A health monitoring and management system for equipment maintenance equipment

Abstract

The present invention relates to the field of intelligent equipment maintenance technology, and specifically, to an equipment maintenance equipment health monitoring and management system, comprising a lifting stability detection module, a lifting stability analysis module, a noise detection and analysis module, a guide rail deformation detection module, a motor wear detection module, a motor wear analysis module, a maintenance equipment health analysis module, and a management database. The system discovers problems in a lifting device in advance by analyzing the lifting stability coefficient of the lifting device, determines whether the device has a fault or abnormality by analyzing the noise evaluation coefficient of the lifting device, evaluates the health of the guide rail by obtaining the deformation coefficient of the guide rail through the deformation parameter of the guide rail, and timely understands the wear condition of the motor by analyzing the wear coefficient of the motor, and then obtains the health index of the maintenance equipment through comprehensive analysis, thereby ensuring the reliability and safety of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent equipment maintenance, and in particular to a health monitoring and management system for equipment maintenance equipment. Background Art

[0002] Maintenance equipment for military equipment refers to the tools, equipment and systems used to maintain and repair military equipment. With the continuous development of military technology and the upgrading of equipment, the complexity and high degree of automation of military equipment are also constantly improving. Therefore, maintenance equipment plays a vital role in the military field.

[0003] The main task of maintenance equipment is to ensure that military equipment is in good working condition, improve the reliability and availability of equipment, and extend the service life of equipment. They involve diagnosis, maintenance, calibration, testing and verification, including various special tools, testing instruments, maintenance stations and workshops, maintenance facilities and related information systems and software, which are of great significance to maintaining national security and military capabilities.

[0004] However, the existing equipment maintenance equipment health monitoring management can often only perform simple fault diagnosis and judgment, and cannot make an accurate assessment of the damage to the maintenance equipment. Specifically, it is manifested in the following aspects: First, the lifting device is one of the important components of equipment maintenance equipment. The traditional detection method is usually visual inspection. The maintenance personnel observe the appearance of the lifting device, connecting parts, etc. to determine whether there are obvious abnormalities. However, this method can only observe the appearance of the lifting device, and cannot deeply detect problems in the internal structure and hidden parts. Some potential faults or hidden damages may not be accurately discovered and evaluated, resulting in delays in problems and failure to take preventive measures in time.

[0005] Second, there is a lack of comprehensive evaluation methods. Existing maintenance equipment health checks often only focus on the status of a single indicator or component, but lack a comprehensive analysis of the overall health of the maintenance equipment. In this case, hidden dangers and problems in the system may be missed, and it is impossible to fully understand the working condition of the maintenance equipment, existing problems and maintenance needs. Summary of the invention

[0006] In order to overcome the shortcomings of the background technology, an embodiment of the present invention provides an equipment maintenance equipment health monitoring and management system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] The objective of the present invention can be achieved through the following technical solutions: The present invention provides a health monitoring and management system for equipment maintenance equipment, including: a lifting stability detection module, which is used to detect the no-load lifting speed of the lifting device in the equipment maintenance equipment, the lifting speed under various loads, the no-load vibration number of the lifting device, and the vibration number under various loads.

[0008] The lifting stability analysis module is used to analyze the lifting stability coefficient of the lifting device according to the lifting speed of the lifting device without load, the lifting speed under various loads, the number of vibrations of the lifting device without load, and the number of vibrations under various loads in the equipment maintenance equipment.

[0009] The noise detection and analysis module is used to detect the noise value and the number of abnormal noises of the lifting device in the equipment maintenance equipment, and then analyze and obtain the noise evaluation coefficient ξ of the lifting device.

[0010] The guide rail deformation degree detection module is used to detect the deformation degree parameters of the guide rails in the equipment maintenance equipment to obtain the deformation degree coefficient κ of the guide rails. The deformation degree parameters include the parallelism between the guide rails and the verticality between the guide rails and the ground.

[0011] The motor wear degree detection module is used to detect the temperature stability, scratch length, and number of wear pixels of the motor in the equipment maintenance equipment.

[0012] The motor wear degree analysis module is used to obtain the motor wear degree coefficient θ according to the temperature stability of the motor in the equipment maintenance equipment, the length of each scratch, and the number of wear pixels.

[0013] The maintenance equipment health analysis module is used to obtain the health index of the maintenance equipment based on the lifting stability coefficient of the lifting device in the equipment maintenance equipment, the noise evaluation coefficient, the deformation coefficient of the guide rail, and the wear coefficient of the motor, and compare it with the preset health index threshold to obtain the health status of the maintenance equipment.

[0014] A management database is used to store the total number of pixels of the motor surface image.

[0015] Preferably, the specific analysis process of the lifting stability detection module is as follows: the first step is to lift the lifting surface of the lifting device in the equipment maintenance equipment to a set height in a no-load state, and monitor the time taken for the lifting process, which is recorded as the no-load lifting time. At the same time, the set height is extracted, and the lifting speed of the lifting device in the no-load state is obtained by dividing the set height by the no-load lifting time, which is recorded as the no-load lifting speed α; according to the set weight, loads of different weights are added to the lifting device in turn, which are recorded as each load, and the time taken for the lifting device to lift to the set height under each load is detected, which is recorded as the lifting time under each load, and the lifting speed under each load is obtained by dividing the set height by the lifting time under each load, which is recorded as α i , where i represents the number of the i-th load, i=1,2,...,n.

[0016] In the second step, the lifting surface of the lifting device in the equipment maintenance equipment is lifted to the set height at the set speed under no-load condition, and the vibration number of the lifting device during the lifting process is obtained by using the vibration sensor installed on the lifting device, which is recorded as the no-load vibration number β of the lifting device; at the same time, each load is added to the lifting device in turn, and the vibration number of the lifting surface of the lifting device during the process of lifting to the set height at the set speed under each load is detected respectively, which is recorded as the vibration number β of the lifting device under each load i .

[0017] Preferably, the specific analysis method of the lifting stability analysis module is: read the no-load lifting speed α, the lifting speed α under each load, and i , the number of vibrations of the lifting device under no-load β, the number of vibrations of the lifting device under various loads β i , substituting it into the formula Get the lifting stability coefficient of the lifting device Among them, α0 and β0 represent the reference values ​​of the set speed uniformity and vibration number, φ1 and φ2 represent the weight factors of the set speed uniformity and vibration number, and n represents the number of loads.

[0018] Preferably, the specific analysis process of the noise detection and analysis module is as follows: the first step is to divide the time points according to the set time length, recorded as each time point, lift the lifting surface of the lifting device in the equipment maintenance equipment to the set height under no-load state, and monitor the noise value generated at each time point during the lifting process, recorded as the noise value η at each time point under no-load state k , where k represents the number of the kth time point, k = 1, 2, ..., q, and each load is added to the lifting device in turn, and the noise value generated by the lifting device at each time point when the lifting surface of the lifting device is lifted to the set height under different loads is detected, which is recorded as the noise value η at each time point under each load ik , substituting it into the formula The noise value η of the lifting device is obtained, where q represents the number of time points and n represents the number of loads.

[0019] In the second step, real-time noise monitoring is performed on the process of the lifting device in the equipment maintenance equipment lifting the lifting surface to the set height under no-load and various loads, and the monitored noise is converted into a noise spectrum, recorded as the no-load noise spectrum and the noise spectrum under various loads. The decibel value of each noise frequency point is read from the no-load noise spectrum and the noise spectrum under various loads, and compared with the preset noise decibel threshold. The frequency point with a decibel value greater than the preset noise decibel threshold is recorded as an abnormal sound frequency point, and the number of abnormal sound frequency points in the no-load noise spectrum and the noise spectrum under various loads is counted respectively, recorded as γ and γ i , substituting it into the formula The abnormal noise frequency γ of the lifting device is obtained, where n represents the number of loads.

[0020] The third step is to read the noise value η of the lifting device and the number of abnormal noises γ of the lifting device respectively, and use the formula The noise evaluation coefficient ξ of the lifting device is obtained, where η0 and γ0 represent the reference values ​​of the noise value and the number of abnormal sounds of the set lifting device respectively, φ3 and φ4 represent the weighting factors of the noise value and the number of abnormal sounds of the set lifting device respectively, and e represents a natural constant.

[0021] Preferably, the specific analysis process of the guide rail deformation degree detection module is as follows: In the first step, the two guide rails of the lifting device in the equipment maintenance equipment are respectively recorded as the left guide rail and the right guide rail, and a number of measurement points are taken from top to bottom on the two guide rails according to the set distance, which are recorded as the measurement points of the left guide rail and the measurement points of the right guide rail. The measurement points of the left guide rail and the measurement points of the right guide rail are corresponded one by one as a group of measurement points, and the horizontal distance between each measurement point group is measured respectively, which is recorded as d x , where x represents the number of the xth measurement point group, x = 1, 2, ..., y, and the average distance between the horizontal distances between the measurement point groups is obtained to obtain the average distance between the guide rails, which is recorded as Substituting this into the formula The parallelism P between the guide rails is obtained, and y represents the number of measurement point groups.

[0022] In the second step, fix the line level meter at each measuring point of the left and right rails in turn, and use a line ruler to measure the vertical distance between the line level meter and the ground at each measuring point, recorded as and Where u represents the number of the uth measurement point, u=1,2,...,v, through the formula The verticality τ between the guide rail and the ground is obtained, where v is the number of measurement points.

[0023] The third step is to read the parallelism P between the guide rails and the verticality τ between the guide rails and the ground respectively, and use the formula The deformation coefficient κ of the guide rail is obtained, where P0 and τ0 represent the set parallelism and perpendicularity reference values, φ5 and φ6 represent the set parallelism and perpendicularity weight factors, and e is a natural constant.

[0024] Preferably, the specific analysis process of the motor wear degree detection module is as follows: the first step is to detect the real-time temperature of the motor during the lifting process of the lifting device in the equipment maintenance equipment under no-load and various loads, and at the same time, take a number of interval time points according to the set time length, record them as each interval time point, read the temperature of the motor at each interval time point, record them as ρ h , where h represents the number of the hth interval time point, h=1,2,...,g, and substitute it into the formula Get the temperature stability σ of the motor.

[0025] In the second step, the motor housing surface is imaged by a camera, recorded as the motor surface image, the motor surface image is converted into a grayscale image, recorded as the motor surface grayscale image, and the number of scratches and the length of each scratch in the motor surface grayscale image are extracted by edge detection technology, and the length of each scratch is recorded as L. l , where l represents the number of the lth scratch, l = 1, 2, ..., j.

[0026] The third step is to read the motor surface image, extract the edge contour of each wear area in the motor surface image through edge detection technology, count the number of pixels in the edge contour of each wear area, record it as the number of wear pixels in each wear area, and obtain the number of wear pixels ε by accumulating the number of wear pixels in each wear area.

[0027] Preferably, the specific analysis method of the motor wear degree analysis module is: reading the temperature stability degree σ of the motor, the length L of each scratch l , the number of worn pixels ε, and substitute it into the formula The wear coefficient of the motor is obtained, where L' represents the reference value of the scratch length, ε 总 represents the total number of pixels of the motor surface image, η1, η2, η3 represent the weight factors of the temperature stability, scratch length, and number of wear pixels, respectively, j represents the number of scratches, and e is a natural constant.

[0028] Preferably, the specific analysis process of the maintenance equipment health analysis module is as follows: the first step is to read the lifting stability coefficient of the lifting device respectively Substitute the noise evaluation coefficient ξ of the lifting device, the deformation coefficient κ of the guide rail, and the wear coefficient θ of the motor into the formula Analyze and obtain the health index of maintenance equipment Among them, w1, w2, w3, and w4 represent the weight factors of the set lifting stability coefficient, noise evaluation coefficient, deformation coefficient, and wear coefficient, respectively, and e is a natural constant.

[0029] In the second step, the health index of the maintenance equipment is compared with the preset health index threshold. If the health index of the maintenance equipment is greater than or equal to the preset health index threshold, it means that the health condition of the maintenance equipment is qualified. If the health index of the maintenance equipment is less than the preset health index threshold, it means that the health condition of the maintenance equipment is unqualified and feedback is given to the system.

[0030] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention obtains the lifting stability coefficient of the lifting device according to the no-load lifting speed of the lifting device, the lifting speed under various loads, the no-load vibration times of the lifting device, and the vibration times under various loads, so as to facilitate determining the cause of the instability of the lifting device, thereby making corresponding improvements and optimizations to improve the lifting stability of the equipment.

[0031] 2. The present invention detects the noise value and the number of abnormal noises of the lifting device, and then analyzes and obtains the noise evaluation coefficient of the lifting device, which is convenient for tracking and grasping the problems in the working process of the lifting device, so as to carry out repairs and maintenance in time, which helps to improve the quality and reliability of the lifting device and reduce shutdowns and losses caused by equipment failures.

[0032] 3. The present invention obtains the deformation coefficient of the guide rail by detecting the parallelism between the guide rails and the verticality between the guide rails and the ground, which helps to timely discover and solve the problems of the guide rails, thereby improving the performance, working accuracy and reliability of the equipment.

[0033] 4. The present invention obtains the wear degree coefficient of the motor according to the temperature stability of the motor, the length of each scratch, and the number of wear pixels, so that the wear problem of the motor can be discovered in time, preventive maintenance measures can be taken, and the service life of the motor can be extended.

[0034] 5. The present invention obtains the health index of the maintenance equipment through comprehensive analysis based on the lifting stability coefficient of the lifting device, the noise evaluation coefficient of the lifting device, the deformation coefficient of the guide rail, and the wear coefficient of the motor, and then obtains the health status of the maintenance equipment. It can predict and diagnose equipment failures, optimize maintenance plans, improve equipment performance, and thus improve the work efficiency and reliability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0036] Figure 1 This is a system module connection diagram of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, a health monitoring and management system for equipment maintenance equipment includes a lifting stability detection module, a lifting stability analysis module, a noise detection and analysis module, a guide rail deformation detection module, a motor wear detection module, a motor wear analysis module, a maintenance equipment health analysis module, and a management database.

[0039] The maintenance equipment health analysis module is connected to the lifting stability analysis module, the guide rail deformation detection module, the motor wear analysis module, the noise detection analysis module, and the management database; the management database is connected to the lifting stability analysis module, the guide rail deformation detection module, the motor wear analysis module, and the noise detection analysis module; the lifting stability analysis module is connected to the lifting stability detection module, and the motor wear analysis module is connected to the motor wear detection module.

[0040] The lifting stability detection module is used to detect the no-load lifting speed of the lifting device in the equipment maintenance equipment, the lifting speed under various loads, the no-load vibration number of the lifting device, and the vibration number under various loads.

[0041] The specific analysis process of the lifting stability detection module is as follows: the first step is to lift the lifting surface of the lifting device in the equipment maintenance equipment to the set height in the no-load state, and monitor the time taken for the lifting process, which is recorded as the no-load lifting time. At the same time, the set height is extracted, and the lifting speed of the lifting device in the no-load state is obtained by dividing the set height by the no-load lifting time, which is recorded as the no-load lifting speed α; according to the set weight, loads of different weights are added to the lifting device in turn, which are recorded as each load, and the time taken for the lifting device to be lifted to the set height under each load is detected, which is recorded as the lifting time under each load, and the lifting speed under each load is obtained by dividing the set height by the lifting time under each load, which is recorded as α i , where i represents the number of the i-th load, i=1,2,...,n; by adding loads of different weights and monitoring the lifting time under each load, the lifting speed under each load can be obtained, thereby evaluating the performance and carrying capacity of the equipment under different load conditions.

[0042] In the second step, the lifting surface of the lifting device in the equipment maintenance equipment is lifted to the set height at the set speed under no-load condition, and the vibration number of the lifting device during the lifting process is obtained by using the vibration sensor installed on the lifting device, which is recorded as the no-load vibration number β of the lifting device; at the same time, each load is added to the lifting device in turn, and the vibration number of the lifting surface of the lifting device during the process of lifting to the set height at the set speed under each load is detected respectively, which is recorded as the vibration number β of the lifting device under each load i ; By monitoring the number of vibrations of the lifting device when it is lifted to a set height at a set speed under no-load condition, the stability of the equipment can be evaluated. If the number of vibrations is small, it indicates that the movement of the equipment during the lifting process is relatively stable and has good stability.

[0043] The lifting stability analysis module is used to analyze the lifting stability coefficient of the lifting device according to the lifting speed of the lifting device without load, the lifting speed under various loads, the number of vibrations of the lifting device without load, and the number of vibrations under various loads in the equipment maintenance equipment.

[0044] The specific analysis method of the lifting stability analysis module is: read the no-load lifting speed α, the lifting speed α under each load respectively i , the number of vibrations of the lifting device under no-load β, the number of vibrations of the lifting device under various loads β i , substituting it into the formula Get the lifting stability coefficient of the lifting device Among them, α0 and β0 represent the reference values ​​of the set speed uniformity and vibration number respectively, φ1 and φ2 represent the weight factors of the set speed uniformity and vibration number respectively, and n represents the number of loads; by reading the lifting speed and vibration number under different loads respectively, the lifting stability coefficients under different load conditions can be compared, and the stability performance of the equipment under different loads can be further evaluated. The higher the lifting stability coefficient of the lifting equipment, the better the speed uniformity and vibration stability of the equipment during the lifting process, and the higher the stability, which is very important for evaluating the performance and safety performance of the equipment, and can help select suitable equipment or optimize equipment design.

[0045] The noise detection and analysis module is used to detect the noise value and the number of abnormal noises of the lifting device in the equipment maintenance equipment, and then analyze and obtain the noise evaluation coefficient ξ of the lifting device.

[0046] The specific analysis process of the noise detection and analysis module is as follows: In the first step, the time points are divided according to the set time length, recorded as each time point, the lifting surface of the lifting device in the equipment maintenance equipment is lifted to the set height under no-load condition, and the noise value generated at each time point during the lifting process is monitored, recorded as the noise value η at each time point under no-load condition k, where k represents the number of the kth time point, k = 1, 2, ..., q, and each load is added to the lifting device in turn, and the noise value generated by the lifting device at each time point when the lifting surface of the lifting device is lifted to the set height under different loads is detected, which is recorded as the noise value η at each time point under each load ik , substituting it into the formula The noise value η of the lifting device is obtained, where q represents the number of time points and n represents the number of loads. By recording the noise values ​​generated by the lifting device at each time point under different loads, the noise level of the equipment under different load conditions can be compared, which helps to evaluate the impact of different loads on noise and provide guidance for selecting appropriate load conditions or optimizing equipment.

[0047] In the second step, real-time noise monitoring is performed on the process of the lifting device in the equipment maintenance equipment lifting the lifting surface to the set height under no-load and various loads, and the monitored noise is converted into a noise spectrum, recorded as the no-load noise spectrum and the noise spectrum under various loads. The decibel value of each noise frequency point is read from the no-load noise spectrum and the noise spectrum under various loads, and compared with the preset noise decibel threshold. The frequency point with a decibel value greater than the preset noise decibel threshold is recorded as an abnormal sound frequency point, and the number of abnormal sound frequency points in the no-load noise spectrum and the noise spectrum under various loads is counted respectively, recorded as γ and γ i , substituting it into the formula The number of abnormal noises γ of the lifting device is obtained, where n represents the number of loads. By monitoring and analyzing the real-time noise of the lifting device during the lifting process under different load conditions, the abnormal noise characteristics of the equipment can be understood. Counting the number of abnormal noise frequency points is helpful to evaluate the noise problem and degree of abnormal noise of the equipment, and provide a basis for improving equipment design and noise control.

[0048] The third step is to read the noise value η of the lifting device and the number of abnormal noises γ of the lifting device respectively, and use the formula The noise evaluation coefficient ξ of the lifting device is obtained, where η0 and γ0 represent the reference values ​​of the noise value and the number of abnormal noises of the lifting device respectively, φ3 and φ4 represent the weight factors of the noise value and the number of abnormal noises of the lifting device respectively, and e represents a natural constant. The noise evaluation coefficient of the lifting device comprehensively considers the noise level and the abnormal noise of the lifting device, and can more comprehensively evaluate the noise performance of the equipment, help to improve the noise performance of the equipment, meet the preset noise requirements, and improve the comfort and safety of the working environment.

[0049] The guide rail deformation degree detection module is used to detect the deformation degree parameters of the guide rails in the equipment maintenance equipment to obtain the deformation degree coefficient κ of the guide rails. The deformation degree parameters include the parallelism between the guide rails and the verticality between the guide rails and the ground.

[0050] The specific analysis process of the guide rail deformation degree detection module is as follows: In the first step, the two guide rails of the lifting device in the equipment maintenance equipment are respectively recorded as the left guide rail and the right guide rail, and a number of measurement points are taken from top to bottom on the two guide rails according to the set distance, which are recorded as the measurement points of the left guide rail and the measurement points of the right guide rail. The measurement points of the left guide rail and the measurement points of the right guide rail are corresponded one by one as a group of measurement points, and the horizontal distance between each measurement point group is measured respectively, which is recorded as d x , where x represents the number of the xth measurement point group, x = 1, 2, ..., y, and the average distance between the horizontal distances between the measurement point groups is obtained to obtain the average distance between the guide rails, which is recorded as Substituting this into the formula The parallelism P between the guide rails is obtained, where y represents the number of measurement point groups; this can help determine whether the guide rails are parallel, and promptly detect and adjust problems in the guide rail installation to avoid the normal operation of the equipment being affected by the non-parallelism of the guide rails.

[0051] In the second step, fix the line level meter at each measuring point of the left and right rails in turn, and use a line ruler to measure the vertical distance between the line level meter and the ground at each measuring point, recorded as and Where u represents the number of the uth measurement point, u=1,2,...,v, through the formula The verticality τ between the guide rail and the ground is obtained, where v is the number of measuring points; by measuring the vertical distance between the line level meter and the ground to evaluate the verticality of the guide rail, problems in the guide rail installation can be discovered and adjusted in time to ensure the stability and operation quality of the equipment.

[0052] The third step is to read the parallelism P between the guide rails and the verticality τ between the guide rails and the ground respectively, and use the formula The deformation coefficient κ of the guide rail is obtained, where P0 and τ0 represent the set parallelism and perpendicularity reference values, φ5 and φ6 represent the set parallelism and perpendicularity weight factors, and e is a natural constant. By calculating the deformation coefficient of the guide rail to evaluate the deformation of the guide rail, the problems existing in the guide rail can be discovered in time, and numerical indicators can be provided for evaluating the quality of the guide rail, thereby promoting the stable operation of the equipment and improving the quality.

[0053] The motor wear degree detection module is used to detect the temperature stability, scratch length, and number of wear pixels of the motor in the equipment maintenance equipment.

[0054] The specific analysis process of the motor wear degree detection module is as follows: In the first step, the real-time temperature of the motor of the lifting device in the equipment maintenance equipment is detected during the lifting process under no-load and various loads, and at the same time, a number of interval time points are taken according to the set time length, recorded as each interval time point, and the temperature of the motor at each interval time point is read, recorded as ρ h, where h represents the number of the hth interval time point, h=1,2,...,g, and substitute it into the formula The temperature stability of the motor is obtained. By real-time monitoring of the motor temperature, the working status and temperature change of the motor can be obtained in time, which helps to determine whether the motor is overheated or has abnormal temperature, and take necessary measures in time, such as shutting down to cool down or adjusting the load, to ensure the safe and stable operation of the equipment.

[0055] In the second step, the motor housing surface is imaged by a camera, recorded as the motor surface image, the motor surface image is converted into a grayscale image, recorded as the motor surface grayscale image, and the number of scratches and the length of each scratch in the motor surface grayscale image are extracted by edge detection technology, and the length of each scratch is recorded as L. l , where l represents the number of the lth scratch, l = 1, 2, ..., j; extracting the number and length of scratches through edge detection technology helps to promptly detect scratch problems on the motor surface, improves maintenance efficiency, and provides quantitative data support for scratch evaluation and management.

[0056] The third step is to read the motor surface image, extract the edge contour of each wear area in the motor surface image through edge detection technology, count the number of pixels in the edge contour of each wear area, record it as the number of wear pixels in each wear area, and obtain the number of wear pixels ε by accumulating the number of wear pixels in each wear area; the number of wear pixels can reflect the degree of wear, thereby helping to understand the wear condition of the motor.

[0057] The specific analysis method for extracting the edge contour of each wear area in the motor surface image by edge detection technology is as follows: read the motor surface image, convert it into a motor surface grayscale image, detect the grayscale value of each pixel in the motor surface grayscale image, and compare the grayscale value of each pixel in the motor surface grayscale image with the set wear grayscale value threshold, record the pixel points that are greater than or equal to the set wear grayscale value threshold as wear pixels, and set the wear grayscale value difference threshold at the same time, compare the grayscale value of the wear pixel point with the grayscale value of the pixels surrounding the wear pixel point, and obtain the grayscale value difference value of each surrounding pixel point. If the grayscale value difference value of a surrounding pixel point is less than the set wear grayscale value difference threshold, then the surrounding pixel point is also recorded as a wear pixel point, and all wear pixels are counted, and the edge contour of each wear area is obtained by connecting the wear pixels.

[0058] The motor wear degree analysis module is used to obtain the motor wear degree coefficient θ according to the temperature stability of the motor in the equipment maintenance equipment, the length of each scratch, and the number of wear pixels.

[0059] The specific analysis method of the motor wear degree analysis module is: reading the temperature stability σ of the motor, the length L of each scratchl , the number of worn pixels ε, and substitute it into the formula The wear coefficient of the motor is obtained, where L' represents the reference value of the scratch length, ε 总 represents the total number of pixels of the set motor surface image, η1, η2, η3 represent the weight factors of the set temperature stability, scratch length, and number of wear pixels, j represents the number of scratches, and e is a natural constant. Comprehensive evaluation of the motor's wear condition helps to timely discover and deal with the motor's wear problems and improve the reliability and service life of the equipment.

[0060] The maintenance equipment health analysis module is used to obtain the health index of the maintenance equipment based on the lifting stability coefficient of the lifting device in the equipment maintenance equipment, the noise evaluation coefficient, the deformation coefficient of the guide rail, and the wear coefficient of the motor, and compare it with the preset health index threshold to obtain the health status of the maintenance equipment.

[0061] The specific analysis process of the maintenance equipment health analysis module is as follows: the first step is to read the lifting stability coefficient of the lifting device respectively. Substitute the noise evaluation coefficient ξ of the lifting device, the deformation coefficient κ of the guide rail, and the wear coefficient θ of the motor into the formula Analyze and obtain the health index of maintenance equipment Among them, w1, w2, w3, and w4 represent the weight factors of the set lifting stability coefficient, noise evaluation coefficient, deformation coefficient, and wear coefficient respectively, and e is a natural constant. By analyzing the health index of the maintenance equipment, the health status of the equipment can be evaluated more comprehensively and accurately, which is helpful to timely discover and deal with equipment problems, and carry out reasonable maintenance and repairs to improve the reliability and life of the equipment.

[0062] In the second step, the health index of the maintenance equipment is compared with the preset health index threshold. If the health index of the maintenance equipment is greater than or equal to the preset health index threshold, it means that the health condition of the maintenance equipment is qualified. If the health index of the maintenance equipment is less than the preset health index threshold, it means that the health condition of the maintenance equipment is unqualified, and feedback is given to the system. The measure of comparing the health index of the maintenance equipment with the preset health index threshold can automatically determine whether the health condition of the maintenance equipment is qualified, and provide timely feedback to relevant personnel, which helps to improve the efficiency and accuracy of maintenance equipment management, timely discover and deal with equipment health problems, thereby ensuring stable operation and reliability of the equipment.

[0063] A management database is used to store the total number of pixels of the motor surface image.

[0064] This system discovers problems in the lifting device in advance by analyzing the lifting stability coefficient of the lifting device, determines whether the equipment has faults or abnormal conditions by analyzing the noise evaluation coefficient of the lifting device, evaluates the health of the guide rail by obtaining the deformation coefficient of the guide rail through the deformation degree parameters of the guide rail, and timely understands the wear condition of the motor by analyzing the wear degree coefficient of the motor, and then comprehensively analyzes to obtain the health index of the maintenance equipment, ensuring the reliability and safety of the equipment.

[0065] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. An equipment maintenance equipment health monitoring and management system, characterized in that: The system specifically includes the following modules: The lifting stability detection module is used to detect the no-load lifting speed of the lifting device in the equipment maintenance equipment, the lifting speed under various loads, the no-load vibration number of the lifting device, and the vibration number under various loads; The lifting stability analysis module is used to analyze the lifting stability coefficient of the lifting device according to the lifting speed of the lifting device without load, the lifting speed under various loads, the number of vibrations of the lifting device without load, and the number of vibrations under various loads in the equipment maintenance equipment. ; The noise detection and analysis module is used to detect the noise value and abnormal noise frequency of the lifting device in the equipment maintenance equipment, and then analyze and obtain the noise evaluation coefficient of the lifting device ; The guide rail deformation degree detection module is used to detect the deformation degree parameters of the guide rail in the equipment maintenance equipment to obtain the guide rail deformation degree coefficient ,The parameters of deformation degree include the parallelism between the rails and the verticality between the rails and the ground; The motor wear degree detection module is used to detect the temperature stability, length of each scratch, and number of wear pixels of the motor in the equipment maintenance equipment; The motor wear degree analysis module is used to obtain the motor wear degree coefficient according to the temperature stability, scratch length and number of wear pixels of the motor in the equipment maintenance equipment. ; The maintenance equipment health analysis module is used to analyze the health index of the maintenance equipment according to the lifting stability coefficient, noise evaluation coefficient, guide rail deformation coefficient, and motor wear coefficient of the lifting device in the equipment maintenance equipment, and compare it with the preset health index threshold to obtain the health status of the maintenance equipment; A management database is used to store the total number of pixels of the motor surface image.

2. The equipment maintenance equipment health monitoring and management system according to claim 1, characterized in that: The specific analysis process of the lifting stability detection module is as follows: The first step is to raise the lifting surface of the lifting device in the equipment maintenance equipment to the set height in the no-load state, and monitor the time taken for the lifting process, which is recorded as the no-load lifting time. At the same time, the set height is extracted, and the lifting speed of the lifting device in the no-load state is obtained by dividing the set height by the no-load lifting time, which is recorded as the no-load lifting speed. ; According to the set weight, loads of different weights are added to the lifting device in turn, recorded as each load, and the time taken for the lifting device to lift to the set height under each load is detected, recorded as the lifting time under each load, and the lifting speed under each load is obtained by dividing the set height by the lifting time under each load, recorded as ,in Indicates The number of the load, ; The second step is to lift the lifting surface of the lifting device in the equipment maintenance equipment to the set height at the set speed under no-load condition, and use the vibration sensor installed on the lifting device to obtain the number of vibrations of the lifting device during the lifting process, which is recorded as the no-load vibration number of the lifting device. ;At the same time, add each load to the lifting device in turn, and detect the number of vibrations of the lifting surface of the lifting device under each load during the process of lifting to the set height at the set speed, which is recorded as the number of vibrations of the lifting device under each load .

3. The equipment maintenance equipment health monitoring and management system according to claim 2, characterized in that: The specific analysis method of the lifting stability analysis module is: Read the no-load lifting speed separately , lifting speed under various loads , Lifting device no-load vibration times , the number of vibrations of the lifting device under each load , substituting it into the formula Get the lifting stability coefficient of the lifting device ,in They respectively represent the reference values ​​of the set speed uniformity and vibration frequency. They represent the weight factors of the set speed uniformity and vibration frequency respectively. Indicates the amount of load.

4. The equipment maintenance equipment health monitoring and management system according to claim 3, characterized in that: The specific analysis process of the noise detection and analysis module is as follows: The first step is to divide the time points according to the set time length, record them as each time point, lift the lifting surface of the lifting device in the equipment maintenance equipment to the set height under no-load condition, and monitor the noise value generated at each time point during the lifting process, record them as the noise value at each time point under no-load condition ,in Indicates The number of the time point, At the same time, add various loads to the lifting device in turn, and detect the noise value generated by the lifting device at each time point when the lifting surface of the lifting device is lifted to the set height under different loads, and record it as the noise value at each time point under each load , substituting it into the formula Get the noise value of the lifting device ,in represents the number of time points, Indicates the amount of load; In the second step, real-time noise monitoring is performed on the process in which the lifting device in the equipment maintenance equipment lifts the lifting surface to the set height under no-load and various loads, and the monitored noise is converted into a noise spectrum, recorded as a no-load noise spectrum and a noise spectrum under various loads. The decibel value of each noise frequency point is read from the no-load noise spectrum and the noise spectrum under various loads, and compared with the preset noise decibel threshold. The frequency point with a decibel value greater than the preset noise decibel threshold is recorded as an abnormal sound frequency point, and the number of abnormal sound frequency points in the no-load noise spectrum and the noise spectrum under various loads is counted respectively, recorded as and , substituting it into the formula Get the number of abnormal noises of the lifting device ,in Indicates the amount of load; The third step is to read the noise value of the lifting device respectively. , the number of abnormal noises of the lifting device , through the formula Get the noise evaluation coefficient of the lifting device ,in Respectively represent the reference value of the noise value and the number of abnormal noises of the set lifting device. They represent the noise value and the weight factor of the abnormal noise frequency of the set lifting device respectively. Represents a natural constant.

5. The equipment maintenance equipment health monitoring and management system according to claim 4, characterized in that: The specific analysis process of the guide rail deformation degree detection module is as follows: In the first step, the two guide rails of the lifting device in the equipment maintenance equipment are respectively recorded as the left guide rail and the right guide rail. According to the set distance, several measuring points are taken from top to bottom on the two guide rails, which are recorded as the measuring points of the left guide rail and the measuring points of the right guide rail. The measuring points of the left guide rail and the measuring points of the right guide rail are matched one by one as a group of measuring points, and the horizontal distance between each measuring point group is measured respectively, which is recorded as ,in Indicates The number of each measurement point group, , calculate the average value of the horizontal distance between each measurement point group to get the average distance between the guide rails, recorded as , substituting it into the formula Get the parallelism between the rails , Indicates the number of measurement point groups; In the second step, fix the line level meter at each measuring point of the left and right rails in turn, and use a line ruler to measure the vertical distance between the line level meter and the ground at each measuring point, recorded as and ,in Indicates The number of the measuring point, , through the formula Get the verticality between the rail and the ground ,in is the number of measurement points; The third step is to read the parallelism between the guide rails respectively. , verticality between the rail and the ground , through the formula Get the deformation coefficient of the guide rail ,in Respectively represent the set parallelism and verticality reference values. Respectively represent the weight factors of the set parallelism and perpendicularity, is a natural constant.

6. The equipment maintenance equipment health monitoring and management system according to claim 5, characterized in that: The specific analysis process of the motor wear degree detection module is as follows: The first step is to detect the real-time temperature of the motor of the lifting device in the equipment maintenance equipment during the lifting process under various loads, and at the same time, take a number of interval time points according to the set time length, record them as each interval time point, and read the temperature of the motor at each interval time point, record them as ,in Indicates The number of the interval time point, , substituting it into the formula Get the temperature stability of the motor ; In the second step, the motor housing surface is imaged by a camera, recorded as the motor surface image, the motor surface image is converted into a grayscale image, recorded as the motor surface grayscale image, and the number of scratches and the length of each scratch in the motor surface grayscale image are extracted by edge detection technology, and the length of each scratch is recorded as ,in Indicates The number of scratches, ; The third step is to read the motor surface image, extract the edge contour of each wear area in the motor surface image through edge detection technology, count the number of pixels in the edge contour of each wear area, record it as the number of wear pixels in each wear area, and get the number of wear pixels by accumulating the number of wear pixels in each wear area. .

7. The equipment maintenance equipment health monitoring and management system according to claim 6, characterized in that: The specific analysis method of the motor wear degree analysis module is: Read the temperature stability of the motor , length of each scratch , Number of worn pixels , substituting it into the formula Get the wear coefficient of the motor ,in Indicates the set scratch length reference value, Indicates the total number of pixels of the motor surface image. They represent the weight factors of the set temperature stability, scratch length, and number of worn pixels, respectively. Indicates the number of scratches, is a natural constant.

8. The equipment maintenance equipment health monitoring and management system according to claim 1, characterized in that: The specific analysis process of the maintenance equipment health analysis module is as follows: The first step is to read the lifting stability coefficient of the lifting device respectively , Noise evaluation coefficient of lifting device , Deformation coefficient of guide rail , Motor wear coefficient , substituting it into the formula Analyze and obtain the health index of maintenance equipment ,in They represent the weight factors of the set lifting stability coefficient, noise evaluation coefficient, deformation coefficient, and wear coefficient, respectively. is a natural constant; In the second step, the health index of the maintenance equipment is compared with the preset health index threshold. If the health index of the maintenance equipment is greater than or equal to the preset health index threshold, it means that the health condition of the maintenance equipment is qualified. If the health index of the maintenance equipment is less than the preset health index threshold, it means that the health condition of the maintenance equipment is unqualified and feedback is given to the system.

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