CN110937489B - Online fault monitoring and early warning method and system for escalator - Google Patents
Online fault monitoring and early warning method and system for escalator Download PDFInfo
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- CN110937489B CN110937489B CN201911121555.0A CN201911121555A CN110937489B CN 110937489 B CN110937489 B CN 110937489B CN 201911121555 A CN201911121555 A CN 201911121555A CN 110937489 B CN110937489 B CN 110937489B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B29/00—Safety devices of escalators or moving walkways
- B66B29/005—Applications of security monitors
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Abstract
The invention discloses an on-line fault monitoring and early warning method and system for an escalator, wherein the method comprises the following steps: acquiring data of a plurality of sensors; fusing sensor characteristics to obtain real-time signal characteristics of a key component; converting the real-time signal characteristics and the normal signal characteristics into distribution changes and comparing the distribution changes to obtain a health value; integrating a state sample library; and receiving the fault code sent by the escalator, analyzing the fault code to obtain a fault state, sending fault state data to the client and distributing a maintenance work order. The escalator is in a monitoring and fault early warning state, the escalator operation efficiency is improved, the safety of the escalator is ensured, and meanwhile the labor cost is saved; establishing a state sample library and optimizing a machine learning model; the work order is automatically distributed, and timely reaction and arrangement of maintenance work are facilitated.
Description
Technical Field
The invention relates to the field of escalator monitoring, in particular to an escalator online fault monitoring and early warning method and system.
Background
An escalator is an escalator with a circulating step, and is a fixed electric driving device used for transporting passengers upwards or downwards. The escalator is widely applied to floors, brings great convenience to daily life and work of people, and meanwhile derives the problem of safe operation of the escalator.
Generally, the escalator is maintained by two modes of fault inspection and maintenance after the accident and regular maintenance of fixed projects, so that the accident probability is reduced and the accident is prevented. But the post-accident fault inspection and maintenance means that the fault has occurred, resulting in irreversible effect of the occurrence of the fault. Regular maintenance of fixed projects can play a role in preventing equipment faults and accidents, but the planning is too strong, the maintenance period is fixed, and the equipment is repaired when the equipment is due no matter what the actual state of the equipment is. To a certain extent, the maintenance is excessive, and the cost of equipment maintenance is directly increased. And the requirements on the working experience and the capability of maintenance personnel are higher, the labor cost is high, and the operation cost of enterprises is increased.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides an on-line fault monitoring and early warning method and system for an escalator.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect of the invention, an online fault monitoring and early warning method for an escalator comprises the following steps:
acquiring data of a plurality of sensors mounted on key components of each escalator;
extracting the characteristics of the data of the sensors and fusing the characteristics to obtain the real-time signal characteristics of the key parts of the escalators;
converting the real-time signal characteristics and the normal signal characteristics into distribution changes by using a machine learning model, comparing the distribution changes to obtain the health value of the escalator, and monitoring the health condition of the escalator in real time;
integrating real-time signal features and comparison data into a state sample library and for optimizing a machine learning model;
when the health value of the escalator is lower than a set threshold value, receiving a fault code sent by the escalator and recording data of a sensor on a fault component at the moment, and real-time signal characteristics and the health value of the fault component;
and analyzing the fault code to obtain a fault state, sending fault state data to the client and distributing a maintenance work order.
According to a first aspect of the invention, the sensors comprise a vibration sensor, an elevator temperature sensor, a current sensor, an ambient noise sensor and an ambient temperature sensor; the vibration sensors are respectively arranged on the motor shell, the shell at the input bearing position and the output bearing position of the speed reducer, the left side and the right side of the main driving bearing, and the top of the left side and the top of the right side of the tensioning frame; the elevator temperature sensors are arranged on the left side and the right side of the escalator handrail belt truss; the current sensor is arranged in a current output phase in the escalator electric cabinet; the environment noise sensor is arranged inside the elevator pit; the environment temperature sensor is arranged inside the elevator pit.
According to a first aspect of the invention, the sampling frequency of the sensor is as follows: fs ═ r × Fmax; wherein Fs is sampling frequency, r is a value range of [2,5], and Fmax is the maximum analysis frequency of key parts of the escalator.
According to the first aspect of the present invention, the extracting and fusing the characteristics of the data of the plurality of sensors to obtain the real-time signal characteristics of the key components of the escalator specifically comprises: extracting the characteristics of data of a plurality of sensors, reducing the dimension by using a principal component analysis method, and then extracting the characteristics accounting for 90% of information components as the real-time signal characteristics of key parts of the escalator; the real-time signal characteristics of the key parts of the escalator comprise bearing real-time signal characteristics, gear real-time signal characteristics, motor real-time signal characteristics and hand strap real-time signal characteristics.
According to the first aspect of the present invention, the information component in the principal component analysis method is calculated as follows:
the denominator is the sum of squares of all singular values of the input features, and the numerator is the sum of squares of top k large singular values of the input features.
According to the first aspect of the present invention, before extracting the features of the sensor data, preprocessing of moving average, detrending item, and outlier removal is performed on the sensor data in sequence.
According to the first aspect of the present invention, when the distribution of the real-time signal features is compared with the distribution of the normal signal features, cross-overlapping is performed, and the percentage of the overlapped portion to the distribution of the normal signal features is calculated, and the percentage is taken as the health value.
In a second aspect of the present invention, an on-line fault monitoring and early warning system for an escalator includes:
a plurality of data collection devices, the data collection devices being a plurality of sensors mounted on key components of the respective escalator;
a data processing unit, the data processing unit comprising:
the data acquisition module is used for acquiring data of the plurality of sensors;
the edge calculation module is used for extracting the characteristics of the data of the sensors and fusing the characteristics to obtain the real-time signal characteristics of the key parts of the escalators;
a cloud platform, the cloud platform comprising:
the health prediction module is used for converting the real-time signal characteristics and the normal signal characteristics into distribution changes by using the machine learning model and comparing the distribution changes to obtain the health value of the escalator, and monitoring the health condition of the escalator in real time;
the fault processing module is used for monitoring that the health value of the escalator is lower than a set threshold value, receiving a fault code sent by the escalator, and analyzing the fault code to obtain a fault state;
the information storage management module is used for storing various data information of the escalator;
and the client is used for receiving the fault state data and the maintenance work order sent by the cloud platform.
According to a second aspect of the invention, the sensors comprise a vibration sensor, an elevator temperature sensor, a current sensor, an ambient noise sensor and an ambient temperature sensor; the vibration sensors are respectively arranged on the motor shell, the shell at the input bearing position and the output bearing position of the speed reducer, the left side and the right side of the main driving bearing, and the top of the left side and the top of the right side of the tensioning frame; the elevator temperature sensors are arranged on the left side and the right side of the escalator handrail belt truss; the current sensor is arranged in a current output phase in the escalator electric cabinet; the environment noise sensor is arranged inside the elevator pit; the environment temperature sensor is arranged inside the elevator pit.
According to the second aspect of the present invention, the client is configured to call and display various types of data information of the information storage management module, where the data information includes a location of the escalator fault, a name of a fault key component, a fault trigger time, a fault code, a fault source, and a fault state.
The technical scheme at least has the following beneficial effects: the health states of all key parts of the escalator are monitored in real time, once the health state of a certain key part is found to be abnormal, risk early warning is sent out, a fault code sent by the escalator is received, the fault code and corresponding information data are sent to a client side, a work order is distributed, and timely reaction and maintenance work arrangement are facilitated. Meanwhile, a state sample base is established, and a machine learning model is optimized, so that the accuracy of the health value predicted by the machine learning model and used for reflecting the health state is higher. Based on real-time monitoring and early warning, frequent after-the-fact maintenance and regular maintenance are avoided, the safety of the escalator is guaranteed, and meanwhile labor cost is saved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a step diagram of an on-line fault monitoring and early warning method of an escalator in an embodiment of the invention;
fig. 2 is a block diagram of an on-line fault monitoring and early warning system of an escalator in accordance with an embodiment of the present invention;
FIG. 3 is a graph comparing the distribution of real-time signal features and normal signal features;
FIG. 4 is a diagram of the steps for extracting real-time signal features of a bearing;
FIG. 5 is a diagram of the steps for extracting real-time signal features of a gear;
fig. 6 is a diagram of the steps for extracting real-time motor signal features.
Detailed Description
Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
Referring to fig. 1, an embodiment of the present invention provides an on-line fault monitoring and early warning method for an escalator, including the following steps:
s100, acquiring data of a plurality of sensors installed on key parts of each escalator;
s200, extracting the characteristics of the data of the sensors and fusing the characteristics to obtain the real-time signal characteristics of the key parts of the escalators;
s300, converting the real-time signal characteristics and the normal signal characteristics into distribution changes by using a machine learning model, comparing the distribution changes to obtain the health value of the escalator, and monitoring the health condition of the escalator in real time;
step S400, integrating the real-time signal characteristics and the comparison data into a state sample library and optimizing a machine learning model;
s500, when the health value of the escalator is lower than a set threshold value, receiving a fault code sent by the escalator and recording data of a sensor on a fault component at the moment, and real-time signal characteristics and the health value of the fault component;
step S600, analyzing the fault code to obtain a fault state, sending fault state data to the client 40 and distributing a maintenance work order.
In this embodiment, the health states of all the key components of the escalator are monitored in real time, once the health state of a certain key component is found to be abnormal, a risk early warning is sent out, a fault code sent by the escalator is received, the fault code and corresponding information data are sent to the client 40, a work order is distributed, and the timely reaction and arrangement of maintenance work are facilitated. Meanwhile, a state sample base is established, and a machine learning model is optimized, so that the accuracy of the health value predicted by the machine learning model and used for reflecting the health state is higher. Based on real-time monitoring and early warning, frequent after-the-fact maintenance and regular maintenance are avoided, the safety of the escalator is guaranteed, and meanwhile labor cost is saved.
Further, the sensors include a vibration sensor, an elevator temperature sensor, a current sensor, an ambient noise sensor, and an ambient temperature sensor; the vibration sensors are respectively arranged on the motor shell, the shell at the input bearing position and the output bearing position of the speed reducer, the left side and the right side of the main driving bearing and the top of the left side and the top of the right side of the tensioning frame. The elevator temperature sensors are arranged on the left side and the right side of the escalator handrail truss and are used for respectively detecting the real-time temperature conditions of the handrail on the left side and the right side. The current sensor is arranged in a current output phase in the escalator electric cabinet, and is used for measuring current data of the motor, and the current data is used as working condition judgment data. The environmental noise sensor is arranged in the elevator pit and used for measuring noise data in the elevator pit. The environment temperature sensor is arranged in the elevator pit and used for measuring the temperature data of the environment in the elevator pit.
Further, the sampling frequency of the sensor is according to the sampling theorem as follows: fs ═ r × Fmax; wherein Fs is sampling frequency, r is a value range of [2,5], and Fmax is the maximum analysis frequency of key parts of the escalator. For the escalator, the maximum analysis frequency of key parts of the escalator is sequentially a motor, a reducer, a main driving bearing and a bearing of a tensioning frame from high to low.
Furthermore, preprocessing of moving average, trend removing items and abnormal point removing is carried out on the sensor data in sequence, abnormal components in the sensor data are removed, and accuracy of feature extraction is improved.
Further, the extraction of the characteristics of the data of the plurality of sensors and the fusion of the characteristics to obtain the real-time signal characteristics of the key components of each escalator specifically include: extracting the characteristics of data of a plurality of sensors, reducing the dimension by using a principal component analysis method, and then extracting the characteristics accounting for 90% of information components as the real-time signal characteristics of key parts of the escalator; the real-time signal characteristics of the key parts of the escalator comprise bearing real-time signal characteristics, gear real-time signal characteristics, motor real-time signal characteristics and hand strap real-time signal characteristics.
Further, the information component in the principal component analysis method is calculated as follows:
the denominator is the sum of squares of all singular values of the input features, and the numerator is the sum of squares of top k large singular values of the input features.
Referring to fig. 4, specifically, extracting the real-time signal feature of the bearing includes the following steps:
detecting whether the sensor data contains signal frequency components of non-circumferential bearings such as shaft frequency, gear meshing frequency and sidebands; if yes, performing FFT to remove, and performing IFFT; if not, carrying out the next step;
extracting time domain signal characteristics;
filtering according to the demodulation frequency band;
carrying out Hilbert transform envelope demodulation, and extracting and outputting an envelope signal characteristic value;
and carrying out FFT (fast Fourier transform) on the signal subjected to Hilbert transform envelope demodulation, and extracting and outputting an envelope spectrum characteristic value.
Referring to fig. 5, the main failure modes of the reduction gearbox gear are tooth surface wear, gear tooth spalling, pitting or breakage and the like. The monitoring of the gear of the reduction gearbox is realized by monitoring the gear meshing frequency and the harmonic amplitude thereof and the sideband of the meshing frequency. The method for extracting the real-time signal characteristics of the gear comprises the following steps:
extracting and outputting gear time domain characteristics from the sensor data;
and extracting and outputting the gear frequency domain characteristics after the sensor data is subjected to FFT.
Referring to fig. 6, the motor of the escalator is mainly a three-phase asynchronous alternating current motor, the rotor fault of the motor is mainly rotor unbalance, rotor crack or looseness, and the stator fault is mainly stator eccentricity or connection looseness. The method for extracting the real-time signal characteristics of the motor comprises the following steps:
performing FFT on the sensor data;
and extracting real-time signal characteristics of the frequency.
The method for solving the mean value of the sensor data is adopted for extracting the real-time signal characteristics of the hand strap.
Referring to fig. 3, further, when the distribution of the real-time signal features is compared with the distribution of the normal signal features, cross-overlapping is performed, and the percentage of the overlapped portion to the distribution of the normal signal features is calculated, and the percentage is taken as a health value. The health value is reflected through the graph and the number, so that the method is effective and direct, and the requirement on the professional ability of maintenance personnel can be reduced.
The escalator health value management system has the advantages that maintenance and management are carried out on data of all sensors, characteristic parameters of all key parts, health values and fault states of the escalator, real-time signal characteristics and comparison data are integrated into a state sample library and used for optimizing a machine learning model, and the health value management system is favorable for improving accuracy of the health values.
And analyzing the fault code to obtain a fault state, sending fault state data to the client 40 and distributing a maintenance work order, so that personnel can conveniently arrange. Maintenance personnel can check maintenance work order arrangement through the client 40, and can call data stored by the cloud platform 30 through the client 40, wherein the data comprises the position of a fault escalator, the name of a fault key component, fault trigger time, a fault code, a fault source and a fault state. The escalator fault detection device is beneficial to quickly confirming the fault problem of the escalator, improves the maintenance speed, and can reduce the professional requirements on maintenance personnel.
In conclusion, the online fault monitoring and early warning method for the escalator has the following advantages:
the safety reliability and the stability of the escalator operation are improved; the escalator operation data is collected in real time, analyzed and processed, real-time early warning and early warning are achieved, and the escalator operation data is prevented from getting in the bud;
the occurrence of faults in the running process of the escalator is reduced; fault diagnosis and health prediction are carried out on key parts of the escalator, and the time for stopping faults is shortened by timely overhauling the parts with performance degradation;
the actual operation efficiency of the escalator is improved; because the planned maintenance mode is changed into the maintenance mode according to the requirement, maintenance personnel do not need to go to the site for detection regularly, the detection is more targeted, and the maintenance is more efficient; the escalator is constantly in a monitoring state through real-time state detection, so that the escalator operation efficiency is improved;
the actual running cost of the escalator is reduced; the escalator is monitored on line, so that the number of maintenance at regular intervals is reduced, and the maintenance cost is reduced; the labor intensity of the maintenance personnel and the requirement on the professional skills of the maintenance personnel are reduced, and the labor cost of the maintenance personnel is reduced; spare parts for fault replacement are reduced, and the operation cost of the escalator is reduced.
Referring to fig. 2, an on-line fault monitoring and early warning system of an escalator according to another embodiment of the present invention includes:
a plurality of data collection devices 10, the plurality of data collection devices 10 being a plurality of sensors mounted on each escalator key component;
a data processing unit 20, the data processing unit 20 comprising:
the data acquisition module 21 is used for acquiring data of a plurality of sensors;
the edge calculation module 22 is used for extracting the characteristics of the data of the sensors and fusing the characteristics to obtain the real-time signal characteristics of the key parts of the escalator;
the health prediction module 31 is used for converting the real-time signal characteristics and the normal signal characteristics into distribution changes by using a machine learning model and comparing the distribution changes to obtain the health value of the escalator, and monitoring the health condition of the escalator in real time;
the fault processing module 32 is used for monitoring that the health value of the escalator is lower than a set threshold value, receiving a fault code sent by the escalator, and analyzing the fault code to obtain a fault state;
the information storage management module 36 is used for storing various data information of the escalator;
and the client 40 is configured to receive the fault state data and the maintenance work order sent by the cloud platform 30.
Further, the sensors include a vibration sensor, an elevator temperature sensor, a current sensor, an ambient noise sensor, and an ambient temperature sensor; the vibration sensors are respectively arranged on the motor shell, the shell at the input bearing position and the output bearing position of the speed reducer, the left side and the right side of the main driving bearing, and the top of the left side and the top of the right side of the tensioning frame; the elevator temperature sensors are arranged on the left side and the right side of the escalator handrail belt truss; the current sensor is arranged in a current output phase in the escalator electric cabinet; the environmental noise sensor is arranged in the elevator pit; the environment temperature sensor is arranged inside the elevator pit. The elevator is also provided with a signal conversion module which is arranged in an electric cabinet of the elevator pit and used for acquiring fault codes of the escalator, is connected with the escalator through hard wiring and is accessed into the data processing unit 20 through a 485 protocol.
Further, the edge calculation module 22 includes a signal preprocessing module, a bearing feature extraction module, a gear feature extraction module, a motor feature extraction module, and a handrail feature extraction module. And the signal preprocessing module is used for sequentially preprocessing the sensor data by means of moving average, trend removing items and abnormal point removing. The bearing feature extraction module, the gear feature extraction module, the motor feature extraction module and the hand strap feature extraction module are respectively used for extracting bearing real-time signal features, gear real-time signal features, motor real-time signal features and hand strap real-time signal features.
Further, in the on-line fault monitoring and early warning system of the escalator, the cloud platform 30 further comprises an application service module 33, and the application service module 33 comprises a work order maintenance module 34 and a basic data module 35. The work order maintenance module 34 is used for managing and distributing work orders; when a maintenance person is marked as idle, a work order is assigned to the maintenance person. In addition, the work order maintenance module 34 tracks and feeds back each fault code generated by the escalator, including the time of the fault code generation, the type of the fault code, the fault code processing condition, the fault code processing personnel and the like, until the fault code is eliminated, and the same is used for tracing the fault code processing. The basic data module 35 is used for inputting basic data of the escalator, such as the model and parameters of each component.
Further, the client 40 is configured to invoke and display various data information of the information storage management module 36, where the data information includes a location of the escalator, a name of a fault key component, a fault trigger time, a fault code, a fault source, and a fault state. It should be noted that the client 40 includes a web page side and a mobile phone APP side.
In another embodiment of the present invention, the storage medium stores executable instructions for causing the computer to execute the above on-line fault monitoring and warning method for an escalator.
The above is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiments, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means.
Claims (7)
1. The on-line fault monitoring and early warning method of the escalator is characterized by comprising the following steps:
acquiring data of a plurality of sensors mounted on key components of each escalator;
extracting the characteristics of the data of the sensors and fusing the characteristics to obtain the real-time signal characteristics of the key parts of the escalators;
converting the real-time signal characteristics and the normal signal characteristics into distribution changes by using a machine learning model, performing cross overlapping on the distribution of the real-time signal characteristics and the distribution of the normal signal characteristics, calculating the percentage of an overlapped part in the distribution of the normal signal characteristics, and taking the percentage as the health value of the escalator to monitor the health condition of the escalator in real time;
integrating real-time signal features and comparison data into a state sample library and for optimizing a machine learning model;
when the health value of the escalator is lower than a set threshold value, receiving a fault code sent by the escalator and recording data of a sensor on a fault component at the moment, and real-time signal characteristics and the health value of the fault component;
analyzing the fault code to obtain a fault state, sending fault state data to the client and distributing a maintenance work order;
the method comprises the following steps of extracting the characteristics of data of a plurality of sensors and fusing the characteristics to obtain the real-time signal characteristics of key components of each escalator, wherein the characteristics are as follows: extracting the characteristics of data of a plurality of sensors, reducing the dimension by using a principal component analysis method, and then extracting the characteristics accounting for 90% of information components as the real-time signal characteristics of key parts of the escalator; the real-time signal characteristics of the key parts of the escalator comprise bearing real-time signal characteristics, gear real-time signal characteristics, motor real-time signal characteristics and hand strap real-time signal characteristics;
the information component in the principal component analysis method is calculated as follows:
(ii) a The denominator is the sum of squares of all singular values of the input features, and the numerator is the sum of squares of top k large singular values of the input features.
2. The escalator online fault monitoring and early warning method according to claim 1, wherein the plurality of sensors comprise a vibration sensor, an elevator temperature sensor, a current sensor, an ambient noise sensor and an ambient temperature sensor; the vibration sensors are respectively arranged on the motor shell, the shell at the input bearing position and the output bearing position of the speed reducer, the left side and the right side of the main driving bearing, and the top of the left side and the top of the right side of the tensioning frame; the elevator temperature sensors are arranged on the left side and the right side of the escalator handrail belt truss; the current sensor is arranged in a current output phase in the escalator electric cabinet; the environment noise sensor is arranged inside the elevator pit; the environment temperature sensor is arranged inside the elevator pit.
3. The escalator online fault monitoring and early warning method according to claim 2, wherein the sampling frequency of the sensors is as follows: fs = r × Fmax, wherein Fs is sampling frequency, r has a value range of [2,5], and Fmax is the maximum analysis frequency of key parts of the escalator.
4. The escalator online fault monitoring and early warning method as claimed in claim 1, wherein before the features of the sensor data are extracted, preprocessing of moving average, trend removing items and abnormal point removing is performed on the sensor data in sequence.
5. The escalator online fault monitoring and early warning system is characterized in that the escalator online fault monitoring and early warning method according to any one of claims 1 to 4 is applied, and the escalator online fault monitoring and early warning system comprises:
a plurality of data collection devices, the data collection devices being a plurality of sensors mounted on key components of the respective escalator;
a data processing unit, the data processing unit comprising:
the data acquisition module is used for acquiring data of the plurality of sensors;
the edge calculation module is used for extracting the characteristics of the data of the sensors and fusing the characteristics to obtain the real-time signal characteristics of the key parts of the escalators;
a cloud platform, the cloud platform comprising:
the health prediction module is used for converting the real-time signal characteristics and the normal signal characteristics into distribution changes by utilizing the machine learning model, performing cross overlapping on the distribution of the real-time signal characteristics and the distribution of the normal signal characteristics, calculating the percentage of the overlapped part in the distribution of the normal signal characteristics, taking the percentage as the health value of the escalator, and monitoring the health condition of the escalator in real time;
the fault processing module is used for monitoring that the health value of the escalator is lower than a set threshold value, receiving a fault code sent by the escalator, and analyzing the fault code to obtain a fault state;
the information storage management module is used for storing various data information of the escalator;
and the client is used for receiving the fault state data and the maintenance work order sent by the cloud platform.
6. The escalator online fault-monitoring and pre-warning system according to claim 5, wherein the plurality of sensors comprise a vibration sensor, an elevator temperature sensor, a current sensor, an ambient noise sensor and an ambient temperature sensor; the vibration sensors are respectively arranged on the motor shell, the shell at the input bearing position and the output bearing position of the speed reducer, the left side and the right side of the main driving bearing, and the top of the left side and the top of the right side of the tensioning frame; the elevator temperature sensors are arranged on the left side and the right side of the escalator handrail belt truss; the current sensor is arranged in a current output phase in the escalator electric cabinet; the environment noise sensor is arranged inside the elevator pit; the environment temperature sensor is arranged inside the elevator pit.
7. The escalator online fault monitoring and early warning system according to claim 6, wherein the client is used for calling and displaying various data information of the information storage and management module, and the data information comprises a fault escalator position, a fault key part name, fault trigger time, a fault code, a fault source and a fault state.
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Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111650919B (en) * | 2020-05-14 | 2021-09-14 | 中铁第四勘察设计院集团有限公司 | Multi-dimensional monitoring escalator fault prediction and health management method and system |
| CN111562096B (en) * | 2020-05-14 | 2022-07-19 | 中铁第四勘察设计院集团有限公司 | Real-time online health state monitoring system of escalator |
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| CN112541430B (en) * | 2020-12-12 | 2023-02-28 | 中铁第四勘察设计院集团有限公司 | Fault diagnosis method for fusion of temperature signal and noise signal |
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| CN113460843A (en) * | 2021-07-05 | 2021-10-01 | 江苏普瑞尔特控制工程有限公司 | Intelligent early warning system of escalator |
| CN114044431B (en) * | 2021-10-08 | 2023-08-01 | 上海三菱电梯有限公司 | Step roller abnormality monitoring method and device of passenger conveying device and passenger conveying device |
| CN114920122A (en) * | 2022-05-05 | 2022-08-19 | 广州广日电梯工业有限公司 | Escalator fault early warning method and system based on transfer learning |
| CN115893167A (en) * | 2022-11-16 | 2023-04-04 | 青岛城市轨道交通科技有限公司 | Escalator vibration trend prediction method based on big data |
| CN116150661B (en) * | 2023-04-19 | 2023-07-07 | 深圳市城市公共安全技术研究院有限公司 | Abnormality diagnosis method and abnormality diagnosis device for elevator traction machine |
| EP4509451A1 (en) * | 2023-08-17 | 2025-02-19 | Inventio Ag | Monitoring method and device for a passenger transportation system |
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Effective date of registration: 20230327 Address after: Units 103 and 203, Block B1, Hantian Science and Technology City, 17 Shenhai Road, Guicheng Street, Nanhai District, Foshan City, Guangdong Province, 528200 (residence application) Patentee after: Anmason Intelligent Technology (Guangdong) Co.,Ltd. Address before: Unit 1601, 16th floor, building 6, area a, Hantian science and Technology City, 17 Shenhai Road, Guicheng Street, Nanhai District, Foshan City, Guangdong Province, 528200 Patentee before: GUANGDONG GLOBAL SMART TECHNOLOGY CO.,LTD. |