate the impact of this interference on the speed data, the noise probability can be calculated by analyzing the volatility and randomness of the data points. The higher the noise probability, the stronger the volatility and the higher the randomness in the data, which indicates that the external vibration source has a greater impact on the sensor.

[0030] Reasons for building windows: Windows can often reveal subtle changes, especially when the overall trend of the observed data is relatively stable. By analyzing the fluctuations of the observed data within the window, some potential, subtle change trends can be discovered, helping to capture local anomalies or mutations.

[0031] Some abnormal phenomena may be short-lived and transient, and difficult to detect on a global scale. However, within the local scope of the window, especially the ups and downs of the observed data around the target moment, these sudden events can be captured more sensitively.

[0032] In one embodiment, a window is constructed in sampling time order with the target moment as the center, and the window size is a preset length; the sampling moments in the window except the target moment are used as reference moments, and in response to the unequal number of reference moments on the left side of the target moment and the right side of the target moment, the data on the side with fewer reference moments are discarded.

[0033] Exemplarily, the preset length is 11, that is, the window size is 11, there are 5 reference moments on the left side of the target moment, and 5 reference moments on the right side of the target moment. If the target moment is the 4th sampling moment and there are only 3 reference moments on the left side of the target moment, all reference data on the left side of the target moment will be discarded. At this time, the window size is 6.

[0034] A coordinate system is constructed with the serial number of the sampling time in the window as the horizontal axis and the rotation speed as the vertical axis. For example, because the window size is set to 11, the horizontal axis of the coordinate system is 1 to 11, and the vertical axis is the value of the rotation speed.

[0035] Calculating the initial noise probability includes: calculating the distance between any two adjacent sampling moments in the window, and the distance satisfies the relationship: , Indicates the sampling time and sampling time The distance and Respectively represent the sampling time and sampling time rotation speed.

[0036] Traverse to obtain the distance between any two sampling moments, calculate the mean of all distances in the window, and use the normalized mean as the initial noise probability.

[0037] By traversing all adjacent sampling moments in the window and calculating the distance, and then obtaining the mean, the stability of the speed change can be reflected. The normalized mean is used as the initial noise probability, which actually quantifies the stability or noise level of the speed signal.

[0038] In one embodiment, calculating the initial noise probability further includes: respectively calculating the distance between any two adjacent sampling moments in the window, and the distance satisfies the relationship: , Indicates the sampling time and sampling time The distance and Respectively represent the sampling time and sampling time The rotation speed, represents the two-norm.

[0039] Traverse to obtain the distance between any two sampling moments, take the mean of all distances as the first distance, take the sum of the two distances involved in the calculation at the target moment as the second distance, calculate the ratio of the second distance to the first distance, and take the normalized ratio as the initial noise probability.

[0040] By analyzing the changes in speed between adjacent moments, it is possible to effectively identify sudden changes or abnormal fluctuations in speed data, which are usually related to noise, signal interference or sensor errors in the system. The stability of the speed signal directly affects the accuracy of subsequent monitoring and analysis, so accurate identification of these sudden changes is critical. By calculating the distance between adjacent sampling moments and normalizing it, the degree of interference of noise on the signal can be effectively quantified, especially in the presence of irregular fluctuations, and the degree of noise impact can be accurately evaluated. This calculation method further reveals whether the fluctuation at the target moment deviates significantly from the normal speed change law by calculating the distance ratio.

[0041] In one embodiment, calculating the initial noise probability further includes: respectively calculating the distance between any two adjacent sampling moments in the window to construct a distance sequence; respectively obtaining the relative angle of any sampling moment in the window, and calculating the sine value of each relative angle to construct a sine sequence.

[0042] The initial noise probability satisfies the relationship: , Indicates the target time The initial noise probability is represents the variance of the distance series, represents the variance of the sine sequence, Represents the normalization function.

[0043] Among them, the relative angle includes: taking any sampling moment in the window as the marking moment, and taking the next sampling moment adjacent to the marking moment as the post-marking moment; in the coordinate system, taking the angle between the line connecting the marking moment and the post-marking moment and the horizontal axis as the relative angle of the marking moment, wherein the counterclockwise direction is the positive direction.

[0044] It should be noted that the distance sequence reflects the amplitude of the rotational speed change at adjacent sampling moments, while the sine sequence reveals the periodic characteristics of the signal by calculating the sine value of the relative angle change. The variances of the two describe the discreteness and instability of the signal change, respectively, and can provide key indicators of whether the signal has abnormal fluctuations or noise. By applying a normalization function to the variance, the degree of noise impact can be converted into a smooth, quantifiable noise probability, thereby more accurately assessing the noise risk of the observed data at each sampling moment.

[0045] At each marked moment, by calculating the angle between the line connecting the next marked moment and the horizontal axis, a quantitative relative angle value is obtained, which not only reflects the speed and direction of the speed change, but also reveals the dynamic characteristics of the signal in different time periods. The convention of counterclockwise as the positive direction further clarifies the positive and negative directions of the angle, making the calculation more standardized and consistent. By analyzing these relative angles, it is possible to identify sudden fluctuations or abnormal patterns that may exist in the process of speed changes, especially when there are periodic or regular changes, and more accurately capture the deviation or unstable state of the signal.

[0046] S3, constructing the rotation speed at each sampling moment in the window into a rotation speed sequence, constructing the traction speed at each sampling moment in the window into a traction speed sequence, and calculating a correction factor according to the rotation speed sequence and the traction speed sequence to correct the initial noise probability and obtain the final noise probability.

[0047] It should be noted that in order to further improve the accuracy of the noise probability of the speed data points and eliminate the interference of external factors or equipment abnormalities on the speed data, the present invention introduces the relationship between the traction speed and the speed of the water-wrapping machine to correct the noise probability of the speed data.

[0048] Specifically, there is a clear positive correlation between the traction speed and rotation speed of the water cloth unwrapping machine. As the rotation speed of the water cloth unwrapping machine increases, the rotation speed of the winding disk and related rotating parts will also increase accordingly, resulting in an increase in the winding speed of the water cloth on the hose. In order to ensure that the water cloth is evenly and tightly wound when the hose passes through the water cloth unwrapping machine, the traction device needs to increase the traction speed so that the hose can pass through the water cloth unwrapping machine head smoothly and continuously to prevent the water cloth from piling up or wrinkling. Conversely, when the rotation speed decreases, the winding speed slows down. In order to avoid the accumulation of water cloth or wrinkles, the traction device must reduce the traction speed to ensure that the winding quality is not affected. Based on this positive correlation, the present invention proposes to introduce traction speed data into the calculation of the speed data noise probability for correction.

[0049] By analyzing the correlation between the traction speed and the rotational speed data, when the correlation between the two is strong, it indicates that the current rotational speed changes normally and the noise probability should be low; when the correlation between the two is weak, it may indicate that the rotational speed data fluctuates abnormally, and the noise probability should be high.

[0050] In one embodiment, the final noise probability satisfies the relationship: , Indicates the target time The final noise probability is Indicates the target time The initial noise probability is represents the speed sequence, represents the traction speed sequence, Indicates similarity, represents the exponential function, Represents the normalization function.

[0051] The final noise probability relationship reflects the consistency of the speed change and the traction speed change, thus providing a more reasonable basis for noise probability correction. When the speed and traction speed are similar, it means that the operation of the water-winding and unwinding machine is relatively normal, and the final noise probability will be correspondingly low. Otherwise, it indicates that there may be abnormal fluctuations and the noise probability will increase.

[0052] S4, determine the filter length of the observation data according to the final noise probability, use Vondrak to filter the observation data, and complete the state monitoring.

[0053] In one embodiment, the final noise probability of multiple sampling moments is obtained by traversal, the initial length of the observed data is preset, and the mean of the final noise probability of the observed data in the initial length is calculated; in response to the final noise probability mean being greater than an abnormal threshold, the initial length is iteratively increased until the final noise probability is not greater than the abnormal threshold or the initial length reaches a preset maximum value, the iteration is stopped, and the length of the observed data is obtained.

[0054] Exemplarily, the initial length is set to 5, the abnormal threshold is set to 0.4, and when the final noise probability mean of the five sampling moments is greater than 0.4, the final noise probability of one sampling moment is added in chronological order, and the final noise probability mean of the six sampling moments is recalculated until the final noise probability mean is no greater than 0.4 or there are 10 iterative data. At this time, the iteration is stopped to obtain the filtering length of the observed data.

[0055] The existing Vondrak is used to filter the observed data. After filtering, the speed data can more accurately reflect the actual operation of the water-winding cloth disintegrating machine. Therefore, the deviation between the actual speed and the preset speed value can be monitored and compared in real time, and the preset speed value can be adjusted in time to achieve dynamic correction. When it is found that there is a significant gap between the actual speed and the preset speed, the preset speed can be appropriately adjusted according to the adjustment strategy, so that the actual speed of the water-winding cloth disintegrating machine gradually approaches the ideal speed.

[0056] An embodiment of the present invention also discloses an automatic water-cloth untangling machine operation status monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an automatic water-cloth untangling machine operation status monitoring method according to the present invention is implemented.

[0057] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0058] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0059] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0060] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the operating status of an automatic water cloth untangling machine, characterized in that: include: Obtain observation data at a target time, the observation data including rotation speed and traction speed, and the target time is any sampling time; Construct a window, use the sequence number of the sampling moment in the window as the horizontal coordinate and the rotation speed as the vertical coordinate to construct a coordinate system, and calculate the initial noise probability at the target moment according to the position of the sampling moment in the coordinate system; The speed at each sampling moment in the window is constructed as a speed sequence, and the traction speed at each sampling moment in the window is constructed as a traction speed sequence. The correction factor is calculated according to the speed sequence and the traction speed sequence to correct the initial noise probability and obtain the final noise probability. The final noise probability satisfies the relationship: , Indicates the target time The final noise probability is Indicates the target time The initial noise probability is represents the speed sequence, represents the traction speed sequence, Indicates similarity, represents the exponential function, represents the normalization function; The filter length of the observed data is determined according to the final noise probability, and the observed data is filtered using Vondrak to complete the state monitoring.

2. The method for monitoring the operating status of the automatic water cloth untangling machine according to claim 1 is characterized in that: The build window includes: Centered on the target time, a window is constructed in the order of sampling time, and the window size is the preset length; The sampling moments except the target moment in the window are taken as reference moments. In response to the unequal number of reference moments on the left side of the target moment and the unequal number of reference moments on the right side of the target moment, the data on the side with fewer reference moments are discarded.

3. The method for monitoring the operating status of the automatic water cloth untangling machine according to claim 1 is characterized in that: The initial noise probability includes: Calculate the distance between any two adjacent sampling moments in the window respectively, and the distance satisfies the relationship: , Indicates the sampling time and sampling time The distance and Respectively represent the sampling time and sampling time Speed; Traverse to obtain the distance between any two sampling moments, calculate the mean of all distances in the window, and use the normalized mean as the initial noise probability.

4. The method for monitoring the operating status of the automatic water cloth untangling machine according to claim 1 is characterized in that: The initial noise probability also includes: Calculate the distance between any two adjacent sampling moments in the window respectively, and the distance satisfies the relationship: , Indicates the sampling time and sampling time The distance and Respectively represent the sampling time and sampling time The rotation speed, represents the two-norm; Traverse to obtain the distance between any two sampling moments, take the mean of all distances as the first distance, take the sum of the two distances involved in the calculation at the target moment as the second distance, calculate the ratio of the second distance to the first distance, and take the normalized ratio as the initial noise probability.

5. The method for monitoring the operating status of the automatic water cloth untangling machine according to claim 1 is characterized in that: The initial noise probability also includes: Calculate the distance between any two adjacent sampling moments in the window to construct a distance sequence; Get the relative angle at any sampling moment in the window respectively, calculate the sine value of each relative angle to construct a sine sequence; The initial noise probability satisfies the relationship: , Indicates the target time The initial noise probability is represents the variance of the distance series, represents the variance of the sine sequence, Represents the normalization function.

6. The method for monitoring the operating status of the automatic water cloth untangling machine according to claim 5 is characterized in that: The relative angles include: Take any sampling time in the window as the marking time, and take the next sampling time adjacent to the marking time as the post-marking time; In the coordinate system, the angle between the line connecting the marking time and the post-marking time and the horizontal axis is taken as the relative angle of the marking time, wherein the counterclockwise direction is taken as the positive direction.

7. The method for monitoring the operating status of the automatic water cloth untangling machine according to claim 1 is characterized in that: Determining the filter length of the observation data according to the final noise probability includes: The final noise probability of multiple sampling moments is obtained by traversing, the initial length of the observation data is preset, and the final noise probability mean of the observation data in the initial length is calculated; In response to the final noise probability mean being greater than the abnormal threshold, the initial length is iteratively increased until the final noise probability is not greater than the abnormal threshold or the initial length reaches a preset maximum value, and the iteration is stopped to obtain the length of the observed data.

8. Automatic water cloth untangling machine operation status monitoring system, characterized in that: include: Processor; and A memory storing computer instructions, wherein when the computer instructions are executed by the processor, the system executes the method for monitoring the operating status of the automatic water-cloth untangling machine according to any one of claims 1 to 7.

Images