CN111650919A - Multi-dimensional monitoring escalator fault prediction and health management method and system - Google Patents
Multi-dimensional monitoring escalator fault prediction and health management method and system Download PDFInfo
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- CN111650919A CN111650919A CN202010409441.2A CN202010409441A CN111650919A CN 111650919 A CN111650919 A CN 111650919A CN 202010409441 A CN202010409441 A CN 202010409441A CN 111650919 A CN111650919 A CN 111650919A
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- G05B23/00—Testing or monitoring of control systems or parts thereof
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- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
- G05B23/0245—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a qualitative model, e.g. rule based; if-then decisions
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Abstract
The invention discloses a method and a system for predicting faults and managing health of an escalator based on multi-dimensional monitoring.
Description
Technical Field
The invention belongs to the technical field of comprehensive monitoring of underground infrastructure, and particularly relates to a multidimensional monitoring escalator fault prediction and health management method and system.
Background
At present, escalators are used in equipment for transporting passengers upwards or downwards between different floor heights of buildings, and are widely used in places where people flow is concentrated, such as railway stations, subway stations, bus stations, shopping malls, airports and the like. The escalator has the working principle that a chain type circulating conveyor belt belongs to forced driving, so that once jamming occurs, equipment can be damaged, and moving parts of the escalator are exposed and directly contact with people, so that huge potential safety hazards exist. The main characteristics of its work are: open, the inclusion of foreign bodies in the gaps is a major risk of personal injury and equipment damage. The escalator in the field of rail transit has multiple types of faults, is easy to expand and difficult to prevent and control, and is vital to ensure the safe operation of the escalator. The fault classification comprises steps, traction chains, driving devices, ladders, handrail devices and safety protection devices. As a key device for carrying and evacuating passengers, the device is also a special device, the accident consequence is severe, and fault prediction and health management are urgently needed to promote a preventive alarm technology.
The current staircase is provided with more than 30 kinds of safety device, but only draws trouble single switching value, only can report to the police after the trouble, does not eliminate the potential safety hazard. The original maintenance mode adopts 'fault maintenance + regular maintenance', excessive maintenance or deficient maintenance conditions exist, the operation state of the escalator cannot be accurately mastered, and capital and resource waste is caused. At present, foreign elevator manufacturers develop monitoring systems aiming at self equipment, the universality is poor, the health trend prejudgment cannot be realized, the functions of the domestic prior art are single, and the monitoring range is small. The existing escalator monitoring adopts a threshold value alarming mode, the accuracy is low, and the engineering practical value is poor. The similar online monitoring technology only monitors a single index and is easy to report by mistake; data redundancy, bandwidth occupation and more invalid data; the design and model selection can not be guided, and the whole life optimization can not be realized.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a method and a system for predicting the fault and managing the health of an escalator based on multi-dimensional monitoring.
To achieve the above object, according to one aspect of the present invention, there is provided a method for predicting failure and managing health of an escalator with multidimensional monitoring, the method comprising the steps of:
acquiring long-term monitoring sample data of the escalator by using a sensing layer of the Internet of things, wherein the long-term monitoring sample data comprises multiple dimensionality monitoring data of multiple parts, and marking the long-term monitoring sample data according to the fault type of the escalator;
constructing a deep iterative neural network learning model, wherein the deep iterative neural network learning model comprises a first neural network learning model and a second neural network learning model, the first neural network is used for acquiring the fault occurrence probability value of each part fault type, and the second neural network is used for acquiring the real-time health state value of the escalator; performing machine learning on the first neural network learning model and the second neural network learning model by using the marked long-term monitoring sample data to obtain a trained first neural network learning model and a trained second neural network learning model;
acquiring real-time monitoring data of the escalator to be monitored, acquiring fault occurrence probability values of fault types of all parts of the escalator to be monitored currently by using a trained first neural network learning model, performing real-time fault judgment on the escalator to be monitored according to the fault occurrence probability values of all the fault types, and performing fault type positioning and fault repair according to fault judgment results; and acquiring the real-time health state value of the escalator by using the trained second neural network learning model, and acquiring the real-time health state of the escalator according to the real-time health state value of the escalator.
As a further improvement of the invention, the first neural network learning model comprises a component fault index weight parameter set and a calculation mode of each component fault occurrence probability value, and also comprises a normal numerical range, a health state threshold value and a fault threshold value which are in one-to-one correspondence with the fault occurrence probability values of each component fault type, and the working states of the component parameters are judged by using the normal numerical range, wherein the working states comprise a normal state, an unhealthy state and a fault state.
As a further improvement of the present invention, the obtaining of the real-time health state value of the escalator by the second neural network specifically comprises:
acquiring a minimum fault mode of equipment to be monitored, and establishing a mapping relation among a parameter data state of a part, a fault type of the part and a fault type of the equipment to form a fuzzy fault tree model;
and acquiring the real-time health state value of the escalator by using the threshold value of the probability value of the fault occurrence of each part and the fuzzy fault tree model.
As a further improvement of the invention, newly added fault data of the escalator is used as new sample data to carry out iterative training on the deep iterative neural network learning model.
As a further improvement of the invention, the minimum failure modes include fixing bolt loosening, bearing failure, braking distance tendency failure, step slipping distance failure, step failure, handrail temperature failure, rotor failure, motor power consumption failure, gear failure and bearing failure.
As a further improvement of the present invention, the multi-dimensional monitoring data includes vibration monitoring data, noise monitoring data, displacement monitoring data, temperature monitoring data, and current monitoring data.
As a further improvement of the invention, the parts of the escalator comprise a driving main machine, a main driving wheel, a step chain tension wheel, a brake, a handrail belt, a speed reducer, a motor and steps.
In order to achieve the above object, according to another aspect of the present invention, there is provided an escalator state monitoring and fault locating system of the internet of things, the system comprising an acquisition module, a fault knowledge base module and a decision module,
the acquisition module is used for acquiring long-term monitoring sample data of the escalator by using a sensing layer of the Internet of things, wherein the long-term monitoring sample data comprises multiple dimensionality monitoring data of multiple parts, and the long-term monitoring sample data is marked according to the fault type of the escalator;
the fault knowledge base module is used for constructing a deep iterative neural network learning model, the deep iterative neural network learning model comprises a first neural network learning model and a second neural network learning model, the first neural network is used for acquiring fault occurrence probability values of fault types of all parts, and the second neural network is used for acquiring real-time health state values of the escalator; performing machine learning on the first neural network learning model and the second neural network learning model by using the marked long-term monitoring sample data to obtain a trained first neural network learning model and a trained second neural network learning model;
the method comprises the steps that a decision module obtains real-time monitoring data of the escalator to be monitored, a trained first neural network learning model is used for obtaining fault occurrence probability values of fault types of all parts of the escalator to be monitored currently, real-time fault judgment is conducted on the escalator to be monitored according to the fault occurrence probability values of all the fault types, and fault type positioning and fault repair are conducted according to fault judgment results; and acquiring the real-time health state value of the escalator by using the trained first neural network learning model, and acquiring the real-time health state of the escalator according to the real-time health state value of the escalator so as to realize the steps of the method.
To achieve the above object, according to another aspect of the present invention, there is provided a terminal device comprising at least one processing unit, and at least one memory unit, wherein the memory unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the steps of the above method.
To achieve the above object, according to another aspect of the present invention, there is provided a computer-readable medium storing a computer program executable by a terminal device, the program, when executed on the terminal device, causing the terminal device to perform the steps of the above method.
Generally, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
according to the method and the system for predicting the fault and managing the health of the escalator based on the multi-dimensional monitoring, single-dimensional monitoring and false fault reporting are avoided through the multi-dimensional real-time online monitoring technology of the escalator based on the fuzzy fault tree, and high-reliability and high-precision state monitoring is realized; a multi-dimensional space-time data processing model is established, the problems of multi-source information fusion and compressed sensing are overcome, and redundancy and transmission bandwidth waste caused by invalid data are avoided.
According to the method and the system for predicting the fault and managing the health of the escalator based on multi-dimensional monitoring, disclosed by the invention, the large data accumulation and mining are realized through the acquisition layer, the characteristic layer and the decision layer, the health analysis of the whole life cycle of the escalator is completed, and the fault diagnosis and prediction are realized through the proximity comparison between the data monitored by the acquisition layer and the fault knowledge base of the characteristic layer; the traditional passive monitoring alarm is reformed into active prediction and risk prevention by making strategies including preventive maintenance, emergency repair, expert system, fault analysis and coping after fault through a decision layer, so that intrinsic safety is realized.
Drawings
Fig. 1 is a schematic diagram of a method for predicting failure and managing health of an escalator with multi-dimensional monitoring according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a multi-dimensional monitoring escalator fault prediction and health management system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an embodiment of an escalator fault prediction and health management system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other. The present invention will be described in further detail with reference to specific embodiments.
Fig. 1 is a schematic diagram of a method for predicting failure and managing health of an escalator based on multidimensional monitoring according to an embodiment of the present invention. As shown in fig. 1, a method for predicting failure and managing health of an escalator with multi-dimensional monitoring comprises the following steps:
acquiring long-term monitoring sample data of the escalator by using a sensing layer of the Internet of things, wherein the long-term monitoring sample data comprises multiple dimensionality monitoring data of multiple parts, and marking the long-term monitoring sample data according to the fault type of the escalator;
constructing a deep iterative neural network learning model, wherein the deep iterative neural network learning model comprises a first neural network learning model and a second neural network learning model, the first neural network is used for acquiring the fault occurrence probability value of each part fault type, and the second neural network is used for acquiring the real-time health state value of the escalator; performing machine learning on the first neural network learning model and the second neural network learning model by using the marked long-term monitoring sample data to obtain a trained first neural network learning model and a trained second neural network learning model;
acquiring real-time monitoring data of the escalator to be monitored, acquiring fault occurrence probability values of fault types of all parts of the escalator to be monitored currently by using a trained first neural network learning model, performing real-time fault judgment on the escalator to be monitored according to the fault occurrence probability values of all the fault types, and performing fault type positioning and fault repair according to fault judgment results; and acquiring the real-time health state value of the escalator by using the trained second neural network learning model, and acquiring the real-time health state of the escalator according to the real-time health state value of the escalator.
As a preferred embodiment, newly added fault data of the escalator can be used as new sample data to carry out iterative training on the deep iterative neural network learning model.
As a preferred embodiment, the first neural network learning model includes a component failure index weight parameter set and a calculation manner of failure probability values of each component, and further includes a normal value range, a healthy state threshold value and a failure threshold value which are in one-to-one correspondence with the failure probability values of the failure types of each component, and the working states of the component parameters are judged by using the normal value range, and include a normal state, an unhealthy state and a failure state.
As a preferred embodiment, the obtaining of the real-time health state value of the escalator by the second neural network specifically includes:
acquiring a minimum fault mode of equipment to be monitored, and establishing a mapping relation among a parameter data state of a part, a fault type of the part and a fault type of the equipment to form a fuzzy fault tree model;
and acquiring the real-time health state value of the escalator by using the threshold value of the probability value of the fault occurrence of each part and the fuzzy fault tree model.
As a preferred embodiment, the minimum failure modes include fixing bolt loosening, bearing failure, braking distance tendency failure, step slip distance failure, step failure, handrail temperature failure, rotor failure, motor power consumption failure, gear failure, and bearing failure.
As an example, table 1 is a schematic table of the corresponding relationship between the multidimensional detection data and the fault types according to the embodiment of the present invention, as shown in table 1, environmental information (time node, temperature and humidity, position information, passenger flow and operation parameters) of the escalator can be used as a noise reduction reference value, and multidimensional monitoring parameters of vibration, noise, temperature and state are used as input, the intensified knowledge base is updated through deep learning iteration, approach analysis and comparison of data models are carried out, health evaluation and fault location after multi-source data analysis are obtained, state recognition of the escalator is achieved, the minimum fault mode of main driving wheel parts is taken as an example of bearing faults, the bearing running quality can be judged through data of three dimensions of vibration, displacement and noise, wherein the faults include but are not limited to inner ring abrasion, outer ring abrasion, retainer abrasion and a locatable bearing damage element.
Table 1 schematic table of correspondence between multidimensional detection data and fault types according to the embodiment of the present invention
As a further improvement of the present invention, the multi-dimensional monitoring data includes vibration monitoring data, noise monitoring data, displacement monitoring data, temperature monitoring data, and current monitoring data.
As a further improvement of the invention, the parts of the escalator comprise a driving main machine, a main driving wheel, a step chain tension wheel, a brake, a handrail belt, a speed reducer, a motor and steps.
Fig. 2 is a schematic diagram of fault prediction and health management of an escalator with multi-dimensional monitoring according to an embodiment of the present invention. As shown in fig. 2, a schematic diagram of fault prediction and health management of an escalator with multi-dimensional monitoring comprises an acquisition module, a fault knowledge base module and a decision-making module,
the acquisition module is used for acquiring long-term monitoring sample data of the escalator by using a sensing layer of the Internet of things, wherein the long-term monitoring sample data comprises multiple dimensionality monitoring data of multiple parts, and the long-term monitoring sample data is marked according to the fault type of the escalator;
the fault knowledge base module is used for constructing a deep iterative neural network learning model, the deep iterative neural network learning model comprises a first neural network learning model and a second neural network learning model, the first neural network is used for acquiring fault occurrence probability values of fault types of all parts, and the second neural network is used for acquiring real-time health state values of the escalator; performing machine learning on the first neural network learning model and the second neural network learning model by using the marked long-term monitoring sample data to obtain a trained first neural network learning model and a trained second neural network learning model;
the method comprises the steps that a decision module obtains real-time monitoring data of the escalator to be monitored, a trained first neural network learning model is used for obtaining fault occurrence probability values of fault types of all parts of the escalator to be monitored currently, real-time fault judgment is conducted on the escalator to be monitored according to the fault occurrence probability values of all the fault types, and fault type positioning and fault repair are conducted according to fault judgment results; and acquiring the real-time health state value of the escalator by using the trained first neural network learning model, and acquiring the real-time health state of the escalator according to the real-time health state value of the escalator so as to realize the steps of the method.
The collecting layer is an internet of things sensing layer, and state monitoring of main parts of the escalator is achieved through a current transformer, a vibration sensor, a temperature sensor, a noise sensor, a signal switch and other multi-state parameter monitoring sensors; the characteristic layer is the fault characteristics (such as step fault, handrail belt fault, bearing gear fault and the like) of main parts, and fault diagnosis and prejudgment are realized by the proximity comparison of data monitored by the acquisition layer and a characteristic layer fault knowledge base; the decision layer is an analysis, decision and control layer of the whole escalator state and comprises functions of preventive maintenance, emergency repair, an expert system, fault analysis, a coping strategy after a fault and the like. The system can realize the following functions: prevention and maintenance: the escalator real-time monitoring device can monitor the basic operation condition of the escalator based on the condition that each escalator real-time monitoring device returns monitoring data, and when some data monitored by the sensors fluctuate violently or exceed preset values, the escalator real-time monitoring device informs a command manager of fault information in an early warning mode through a network, and the command manager carries out task scheduling. Emergency repair: the system is mainly used for receiving maintenance tasks or fault handling tasks assigned by an escalator maintenance base ground level system, feeding data (including videos, audios, pictures and the like) generated in field work back to a platform end, and remotely seeking help through videos if emergencies or problems which cannot be solved are met. An expert system: the management personnel lead in the accessory management and the spare part type of the managed station, and the loss of escalator spare parts and equipment is recorded and calculated by the server end in the daily working process to provide a common spare part loss report form for the management personnel, so that the management efficiency is improved, and the data support is provided for the reserve consumption management of spare parts. And (3) fault analysis: the unit is an escalator fault repair knowledge storage unit, when a fault condition occurs, a maintenance worker can find a fault solution through the unit when the fault cannot be processed, and meanwhile, the unit can analyze the fault type through a big data algorithm.
Fig. 3 is a schematic diagram of an embodiment of an escalator fault prediction and health management system according to an embodiment of the present invention. As shown in fig. 3, as an example, the system includes five parts, namely, an escalator key component, an internet of things sensing device, a data acquisition and transmission system, single data analysis and multidimensional data analysis, and performs all-weather real-time online monitoring on the escalator drive host, a main drive wheel, a step chain tension wheel, a brake, a step handrail belt, a motor, a reducer and other key components through sensors such as noise, temperature, vibration and the like; then, data collected by the sensor are transmitted to a data transmission queue, the collection frequency is controlled by a console, effective data are extracted and stored in a remote dictionary service cluster storage system (the overflow and redundancy of the data of the transmission queue are avoided, and the performance of the transmission queue is not influenced); then, the data is transmitted to a system server (the problems of high fault tolerance, high performance, high load and the like are solved), and single state analysis (comparison with a fault threshold or a non-healthy state) of the data such as vibration, temperature, noise and the like is firstly carried out; then carrying out multidimensional analysis on problematic data in a single state and other multidimensional states of the same part, ensuring the accuracy and reliability of analysis, avoiding false alarm and missed report, meanwhile, regularly analyzing the priority of faults in the single state through the on-off control of a timing analysis system, stripping off data which are easy to cause disasters or accidents, giving an alarm in real time, independently deploying emergency countermeasures, carrying out off-line analysis on other unhealthy data in a server, taking the minimum cut set (minimum fault mode) of the part as a source, and obtaining quantitative analysis of the health state of the whole escalator through an equipment state analysis method for solving a fault tree based on a Boolean algebra method, sequencing the health degree, and guiding subsequent maintenance and inspection; and finally, forming a coping strategy corresponding to the health degree through the health management function of the system, assigning maintenance personnel to execute, and storing and processing results in a database for continuously correcting the correctness of the health degree algorithm.
A terminal device comprising at least one processing unit and at least one memory unit, wherein the memory unit stores a computer program which, when executed by the processing unit, causes the processing unit to carry out the steps of the above-mentioned method.
A computer-readable medium, in which a computer program executable by a terminal device is stored, causes the terminal device to perform the steps of the above-mentioned method when the program is run on the terminal device.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A multi-dimensional monitoring escalator fault prediction and health management method is characterized by comprising the following steps:
acquiring long-term monitoring sample data of the escalator by using a sensing layer of the Internet of things, wherein the long-term monitoring sample data comprises multiple dimensionality monitoring data of multiple parts, and marking the long-term monitoring sample data according to the fault type of the escalator;
constructing a deep iterative neural network learning model, wherein the deep iterative neural network learning model comprises a first neural network learning model and a second neural network learning model, the first neural network is used for acquiring the fault occurrence probability value of each part fault type, and the second neural network is used for acquiring the real-time health state value of the escalator; performing machine learning on the first neural network learning model and the second neural network learning model by using the marked long-term monitoring sample data to obtain a trained first neural network learning model and a trained second neural network learning model;
acquiring real-time monitoring data of the escalator to be monitored, acquiring fault occurrence probability values of fault types of all parts of the escalator to be monitored currently by using a trained first neural network learning model, performing real-time fault judgment on the escalator to be monitored according to the fault occurrence probability values of all the fault types, and performing fault type positioning and fault repair according to fault judgment results; and acquiring the real-time health state value of the escalator by using the trained second neural network learning model, and acquiring the real-time health state of the escalator according to the real-time health state value of the escalator.
2. The escalator fault prediction and health management method based on multi-dimensional monitoring as claimed in claim 1, wherein the first neural network learning model includes a component fault index weight parameter set and a calculation manner of fault occurrence probability values of each component, and further includes a normal value range, a health state threshold value and a fault threshold value which are in one-to-one correspondence with the fault occurrence probability values of fault types of each component, and the normal value range is used to determine the working states of the component parameters, including a normal state, an unhealthy state and a fault state.
3. The escalator fault prediction and health management method based on multidimensional monitoring as claimed in claim 1, wherein the second neural network obtains real-time health status values of the escalator specifically as follows:
acquiring a minimum fault mode of equipment to be monitored, and establishing a mapping relation among a parameter data state of a part, a fault type of the part and a fault type of the equipment to form a fuzzy fault tree model;
and acquiring the real-time health state value of the escalator by using the threshold value of the probability value of the fault occurrence of each part and the fuzzy fault tree model.
4. The method for escalator fault prediction and health management with multidimensional monitoring as claimed in claim 1, wherein the deep iterative neural network learning model is iteratively trained using newly added fault data of an escalator as new sample data.
5. The method of claim 1, wherein the minimum failure modes include fixing bolt loosening, bearing failure, braking distance trend failure, sliding distance failure, step failure, handrail temperature failure, rotor failure, motor power consumption failure, gear failure and bearing failure.
6. The method for escalator fault prediction and health management with multi-dimensional monitoring as claimed in claim 1, wherein said multi-dimensional monitoring data comprises vibration monitoring data, noise monitoring data, displacement monitoring data, temperature monitoring data and current monitoring data.
7. The method of claim 1, wherein the escalator components comprise a drive main machine, a main drive wheel, a step chain tension wheel, a brake, a handrail belt, a reducer, a motor and steps.
8. A multidimensional monitoring escalator fault prediction and health management system is characterized by comprising an acquisition module, a fault knowledge base module and a decision-making module, wherein,
the acquisition module is used for acquiring long-term monitoring sample data of the escalator by using a sensing layer of the Internet of things, wherein the long-term monitoring sample data comprises multiple dimensionality monitoring data of multiple parts, and the long-term monitoring sample data is marked according to the fault type of the escalator;
the fault knowledge base module is used for constructing a deep iterative neural network learning model, the deep iterative neural network learning model comprises a first neural network learning model and a second neural network learning model, the first neural network is used for acquiring fault occurrence probability values of fault types of all parts, and the second neural network is used for acquiring real-time health state values of the escalator; performing machine learning on the first neural network learning model and the second neural network learning model by using the marked long-term monitoring sample data to obtain a trained first neural network learning model and a trained second neural network learning model;
the decision module acquires real-time monitoring data of the escalator to be monitored, acquires the fault occurrence probability value of each fault type of each part of the escalator to be monitored currently by using a trained first neural network learning model, performs real-time fault judgment on the escalator to be monitored according to the fault occurrence probability value of each fault type, and performs fault type positioning and fault repair according to the fault judgment result; acquiring a real-time health state value of the escalator by using the trained second neural network learning model, and acquiring the real-time health state of the escalator according to the real-time health state value of the escalator to realize the steps of the method of any one of claims 1 to 7.
9. A terminal device, comprising at least one processing unit and at least one memory unit, wherein the memory unit stores a computer program which, when executed by the processing unit, causes the processing unit to carry out the steps of the method according to any one of claims 1 to 7.
10. A computer-readable medium, in which a computer program is stored which is executable by a terminal device, and which, when run on the terminal device, causes the terminal device to carry out the steps of the method of any one of claims 1 to 7.
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| CN111562096A (en) * | 2020-05-14 | 2020-08-21 | 中铁第四勘察设计院集团有限公司 | Health state real-time online monitoring system of escalator |
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100468263C (en) * | 2007-09-05 | 2009-03-11 | 东北大学 | Continuous miner remote real-time failure forecast and diagnosis method and device |
| CN102923538A (en) * | 2012-07-06 | 2013-02-13 | 天津大学 | Elevator health management and maintenance system based on Internet of things and collection and assessment method |
| CN105035902A (en) * | 2015-08-10 | 2015-11-11 | 广州特种机电设备检测研究院 | Elevator safety condition evaluation method |
| CN105731209A (en) * | 2016-03-17 | 2016-07-06 | 天津大学 | Intelligent prediction, diagnosis and maintenance method for elevator faults on basis of Internet of Things |
| CN106586796A (en) * | 2016-11-15 | 2017-04-26 | 王蕊 | System and method for monitoring state of escalator |
| CN108178037A (en) * | 2017-12-30 | 2018-06-19 | 武汉大学 | A kind of elevator faults recognition methods based on convolutional neural networks |
| US20180217585A1 (en) * | 2014-12-19 | 2018-08-02 | United Technologies Corporation | Sensor data fusion for prognostics and health monitoring |
| CN108564313A (en) * | 2018-06-14 | 2018-09-21 | 华北水利水电大学 | The method and device of Wind turbines status monitoring and health evaluating based on fault tree |
| CN108584588A (en) * | 2017-12-31 | 2018-09-28 | 浙江工业大学 | A kind of tor door faults detection method based on extensive flow data |
| CN109492790A (en) * | 2018-09-18 | 2019-03-19 | 北京光耀电力科技股份有限公司 | Wind turbines health control method based on neural network and data mining |
| US20190324442A1 (en) * | 2017-08-02 | 2019-10-24 | Strong Force Iot Portfolio 2016, Llc | Self-organizing systems and methods for data collection |
| CN110937489A (en) * | 2019-11-15 | 2020-03-31 | 广东寰球智能科技有限公司 | Online fault monitoring and early warning method and system for escalator |
-
2020
- 2020-05-14 CN CN202010409441.2A patent/CN111650919B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100468263C (en) * | 2007-09-05 | 2009-03-11 | 东北大学 | Continuous miner remote real-time failure forecast and diagnosis method and device |
| CN102923538A (en) * | 2012-07-06 | 2013-02-13 | 天津大学 | Elevator health management and maintenance system based on Internet of things and collection and assessment method |
| US20180217585A1 (en) * | 2014-12-19 | 2018-08-02 | United Technologies Corporation | Sensor data fusion for prognostics and health monitoring |
| CN105035902A (en) * | 2015-08-10 | 2015-11-11 | 广州特种机电设备检测研究院 | Elevator safety condition evaluation method |
| CN105731209A (en) * | 2016-03-17 | 2016-07-06 | 天津大学 | Intelligent prediction, diagnosis and maintenance method for elevator faults on basis of Internet of Things |
| CN106586796A (en) * | 2016-11-15 | 2017-04-26 | 王蕊 | System and method for monitoring state of escalator |
| US20190324442A1 (en) * | 2017-08-02 | 2019-10-24 | Strong Force Iot Portfolio 2016, Llc | Self-organizing systems and methods for data collection |
| CN108178037A (en) * | 2017-12-30 | 2018-06-19 | 武汉大学 | A kind of elevator faults recognition methods based on convolutional neural networks |
| CN108584588A (en) * | 2017-12-31 | 2018-09-28 | 浙江工业大学 | A kind of tor door faults detection method based on extensive flow data |
| CN108564313A (en) * | 2018-06-14 | 2018-09-21 | 华北水利水电大学 | The method and device of Wind turbines status monitoring and health evaluating based on fault tree |
| CN109492790A (en) * | 2018-09-18 | 2019-03-19 | 北京光耀电力科技股份有限公司 | Wind turbines health control method based on neural network and data mining |
| CN110937489A (en) * | 2019-11-15 | 2020-03-31 | 广东寰球智能科技有限公司 | Online fault monitoring and early warning method and system for escalator |
Non-Patent Citations (2)
| Title |
|---|
| 张阔等: "故障树法和改进PSO-PNN网络的电梯故障诊断模型", 《中国安全生产科学技术》 * |
| 陈志平等: "基于大数据的电梯故障诊断与预测研究", 《机电工程》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111562096A (en) * | 2020-05-14 | 2020-08-21 | 中铁第四勘察设计院集团有限公司 | Health state real-time online monitoring system of escalator |
| CN112561280B (en) * | 2020-12-09 | 2023-04-18 | 中铁第四勘察设计院集团有限公司 | Equipment fault prediction method based on self-learning convergence fault knowledge base and application thereof |
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| CN112561280A (en) * | 2020-12-09 | 2021-03-26 | 中铁第四勘察设计院集团有限公司 | Equipment fault prediction method based on self-learning convergence fault knowledge base and application thereof |
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| CN112348419A (en) * | 2021-01-05 | 2021-02-09 | 光谷技术有限公司 | Internet of things processing system and method |
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| CN115238925A (en) * | 2022-07-25 | 2022-10-25 | 北京卓尔忠诚科技有限公司 | Motor equipment supervision method and system |
| CN115238925B (en) * | 2022-07-25 | 2023-12-29 | 北京卓尔忠诚科技有限公司 | Motor equipment supervision method and system |
| CN115310561A (en) * | 2022-09-29 | 2022-11-08 | 中国空气动力研究与发展中心设备设计与测试技术研究所 | Electromagnetic valve fault monitoring method based on integrated instant learning |
| CN116310940A (en) * | 2022-12-29 | 2023-06-23 | 苏州斯曼克磨粒流设备有限公司 | Risk assessment method and system for running state of electromechanical equipment |
| CN116067432A (en) * | 2023-03-06 | 2023-05-05 | 南京市特种设备安全监督检验研究院 | Escalator variable working condition fault diagnosis method |
| CN115931416A (en) * | 2023-03-14 | 2023-04-07 | 枣庄市天工精密机械有限公司 | Sand machine fault detection system drenches based on data analysis |
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