Elevator guide rail safety monitoring method and system in dusty environment

Abstract

The present invention belongs to the field of anomaly identification technology and proposes a method and system for monitoring the safety of elevator guide rails in dusty environments. Specifically, the method comprises the following steps: first, tension sensors and vibration sensors are arranged in the elevator system, and the sensor data of the vibration inside the car is filtered. The abnormal tension trajectory is screened based on the tension value obtained by the tension sensor. Then, the authenticity of the dust particle risk is determined by combining the tension value and vibration data corresponding to the abnormal tension trajectory. Finally, the dust particle risk is fed back to the client or application end. By identifying the variation of vibration characteristics, it is effectively determined whether the abnormal elevator tension trajectory is caused by the accumulation of dust particles, thereby reducing the risk of guide rail and guide shoe wear caused by dust pollution during the use of elevators in high-dust environments, reducing unnecessary high-frequency manual inspections in dusty environments such as dusty areas or industrial areas, and greatly improving elevator safety and maintenance efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of anomaly identification, and in particular relates to a method and system for monitoring the safety of elevator guide rails in a dusty environment. Background Art

[0002] Elevators are essential transportation facilities in buildings, and their operational stability and safety are paramount in their daily operations. Among the hidden dangers of elevators is the safety hazard posed by elevator vibration. Elevators in high-dust areas often experience a high rate of vibration failures. This is due to the dusty environment. When there is a lot of dust in the air, dust particles enter the contact area between the guide rails and guide shoes, causing localized dry friction and causing vibration during elevator operation. Lubricant replenishment can effectively alleviate or resolve this vibration problem. To address dust accumulation, lubricant replenishment creates an oil film barrier that effectively prevents dust from directly contacting the metal of the guide rails. It also reduces the adhesion of dust particles to the guide rails, providing opportunities for particle removal, thereby reducing frictional climb and car vibration. However, traditional lubricant replenishment typically relies on periodic monitoring, which is often conducted at long intervals. This results in a significant delay in the diagnosis of wear caused by dust particle accumulation in the contact area between the guide rails and guide shoes. Therefore, a method and system for monitoring elevator guide rail safety in dusty environments is urgently needed. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for monitoring the safety of elevator guide rails in a dusty environment to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0004] To achieve the above object, according to one aspect of the present invention, a method for monitoring elevator guide rail safety in a dusty environment is provided, the method comprising the following steps:

[0005] Arrange tension sensors and vibration sensors in the elevator system and filter the sensor data of the vibration inside the car;

[0006] Screening abnormal tension tracks based on the tension value obtained by the tension sensor;

[0007] Combine the tension value and vibration data corresponding to the abnormal tension trajectory to determine the authenticity of the dust particle risk;

[0008] Feedback dust particle risks to the client or application.

[0009] Furthermore, a method for arranging tension sensors and vibration sensors in an elevator system and filtering sensor data of vibration inside the car is as follows: using the tension sensor to collect the force of the elevator traction wire rope and record the measured tension value in real time, the tension sensor is arranged at the wire rope anchor end, and the top position of the car is selected as the wire rope anchor end by default, and other optional positions also include the traction sheave and the counterweight end; vibration sensors are arranged at the guide rail fixing bracket and the bottom of the car respectively, and the vibration sensor data is processed by the signal differential method to obtain vibration data;

[0010] When the elevator runs at a constant speed, the tension value and vibration data are recorded from the tension sensor and vibration sensor respectively.

[0011] The signal difference method can eliminate the vibration data inside the car through vibration sensors at two locations.

[0012] The vibration sensor uses a piezoelectric accelerometer. The primary reason is that the vibration caused by dust particles is usually mainly high-medium frequency, so it is suitable for monitoring vibration changes during long-term use caused by the gradual increase in friction due to dust accumulation. Secondly, the piezoelectric accelerometer has low signal noise and high long-term stability, making it suitable for high-precision vibration spectrum analysis.

[0013] When the elevator is running at a constant speed, the tension value and vibration data are recorded from the tension sensor and vibration sensor respectively, with a measurement interval of 0.2 seconds to 1 second; every other measurement interval is regarded as a measurement point;

[0014] The uniform speed operation of the elevator is determined by the speed sensor in the elevator drive control module. It does not mean that the actual speed is uniform, but rather the non-start-stop driving process state of the elevator.

[0015] Furthermore, a method for screening abnormal tension trajectories based on the tension values obtained by the tension sensor is as follows: a self-monitoring period is set, and its value range is [12, 48] hours; any uniform speed process between the start and stop of the elevator is recorded as a trajectory; each tension value in the trajectory is constructed into a uniform tension sequence, and the ratio of the upper quartile value to the lower quartile value in the uniform tension sequence is recorded as the tension gradient; the self-monitoring period after the most recent elevator safety maintenance time is used as the benchmark monitoring period; the maximum value of the tension gradient within the benchmark monitoring period is used as the gradient benchmark; after the benchmark monitoring period, if any tension gradient is greater than the gradient benchmark, the corresponding trajectory is recorded as a tension abnormal trajectory, abbreviated as an abnormal trajectory.

[0016] The definition of the benchmark monitoring cycle enables the monitoring system to adapt to the initial stable state after elevator maintenance. This is because after the elevator is safely maintained, the mechanical state of the system, including wire rope tension, lubrication status, guide rail friction, etc., are restored to the standard state. This standard state is subject to gradual changes in long-term operation, so its tension abnormality judgment standard needs to be regularly maintained and updated.

[0017] The tension gradient is used to highlight the rapidly changing parts of the tension signal, and to improve the sensitivity of detection by amplifying subtle changes, making it easier to identify abnormalities later.

[0018] The reference principle of tension gradient is based on the distribution of tension stage data, reflecting the balanced characteristics of tension distribution during uniform speed operation of the elevator.

[0019] Furthermore, the method for determining the authenticity of dust particle risk by combining the tension value and vibration data corresponding to the tension abnormality trajectory is:

[0020] The preset monitoring period MLp, MLp∈[24,48] hours;

[0021] The currently acquired abnormal tension trajectory is used as the current abnormal tension trajectory; the vibration data is the vibration data sequence;

[0022] During the monitoring period, the time points at which vibration data and tension were obtained were recorded as measurement points;

[0023] For any abnormal tension trajectory, the angle NAK between each element in the vibration data sequence and the x-axis is calculated using the inverse tangent function, where the elements in the vibration data sequence are two-tuples of acceleration in the horizontal plane. That is, the angle NAK between the acceleration of each measuring point in the vibration data sequence and the x-axis is calculated using the inverse tangent function, and the angle NAK is adjusted according to the quadrant so that NAK∈[0°,360°).

[0024] if , then the NAK corresponding measurement point is recorded as the first abnormal point;

[0025] The principle of obtaining the angle NAK between the acceleration binary and the x-axis and identifying the first abnormal point is actually the application of trigonometric functions and angle adjustment and conversion. The introduction of vector direction diagnosis directly associates vibration data with risk characteristics to identify whether the vibration direction is abnormal. By limiting NAK, the normal vibration of the elevator during uniform speed operation can be effectively filtered out. NAK associates direction with risk. Dust accumulation causes dry friction between the guide rail and the guide shoe, which can cause lateral swing (NAK is close to 0° or 180°) or axial impact (NAK is close to 90° or 270°). NAK can directly filter This type of feature is selected to lock on. Therefore, when NAK deviates from the limited value range, it indicates that a sudden change in the direction of the friction between the guide rail and the guide shoe has occurred, providing a basis for fault location. NAK is obtained to capture the specific vibration mode caused by dry friction caused by dust entering the guide rail, and transform the vibration data from scalar energy analysis to vector direction diagnosis, thereby improving the identification accuracy of dust friction and effectively avoiding diagnostic lags, while also avoiding false alarms caused by vibrations caused by normal load changes. The first abnormal point is usually determined at the first moment when a sudden change in vibration direction or tension direction occurs.

[0026] Calculate the combined acceleration of each measuring point based on the acceleration binary, and combine the combined acceleration and tension value to form a temporary variable binary Trb. Then sort the measuring points in descending order according to the tension value, and arrange the sorted measuring points in sequence to form a sequence Trb.Ls. Let i be the serial number of the measuring point in Trb.Ls, and let Ftb(i) be the Trb of the i-th measuring point in Trb.Ls.

[0027] In Trb.Ls, the Euclidean distances between Ftb(i) and its preceding and succeeding first outlier points (Trb) are calculated and recorded as QI.C and HO.C, respectively. If QI.C is greater than HO.C, the corresponding measurement point of Ftb(i) is recorded as the second outlier point. The first and second outlier points are marked as outliers. When the number of outlier marks in Trb.Ls exceeds 75%, the dust particle risk is judged to be true, otherwise it is false.

[0028] The selection principle of the second abnormal point measurement point is based on the sorting and adjacent point relationship. Its basis is to find the point with the worst similarity to the first abnormal point, representing the position where the direction of change of the vibration data is significantly different, thereby maximizing the time and time domain span of the vibration data. When the frequency of such span is large, that is, the number of abnormal marks accounts for a high proportion, it means that the degree of dust particle accumulation is judged to be stronger.

[0029] Since the determination of dust particle risk requires processing the first and second abnormal points, it can effectively quantify the risk of guide rail and guide shoe wear caused by dust pollution during the use of elevators in high-dust environments. However, the acquisition of the angle NAK between the acceleration binary and the x-axis is too sensitive to the direct data of the vibration data sequence, and is prone to directional sensitivity deviation, resulting in inaccurate attribution and decision-making deviation. Maintenance feedback still has a lag, especially during periods when the density of first abnormal points is not strong or the distribution is uneven. This problem is more prominent. However, the existing technology cannot effectively compensate for this deviation. To eliminate this effect, the present invention proposes a more preferred solution as follows:

[0030] Preferably, the method for determining the authenticity of dust particle risk by combining the tension value and vibration data corresponding to the tension abnormal trajectory is as follows: setting a reference period, whose value range is [12, 48] hours; obtaining the vibration data sequence A_diff corresponding to the currently obtained abnormal trajectory; performing alignment check on the tension value sequence and the vibration data sequence through a timestamp matching module, and extracting the following characteristic parameters from the vibration data sequence: performing a fast Fourier transform on the vibration data sequence to form a transformation binary sequence B_diff, obtaining the energy spectrum of the 0Hz-5kHz frequency range, and taking the energy ratio of the 1kHz-5kHz frequency range as the high-frequency band energy ratio;

[0031] The accumulation of dust particles will lead to increased high-frequency friction between the guide rail and the guide shoe, which is manifested as a significant increase in the proportion of high-frequency energy.

[0032] Set the first vibration condition: the energy proportion of the high frequency band corresponding to the current abnormal trajectory is greater than the dynamic threshold; the dynamic threshold is E threshold =E M ×1.15, E M The 95th percentile value of the high-frequency energy proportion corresponding to each abnormal trajectory in the reference period; the first vibration condition reflects that the high-frequency energy exceeds the standard;

[0033] Calculate the ratio of the standard deviation to the mean of the vibration data in the two vertical directions within the horizontal direction, and record them as the coefficient of variation of acceleration CV_x and CV_y. Set the second vibration condition as follows: the coefficient of variation of acceleration CV_x or CV_y is greater than 0.25. This second vibration condition indicates that the coefficient of variation is too high.

[0034] The coefficient of variation (CV) is a normalized measure of vibration data fluctuations relative to the average level. It is defined here as the ratio of the signal's standard deviation to its mean, without relying on absolute units. This characteristic allows data to be compared across different sensors and system operating conditions, ensuring relative robustness to the shape of the signal distribution. It can effectively measure fluctuation intensity even when the vibration data exhibits a non-Gaussian distribution.

[0035] The average value of the vibration data of any measuring point and its 9-19 measuring points in the reverse time direction is the vibration window value, and the average value of each vibration window value within the reference period is recorded as the working condition vibration reference value;

[0036] The number of measuring points used is determined based on the number of elevator starts and stops. The larger the number of elevator starts and stops during the day, the larger the value used. This is to enhance the system's adaptability to elevators with different usage pressures, because the window value for building a vibration reference value should be a balance between time and the number of starts and stops. If only time or the number of starts and stops is considered, it is easy to cause the system robustness to decrease, leading to further warning of false touch problems.

[0037] The vibration window value is calculated from the vibration data, so the data structure is still a two-tuple. The working condition vibration reference value is calculated from the vibration window value, so the data structure is still a two-tuple. The values in the two-tuple correspond to the x-direction and the y-direction.

[0038] Calculate the percentage difference between the mean value of the current vibration data in the x-direction and y-direction and the corresponding working condition vibration reference value in the reference period and record it as Δμ_x i , Δμ_y i ; If Δμ_x i and Δμ_y i The third vibration condition is set as follows: during the current abnormal trajectory operation, Δμ_x and Δμ_y exceed 10% for five or more consecutive measurement points; this third vibration condition indicates that the mean difference continues to exceed the standard.

[0039] If any two or more vibration conditions are met, the dust particle risk is determined to be true, otherwise the dust particle risk is determined to be false.

[0040] The first vibration condition, the second vibration condition and the third vibration condition are all vibration conditions.

[0041] By coupling the abnormal tension trajectory with the vibration spectrum characteristic variation analysis, we can accurately distinguish the abnormal tension caused by mechanical wear due to dust accumulation, and achieve targeted diagnosis of elevator guide rail risks in dusty environments. The dynamic benchmark update mechanism overcomes the over-sensitivity to monitoring accuracy. This fault-tolerant design significantly improves the anti-interference ability under complex working conditions, achieves a precise management mode of "abnormality means maintenance", and improves the stability of system operation.

[0042] Beneficial Effects: Because dust particle risk results are derived from vibration data analysis of abnormal tension trajectories, the accumulation of dust particles increases the friction between the guide rail and the guide shoe, and the variation characteristics of the vibration data are enhanced. This variation can be used to effectively determine whether abnormal elevator tension trajectories are caused by dust particle accumulation. This dynamic monitoring method triggers maintenance only when dust impacts are significant, reducing unnecessary and frequent manual inspections in dusty environments such as sandy and industrial areas, significantly improving elevator safety and maintenance efficiency.

[0043] Furthermore, the method of feeding back dust particle risk to the client or application is:

[0044] If the current abnormal trajectory determines that the dust particle risk is true, the guide rail is considered to be contaminated by dust particles, and a dust particle contamination warning signal is sent to the administrator client to warn that maintenance is required on the guide rail with dust particle risk.

[0045] Alternatively, an early warning signal of dust particle contamination can be sent to the application end, where lubricant maintenance can be performed.

[0046] If the dust particle risk judgment result of the current abnormal trajectory is false, the warning will not be triggered.

[0047] Furthermore, a warning signal of dust particle pollution is sent to the application end, and a method for performing lubricating fluid maintenance at the application end is: the application end is an automatic lubrication system, and when the automatic lubrication system obtains a warning signal of dust particle pollution, the automatic lubrication function is started to replenish the lubricating fluid for the guide rail.

[0048] Preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0049] The present invention also provides an elevator guide rail safety monitoring system for a dusty environment. The elevator guide rail safety monitoring system for a dusty environment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the elevator guide rail safety monitoring method for a dusty environment are implemented. The elevator guide rail safety monitoring system for a dusty environment can be run on computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers. The executable system may include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs it in the following system units:

[0050] A sensor element deployment unit is used to arrange tension sensors and vibration sensors in the elevator system and filter sensor data of vibration inside the car;

[0051] A tension anomaly sensing unit, used to screen tension anomaly trajectories based on tension values obtained by a tension sensor;

[0052] The dust particle risk identification unit is used to determine the authenticity of the dust particle risk by combining the tension value and vibration data corresponding to the tension abnormality trajectory;

[0053] The monitoring feedback unit is used to feed back dust particle risks to the client or application.

[0054] The present invention provides a method and system for monitoring elevator guide rail safety in dusty environments. By identifying variations in vibration characteristics, the system effectively determines whether abnormal elevator tension trajectory is caused by dust particle accumulation. This reduces the risk of guide rail and guide shoe wear caused by dust pollution during elevator operation in high-dust environments, thereby extending the service life and operational safety of elevators. This dynamic monitoring method triggers maintenance only when dust impacts are significant, reducing unnecessary and frequent manual inspections in dusty environments such as sandy or industrial areas, significantly improving elevator safety and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and other features of the present invention will become more apparent through a detailed description of the embodiments shown in conjunction with the accompanying drawings. In the drawings of the present invention, the same reference numerals represent the same or similar elements. Obviously, the drawings described below are only some embodiments of the present invention. It is possible for a person skilled in the art to derive other drawings based on these drawings without inventive effort. In the drawings:

[0056] Figure 1 Shown is a flow chart of a method for monitoring elevator guide rail safety in a dusty environment;

[0057] Figure 2 Shown is a structural diagram of an elevator guide rail safety monitoring system in a dusty environment. DETAILED DESCRIPTION

[0058] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict.

[0059] like Figure 1 The figure shows a flow chart of a method for monitoring elevator guide rail safety in a dusty environment. Figure 1 A method for monitoring elevator guide rail safety in a dusty environment according to an embodiment of the present invention is described below. The method comprises the following steps:

[0060] Tension sensors and vibration sensors are placed in the elevator system, and the sensor data of the vibration inside the car is filtered; abnormal tension trajectories are screened based on the tension values obtained by the tension sensors; the authenticity of the dust particle risk is determined by combining the tension values and vibration data corresponding to the abnormal tension trajectories; and the dust particle risk is fed back to the client or application end.

[0061] Furthermore, a method for arranging tension sensors and vibration sensors in an elevator system and filtering sensor data of vibration inside the car is as follows: using the tension sensor to collect the force of the elevator traction wire rope and record the measured tension value in real time, the tension sensor is arranged at the wire rope anchor end, and the top position of the car is selected as the wire rope anchor end by default, and other optional positions also include the traction sheave and the counterweight end; vibration sensors are arranged at the guide rail fixing bracket and the bottom of the car respectively, and the vibration sensor data is processed by the signal differential method to obtain vibration data;

[0062] When the elevator runs at a constant speed, the tension value and vibration data are recorded from the tension sensor and vibration sensor respectively.

[0063] The signal difference method can eliminate the vibration data inside the car through vibration sensors at two locations.

[0064] The vibration sensor uses a piezoelectric accelerometer. The primary reason is that the vibration caused by dust particles is usually mainly high-medium frequency, so it is suitable for monitoring vibration changes during long-term use caused by the gradual increase in friction due to dust accumulation. Secondly, the piezoelectric accelerometer has low signal noise and high long-term stability, making it suitable for high-precision vibration spectrum analysis.

[0065] The data obtained by the vibration sensor is presented in the computer as a binary vector of acceleration in the horizontal direction. The process of obtaining vibration data by the signal difference method is as follows: Assume that the measured acceleration of the sensor at the bottom of the car is A cab =(a cab (x), a cab (y)), the measured acceleration of the guide rail fixing bracket sensor is A rail =(a rail (x), a rail (y)), the vibration data is: A diff =A cab -A rail . Among them a cab (x) and a cab (y) is the acceleration of the vibration sensor at the bottom of the car in two directions on the horizontal plane, a rail (x) and a rail (y) is the acceleration of the vibration sensor of the guide rail fixing bracket in two directions on the horizontal plane;

[0066] The purpose of processing sensor data through signal differentiation is to effectively eliminate internal elevator car noise interference and extract the actual contact vibration data between the guide rail and guide shoe. The principle is to place vibration sensors at the bottom of the car and on the guide rail mounting bracket, synchronously collecting acceleration data at both points. Since structural resonance within the car, passenger behavior, or motor vibration, will appear simultaneously at both measurement points, these common-mode interference can be eliminated by using the difference between the two signals. The differential result primarily retains local vibration changes caused by guide rail friction or dust accumulation, offering greater sensitivity and accuracy, thereby facilitating the early identification and analysis of dust vibration sources.

[0067] When the elevator is running at a constant speed, the tension value and vibration data are recorded from the tension sensor and vibration sensor respectively, with a measurement interval of 1 second; every measurement interval is regarded as a measurement point;

[0068] The uniform speed operation of the elevator is determined by the speed sensor in the elevator drive control module. It does not mean that the actual speed is uniform, but rather the non-start-stop driving process state of the elevator.

[0069] Furthermore, a method for screening abnormal tension trajectories based on the tension values obtained by the tension sensor is as follows: a self-monitoring period is set, the value of which is 24 hours; any uniform speed process between the start and stop of the elevator is recorded as a trajectory; each tension value in the trajectory is constructed into a uniform tension sequence, and the ratio of the upper quartile value to the lower quartile value in the uniform tension sequence is recorded as the tension gradient; the self-monitoring period after the most recent elevator safety maintenance time is used as the benchmark monitoring period; the maximum value of the tension gradient within the benchmark monitoring period is used as the gradient benchmark; after the benchmark monitoring period, if any tension gradient is greater than the gradient benchmark, the corresponding trajectory is recorded as a tension abnormal trajectory, abbreviated as an abnormal trajectory.

[0070] Furthermore, the method for determining the authenticity of dust particle risk by combining the tension value and vibration data corresponding to the tension abnormality trajectory is:

[0071] The preset monitoring period MLp is 24 hours;

[0072] The currently acquired abnormal trajectory is used as the current abnormal trajectory; the vibration data is the vibration data sequence;

[0073] During the monitoring period, the time points at which vibration data and tension were obtained were recorded as measurement points;

[0074] For any tension abnormality trajectory, the angle NAK between each element in the vibration data sequence and the x-axis is calculated according to the inverse tangent function, where the elements in the vibration data sequence are acceleration pairs in the horizontal direction. That is, the angle NAK between the acceleration of each measuring point in the vibration data sequence and the x-axis is calculated by the inverse tangent function. Specifically, NAK=arctan(ay / a x ), where a y and a x is the acceleration value in the y direction and the x direction in the acceleration binary; and NAK is adjusted according to the quadrant so that NAK∈[0°,360°);

[0075] if , then the NAK corresponding measurement point is recorded as the first abnormal point;

[0076] The essence of the NAK angle here is the vibration direction angle, which represents the direction of the acceleration vector in the xy plane. Since the natural friction path of the guide rail-guide shoe system has four-quadrant symmetry, the elevator guide rails are usually symmetrically arranged double T-shaped steel rails, and the guide shoes clamp them in four directions, including front-to-back and left-to-right directions, corresponding to the four natural contact directions in the horizontal plane. In actual structures, it is inevitable that the guide rail and guide shoe will be offset when they are in contact, and the direction of the contact force will not be precisely aligned. Instead, they are usually clustered at ±15° to 30° away from the ideal orthogonal direction. That is, the 60° offset band in each direction is the relatively stable direction of the friction-guide force. This method uses whether the vibration direction angle deviates from this relatively stable direction as the first abnormal point judgment condition. If it deviates, it means that its vibration direction deviates from the natural vibration inertia range of the system.

[0077] The total acceleration of each measuring point is calculated based on the acceleration binary group. The total acceleration calculation method is sqrt(a y 2 +a x 2 ), sqrt() is the square root function; the combined acceleration and tension values are combined into a temporary variable binary group Trb, and each measuring point is sorted in descending order according to the size of the tension value, and the sorted measuring points are arranged in sequence to form a sequence Trb.Ls; i is the serial number of the measuring point in Trb.Ls, and Ftb(i) is the Trb of the i-th measuring point in Trb.Ls;

[0078] In Trb.Ls, the Euclidean distances between Ftb(i) and its preceding and succeeding first outlier points (Trb) are calculated and recorded as QI.C and HO.C, respectively. If QI.C is greater than HO.C, the corresponding measurement point of Ftb(i) is recorded as the second outlier point. The first and second outlier points are marked as outliers. When the number of outlier marks in Trb.Ls exceeds 75%, the dust particle risk is judged to be true, otherwise it is false.

[0079] The first and last two elements in Ftb(i) do not count QI.C and HO.C;

[0080] Trb is the friction state mapping vector of a measuring point at the current measuring point. QI.C and HO.C represent the differences in Trb values between a measuring point and its previous and next first outlier points, respectively. Therefore, when QI.C > HO.C, it means that the friction state of the current measuring point i is closer to the state of the next first outlier point and deviates from the state of the previous first outlier point, showing a trend of state mutation or fracture. This reflects the unnatural transfer of the friction state mapping vector from the previous outlier point to the next outlier point in the process of series transmission and diffusion, including forward attenuation or backward enhancement. This reflects the nonlinear existence of this vibration state with the change of tension, or the isolation of the vibration state, indicating the uneven diffusion of disturbances in the vibration data.

[0081] Preferably, the method for determining the authenticity of dust particle risk by combining the tension value and vibration data corresponding to the tension abnormality trajectory is as follows: setting a reference period, which is set to 24 hours; the vibration data sequence A_diff corresponding to the current abnormal trajectory; and aligning the tension value sequence and the vibration data sequence using a timestamp matching module;

[0082] Alignment check refers to the time alignment of the acquired data to prevent mismatches in acquisition time between the tension value sequence and the vibration data sequence, including preventing data gaps or misalignment in the data time dimension. Data gaps refer to the presence of only one type of data in the tension value sequence and the vibration data sequence at the same time point. Data misalignment in the data time dimension refers to the allocation of tension values and vibration data acquired at different times to the same time element in the sequence.

[0083] Extract the following characteristic parameters from the vibration data sequence: perform a fast Fourier transform on the vibration data sequence to form a transform binary sequence B_diff, obtain the energy spectrum of the 0Hz-5kHz frequency range, and use the ratio of the energy in the 1kHz-5kHz frequency range as the high-frequency energy ratio;

[0084] The mathematical expression of the high-frequency energy ratio is as follows:

[0085] ;

[0086] B_diff(f) represents the synthetic vibration intensity corresponding to the frequency band energy f, which is similar to the total amplitude of two-dimensional vibration. The calculation process is: B_diff(f) = sqrt(|B x (f)| 2 +|B y (f)| 2 ), sqrt() is the square root function, B x (f) and B y (f) are the fast Fourier transform results of the vibration data sequence A_diff in the x direction and y direction respectively.

[0087] Set the first vibration condition: the energy proportion of the high frequency band corresponding to the current abnormal trajectory is greater than the dynamic threshold; the dynamic threshold is E threshold =E M ×1.15, E M is the 95th percentile value of the high-frequency energy proportion corresponding to each abnormal trajectory in the reference period; the ratio of the standard deviation of the vibration data corresponding to the two vertical directions in the horizontal direction to the mean is calculated respectively, and recorded as the acceleration variation coefficient CV_x and CV_y; the second vibration condition is set as: the acceleration variation coefficient CV_x or CV_y exceeds 0.25; the average value of the vibration data of any measuring point and the 9 measuring points in the reverse time direction is the vibration window value, and the average value of each vibration window value in the reference period is recorded as the working condition vibration reference value;

[0088] The vibration window value is calculated from the vibration data, so the data structure is still a two-tuple. The working condition vibration reference value is calculated from the vibration window value, so the data structure is still a two-tuple. The values in the two-tuple correspond to the x-direction and the y-direction.

[0089] Calculate the percentage difference between the mean value of the current vibration data in the x-direction and y-direction and the corresponding working condition vibration reference value in the reference period and record it as Δμ_x i , Δμ_y i ; If Δμ_x i and Δμ_y i The persistently exceeding limits indicates that the vibration pattern deviates from normal, possibly due to dust accumulation. The third vibration condition is set as follows: During the current abnormal trajectory operation, Δμ_x and Δμ_y exceed 10% for five or more consecutive measurement points. If any two or more of these vibration conditions are met, the dust particle risk is determined to be true; otherwise, the dust particle risk is determined to be false.

[0090] The first vibration condition, the second vibration condition and the third vibration condition are all vibration conditions.

[0091] Furthermore, the method of feeding back dust particle risk to the client or application is:

[0092] If the current abnormal trajectory determines that the dust particle risk is true, the guide rail is considered to be contaminated by dust particles, and a dust particle contamination warning signal is sent to the administrator client to warn that maintenance is required on the guide rail with dust particle risk.

[0093] Alternatively, an early warning signal of dust particle contamination can be sent to the application end, where lubricant maintenance can be performed.

[0094] If the dust particle risk judgment result of the current abnormal trajectory is false, the warning will not be triggered.

[0095] Furthermore, a warning signal of dust particle pollution is sent to the application end, and a method for performing lubricating fluid maintenance at the application end is: the application end is an automatic lubrication system, and when the automatic lubrication system obtains a warning signal of dust particle pollution, the automatic lubrication function is started to replenish the lubricating fluid for the guide rail.

[0096] An embodiment of the present invention provides an elevator guide rail safety monitoring system for a dusty environment, such as Figure 2 The figure shows a structural diagram of an elevator guide rail safety monitoring system for a dusty environment according to the present invention. The elevator guide rail safety monitoring system for a dusty environment according to this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned embodiment of the elevator guide rail safety monitoring method for a dusty environment are implemented.

[0097] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system:

[0098] A sensor element deployment unit is used to arrange tension sensors and vibration sensors in the elevator system and filter sensor data of vibration inside the car;

[0099] A tension anomaly sensing unit, used to screen tension anomaly trajectories based on tension values obtained by a tension sensor;

[0100] The dust particle risk identification unit is used to determine the authenticity of the dust particle risk by combining the tension value and vibration data corresponding to the tension abnormality trajectory;

[0101] The monitoring feedback unit is used to feed back dust particle risks to the client or application.

[0102] The elevator guide rail safety monitoring system for dusty environments can be run on computing devices such as desktop computers, laptops, PDAs, and cloud servers. Systems capable of running the elevator guide rail safety monitoring system for dusty environments may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that the example is merely an illustration of an elevator guide rail safety monitoring system for dusty environments and does not constitute a limitation on the entire elevator guide rail safety monitoring system for dusty environments. The system may include more or fewer components than the example, or a combination of certain components, or different components. For example, the elevator guide rail safety monitoring system for dusty environments may also include input and output devices, network access devices, buses, and the like.

[0103] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the operation system of the elevator guide rail safety monitoring system for a dusty environment, and utilizes various interfaces and lines to connect various parts of the entire operation system of the elevator guide rail safety monitoring system for a dusty environment.

[0104] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the elevator guide rail safety monitoring system for dusty environments by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0105] Although the present invention has been described in considerable detail and with particularity with respect to several embodiments, it is not intended to limit the present invention to any of these details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the present invention. In addition, the present invention has been described above with respect to embodiments foreseen by the inventors for the purpose of providing a useful description, and those insubstantial modifications of the present invention that are not currently foreseen may still represent equivalent modifications of the present invention.

Claims

1. A method for monitoring elevator guide rail safety in a dusty environment, characterized in that: The method includes the following steps: arranging tension sensors and vibration sensors in the elevator system and filtering sensor data of vibration inside the elevator car; screening for abnormal tension trajectories based on tension values obtained by the tension sensors; determining the authenticity of dust particle risks by combining the tension values and vibration data corresponding to the abnormal tension trajectories; and feeding back the dust particle risks to a client or application. The method for arranging tension sensors and vibration sensors in an elevator system and filtering sensor data of internal car vibration is as follows: the tension sensor is used to collect the force of the elevator traction wire rope and record the measured tension value in real time. The tension sensor is arranged at the anchor end of the wire rope; vibration sensors are arranged at the guide rail fixing bracket and the bottom of the car respectively, and the vibration sensor data is processed by signal differential method to obtain vibration data; When the elevator runs at a constant speed, the tension value and vibration data are recorded from the tension sensor and vibration sensor respectively; The method for screening abnormal tension trajectories based on the tension values obtained by the tension sensor is as follows: set a self-monitoring period with a value range of [12, 48] hours; any uniform speed process between the start and stop of the elevator is recorded as a trajectory; each tension value in the trajectory is constructed into a uniform tension sequence, and the ratio of the upper quartile value to the lower quartile value in the uniform tension sequence is recorded as the tension gradient; the self-monitoring period after the most recent elevator safety maintenance time is used as the benchmark monitoring period; the maximum value of the tension gradient within the benchmark monitoring period is used as the gradient benchmark; after the benchmark monitoring period, if any tension gradient is greater than the gradient benchmark, the corresponding trajectory is recorded as a tension abnormal trajectory, abbreviated as an abnormal trajectory; The method of combining the tension value and vibration data corresponding to the tension abnormality trajectory to determine the authenticity of the dust particle risk is to record the time point of obtaining the vibration data and tension as the measurement point during the monitoring period; For any abnormal tension trajectory, the angle NAK between each element in the vibration data sequence and the x-axis is calculated according to the arc tangent function; if , then the NAK corresponding measurement point is recorded as the first abnormal point; Calculate the combined acceleration of each measuring point based on the acceleration binary, and combine the combined acceleration and tension value to form a temporary variable binary tuple Trb. Then sort the measuring points in descending order according to the tension value, and arrange the sorted measuring points in sequence to form a sequence Trb.Ls. Let i be the serial number of the measuring point in Trb.Ls, and let Ftb(i) be the temporary variable binary tuple of the i-th measuring point in Trb.Ls. In Trb.Ls, the Euclidean distances between Ftb(i) and its preceding and succeeding first abnormal point are calculated and recorded as QI.C and HO.C, respectively. If QI.C is greater than HO.C, the corresponding measurement point of Ftb(i) is recorded as the second abnormal point. The first and second abnormal points are marked as abnormal. When the number of abnormal marks in Trb.Ls exceeds 75%, the dust particle risk is judged to be true, otherwise it is false.

2. The method for monitoring elevator guide rail safety in a dusty environment according to claim 1, wherein: The method for determining the authenticity of dust particle risk by combining the tension value and vibration data corresponding to the tension abnormal trajectory is as follows: the current abnormal trajectory corresponds to the vibration data sequence A_diff; the tension value sequence and the vibration data sequence are aligned and verified using the timestamp matching module; the following characteristic parameters are extracted from the vibration data sequence: the vibration data sequence is fast Fourier transformed to form a transformed binary sequence B_diff, and the energy spectrum of the 0Hz-5kHz frequency range is obtained, and the proportion of the energy in the 1kHz-5kHz frequency range is used as the high-frequency energy proportion; Set the first vibration condition: the energy proportion of the high frequency band corresponding to the current abnormal trajectory is greater than the dynamic threshold; the dynamic threshold is E threshold =E M ×1.15, E M The 95th percentile value of the high-frequency energy proportion corresponding to each abnormal trajectory in the reference period; Calculate the ratio of the standard deviation to the mean of the vibration data in the two vertical directions within the horizontal direction, and record them as the acceleration variation coefficients CV_x and CV_y. Set the second vibration condition as follows: the acceleration variation coefficient CV_x or CV_y is greater than 0.

25. The average value of the vibration data of any measuring point and its 9-19 measuring points in the reverse time direction is the vibration window value. The average value of each vibration window value in the reference period is recorded as the working condition vibration reference value; the percentage difference between the average value of the current vibration data in the x-direction and y-direction and the corresponding working condition vibration reference value in the reference period is calculated and recorded as Δμ_x i , Δμ_y i The third vibration condition is set as follows: during the current abnormal trajectory operation, Δμ_x and Δμ_y are both greater than 10% at 5 or more consecutive measurement points; If any two or more vibration conditions are met, the dust particle risk is determined to be true, otherwise the dust particle risk is determined to be false.

3. The method for monitoring elevator guide rail safety in a dusty environment according to claim 1, wherein: The method for feeding back dust particle risk to the client or application is as follows: if the current abnormal trajectory determines that the dust particle risk result is true, the guide rail is considered to be contaminated by dust particles, and a dust particle contamination warning signal is sent to the administrator client to warn that maintenance is required for the guide rail with dust particle risk; Alternatively, an early warning signal of dust particle contamination can be sent to the application end, where lubricant maintenance can be performed.

4. The method for monitoring elevator guide rail safety in a dusty environment according to claim 3, wherein: The early warning signal of dust particle pollution is sent to the application end. The method for performing lubricating fluid maintenance at the application end is: the application end is an automatic lubrication system. When the automatic lubrication system obtains the early warning signal of dust particle pollution, the automatic lubrication function is started to replenish the lubricating fluid for the guide rail.

5. An elevator guide rail safety monitoring system for dusty environments, characterized in that: The elevator guide rail safety monitoring system for a dusty environment includes: a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the elevator guide rail safety monitoring method for a dusty environment described in any one of claims 1 to 4 are implemented. The elevator guide rail safety monitoring system for a dusty environment runs on a desktop computer, a laptop computer, a PDA, and a computing device in a cloud data center.

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