32results about How to "Life Prediction is Accurate" patented technology

The invention discloses a safety management system for a high-speed train braking component, which comprises a driving data acquisition module, a wear data acquisition module, a time life prediction module and a mileage life prediction module, wherein the driving data acquisition module is used for acquiring driving data of the high-speed train; the wear data acquisition module is used for acquiring wear data w of a braking component on an axle; the time life prediction module is used for acquiring a fitting function T(w) for detection days t and the wear data w through fitting of a least square method, and the detection days t0 when the braking component reaches a wear limit are deduced; and the mileage life prediction module is used for acquiring a fitting function M(w) for a driving mileage m and the wear data w through fitting of the least square method, and the driving mileage m0 when the braking component reaches the wear limit is deduced. Detection data on the braking components corresponding to each high-speed train are unified, service lives of different braking components can be predicted accurately, the maintenance efficiency and the replacement efficiency can be mastered reasonably and effectively, and the driving safety of the high-speed train is improved.

The invention discloses a battery life prediction method and system, an electronic device and a storage medium. The method comprises the following steps: acquiring historical data of battery capacity; preprocessing the historical data to obtain principal component data and secondary component data of battery capacity attenuation; inputting the principal component data and the secondary component data into a pre-trained long short-term memory neural network; receiving an output result of the long short-term memory neural network, and processing the output result to obtain an attenuation sequence of the battery capacity; and judging whether the numerical value in the attenuation sequence reaches a preset battery failure threshold value or not so as to predict the residual life of the battery, and inputting historical data and a prediction result into a long-short-term memory neural network for reverse training, so that generalization, long-term and effective prediction of the service life of the battery can be realized.

There are provided measuring units of measuring the amount of fluoride ions in the waste material from a fuel cell which receives a fuel gas and an oxidizing agent gas containing oxygen to undergo electrochemical reaction by which electricity is generated and a life predicting unit of predicting the life of the fuel cell by the use of the amount of fluoride ions thus measured.

The invention relates to a method for predicting residual mechanical life of a universal circuit breaker contact system based on deep learning. The method adopts a deep learning method to carry out life prediction research, and comprises the following steps: firstly, proposing a mechanical life prediction vibration signal effective fragment concept; secondly, introducing a VMD algorithm and double thresholds based on short-time energy to automatically calibrate the interval; constructing a multi-channel convolutional auto-encoder network (MCCAE) again, adopting an unsupervised learning mode for training, and extracting effective fragment deep degradation time sequence features; and finally, constructing a long-short term memory (LSTM) neural network, taking the time sequence characteristics as input, adopting a supervised learning training mode, completing the steps of prediction and the like, and effectively completing the prediction of the residual mechanical life of the contact system of the universal circuit breaker.

The invention discloses an IGBT life prediction method and system based on composite failure mode coupling. The method comprises: firstly, calculating a simultaneous failure probability model of a bonding wire and a solder layer; then, calculating the expectation of the simultaneous failure probability model, and recording the expectation as the service life under the coupling effect; establishing a coupling function relational expression; predicting the service life of the solder layer and the service life of the bonding wire; establishing an IGBT service life prediction model without considering the coupling effect; and establishing an IGBT service life prediction model considering the coupling effect. According to the invention, more accurate life prediction can be carried out on the IGBT module under the coupling effect of the solder layer and the bonding wire.

The invention discloses a method for predicting the service life of structural concrete under sulfate environment erosion, which comprises the following steps: based on the Fick's second law and the law of conservation of mass, solving a sulfate transmission constitutive equation by adopting an alternating implicit finite difference method to meet unconditional convergence conditions, and performing programming and calculating by using MATLAB (Matrix Laboratory) software, and achieving analogue simulation and life prediction of the transmission process of sulfate ion erosion in the concrete. The method is accurate in calculation, high in stability and high in efficiency, and based on the Fick law and the chemical reaction kinetics theory, the diffusion-reaction model of the sulfate ions in the concrete structure with the boundary moving and the calculation method of the erosion product generation amount are achieved.

The invention discloses a novel high-reliability slewing bearing service life evaluation method. The method comprises the following steps of: extracting an effective average value of positive and negative vibration signals in a short time according to a denoised slewing bearing vibration acceleration signal to obtain time-space information data, and determining actual balance position informationof the slewing bearing; determining whether the average value of the positive and negative vibration signals has false fluctuation or not according to the obtained information of the actual balance position, if so, repairing the signal data, and subsequently obtaining two groups of time indexes through adoption of a smooth curve method; and establishing a relationship between the obtained index and the residual service life by utilizing the obtained high-quality time index and the fault index of the slewing bearing through a time and fault index hybrid embedded long-short-term memory network based on the generative adversarial network. According to the method, high-quality sample data are obtained under complex working conditions, the number of samples is increased, the reliability of lifeprediction under the complex working conditions is effectively improved, and the method has a certain application value.

In a system for preparing a wire harness production, a modified wiring pattern generator generates modified wire harness wiring pattern information related to at least one second pattern in which at least one of parts, electric circuits, and wirings of a first pattern including the parts, the electric circuits, and the wirings of a standard type vehicle is altered. A parts information storage stores parts specification information including specifications of the parts, the electric circuits and the wiring included in the second patterns. A simulator simulates whether each of the second patterns is practical while referring to the parts specification information. A sub wiring pattern modifier modifies at least one of first sub patterns constituting the first pattern to at least one of second sub patterns so as to constitute the second pattern judged as practical by the simulator. A jig arrangement pattern information generator generates jig arrangement pattern information related to jig arrangement patterns prepared by applying at least one sub jig arrangement pattern to each of a plurality of wire harness path patterns of the standard type vehicle included in wire harness path pattern information. Path decision information selects one wire harness path pattern among the plurality of wire harness path patterns. Alternation information indicates at least one of alternation portions altered from the first pattern. A jig arrangement pattern selector selects one jig arrangement pattern corresponding to the selected wire harness path pattern among the plurality of wire harness path patterns. A path plan generator generates a path plan by integrating the modified second sub pattern having the alternation portions which is indicated in the alternation information and the selected jig arrangement pattern.

The invention relates to an airport multi-platform multi-device complex system health management method, which combines nominal values of components for physical modeling, can simply perform physicalmodeling on different components, and can estimate the remaining life of the components in real time according to the change of the environment. Meanwhile, a data modeling mode is used for an automatic switch type device, data models of pull-in time and time of different types of components under different working conditions can be established, accurate service life prediction is carried out, important reference is provided for predictive diagnosis, and the failure rate of an airport electrical system is effectively reduced.

The invention relates to the technical field of rail traffic, in particular to a fastener system capable of monitoring dynamic characteristics of a rail and a monitoring method thereof.The fastener system comprises an under-rail elastic cushion, a rigid base plate, an under-plate elastic cushion and a sleeper and further comprises a vibration electric energy conversion device, a plurality of wireless sensing integrated assemblies and a terminal data processing unit; according to the application, the elastic pad is extruded and deformed to cause the dynamic change of the resistance or voltage of the wireless sensing integrated assembly in the elastic pad, and the change electric signal is transmitted to the terminal data processing unit to be processed and analyzed, so that the system integration degree is high; according to the monitoring method, through the wireless intelligent sensing technology, the electric signal data are collected and transmitted in real time through the multiple wireless sensing integrated assemblies along with extrusion deformation of the under-rail elastic cushion and the under-plate elastic cushion, and then the dynamic characteristics and disease phenomena of the rail system are monitored and fed back in real time; dynamic, reliable and accurate big data support and part service life prediction are provided for intelligent operation and maintenance of rail transit.

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 discloses a method for extracting the operating condition of the universal battery in the region for the prediction of the service life of the vehicle battery. Shelving period; 2), data collection of actual working conditions in a certain area: real-time collection of battery operating information of different vehicles in the area through the electric vehicle management system; 3), voltage and SOC extraction: extract the operating voltage range and SOC of the battery according to the vehicle operating data Scope; 4), vehicle-mounted operating current extraction step: extract the throughput per unit time of the battery in this area; segment the operating current, and extract the frequency of occurrence of each segment operating current; combine the throughput and the frequency to obtain the throughput per unit time Operating current spectrogram; 5), extraction step of storage and operating temperature: obtain the ambient temperature of the battery in the area as the storage temperature of the battery, and calculate the operating temperature according to the relationship between the temperature rise of the vehicle and the initial temperature.

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 and device for life prediction of a balance control module based on a health index. The method includes the following steps: Step S1: Based on the data of the balance control module in each operation, calculate its characteristics in each operation value; Step S2: Construct a health index model, determine the model parameters based on the eigenvalues ​​of the balance control module samples; and based on the extracted eigenvalues, use the health index model after parameter determination to calculate the health indicators of the balance control module in each action ;Step S3: Build a life prediction model, train the prediction model parameters based on the health index sequence and the remaining service life sequence of the balanced control module sample, and obtain the trained life prediction model; Step S4: Put the historical health indicators of the balanced control module to be tested The sequence is input to the trained life prediction model, and its remaining service life is output. The invention can predict the service life of the balance control module with high accuracy.

The invention is a method for predicting the remaining mechanical life of a contact system of a universal circuit breaker based on deep learning. This method uses the deep learning method to carry out life prediction research. Firstly, the concept of effective segment of vibration signal for mechanical life prediction is proposed; secondly, VMD algorithm and double threshold based on short-term energy are introduced to automatically calibrate its interval; again, multi-channel convolutional self-encoding is constructed. Machine network (MCCAE), trained by unsupervised learning method, extracts deep degraded temporal features of effective segments; finally builds long short-term memory neural network (LSTM), takes temporal features as input, and uses supervised learning training method to complete prediction and other steps, The remaining mechanical life prediction of the universal circuit breaker contact system can be effectively completed.

The invention provides a water pump remaining service life prediction method and device, equipment and a storage medium, and relates to the technical field of industrial equipment maintenance. The method comprises the steps of collecting vibration information, pressure value information and water pump flow value information of a water pump; performing time-frequency domain information extraction on the vibration information, and constructing a feature set of the water pump; and performing data dimension reduction on the feature set, predicting the feature set after dimension reduction, and determining the remaining service life of the water pump. The invention comprises that various information of the water pump during operation is obtained, and features of the water pump are extracted through a preset prediction model for prediction. Various operation data of the water pump are fully considered, and the prediction result is more accurate. And time-frequency domain information is extracted from vibration information, and input is constructed with water pump inlet and outlet pressure and a water pump fluid flow value, so that service life prediction is more accurate. The accuracy of predicting the residual life of the water pump is guaranteed, maintenance personnel can maintain or replace the water pump conveniently, and the production safety is improved.

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 discloses a bearing cross-working-condition fault prediction method based on adversarial transfer learning, and the method comprises the steps: setting different working conditions, carrying out the signal collection of a bearing, and obtaining a vibration signal; recognizing the vibration signal based on a continuous abnormal point detection method of peak measurement to obtain a health stage signal and a degradation stage signal; performing preprocessing and fault feature extraction on the degradation stage signal to obtain a fault feature set; inputting the fault feature set into a domain adversarial transfer learning double-branch neural network and carrying out updating training to obtain a prediction model; and obtaining to-be-tested data and inputting the to-be-tested data into the prediction model to obtain a fault prediction result. By using the method, accurate service life prediction of the high-speed shaft ball bearing of the gear box of the large fan under different working conditions is realized, wherein the service life of the high-speed shaft ball bearing faces to actual working condition characteristics. The bearing cross-working-condition fault prediction method based on adversarial transfer learning can be widely applied to the field of engineering part service life prediction.

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.