160 results about "Health state valuation" patented technology

A rechargable battery with an internal microcontroller. The microcontroller contains a memory in which data regarding the environment to which the battery is exposed are stored. These data are read by a processor integral with the charger used to charge the battery. If these data indicates the battery may have been subjected to a harsh environment for an excessive period of time, the charger performs a complete state of health evaluation of the battery.

A wearable platform embodied in a belt or patch provides physiological monitoring of soldiers during field operations or trauma victims at accident sites and makes health state assessments. The platform includes sensors for heart rate, body motion, respiration rate and intensity, and temperature and further contains a microprocessor and short range transmitter. An analog circuit running an algorithm obtains the R-wave period from the EKG signal and produces electrical pulses with the period between pulses corresponding to the R-wave period. A rule based processing engine having an evaluation algorithm is capable of making a medical evaluation of subject condition and determines a confidence level for the evaluation. The rules are subject to variation depending upon the subject population. The information is communicated wirelessly to a local hub for relay to a remote monitor.

An evaluation and support system of electric power device state comprises system management module for managing test process, authority, organization and personnel; data management module for classifying and managing device data such as device type, device parts, device parameter, weight ant test parameter; state evaluation management module for obtaining healthy state result of electric power device by calculating and analyzing means.

The present invention provides a multi-disease chronic disease information management system relating to the computer field and used for solving the problem that the conventional chronic disease management system can not realize the multi-department multi-disease same system management. The system provided by the present invention comprises a database, an application server, a plurality of hospital clients and a plurality of patient clients. The database stores the health examination data of patients, the doctor suggestion, the health data reference ranges of the examination items and the health state assessment indexes of various chronic diseases, and the application server obtains the health examination data of the appointed patient, the corresponding health data reference ranges, the health state assessment indexes of various chronic diseases and the doctor suggestion from the database according to a first query instruction sent by the hospital / patient client to obtain a chronic disease assessment result and return the chronic disease assessment result of the current appointed patient and the data to the hospital / patient client. By the above scheme, the communication interaction of the multiple medical departments and the patients about the multi-disease chronic disease is realized.

The invention discloses an equipment cluster health state evaluation method based on industrial big data and solves a health state evaluation problem of equipment clusters. Based on the full service period big data environment of the equipment clusters, an entity state information slicing management model is constructed to pre-process data; a condition similarity clustering method is utilized to carry out equipment cluster division, a regression method is utilized to establish a mirror image model, the mirror image model and an entity health state are mutually mapped, and a health state quantification model of the different equipment clusters is acquired; fusion and reconstruction of the health state data of the different equipment clusters are carried out to acquire health degrees of the different pieces of equipment, fitting of an equipment health state degeneration curve is carried out, and residual service life of the equipment is predicted. The method is advantaged in that a big data clustering modeling method is utilized, not only can effectively different evaluation on the equipment cluster health state be carried out, but also model redundancy of cluster modeling can be reduced, the internal structure of the model is simplified, normal operation of the cluster equipment is guaranteed, and use values of the equipment can be mined to a maximum degree.

The invention discloses a method for assessing an SOH (state of health) of a lithium ion battery. The method comprises the following steps: a, establishing an internal resistance model of the lithium ion battery; b, acquiring current and terminal voltage data of the lithium ion battery under different SOCs (states of charge), and storing the data if the change rate of two adjacent current values is not greater than a set value; c, performing fitting on the stored data by utilizing the internal resistance model of the lithium ion battery to obtain an R2-SOC curve and a tau-SOC curve of the lithium ion battery during a discharge process, and further, working out the ohmic resistance and the diffusion resistance of the lithium ion battery at the T moment; d, calculating the SOH value of the lithium ion battery; e, performing assessment on the SOH of the battery according to the SOH value of the lithium ion battery. According to the method for assessing the SOH of the lithium ion battery disclosed by the invention, different SOH assessment methods are adopted by the lithium ion battery under different SOCs, and therefore, the accuracy of an assessment result is greatly improved. By adopting the method, maintenance personnel can discover and eliminate hidden troubles of the battery in time to ensure the normal operation of an electric vehicle.

The invention discloses a health assessment and fault diagnosis method for rotating machinery based on fisher discriminant analysis and a mahalanobis distance, which belongs to the technical field of condition-based maintenance of the rotating machinery. The method comprises the steps of extracting an energy eigenvector based on wavelet packet decomposition, constructing a discriminant analysis function, conducting health status assessment, conducting fault detection on the rotating machinery, and finally conducting fault diagnosis on the rotating machinery. The method constructs a comprehensive frame integrating the status assessment, the fault detection and the fault diagnosis, solves the hotspot problem in comprehensive health management of the rotating machinery at present, achieves intelligent maintenance of the rotating machinery, can establish an assessment and diagnosis model without full life status monitoring data of the rotating machinery, reduces the dependence on historical data, and is very high in engineering applicability.

The invention discloses a method and system for evaluating the health state of a battery. The method comprises: S1, acquiring life decay data of a plurality of batteries of the same type; S2, according to the life decay data, constructing target characteristic parameters of the batteries and calculating a capacity data sequence; and S3, on the basis of a least square method, fitting of the capacity data sequence and the target characteristic parameters is carried out to construct a health state assessment model for calculating effective capacities of the batteries. The target characteristic parameters include at least one of the following parameters: an equal-voltage-drop discharging ampere-hour value, an equal-ampere-hour discharging voltage difference, an equal-voltage-drop charging ampere-hour value, and an equal-ampere-hour charging voltage difference. According to the invention, no complicated testing condition or model as well as no complete charging and discharging voltage curves on the constant current condition of the battery is needed; and the health state of the battery can be predicted accurately.

The invention provides a clustered-type bridge health real-time monitoring system based on wireless communication technology. The system comprises bridge heath state data collecting and processing subsystems arranged on all bridges and a bridge heath real-time monitoring center which is arranged at a background and is used for receiving bridge health monitoring data uploaded by the bridge heath state data collecting and processing subsystems, thereby obtaining the heath state evaluation, comparison and life forecast as well as bridge maintaining and tending strategies; and each subsystem performs data transmission with the bridge heath real-time monitoring center in a wireless mode. According to the system, the bridge heath state data collecting and processing subsystem is installed on a specific bridge, the bridge heath real-time monitoring center is installed at the background, and the data transmission between the subsystem and the bridge heath real-time monitoring center is realized by adopting the wireless communication technology, thus the construction difficulty and cost are reduced, and the arrangement of monitoring points is more flexible.

The invention relates to an equipment health state evaluation and recession prediction method based on multi-channel sensing signals. The operation steps of the method are as follows: the first step: joint noise filtration of the multiple sensing signals: adopting a multi-scale wavelet filtering algorithm to perform the joint noise filtration on the multi-channel sensing signal collected from equipment so as to improve the quality of the multiple sensing signals; the second step: generation of an original characteristic set; the third step: extraction of local characteristics of the signals; the fourth step: modeling of a reference self-organizing mapping model; and the fifth step: health quantitative evaluation and residual life prediction. By adopting the method, the quantitative evaluation and the life prediction of performance recession of the equipment can be realized, the operation reliability of the equipment can be further improved and the maintenance cost can be reduced.

The invention discloses a power MOSFET health state assessment and residual life prediction method. The concrete steps are that: drain and source electrode voltage, drain and source electrode current and threshold voltage of a health power MOSFET are firstly acquired, mahalanobis distance of the health power MOSFET is acquired and Box-Cox conversion is performed so that the mahalanobis distance in normal distribution is acquired, then a health state assessment reference threshold of the power MOSFET is confirmed, and the mahalanobis distance in normal distribution is selected as a health state characteristic parameter for assessing the measured power MOSFET; then the health state assessment is performed on the drain and source electrode voltage, the drain and source electrode current and the threshold voltage monitoring the measured power MOSFET; and finally different residual life prediction models are established according to different health states of the measured MOSFET. Multi-characteristic parameters of the power MOSFET are converted into a single-characteristic parameter to perform the health state assessment. Meanwhile, temperature and voltage stress of working conditions are considered, and the residual life prediction models under the normal states and the abnormal states are established so that the residual life of the measured power MOSFET can be accurately predicted.

The invention relates to a health state assessment method for a rotary machine based on statistical methods such as kernel density estimation and K-L divergence calculation. The method comprises the following steps that 1) original vibration data of a monitored object is collected; 2) time-domain and frequency-domain features are extracted from the original vibration data; 3) the dimensions of the time-domain and frequency-domain features are reduced to obtain sensitive features; 4) a movable sliding window of the width k is used to dynamically select sample sets, and the statistical characteristics of the sample sets are calculated; 5) the probability density function of each sample set under each sensitive feature is calculated; 6) the K-L divergence between the probability density functions of two adjacent sample sets under the same sensitive feature is calculated; and 7) an integrated K-L divergence is calculated and serves as a health assessment index of the monitored object. The health state assessment method has the advantages that the statistical uncertainty of the samples is fully considered, and the accuracy and generalization performance of a health state assessment model are improved.

A rechargable battery with an internal microcontroller. The microcontroller contains a memory in which data regarding the environment to which the battery is exposed are stored. These data are read by a processor integral with the charger used to charge the battery. If these data indicates the battery may have been subjected to a harsh environment, such as being exposed to an excessive temperature, the charger performs a fulle state of health evaluation of the battery.

The invention discloses a vertical mill operation regulation and control system based on data mining and a method thereof. The method is characterized by using an integration characteristic screening method to carry out mining analysis on work condition data and acquiring a vertical mill health state assessment index; carrying out cluster mining analysis on a work condition state, acquiring a characteristic of each condition cluster, and acquiring each state distribution condition in historical work conditions; defining operation state types in the historical work conditions and acquiring a stable mode condition database; using an ARIMA algorithm to carry out training model on a determined characteristic value in a vertical mill health state characteristic acquisition module and predict a parameter change trend, and using a predicted value to assist state identification; combining the predicted value given by an ARIMA model, determining a vertical mill operation state; when the state is determined to be abnormal, reading a work condition record in the stable work condition mode database, acquiring a recommended regulation and control target value and regulating and controlling a controllable parameter at the moment. In the invention, vertical-mill qualitative and quantitative regulation and control suggestions can be accurately provided and a mill machine can work stably for a long time.

The invention discloses a data mining-based slag grinding system health state identification system and method. According to the data mining-based slag grinding system health state identification system and method of the invention, a comprehensive feature screening method is utilized to carry out mining analysis on working condition data so as to obtain key parameters which affect the stability of a vertical mill, and the key parameters are adopted as indicators of vertical mill health state assessment; based on the identified indicators of the vertical mill health state assessment, clustering and mining analysis is performed on working condition states, so that the features of various of working condition clusters are obtained; the state distribution conditions of historical working conditions are obtained, and operating state categories in the historical working conditions are defined; and the ARIMA algorithm is adopted to train a model for determined feature values in a vertical mill health state feature acquisition module, and the change trend of parameters is predicted, and a prediction value is utilized to assist state identification. The data mining-based slag grinding system health state identification system and method of the invention have the advantages of high recognition precision, great generalization ability and high performance, and is suitable for the health state identification and diagnosis of a slag grinding system.

The invention provides a health state evaluation method for an energy-storage battery pack. The method comprises the following steps that I) historical data of the capacity of the energy-storage battery pack is obtained; II) a gray prediction algorithm is used to establish a prediction model for the present capacity of the energy-storage battery pack; III) the present of the energy-storage battery pack is predicted; IV) a neural network algorithm is trained is form a battery pack health state model; V) the predicted present capacity is input to the battery pack health state model to obtain a predicted internal resistance consistency coefficient; and VI) whether the energy storage battery pack is healthy is determined according to the internal resistance consistency coefficient. The method can fully adapt to high nonlinear characteristics of an electrochemical system of batteries, and has the advantages of low data operand, less sample data and high prediction precision.

The invention discloses a method for evaluating the health state of a wind turbine generator based on multi-dimensional SCADA data, and aims at solving the problem of incomplete evaluation caused by single parameter used for evaluating the health state of the wind turbine generator in the traditional method, and extracting a plurality of characteristic parameters representing unit degradation information. According to the method, mutual information between parameters in the SCADA system is calculated through an empirical Copula function, and the magnitude of the mutual information can reflectthe degree of influence of the parameters on the performance of the fan. The parameters with large mutual information values are used as health assessment objects, and compared with a traditional method that a wind power curve is used as an assessment object, assessment of the health state of the wind turbine generator is more comprehensive and accurate. A method for dividing working conditions according to wind speed intervals is introduced into the model. And the method is combined with kernel principal component analysis to establish a wind turbine generator health state evaluation model based on adaptive KPCA. The diagnosis result shows that the wind turbine generator health evaluation result of the model is superior to the evaluation result of a traditional method.

The invention discloses a fuzzy multi-attribute evaluation-based satellite power supply health degree calculation method. The method realizes the health state evaluation after the failure occurrence of a satellite power supply. According to the method, firstly, an evaluation index attribute set and a reasonable evaluation set are established based on the failure analysis result of a satellite power supply failure mode. Secondly, a fuzzy evaluation matrix is obtained through expert evaluation or other methods. Thirdly, based on the analytic hierarchy process, the initial weight of each evaluation index in the same level is determined. Meanwhile, qualitative attributes are quantified, and then a final comprehensive evaluation result is obtained. In the aspect of health management, results can be divided into 5 levels of health, good, attention, deterioration and disease. In this way, the health degree of an evaluated object can be characterized in the above 5 levels. By adopting the method, the health degree of a satellite power supply during the running process can be calculated in real time. Therefore, the on-orbit safety of the satellite power supply can be improved. Meanwhile, adecision is provided to assist the ground transportation personnel to realize the on-orbit operation management of satellites.

The invention discloses a prognostics and health management (PHM) system for a single-level aircraft structure and an implementation method. The PHM system for the aircraft structure consists of an onboard PHM system and a ground PHM system. A PHM regional manager for the aircraft structure can effectively communicate with each PHM member-level system of the structure, drives the corresponding member-level systems to finish corresponding PHM tasks, receives PHM data information from the member-level systems, and stores, files and displays the PHM data information. A database driving module divides a data information stream of the whole PHM system into the following branches, and finally converges the branches into a PHM database. When historical PHM data are required by a health status evaluation module and a health status prediction module, the database driving module transmits a request to the PHM database, and transmits corresponding data to the corresponding modules. When the maintenance history of the structure is required by the generation of a decision, the database driving module transmits a request to the PHM database, and transmits corresponding historical decision data to a decision generation module.

The invention discloses an unmanned aerial vehicle system health status evaluation device and is applied to the evaluation of the health status of K unmanned aerial vehicles. The unmanned aerial vehicle system health status evaluation device is characterized by comprising an information collecting module, an information processing module, an evaluation processing module, a health status score displaying module and a health status information database; the information collecting module is used for obtaining the flight data of the unmanned aerial vehicles in flying processes; the information processing module is used for processing the flight data to obtain standard flight data blocks; the evaluation processing module processes the standard flight data by using an entropy coefficient G1 power evaluation method according to a set health status evaluation index system, and the weight of each evaluation index and the score of unmanned aerial vehicle system health status are obtained by the evaluation processing module and sent to the health status score displaying module for being displayed. The unmanned aerial vehicle system health status evaluation device can conduct colleting, monitoring and processing on the flying data of the unmanned aerial vehicles to obtain the visual unmanned aerial vehicle health status scores for unmanned aerial vehicle decision support.

The invention provides a system and method for health management of electrical equipment. Through the data fitting method, the mapping relation is established and the operation parameters are inputted. According to the mapping relation, the final result is analyzed, and the data fitting method is constantly corrected according to the final result, and the data fitting method is constantly updated.The final result of the analysis is the secondary evaluation and prediction result of the health assessment and fault prediction. By installing various sensors in electrical equipment and acquiring internal data through communication, real-time monitoring and regular patrol inspection can be carried out to collect data. The invention not only establishes a data model and a neural network, the invention also carries out self-learning through a data fitting mode, and conducts health state evaluation and fault prediction on the electrical equipment through a mapping relationship. The invention comprises three evaluation and prediction modes, and combines the three evaluation and prediction modes to carry out health state evaluation and fault prediction on the electrical equipment, so as to reduce the possibility of misjudgment.

The invention discloses a method and system for detecting the device state based on working condition data, and belongs to the technical field of device health condition evaluation and prediction. The method comprises obtaining the working condition data of a device and the importance degree corresponding to each working condition data; carrying out normalization processing to the working condition data to obtain normalized working condition data; obtaining the health degree corresponding to the normalized working condition data by means of a health SOM model; and carrying out weighted fusion to the importance degree and the health degree of each working condition data in order to obtain the health degree of the device. The method for detecting the device state based on the working condition data solves the problem in the prior art that simulation data is employed to detect the device state, and there is a significant difference between the detected device state and the device state in practical application, furthermore, that the contribution degrees of different parameters to the device state are different is considered, and the health degree and the importance degree are subjected to weighted fusion to obtain the health degree of the device.

The invention provides a power distribution equipment health state assessment method and system. The power distribution equipment health state evaluation method comprises the following steps: establishing a power distribution equipment state evaluation model by adopting a multi-stage fuzzy comprehensive evaluation method; Analyzing the defect severity of each part of the power distribution equipment in the power distribution equipment state evaluation model by using an analytic hierarchy process, establishing an evaluation matrix of each part of the power distribution equipment, and determining the weight of each evaluation index; Analyzing an evaluation matrix of each part of the power distribution equipment by utilizing a fuzzy evaluation method and the weight of each evaluation index, and obtaining a comprehensive initial score of the power distribution equipment through a weighting membership degree principle; Constructing an identification framework of the power distribution equipment state evaluation model, and determining the weight of the defect severity; And performing weighted calculation on the comprehensive initial score of the power distribution equipment and the weight of the defect severity to obtain a comprehensive score value of the health state evaluation of the power distribution equipment.

The invention discloses a model used for evaluating the health state of electrical equipment, relates to the field of the electrical technology, can accurately evaluate the operation health state of the electrical equipment, and is convenient to carry out operations including reasonable use and maintenance, fault prediction, power distribution management and the like on the electrical equipment. The model used for evaluating the health state of the electrical equipment comprises an evaluation index module, a comment module, a fuzzy assessment module and a grey correlation module, wherein the evaluation index module comprises a plurality of state quantity indexes which affect the health state of the electrical equipment and the weight of each state quantity index; the comment module comprises a comment set and is used for assessing the health state of the electrical equipment; the fuzzy assessment module comprises a membership function established on the basis of the comment set, and is used for combining with the weight of each state quantity index through the membership function to carry out fuzzy assessment on the health state of the electrical equipment to obtain a fuzzy assessment conclusion; and the grey correlation module comprises a clear assessment standard established on the basis of the comment set, and is used for carrying out clear assessment processing on the fuzzy assessment conclusion to obtain an accurate health state evaluation result.

The invention discloses a bridge structure damage identification method and system; the method comprises the following steps: collecting flexibility information of environment variables and a bridge structure; evaluating the health state of the bridge structure, determining whether the bridge structure needs damage identification or not, and sending safety alarms; using a damage position identification model to identify damaged positions; using a damage level identification model to identify the damage level. The system comprises a data acquisition module, a health state evaluation module, an alarm module, a damage position identification module, a damage level identification module and a display module. The bridge structure damage identification system is simple in structure, easy to realize, suitable for various bridge structures, and can timely provide detail bridge structure health state information for a bridge management department, thus ensuring safety operation of the bridge structures.

The present invention provides a health state assessment method based on physiological data and psychological data, a health state assessment device, and a system for rehabilitation. The health stateassessment method includes the following steps that: physiological data and psychological data related to health states are acquired, the physiological data are normalized, and data dimensionality reduction and / or data cleaning is performed on the psychological data; data weighting and value assignment are performed on the normalized physiological data and the psychological data which have been subjected to the data reduction and / or data cleaning; and health state assessment is carried out according to physiological and psychological comprehensive data which have been subjected to the weighting and value assignment. The health state assessment method is in line with the actual situation of each patient; the evaluation result of the method is more accurate; and rapid and effective rehabilitation can be achieved.

The invention is applicable to the technical field of equipment monitoring, and discloses an equipment fault prediction and health assessment method and system and terminal equipment, and the method comprises the steps: receiving monitoring data sent by a vehicle-mounted terminal, carrying out the preprocessing of the monitoring data, and enabling the vehicle-mounted terminal to be located in theequipment; Performing fault diagnosis on the equipment according to the preprocessing data to obtain fault information; Performing fault prediction on the equipment according to the preprocessing datato obtain fault prediction information; And performing health state assessment on the equipment according to the pre-processed data to obtain health state information. The fault and health state of the equipment can be predicted in advance, preventive maintenance is carried out on the equipment according to the fault and health state, the problem of excessive maintenance or repair loss can be solved, the maintenance cost is reduced, and normal use of the equipment is guaranteed.

The invention relates to a method for evaluating health status of a switching power supply based on least square fitting, belonging to the technical field of evaluation of the health status of the switching power supplies, and aiming at solving the problems that an existing method for evaluating the health status of a switching power supply is difficult for data acquisition and bad for evaluation result reliability. The method disclosed by the invention comprises the steps: firstly establishing a switching power supply simulation model, determining the key stress factor influencing the output characteristic of the switching power supply and the fluctuation range of the key stress factor; then determining key components of the switching power supply, and establishing a degradation model of each key component; sequentially carrying out quantitative degradation injection on the degradation models of the key components; then constructing an output characteristic parameter degradation model of the switching power supply; and acquiring the key stress factor values and the output characteristic parameter value of the switching power supply, thus obtaining the health status evaluation result of the switching power supply according to the output characteristic parameter degradation model of the switching power supply. The method disclosed by the invention is used for evaluating the health status of the switching power supply.

The invention discloses a measurement and control equipment health state evaluating system and method based on online monitored data, and belongs to the field of electric power system operation and management. The method comprises the steps of: adding some information monitoring functions reflecting the health state of measurement and control equipment to the inner part of the measurement and control equipment, uploading the information monitoring functions to a measurement and control equipment health state evaluating work station, and carrying out real-time monitoring and evaluating on the health state of the measurement and control equipment. According to the method, an online monitored information classification model is established, the health state of the equipment is determined according to line-crossing and alarming conditions of diffident kinds of equipment information, a health state evaluating method is established based on online information, and the health state of the equipment is evaluated. The method provided by the invention is the health state evaluating method based on the online monitored information, the defects of a conventional evaluation model in which evaluation is carried out mainly based on history information and manual evaluation are overcome, and the health state of the measurement and control equipment is more accurately reflected in real time.

The invention discloses a health reserve evaluation method. The health reserve evaluation method comprises the following steps: a system converts received human pulse beat signals, heart sound signalsand ECG signals into point process signal data; the system performs composite variable analysis of a health reserve value on the point process signal data, and forms a monotonic function reflecting the healthy reserve state; and the system performs qualitative and quantitative determination of human health according to statistical distribution parameters of the health reserve values of the population. A health reserve evaluation system / device includes a data acquisition unit, a data conversion unit, a data analysis unit, a health status evaluation unit, and a health reserve evaluation application. The evaluation method of the health state and change thereof is simple and reliable, is easy to implement, can perform repeat measurement and evaluation, can be widely used in many fields such as health evaluation, health guidance, exercise rehabilitation guidance and the like of the general population.