Safety monitoring method and system for elevator guide rail in sand and dust environment

Abstract

The invention belongs to the technical field of anomaly recognition, and provides an elevator guide rail safety monitoring method and system in a sand and dust environment, and the method specifically comprises the steps: firstly, arranging a tension sensor and a vibration sensor in an elevator system, filtering the data of the vibration sensor in a lift car, and screening a tension anomaly track according to a tension value obtained by the tension sensor; judging the authenticity of the dust particle risk according to the tension value corresponding to the tension abnormal track and the vibration data, and finally feeding back the dust particle risk to the client or the application end. Whether the abnormal tension track of the elevator is caused by dust particle accumulation or not is effectively judged through vibration characteristic variation recognition, so that the abrasion risk of a guide rail and a guide shoe caused by dust pollution in the elevator application process in the high-dust environment is reduced, and unnecessary high-frequency manual inspection in the dust environment of a sand-dust area or an industrial area is reduced; and the safety and the maintenance efficiency of the elevator are greatly improved.

Description

Technical Field

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

[0002] As an important transportation facility in a building, the stability and safety of an elevator during operation are the primary tasks in its daily operation. Among the potential problems of an elevator, there are safety hazards caused by elevator vibration. During the use of elevators in high-dust areas, there are often relatively high elevator vibration failure rates, which are caused by the dust environment. When there is a lot of dust in the air, dust particles enter the contact area between the guide rail and the guide shoe, forming local dry friction, resulting in vibration during elevator operation. By replenishing lubricating fluid, this vibration problem can be effectively alleviated or solved. Regarding the problem of dust accumulation, the replenishment of lubricating oil can form an oil film barrier, which can effectively prevent dust from directly contacting the guide rail metal, and at the same time reduce the adhesion of dust particles to the guide rail and provide an opportunity for particle removal, thereby reducing the problem of friction increase and car vibration. However, traditional lubricating fluid replenishment usually relies on regular monitoring, and the intervals of regular monitoring are usually relatively long, resulting in a serious diagnostic lag in the wear caused by the accumulation of dust particles in the contact area between the guide rail and the guide shoe. Therefore, there is an urgent need for a method and system for safety monitoring of elevator guide rails in a dust environment. Summary of the Invention

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

[0004] To achieve the above purpose, according to one aspect of the present invention, there is provided a method for safety monitoring of elevator guide rails in a dust environment, the method comprising the following steps: Arrange a tension sensor and a vibration sensor in the elevator system, and filter the sensor data of the vibration inside the car; Select tension anomaly trajectories according to the tension values obtained by the tension sensor; Combine the tension values corresponding to the tension anomaly trajectories and the vibration data to determine the authenticity of the dust particle risk; Feed back the dust particle risk to the client or the application end.

[0005] Further, the method of arranging a tension sensor and a vibration sensor in an elevator system and filtering the sensor data of the vibration inside the car is as follows: The tension of the elevator traction steel wire rope is collected by the tension sensor and the measured tension value is recorded in real time. The tension sensor is arranged at the wire rope anchoring end. By default, the top position of the car is selected as the wire rope anchoring end, and its optional positions also include the traction sheave and the counterweight end. Vibration sensors are respectively arranged on the guide rail fixing bracket and the bottom of the car. The vibration sensor data is processed by the signal difference method to obtain vibration data. When the elevator is running at a constant speed, the tension value and the vibration data are respectively recorded from the tension sensor and the vibration sensor.

[0006] The signal difference method can exclude the vibration data inside the car through the vibration sensors at two positions.

[0007] Among them, the vibration sensor uses a piezoelectric acceleration sensor. The primary reason is that the vibration caused by dust particles is mainly in the high and medium frequencies, so it is suitable for monitoring the vibration changes during long-term use caused by the gradual increase in friction due to sand and dust accumulation. Secondly, the signal noise of the piezoelectric acceleration sensor is low and the long-term stability is high, so it is suitable for high-precision vibration spectrum analysis.

[0008] When the elevator is running at a constant speed, the tension value and the vibration data are respectively recorded from the tension sensor and the vibration sensor, and the measurement interval is from 0.2 seconds to 1 second; every other measurement interval is used as a measurement point. Among them, the elevator running at a constant speed is determined by the speed sensor in the elevator drive control module. It is not that the actual speed is running at a constant speed, but the driving process state in the elevator without starting and stopping.

[0009] Further, the method of screening the tension abnormal trajectory according to the tension value obtained by the tension sensor is as follows: Set the self-monitoring period, and its value range is [12, 48] hours; The uniform process between any two elevator starts and stops is recorded as one 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 nearest elevator safety maintenance time to the current time is used as the reference monitoring period; The maximum value of the tension gradient within the reference monitoring period is used as the gradient reference; After the reference monitoring period, if the tension gradient of any time is greater than the gradient reference, the corresponding trajectory is recorded as a tension abnormal trajectory, simply recorded as an abnormal trajectory.

[0010] By defining the reference monitoring period, the monitoring system can adapt to the initial stable state after elevator maintenance, because after elevator safety maintenance, the mechanical state of the system, including wire rope tension, lubrication state, guide rail friction, etc., is restored to the standard state, and this standard state has a gradual change during long-term operation. Therefore, the tension abnormal determination standard needs to be maintained and updated regularly.

[0011] The tension gradient is used to highlight the rapidly changing part of the tension signal, improve the detection sensitivity by amplifying subtle changes, and facilitate subsequent identification of abnormalities at the edge.

[0012] The reference principle of the tension gradient is based on the distribution of tension stage data, reflecting the equilibrium characteristics of the tension distribution during the uniform operation of the elevator.

[0013] Furthermore, the method for determining the authenticity of dust particle risk by combining the tension value corresponding to the tension anomaly trajectory and vibration data is as follows: Preset the monitoring period MLp, where MLp ∈ [24, 48] hours; Take the currently obtained tension anomaly trajectory as the current tension anomaly trajectory; the vibration data is a vibration data sequence. During the monitoring period, record the time points for obtaining vibration data and tension as measurement points. For any tension anomaly trajectory, calculate the angle NAK between each element in the vibration data sequence and the x-axis according to the arctangent function, where the elements in the vibration data sequence are acceleration binary groups in the horizontal plane direction, that is, calculate the angle NAK between the acceleration of each measurement point in the vibration data sequence and the x-axis according to the arctangent function, and adjust the angle of NAK according to the quadrant to make NAK ∈ [0°, 360°). If , then record the measurement point corresponding to NAK as the first abnormal point. In fact, the principle of obtaining the angle NAK between the acceleration binary group and the x-axis and discriminating the first abnormal point is the application of the adjustment and conversion of trigonometric functions and angles. Introducing vector direction diagnosis directly associates the vibration data with risk characteristics to identify whether the vibration direction is abnormal; by limiting NAK, the conventional vibration during the uniform operation of the elevator can be effectively filtered; NAK associates the direction with the risk. The dry friction between the guide rail and the guide shoe caused by dust accumulation will cause lateral swing (NAK close to 0° or 180°) or axial impact (NAK close to 90° or 270°). NAK can directly screen and lock such characteristics. Therefore, when NAK deviates from the limited value range, it indicates that the directionality of the friction force between the guide rail and the guide shoe has mutated, providing a basis for fault location; the acquisition of NAK is to capture the specific vibration mode caused by the dry friction induced by dust entering the guide rail, transform the vibration data from scalar energy analysis to vector direction diagnosis, thereby improving the recognition accuracy of dust friction and effectively avoiding and reducing diagnostic hysteresis, and at the same time avoiding false alarms caused by vibrations caused by normal load changes; the determination of the first abnormal point is usually the earliest moment when the vibration direction or the tension direction appears to mutate.

[0014] Calculate the resultant acceleration of each measurement point according to the acceleration pair, form the critical change pair Trb with the resultant acceleration and the tension value, sort each measurement point in descending order according to the tension value, and arrange the sorted measurement points in sequence to form the sequence Trb.Ls; let i be the serial number of the measurement point in Trb.Ls, and Ftb(i) be the Trb of the i-th measurement point in Trb.Ls; In Trb.Ls, calculate the Euclidean distances between Ftb(i) and the Trbs of its previous and next first abnormal points respectively, and denote them as QI.C and HO.C; if QI.C is greater than HO.C, then mark the measurement point corresponding to Ftb(i) as the second abnormal point; mark the first abnormal point and the second abnormal point. When the proportion of the number of abnormal marks in Trb.Ls exceeds 75%, it is judged that the dust particle risk is true, otherwise it is false.

[0015] The selection principle of the measurement point of the second abnormal point is based on the sorting and the relationship with adjacent points. Its basis is to find the point with the worst similarity to the first abnormal point, which represents the position where the change direction of the vibration data is significantly different, so as to maximize the time and the time domain span of the vibration data. When the frequency of this span is large, that is, the proportion of the number of abnormal marks is high, it indicates that the degree of dust particle accumulation judged is stronger.

[0016] Since the determination of the dust particle risk needs to process the first abnormal point and the second abnormal point, it can effectively quantify the wear risk of the guide rail and the guide shoe caused by dust pollution during the elevator application process in a high-dust environment. However, the acquisition of the angle NAK between the acceleration pair and the x-axis is too sensitive to the direct data of the vibration data sequence, and there is an easy direction sensitivity deviation, resulting in inaccurate attribution and decision-making deviation. The maintenance feedback still has hysteresis, especially in the period when the density of the 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 phenomenon. In order to eliminate this influence, the present invention proposes a more preferred solution as follows: Preferably, the method for determining the authenticity of the dust particle risk by combining the tension value corresponding to the tension abnormal trajectory and the vibration data is: set a reference period, and its value range is [12, 48] hours; obtain the vibration data sequence A_diff corresponding to the currently obtained abnormal trajectory; perform alignment verification on the tension value sequence and the vibration data sequence through the timestamp matching module, and extract the following characteristic parameters from the vibration data sequence: perform a fast Fourier transform on the vibration data sequence to form a transformed pair sequence B_diff, obtain the energy spectrum in the frequency band of 0 Hz - 5 kHz, and use the proportion of the energy in the frequency band of 1 kHz - 5 kHz as the high-frequency band energy ratio; The accumulation of dust particles will cause the high-frequency friction between the guide rail and the guide shoe to intensify, manifested as a significant increase in the high-frequency band energy ratio.

[0017] 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 It is 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; Calculate the ratio of the standard deviation of the vibration data corresponding to the two vertical directions in the horizontal direction to the mean value, and record them as the acceleration variation coefficients CV_x and CV_y; set the second vibration condition as: the acceleration variation coefficient CV_x or CV_y exceeds 0.25; the second vibration condition reflects that the variation coefficient is too high; The coefficient of variation is a normalized indicator for measuring the fluctuation of vibration data relative to the average level. It is defined here as the ratio of the signal standard deviation to the mean without relying on absolute units. This characteristic allows data to be compared across different sensors and system conditions, ensuring that the signal distribution shape is relatively robust. Even if the vibration data presents a non-Gaussian distribution, the intensity of fluctuations can still be effectively measured.

[0018] 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 in the reference period is recorded as the working condition vibration reference value; 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.

[0019] 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. Calculate the percentage difference between the mean value of the current vibration data in the x-direction and the vibration reference value of the corresponding working condition 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 5 or more consecutive measurement points; the third vibration condition reflects that the mean difference continues to exceed the standard.

[0020] 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.

[0021] Among them, the first vibration condition, the second vibration condition, and the third vibration condition are all vibration conditions.

[0022] By coupling the abnormal tension trajectory with the analysis of the variation characteristics of the vibration spectrum, the abnormal tension caused by mechanical wear due to dust accumulation is accurately distinguished, realizing the targeted diagnosis of the risks of elevator guide rails in a dust environment. The dynamic benchmark update mechanism overcomes the over-sensitive characteristics of the monitoring accuracy. This fault-tolerant design significantly improves the anti-interference ability under complex working conditions, achieves an accurate management mode of "abnormality means maintenance", and improves the stability of system operation.

[0023] Beneficial effects: Since the risk result of dust particles is obtained by analyzing the vibration data based on the abnormal tension trajectory, when dust particles accumulate, the friction force between the guide rail and the guide shoe will increase, and the variation characteristics of the vibration data will also increase. Therefore, the identification of this variation characteristic is used to effectively judge whether the abnormal tension trajectory of the elevator is caused by the accumulation of dust particles. This dynamic monitoring method only triggers maintenance when the dust impact is obvious, reducing unnecessary high-frequency manual inspections in dust environments such as desert areas or industrial areas, and greatly improving the safety and maintenance efficiency of elevators.

[0024] Furthermore, the method of feeding back the dust particle risk to the client or the application end is as follows: If the risk result of dust particles is judged to be true based on the current abnormal trajectory, it is considered that the guide rail is contaminated by dust particles, and a warning signal of dust particle contamination is sent to the administrator client to warn that maintenance of the guide rail with dust particle risk is required; Or, a warning signal of dust particle contamination is sent to the application end, and lubricant maintenance is performed at the application end.

[0025] If the risk result of dust particles is judged to be false based on the current abnormal trajectory, no warning is triggered.

[0026] Furthermore, the method of sending a warning signal of dust particle contamination to the application end and performing lubricant maintenance at the application end is as follows: The application end is an automatic lubrication system. When the automatic lubrication system obtains the warning signal of dust particle contamination, it starts the automatic lubrication function to supplement the lubricant for the guide rail.

[0027] Preferably, among them, for all undefined variables in the present invention, if there is no clear definition, they can all be artificially set thresholds.

[0028] The present invention also provides an elevator guide rail safety monitoring system for a dust environment. The elevator guide rail safety monitoring system for a dust 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 in the elevator guide rail safety monitoring method for a dust environment are implemented. The elevator guide rail safety monitoring system for a dust environment can run on computing devices such as desktop computers, laptop computers, palm computers, and cloud data centers. The operable system can include, but is not limited to, a processor, a memory, and a server cluster. The processor executes the computer program and runs in the following units of the system: A sensing element deployment unit, configured to arrange a tension sensor and a vibration sensor in an elevator system and filter the sensor data of the vibration inside the car; A tension anomaly perception unit, configured to screen out tension anomaly trajectories according to the tension values obtained by the tension sensor; A dust particle risk discrimination unit, configured to determine the authenticity of the dust particle risk by combining the tension value corresponding to the tension anomaly trajectory and the vibration data; A monitoring feedback unit, configured to feedback the dust particle risk to a client or an application end.

[0029] The beneficial effects of the present invention are as follows: The present invention provides an elevator guide rail safety monitoring method and system for a dust environment, and uses the identification of the variation of vibration characteristics to effectively judge whether the elevator tension anomaly trajectory is caused by dust particle accumulation, thereby reducing the risk of wear of the guide rail and guide shoe caused by dust pollution during the application of the elevator in a high-dust environment, and improving the service life and operation safety of the elevator. This dynamic monitoring method only triggers maintenance when the dust impact is obvious, reduces unnecessary high-frequency manual inspections in dust environments such as dust-laden areas or industrial areas, and greatly improves the elevator safety and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] By describing the embodiments shown in the accompanying drawings in detail, the above and other features of the present invention will become more obvious. The same reference numerals in the drawings of the present invention represent the same or similar elements. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 Shown is a flowchart of an elevator guide rail safety monitoring method for a dust environment; Figure 2 Shown is a structural diagram of an elevator guide rail safety monitoring system for a dust environment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The concept, specific structure, and technical effects of the present invention will be clearly and completely described below in conjunction with embodiments and the accompanying drawings to fully understand the purpose, solution, and effects of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0032] As Figure 1 shown is a flowchart of a safety monitoring method for elevator guide rails in a dust environment. The following will be described in conjunction with Figure 1 to illustrate a safety monitoring method for elevator guide rails in a dust environment according to an embodiment of the present invention. The method includes the following steps: Arrange a tension sensor and a vibration sensor in the elevator system, and filter the sensor data of the vibration inside the car; screen the abnormal tension trajectories according to the tension values obtained by the tension sensor; combine the tension values corresponding to the abnormal tension trajectories and the vibration data to determine the authenticity of the dust particle risk; and feedback the dust particle risk to the client or the application end.

[0033] Further, the method of arranging a tension sensor and a vibration sensor in the elevator system and filtering the sensor data of the vibration inside the car is as follows: Collect the force on the elevator traction steel wire rope through the tension sensor and record the measured tension value in real time. The tension sensor is arranged at the anchoring end of the steel wire rope. By default, the top position of the car is selected as the anchoring end of the steel wire rope, and its optional positions also include the traction wheel and the counterweight end; arrange vibration sensors on the guide rail fixing bracket and the bottom of the car respectively, and process the vibration sensor data through the signal difference method to obtain the vibration data; When the elevator runs at a constant speed, record the tension value and the vibration data from the tension sensor and the vibration sensor respectively.

[0034] The signal difference method can exclude the vibration data inside the car through the vibration sensors at two positions.

[0035] Among them, the vibration sensor uses a piezoelectric acceleration sensor. The primary reason is that the vibration caused by dust particles is mainly in the high and medium frequencies, so it is suitable for monitoring the vibration changes during long-term use caused by the gradual increase in friction due to dust accumulation. Secondly, the signal noise of the piezoelectric acceleration sensor is low and the long-term stability is high, so it is suitable for high-precision vibration spectrum analysis.

[0036] Among them, the data obtained by the vibration sensor is presented in the form of a two-dimensional vector of acceleration in the horizontal plane direction in the computer. The specific process of obtaining the vibration data by the signal difference method is as follows: Let the measured acceleration of the sensor at the bottom of the car be A cab =(a cab (x), a cab (y)), and the measured acceleration of the sensor on the guide rail fixing bracket be A rail =(a rail(x), a rail (y)), the vibration data is: A diff =A cab -A rail . Where a cab (x) and a cab (y) are the accelerations of the vibration sensors at the bottom of the car in two horizontal directions. a rail (x) and a rail (y) are the accelerations of the vibration sensors on the guide rail fixing bracket in two horizontal directions; The purpose of processing the sensor data by the signal difference method is to effectively eliminate the noise interference inside the elevator car and extract the true contact vibration data between the guide rail and the guide shoe. The principle is to arrange vibration sensors at the bottom of the car and on the guide rail fixing bracket respectively, and synchronously collect the acceleration data of the two points. Since the structural resonance inside the car, passenger behavior or motor vibration will appear simultaneously in the two measurement points and belong to the common components, this part of the common-mode interference can be eliminated by using the signal difference between the two. The differential result mainly retains the local vibration changes caused by rail friction or dust accumulation, and has higher sensitivity and accuracy, thus promoting the early identification and analysis of the dust vibration source.

[0037] When the elevator runs at a constant speed, the tension value and vibration data are respectively recorded from the tension sensor and the vibration sensor, and the measurement interval is 1 second; every other measurement interval is used as a measurement point; Among them, the determination of the elevator running at a constant speed is based on the running state determined by the speed sensor in the elevator drive control module. It is not that the actual speed is constant, but the driving process state in the elevator without starting and stopping.

[0038] Further, the method for screening the abnormal tension trajectory according to the tension value obtained by the tension sensor is: set the self-monitoring period, and its value is 24 hours; the uniform process between any two elevator starts and stops is recorded as one trajectory; the tension values in the trajectory are 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 nearest elevator safety maintenance time to the current time is used as the reference monitoring period; the maximum value of the tension gradient within the reference monitoring period is used as the gradient reference; after the reference monitoring period, if the tension gradient in any one time is greater than the gradient reference, the corresponding trajectory is recorded as an abnormal tension trajectory, simply recorded as an abnormal trajectory.

[0039] Further, the method for determining the authenticity of the dust particle risk by combining the tension value and vibration data corresponding to the abnormal tension trajectory is: Preset the monitoring period MLp, and its value is 24 hours; Take the currently obtained abnormal trajectory as the current abnormal trajectory; the vibration data is the vibration data sequence; During the monitoring period, the time points when vibration data and tension are acquired are recorded as measurement points; For any tension anomaly trajectory, calculate the angle NAK between each element in the vibration data sequence and the x-axis according to the arctangent function, where the elements in the vibration data sequence are acceleration binary tuples in the horizontal plane direction, that is, calculate the angle NAK between the acceleration of each measurement point in the vibration data sequence and the x-axis. Specifically: NAK = arctan(a y / a x ), where a y and a x are the acceleration values in the y-direction and x-direction in the acceleration binary tuple; and adjust the angle of NAK according to the quadrant so that NAK ∈ [0°, 360°); If , then mark the measurement point corresponding to NAK as the first anomaly point; The essence of the NAK angle here is the vibration direction angle, which represents the direction of the acceleration vector in the x-y plane. Due to the four-quadrant symmetry of the natural friction path of the guide rail-guide shoe system, elevator guide rails are usually symmetrically arranged double T-shaped steel rails, and there are four directions for the guide shoe to clamp it, including the front-back and left-right directions, corresponding to 4 natural contact directions in the horizontal plane. In the actual structure, it is inevitable that the guide rail and the guide shoe are offset when in contact, and the contact force direction will not be precisely aligned , but usually clusters within ±15° to 30° deviating from the ideal orthogonal direction, that is, the 60° offset band in each direction is the direction where the friction-guiding force is relatively stable. This method uses whether the vibration direction angle deviates from this relatively stable direction as the judgment condition for the first anomaly point. If it deviates, it means that its vibration direction deviates from the natural vibration inertia range of the system.

[0040] Calculate the resultant acceleration of each measurement point according to the acceleration binary tuple. The calculation method of the resultant acceleration is sqrt(a y 2 +a x 2 ), where sqrt() is the square root function; form the critical change binary tuple Trb with the resultant acceleration and the tension value, and sort each measurement point in descending order according to the magnitude of the tension value, and arrange the sorted measurement points in sequence to form the sequence Trb.Ls; use i as the serial number of the measurement point in Trb.Ls, and use Ftb(i) as the Trb of the i-th measurement point in Trb.Ls; In Trb.Ls, calculate the Euclidean distances between Ftb(i) and the Trb of its previous and next first outliers respectively, and denote them as QI.C and HO.C; if QI.C is greater than HO.C, mark the measuring point corresponding to Ftb(i) as the second outlier; mark the first outliers and the second outliers as abnormal. When the proportion of the number of abnormal marks in Trb.Ls exceeds 75%, it is determined that the dust particle risk is true, otherwise it is false.

[0041] Among them, the first and last two elements in Ftb(i) are not used to calculate QI.C and HO.C; Trb is the friction state mapping vector of a certain measuring point at the current measuring point. QI.C and HO.C respectively represent the differences in Trb values between a measuring point and its previous and next first outliers. 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 and deviates from the state of the previous first outlier, showing a trend of state mutation or discontinuous transfer, reflecting the unnatural transfer in the process of the series transmission and diffusion of the friction state mapping vector from the previous outlier level to the next outlier, including forms of forward attenuation or backward enhancement transfer, thereby reflecting the non-linear existence quantity of this vibration state during the tension change process, or the isolation of the vibration state, representing the unbalanced diffusion of vibration data perturbations.

[0042] Preferably, the method for determining the authenticity of dust particle risk by combining the tension value corresponding to the tension anomaly trajectory and the vibration data is as follows: set a reference period, and its value is 24 hours; the current anomaly trajectory corresponds to the vibration data sequence A_diff; use the timestamp matching module to perform alignment verification on the tension value sequence and the vibration data sequence; The so-called alignment verification refers to aligning the obtained data in time to prevent mismatches in the acquisition time between the tension value sequence and the vibration data sequence, including preventing data gaps or misalignments in the data time dimension; a data gap means that only one type of data exists in the tension value sequence and the vibration data sequence at the same time point; a misalignment in the data time dimension means that the tension values and vibration data obtained at different times are assigned to the same time element in the sequence; Extract the following characteristic parameters from the vibration data sequence: perform a fast Fourier transform on the vibration data sequence to form a transformed binary group sequence B_diff, obtain the energy spectrum in the 0Hz - 5kHz frequency band, and use the proportion of the energy in the 1kHz - 5kHz frequency band as the high-frequency band energy ratio; The mathematical expression of the high-frequency band energy ratio is as follows: ; B_diff(f) represents the synthetic vibration intensity corresponding to the energy of the frequency band f, similar to the total amplitude of two-dimensional vibration. Its calculation process is as follows: B_diff(f) = sqrt(|B x (f)| 2 +|B y (f)| 2 ), where sqrt() is the square root function, and B x (f) and B y (f) are the results of the fast Fourier transform of the vibration data sequence A_diff in the x-direction and y-direction respectively.

[0043] Set the first vibration condition: the proportion of the energy in the high-frequency band corresponding to the current abnormal trajectory is greater than the dynamic threshold; where the dynamic threshold is E threshold =E M ×1.15, and E M is the 95th percentile value of the proportion of the energy in the high-frequency band corresponding to each abnormal trajectory within the reference period; calculate the ratios of the standard deviation to the mean of the vibration data corresponding to two perpendicular directions in the horizontal direction respectively, denoted as the acceleration coefficient of variation CV_x and CV_y; set the second vibration condition as: the acceleration coefficient of variation CV_x or CV_y exceeds 0.25; the average value of the vibration data of any measurement point and its 9 measurement 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 denoted as the working condition vibration reference value; The vibration window value is calculated from the vibration data, so the data structure is still a binary tuple. The working condition vibration reference value is calculated from the vibration window value, so the data structure is still a binary tuple. The values in the binary tuple correspond to the x-direction and y-direction; Calculate the percentage differences between the current vibration data means in the x-direction and y-direction and the corresponding working condition vibration reference values within the reference period respectively, and denote them as Δμ_x i and Δμ_y i ; if both Δμ_x i and Δμ_y i continue to exceed the standard, it indicates that the vibration mode deviates from the normal state, which may be caused by dust accumulation. Set the third vibration condition as: during the working period of the current abnormal trajectory, Δμ_x and Δμ_y both exceed 10% at 5 or more consecutive measurement points; if any two or more vibration conditions are satisfied, it is determined that the dust particle risk is true, otherwise it is determined that the dust particle risk is false.

[0044] Among them, the first vibration condition, the second vibration condition, and the third vibration condition are all vibration conditions.

[0045] Furthermore, the method of feeding back the dust particle risk to the client or the application is: If the result of judging the dust particle risk based on the current abnormal trajectory is true, it is considered that the guide rail is contaminated by dust particles, and a warning signal of dust particle contamination is sent to the administrator client to warn that the guide rail with dust particle risk needs to be maintained; Alternatively, a warning signal of dust particle contamination is sent to the application side, and lubricating fluid maintenance is performed on the application side.

[0046] If the result of judging the dust particle risk based on the current abnormal trajectory is false, no warning is triggered.

[0047] Furthermore, the method of sending a warning signal of dust particle contamination to the application side and performing lubricating fluid maintenance on the application side is as follows: The application side is an automatic lubrication system. When the automatic lubrication system obtains a warning signal of dust particle contamination, it starts the automatic lubrication function to supplement the lubricating fluid to the guide rail.

[0048] An elevator guide rail safety monitoring system in a sand and dust environment provided by an embodiment of the present invention, such as Figure 2 shown in the structural diagram of an elevator guide rail safety monitoring system in a sand and dust environment of the present invention. An elevator guide rail safety monitoring system in a sand and dust environment of 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, it implements the steps in the above-mentioned embodiment of the elevator guide rail safety monitoring method in a sand and dust environment.

[0049] The system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it runs in the following units of the system: A sensing element deployment unit, configured to arrange a tension sensor and a vibration sensor in the elevator system and filter the sensor data of the vibration inside the car; A tension anomaly perception unit, configured to screen tension anomaly trajectories according to the tension values obtained by the tension sensor; A dust particle risk discrimination unit, configured to determine the authenticity of the dust particle risk by combining the tension values corresponding to the tension anomaly trajectories and the vibration data; A monitoring and feedback unit, configured to feedback the dust particle risk to the client or the application side.

[0050] The elevator guide rail safety monitoring system in a dust environment can run on computing devices such as desktop computers, laptop computers, palmtop computers, and cloud servers. The elevator guide rail safety monitoring system in a dust environment, the operable system can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above examples are only examples of the elevator guide rail safety monitoring system in a dust environment and do not constitute a limitation on the elevator guide rail safety monitoring system in a dust environment. It can include more or fewer components than the examples, or combine certain components, or different components. For example, the elevator guide rail safety monitoring system in a dust environment can also include input / output devices, network access devices, a bus, etc.

[0051] The so-called processor can be a central processing unit (CPU), or it can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor is the control center of the operable system of the elevator guide rail safety monitoring system in a dust environment, and uses various interfaces and lines to connect all parts of the operable system of the elevator guide rail safety monitoring system in a dust environment.

[0052] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the elevator guide rail safety monitoring system in a dust environment by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0053] Although the description of the present invention has been quite detailed and particularly describes several of the described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above in terms of embodiments foreseeable by the inventors for the purpose of providing a useful description, and non-substantive modifications to the present invention that are not currently foreseeable may still represent equivalent modifications of the present invention.

Claims

1. A safety monitoring method for elevator guide rails in a dust environment, characterized in that, The method includes the following steps: arranging a tension sensor and a vibration sensor in an elevator system, and filtering the sensor data of the vibration inside the car; screening the tension abnormal trajectories according to the tension values obtained by the tension sensor; combining the tension values corresponding to the tension abnormal trajectories and the vibration data to determine the authenticity of the dust particle risk; and feeding back the dust particle risk to the client or the application end. The method for combining the tension values corresponding to the tension abnormal trajectories and the vibration data to determine the authenticity of the dust particle risk is as follows: calculating the included angle for the tension abnormal trajectories according to the vibration data to identify the first abnormal point; obtaining the vibration data corresponding to the tension abnormal trajectories from the vibration sensor, constructing a binary tuple sequence with the vibration data and the tension values in descending order of the tension values to identify the second abnormal point, and marking the dust particle risk according to the ratio of the first abnormal point to the second abnormal point.

2. The elevator guide rail safety monitoring method in a dust environment according to claim 1, characterized in that, The method for arranging a tension sensor and a vibration sensor in an elevator system and filtering the sensor data of the vibration inside the car is as follows: collecting the force on the elevator hoisting wire rope through the tension sensor and recording the measured tension values in real time, and arranging the tension sensor at the wire rope anchorage end; arranging vibration sensors at the guide rail fixing bracket and the bottom of the car respectively, and processing the vibration sensor data through the signal difference method to obtain the vibration data. When the elevator runs at a constant speed, record the tension value and the vibration data from the tension sensor and the vibration sensor respectively.

3. The elevator guide rail safety monitoring method in a sand and dust environment according to claim 1, characterized in that, The method for screening the tension abnormal trajectories according to the tension values obtained by the tension sensor is as follows: setting a self-monitoring period, the value range of which is [12, 48] hours; regarding the constant-speed process between any two elevator starts and stops as one trajectory; constructing the tension values in the trajectory into a constant-speed tension sequence, and recording the ratio of the upper quartile value to the lower quartile value in the constant-speed tension sequence as the tension gradient; taking the self-monitoring period after the nearest elevator safety maintenance time to the current time as the reference monitoring period; taking the maximum value of the tension gradient in the reference monitoring period as the gradient reference; after the reference monitoring period, if the tension gradient of any time is greater than the gradient reference, then record the corresponding trajectory as a tension abnormal trajectory, briefly recorded as an abnormal trajectory.

4. A method for safety monitoring of elevator guide rails in a dust environment according to claim 1, characterized in that, The method for combining the tension values corresponding to the tension abnormal trajectories and the vibration data to determine the authenticity of the dust particle risk is as follows: within the monitoring period, recording the time points for obtaining the vibration data and the tension as measurement points. For any abnormal tension trajectory, calculate the angle NAK between each element in the vibration data sequence and the x-axis according to the arctangent function; if , then mark the measuring point corresponding to NAK as the first abnormal point; Calculating the resultant acceleration of each measurement point according to the acceleration binary tuple, constructing a critical change binary tuple Trb with the resultant acceleration and the tension value, sorting each measurement point in descending order according to the magnitude of the tension value, and arranging the sorted measurement points in sequence to form a sequence Trb.Ls; using i as the serial number of the measurement point in Trb.Ls, and using Ftb(i) as the Trb of the i-th measurement point in Trb.Ls. In Trb.Ls, calculate the Euclidean distances between Ftb(i) and the Trbs of its previous and next first abnormal points respectively, and record them as QI.C and HO.C respectively. If QI.C is greater than HO.C, then mark the measurement point corresponding to Ftb(i) as the second abnormal point; making abnormal marks on the first abnormal point and the second abnormal point, and when the proportion of the number of abnormal marks in Trb.Ls exceeds 75%, then judge that the dust particle risk is true, otherwise it is false.

5. The elevator guide rail safety monitoring method in a dust environment according to claim 1, wherein, The method for determining the authenticity of dust particle risk by combining the tension value corresponding to the abnormal tension trajectory and vibration data is as follows: The current abnormal trajectory corresponds to the vibration data sequence A_diff; the alignment check of the tension value sequence and the vibration data sequence is performed through the timestamp matching module; the following characteristic parameters are extracted from the vibration data sequence: the vibration data sequence is subjected to fast Fourier transform to form the transformation binary sequence B_diff, the energy spectrum in the frequency range of 0 Hz - 5 kHz is obtained, and the ratio of the energy in the frequency range of 1 kHz - 5 kHz is used as the high-frequency energy ratio. Set the first vibration condition: the energy proportion of the current abnormal trajectory corresponding to the high-frequency band is greater than the dynamic threshold; where the dynamic threshold is E threshold =E M ×1.15, E M is the 95th percentile value of the energy proportion of each abnormal trajectory corresponding to the high-frequency band within the reference period; The ratios of the standard deviation to the mean of the vibration data corresponding to two vertical directions in the horizontal direction are calculated respectively, denoted as the acceleration variation coefficients 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 measurement point and 9 - 19 measurement points in its reverse time direction is the vibration window value, and the average value of each vibration window value within the reference period is denoted as the vibration reference value for the working condition; Calculate the percentage difference between the current vibration data mean value in the x - direction and the y - direction and the corresponding vibration reference value for the working condition within the reference period, and denote it as Δμ_x i , Δμ_y i ; Set the third vibration condition as: during the current abnormal trajectory operation, Δμ_x and Δμ_y are both greater than 10% for 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.

6. A method for safely monitoring an elevator guide rail in a dust environment according to claim 1, characterized in that, The method for feedbacking the dust particle risk to the client or the application is as follows: If the result of determining the dust particle risk for the current abnormal trajectory is true, it is considered that the guide rail is contaminated by dust particles, and a warning signal of dust particle contamination is sent to the administrator client to warn that the guide rail with dust particle risk needs to be maintained. Alternatively, a warning signal of dust particle contamination is sent to the application, and the lubricant maintenance is performed at the application.

7. A method for safety monitoring of elevator guide rails in a dust environment according to claim 6, characterized in that The method for sending a warning signal of dust particle contamination to the application and performing lubricant maintenance at the application is as follows: The application is an automatic lubrication system. When the automatic lubrication system obtains a warning signal of dust particle contamination, the automatic lubrication function is started to supplement the lubricant for the guide rail.

8. An elevator guide rail safety monitoring system for a dust environment, characterized in that, The elevator guide rail safety monitoring system in a sand and dust 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 in any one of the methods for monitoring the elevator guide rail safety in a sand and dust environment as claimed in claims 1 - 7 are implemented. The elevator guide rail safety monitoring system in a sand and dust environment runs on computing devices such as desktop computers, laptop computers, palmtop computers, and cloud data centers.

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