System and method for predicting maintenance period of elevator

Abstract

The invention relates to the technical field of elevator maintenance, in particular to a system and method for predicting the maintenance period of an elevator, and the system comprises an internet-of-things sensing module, a fault detection module, a message bus, a data analysis module, a maintenance period calculation module and a maintenance terminal; the internet-of-things sensing module and the fault detection module are both installed in an elevator car and connected with the message bus through a mobile network. The message bus is respectively connected with the data analysis module and the maintenance terminal, the data analysis module is also respectively connected with the maintenance period calculation module and the maintenance terminal, and the maintenance period calculation module is connected with the message bus. According to the elevator maintenance method and system, the elevator can be maintained in a targeted mode according to needs, maintenance manpower resources are saved, and the maintenance efficiency and the elevator safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator maintenance, and in particular to a system and method for predicting an elevator maintenance cycle. Background Art

[0002] At present, according to the relevant requirements of elevator maintenance, elevator maintenance can be divided into four types: half-monthly, quarterly, semi-annual, and annual. However, for elevators equipped with IoT sensor equipment, the maintenance cycle requirements can be appropriately advanced or relaxed as needed.

[0003] At the same time, with the improvement of urbanization level, the number of elevators has continued to grow. However, the growth rate of professional elevator maintenance personnel cannot keep up with the growth rate of elevators, resulting in a shortage of maintenance personnel, which may lead to low-quality and high-risk problems. Repeated maintenance of elevators in good operating condition wastes the energy of maintenance personnel, while the safety hazards of elevators in poor operating condition are often not discovered and handled in advance. With the advancement of technology, the reliability of elevator components and the overall system has been greatly improved. If the traditional maintenance cycle is still carried out indiscriminately, it will inevitably lead to a waste of resources. Therefore, under the premise of complying with the mandatory provisions of laws and regulations, it is very necessary to carry out maintenance on demand according to the actual working conditions of the elevator.

[0004] In view of this, this application is filed. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a system and method for predicting the elevator maintenance cycle, which can realize targeted and on-demand maintenance of elevators, save maintenance human resources, and improve maintenance efficiency and elevator safety.

[0006] The present invention is achieved through the following technical solutions:

[0007] A system for predicting an elevator maintenance cycle is provided, comprising an Internet of Things sensing module, a fault detection module, a message bus, a data analysis module, a maintenance cycle calculation module and a maintenance terminal; the Internet of Things sensing module and the fault detection module are both installed in an elevator car and are respectively connected to the message bus through a mobile network; the message bus is respectively connected to the data analysis module and the maintenance terminal, the data analysis module is also respectively connected to the maintenance cycle calculation module and the maintenance terminal, and the maintenance cycle calculation module is connected to the message bus.

[0008] Furthermore, a variety of sensors are integrated into the IoT sensor module to collect data on the real-time operating status of the elevator.

[0009] Furthermore, the fault detection module is used to pre-process the raw data collected by the IoT sensor module to detect and identify whether the elevator currently has a fault.

[0010] Furthermore, the maintenance cycle calculation module is used to calculate the date and project of the next maintenance and generate a maintenance work order.

[0011] In this solution, the IoT sensor module integrates a variety of sensors, such as speed, temperature, acceleration, vibration frequency, tilt angle, etc., which are used to collect data on the real-time operation status of the elevator; the fault detection module pre-processes the raw data collected by the IoT sensor module to detect and identify whether the elevator currently has a fault, such as trapped people, open door and elevator, overspeed, frequent door opening and closing, etc.; the message bus is connected to the data analysis module, and the data analysis module is connected to the maintenance cycle calculation module to provide the raw data for maintenance cycle calculation. The maintenance cycle calculation module performs comprehensive calculations based on the collected data such as the current operation status of the elevator, elevator operation records, historical maintenance results, and historical inspection results, and obtains the recommended date and key maintenance items for the next maintenance, and generates a maintenance work order; the maintenance cycle calculation module is connected to the message bus to push the maintenance work order to the maintenance terminal; the maintenance terminal is connected to the message bus and is used by elevator maintenance personnel to receive maintenance work order notifications and perform subsequent operations and maintenance.

[0012] A method for predicting an elevator maintenance cycle using a system for predicting an elevator maintenance cycle comprises the following steps:

[0013] S1. The IoT sensor module collects real-time parameter data of the elevator through built-in multiple sensors, including speed, temperature, acceleration, vibration frequency and tilt angle;

[0014] S2. The fault detection module pre-processes the parameter data collected by the IoT sensor module, filters out invalid jitter data, and performs elevator fault identification to detect whether there is a fault at present;

[0015] S3. If a fault is detected in the elevator, the fault detection module will report the identified fault to the message bus through the MQTT protocol;

[0016] S4. Use the data analysis module to analyze and process the reported data, including filtering the obviously jittery data and removing expired data; then combine the elevator's running time, running distance, historical fault records, historical maintenance and inspection record data to establish an elevator safety assessment model;

[0017] S5. Use the maintenance cycle calculation module in conjunction with the elevator safety assessment model established in step S4 to calculate the current safety level of the elevator and the items that need to be focused on in the next maintenance, generate a maintenance work order and push it to the message bus;

[0018] S6. The maintenance terminal receives the maintenance work order from the message bus, executes it and provides result feedback;

[0019] S7. Supplement the maintenance record of this time into the evaluation model established in step S4 to provide data support for the calculation of the next maintenance cycle.

[0020] Furthermore, in step S4, the method for establishing an elevator safety assessment model is as follows: first determine the severity of the consequences of the current risks, denoted as S, divide the severity of the risks into several levels according to the impact of the risks on people, the environment, and property, and determine the probability of the risk occurring, denoted as P; determine the risk category after determining the probability level of the risk occurrence based on historical data and statistical analysis, and divide the risks into different categories based on the levels of S and P, make a comprehensive safety status level determination, weight all individual assessment items, and calculate the comprehensive safety status score of the elevator.

[0021] Preferably, in step S3, if the mobile network is not connected, the data is cached and uploaded in batches after the network is restored.

[0022] Furthermore, in step S5, the next maintenance cycle is calculated as follows:

[0023] D n =D n-1 +ΔD;

[0024] Among them, D n Indicates the estimated date of the next maintenance, D n-1 Indicates the date when the last maintenance was performed.

[0025] ΔD is the calculated maintenance period offset, which can be expressed as:

[0026]

[0027] Among them, T1 represents the initial impact value of the elevator's historical maintenance results on the maintenance cycle, α represents the impact coefficient of T1 on the overall maintenance cycle, T2 represents the initial impact value of the elevator's current operating parameters on the maintenance cycle, β represents the impact coefficient of T2 on the overall maintenance cycle, n is the number of elevator parts, P i It represents the service life of the i-th component, γ represents the influence coefficient of the corresponding component on the overall safety of the elevator, θ represents the influence coefficient of the service life of the elevator components on the overall maintenance period, and the specific values ​​of α, β, γ, and θ are obtained by the data analysis module through comprehensive analysis of historical maintenance, fault records, and related records of other similar elevators in China.

[0028] Furthermore, the calculated maintenance period is compared with the maintenance period required by laws and regulations, and the minimum value is taken as the next maintenance period:

[0029] T n =min(D n, L n );

[0030] Among them, T n To determine the next maintenance cycle, L n It is the next latest maintenance period required by laws and regulations, and min() is the minimum function.

[0031] Beneficial effects of the present invention:

[0032] The present invention establishes an evaluation model for comprehensive analysis by combining data such as the elevator's operating status, historical fault records, maintenance records, elevator service life, operating records, and service life of each component with fault data of other similar elevators in China, thereby achieving targeted and on-demand maintenance of elevators, saving maintenance human resources, and improving maintenance efficiency and elevator safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a system structure block diagram of the present invention;

[0034] Figure 2 It is a connection diagram of the system implementation method of the present invention. DETAILED DESCRIPTION

[0035] In order to clearly illustrate the technical features of this solution, this solution is described below through a specific implementation method.

[0036] Embodiment 1:

[0037] A system for predicting an elevator maintenance period comprises an Internet of Things sensing module, a fault detection module, a message bus, a data analysis module, a maintenance period calculation module and a maintenance terminal.

[0038] The IoT sensor module and fault detection module are both installed in the elevator car and connected to the message bus through the mobile network. The IoT sensor module integrates multiple sensors, such as speed, temperature, acceleration, vibration frequency, tilt angle, etc., to collect data on the real-time operation status of the elevator. The fault detection module pre-processes the raw data collected by the IoT sensor module to detect and identify whether there is a fault in the elevator, such as trapped people, open door and elevator, overspeed, frequent opening and closing of doors, etc.

[0039] The message bus is connected to the data analysis module and the maintenance terminal respectively. The data analysis module is also connected to the maintenance cycle calculation module and the maintenance terminal respectively. The maintenance cycle calculation module is connected to the message bus. The message bus is connected to the data analysis module, which is connected to the maintenance cycle calculation module to provide the original data for the maintenance cycle calculation. The maintenance cycle calculation module performs comprehensive calculations based on the collected data such as the current operating status of the elevator, elevator operation records, historical maintenance results, and historical inspection results, and obtains the recommended date and key maintenance items for the next maintenance, and generates a maintenance work order; the maintenance cycle calculation module is connected to the message bus to push maintenance work orders to the maintenance terminal; the maintenance terminal is connected to the message bus and is used by elevator maintenance personnel to receive maintenance work order notifications and perform subsequent operations and maintenance.

[0040] Embodiment 2:

[0041] A method for predicting an elevator maintenance cycle using a system for predicting an elevator maintenance cycle comprises the following steps:

[0042] S1. The IoT sensor module collects real-time parameter data of the elevator through built-in multiple sensors, including speed, temperature, acceleration, jitter frequency and tilt angle.

[0043] S2. The fault detection module pre-processes the parameter data collected by the IoT sensor module, filters out invalid jitter data, and performs elevator fault identification to detect whether there is a fault at present.

[0044] S3. If a fault is detected in the elevator, the fault detection module will report the identified fault to the message bus through the MQTT protocol; if the mobile network cannot be connected, the data will be cached and uploaded in batches after the network is restored.

[0045] S4. Use the data analysis module to analyze and process the reported data, including filtering the data with obvious jitter and eliminating expired data; then, combine the elevator's running time, running distance, historical fault records, historical maintenance and inspection record data to establish an elevator safety assessment model.

[0046] The method for establishing an elevator safety assessment model is as follows: first determine the severity of the consequences of the current risks, denoted as S, and divide the risk severity into several levels based on the impact of the risks on people, the environment, and property, and determine the probability of risk occurrence, denoted as P; based on historical data and statistical analysis, determine the probability level of risk occurrence and then determine the risk category, combine the levels of S and P to divide the risks into different categories, make a comprehensive safety status determination, weight all individual assessment items, and calculate the comprehensive safety status score of the elevator.

[0047] S5. Use the maintenance cycle calculation module in conjunction with the elevator safety assessment model established in step S4 to calculate the current safety level of the elevator and the items that need to be focused on in the next maintenance, generate a maintenance work order and push it to the message bus.

[0048] The calculation method for the next maintenance period is as follows:

[0049] D n =D n-1 +ΔD;

[0050] Among them, D n Indicates the estimated date of the next maintenance, D n-1 Indicates the date when the last maintenance was performed.

[0051] ΔD is the calculated maintenance period offset, which can be expressed as:

[0052]

[0053] Among them, T1 represents the initial impact value of the elevator's historical maintenance results on the maintenance cycle, α represents the impact coefficient of T1 on the overall maintenance cycle, T2 represents the initial impact value of the elevator's current operating parameters on the maintenance cycle, β represents the impact coefficient of T2 on the overall maintenance cycle, n is the number of elevator parts, P i It represents the service life of the i-th component, γ represents the influence coefficient of the corresponding component on the overall safety of the elevator, θ represents the influence coefficient of the service life of the elevator components on the overall maintenance period, and the specific values ​​of α, β, γ, and θ are obtained by the data analysis module through comprehensive analysis of historical maintenance, fault records, and related records of other similar elevators in China.

[0054] Compare the calculated maintenance period with the maintenance period required by laws and regulations and take the minimum value as the next maintenance period:

[0055] T n =min(D n , L n );

[0056] Among them, T n To determine the next maintenance cycle, L n It is the next latest maintenance period required by laws and regulations, and min() is the minimum function.

[0057] S6. The maintenance terminal receives the maintenance work order from the message bus, executes it and provides result feedback.

[0058] S7. Supplement the maintenance record of this time into the evaluation model established in step S4 to provide data support for the calculation of the next maintenance cycle.

[0059] Specific application cases:

[0060] The last maintenance result of the No. 1 elevator in Unit 1 of a residential building was normal, and all components were running well. The initial value of T1 was 100, and the value of α was 0.3. However, the elevator stopped and trapped people recently. The value of T2 was -10, and the value of β was 0.5. The impact of each key component of the elevator, such as the motor and brake, on the maintenance cycle is calculated based on the service life. The value of θ is 0.2, and the calculation results are:

[0061] ΔD=100×0.3-10×0.5-10×0.2=23

[0062] That is to say, the next maintenance cycle is recommended to be after 23 days. However, according to the requirements of relevant regulatory authorities, the latest maintenance date is 10 days later, so the final maintenance time is determined to be 10 days later.

[0063] Of course, the above description is not limited to the above examples. Technical features not described in the present invention can be achieved by or by using existing technologies, which will not be described here. The above embodiments and drawings are only used to illustrate the technical scheme of the present invention and are not limitations of the present invention. The present invention is described in detail with reference to the preferred implementation methods. Ordinary technicians in this field should understand that changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention do not depart from the purpose of the present invention, and should also fall within the scope of protection of the claims of the present invention.

Claims

1. A system for predicting elevator maintenance cycle, Features: It includes an Internet of Things sensing module, a fault detection module, a message bus, a data analysis module, a maintenance cycle calculation module and a maintenance terminal; the Internet of Things sensing module and the fault detection module are both installed in the elevator car and are respectively connected to the message bus through a mobile network; the message bus is respectively connected to the data analysis module and the maintenance terminal, the data analysis module is also respectively connected to the maintenance cycle calculation module and the maintenance terminal, and the maintenance cycle calculation module is connected to the message bus.

2. The system for predicting elevator maintenance cycle according to claim 1, Features: The IoT sensor module integrates a variety of sensors to collect data on the real-time operating status of the elevator.

3. The system for predicting elevator maintenance cycle according to claim 2, Features: The fault detection module is used to pre-process the raw data collected by the IoT sensor module to detect and identify whether the elevator is currently faulty.

4. The system for predicting elevator maintenance cycle according to claim 3, Features: The maintenance cycle calculation module is used to calculate the date and project of the next maintenance and generate a maintenance work order.

5. A method for predicting an elevator maintenance period using the system for predicting an elevator maintenance period according to claim 4, Features: The following steps are involved: S1. The IoT sensor module collects real-time parameter data of the elevator through built-in multiple sensors, including speed, temperature, acceleration, vibration frequency and tilt angle; S2. The fault detection module pre-processes the parameter data collected by the IoT sensor module, filters out invalid jitter data, and performs elevator fault identification to detect whether there is a fault at present; S3. If a fault is detected in the elevator, the fault detection module will report the identified fault to the message bus through the MQTT protocol; S4. Use the data analysis module to analyze and process the reported data, including filtering the data with obvious jitter and removing expired data; Then, a model for evaluating the elevator's safety is established by combining the elevator's operating time, operating distance, historical fault records, historical maintenance and inspection records. S5. Use the maintenance cycle calculation module in conjunction with the elevator safety assessment model established in step S4 to calculate the current safety level of the elevator and the items that need to be focused on in the next maintenance, generate a maintenance work order and push it to the message bus; S6. The maintenance terminal receives the maintenance work order from the message bus, executes it and provides result feedback; S7. Supplement the maintenance record of this time into the evaluation model established in step S4 to provide data support for the calculation of the next maintenance cycle.

6. The method for predicting the elevator maintenance cycle according to claim 5, Features: In step S4, the method for establishing an elevator safety assessment model is as follows: first determine the severity of the consequences of the current risks, denoted as S, divide the severity of the risks into several levels according to the impact of the risks on people, the environment, and property, and determine the probability of the risk occurrence, denoted as P; determine the risk category after determining the probability level of the risk occurrence based on historical data and statistical analysis, combine the levels of S and P to divide the risks into different categories, make a comprehensive safety status determination, weight all individual assessment items, and calculate the comprehensive safety status score of the elevator.

7. The method for predicting the elevator maintenance cycle according to claim 5, Features: In step S3, if the mobile network is not connected, the data is cached and uploaded in batches after the network is restored.

8. The method for predicting the elevator maintenance cycle according to claim 5, Features: In step S5, the next maintenance cycle is calculated as follows: D n =D n-1 +ΔD; Among them, D n Indicates the estimated date of the next maintenance, D n-1 It indicates the date of the last maintenance execution, and ΔD is the calculated maintenance cycle offset, which can be expressed as: Among them, T 1 represents the initial impact of the elevator's historical maintenance results on the maintenance cycle, and α represents T 1 The influence coefficient on the overall maintenance cycle, T 2 represents the initial impact value of the elevator's current operating parameters on the maintenance cycle, and β represents T 2 The impact coefficient on the overall maintenance cycle, n is the number of elevator parts, P i It represents the service life of the i-th component, γ represents the influence coefficient of the corresponding component on the overall safety of the elevator, θ represents the influence coefficient of the service life of the elevator components on the overall maintenance period, and the specific values ​​of α, β, γ, and θ are obtained by the data analysis module through comprehensive analysis of historical maintenance, fault records, and related records of other similar elevators in China.

9. The method for predicting the elevator maintenance cycle according to claim 7, Features: Compare the calculated maintenance period with the maintenance period required by laws and regulations and take the minimum value as the next maintenance period: T n =min(D n ,L n ); Among them, T n To determine the next maintenance cycle, L n It is the next latest maintenance period required by laws and regulations, and min() is the minimum function.

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