A control device including a solder joint life predictor includes: a temperature sensor that measures temperature of a solder joint on an electronic circuit board that drives a heater and a motor; a storage that stores a reference acceleration factor that is an acceleration factor based on a test condition of a thermal shock test and a reference condition in an environment in which the electrical appliance is used; a calculator that calculates an actual acceleration factor from a temperature variation range and a maximum reached temperature of the solder joint during one cycle from start to end of driving of the heater or the motor; and a determiner that predicts the life of the solder joint by comparing the integrated value of the acceleration factor ratios with a threshold.

This invention discloses a method for predicting the service life of a retired power battery. The service life of a retired power battery may be predicted by its power battery life attenuation curve. The power battery life attenuation curve is obtained by establishing a power battery life model and a charge and discharge characteristic curve of the power battery by utilizing the temperature T, the discharge rate C and the discharge depth DOD in the charging and discharging process of the power battery. This invention establishes a three-dimensional relation graph with a cycle life with respect to the capacity loss rate and the functional relationship ω=f(T,C) by using the power battery life attenuation curve. The three-dimensional relation graph is applied to the same type of battery. And the attenuation of the battery in the full life cycle may be predicted.

A system and method of simulating device aging based on a digital waveform representative of a workload of an electronic device are disclosed. In one aspect, the method comprises grouping contiguous sets of cycles into segments, each set corresponding to a segment. Each segment has values for a combination of segment parameters that are unique from each of the other segments and a start point that is separated from a start point of an adjacent segment by a pre-defined distance criterion. Grouping the sets into the segments comprises, for each segment: sampling one or more sequential cycles of the workload, generating the segment based on the sampled contiguous cycles having a period exceeding a threshold period, and determining the values for the combination of segment parameters. The method further comprises applying an aging model to the segments to simulate the aging. The segments are a representation of the digital waveform.

The disclosure discloses a multi-time-scale reliability evaluation method of a wind power IGBT considering fatigue damage and a system thereof. Lifetime information of a power device is comprehensively extracted by using multiple time scales. An electro-thermal coupling model of an IGBT module is established to obtain a junction temperature data. A steady-state junction temperature database of the IGBT in different aging states is established. Based on a SCADA monitoring data, the junction temperature data is outputted in real-time through the electro-thermal coupling model and a real-time thermal stress cycle number is calculated in a short-term time-scale profile, and a wind speed probability distribution curve is obtained in a long-term time-scale profile. A maximum thermal stress cycle number that the IGBT can withstand in different aging stages is obtained in advance and a cumulative damage degree and an estimated lifetime of the IGBT of the wind power converter are calculated.

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.

An integrated circuit includes first circuits that are configured to implement a user design for the integrated circuit, second circuits that are unused by the user design, and configuration circuitry that couples the second circuits together through a network of conductors. Transistors in the second circuits turn on and off in response to a varying signal that propagates through the second circuits and through the network of conductors while the first circuits implement the user design.

A method of estimating future aging of a transformer includes generating probabilistic models of factors that affect effective aging of the transformer, generating probabilistic profiles of the factors that affect effective aging of the transformer based on the probabilistic models, generating expected hot spot profiles from the probabilistic profiles, simulating a plurality of aging scenarios of the transformer based on the expected hot spot profiles and ambient temperature profiles, and estimating future aging of the transformer from the plurality of aging scenarios.

A method for estimating life of a component includes obtaining fracture data corresponding to a component. The fracture data includes a first dataset corresponding to a threshold region where the crack in the component is dormant below a fatigue threshold. The method further includes determining initial estimates of parameters of a crack growth rate model and parameters of temperature models corresponding to the crack growth rate model based on the fracture data. The method also includes computing optimized parameters of temperature models corresponding to the crack growth rate model, and a scatter parameter via simulation of a joint optimization method using the initial estimates. The method includes determining a cumulative distribution function based on the optimized parameters and the scatter parameter and estimating life of the component based on the cumulative distribution function.

A system and method is disclosed for predicting and comparing wear scenarios in a rail system. The method can include generating and running a contact model of the interaction between a rail and a train car to produce a simulated loading on the rail for a predetermined time period; generating and running a wear model based on the material properties and/or friction modifier properties of the rail using the simulated loading to produce a simulated wear profile of the rail for the predetermined time period; obtaining a grinding profile to be performed on the rail during the predetermined time period; and generating an updated rail profile by modifying the rail profile by the simulated wear profile and the grinding profile. The method can predict and compare crack growth over time, and provide a financial model and comparison of costs and benefits for different maintenance protocols for the rail system.

A system and method is disclosed for predicting and comparing wear scenarios in a rail system. The method can include generating and running a contact model of the interaction between a rail and a train car to produce a simulated loading on the rail for a predetermined time period; generating and running a wear model based on the material properties and/or friction modifier properties of the rail using the simulated loading to produce a simulated wear profile of the rail for the predetermined time period; obtaining a grinding profile to be performed on the rail during the predetermined time period; and generating an updated rail profile by modifying the rail profile by the simulated wear profile and the grinding profile. The method can predict and compare crack growth over time, and provide a financial model and comparison of costs and benefits for different maintenance protocols for the rail system.