967 results about "Health index" patented technology

A method and system monitor a communications network, e.g., a controller area network (CAN), and more specifically, an in-vehicle communications network, by maintaining a count of each type of error code and a histogram of all network messages seen by each of the controllers during a measurement period; and by determining a bus health index of the communication bus based upon a percentage of a given type of error to the total count of all errors during a measurement period. An individual controller or controller area network bus segment can be indicated as having a communications problem as a result of the health index.

Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

A method 1300 and apparatus for deriving a health index for determining cardiovascular health of a person is disclosed herein. In a described embodiment, the method 1300 includes (i) obtaining values of respiratory rate, SpO2 and heart rate from a PPG signal of the person at step 1304; (ii) assigning a score based on the values from a reference scoring system at step 1306; and (iii) deriving the health index based on the assigned scores at step 1308 / 1314. A method and device for obtaining the PPG signal at optimum pressure is also disclosed.

The invention discloses a CNN and LSTM-based rolling bearing residual service life prediction method, and relates to the field of rolling bearing life prediction. The method aims to solve the problemthat residual service life (RUL) prediction of a rolling bearing is difficult in two modes of performance degradation gradual change faults and sudden faults. The method comprises the following stepsof: firstly, carrying out FFT (Fast Fourier Transform) on an original vibration signal of the rolling bearing, then carrying out normalization processing on a frequency domain amplitude signal obtained by preprocessing, and taking the frequency domain amplitude signal as the input of a CNN (Convolutional Neural Network); The CNN is used for automatically extracting data local abstract informationto mine deep features, and the problem that a traditional feature extraction method depends too much on expert experience is avoided. the deep features are input into an LSTM network, a trend quantitative health index is constructed, and a failure threshold value is determined at the same time; And finally, smoothing processing is carried out by using a moving average method, eliminating local oscillation, and a future failure moment is predicted by using polynomial curve fitting to realize rolling bearing RUL prediction. And the prediction result can be well close to the real life value.

The invention relates to a method for remain useful life prognostic of a lithium ion battery with a model active updating strategy. According to a time series obtained through a voltage range of a discharge curve, conversion is conducted so that an equivalent discharge difference series obtained by discharge circulation at each time can be obtained, and therefore a health index time series of the ion battery is obtained; according to correspondence of a discharge voltage series and a time series, prognostic is conducted on the health index series to determine the remain useful life of the battery. Sampling entropy characteristic extraction and modeling are conducted on a charge voltage curve so that a relationship between a complete and accurate charge / discharge process and a battery performance index can be provided. On the basis of a performance index model, a short-term time series prognostic result is continuously updated to a known performance index data series and correlation analysis is conducted. According to the difference of the correlation degrees, retraining is conducted in the mode of training set expansion. The method is different from an existing iteration updating draining method, the prognostic model is updated dynamically, and therefore the prognostic precision is improved.

A method for monitoring performance of an advanced process control system for at least one process output includes calculating a variance of a prediction error for a processing performance and / or a probability for violating specification limits of the processing performance of the at least one process output. If the variance of the prediction error is calculated, the method also includes calculating a model health index. If the probability for violating specification limits is calculated, the method further includes calculating a process health index.

The invention discloses a bracelet capable of collecting multi-parameter health indexes. The bracelet comprises a body and a wrist strap. The body comprises a shell body, a displaying screen, a power supply, a clock part and a main control system. The displaying screen, the power supply, the clock part and the main control system are arranged in the shell body. The main control system comprises a main printed board, a microprocessor, a storage, an analogue signal processing chip and a Bluetooth transmission device. The microprocessor, the storage, the analogue signal processing chip and the Bluetooth transmission device are arranged on the main printed board. An infrared temperature sensor, an alarming device, a gravitational acceleration sensor for step counting and a charging connector are arranged on the main printed board. A flexible connecting wire cable, a measuring printed board, a body surface temperature sensor, a photosensitive sensor and a light source are arranged on the wrist strap. The body surface temperature sensor, the photosensitive sensor and the light source are integrated on the measuring printed board. A tensioning device is arranged on the wrist strap. The bracelet is small and light, wearing and using are convenient, various health parameters can be monitored accurately, human body parameters are collected near the Neiguan point under a wrist of a palm side, the number of blood vessels under the wrist is large, pulsation is obvious, and accordingly the accuracy of data testing and obtaining through the bracelet is greatly improved.

A network diagnostic device is provided, which comprises a passive real-time measurement tool that is useful for, among other things, expediting fault identification, isolation, and repair of a communication network or bus. The device also facilitates prediction of failures by identifying marginal operating conditions. The device analyzes data flowing through the communication network, including through an analysis of variations in bit waveform shape carried by the network physical interconnect media. In one embodiment, an implementation of the network diagnostic device is particularly useful in a DeviceNet-compatible network or, more generally, a Controller Area Network (CAN). The device identifies faults by comparing measurements made on the actual DeviceNet bus with worst-case acceptable criteria. The device interfaces with a remote monitoring computer via an Ethernet compatible medium to display parsed bit-level waveforms, network warnings and errors, as well as an overall network health index.

A system, method and computer program product for maintaining an infrastructure of components. The system receives structured data, unstructured data, and infrastructure data from a database. The system runs at least one statistical and optimization modeler with one or more of: the received structured data, the received unstructured data and the received infrastructure data, in order to calculate a health index of at least one component of the infrastructure. The health index represents a health attribute of the at least one component. The system establishes at least one maintenance plan of the infrastructure, based on the calculated health index. Each established maintenance plan is associated with at least one health index. The system compares health indices of the at least one established maintenance plan. The system selects a plan, among the at least one established maintenance plan, whose health index is a maximum among the compared health indices.

A method for monitoring performance of an advanced process control system for at least one process output includes calculating a variance of a prediction error for a processing performance and / or a probability for violating specification limits of the processing performance of the at least one process output. If the variance of the prediction error is calculated, the method also includes calculating a model health index. If the probability for violating specification limits is calculated, the method further includes calculating a process health index.

Starter control valve failure prediction machines, systems, program products, and computer implemented methods to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic and to make predictions of starter control valve failures, are provided. A computer implemented method according to an embodiment of the present invention can include the steps of generating a continuous starter control valve deterioration trend function responsive to a plurality of health indices derived from gas turbine engine startup data downloaded from gas turbine engine sensors for a plurality of startups and analyzing the continuous starter control valve deterioration trend function to identify potential starter control valve failure points where the points on the starter control valve deterioration trend function correlate to a starter control valve health prognostic responsive to historic gas turbine engine startup data downloaded from gas turbine engine sensors.

The invention discloses a health monitoring method for a numerical control machine tool, and the method comprises the steps: (1), executing a self-inspection G instruction, and collecting the operation state data; (2), building the mapping relation between the collected operation state data and the self-inspection G instruction, and forming an instruction domain mapping ensemble; (3), dividing the instruction domain mapping ensemble according to the self-inspection G instruction, extracting the instruction domain mapping under the self-inspection G instruction, obtaining the operation state data under the self-inspection G instruction, calculating an actual characteristic value according to the operation state data, comparing the actual characteristic value with a standard characteristic value, and obtaining a health index through calculation. The method improves the calculation basis of a key health index of the numerical control machine tool, the calculation method of the health index and the display mode of the health index, and can effectively solve a problem that the judgment of the health condition of the numerical control machine tool is not timely and accurate. Moreover, the obtained health index of the numerical control machine tool is displayed in a visualized manner, thereby improving the interaction effect of the numerical control machine tool with a person.

The invention discloses a real-time evaluation method for the health state of a wind turbine unit. The method comprises the steps that firstly, on the basis of historical running data of the wind turbine unit, running work condition dividing of the wind turbine unit is achieved by applying a clustering technology, and standard state cloud models of the wind turbine unit under all work conditions are calculated; secondly, work condition identification is conducted on real-time data stream of the wind turbine unit through a stream-type clustering algorithm, and cloud models of the real-time states of the unit are calculated; thirdly, deviation values between the cloud models of the real-time states and the standard state cloud models are calculated and taken as health indexes of the wind turbine unit; lastly, the health state of the wind turbine unit is evaluated according to the magnitude of the health indexes. According to the method, the running states of the wind turbine unit are described through the cloud models, the health state and the development tendency of the wind turbine unit are acquired by introducing a time window method, therefore, the uncertainty of state monitoring information of the wind turbine unit is fully taken into account, the accuracy of an evaluation result is greatly improved, and powerful support can be provided for making a wind turbine unit maintenance plan.

The invention provides a method for detecting application server performance. The method comprises the following steps: real-time testing data of each performance index of an application server is obtained; a health index of the application server is calculated according to the real-time testing data of each performance index and preset weight; a real-time performance state of the application server is determined according to the health index; and corresponding processing is conducted to the application server according to the real-time performance state of the application server. The processing comprises that when the real-time performance state of the application server is outside a preset normal range, alarm information is generated. The invention further provides a system for detecting the application server performance. According to the method and the system for detecting the application server performance, accuracy and efficiency of performance detection are high.

A multi processor computing system managing tasks based on the health index of the plurality of processors and the priority of tasks to be scheduled. The method comprise receiving the tasks to be scheduled on the computing system; preparing a queue of the tasks based on a scheduling algorithm; computing a health index value for each processor of the computing system; and scheduling the tasks on processors based on the health index value of the processors. A task from a processor with a lower health index may be moved to an available processor with a higher health index.

The invention discloses an aeroengine remaining service life prediction method based on an LSTM network and an ARIMA model. The method comprises the following steps: n engine health index (LSTM-HI) models based on an LSTM depth neural network are established according to engine historical degradation data; According to the engine sensor data, the ARIMA model is trained and the engine sensor parameters are predicted backward in many steps. By predicting the sensor parameters, according to LSTM-HI index is used to evaluate whether the engine is degraded to failure, and the remaining service lifeand its probability distribution are obtained. The invention provides a novel method for predicting the remaining service life of an aeroengine, which has high accuracy and feasibility, and plays anactive role in promoting the real-time health management of the aeroengine and reducing the maintenance cost.

The invention provides a self-adaptive cache adjustment control method and device and a self-adaptive player. The method comprises the following steps: continually computing a receiving occupying rate of a receiving buffering region, a playing occupying rate of a playing buffering region and a current network health index in a process of playing a streaming media file by the player; and dynamically adjusting the size of the current receiving buffering region and the current playing buffering region according to the current network health index, the receiving occupying rate and the playing occupying rate. A general buffering region is composed of the current playing buffering region and the current receiving buffering region; the current playing buffering region and the current receiving buffering region are dynamically changed along with the current network health index and the current network health index can reflect the speed of a current network, so that the general buffering region can be dynamically changed along with the current network health index; therefore, the method provided by the invention can dynamically adjust the size of the general buffering region according to the network speed and the aims of not discarding data packets and also guaranteeing the sufficient utilization of resources can be realized.

The invention provides a lithium ion battery remaining useful life prediction method and system based on a PCA-NARX neural network, so as to solve the problems of large difficulty and high redundancyin lithium ion battery remaining useful life prediction health index measurement. The method comprises steps: 1) a constant current discharge voltage change law of the lithium ion battery at differentdischarge periods is analyzed, and parameters that reflect the degradation of battery performance are extracted; 2) the correlation between the extracted parameters and the correlation between the extracted parameters and the lithium ion battery capacity are verified, a PCA algorithm is used to remove redundancy of the parameters, and a main component obtained after redundancy removal is used asa health indicator for the lithium ion battery; and 3) the obtained health indicator for the lithium ion battery is inputted to an NARX neural network, and lithium ion battery capacity estimation andremaining useful life prediction are carried out. An experiment result proves that the method is high in prediction precision and can be used for accurate prediction on the lithium ion battery remaining useful life.

Provided is a film-type biomedical signal measuring apparatus configured in a such a way that a plurality of metallic thin film electrodes and a circuit unit are formed on a film-type piezoelectric element so as to easily attach the apparatus to the skin and an electrical signal as well as an electrical signal of a human body is simultaneously measured using the plurality of metallic thin film electrodes and the circuit unit. Accordingly, the film-type biomedical signal measuring apparatus simultaneously measures electrocardiogram (ECG) and ballistocardiogram (BCG) from the simultaneously measured electrical signal and vibration signal of the human body and extracts biomedical information of various types of health indexes such as a heart rate, a stress index, BCG, a blood pressure, an amount of physical activity, a respiration rate, and VO2max from the two different biomedical signals.

The invention provides a bearing health assessment and prediction method and system. The method comprises the following steps: acquiring a vibration signal in the running process of a bearing; processing the vibration signal to acquire time domain statistical characteristic, a time frequency domain energy characteristic and a trigonometric function characteristic of the vibration signal; selecting the characteristics which are mostly sensitive to the bearing degradation according to the characteristic index of the bearing degradation, so as to form characteristic vectors; selecting the characteristic vectors during the normal running of the bearing as input vectors to train a self-organizing characteristic mapping network and establishing a self-organizing characteristic mapping network model; solving the minimum quantization error corresponding to each characteristic vector in the self-organizing characteristic mapping network model; constructing health indexes which reflect the bearing degradation performance by utilizing the minimum quantization errors; and predicting the trend of the bearing degradation by utilizing an extreme learning machine prediction method and the health indexes. According to the bearing health assessment and prediction method and system, the degradation degree in the bearing degradation process can be correctly described, and the trend of the bearing degradation can be accurately predicted.

The invention provides a health evaluation method and system for key components and a complete machine of a numerical control machine tool and an evaluation terminal. The system comprises a data acquisition and preprocessing module, a sample working condition identification module, a health index fusion module, an evaluation index mapping module, a baseline value construction and key component relative evaluation module and a complete machine health evaluation module. Based on cooperation of the above modules, through analysis of redundancy, relevance, sensitivity and stability of measuring point information, sensor selection and layout optimization are achieved, numerical control machine tool sensing information is obtained, and evaluation is conducted. The trend characteristics of the numerical control machine tool can be fused, evaluation indexes of the degradation state of the whole life cycle of the numerical control machine tool are reflected, the problem that the weights of front and back time sequence information are unbalanced due to the fact that the input information cycle is too long is effectively solved, and the health evaluation accuracy is improved. Health evaluation is carried out by establishing the baseline value, and the health evaluation precision of the key components and the complete machine of the numerical control machine tool is improved.

The invention relates to a power distribution network health index assessment method comprising the following steps: 1, determining power distribution network health index connotation and purpose; 2, determining a power distribution network health index assessment object; 3, determining a power distribution network health index assessment index system; 4, evaluating a transformer and distribution equipment health index; 5, evaluating the power distribution network health index. In a long term, the reliability research pays more attention on a single device at certain moment, a single section (static state), a fault result, and a cause and effect relation; the novel method provides a colony equipment and network health index assessment method, can study the colony equipment and network dynamic health level and power supply reliability, can guide power distribution network planning and operation unkeep asset management, can provide equipment and network health status quantification analysis, and macroscopic and microscopic are combined to provide base theory and practical technical means.

The invention relates to a current transformer operation evaluation method, which comprises the following steps: 1) original data of a field current transformer acquired by a system are monitored in an online mode via a remote metering device, and the original data are uploaded to a data server of an upper PC; 2) the upper PC classifies the original data, and according to the original data after classification, a mathematical model for current transformer operation evaluation is built; and 3) according to the built mathematical model for current transformer operation evaluation, current transformer operation is evaluated, a final health index HI of the current transformer is acquired, and according to the final health index HI, an operation trend of the current transformer is predicted and early warning is carried out. Compared with the prior art, the current transformer operation evaluation method has the advantages of being scientific and reasonable, a large specific number of research objects, wide covering range, systemaltization, intelligence and the like.

The invention discloses a living security health remote monitoring system based on data integration and relates to a living security health remote monitoring system which is assembled on an intelligent wheelchair. Aiming at the living environment at which the intelligent wheelchair is used, the living security health remote monitoring system is used for determining monitoring factors of environment security and human body health, selecting a wireless sensor network based on the ZigBee technology according to the characteristics of each monitoring factor, carrying out environment security and human body health index monitoring, and alarming according to danger information; in order to improve the accuracy of the alarming information, the data integration technology is used while processing the sensor information integrated into the intelligent wheelchair; for three important indexes in the security alarming, a BP (back propagation) neural network method is adopted for data integration treatment; on the basis, the judgment accuracy of the system to dangerous events is improved; the problem of false alarm rate in the security health remote monitoring system is solved; and the purpose of living security health remote monitoring of the intelligent wheelchair is achieved.

The present invention discloses an automatic bio-signal supervising system for medical care. The automatic bio-signal supervising system utilizes a head-mounted bio-signal acquisition device, wore upon the user's head, to acquire the bio-signals, convert them into the digital bio-signals, and transmit the digital bio-signals to a signal analysis processor to perform analysis process. And the automatic bio-signal supervising system delivers a corresponding control signal to the environment control equipment based on the result of analysis, and control the environment condition to achieve the remote supervising and medical care purpose. Furthermore, the signal analysis processor in the present invention further provides a real-time warning signal or a health index, which supervise the personal biological information to avoid the unpredictable situation.

The invention discloses a car networking system for automobile and a safety monitoring method. The car networking system comprises an intelligent bracelet and a vehicle-mounted terminal, the intelligent bracelet comprises a bracelet controller, a movement sensor, a storage module, a bracelet display module and a vibration motor, the movement sensor, storage module, bracelet display module and vibration motor are respectively connected with the bracelet controller, the intelligent bracelet communicates with the vehicle-mounted terminal through WIFI network and 3G network, and the input end of the bracelet controller is connected with each of a body temperature controller and a pressure sensor. The car networking system for the automobile has advantages that the real-time monitoring information of the vehicle is added to the intelligent bracelet, so that the function of the bracelet is enriched, and seamless and real-time monitoring for the vehicle is realized; the body health index is effectively combined with safe driving so that the car networking system realizes monitoring driving safety; the car networking system relates to the life field of users and enables the users to get rid of sub-health living customs; the car networking system is low in cost, convenient to carry and easy to operate.

The invention discloses a transformer reliability evaluation method based on an integral health index, and belongs to the technical field of transformer reliability evaluation. The method comprises the following steps that: feature quantities and feature factors of transformer states are selected, and an expression between the feature factors and the feature factors is determined; a weight of each feature factor is determined, the weights are multiplied by values of the corresponding feature factors, and a health index is obtained; a correction coefficient rule is used for correcting the obtained health index to obtain the corrected integral health index; the expected service life of a transformer is determined; the expected service life is used for calculating an aging coefficient of the transformer according to the integral health index value when the transformer is put into operation or quilts the operation; the aging coefficient is used for calculating a time-variation expression of the integral health index of the transformer; and the integral health index is used for obtaining a time-variation expression of the fault rate of the transformer, so that the fault rate is obtained. The method has the advantages that the internal conditions such as main insulation of the transformer and external conditions such as overhaul and operation of the transformer are sufficiently considered during the transformer reliability evaluation, and the transformer reliability can be more precisely and efficiently evaluated.