CN111401583A - Escalator full life cycle health management system based on predictive maintenance - Google Patents
Escalator full life cycle health management system based on predictive maintenance Download PDFInfo
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Abstract
The invention relates to a predictive maintenance-based escalator full-life-cycle health management system, which comprises an intelligent sensing module, a health assessment and fault prediction module, an intelligent network module and an intelligent decision module, wherein the intelligent sensing module is used for sensing the health of an escalator; the intelligent sensing module acquires data and information of the escalator; the health evaluation and fault prediction module carries out health state evaluation and fault prediction on the whole equipment and key components through the data and information acquired by the intelligent sensing module; the intelligent network module constructs a network environment capable of guiding an entity space, establishes a digital mirror image model mapped with an entity escalator system, forms a single equipment record of the escalator, and performs cluster management on the escalator; and the intelligent decision module is used for visually displaying the analysis result and giving a maintenance suggestion according to the analysis result of the fault diagnosis. The escalator fault early warning system can guarantee the operation safety of the escalator, can early warn faults of the escalator in advance, and can evaluate the health state of key parts of the escalator.
Description
Technical Field
The application relates to a predictive maintenance-based escalator full life cycle health management system which is suitable for the technical field of escalator state monitoring and fault diagnosis.
Background
In recent years, with the rapid development of social economy, subway stations, high-speed railway stations and superstores are rapidly increased, the number of escalators is increased by more than 10% per year, and most of the escalators work in public places with intensive people flow, so that the escalators are long in operation time, large in load and even run in a peak period or overload state. Taking a Beijing subway as an example, as of 2018, 15 lines governed by the Beijing subway have an escalator 2170 part in total, the operation lasts for 19-20 hours every day, more than 1000 thousands of passengers are transported every day, and the number of people taking the escalator to get in and out of a station is about 70%. Accidents of the escalator are increased, so that the escalator is stopped, safety accidents are possibly caused, even great economic loss or personnel injury is caused, and the safety accidents of the escalator are frequently reported in recent years. According to the statistics of authorities, more than 80% of accidents from escalators in recent years are caused by improper management, particularly untimely and short-time maintenance, so that the operation safety and maintenance of the escalators should be emphasized.
The escalator belongs to special equipment, maintenance strategies mainly adopt a mode of planned maintenance and a mode of after maintenance, the planned maintenance refers to regular inspection according to half month, quarter and year, professional maintenance personnel make corresponding investigation records according to the actual running condition of the escalator, records abnormal records or maintenance records and enters files, the maintenance records also adopt paper records, and a machine account and a track book of the equipment are established; the term "post-repair" refers to the inspection and repair of the escalator after a failure, and has hysteresis. The maintenance cost of the maintenance mode is high, and redundant maintenance exists; secondly, the informatization degree is low, the maintenance is highly dependent on the experience of maintenance personnel, the maintenance recorded information is isolated, and the experience is difficult to solidify; and thirdly, the running state of the escalator cannot be monitored in real time, and timely early warning or alarming can not be carried out on faults according to the running state.
At present, monitoring systems are provided by larger escalator manufacturers aiming at escalators of own brands, and the systems mainly aim at visually displaying the whole operation state of the escalator on an upper computer of a monitoring room, so that the functions of transmitting and alarming fault signals, recording operation data and the like are realized. However, these systems have poor compatibility, and cannot monitor escalators of other brands, and these monitoring systems mainly realize visualization of the operation state of the equipment. In order to solve the problems, in the prior art, a sensor is additionally arranged or an image video acquisition mode is adopted to realize online state monitoring of the escalator, and the modes only aim at certain parameter indexes of the operation of the escalator, so that the online monitoring state is incomplete; some escalator relatively and comprehensively considers the state monitoring of the escalator, also adds a fault alarm function, essentially belongs to state prediction, fails to realize early warning of the fault of the escalator, belongs to passive monitoring, and lacks the on-line health state assessment of key components.
Disclosure of Invention
The invention aims to design a predictive maintenance-based escalator full life cycle health management system, which can reduce accidental fault shutdown of escalator equipment, guarantee the operation safety of an escalator, early warn the fault of the escalator in advance and evaluate the health state of key parts of the escalator.
The application relates to a predictive maintenance-based escalator full-life-cycle health management system, which comprises an intelligent sensing module, a health assessment and fault prediction module, an intelligent network module and an intelligent decision module;
the intelligent sensing module acquires data and information of the escalator;
the health evaluation and fault prediction module carries out health state evaluation and fault prediction on the whole equipment and key components through the data and information acquired by the intelligent sensing module;
the intelligent network module constructs a network environment capable of guiding an entity space, establishes a digital mirror image model mapped with an entity escalator system, forms a single equipment record of the escalator, and performs cluster management on the escalator;
and the intelligent decision module is used for visually displaying the analysis result and giving a maintenance suggestion according to the analysis result of the fault diagnosis.
The intelligent sensing module comprises a data acquisition system, and the data acquisition system acquires data of the external sensor, data of the escalator controller, equipment information of the escalator and maintenance records. The data and information that intelligence perception module gathered mainly include: the escalator control system comprises escalator equipment information, escalator operation state measurement parameters, escalator operation working condition data, escalator use environment parameters and escalator maintenance records.
The intelligent network module comprises an escalator digital mirror image module and a cluster management module; the escalator digital mirror image module can check the field condition of the escalator and form a single equipment history of the escalator; the cluster management module is built with models of different grades and comprises a cluster benchmarking module; the cluster benchmarking module can perform cluster benchmarking on various vibration characteristic parameters of the escalators under different loads, and escalator groups corresponding to outliers in a cluster benchmarking result are regarded as abnormal escalators.
The intelligent decision module comprises a visual management module and an operation and maintenance decision analysis module; the visual management module comprises early warning state display, cluster benchmarking display, equipment health state display, key component performance decline display, early warning information display and statistical analysis display; the operation and maintenance decision analysis module can adopt a graded maintenance strategy for the escalator equipment and send alarm information according to the analysis result of the health assessment and fault prediction module.
The method for performing health evaluation and fault prediction by using the health evaluation and fault prediction module comprises the following steps of:
(1) data pre-processing
Denoising the acquired original sample data to improve the data quality;
(2) feature extraction
Extracting characteristic values of the sample data subjected to data preprocessing;
(3) feature selection
Selecting effective characteristics from the extracted characteristics as characteristic input of subsequent modeling;
(4) building health assessment model for key parts of escalator
The selected characteristic value is used for establishing a health assessment model of the key component, a sample is selected for training, and the health assessment model from the characteristic value to the health value is established;
(5) escalator health assessment
The health state of each key component of the escalator is evaluated in real time and the performance decline trend of each key component is predicted by inputting the original data of each key component measuring point collected in real time into each key component health evaluation model;
(6) fault prediction
And intelligently diagnosing the key components according to the health evaluation model of each key component, and giving fault reasons and maintenance suggestions by the system if the fault occurs.
The data acquisition device comprises edge data acquisition equipment, wherein the edge data acquisition equipment is provided with a driving motor acceleration sensor, a driving motor current monitor, a gear box vibration sensor, a main driving chain wheel encoder, a step chain wheel vibration sensor and a step chain wheel encoder; the driving motor acceleration sensor is arranged on a bearing base of the motor, and the driving motor current monitor is used for monitoring three-phase current; the vibration sensor of the gear box is arranged on a bearing base of the gear box, and the vibration sensor of the main driving sprocket and the encoder of the main driving sprocket are arranged on a bearing seat of the main driving sprocket; the step chain wheel vibration sensor and the step chain wheel encoder are arranged on a bearing seat of the step chain wheel. Preferably, the edge data acquisition equipment is also provided with a handrail temperature sensor, a band-type brake temperature sensor and a rotating speed sensor, wherein the handrail temperature sensor is arranged on the left truss and the right truss of the handrail; the band-type brake temperature sensor is arranged on the surface of the band-type brake; the rotating speed sensor is arranged on the driving motor.
The beneficial technical effect of this application includes:
(1) the access of escalator controllers of different manufacturers and models can be realized, and an equipment record file is established for each escalator in the system, so that unified management can be realized;
(2) the traditional planned maintenance and post-repair are improved into predictive maintenance, the health state and fault early warning of each key part of the escalator can be evaluated in real time, and the whole health state of the escalator is evaluated in real time on line by combining the risk coefficient of the escalator;
(3) the performance of each monitored escalator can be effectively predicted, and the performance decline trend is predicted, so that the fault risk is predicted, and accurate positioning can be realized;
(4) the digital mirror image of the escalator can be formed, the performance and cluster management of each escalator are comprehensively evaluated, the visual maintenance is realized, the maintenance efficiency is improved, and the labor cost is reduced;
(5) the fault early warning system can realize graded maintenance on the fault early warning of the escalator, send early warning information to related management, operation, maintenance and other personnel aiming at different early warning grades, and determine specific response time according to different grades.
Drawings
Fig. 1 is an overall structural diagram of the escalator full life cycle health management system of the present application.
Fig. 2 is a diagram of a typical fault and maintenance strategy analysis for an escalator.
Fig. 3 is a graph showing the relationship between the influence of damage to components of an escalator and the frequency of occurrence of damage to the components.
Fig. 4 shows an exemplary preferred block diagram of a smart sensor module.
Fig. 5 is a flow chart of an early warning process in a preferred embodiment of the health assessment and fault prediction module of the present application.
Fig. 6 is a schematic diagram of a cluster targeting result in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict. In the present application, directional terms such as "front, back, left, right", "up, down, outside, inside" and the like are used for convenience of description and do not limit the scope of protection. Those skilled in the art will appreciate that simple substitutions between the above orientations are possible without departing from the scope of the present application.
The whole framework diagram of the escalator full life cycle health management system according to the application is shown in figure 1. The system consists of a 'physical space' and a 'Saybook space'. In the entity space, the escalator is intelligently sensed, and the core problem is data acquisition of various kinds of information. In the competition space, the health value of each escalator is formed through data preprocessing, feature extraction and information fusion of the entity space of the escalator, so that the health evaluation of each device is realized; the digital mirror image of each escalator is formed in the Saybook space, so that the performance and health state of each escalator, and the performance condition and potential fault risk of each key component can be reflected; meanwhile, by combining actual working conditions and environmental factors in the solid space, the digital mirror image model can predict the decline trend and the residual life of the escalator in real time, and feeds the decline trend and the residual life back to equipment in the solid space to guide the graded maintenance of the escalator.
The escalator full-life-cycle health management system comprises an intelligent sensing module, a health assessment and fault prediction module, an intelligent network module and an intelligent decision module. The intelligent sensing module collects data and information of the full life cycle of the escalator, breaks through barriers of independent sensing and information isolated islands of the conventional escalator equipment, and establishes a uniform data environment; the system comprises the acquisition of multi-source data, such as equipment information, operation information, working condition information, environment information, maintenance information and the like. The health assessment and fault prediction module is the technical core of predictive maintenance of escalator equipment, and carries out health state assessment and fault prediction on the whole equipment and key parts through data and information acquired by the intelligent sensing module. The intelligent network module constructs a network environment capable of guiding an entity space, establishes a digital mirror image model mapped with an entity escalator system, forms a single equipment record of the escalator, and performs cluster management on the escalator. The intelligent decision module proposes a series of maintenance measures including a fault processing method, spare part management, maintenance personnel management and the like aiming at the position, type, reason and the like of the fault according to the analysis results of fault early warning and fault diagnosis and aiming at the minimum maintenance cost, so as to complete escalator fault maintenance optimization and reduce operation and maintenance cost.
Intelligent sensing module
The data and information of the escalator full life cycle collected by the intelligent sensing module mainly comprise the following key data:
(1) equipment information of the escalator: each escalator is provided with an electronic identity card with a unique identifier, and information of production, manufacturing, installation, maintenance, repair, inspection and the like of the escalator is recorded in the 'identity card', and the electronic identity card of each escalator can be generated by using a radio frequency identification technology for example;
(2) measuring parameters of the running state of the escalator: the escalator health state data is mainly data which can reflect the health state of the escalator and is obtained from a sensor for measuring the point of a key part of the escalator, such as vibration, temperature, current and the like of the key part. The selection of the key components can be generally defined according to the occurrence frequency and the influence of the faults, equipment with low occurrence frequency and serious influence once the faults occur is selected, for example, long-time downtime and high maintenance cost are caused, and predictive maintenance and risk management are carried out on the components;
(3) the working condition data of the escalator operation are as follows: the escalator control system mainly refers to state data during escalator operation, and comprises data such as an escalator operation mode (ascending/descending/sudden stop and the like), fault information and the like, wherein the data can be directly obtained from a controller of the escalator;
(4) environmental parameters in the use process of the escalator are as follows: including all environmental factors that may affect escalator performance and operating conditions, such as temperature, humidity, mechanical shock, vibration, etc.;
(5) maintenance recording of the escalator: all point inspection, maintenance, repair and maintenance replacement records in the life cycle of the escalator. The data can be used as a reference for updating the state of the escalator and is mutually compared with the state data of the escalator to serve as an updating node of the state of the escalator to update the health prediction model of the equipment. The long-term accumulation of the data records is helpful for counting the mean time between failures of key parts of the equipment, and the mean time is used as the basis for judging the quantity of safety spare parts and improving the design of the escalator.
As shown in FIG. 4, an exemplary preferred block diagram of a smart perception module is shown. The intelligent sensing module is connected with the edge data acquisition equipment and the escalator controller, the intelligent sensing module comprises a data acquisition system, the data acquisition system acquires external sensor data, the escalator controller data and can read the equipment information and maintenance record of the escalator, wherein the edge data acquisition equipment is provided with a driving motor acceleration sensor, a driving motor current monitor, a gear box vibration sensor, a main driving sprocket encoder, a step sprocket vibration sensor, a step sprocket encoder, a handrail belt temperature sensor, a band-type brake temperature sensor, a rotating speed sensor and an environment temperature and humidity sensor. The driving motor acceleration sensor is arranged on a bearing base of the motor, the driving motor current monitor is used for monitoring three-phase current, and the monitoring fault types of the driving motor acceleration sensor and the driving motor current monitor comprise rotor faults such as rotor unbalance or eccentricity and the like, bearing faults, falling or fracture, phase dislocation or phase failure and the like of a main machine fixing bolt. The gearbox vibration sensor is arranged on a bearing base of the gearbox, and monitored fault types comprise gear faults such as gear malocclusion, gear breakage and the like and bearing faults. The main driving chain wheel vibration sensor and the main driving chain wheel encoder are arranged on the bearing seat, and the monitored fault types comprise bearing faults and main driving chain abrasion. The step chain wheel vibration sensor and the step chain wheel encoder are arranged on the bearing seat, and the monitored fault types comprise bearing faults and step chain abrasion. The handrail temperature sensor is arranged on the left truss and the right truss of the handrail, and the monitored fault types comprise overhigh temperature, abrasion and the like of the handrail. The band-type brake temperature sensor is arranged on the surface of a band-type brake, and the monitored fault types comprise band-type brake heating abrasion and the like caused by mechanical over-tightening of the band-type brake or circuit failure. The rotating speed sensor is arranged on the driving motor and used for monitoring the rotating speed of the escalator during operation. The ambient temperature and humidity sensor measures ambient parameters including ambient temperature and ambient humidity.
The output signal of the escalator controller can be connected to the intelligent sensing module through a communication interface such as RS-485. The escalator state signals required to be collected comprise operation modes, overhaul states, normal stop, emergency stop and starting conditions of safety protection devices, wherein the operation modes comprise ascending operation and descending operation, the emergency stop comprises upper emergency stop, middle emergency stop and lower emergency stop, and the safety protection devices comprise anti-reversal safety protection devices, step chain fracture protection devices, driving chain safety protection devices and handrail belt inlet safety protection devices. Aiming at the problem that data structures of different escalator manufacturers are not uniform, the data processing module arranged in the intelligent sensing module extracts and analyzes the received state information of the escalator, and converts the state information into a data structure with uniform standard, so that the equipment of different manufacturers can be accessed.
The equipment information of the escalators can be realized by using RFID (radio frequency identification) module communication, each escalator is provided with an RFID electronic tag, and the electronic tag comprises the ID, the manufacturing information, the installation information and the like of the escalator. The RFID module is internally provided with a reader-writer to read information of the escalator equipment and upload the information to the intelligent sensing module through TCP/IP. The maintenance information of the escalator can be uploaded to the intelligent perception module through a communication protocol such as TCP/IP.
Health assessment and fault prediction module
And the health evaluation and fault prediction module carries out health state evaluation and fault prediction on the whole equipment and key components through the data acquired by the intelligent sensing module. Regarding the selection of key components, according to the criticality analysis, see the typical failure and maintenance strategy analysis diagram shown in fig. 2, the key components of the escalator may be four major components of a gear box, a driving motor, a chain and a handrail. Corresponding data are obtained through the intelligent sensing module, the data are sorted firstly, abnormal points are removed, and preprocessing is carried out. Because the escalator is generally used in public places and is influenced by the flow of personnel in the peak period/peak leveling period, and the load changes dynamically, working conditions are identified from controller data, the data are segmented according to the working conditions, and data analysis methods such as characteristic value extraction, screening and classification are carried out, so that a health evaluation model of the escalator system is finally established, the intelligent evaluation of the health state of the whole escalator is realized, the health state evaluation and performance decline trend analysis of key components are realized, and the fault prediction of the escalator is realized.
The fault prediction is to comprehensively process the equipment data according to the real-time state data acquired by the intelligent sensing module, analyze the fault mode and feed the fault processing information back to the relevant maintenance personnel in time to assist the fault in rapidly solving. The fault analysis is used for carrying out statistical analysis on system faults based on historical data, and the system faults can be classified according to different dimensions and generate required analysis reports. Meanwhile, the change trend and the characteristics of various faults can be known by analyzing the relation between the fault of the escalator system and variables such as the running time, the motor load and the like, so that a basis is provided for the optimization of fault early warning and diagnosis algorithms; and finally, the health evaluation function judges the current health state of the escalator through a corresponding evaluation rule, so that the current health state of the escalator is judged, and support is provided for fault prediction.
The escalator system fault monitoring method and device can monitor the escalator system fault caused by the failure of different key components. The method for health assessment and fault prediction comprises the steps of raw data collection, data preprocessing, feature extraction, feature selection, establishment of a health assessment model of each key component of the escalator, health assessment of the escalator and fault prediction. The method specifically comprises the following steps:
(1) data pre-processing
Considering that the field environment of escalator operation is complex, the quality of the collected vibration data is poor, a large amount of noise exists, and the prediction effect and generalization capability of the model are directly influenced. The method comprises the steps of carrying out data preprocessing on the acquired original sample data, and improving the data quality through methods of data cleaning, noise reduction, filtering and the like. For example, for vibration signal monitoring points of an escalator, whether a vibration waveform deviates or not is judged by methods of calculating an average value, counting the difference between positive and negative data points and the like on an original signal, a trend item of the signal exists, and the trend item of the signal is removed by methods of a least square method, sorting the signal and subtracting the average value and the like.
(2) Feature extraction
The characteristic value extraction is performed on the sample data after data preprocessing, and may include, for example, time domain characteristic extraction, frequency domain characteristic extraction, time-frequency domain characteristic extraction, and order domain characteristic extraction. For time domain features including but not limited to peak-to-peak, effective value, kurtosis, pulse index, waveform index, margin index, skewness index, etc.; for frequency domain features including, but not limited to, FFT spectrum, power spectrum, envelope spectrum, cepstrum, and the like; for time-frequency domain features, including but not limited to energy variation features of time series and frequency domain, etc.; for order domain features, including but not limited to order spectra, order power spectra, order envelope spectra, and the like. In order to adapt to the variable load working condition of the escalator, time domain non-stationary data is converted into angle domain stationary data, so that the influence of load fluctuation on vibration data analysis is avoided; and then, extracting time domain and frequency domain fault characteristics of the original vibration data according to escalator parameters, and extracting order domain fault characteristics of the angular domain stable vibration data, wherein the escalator parameters comprise gear tooth number, bearing fault characteristic frequency, resonant frequency and the like.
(3) Feature selection
The characteristics irrelevant to the recession of the key parts of the escalator cause difficulty in building the model, so that effective characteristics are required to be selected from the extracted characteristics to be used as characteristic input of subsequent modeling, and according to the failure mechanism analysis of the escalator and the data analysis of the measuring points of the key parts of the escalator. Through the characteristic classifiability evaluation, variable sequencing can be performed by using a Fisher criterion, for example, and after the fisher value of each variable is calculated, a plurality of variables with the highest fisher values are selected as characteristic values in input model training.
(4) Building health assessment model for key parts of escalator
And using the characteristic values selected from the training data to establish a health evaluation model of each key component of the escalator, selecting the normal operation of the key component as a health data label, and selecting the fault as a failure label. And (3) taking the health label of each key part as a reference, taking the failure label as a sample of a decline state for training, establishing a model from the characteristics to the health value, and obtaining a health evaluation model of each key part of the escalator.
(5) Escalator health assessment
The health value is defined as an index for measuring equipment degradation, the range is 0-1, and the lower the health value is, the lower the health degree of the escalator system is. And inputting all data into a health evaluation model for calculation to obtain data distribution of the health label and the failure label, and dividing a set threshold value according to the data distribution to mark the limit that the health degree is reduced to the occurrence problem as a set value of early warning before failure.
The health state of each key component of the escalator can be evaluated in real time by inputting the original data of each key component measuring point collected in real time into each key component health evaluation model, and the performance degradation trend of each key component can be predicted by the change of the health value. And the health state evaluation of the escalator needs to comprehensively consider the health states of all key components.
(6) Fault prediction
Firstly, intelligently diagnosing key components (such as a main machine bearing, a gear box, a main driving chain wheel and the like) according to a health evaluation model of each key component, and giving fault reasons and maintenance suggestions by a system if a fault occurs. Particularly, the fault early warning grade of the escalator is divided into the following steps from low to high: normal, concern, early warning and alarm. The early warning grade of the key component is the maximum early warning grade in all vibration measuring points on the key component; the whole escalator early warning state is the maximum early warning grade in each key part. The early warning process is shown in figure 5.
Intelligent network module
The intelligent network module constructs a network environment capable of guiding an entity space, establishes a digital mirror image model mapped with an entity escalator system, forms a single equipment record of the escalator, and performs cluster management on the escalator. The intelligent network module comprises an escalator digital mirror image module and a cluster management module.
Firstly, in a race space, the mechanism and the operating environment parameters of the escalator are organically combined with a group, a digital mirror image model of the escalator is established, the history of the escalator single equipment is formed, and the history comprises but is not limited to time, the ID, the configuration information, the environment information, the working condition information, the overall health value, the health value of key components, maintenance records, service time, videos and other multi-dimensional information, and the history information of the equipment is updated through an RFID module. The virtual model of the equipment is constructed by digital twins, and the field condition of the escalator can be really known by real-time acquisition, alarming, operation state watching boards, operation historical data playback and the like. Meanwhile, for a new escalator, modeling of the new escalator is achieved through digital mirror images of the escalator, the current life cycle stage of the new escalator is predicted according to the collected data conditions, and the digital mirror images of the escalator are updated along with data accumulation.
And secondly, establishing a data model driven by data by a health assessment and fault prediction module, monitoring the running condition of the escalator entity in real time in the Saybook space, and guiding the operation and maintenance decision of the escalator in the entity space according to the model prediction result. The digital twin core is that models of different levels from component level, equipment level to cluster level are built, algorithms such as machine learning, industrial artificial intelligence, intelligent decision, optimization and the like are applied, and algorithm models of different levels are built to express digital twin bodies of different levels;
thirdly, a health assessment and fault prediction model of a component level and an equipment level can be realized through a health assessment and fault prediction module, a cluster level model of the escalator is constructed, different occasions, use frequencies and load changes of the escalator are combined, the cluster level model can be used for transverse comparison of escalator clusters, namely under different load working conditions, cluster benchmarking is carried out on various data characteristic parameters of the escalators, abnormal equipment in an escalator group can be identified by identifying outliers in benchmarking results, and abnormal performance of escalator equipment is analyzed;
finally, the digital twin has the advantages that the intercommunication of the models can be realized, the models are different at a network layer without depending on the individual, brand or environment of the escalator, and the models can be intercommunicated and used among different escalators. Particularly for the newly-accessed escalator, because no historical data exists, the clustered model can be used for the newly-accessed escalator for reference, the current life cycle stage of the new escalator is predicted according to the acquired data condition, the digital mirror image of the escalator is updated along with the accumulation of the data, and meanwhile, the existing escalator model can be updated and iterated, so that the prediction accuracy of the model is continuously improved.
For an escalator, generally, most escalator groups are in a normal running state, and only a few escalator groups are in an abnormal running state. Based on the above, the same characteristic parameters of the respective moving escalators under the same working condition are displayed in a centralized manner, and the escalator corresponding to the finally-appearing outlier is regarded as an abnormal escalator. The cluster benchmarking module in the system can perform cluster benchmarking on various vibration characteristic parameters (such as early warning evaluation value, peak-to-peak value, effective value, kurtosis, pulse index, waveform index, margin index, skewness index and the like) of each escalator under different loads, and an escalator group corresponding to an outlier in a cluster benchmarking result can be regarded as an abnormal escalator.
Intelligent decision-making module
The intelligent decision module mainly comprises a visual management module and an operation and maintenance decision analysis module. The intelligent decision module mainly realizes decision and execution, and mainly comprises hierarchical maintenance of escalator fault early warning and visual display of analysis results, including escalator health state display, key component performance decline trend display, fault early warning information statistics and the like. Aiming at the graded maintenance of the escalator, different early warning grades are maintained according to the risk coefficients of different parts of the escalator, the early warning information is sent to related personnel for production, management, operation, maintenance and the like according to different early warning grades, and specific response time is determined according to different grades.
The visual management module can display the early warning state, the cluster benchmarks, the equipment health state, the performance decline of key components, the early warning information and the statistical analysis. Aiming at the statistics of the early warning information, the statistics of the early warning information of the whole machine, the early warning information of key components and the failure times can be carried out: the whole machine early warning information statistical function can be used for counting the early warning quantity generated by all escalators, and the health distribution condition of the whole machine can be visually displayed. The key component early warning information statistics can be used for counting the early warning number of key components of the escalator, and the health distribution condition of large components can be visually displayed. The failure times statistics can be used for carrying out statistics on the occurrence times of various failures related to a certain key measuring point in a certain time period. The failure frequency counting function can visually display the occurrence frequency of various failures, and operation and maintenance personnel on the failure site who occur for many times can pay key attention to the failure frequency counting function.
The operation and maintenance decision analysis module can adopt a grading maintenance strategy for the escalator equipment. According to the analysis result of the health assessment and fault prediction module, the alarm information is sent to related management, operation, maintenance and the like personnel aiming at different alarm levels, and meanwhile, the current dimension information such as spare part condition, maintenance time, maintenance and the like is considered, so that specific response time is determined, and hierarchical maintenance is realized. For example, oil supply and lubrication maintenance are scheduled in advance according to the prediction of the wear conditions of the drive chain and the step chain, so that the safety is guaranteed. Finally, the 'predictive' maintenance of the escalator in the subway station is realized to replace the traditional planned maintenance, and the intellectualization and effectiveness of equipment maintenance scheduling are improved.
As shown in fig. 3, a four quadrant graph similar to fig. 2 may be used to assist in the analysis. The abscissa may represent the effect caused by the damage to the component and the ordinate represents the frequency with which the damage to the component occurs. A coordinate system is divided into four quadrants by using a certain threshold value through a horizontal coordinate and a vertical coordinate, and each quadrant corresponds to different data acquisition strategies and maintenance strategies. The threshold value is not a fixed value, but is set after counting the failure information of the escalator, and can be adjusted as needed.
Quadrant 1: the components falling in this quadrant have high failure frequency and high failure severity, which are generally attributed to design defects, and should be fed back to the production side for design improvement.
Quadrant 2: although the parts falling in the quadrant have higher failure occurrence frequency, the parts are usually replaced in time by adding spare parts in time according to the degree of severity.
Quadrant 3: components falling in this quadrant, due to their low frequency of occurrence and low severity of failure, are often replaced by enhanced routine maintenance according to their designed average life.
Quadrant 4: although the frequency of failure of the component falling in this quadrant is low, the influence of the failure is serious, and therefore, predictive maintenance should be performed.
According to the maintenance strategy four-quadrant, the graded maintenance strategy of the escalator equipment group is as follows: a long-time escalator maintenance scheduling model is established by establishing a performance decline model of each escalator key component and considering dimension information such as risk coefficient, fault influence, emergency degree of current maintenance task, spare part condition, maintenance time and the like of each key component, so that the traditional planned maintenance is replaced, and the intelligence and effectiveness of equipment maintenance scheduling are improved.
Examples
The technical scheme of the application is introduced by taking the escalator of a certain subway station as an example. The subway station has a common escalator 10 part, and relates to four different escalator brands. The escalator operates for 19-20 hours every day, the flow of people is large in the morning and evening at peak time, and the load pressure of the escalator is large. In order to improve the quality of urban rail transit passenger service, generally, scheduled maintenance work of the escalator is generally arranged to be operated at night non-operation time, generally from zero to four points in the morning. The maintenance of the escalator in the subway station faces the following challenges: the equipment is loaded, the distribution is wide, the working condition is complex, and the maintenance strength of the equipment is high; the equipment brands are various, the data structure is not uniform, and the difficulty of equipment maintenance management is increased; the equipment maintenance is basically planned and maintained, and is highly dependent on people, and the redundant maintenance phenomenon is serious; the BAS system of the current subway can only monitor the running state of equipment, belongs to passive monitoring and lacks the health state evaluation of key components; and early warning after the fault, and failure prejudgment and risk assessment are lacked.
The system designed by the patent can realize predictive maintenance of the escalator. The escalator online health state monitoring system is not limited to manufacturers and interfaces of equipment, can deal with different working conditions, and achieves online health state assessment and cluster management of the escalator, so that accidental fault shutdown of the equipment is reduced, operation safety of the escalator is guaranteed, service state change trend of key parts of the escalator is monitored online, and data support and scientific basis are provided for making a reasonable maintenance plan.
The test of 6 months of the escalator implementation period and the experimental length of 4 hours or more each day contains test data of different load working conditions. The intelligent sensing module is used for realizing information acquisition of key components such as a driving motor, a speed reducer, a main driving chain, a step chain, a handrail belt, a band-type brake and the like, acquisition of output signals of an escalator controller and acquisition of escalator equipment information and maintenance information. The method comprises the steps of carrying out data preprocessing on original sample data acquired by an intelligent sensing module, then carrying out extraction, screening and classification on characteristic values, finally modeling the health decline conditions of all key parts of the escalator, calculating the health degree of all the key parts to evaluate the service state and the performance decline trend of the key parts, and further comprehensively evaluating the health state of the escalator. Taking the cluster benchmarking of the effective value of the vibration of the escalator driving motor base in a certain period of time as an example, a schematic diagram of the cluster benchmarking result is shown in fig. 6, and the escalator group corresponding to the outlier in the diagram can be regarded as an abnormal escalator.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The escalator full-life-cycle health management system based on predictive maintenance is characterized by comprising an intelligent sensing module, a health assessment and fault prediction module, an intelligent network module and an intelligent decision module;
the intelligent sensing module acquires data and information of the escalator;
the health evaluation and fault prediction module carries out health state evaluation and fault prediction on the whole equipment and key components through the data and information acquired by the intelligent sensing module;
the intelligent network module constructs a network environment capable of guiding an entity space, establishes a digital mirror image model mapped with an entity escalator system, forms a single equipment record of the escalator, and performs cluster management on the escalator;
and the intelligent decision module is used for visually displaying the analysis result and giving a maintenance suggestion according to the analysis result of the fault diagnosis.
2. The escalator full-life cycle health management system according to claim 1, wherein the intelligent sensing module comprises a data acquisition system, and the data acquisition system acquires external sensor data, escalator controller data, escalator equipment information and maintenance records.
3. The escalator full-life cycle health management system according to claim 2, wherein the data and information collected by the intelligent sensing module comprises: the escalator control system comprises escalator equipment information, escalator operation state measurement parameters, escalator operation working condition data, escalator use environment parameters and escalator maintenance records.
4. The escalator full-life cycle health management system according to any one of claims 1-3, wherein the intelligent network module comprises an escalator digital mirror module and a cluster management module;
the escalator digital mirror image module can check the field condition of the escalator and form a single equipment history of the escalator; the cluster management module is built with models of different grades and comprises a cluster benchmarking module; the cluster benchmarking module can perform cluster benchmarking on various vibration characteristic parameters of the escalators under different loads, and escalator groups corresponding to outliers in a cluster benchmarking result are regarded as abnormal escalators.
5. The escalator full-life cycle health management system according to any one of claims 1-3, wherein the intelligent decision module comprises a visual management module and an operation and maintenance decision analysis module;
the visual management module comprises early warning state display, cluster benchmarking display, equipment health state display, key component performance decline display, early warning information display and statistical analysis display;
the operation and maintenance decision analysis module can adopt a graded maintenance strategy for the escalator equipment and send alarm information according to the analysis result of the health assessment and fault prediction module.
6. The escalator full-life cycle health management system according to any one of claims 1-3, wherein the method for health assessment and fault prediction using the health assessment and fault prediction module comprises the steps of:
(1) data pre-processing
Denoising the acquired original sample data to improve the data quality;
(2) feature extraction
Extracting characteristic values of the sample data subjected to data preprocessing;
(3) feature selection
Selecting effective characteristics from the extracted characteristics as characteristic input of subsequent modeling;
(4) building health assessment model for key parts of escalator
The selected characteristic value is used for establishing a health assessment model of the key component, a sample is selected for training, and the health assessment model from the characteristic value to the health value is established;
(5) escalator health assessment
The health state of each key component of the escalator is evaluated in real time and the performance decline trend of each key component is predicted by inputting the original data of each key component measuring point collected in real time into each key component health evaluation model;
(6) fault prediction
And intelligently diagnosing the key components according to the health evaluation model of each key component, and giving fault reasons and maintenance suggestions by the system if the fault occurs.
7. The escalator full-life cycle health management system according to claim 4, wherein the method for health assessment and fault prediction using the health assessment and fault prediction module comprises the steps of:
(1) data pre-processing
Denoising the acquired original sample data to improve the data quality;
(2) feature extraction
Extracting characteristic values of the sample data subjected to data preprocessing;
(3) feature selection
Selecting effective characteristics from the extracted characteristics as characteristic input of subsequent modeling;
(4) building health assessment model for key parts of escalator
The selected characteristic value is used for establishing a health assessment model of the key component, a sample is selected for training, and the health assessment model from the characteristic value to the health value is established;
(5) escalator health assessment
The health state of each key component of the escalator is evaluated in real time and the performance decline trend of each key component is predicted by inputting the original data of each key component measuring point collected in real time into each key component health evaluation model;
(6) fault prediction
And intelligently diagnosing the key components according to the health evaluation model of each key component, and giving fault reasons and maintenance suggestions by the system if the fault occurs.
8. The escalator full-life cycle health management system according to claim 5, wherein the method for health assessment and fault prediction using the health assessment and fault prediction module comprises the steps of:
(1) data pre-processing
Denoising the acquired original sample data to improve the data quality;
(2) feature extraction
Extracting characteristic values of the sample data subjected to data preprocessing;
(3) feature selection
Selecting effective characteristics from the extracted characteristics as characteristic input of subsequent modeling;
(4) building health assessment model for key parts of escalator
The selected characteristic value is used for establishing a health assessment model of the key component, a sample is selected for training, and the health assessment model from the characteristic value to the health value is established;
(5) escalator health assessment
The health state of each key component of the escalator is evaluated in real time and the performance decline trend of each key component is predicted by inputting the original data of each key component measuring point collected in real time into each key component health evaluation model;
(6) fault prediction
And intelligently diagnosing the key components according to the health evaluation model of each key component, and giving fault reasons and maintenance suggestions by the system if the fault occurs.
9. The escalator full-life cycle health management system according to claim 2 or 3, wherein the data acquisition device comprises an edge data acquisition device provided with a drive motor acceleration sensor, a drive motor current monitor, a gear box vibration sensor, a main drive sprocket encoder, a step sprocket vibration sensor and a step sprocket encoder; the driving motor acceleration sensor is arranged on a bearing base of the motor, and the driving motor current monitor is used for monitoring three-phase current; the vibration sensor of the gear box is arranged on a bearing base of the gear box, and the vibration sensor of the main driving sprocket and the encoder of the main driving sprocket are arranged on a bearing seat of the main driving sprocket; the step chain wheel vibration sensor and the step chain wheel encoder are arranged on a bearing seat of the step chain wheel.
10. The escalator full-life cycle health management system according to claim 9, wherein the edge data acquisition equipment is further provided with a handrail temperature sensor, a band-type brake temperature sensor and a rotation speed sensor, the handrail temperature sensors are arranged on the left and right trusses of the handrail; the band-type brake temperature sensor is arranged on the surface of the band-type brake; the rotating speed sensor is arranged on the driving motor.
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