The present disclosure discloses a method for calculating the service life of a material under the action of a thermal shock load. The method includes steps of obtaining test results at different thermal shock temperatures and a thermal shock cycle number according to a thermal shock test, and calculating a temperature rise rate to temperature drop rate ratio R.sub.v; calculating a corresponding stress intensity factor ΔK according to a crack length a measured in the test; calculating a thermal stress σ at the notch and a notch stress concentration coefficient k.sub.t of the test specimen; calculating a stress intensity factor threshold ΔK.sub.th according to the crack length a measured in the test; and substituting the obtained the stress intensity factor ΔK, stress intensity factor threshold ΔK.sub.th and temperature rise rate to temperature drop rate ratio R.sub.v into a thermal fatigue crack growth model.

A battery model construction method includes: a step ST2 for constructing a battery model; steps ST3 and ST4 for evaluating, for each sample battery, the prediction error between a measured value of the SOH and a predicted value according to the battery model, and determining whether there is inherent bias in the prediction error for each sample battery; steps ST5 and ST6 for constructing a first error prediction model associating explanatory variables defined on the basis of usage history parameters with an objective variable, and determining whether a first correlation exists between the measured value of the average prediction error acquired in steps ST3 and ST4 and the predicted value according to the first error prediction model; and a step ST7 for reconstructing the battery model in the case where it is determined that there is bias and that the first correlation exists in steps ST5 and ST6.

A system and method for simulating an electronic circuit is disclosed. The method includes creating a finite set of circuit or device parameter points selected from within an n-dimensional parameter space. The method includes determining, for each circuit or device parameter point of the set, a corresponding response value of the performance metric and a corresponding probability of occurrence. The method includes determining, for a predetermined value of the performance metric, the total probability of occurrence.

A circuit health state prediction method and system based on an integrated deep neural network are provided and relates to a technique for predicting a power electronic circuit failure. The invention serves to identify and diagnose a health state of a simulation circuit based on historical data by using an integrated deep neural network, and the method includes: carrying out parameter aging simulation experiments for different devices; extracting a series of time domain features of output signals through a temporal transformation method, and establishing health indices of the devices based on an improved angular similarity; predicting a health state of the simulation circuit in degeneration by using CAE and LSTM-RNN; and predicting validity of the circuit health state prediction method by referring to relevant evaluation indices. The invention is capable of effectively predicting the health state of the simulation circuit and is highly accurate and easy to implement.

A method for aging simulation model establishment includes following operations. Provide a planar p-type metal-oxide-semiconductor field-effect transistor (pMOSFET) having a source and a drain. Measure a first reliability degradation data of the pMOSFET. Select a model for the pMOSFET with modeling parameters related to hot carrier induced punch-through (HEIP). The modeling parameters comprise hot carrier injection (HCI) parameters used to fix a simulated current relation between the source and the drain. Construct the modeling parameters by aging parameters multiplied corresponding flags. Perform a simulation of the pMOSFET with the model to have a second reliability degradation data. Update the aging parameters and re-performing the simulation if the first reliability degradation data and second reliability degradation data are not matched. Collect the aging parameters when the first reliability degradation data and the second reliability degradation data are matched to establish the aging simulation model for the pMOSFET.

A device and method for building life cycle sustainability assessment using probabilistic analysis method, the device and method being capable of assessing and predicting building life cycle sustainability, and a recording medium storing the method. The device includes: a first storage unit storing a reference environmental impact assessment value of a reference building, and first and second environmental impact coefficient groups; an input unit receiving area information, amounts of building materials and energy sources; a probability distribution calculating unit storing a set value, and deducing probability distributions of the building materials and the energy sources; a first arithmetic unit calculating probability distributions of first and second environmental impact assessment values, and a probability distribution of a life cycle environmental impact assessment value; and a first output unit deducing a probability distribution of an environmental impact index, and outputting the deduced probability distribution of the environmental impact index.

A system and method for the predictive maintenance of electronic components that includes sensors at at least one position via which present values of system parameters, such as temperature and voltage, and a signal propagation time at the at least one position are determined, where values of the system parameters and the signal propagation time presently determined by the sensors are retrieved by a central monitoring unit, an individual valid limit value is determined for the signal propagation time at each of the at least one position via the central monitoring unit based on the presently determined values of the system parameters, and the presently determined signal propagation time at each of the at least one position is compared with the associated valid limit value, and a notification is sent to a superordinate level, if the signal propagation time exceeds the limit value to trigger replacement of the electronic component.

A method for three-dimensional printing includes dithering a set surface of the printing object and printing the printing object with the dithered surface. The dithering includes determining the set surface of the printing object, providing a spatially high-frequent dithering signal, and modifying the set surface as a function of the dithering signal. A non-transitory computer-readable medium includes instructions that implement the method. A 3D printing device includes a printing device and a control unit configured to control the printing device using the method.

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining service life data of a part including a maintenance operation count of the part. The life expectancy of the part is formulated as a function of a life span of the part and the maintenance operation count at a point in time based on the obtained service life data and data interpolated therefrom. A life expectancy model is built based on the function and a plurality of life expectancies are predicted by applying simulated inputs to the life expectancy model. The life expectancies are produced after verification to indicate when to replace the part.

The present invention discloses a multiaxial creep-fatigue prediction method based on ABAQUS, which comprises: S1: establishing an ABAQUS finite element model, and defining the viscoplastic constitutive equation of the material to be tested by means of the user subroutine UMAT; S2: determining the model parameters required by the viscoplastic constitutive equation; S3: establishing the fatigue damage calculation model and creep damage calculation model of the multiaxial stress-strain state of the material to be tested; S4: establishing an ABAQUS finite element model under the multiaxial stress-strain state, and calculating the stress-strain tensor of each cycle based on the defined viscoplastic constitutive equation and the model parameters; S5: calculating the equivalent stress and equivalent plastic strain by means of the user subroutine USDFLD, and superimposing the fatigue damage and creep damage of each cycle according to the linear cumulative damage criterion to obtain the crack initiation life of the material to be tested based on the fatigue damage calculation model and creep damage calculation model in combination with the stress-strain tensor.