Hoisting equipment health assessment and life prediction method and system based on exponential smoothing method

Abstract

The application provides a hoisting equipment health assessment and life prediction method and system based on an exponential smoothing method, and the method comprises the following steps: acquiring operation data of the hoisting equipment indicating the health state, wherein the operation data comprises the average hoisting weight of each working cycle of the main force structure, the cumulative braking times of the brake, the cumulative action times of the contactor, and the starting times of the motor; confirming the health state of each component of the hoisting equipment based on the operation data of each component of the hoisting equipment and the preset rated life limit value of each component of the hoisting equipment; assessing the overall health state of the hoisting equipment based on the health state of each component of the hoisting equipment; and applying the exponential smoothing method to predict the life of each component of the hoisting equipment based on the health state of each component of the hoisting equipment. The method can collect the operation data of the hoisting equipment, import the operation data into a health assessment model, obtain the health state assessment result of the hoisting equipment, and realize the residual life prediction of the hoisting equipment through the exponential smoothing method.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring technology for engineering machinery, and in particular to a method and system for health assessment and life prediction of lifting equipment based on exponential smoothing. Background Technology

[0002] Cranes are a typical type of electromechanical special equipment. As an important material handling device, they are increasingly widely used in modern production processes as production scale expands and automation levels increase. However, currently, the maintenance of cranes mainly includes two categories: periodic preventive maintenance and restorative maintenance after malfunctions or accidents. This results in poor timeliness in detecting equipment health problems, frequent sudden shutdowns, severely impacting production efficiency and posing significant safety hazards.

[0003] Chinese patent application CN111178674A, entitled "An Industrial Big Data-Driven Health Management and Control Service System for Lifting Machinery," provides an industrial big data-driven health management and control service system for lifting equipment. It includes a remote operation and maintenance service module and a health management service module, capable of diagnosing the status of lifting equipment and predicting faults in healthy and sub-healthy states. However, when assessing equipment health status, this system requires acquiring full lifecycle data from the crane's demand, design, manufacturing, operation and maintenance, and scrapping stages, and then training the model. In practical applications, the operating conditions, status data, inspection and testing data, and operating environment data of each lifting device are different, making data sample acquisition difficult, training cycles long, and hindering the development of a universal model and implementation.

[0004] Chinese patent application CN108760371A, entitled "A Method for Calculating the Remaining Life of In-Service Lifting Machinery Structures," proposes a method for monitoring the health status and estimating the remaining life of lifting machinery structures based on historical lifting conditions and stress spectrum data from the measured stage, thereby reducing operational risks. However, this method only calculates the remaining life of the lifting machinery structure and lacks health status assessment and remaining life prediction for electrical system components such as brakes, contactors, and motors, resulting in an insufficiently comprehensive reflection of the overall health status of the crane. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method and system for health assessment and life prediction of lifting equipment based on exponential smoothing, so as to eliminate or improve one or more defects existing in the prior art.

[0006] The technical solution of the present invention is as follows:

[0007] A method for health assessment and life prediction of lifting equipment based on exponential smoothing, the method comprising the following steps:

[0008] The operation data indicating the health status of the lifting equipment is obtained. The lifting equipment includes the following components: main load-bearing structure, brake, contactor and motor. The operation data includes: the average lifting capacity of the main load-bearing structure in each working cycle, the cumulative number of braking times of the brake, the cumulative number of actuations of the contactor, and the number of starts of the motor.

[0009] Based on the operating data of each component of the lifting equipment and the preset rated life limit of each component, the health status of each component of the lifting equipment is confirmed.

[0010] Based on the health status of each component of the lifting equipment, assess the overall health status of the lifting equipment;

[0011] Based on the health status of each component of the lifting equipment, the exponential smoothing method is applied to predict the lifespan of each component.

[0012] In some embodiments, the method further includes:

[0013] Predictive maintenance recommendations are given based on the overall health status of the lifting equipment and / or the life prediction results of each component of the lifting equipment.

[0014] In some embodiments, the preset rated life limit of the main load-bearing structure is the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment. The steps for confirming the health status of the main load-bearing structure include:

[0015] After obtaining the average lifting capacity of the main load-bearing structure of the lifting equipment in each working cycle, calculate the ratio of this ratio to the rated lifting capacity of the lifting equipment, cube the ratio, and sum the results. Then divide the sum by the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment to obtain the damage coefficient D of the main load-bearing structure of the lifting equipment. Metal :

[0016]

[0017] Where WorkPeriodLoad is the average lifting capacity within each work cycle; LoadN is the rated lifting capacity; and WorkPeriodN is the maximum number of work cycles corresponding to the rated lifting capacity under the permissible work level.

[0018] According to the formula:

[0019] H Metar =(1-D Metar )×100

[0020] Calculate the health fraction H of the main load-bearing structure of the lifting equipment. Metal .

[0021] In some embodiments, the preset rated life limit of the brake is an upper limit of the cumulative number of braking cycles, and the step of determining the current health status of the brake includes:

[0022] Calculate the cumulative number of braking events (BrakeCnt) and the upper limit of the cumulative number of braking events (BrakeCnt). Lim The ratio of the values ​​yields the damage coefficient D of the brake. Brake :

[0023]

[0024] According to the formula:

[0025] H Brake =(1-D Brake )×100

[0026] Calculate the health score H of the brake. Brake ;

[0027] The contactor's preset rated lifespan limit is the number of electrical lifetimes. The steps for determining the contactor's current health status include:

[0028] Calculate the cumulative number of contactor operations (ContactCnt) and the electrical life count (ContactCnt). Lim The ratio of the two values ​​yields the damage coefficient D of the contactor. Contact :

[0029]

[0030] According to the formula:

[0031] H Contact =(1-D Contact )×100

[0032] Calculate the health score H of the contactor Contact ;

[0033] The preset rated lifespan limit of the motor is the upper limit of the number of motor starts. The steps to determine the current health status of the motor include:

[0034] Calculate the number of motor starts (MotorCnt) and the upper limit of the number of motor starts (MotorCnt). Lim The ratio of these values ​​yields the motor's damage coefficient D. Mtor :

[0035]

[0036] According to the formula:

[0037] H Motor =(1-D Motor )×100

[0038] Calculate the health score D of the motor Motor .

[0039] In some embodiments, the step of assessing the overall health status of the lifting equipment includes:

[0040] The minimum health score among the main load-bearing structures, brakes, contactors, and motors is taken and normalized to obtain the overall health score y of the lifting equipment. 健康分数 ;

[0041] x = MIN(H) Metal H Brake H Contact H Motor )

[0042]

[0043] Where x∈(-∞, 100], y 健康分数 ∈(0, 100).

[0044] In some embodiments, the step of predicting the lifespan of each component of the lifting equipment based on its health status using an exponential smoothing method includes:

[0045] i. Starting from the moment a component is connected to the system, obtain the component's health score H1 for the first day, and then set the predicted value...

[0046] ii. Calculate the predicted health score for each component up to day t after it is connected to the system, using the first exponential smoothing formula:

[0047]

[0048] in, This is a smoothed prediction of the component's health status on day t after it is connected to the system. H is a smoothed prediction of the health status of this component on day t-1 of the system. t-1 The actual health score of the component on day t-1 after it is connected to the system, where α is the smoothing coefficient;

[0049] iii. Using the quadratic exponential smoothing formula, calculate the predicted health score of each component on day t after quadratic smoothing:

[0050]

[0051] in, This is the second-order smoothed prediction of the health status of the component on day t after it is connected to the system. This is a smoothed prediction value from the previous step. The smoothed prediction value of the health status of the access component to the system on day t-1 is given by α, where α is the smoothing coefficient.

[0052] iv. Based on the current prediction results, calculate the intercept a of the current trend in health score changes. t and slope b t for:

[0053]

[0054] v. Based on the current trend of health score changes, the predicted value on day t+T is:

[0055]

[0056] make The value of T is calculated, which represents the predicted remaining lifespan of the component after t days of connection to the system.

[0057] In some embodiments, when using the exponential smoothing method to predict the lifespan of various components of the lifting equipment:

[0058] If a component is connected to the system within 10 days, the lifespan prediction results for that component will not be displayed.

[0059] If a component has been connected to the system for a cumulative period of 10 days, after the daily health score calculation for that component is completed, the system will automatically predict the lifespan of that component for the next day to obtain the remaining lifespan of the main load-bearing structures, brakes, contactors, and / or motors of the lifting equipment.

[0060] In some embodiments, based on the overall health score y of the lifting equipment 健康分数 The health status of lifting equipment is dynamically graded and displayed.

[0061] If the health score is y 健康分数 If the value is less than 60, the health status of the lifting equipment is considered poor.

[0062] If 60 ≤ health score y 健康分数 If the value is less than 80, the health status of the lifting equipment is considered normal.

[0063] If 80 ≤ health score y 健康分数 If the value is ≤100, the lifting equipment is considered to be in good health.

[0064] A health assessment and life prediction system for lifting equipment is disclosed. The system includes a monitoring unit and a cloud platform. The monitoring unit collects operational data indicating the health status of the lifting equipment in real time and transmits the data to the cloud platform. The lifting equipment includes components such as a main load-bearing structure, a brake, a contactor, and a motor. The operational data includes the average lifting capacity of the main load-bearing structure per work cycle, the cumulative number of braking actions of the brake, the cumulative number of actuations of the contactor, and the number of starts of the motor. The cloud platform receives and displays the operational data sent by the monitoring unit, performs health assessments and life predictions of the lifting equipment, and provides maintenance recommendations. The cloud platform includes a processor and a memory. The memory stores preset rated life limits for each component of the lifting equipment and computer instructions. The processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the cloud platform implements the steps described above.

[0065] In some embodiments, the system further includes a terminal for accessing the cloud platform and remotely viewing and / or receiving operating data, health assessments, and lifespan predictions of the lifting equipment.

[0066] The lifting equipment health assessment and life prediction method based on exponential smoothing in this invention can collect real-time operating data of the lifting equipment, import it into a pre-established lifting equipment health assessment model, obtain the lifting equipment health status assessment result, and apply the result to predict the remaining life of the lifting equipment through the exponential smoothing method.

[0067] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the written description, claims, and drawings.

[0068] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0069] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. The components in the drawings are not drawn to scale but are merely illustrative of the principles of the invention. For ease of illustration and description of certain parts of the invention, corresponding portions in the drawings may be enlarged, i.e., may appear larger relative to other components in an exemplary device actually manufactured according to the invention. In the drawings:

[0070] Figure 1 This is a schematic block diagram of a method for health assessment and life prediction of lifting equipment based on exponential smoothing in one embodiment of the present invention.

[0071] Figure 2 This is a schematic block diagram of a method for health assessment and life prediction of lifting equipment based on exponential smoothing, according to another embodiment of the present invention.

[0072] Figure 3 This is a schematic diagram of the composition of a lifting equipment health assessment and life prediction system according to an embodiment of the present invention.

[0073] Figure 4 This is a schematic diagram of the composition of a lifting equipment health assessment and life prediction system according to another embodiment of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0075] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0076] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0077] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0078] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0079] This invention provides a method and system for health assessment and life prediction of lifting equipment based on exponential smoothing. According to the current health status of each component of the lifting equipment, the remaining life is predicted based on exponential smoothing. By analyzing the time series change trend of the health status of each component of each piece of equipment in real time, the remaining life of the components can be sensitively calculated, which helps equipment maintenance personnel to reasonably arrange maintenance tasks according to the remaining life of different equipment and different components, thereby improving maintenance efficiency.

[0080] According to one aspect of the invention, such as Figure 1 As shown, in some embodiments, the method for health assessment and life prediction of lifting equipment based on exponential smoothing includes the following steps:

[0081] S110: Obtain operational data indicating the health status of the lifting equipment. The lifting equipment includes the following components: main load-bearing structure, brake, contactor, and motor. The operational data includes: the average lifting capacity of the main load-bearing structure per work cycle, the cumulative number of braking actions of the brake, the cumulative number of actuations of the contactor, and the number of starts of the motor.

[0082] S120: Based on the operating data of each component of the lifting equipment and the preset rated life limit of each component of the lifting equipment, confirm the health status of each component of the lifting equipment.

[0083] S130: Based on the health status of each component of the lifting equipment, assess the overall health status of the lifting equipment;

[0084] S140: Based on the health status of each component of the lifting equipment, the lifespan of each component of the lifting equipment is predicted using the exponential smoothing method.

[0085] In the above embodiments, the operational data of the lifting equipment used to indicate its health status can be obtained in multiple ways, such as by relying on the lifting equipment's own control circuit or monitoring unit to collect and transmit the data. The lifting equipment health assessment and lifespan prediction method based on exponential smoothing in this invention can collect real-time operational data of the lifting equipment, import it into a pre-established lifting equipment health assessment model to obtain the lifting equipment health status assessment result, and apply this result to predict the remaining lifespan of the lifting equipment using the exponential smoothing method.

[0086] In some embodiments, such as Figure 2 As shown, the method further includes:

[0087] S150: Provide predictive maintenance recommendations based on the overall health status of the lifting equipment and / or the life prediction results of individual components of the lifting equipment.

[0088] The crane health assessment and life prediction method based on exponential smoothing in this invention can realize a dynamic predictive maintenance task decision-making mode based on real-time data based on the overall health status of the crane equipment and / or the life prediction results of each component of the crane equipment, thereby reducing temporary downtime and production losses.

[0089] In some embodiments, the main load-bearing structure of the lifting equipment varies depending on the type of lifting equipment. The average lifting capacity of the main load-bearing structure per working cycle can be used as an indicator to assess the health status of the main load-bearing structure, and the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment can be used as a preset rated life limit. The steps to confirm the health status of the main load-bearing structure include:

[0090] After obtaining the average lifting capacity of the main load-bearing structure of the lifting equipment in each working cycle, calculate the ratio of this ratio to the rated lifting capacity of the lifting equipment, cube the ratio, and sum the results. Then divide the sum by the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment to obtain the damage coefficient D of the main load-bearing structure of the lifting equipment. Metal :

[0091]

[0092] Where WorkPeriodLoad is the average lifting capacity per work cycle; LoadN is the rated lifting capacity; WorkPeriodN is the maximum number of work cycles corresponding to the rated lifting capacity under the allowable work level; the specific values ​​are shown in the table below, depending on the work level of the lifting equipment:

[0093] WorkLevel A2 A3 A4 A5 A6 A7 A8 WorkPeriodN 16000 32000 63000 125000 250000 500000 1000000

[0094] According to the formula:

[0095] H Metal =(1-D Metal )×100

[0096] Calculate the health fraction H of the main load-bearing structure of the lifting equipment. Metal .

[0097] In some embodiments, the preset rated life limit of the brake is an upper limit of the cumulative number of braking cycles, and the step of determining the current health status of the brake includes:

[0098] Calculate the cumulative number of braking events (BrakeCnt) and the upper limit of the cumulative number of braking events (BrakeCnt). Lim The ratio of the values ​​yields the damage coefficient D of the brake. Brake :

[0099]

[0100] According to the formula:

[0101] H Brake =(1-D Brake )×100

[0102] Calculate the health score H of the brake.Brake .

[0103] In some embodiments, the preset rated life limit of the contactor is the number of electrical lifetimes, and the step of determining the current health status of the contactor includes:

[0104] Calculate the cumulative number of contactor operations (ContactCnt) and the electrical life count (ContactCnt). Lim The ratio of the two values ​​yields the damage coefficient D of the contactor. Contact :

[0105]

[0106] According to the formula:

[0107] H Contact =(1-D Contact )×100

[0108] Calculate the health score H of the contactor Contact .

[0109] In some embodiments, the preset rated lifespan limit of the motor is the upper limit of the number of motor starts, and the steps for determining the current health status of the motor include:

[0110] Calculate the number of motor starts (MotorCnt) and the upper limit of the number of motor starts (MotorCnt). Lim The ratio of these values ​​yields the motor's damage coefficient D. Motor :

[0111]

[0112] According to the formula:

[0113] H Motor =(1-D Motor )×100

[0114] Calculate the health score H of the motor Motor .

[0115] Based on the above methods and steps, the health score H of the main load-bearing structure of the lifting equipment can be obtained. Metal Health score H of the brake Brake Health score H of the contactor Contact and the motor's health score H Motor These parameters can reflect the health status of individual components of the lifting equipment, and can also be used to assess the overall health status of the lifting equipment.

[0116] In some embodiments, the step of assessing the overall health status of the lifting equipment includes:

[0117] The minimum health score among the main load-bearing structures, brakes, contactors, and motors is taken and normalized to obtain the overall health score y of the lifting equipment. 健康分数 ;

[0118] x = MIN(H) Metal H Brake H Contact H Motor )

[0119]

[0120] Where x∈(-∞, 100], y 健康分数 ∈(0, 100).

[0121] Normalization can converge the data to the (0,100) range, with values ​​less than 60 indicating unhealthy devices, which aligns with users' habitual perceptions and is easier to understand.

[0122] In some embodiments, based on the health status of each component of the lifting equipment, the exponential smoothing method can be applied to predict the lifespan of each component of the lifting equipment. The steps include:

[0123] i. Starting from the moment a component is connected to the system, obtain the component's health score H1 for the first day, and then set the predicted value...

[0124] ii. Calculate the predicted health score for each component up to day t after it is connected to the system, using the first exponential smoothing formula:

[0125]

[0126] in, This is a smoothed prediction of the component's health status on day t after it is connected to the system. H is a smoothed prediction of the health status of this component on day t-1 of the system. t-1 The actual health score of the component on day t-1 after it is connected to the system, where α is a smoothing coefficient, and based on experience, for example, α = 0.8;

[0127] iii. Using the quadratic exponential smoothing formula, calculate the predicted health score of each component on day t after quadratic smoothing:

[0128]

[0129] in, This is the second-order smoothed prediction of the health status of the component on day t after it is connected to the system. This is a smoothed prediction value from the previous step. The second-smoothed prediction value of the health status of the component accessing the system on day t-1 is given by α, which is the smoothing coefficient. Based on experience, for example, α = 0.8.

[0130] iv. Based on the current prediction results, calculate the intercept a of the current trend in health score changes. t and slope b t for:

[0131]

[0132] v. Based on the current trend of health score changes, the predicted value on day t+T is:

[0133]

[0134] make The value of T is calculated, which represents the predicted remaining lifespan of the component after t days of connection to the system.

[0135] Furthermore, when using the exponential smoothing method to predict the lifespan of various components of the lifting equipment:

[0136] If a component is connected to the system within 10 days, the lifespan prediction results for that component will not be displayed.

[0137] If a component has been connected to the system for a cumulative period of 10 days, after the daily health score calculation for that component is completed, the system will automatically predict the lifespan of that component for the next day to obtain the remaining lifespan of the main load-bearing structures, brakes, contactors, and / or motors of the lifting equipment.

[0138] Furthermore, based on the overall health score y of the lifting equipment 健康分数 The system dynamically grades and displays the health status of lifting equipment, which can be divided into three states: good, fair, and poor.

[0139] If the health score is y 健康分数 If the value is less than 60, the health status of the lifting equipment is considered poor.

[0140] If 60 ≤ health score y 健康分数 If the value is less than 80, the health status of the lifting equipment is considered normal.

[0141] If 80 ≤ health score y 健康分数 If the value is ≤100, the lifting equipment is considered to be in good health.

[0142] Based on the above method, the health status of various components of the lifting equipment can be dynamically classified and displayed, and replacement or inspection suggestions can be given for different components. For example, if the health status of a certain component is poor, then that component should be replaced.

[0143] According to another aspect of the present invention, a system for health assessment and life prediction of lifting equipment is also provided, such as... Figure 3 and Figure 4 As shown, the system may include a monitoring unit 10 and a cloud platform 20.

[0144] The monitoring unit is used to collect real-time operational data indicating the health status of the lifting equipment and transmit the data to the cloud platform 20. This monitoring unit can be various sensors or monitoring devices. The monitoring unit 10 can upload the monitored or collected operational data to the cloud platform 20 via high-speed 4G / 5G modules, mobile communication networks, and the Internet, but is not limited to these. For example, it can also transmit the data to the cloud platform's server or a local server via Bluetooth, WIFI, etc., and then the local server can transmit it to the cloud platform via the network. The lifting equipment includes the following components: main load-bearing structure, brake, contactor, and motor. Operational data includes: the average lifting capacity of the main load-bearing structure per work cycle, the cumulative number of brake applications, the cumulative number of contactor actuations, and the number of motor starts.

[0145] In some embodiments, the lifting equipment may be various types of lifting machinery, such as light lifting equipment, bridge cranes, boom cranes, and cable cranes. The above-mentioned operating data may also include other parameters, such as controller operating time, lifting distance, etc.

[0146] The cloud platform 20 of this embodiment of the invention is used to receive and display operating data sent by the monitoring unit 10, perform health assessments and life predictions of the lifting equipment, and provide maintenance suggestions. The cloud platform 20 includes a processor and a memory. The memory stores preset rated life limits for each component of the lifting equipment and computer instructions. The processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the cloud platform 20 implements the steps of the aforementioned method.

[0147] The cloud platform of this invention can display real-time monitoring data collected and uploaded by edge monitoring products, and calculate other performance parameters based on the real-time monitoring data. In some embodiments, the functions implemented by the cloud platform include: real-time monitoring of the operating status of lifting equipment, equipment alarm notification and record query, equipment health assessment, predictive maintenance of equipment, and user system configuration.

[0148] In some embodiments, the system further includes a terminal 30, which is used to access the cloud platform 20 and remotely view and / or receive operating data, health assessments, and life predictions of the lifting equipment. The terminal can be a mobile phone, smart tablet, personal computer, etc. Users can conveniently access the cloud platform via mobile WeChat, PC browsers, etc., to remotely view equipment operating parameters, alarm status, health assessment and life prediction details anytime, anywhere, and receive equipment alarm information, health assessment and life prediction results, and corresponding maintenance measures via mobile WeChat, SMS, and email.

[0149] The method of the present invention will be described below using an electric hoist bridge crane with a rated lifting capacity of 5T and a working class of A4 as an example. The cloud platform performs dynamic health assessments on the crane based on pre-set basic information of the lifting equipment, life information of each component of the equipment, and real-time monitored operating data.

[0150] (1) The monitoring unit collects the operational data of the health status of each lifting device in real time and transmits it to the cloud platform. The operational data includes: the average lifting capacity of the main load-bearing structure in each working cycle, the cumulative number of braking times of the brake, the cumulative number of action times of the contactor, and the number of motor starts.

[0151] The operating status of the lifting equipment (South Crossing East Second of the Steel Wire Rope) was recorded from December 18, 2021 to January 21, 2022. The operating data is shown in the table below:

[0152]

[0153] (2) The cloud platform confirms the current health status of each component of the lifting equipment based on the real-time operating data of each lifting equipment and the preset rated life limit of each equipment.

[0154] 2.1 The health assessment method for the main load-bearing structures is as follows:

[0155] Record the average lifting capacity of the lifting equipment in each working cycle, calculate the ratio of this ratio to the rated lifting capacity of the lifting equipment, cube the ratio, and sum the results. Then divide this sum by the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment to obtain the damage coefficient D of the main load-bearing structure of the lifting equipment. Metal .

[0156]

[0157] in,

[0158] WorkPeriodLoad is the average lifting weight within each work cycle;

[0159] LoadN is the rated lifting capacity;

[0160] WorkPeriodN represents the maximum number of working cycles corresponding to the rated lifting capacity under the permissible working class, which is 63,000 cycles depending on the working class of the lifting equipment.

[0161] According to the system's calculation results, the damage coefficient D after 3336 working cycles is... Metal =0.02%.

[0162] The health value of the main load-bearing structure of the lifting equipment is:

[0163] H Metal =(1-D Metal )×100=98.

[0164] 2.2 The health assessment methods for brakes, contactors, and motors are as follows:

[0165] Record the cumulative number of operations of the brake, lifting / lowering contactor, and motor, calculate the ratio of this number to the upper limit of the number of operations, and obtain the damage coefficient D of the brake, lifting / lowering contactor, and motor. Brake D Contact D Motor The corresponding health value is H = (1-D)*100%.

[0166]

[0167] in,

[0168] BrakeCnt represents the cumulative number of braking operations;

[0169] BrakeCnt Lim The maximum number of cumulative braking cycles is 2 million.

[0170] H Brake =96

[0171]

[0172] in,

[0173] ContactCnt represents the cumulative number of actions of the contactor;

[0174] ContactCnt Lim The electrical life of the contactor is 500,000 cycles.

[0175] H Contact下降 =93

[0176] H Contact上升 =94

[0177]

[0178] in,

[0179] MotorCnt represents the number of times the motor has been started.

[0180] MotorCntL im This is the maximum number of times the motor can be started, 1 million times.

[0181] H Motor =93

[0182] (3) Assess the overall health status of the equipment based on the health status of each component of the lifting equipment.

[0183] The overall health assessment score of the lifting equipment is obtained by taking the minimum value among the health scores of the main load-bearing structure, brake, lifting / lowering contactor, and motor, and then normalizing it.

[0184] H totar =MIN(H Metar H Brake H Contact H Motor ) = 93

[0185]

[0186] Among them, H total y is the minimum health score among all components. 健康分数 This is the health assessment score for the entire lifting equipment.

[0187] (4) Based on the health status of each component of the lifting equipment, the lifespan of each component of each equipment is predicted using the exponential smoothing method. The main steps are as follows:

[0188] i. After obtaining the health status assessment score H1 of the main load-bearing structure, brake, and rising / falling contactor on the first day of equipment connection to the system, let the predicted value...

[0189] ii. Calculate the component health score prediction results up to the t-th day after system access using the first exponential smoothing formula:

[0190]

[0191] in, Let be the predicted health status of a certain component connected to the system on day t. H is the predicted health status of the access system on day t-1. t-1 This represents the actual health status score on day t-1. Here, α is the smoothing coefficient, which, empirically, is 0.8.

[0192] iii. Calculate the predicted component health score after double smoothing on day t using the quadratic exponential smoothing formula:

[0193]

[0194] in, Let be the quadratic smoothed prediction of the health status of a certain component connected to the system on day t. This is the first smoothed prediction value from the previous step. This is the second-smoothed predicted value of the health status of the access system on day t-1.

[0195] Where α is the smoothing coefficient, and empirically, α = 0.8.

[0196] iv. Based on the current prediction results, the intercept 'a' of the current health score trend can be calculated. t and slope b t for:

[0197]

[0198] v. Based on the current trend of health score changes, the predicted value on day t+T is:

[0199]

[0200] make T is obtained, which means that after t days of access to the system, the predicted remaining lifespan of this component of the device is T days.

[0201] vi. Within 10 days of equipment access to the system, lifespan prediction results will not be displayed. After 10 days of cumulative system access, once the platform completes the daily calculation of new component health scores, it will automatically predict the component lifespan for the following day, determining the remaining lifespan of the main load-bearing structures, brakes, contactors, and motors of the lifting equipment.

[0202] (5) Based on the overall health assessment results of the lifting equipment and the life prediction results of each component of the lifting equipment, predictive maintenance recommendations are given.

[0203] The cloud platform dynamically classifies and displays the health status of lifting equipment based on its health assessment score. The health status is divided into three levels: good, fair, and poor.

[0204] The classification is based on:

[0205] Poor: Normalized health score <60;

[0206] Generally: 60 ≤ normalized health score < 80;

[0207] Good: 80 ≤ normalized health score ≤ 100.

[0208] The current health assessment score for the lifting equipment is 94.5, indicating good health. No maintenance is required for any components of the lifting equipment.

[0209] The method and system for health assessment and life prediction of lifting equipment based on exponential smoothing according to embodiments of the present invention can achieve at least the following beneficial effects:

[0210] (1) This method changes the traditional approach of periodic inspection and preventive maintenance and fire-fighting maintenance after failures or accidents during the use of lifting equipment. This method uses real-time data to dynamically assess the health status of the components and the whole machine of the lifting equipment, which can accurately locate components that may be abnormal, and carry out targeted maintenance to reduce the losses caused by sudden equipment downtime and prevent problems before they occur.

[0211] (2) This method predicts the remaining life based on the exponential smoothing method according to the current health status of each component of the lifting equipment. By analyzing the time series change trend of the health status of each component of each lifting equipment in real time, it can sensitively calculate the remaining life of the components and help equipment maintenance personnel to reasonably arrange maintenance tasks according to the remaining life of different equipment and different components, thereby improving maintenance efficiency.

[0212] (3) The system can intelligently generate suggested maintenance measures for components and send them to equipment maintenance personnel via WeChat, SMS and email. This can reduce the technical threshold for equipment management personnel and enable them to carry out their work in conjunction with the system's suggestions.

[0213] (4) The present invention uses a lifting equipment intelligent monitoring unit and cloud platform to collect the operating data of the lifting equipment in a low-cost manner, realize online monitoring and health management of the lifting equipment, and is easy to install and maintenance-free.

[0214] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0215] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0216] The software can be stored in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0217] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0218] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for health assessment and life prediction of lifting equipment based on exponential smoothing, characterized in that, The method includes the following steps: The operation data indicating the health status of the lifting equipment is obtained. The lifting equipment includes the following components: main load-bearing structure, brake, contactor and motor. The operation data includes: the average lifting capacity of the main load-bearing structure in each working cycle, the cumulative number of braking times of the brake, the cumulative number of actuations of the contactor, and the number of starts of the motor. Based on the operating data of each component of the lifting equipment and the preset rated life limit of each component, the health status of each component of the lifting equipment is confirmed; the preset rated life limit of the main load-bearing structure is the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment. The steps for confirming the health status of the main load-bearing structure include: After obtaining the average lifting capacity of the main load-bearing structure of the lifting equipment in each working cycle, calculate the ratio of this ratio to the rated lifting capacity of the lifting equipment, cube the ratio, and sum the results. Then divide the sum by the maximum number of working cycles corresponding to the rated lifting capacity of the lifting equipment to obtain the damage coefficient of the main load-bearing structure of the lifting equipment. : in, This represents the average lifting capacity within each work cycle. Rated lifting capacity; The maximum number of working cycles corresponding to the rated lifting capacity under the permissible working level; According to the formula: Calculate the health score of the main load-bearing structure of the lifting equipment. ; The preset rated lifespan limit of the brake is the upper limit of the cumulative number of braking cycles. The steps for determining the current health status of the brake include: Calculate the cumulative number of braking cycles of the brake. With the maximum number of cumulative braking times The ratio of the values ​​yields the damage coefficient of the brake. : According to the formula: Calculate the health score of the brake. ; The contactor's preset rated lifespan limit is the number of electrical lifetimes. The steps for determining the contactor's current health status include: Calculate the cumulative number of contactor actions With electrical lifetime count The ratio of the two values ​​yields the damage coefficient of the contactor. : According to the formula: Calculate the health score of the contactor ; The preset rated lifespan limit of the motor is the upper limit of the number of motor starts. The steps to determine the current health status of the motor include: Calculate the number of motor starts Maximum number of motor starts The ratio of these values ​​yields the motor's damage coefficient. : According to the formula: Calculate the health score of the motor ; Based on the health score of the main load-bearing structure of the lifting equipment Health score of the brake Health score of contactor Motor health score Assess the overall health status of the lifting equipment; Based on the health score of the main load-bearing structure of the lifting equipment Health score of the brake Health score of contactor Motor health score The lifespan of various components of the lifting equipment is predicted by applying the exponential smoothing method.

2. The method according to claim 1, characterized in that, The method further includes: Predictive maintenance recommendations are given based on the overall health status of the lifting equipment and / or the life prediction results of each component of the lifting equipment.

3. The method according to claim 2, characterized in that, The steps for assessing the overall health of lifting equipment include: The minimum health score among the main load-bearing structures, brakes, contactors, and motors is taken and normalized to obtain the overall health score of the lifting equipment. ; in, .

4. The method according to claim 3, characterized in that, Based on the health status of each component of the lifting equipment, the steps for predicting the lifespan of each component using the exponential smoothing method include: The health score of a component is obtained from the first day it is connected to the system. Then, let the predicted value ; Using the first exponential smoothing formula, calculate the predicted health score for each component up to day t after it is connected to the system: (t=2,3,……,n) in, This is a smoothed prediction of the component's health status on day t after it is connected to the system. This is a smoothed prediction of the health status of the component on day t-1 of its connection to the system. This is the actual score of the component's health status on day t-1 after it was connected to the system. For smoothing coefficients; Using the quadratic exponential smoothing formula, calculate the predicted health score of this component on day t after quadratic smoothing: (t=2,3,……,n) in, This is the second-order smoothed prediction of the component's health status on day t after it is connected to the system. This is a smoothed prediction value from the previous step. To obtain the quadratic smoothed prediction value of the health status of this component on day t-1 of the access system, For smoothing coefficients; Based on the current prediction results, calculate the intercept of the current trend in health score. and slope for: (t=2,3,……,n) Given the current trend in health score changes, the predicted value on day t+T is: make The value of T is calculated, which represents the predicted remaining lifespan of the component after t days of connection to the system.

5. The method according to claim 3, characterized in that, When using the exponential smoothing method to predict the lifespan of various components of lifting equipment: If a component is connected to the system within 10 days, the lifespan prediction results for that component will not be displayed. If a component has been connected to the system for a cumulative period of 10 days, after the daily health score calculation for that component is completed, the system will automatically predict the lifespan of that component for the next day to obtain the remaining lifespan of the main load-bearing structures, brakes, contactors, and / or motors of the lifting equipment.

6. The method according to claim 3, characterized in that, Based on the overall health score of the lifting equipment The health status of lifting equipment is dynamically graded and displayed. If health score If the value is less than 60, the health status of the lifting equipment is considered poor. If 60 ≤ Health Score If the value is less than 80, the health status of the lifting equipment is considered normal. If 80 ≤ Health Score If the value is ≤100, the lifting equipment is considered to be in good health.

7. A health assessment and life prediction system for lifting equipment, characterized in that, The system includes a monitoring unit and a cloud platform; The monitoring unit is used to collect real-time operational data indicating the health status of the lifting equipment and transmit the data to the cloud platform. The lifting equipment includes the following components: main load-bearing structure, brake, contactor, and motor. The operational data includes: the average lifting capacity of the main load-bearing structure in each working cycle, the cumulative number of braking times of the brake, the cumulative number of actuations of the contactor, and the number of starts of the motor. The cloud platform is used to receive and display the operating data sent by the monitoring unit, perform health assessments and life predictions of the lifting equipment, and provide maintenance and repair suggestions. The cloud platform includes a processor and a memory. The memory stores preset rated life limits for each component of the lifting equipment and computer instructions. The processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the cloud platform implements the steps of the method as described in any one of claims 1-6.

8. The system according to claim 7, characterized in that, The system also includes a terminal, which is used to access the cloud platform and remotely view and / or receive operating data, health assessments, and life predictions of the lifting equipment.

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