CN114267178A - Intelligent operation maintenance method and device for station - Google Patents
Intelligent operation maintenance method and device for station Download PDFInfo
- Publication number
- CN114267178A CN114267178A CN202111654341.7A CN202111654341A CN114267178A CN 114267178 A CN114267178 A CN 114267178A CN 202111654341 A CN202111654341 A CN 202111654341A CN 114267178 A CN114267178 A CN 114267178A
- Authority
- CN
- China
- Prior art keywords
- data
- result
- real
- maintenance
- equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The embodiment of the application discloses an intelligent operation maintenance method and device for a station. According to the technical scheme provided by the embodiment of the application, the running state data of each device in a station range is obtained in real time, so that the real-time data and the historical data of the running state of each device are obtained; performing intelligent analysis processing on the real-time data and the historical data of the running state of each equipment to obtain a health state result of each equipment, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result; and generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result. The technical scheme provided by the embodiment of the application can solve the problem of decentralized maintenance of station operation, and improves centralization and refinement of station operation maintenance.
Description
Technical Field
The embodiment of the application relates to the technical field of station operation, in particular to an intelligent operation maintenance method and device for a station.
Background
With the rapid development of national economy and the acceleration of urbanization process, urban rail transit enters a large development period. With the continuous increase of urban rail transit operation mileage and the continuous enlargement of wire network scale in China, the operation and maintenance pressure of a subway system rapidly rises, so that a subway operation service loss gap is in an increasing trend, and the method becomes an important adverse factor for restricting the sustainable development of urban rail transit in China.
In order to cope with the current situation that the loss gap of the existing subway operation business is in an ascending trend, the operation maintenance of a station needs to be intelligently upgraded. However, the current station intelligent operation and maintenance method and system mainly focus on a single-professional fault detection function, and cannot perform systematic operation and maintenance on all equipment in a station, so that operation and maintenance are decentralized, the resource utilization rate is low, and the operation cost is increased.
Disclosure of Invention
The embodiment of the application provides an intelligent operation maintenance method and device for a station, which can solve the problem of decentralized station operation maintenance and promote centralization and refinement of station operation maintenance.
In a first aspect, an embodiment of the present application provides an intelligent operation maintenance method for a station, including:
acquiring running state data of each device in a station range in real time to obtain real-time data and historical data of the running state of each device;
performing intelligent analysis processing on the real-time data and the historical data of the running state of each equipment to obtain a health state result of each equipment, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result;
and generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
Further, the obtaining of the running state data of each device in the station range in real time to obtain the real-time data and the historical data of the running state of each device specifically includes:
the method comprises the steps that running state data of each device are monitored in real time through a comprehensive monitoring system, a shielding door monitoring system, an automatic ticketing monitoring system, an environment monitoring system and an escalator state detection system, so that real-time data and historical data of the running state of each device are obtained, and the real-time data comprise real-time running data and real-time fault data;
and storing the real-time data and the historical data into a data storage device, and sending the real-time data and the historical data to a server background for intelligent analysis and processing.
Further, the intelligent analysis processing is performed on the real-time data and the historical data of the running state of each device to obtain the health state result of each device, and the method specifically comprises the following steps:
carrying out data preprocessing on the real-time data and the historical data of each device;
analyzing and processing the data after data preprocessing through different application containers;
and outputting the health state result of each device after analysis and processing.
Further, the fault diagnosis result comprises a performance evaluation result, a system abnormal information result, a fault type information result and a fault cause information result;
the intelligent analysis processing is carried out on the real-time data and the historical data of the running state of each equipment to obtain the health state result of each equipment, and the intelligent analysis processing specifically comprises the following steps:
preprocessing the real-time data and the historical data of the running state of each device, inputting the preprocessed real-time data and the preprocessed historical data into a preset performance evaluation model, extracting performance evaluation index data from the preset performance evaluation model, analyzing and processing the data, and outputting a corresponding performance evaluation result;
preprocessing the real-time data and the historical data of the running states of the equipment, and then processing the data through a clustering algorithm to obtain data sets corresponding to different working conditions;
inputting the data sets under different working conditions into corresponding different anomaly detection models for data analysis and processing, and outputting corresponding system anomaly information results, wherein the system anomaly information results comprise anomaly information results corresponding to different working conditions;
inputting data sets of different working conditions into a preset fault diagnosis model, extracting fault diagnosis index data from the fault diagnosis model for data analysis and processing, and outputting a fault type information result;
and inputting the historical data into a preset root cause analysis model, carrying out data analysis processing in the root cause analysis model, and outputting a fault cause information result.
Further, the health prediction result comprises a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result;
the intelligent analysis processing is carried out on the real-time data and the historical data of the running state of each equipment to obtain the health state result of each equipment, and the intelligent analysis processing specifically comprises the following steps:
screening the historical data of each device to obtain fault maintenance data;
and inputting the fault maintenance data into a preset first degradation model, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result.
Further, the method further comprises:
inputting the fault maintenance data into a preset second degradation model, and carrying out data analysis processing in the preset second degradation model to output a degradation period judgment result;
continuously monitoring equipment which does not enter the degradation period according to the degradation period judgment result;
and inputting the fault maintenance data of the equipment which enters the degradation period into a preset first degradation model according to the degradation period judgment result, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result.
Further, the generating of the maintenance and management strategy of the corresponding device according to the obtained health status result specifically includes:
according to the obtained health state evaluation result, an equipment maintenance strategy is formulated by combining assets, maintenance personnel work arrangement and material information, and a corresponding maintenance work task is sent to a corresponding maintenance work end according to the equipment maintenance strategy;
and formulating an equipment management strategy according to the obtained health state evaluation result, and sending a corresponding equipment management work task to a corresponding management work terminal according to the equipment management strategy.
In a second aspect, an embodiment of the present application provides an intelligent operation maintenance device for a station, including:
the data acquisition unit is used for acquiring the running state data of each device in a station range in real time to obtain the real-time data and the historical data of the running state of each device;
the result output unit is used for intelligently analyzing and processing the real-time data and the historical data of the running state of each device to obtain the health state result of each device, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result;
and the maintenance and management unit is used for generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
Further, the data acquisition unit is also used for carrying out real-time monitoring on the running state data of each device through a comprehensive monitoring system, a shielding door monitoring system, an automatic ticketing monitoring system, an environment monitoring system and an escalator state detection system to obtain real-time data and historical data of the running state of each device, wherein the real-time data comprises real-time running data and real-time fault data;
and storing the real-time data and the historical data into a data storage device, and sending the real-time data and the historical data to a server background for intelligent analysis and processing.
Further, the result output unit is further configured to perform data preprocessing on the real-time data and the historical data of each device;
analyzing and processing the data after data preprocessing through different application containers;
and outputting the health state result of each device after analysis and processing.
Further, the fault diagnosis result comprises a performance evaluation result, a system abnormal information result, a fault type information result and a fault cause information result;
the result output unit is further used for preprocessing the real-time data and the historical data of the running states of the equipment, inputting the preprocessed real-time data and the preprocessed historical data into a preset performance evaluation model, extracting performance evaluation index data from the preset performance evaluation model, analyzing and processing the data, and outputting a corresponding performance evaluation result;
preprocessing the real-time data and the historical data of the running states of the equipment, and then processing the data through a clustering algorithm to obtain data sets corresponding to different working conditions;
inputting the data sets under different working conditions into corresponding different anomaly detection models for data analysis and processing, and outputting corresponding system anomaly information results, wherein the system anomaly information results comprise anomaly information results corresponding to different working conditions;
inputting data sets of different working conditions into a preset fault diagnosis model, extracting fault diagnosis index data from the fault diagnosis model for data analysis and processing, and outputting a fault type information result;
and inputting the historical data into a preset root cause analysis model, carrying out data analysis processing in the root cause analysis model, and outputting a fault cause information result.
Further, the health prediction result comprises a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result;
the result output unit is also used for screening the historical data of each device to obtain fault maintenance data;
and inputting the fault maintenance data into a preset first degradation model, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result.
Further, the apparatus further comprises:
the result output module is also used for inputting the fault maintenance data into a preset second degradation model, and carrying out data analysis processing in the preset second degradation model to output a degradation period judgment result;
continuously monitoring equipment which does not enter the degradation period according to the degradation period judgment result;
and inputting the fault maintenance data of the equipment which enters the degradation period into a preset first degradation model according to the degradation period judgment result, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result.
Further, the maintenance and management unit is used for making an equipment maintenance strategy according to the obtained health state evaluation result by combining assets, maintenance personnel work arrangement and material information, and sending a corresponding maintenance work task to a corresponding maintenance work end according to the equipment maintenance strategy;
and formulating an equipment management strategy according to the obtained health state evaluation result, and sending a corresponding equipment management work task to a corresponding management work terminal according to the equipment management strategy.
In a third aspect, an embodiment of the present application provides an electronic device, including:
a memory and one or more processors;
the memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent operation and maintenance method for a station as described in the first aspect.
In a fourth aspect, the present application provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are used for executing the intelligent operation and maintenance method for a station as described in the first aspect.
According to the method and the device, the real-time data and the historical data of the running state of each device in the station are intelligently analyzed and processed, the health state result of each device is obtained and visually displayed, and the maintenance and management strategy of the corresponding device is generated according to the obtained health state result. By adopting the technical means, the health state results corresponding to the equipment can be obtained by carrying out intelligent analysis and processing on the running state data of the equipment and visually displayed, so that the equipment of the station is centrally monitored, and management personnel can conveniently check the health state results through the visual display, thereby improving the intellectualization and the centralization of the station operation and maintenance; and a maintenance and management strategy corresponding to the equipment is generated according to the obtained health state result of each equipment, so that the pertinence to the maintenance and management of the equipment is improved, and the refinement of the station operation maintenance is improved.
Drawings
Fig. 1 is a flowchart of an intelligent operation maintenance method for a station according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a data acquisition process according to a first embodiment of the present application;
FIG. 3 is a schematic diagram of a fault diagnosis process in the first embodiment of the present application;
FIG. 4 is a schematic diagram of a health prediction process according to a first embodiment of the present application;
fig. 5 is a schematic structural diagram of an intelligent operation maintenance device for a station according to a second embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The application provides an intelligent operation maintenance method and device for a station, and aims to obtain health state results of each device and visually display the health state results by intelligently analyzing and processing real-time data and historical data of operation states of each device in the station during operation maintenance of the station, and generate maintenance and management strategies corresponding to the devices according to the obtained health state results, so that the intellectualization, centralization and refinement of the operation maintenance of the station are improved. Compare in traditional station operation maintenance mode, it detects to the trouble of specialty usually, for example specially to the fault detection of platform door, can't carry out systematic operation and maintenance to all equipment in the station, leads to the operation maintenance decentralization of station to lead to the utilization ratio of resource not high, and then lead to the increase of operation cost. Based on this, the intelligent operation maintenance method for the station provided by the embodiment of the application is provided to solve the problem of decentralization of the existing station operation maintenance.
The first embodiment is as follows:
fig. 1 is a flowchart of an intelligent operation and maintenance method for a station according to an embodiment of the present disclosure, where the intelligent operation and maintenance method for a station provided in this embodiment may be executed by an intelligent operation and maintenance device for a station, the intelligent operation and maintenance device for a station may be implemented in a software and/or hardware manner, and the intelligent operation and maintenance device for a station may be composed of two or more physical entities or may be composed of one physical entity. Generally, the intelligent operation and maintenance device of the station may be a terminal device, such as a computer device.
The following description will be given taking a computer device as an example of a main body for executing an intelligent operation maintenance method for a station. Referring to fig. 1, the intelligent operation maintenance method for the station specifically includes:
s101, acquiring running state data of each device in a station range in real time to obtain real-time data and historical data of running states of each device.
Integrated monitoring systems (ISCS) are large computer synthesis systems based on modern computer technology, network technology, automation technology and information technology. The integrated monitoring system (ISCS) integrates a plurality of automatic professional subsystems, and uniformly monitors each professional under the support of the integrated platform, so that the information sharing of each professional system and the linkage control function among the systems are realized, the application efficiency is improved, and an informatization basis is provided for realizing urban rail transit operation management. The subsystems of the integrated monitoring system (ISCS) capable of being deeply integrated comprise a power monitoring subsystem (PSCADA), an environment and equipment monitoring subsystem (BAS), a Fire Alarm Subsystem (FAS) and a screen door system (PSD), and the subsystems of the integrated monitoring system (ISCS) capable of being interconnected comprise a signal System (SIG), an automatic ticket selling system (AFC), a closed circuit television system (CCTV), a broadcasting system (PA), an Access Control System (ACS), a Passenger Information System (PIS) and a clock system (CLK).
Referring to fig. 2, the operation state data of various devices in a station yard is comprehensively monitored in real time by an integrated monitoring system (ISCS) including power devices, fire alarm devices, broadcasting devices, platform door systems, and the like. The method comprises the steps of monitoring the operation state data of each shielding door in real time through a shielding door monitoring system (PSD monitoring system), wherein the operation state data of each shielding door comprises a current value, a voltage value, power, an opening and closing state, a fault type, fault times and the like. The method comprises the steps of detecting running state data of each automatic ticket vending machine in real time through an automatic ticket selling monitoring system (AFC), wherein the running state data of each automatic ticket vending machine comprises ticket selling quantity, running speed of the ticket vending system, failure times, failure types and the like. The running state data of each environment monitoring device is monitored in real time through an environment monitoring system, and the environment monitoring devices comprise temperature acquisition devices, closed circuit television devices, smoke monitoring devices and the like. The method comprises the steps of monitoring the running state data of each escalator in real time through an escalator state detection system, wherein the running state data of each escalator comprises a current value, a voltage value, running power, failure times, failure types and the like.
The method comprises the steps that running state data of each device are monitored in real time through a comprehensive monitoring system, a shielding door monitoring system, an automatic ticketing monitoring system, an environment monitoring system and an escalator state detection system, so that real-time data and historical data of the running state of each device are obtained, and the real-time data comprise real-time running data and real-time fault data; and storing the real-time data and the historical data into a data storage device, and sending the real-time data and the historical data to a server background for intelligent analysis and processing.
And S102, intelligently analyzing and processing the real-time data and the historical data of the running state of each equipment to obtain the health state result of each equipment, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result.
The method comprises the steps of obtaining time series data by integrating real-time data received in real time and historical data obtained from data storage equipment, and carrying out data preprocessing on the time series data formed by the real-time data and the historical data of each equipment, wherein the data preprocessing comprises the steps of carrying out data cleaning on the data, putting the cleaned data into a corresponding message queue, carrying out pipeline management on the message queue and carrying out distributed computation on the message queue. And analyzing and processing the data after data preprocessing through different application containers, wherein different functions realized through the different application containers comprise centralized early warning analysis, cause analysis, abnormality detection analysis, availability analysis, health state analysis, energy consumption analysis, root cause analysis, load analysis and the like. And after the analysis processing is carried out through different application containers, the health state results of the analyzed and processed equipment are output and are visually displayed. The visual display can display corresponding data through an electronic display screen, and can also display a corresponding analysis result curve graph or a three-dimensional chart of the analysis result through the electronic display screen.
Further, referring to fig. 3, the health status result includes a fault diagnosis result and a health prediction result. The fault diagnosis result comprises a performance evaluation result, a system abnormal information result, a fault type information result and a fault cause information result.
And preprocessing the real-time data and the historical data of the running state of each device, inputting the preprocessed data into a preset performance evaluation model, extracting performance evaluation index data from the preset performance evaluation model, analyzing and processing the data, and outputting a corresponding performance evaluation result. The performance evaluation result is that the performance and the state of the system are quantitatively graded by defining and calculating key performance indexes capable of reflecting the performance state of the system, and the grading result is visually displayed on the user side in a report form.
Preprocessing the real-time data and the historical data of the running states of the equipment, and then processing the data through a clustering algorithm to obtain data sets corresponding to different working conditions; and inputting the data sets under different working conditions into corresponding different anomaly detection models for data analysis and processing, and outputting corresponding system anomaly information results, wherein the system anomaly information results comprise anomaly information results corresponding to different working conditions. The system anomaly information comprises anomaly types and anomaly probabilities. The anomaly detection model is obtained by training through an unsupervised machine learning algorithm, such as a random forest, a One-Class SVM and the like, a statistical probability algorithm or an approximation contrast algorithm and the like. The abnormal information result of the system is obtained by analyzing and processing the data through the abnormal detection model, and real-time or periodic abnormal detection can be carried out according to the requirements of users, so that multi-dimensional and multi-standard abnormal detection and grading early warning under different working conditions are realized.
In an embodiment, the construction process of the anomaly detection model includes preprocessing data, constructing a feature project, constructing and training a model according to the preprocessed data and the feature project to obtain a corresponding preliminary anomaly detection model, and evaluating and optimizing the preliminary anomaly detection model to obtain a final anomaly detection model. And (4) carrying out data processing on the data of different working conditions on the final abnormal detection model after training and optimization, and outputting a corresponding abnormal information result. The data preprocessing comprises classifying original data into data under different working conditions, extracting information of the data under each working condition, cleaning the data, and realizing data classification and abnormal data elimination. Taking the belt running condition that the working condition is in the sliding work of the shielding door as an example, the data classification mainly separates the data related to the belt running condition from the original data, such as the belt vibration frequency, the belt vibration strength, the belt vibration horizontal deviation value and the like, and the data obtained by the specific data classification can be obtained by classification according to the actual situation. Before model training, the data separated by data classification is analyzed to construct a model of feature engineering. As the operation mechanisms of all the devices are different, the characteristic extraction mode can be extracted according to the actual situation.
Illustratively, the extracted feature quantities are a belt vibration frequency value, a belt vibration intensity value, a belt vibration level offset value, and the like. And constructing and training a model according to the preprocessed data and the feature engineering, wherein different types of target equipment have different principles and failure mechanisms. Therefore, it is necessary to model each operating condition of each device to improve the detection of the abnormality and the fault corresponding to the specific operating condition. The present embodiment is described by taking the belt operating condition of the shield door as an example. Separating a belt vibration frequency value, a belt vibration strength value and belt vibration horizontal offset value data of a shield door belt from the original data; extracting characteristic quantities which are a belt vibration frequency value, a belt vibration strength value and a belt vibration level deviation value; the mechanism model for detecting the belt abnormality is Conb＝S(fi,bi,ii) Wherein ConbRepresenting the belt state function, fiRepresenting the vibration frequency value of the belt, the vibration frequency of a tighter belt is higher, the vibration frequency of a looser belt is lower, biRepresenting the vibration intensity of the belt, taking the initial vibration horizontal line of the belt as a base line, and when the vibration horizontal line of the belt deviates obviously, indicating that the belt state is abnormal, iiRepresenting belt vibration level deflection values, a looser belt has a greater amplitude and a tighter belt has a lesser amplitude. Belt state function S (f) by using One-Class SVM algorithmi,bi,ii) And training the formed state space, determining the boundary of the normal state and the abnormal state, and obtaining a preliminary abnormal detection model. And carrying out anomaly detection according to the boundary between the normal state and the abnormal state, and outputting a corresponding anomaly information result.
Because the unsupervised machine learning algorithm model is used in the anomaly detection in a high probability, the evaluation mode of the model is to find the anomaly points and then manually confirm the anomaly points, that is, the output of the model only has two states of anomaly and normal, so the algorithm can be evaluated by using the statistics in the binary classification confusion matrix, and the form of the binary classification confusion matrix is as follows:
| predicted value is 1 | Predicted value is 0 | |
| True value of 1 | TP | FN |
| True value of 0 | FP | TN |
Four main model evaluation indices can be derived from the above table: true Positive (TP): the true class of the sample is a positive example, and the result of the model prediction is also a positive example. True Negative (True Negative, TN): the true class of the sample is a negative case and the model predicts it as a negative case. False Positive (FP): the true class of a sample is a negative example, but the model predicts it as a positive example. False Negative (FN): the true class of a sample is a positive example, but the model predicts it as a negative example. Different from the general machine learning model optimization direction, due to actual business needs, the intelligent operation and maintenance abnormity detection usually needs to reduce extra workload brought to the maintainers by false alarm as much as possible. Therefore, there is a need in training with use to optimize model performance by reducing False Positives (FP). Therefore, the preliminary abnormal detection model is optimized for reducing the expression of False Positives (FP) to obtain a final abnormal detection model, and a corresponding abnormal information result is output according to the final abnormal detection model.
And deploying the trained final anomaly detection model in the local equipment of the user, and capturing the drift of the data after a period of time through periodic training. And taking the real-time data as detection data, and obtaining an abnormal information result through model calculation, wherein the abnormal information result comprises an abnormal type and an abnormal probability.
Preprocessing the real-time data and the historical data of the running states of the equipment, and then processing the data through a clustering algorithm to obtain data sets corresponding to different working conditions; and inputting the data sets of different working conditions into a preset fault diagnosis model, extracting fault diagnosis index data from the fault diagnosis model to perform data analysis and processing, and outputting a fault type information result. The fault diagnosis model is obtained by performing adjustment and training by using a signal processing technology such as FFT (fast Fourier transform), envelope adjustment, signal decomposition, a data mining technology or a machine learning technology, and based on historical data of the running state of the target equipment, the fault diagnosis model positions the fault position and the fault type of the target equipment in the running process. The fault diagnosis model is deployed on site, the early warning information is compared with the features in the fault feature library and matched, when the mapping included angle between the early warning information and a certain fault feature is smaller than a threshold value, the fault diagnosis model judges that the early warning information belongs to a certain type of fault, and the fault diagnosis model is confirmed by a user side and then stored. And analyzing and processing the fault diagnosis model to obtain corresponding fault characteristics, and outputting corresponding pre-matched early warning information according to the fault characteristics.
And inputting the historical data into a preset root cause analysis model, carrying out data analysis processing in the root cause analysis model, and outputting a fault cause information result. The root cause analysis mode is established by constructing a target system fault tree by using a Bayesian network, calculating the prior probability of each node by using historical fault data and an algorithm, and analyzing the fault dependency of the coupling relation of the sub-components.
Further, referring to fig. 4, the health prediction result includes a remaining life prediction result, a failure type occurrence probability prediction result, and a reliability change trend prediction result.
Screening the historical data of each device to obtain fault maintenance data; and inputting the fault maintenance data into a preset first degradation model, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result.
Exemplarily, screening historical data of each device to obtain fault maintenance data; inputting the fault maintenance data into a preset first degradation model, extracting corresponding degradation index data from the preset first degradation model, performing data analysis processing to obtain a corresponding fitted degradation curve, analyzing according to a life ending threshold of a preset degradation index and the fitted degradation curve to obtain the remaining service life of corresponding equipment, outputting a corresponding remaining life prediction result, and performing visual display in a report form at a user side.
Exemplarily, screening historical data of each device to obtain fault maintenance data; the method comprises the steps of inputting fault maintenance data into a preset first degradation model, fitting the health index score of target equipment in the preset first degradation model, calculating the probability of occurrence of certain faults of the target equipment, judging whether the target equipment has obvious degradation or unstable states according to the change rule of the probability of occurrence of the faults in a period of time, and predicting the probability of occurrence of the faults in a future period of time according to the judgment result. For example, the method includes the steps of predicting faults of an escalator which stops running, screening historical data of the escalator to obtain fault maintenance data of the escalator, inputting the fault maintenance data of the escalator into a preset first degradation model, analyzing the fault maintenance data of the escalator in the preset first degradation model to obtain corresponding health index grading data, fitting the health index grading data to obtain a corresponding health index curve, calculating a corresponding fault occurrence probability curve according to the health index curve, analyzing according to a change rule of the fault occurrence probability curve, and predicting the probability that the escalator stops running in a future period of time.
Exemplarily, screening historical data of each device to obtain fault maintenance data; inputting the fault maintenance data into a preset first degradation model, obtaining a corresponding failure change curve based on Weibull analysis processing in the preset first degradation model, obtaining a corresponding reliability change trend prediction result according to the failure change curve, and visually displaying the corresponding reliability change trend prediction result on a user side in a chart form. According to the reliability change trend prediction result, a corresponding maintenance strategy can be formulated and sent to the management working end, so that the storage and maintenance personnel of the parts can be reasonably arranged, and the maintenance accuracy and effectiveness can be improved.
Further, inputting the fault maintenance data into a preset second degradation model, and performing data analysis processing in the preset second degradation model to output a degradation period judgment result; continuously monitoring equipment which does not enter the degradation period according to the degradation period judgment result; and inputting the fault maintenance data of the equipment which enters the degradation period into a preset first degradation model according to the degradation period judgment result, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result.
Illustratively, fault maintenance data are input into a preset second degradation model, health index data are extracted from the preset second degradation model to be subjected to data analysis processing, degradation period judgment results are obtained, the judgment results comprise a degradation period entering state, a non-degradation period entering state and a degradation period entering state and are fatigue loss, and the degradation period judgment results are visually displayed at a user side. And continuously monitoring the equipment which does not enter the degradation period according to the degradation period judgment result. Inputting the fault maintenance data of the equipment which enters the degradation period into a preset first degradation model according to the degradation period judgment result, extracting corresponding degradation index data from the preset first degradation model, carrying out data analysis processing to obtain a corresponding fitted degradation curve, analyzing according to a life ending threshold value of a preset degradation index and the fitted degradation curve to obtain the residual service life of the corresponding equipment, outputting a corresponding residual life prediction result, and carrying out visual display on a user side in a form of a report. And further, inputting fault maintenance data which enters the degradation period and is fatigue loss equipment into a third degradation model according to the degradation period judgment result, extracting environmental covariate data from the third degradation model for data analysis and processing, and outputting a corresponding residual life prediction result.
And S103, generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
And according to the obtained health state evaluation result, an equipment maintenance strategy is formulated by combining assets, maintenance personnel work arrangement and material information, a corresponding maintenance work task is sent to a corresponding maintenance work end according to the equipment maintenance strategy, and the maintenance work end allocates corresponding substances and maintenance personnel and arranges the maintenance work according to the received maintenance strategy. And formulating an equipment management strategy according to the obtained health state evaluation result, and sending a corresponding equipment management work task to a corresponding management work terminal according to the equipment management strategy. And the management working end carries out corresponding management working arrangement according to the received management strategy.
Compared with the traditional station equipment maintenance mode with planned maintenance as a main mode and with a small amount of state maintenance as an auxiliary mode, the station intelligent operation maintenance method has the advantages that the intelligent detection equipment is equipped to monitor the real-time operation state of the equipment in the station range, and real-time data and historical data of the real-time operation state are obtained; a data analysis system is equipped to intelligently analyze real-time data and historical data of a real-time running state, so that fault diagnosis and health prediction of the system, equipment and elements are realized, and visual presentation is carried out; meanwhile, a production management plate is associated to provide data support for optimizing operation maintenance management of the station; and a production management plate is equipped to perform platform integration on a maintenance strategy, a management working strategy, material management, intelligent storage, a maintenance management platform, maintenance equipment and the like, so that mutual information intercommunication and paperless management of information are realized. In the embodiment, the operation and maintenance of the paperless station can be realized, and the utilization rate of station resources is improved; the utilization rate of station resources is improved; the overall availability of the rail transit is improved, the failure rate of equipment in a station battlefield is reduced, and the operation and maintenance cost of the full life cycle of station equipment is optimized.
The real-time data and the historical data of the running state of each equipment in the station are intelligently analyzed and processed to obtain the health state result of each equipment and visually display the health state result, and the maintenance and management strategy of the corresponding equipment is generated according to the obtained health state result. By adopting the technical means, the health state results corresponding to the equipment can be obtained by carrying out intelligent analysis and processing on the running state data of the equipment and visually displayed, so that the equipment of the station is centrally monitored, and management personnel can conveniently check the health state results through the visual display, thereby improving the intellectualization and the centralization of the station operation and maintenance; and a maintenance and management strategy corresponding to the equipment is generated according to the obtained health state result of each equipment, so that the pertinence to the maintenance and management of the equipment is improved, and the refinement of the station operation maintenance is improved.
Example two:
on the basis of the foregoing embodiment, fig. 5 is a schematic structural diagram of an intelligent operation maintenance device for a station according to a second embodiment of the present application. Referring to fig. 5, the intelligent operation maintenance device for a station provided in this embodiment specifically includes: a data acquisition unit 21, a result output unit 22, and a maintenance and management unit 23.
The data acquisition unit 21 is configured to acquire running state data of each device in a station range in real time to obtain real-time data and historical data of running states of each device;
the result output unit 22 is configured to perform intelligent analysis processing on the real-time data and the historical data of the operation states of the devices to obtain health state results of the devices, and perform visual display on the health state results, where the health state results include fault diagnosis results and health prediction results;
and the maintenance and management unit 23 is used for generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
Further, the data obtaining unit 21 is further configured to perform real-time monitoring on the operation state data of each device through a comprehensive monitoring system, a screen door monitoring system, an automatic ticketing monitoring system, an environment monitoring system and an escalator state detection system to obtain real-time data and historical data of the operation state of each device, where the real-time data includes real-time operation data and real-time fault data; and storing the real-time data and the historical data into a data storage device, and sending the real-time data and the historical data to a server background for intelligent analysis and processing.
Further, the result output unit 22 is further configured to perform data preprocessing on the real-time data and the historical data of each device; analyzing and processing the data after data preprocessing through different application containers; and outputting the health state result of each device after analysis and processing.
Further, the fault diagnosis result comprises a performance evaluation result, a system abnormal information result, a fault type information result and a fault cause information result;
the result output unit 22 is further configured to perform data preprocessing on the real-time data and the historical data of the operation states of the devices, input the preprocessed real-time data and the preprocessed historical data into a preset performance evaluation model, extract performance evaluation index data from the preset performance evaluation model to perform data analysis processing, and output a corresponding performance evaluation result; preprocessing the real-time data and the historical data of the running states of the equipment, and then processing the data through a clustering algorithm to obtain data sets corresponding to different working conditions; inputting the data sets under different working conditions into corresponding different anomaly detection models for data analysis and processing, and outputting corresponding system anomaly information results, wherein the system anomaly information results comprise anomaly information results corresponding to different working conditions; inputting data sets of different working conditions into a preset fault diagnosis model, extracting fault diagnosis index data from the fault diagnosis model for data analysis and processing, and outputting a fault type information result; and inputting the historical data into a preset root cause analysis model, carrying out data analysis processing in the root cause analysis model, and outputting a fault cause information result.
Further, the health prediction result comprises a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result;
the result output unit 22 is further configured to perform screening processing on the historical data of each device to obtain fault maintenance data; and inputting the fault maintenance data into a preset first degradation model, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result.
Further, the apparatus further comprises:
the result output module 22 is further configured to input the fault maintenance data into a preset second degradation model, perform data analysis processing in the preset second degradation model, and output a degradation period determination result; continuously monitoring equipment which does not enter the degradation period according to the degradation period judgment result; and inputting the fault maintenance data of the equipment which enters the degradation period into a preset first degradation model according to the degradation period judgment result, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result.
Further, the maintenance and management unit 23 is configured to formulate an equipment maintenance strategy according to the obtained health status evaluation result by combining assets, maintenance staff work arrangement and material information, and send a corresponding maintenance work task to a corresponding maintenance work end according to the equipment maintenance strategy; and formulating an equipment management strategy according to the obtained health state evaluation result, and sending a corresponding equipment management work task to a corresponding management work terminal according to the equipment management strategy.
In the embodiment of the application, the real-time data and the historical data of the operation state of each equipment in the station are intelligently analyzed and processed to obtain the health state result of each equipment, the health state result is visually displayed, and the maintenance and management strategy of the corresponding equipment is generated according to the obtained health state result. By adopting the technical means, the health state results corresponding to the equipment can be obtained by carrying out intelligent analysis and processing on the running state data of the equipment and visually displayed, so that the equipment of the station is centrally monitored, and management personnel can conveniently check the health state results through the visual display, thereby improving the intellectualization and the centralization of the station operation and maintenance; and a maintenance and management strategy corresponding to the equipment is generated according to the obtained health state result of each equipment, so that the pertinence to the maintenance and management of the equipment is improved, and the refinement of the station operation maintenance is improved.
The station intelligent operation maintenance device provided by the second embodiment of the present application can be used for executing the station intelligent operation maintenance method provided by the first embodiment of the present application, and has corresponding functions and beneficial effects.
Example three:
an embodiment of the present application provides an electronic device, and with reference to fig. 6, the electronic device includes: a processor 31, a memory 32, a communication module 33, an input device 34, and an output device 35. The number of processors in the electronic device may be one or more, and the number of memories in the electronic device may be one or more. The processor, memory, communication module, input device, and output device of the electronic device may be connected by a bus or other means.
The memory 32 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the intelligent operation and maintenance method for a station according to any embodiment of the present application (for example, a data acquisition unit, a result output unit, and a maintenance and management unit in an intelligent operation and maintenance device for a station). The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to use of the device, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The communication module 33 is used for data transmission.
The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory, that is, the above-mentioned intelligent operation and maintenance method for the station is realized.
The input device 34 may be used to receive entered numeric or character information and to generate key signal inputs relating to user settings and function controls of the apparatus. The output device 35 may include a display device such as a display screen.
The electronic device provided by the embodiment can be used for executing the intelligent operation maintenance method for the station provided by the embodiment one, and has corresponding functions and beneficial effects.
Example four:
an embodiment of the present application further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform an intelligent operation and maintenance method for a station, where the intelligent operation and maintenance method for a station includes: acquiring running state data of each device in a station range in real time to obtain real-time data and historical data of the running state of each device; performing intelligent analysis processing on the real-time data and the historical data of the running state of each equipment to obtain a health state result of each equipment, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result; and generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations, e.g., in different computer systems connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application and containing computer-executable instructions is not limited to the above-mentioned intelligent operation and maintenance method for a station, and may also perform related operations in the intelligent operation and maintenance method for a station provided in any embodiment of the present application.
The intelligent operation and maintenance device, the storage medium, and the electronic device for a station provided in the foregoing embodiments may execute the intelligent operation and maintenance method for a station provided in any embodiment of the present application, and refer to the intelligent operation and maintenance method for a station provided in any embodiment of the present application without detailed technical details described in the foregoing embodiments.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (10)
1. An intelligent operation maintenance method for a station is characterized by comprising the following steps:
acquiring running state data of each device in a station range in real time to obtain real-time data and historical data of the running state of each device;
performing intelligent analysis processing on the real-time data and the historical data of the running state of each equipment to obtain a health state result of each equipment, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result;
and generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
2. The intelligent operation maintenance method for the station as claimed in claim 1, wherein the obtaining of the running state data of each device in the station range in real time to obtain the real-time data and the historical data of the running state of each device specifically comprises:
the method comprises the steps that running state data of each device are monitored in real time through a comprehensive monitoring system, a shielding door monitoring system, an automatic ticketing monitoring system, an environment monitoring system and an escalator state detection system, so that real-time data and historical data of the running state of each device are obtained, and the real-time data comprise real-time running data and real-time fault data;
and storing the real-time data and the historical data into a data storage device, and sending the real-time data and the historical data to a server background for intelligent analysis and processing.
3. The intelligent operation maintenance method for the station according to claim 1, wherein the real-time data and the historical data of the operation state of each device are intelligently analyzed and processed to obtain the health state result of each device, and specifically:
carrying out data preprocessing on the real-time data and the historical data of each device;
analyzing and processing the data after data preprocessing through different application containers;
and outputting the health state result of each device after analysis and processing.
4. The intelligent operation maintenance method for the station according to claim 1, wherein the fault diagnosis result includes a performance evaluation result, a system abnormality information result, a fault type information result, and a fault cause information result;
the intelligent analysis processing is carried out on the real-time data and the historical data of the running state of each equipment to obtain the health state result of each equipment, and the intelligent analysis processing specifically comprises the following steps:
preprocessing the real-time data and the historical data of the running state of each device, inputting the preprocessed real-time data and the preprocessed historical data into a preset performance evaluation model, extracting performance evaluation index data from the preset performance evaluation model, analyzing and processing the data, and outputting a corresponding performance evaluation result;
preprocessing the real-time data and the historical data of the running states of the equipment, and then processing the data through a clustering algorithm to obtain data sets corresponding to different working conditions;
inputting the data sets under different working conditions into corresponding different anomaly detection models for data analysis and processing, and outputting corresponding system anomaly information results, wherein the system anomaly information results comprise anomaly information results corresponding to different working conditions;
inputting data sets of different working conditions into a preset fault diagnosis model, extracting fault diagnosis index data from the fault diagnosis model for data analysis and processing, and outputting a fault type information result;
and inputting the historical data into a preset root cause analysis model, carrying out data analysis processing in the root cause analysis model, and outputting a fault cause information result.
5. The intelligent operation maintenance method for the station as claimed in claim 1, wherein the health prediction result comprises a remaining life prediction result, a failure type occurrence probability prediction result and a reliability change trend prediction result;
the intelligent analysis processing is carried out on the real-time data and the historical data of the running state of each equipment to obtain the health state result of each equipment, and the intelligent analysis processing specifically comprises the following steps:
screening the historical data of each device to obtain fault maintenance data;
and inputting the fault maintenance data into a preset first degradation model, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result, a fault type occurrence probability prediction result and a reliability change trend prediction result.
6. The intelligent operation and maintenance method for the station as claimed in claim 5, wherein the method further comprises:
inputting the fault maintenance data into a preset second degradation model, and carrying out data analysis processing in the preset second degradation model to output a degradation period judgment result;
continuously monitoring equipment which does not enter the degradation period according to the degradation period judgment result;
and inputting the fault maintenance data of the equipment which enters the degradation period into a preset first degradation model according to the degradation period judgment result, and carrying out data analysis processing in the preset first degradation model to output a residual life prediction result.
7. The intelligent operation and maintenance method for the station according to claim 1, wherein the maintenance and management strategy for the corresponding device is generated according to the obtained health status result, and specifically comprises:
according to the obtained health state evaluation result, an equipment maintenance strategy is formulated by combining assets, maintenance personnel work arrangement and material information, and a corresponding maintenance work task is sent to a corresponding maintenance work end according to the equipment maintenance strategy;
and formulating an equipment management strategy according to the obtained health state evaluation result, and sending a corresponding equipment management work task to a corresponding management work terminal according to the equipment management strategy.
8. The utility model provides an intelligent operation maintenance device at station which characterized in that includes:
the data acquisition unit is used for acquiring the running state data of each device in a station range in real time to obtain the real-time data and the historical data of the running state of each device;
the result output unit is used for intelligently analyzing and processing the real-time data and the historical data of the running state of each device to obtain the health state result of each device, and performing visual display, wherein the health state result comprises a fault diagnosis result and a health prediction result;
and the maintenance and management unit is used for generating a maintenance and management strategy of the corresponding equipment according to the obtained health state result.
9. An electronic device, comprising:
a memory and one or more processors;
the memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the method of any one of claims 1-7 when executed by a computer processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111654341.7A CN114267178B (en) | 2021-12-30 | 2021-12-30 | Intelligent operation maintenance method and device for station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111654341.7A CN114267178B (en) | 2021-12-30 | 2021-12-30 | Intelligent operation maintenance method and device for station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114267178A true CN114267178A (en) | 2022-04-01 |
| CN114267178B CN114267178B (en) | 2023-09-26 |
Family
ID=80831820
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111654341.7A Active CN114267178B (en) | 2021-12-30 | 2021-12-30 | Intelligent operation maintenance method and device for station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114267178B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115204536A (en) * | 2022-09-16 | 2022-10-18 | 苏州智能交通信息科技股份有限公司 | Building equipment fault prediction method, device, equipment and storage medium |
| CN115659812A (en) * | 2022-10-29 | 2023-01-31 | 思维实创(哈尔滨)科技有限公司 | Escalator service life prediction method, system, equipment and medium based on urban rail ISCS |
| CN115759357A (en) * | 2022-10-29 | 2023-03-07 | 思维实创(哈尔滨)科技有限公司 | Power supply equipment safety prediction method, system, equipment and medium based on PSCADA data |
| CN116418421A (en) * | 2023-06-09 | 2023-07-11 | 北京神州明达高科技有限公司 | Communication equipment detection method based on frequency reception |
| CN117558134A (en) * | 2024-01-12 | 2024-02-13 | 北京城市轨道交通咨询有限公司 | Operation and maintenance management system, method, equipment and medium for rail transit vehicle |
Citations (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1787116A (en) * | 2005-11-10 | 2006-06-14 | 上海交通大学 | System for intelligent maintaince of muclear power paltn based on state monitoring |
| CN102313577A (en) * | 2011-06-24 | 2012-01-11 | 上海大学 | Equipment health state evaluation and recession prediction method based on multi-channel sensing signals |
| CN103310051A (en) * | 2013-06-05 | 2013-09-18 | 天泽信息产业股份有限公司 | Method for predicting failure rate of vehicle-mounted information terminal in entire life cycle |
| US20140120961A1 (en) * | 2012-10-26 | 2014-05-01 | Lookout, Inc. | System and method for secure message composition of security messages |
| CN104573850A (en) * | 2014-12-18 | 2015-04-29 | 湖南大唐先一科技有限公司 | Method for evaluating state of thermal power plant equipment |
| CN104766138A (en) * | 2015-03-27 | 2015-07-08 | 大唐淮南洛河发电厂 | Thermal power plant equipment property evaluation and early warning method and system based on industrial internet |
| US20170337098A1 (en) * | 2016-05-19 | 2017-11-23 | Chiun Mai Communication Systems, Inc. | Cloud device, terminal device, and method for handling abnormalities therein |
| CN108062088A (en) * | 2017-12-15 | 2018-05-22 | 广州广电运通金融电子股份有限公司 | Station terminal failure risk method for early warning, device, terminal and storage medium |
| CN108829933A (en) * | 2018-05-22 | 2018-11-16 | 北京天泽智云科技有限公司 | A kind of method of the predictive maintenance and health control of semiconductor manufacturing facility |
| CN109242104A (en) * | 2018-08-04 | 2019-01-18 | 大唐国际发电股份有限公司张家口发电厂 | A kind of system for analyzing real-time discovering device failure exception using data |
| CN110059775A (en) * | 2019-05-22 | 2019-07-26 | 湃方科技(北京)有限责任公司 | Rotary-type mechanical equipment method for detecting abnormality and device |
| CN110209999A (en) * | 2018-02-28 | 2019-09-06 | 广州地铁集团有限公司 | A kind of mobile unit failure trend prediction method |
| JP2019185422A (en) * | 2018-04-11 | 2019-10-24 | 株式会社Ｙｅ Ｄｉｇｉｔａｌ | Failure prediction method, failure prediction device, and failure prediction program |
| CN110516848A (en) * | 2019-07-31 | 2019-11-29 | 国网江苏省电力有限公司 | A kind of electric equipment maintenance Cost Optimization Approach based on survival analysis model |
| CN110597235A (en) * | 2019-10-10 | 2019-12-20 | 苏州知因扬鼎智能科技有限公司 | Universal intelligent fault diagnosis method |
| CN110658373A (en) * | 2019-09-30 | 2020-01-07 | 国网湖南省电力有限公司 | Power distribution network electricity stealing monitoring method based on multi-algorithm fusion, model and readable storage medium |
| CN110866365A (en) * | 2019-11-22 | 2020-03-06 | 北京航空航天大学 | Mechanical equipment intelligent fault diagnosis method based on partial migration convolutional network |
| CN110955651A (en) * | 2019-11-28 | 2020-04-03 | 武汉科技大学 | Motor fault data enhancement method based on deep convolution generation type countermeasure network |
| US20200209842A1 (en) * | 2017-09-06 | 2020-07-02 | Nippon Telegraph And Telephone Corporation | Anomalous sound detection apparatus, anomaly model learning apparatus, anomaly detection apparatus, anomalous sound detection method, anomalous sound generation apparatus, anomalous data generation apparatus, anomalous sound generation method and program |
| CN111401583A (en) * | 2020-03-18 | 2020-07-10 | 北京天泽智云科技有限公司 | Escalator full life cycle health management system based on predictive maintenance |
| CN111402106A (en) * | 2020-03-16 | 2020-07-10 | 北京锦鸿希电信息技术股份有限公司 | Device management method, device, system and storage medium |
| CN111563692A (en) * | 2020-05-20 | 2020-08-21 | 深圳达实智能股份有限公司 | Intelligent operation and maintenance system for rail transit |
| CN111736636A (en) * | 2020-07-10 | 2020-10-02 | 国电大渡河枕头坝发电有限公司 | Flooded waterwheel room early warning method and system based on knowledge graph |
| CN111985546A (en) * | 2020-08-10 | 2020-11-24 | 西北工业大学 | Aircraft engine multi-working-condition detection method based on single-classification extreme learning machine algorithm |
| CN112017195A (en) * | 2020-08-26 | 2020-12-01 | 上海三维工程建设咨询有限公司 | Intelligent integrated monitoring system applied to urban rail transit |
| CN112348205A (en) * | 2019-08-09 | 2021-02-09 | 比亚迪股份有限公司 | Rail transit operation and maintenance method, device, system, equipment and medium |
| CN112529232A (en) * | 2019-08-30 | 2021-03-19 | 比亚迪股份有限公司 | Station equipment fault prediction method and system and rail transit management system |
| US20210133607A1 (en) * | 2019-10-31 | 2021-05-06 | Shoreline Iot, Inc. | Systems and methods for self-learning artificial intelligence of things (aiot) devices and services |
| CN112859822A (en) * | 2021-04-25 | 2021-05-28 | 北京邮电大学 | Equipment health analysis and fault diagnosis method and system based on artificial intelligence |
| CN112884325A (en) * | 2021-02-22 | 2021-06-01 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Method and system for application analysis and health condition evaluation of customer station equipment |
| CN113033673A (en) * | 2021-03-24 | 2021-06-25 | 河南中烟工业有限责任公司 | Training method and system for motor working condition abnormity detection model |
| CN113189941A (en) * | 2021-03-16 | 2021-07-30 | 珠海市钰海电力有限公司 | Intelligent fault early warning method and system for power plant power generation equipment |
| CN113486571A (en) * | 2021-06-15 | 2021-10-08 | 浙江大学 | Method for predicting residual service life of machining equipment |
-
2021
- 2021-12-30 CN CN202111654341.7A patent/CN114267178B/en active Active
Patent Citations (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1787116A (en) * | 2005-11-10 | 2006-06-14 | 上海交通大学 | System for intelligent maintaince of muclear power paltn based on state monitoring |
| CN102313577A (en) * | 2011-06-24 | 2012-01-11 | 上海大学 | Equipment health state evaluation and recession prediction method based on multi-channel sensing signals |
| US20140120961A1 (en) * | 2012-10-26 | 2014-05-01 | Lookout, Inc. | System and method for secure message composition of security messages |
| CN103310051A (en) * | 2013-06-05 | 2013-09-18 | 天泽信息产业股份有限公司 | Method for predicting failure rate of vehicle-mounted information terminal in entire life cycle |
| CN104573850A (en) * | 2014-12-18 | 2015-04-29 | 湖南大唐先一科技有限公司 | Method for evaluating state of thermal power plant equipment |
| CN104766138A (en) * | 2015-03-27 | 2015-07-08 | 大唐淮南洛河发电厂 | Thermal power plant equipment property evaluation and early warning method and system based on industrial internet |
| US20170337098A1 (en) * | 2016-05-19 | 2017-11-23 | Chiun Mai Communication Systems, Inc. | Cloud device, terminal device, and method for handling abnormalities therein |
| US20200209842A1 (en) * | 2017-09-06 | 2020-07-02 | Nippon Telegraph And Telephone Corporation | Anomalous sound detection apparatus, anomaly model learning apparatus, anomaly detection apparatus, anomalous sound detection method, anomalous sound generation apparatus, anomalous data generation apparatus, anomalous sound generation method and program |
| CN108062088A (en) * | 2017-12-15 | 2018-05-22 | 广州广电运通金融电子股份有限公司 | Station terminal failure risk method for early warning, device, terminal and storage medium |
| CN110209999A (en) * | 2018-02-28 | 2019-09-06 | 广州地铁集团有限公司 | A kind of mobile unit failure trend prediction method |
| JP2019185422A (en) * | 2018-04-11 | 2019-10-24 | 株式会社Ｙｅ Ｄｉｇｉｔａｌ | Failure prediction method, failure prediction device, and failure prediction program |
| CN108829933A (en) * | 2018-05-22 | 2018-11-16 | 北京天泽智云科技有限公司 | A kind of method of the predictive maintenance and health control of semiconductor manufacturing facility |
| CN109242104A (en) * | 2018-08-04 | 2019-01-18 | 大唐国际发电股份有限公司张家口发电厂 | A kind of system for analyzing real-time discovering device failure exception using data |
| CN110059775A (en) * | 2019-05-22 | 2019-07-26 | 湃方科技(北京)有限责任公司 | Rotary-type mechanical equipment method for detecting abnormality and device |
| CN110516848A (en) * | 2019-07-31 | 2019-11-29 | 国网江苏省电力有限公司 | A kind of electric equipment maintenance Cost Optimization Approach based on survival analysis model |
| CN112348205A (en) * | 2019-08-09 | 2021-02-09 | 比亚迪股份有限公司 | Rail transit operation and maintenance method, device, system, equipment and medium |
| CN112529232A (en) * | 2019-08-30 | 2021-03-19 | 比亚迪股份有限公司 | Station equipment fault prediction method and system and rail transit management system |
| CN110658373A (en) * | 2019-09-30 | 2020-01-07 | 国网湖南省电力有限公司 | Power distribution network electricity stealing monitoring method based on multi-algorithm fusion, model and readable storage medium |
| CN110597235A (en) * | 2019-10-10 | 2019-12-20 | 苏州知因扬鼎智能科技有限公司 | Universal intelligent fault diagnosis method |
| US20210133607A1 (en) * | 2019-10-31 | 2021-05-06 | Shoreline Iot, Inc. | Systems and methods for self-learning artificial intelligence of things (aiot) devices and services |
| CN110866365A (en) * | 2019-11-22 | 2020-03-06 | 北京航空航天大学 | Mechanical equipment intelligent fault diagnosis method based on partial migration convolutional network |
| CN110955651A (en) * | 2019-11-28 | 2020-04-03 | 武汉科技大学 | Motor fault data enhancement method based on deep convolution generation type countermeasure network |
| CN111402106A (en) * | 2020-03-16 | 2020-07-10 | 北京锦鸿希电信息技术股份有限公司 | Device management method, device, system and storage medium |
| CN111401583A (en) * | 2020-03-18 | 2020-07-10 | 北京天泽智云科技有限公司 | Escalator full life cycle health management system based on predictive maintenance |
| CN111563692A (en) * | 2020-05-20 | 2020-08-21 | 深圳达实智能股份有限公司 | Intelligent operation and maintenance system for rail transit |
| CN111736636A (en) * | 2020-07-10 | 2020-10-02 | 国电大渡河枕头坝发电有限公司 | Flooded waterwheel room early warning method and system based on knowledge graph |
| CN111985546A (en) * | 2020-08-10 | 2020-11-24 | 西北工业大学 | Aircraft engine multi-working-condition detection method based on single-classification extreme learning machine algorithm |
| CN112017195A (en) * | 2020-08-26 | 2020-12-01 | 上海三维工程建设咨询有限公司 | Intelligent integrated monitoring system applied to urban rail transit |
| CN112884325A (en) * | 2021-02-22 | 2021-06-01 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | Method and system for application analysis and health condition evaluation of customer station equipment |
| CN113189941A (en) * | 2021-03-16 | 2021-07-30 | 珠海市钰海电力有限公司 | Intelligent fault early warning method and system for power plant power generation equipment |
| CN113033673A (en) * | 2021-03-24 | 2021-06-25 | 河南中烟工业有限责任公司 | Training method and system for motor working condition abnormity detection model |
| CN112859822A (en) * | 2021-04-25 | 2021-05-28 | 北京邮电大学 | Equipment health analysis and fault diagnosis method and system based on artificial intelligence |
| CN113486571A (en) * | 2021-06-15 | 2021-10-08 | 浙江大学 | Method for predicting residual service life of machining equipment |
Non-Patent Citations (3)
| Title |
|---|
| 孙毅;黄金龙;吴宁;蔡杰;: "向家坝电站运行设备异常状态识别功能的设计与实现", 西北水电, no. 04 * |
| 张志军: "《大数据技术在高效中的应用研究》", 北京邮电大学出版社 * |
| 王;韩林生;高艳波;李超;: "基于人工神经网络的海洋锚系浮标表层水温序列异常检测研究", 海洋技术学报, no. 05, pages 232 - 234 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115204536A (en) * | 2022-09-16 | 2022-10-18 | 苏州智能交通信息科技股份有限公司 | Building equipment fault prediction method, device, equipment and storage medium |
| CN115659812A (en) * | 2022-10-29 | 2023-01-31 | 思维实创(哈尔滨)科技有限公司 | Escalator service life prediction method, system, equipment and medium based on urban rail ISCS |
| CN115759357A (en) * | 2022-10-29 | 2023-03-07 | 思维实创(哈尔滨)科技有限公司 | Power supply equipment safety prediction method, system, equipment and medium based on PSCADA data |
| CN116418421A (en) * | 2023-06-09 | 2023-07-11 | 北京神州明达高科技有限公司 | Communication equipment detection method based on frequency reception |
| CN117558134A (en) * | 2024-01-12 | 2024-02-13 | 北京城市轨道交通咨询有限公司 | Operation and maintenance management system, method, equipment and medium for rail transit vehicle |
| CN117558134B (en) * | 2024-01-12 | 2024-04-12 | 北京城市轨道交通咨询有限公司 | Operation and maintenance management system, method, equipment and medium for rail transit vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114267178B (en) | 2023-09-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114267178B (en) | Intelligent operation maintenance method and device for station | |
| CN108415789B (en) | Node fault prediction system and method for large-scale hybrid heterogeneous storage system | |
| EP3902992B1 (en) | Scalable system and engine for forecasting wind turbine failure | |
| KR101984730B1 (en) | Automatic predicting system for server failure and automatic predicting method for server failure | |
| CN108038049B (en) | Real-time log control system and control method, cloud computing system and server | |
| US11403164B2 (en) | Method and device for determining a performance indicator value for predicting anomalies in a computing infrastructure from values of performance indicators | |
| CN111459700B (en) | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium | |
| KR20180108446A (en) | System and method for management of ict infra | |
| US11307916B2 (en) | Method and device for determining an estimated time before a technical incident in a computing infrastructure from values of performance indicators | |
| CN104777827A (en) | Method for diagnosing fault of high-speed railway signal system vehicle-mounted equipment | |
| CN106708738B (en) | Software test defect prediction method and system | |
| US11675643B2 (en) | Method and device for determining a technical incident risk value in a computing infrastructure from performance indicator values | |
| CN111915026A (en) | Fault processing method and device, electronic equipment and storage medium | |
| CN117196066A (en) | Intelligent operation and maintenance information analysis model | |
| CN113919207A (en) | Top-level open type electrical intelligent health monitoring and management system | |
| JP7062505B2 (en) | Equipment management support system | |
| Wang et al. | LSTM-based alarm prediction in the mobile communication network | |
| Jingyu et al. | Statistical analysis of distribution network fault information based on multi-source heterogeneous data mining | |
| CN113807704A (en) | Intelligent algorithm platform construction method for urban rail transit data | |
| CN113805564A (en) | Equipment fault early warning and diagnosis system based on CEP | |
| Gruber et al. | Condition‐Based Maintenance v ia a Targeted B ayesian Network Meta‐Model | |
| CN116579677B (en) | Full life cycle management method and system for high-speed railway electric service vehicle-mounted equipment | |
| Xu et al. | Alarm Ranking Model for Intelligent Management of Metro Systems Based on Statistical Machine Learning Methods | |
| CN117172545A (en) | Risk early warning method and device and computer readable storage medium | |
| CN117827608A (en) | Intelligent early warning and disposal method based on historical monitoring data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |