31results about How to "Life Prediction is Accurate" patented technology

The invention discloses an optimization design method for the storage life of a rubber seal ring. The method includes the steps of conducting the aging acceleration test on a rubber test piece under different installation compression ratios and different temperatures to contain a test curve of the deformation rate and the aging time tau of the rubber test piece, determining A, K and/or alpha through the least square method, determining Z and delta E/R through the least square method, calculating the As value, the value (please see the symbol in the specification) and the Ks value under different installation compression ratios and the storage temperature of Ts, and substituting the values into the formula (please see the formula in the specification) to obtain the aging time tau under different installation compression ratios. By considering the rule of influences of compression ratios on the storage life of the rubber seal ring, the prediction of the life of the rubber seal ring is more accurate; the storage life of the rubber seal ring can be prolonged through optimization design, and guidance is provided for the life design of the seal ring.

The invention provides a self-adaptive filtering method for data of a pipeline magnetic flux leakage inner detector, and relates to the technical field of fault diagnosis techniques of pipelines. The method comprises the following steps that magnetic flux leakage signals are acquired by the pipeline inner detector, are amplified and stored, and are transformed into the actual digital voltage signals, the digital voltage signals are subjected to baseline correction, and then the abnormal data are searched, eliminated and compensated; the single-channel signal classification are conducted, and different types of signals are subjected to line filtering through corresponding single-channel self-adaptive filtering algorithms; the influence degree of noises between the adjacent channels is compared and analyzed to implement multi-channel equalization on the magnetic flux leakage signals; and the multi-channel signal area classification is conducted, and the different signal areas are subjected to surface filtering through corresponding multi-channel self-adaptive filtering algorithms. According to the method provided by the invention, the predication of the service life of the pipeline is more accurate, the feature is much easier to extract in the fault diagnosis of the pipeline, the filtering effect is more obvious, and the function of rapidly and accurately processing big data is achieved.

The invention discloses a method for predicting the service life of a key system of rail transit vehicles. The method comprises the following steps of firstly, determining the key system of urban rail vehicles, and bringing in a proportional hazard model to carry out service life prediction on the key system; secondly, screening historical overhauling data of the key system to obtain concomitant variable factors, and processing concomitant variables by applying a principal component analysis method so as to obtain a concomitant variable matrix; thirdly, solving a parameter estimation value in the proportional hazard model by adopting a maximum likelihood estimation method and a Newton iteration method; finally, calculating the predicted service life of the key system of the rail transit vehicles by utilizing the obtained proportional hazard model. According to the method disclosed by the invention, the influence of the concomitant variable factors on the service life of the key system of the rail transit vehicles is considered, and the defect that the service life prediction is inaccurate as only system failure is considered in traditional service life prediction is overcome.

To provide a data processing device of production equipment that can generate data usable by an operator or an administrator by performing processing for a collected plurality of types of data. A data processing device includes a reference-data acquiring unit configured to acquire, in production equipment, reference data including information concerning time in which a reference for grouping of data operates, a target-data acquiring unit configured to acquire target data concerning a state of the production equipment detected by detectors provided in the production equipment, and a combined-data generating unit configured to generate, for each group of the reference data, combined data for each group obtained by combining, with the reference data, data detected in the same period of time as an operation period of time of the reference data in the target data.

In preparing a wire harness production, modified wire harness wiring pattern information is generated. The generated information relates to second patterns, in which some parts of a first pattern are altered. Each of the second patterns is simulated for practicality. Where practical, according to the simulation, a first sub-pattern is modified to one of the second sub-patterns to achieve the desired second pattern. Jig arrangement pattern information is generated by applying at least one sub jig arrangement pattern to each of the wire harness path patterns of a standard type vehicle included in wire harness path pattern information. Path decision information is used to select one wire harness path pattern. A jig arrangement pattern selector selects a jig arrangement pattern. A path plan is generated by integrating the modified second sub pattern having the necessary alterations and the selected jig arrangement pattern.

The invention discloses a balance control module service life prediction method and device based on health indexes. The method comprises the following steps: S1, calculating the characteristic value of a balance control module during each action based on the data of the balance control module in each action; S2, constructing a health index model, and determining model parameters based on the characteristic values of balance control module samples; based on the extracted characteristic values, calculating health indexes of the balance control module in each action by adopting the health index model after parameter determination; S3, constructing a life prediction model, and training prediction model parameters based on a health index sequence and a residual service life sequence of the balance control module samples to obtain a trained life prediction model; and S4, inputting a historical health index sequence of a balance control module to be detected into the trained life prediction model, and outputting the remaining service life of the balance control module. The service life of the balance control module can be predicted, and the accuracy is high.

The invention discloses a mining lithium battery life prediction method based on a grey vector machine and a management system. The life prediction method adopts a mining lithium battery capacity as atraining sample to establish a grey RVM regression prediction model; a DGM is built according to the training sample data, a predicted value of the DGM is used as input, the original training sampleis used as output, and an RVM regression prediction model is obtained by training; a DGM(1,1) is adopted to performing capacity short-term prediction, a predicted value is taken as the input of the RVM regression prediction model to obtain a short-term regression prediction result and a prediction probability value of the obtained capacity, a metabolic process is introduced, so that the training sample data are updated; and relevance is analyzed and judged through grey correlation, the RVM model is dynamically updated according to the result so as to obtain a new relevance vector, thereby obtaining a long-term trend prediction result of the method. According to the method, lithium battery monitoring data is acquired in real time, so that the service life prediction precision of the mininglithium battery is more accurate.

The invention aims to provide a vibration curve normalized average life prediction method of equipment components. The load is normalized, the loading force is comprehensively considered, the use state and the predicted use time of the equipment components are accurately analyzed based on the ambient temperature and humidity, and lubrication. In order to achieve the aim, a rotary machinery system fault diagnosis method based on an adaptive noise reduction algorithm is provided and includes acquiring a trend curve and calculating the average curve of a component, comparing the trend curve with the average curve of the component, and determining the accurate component service life information based on the distance D between the trend curve and the average curve of the component on a specific time point. Therefore, the root cause of the component fault is obtain through analysis according to the influence factors of the work state and the environmental parameters to the vibration curve.

A life prediction apparatus is configured to accurately predict a life of a cable wired at a joint part of a robot. The life prediction apparatus includes a fatigue-level estimation unit for estimating a fatigue level of the cable based on encoder information of an actuator which moves the joint part of the robot, and a life prediction unit for predicting the life of the cable based on a fatigue level of the cable estimated by the fatigue-level estimation unit and an allowable value of the cable.

The invention relates to a method for predicting the service life of a transformer substation relay protection device. The method comprises the following steps: S1, selecting evaluation indexes, and obtaining the sample data of the corresponding evaluation indexes; s2, quantifying the sample data; s3, establishing a multi-classification support vector machine model; s4, training and optimizing themulti-class support vector machine model by utilizing the quantized sample data; and S5, obtaining evaluation indexes of the relay protection device, inputting the evaluation indexes into the multi-classification support vector machine model, and outputting the predicted life length of the relay protection device by the multi-classification support vector machine model.