Time–Frequency Domain Characteristics of Acoustic Emission Signals and Critical Fracture Precursor Signals in the Deep Granite Deformation Process

To study the crack evolution law and failure precursory characteristics of deep granite rocks in the process of deformation and failure under high confining pressure, granite samples obtained from a depth of 1150 m are tested using a TAW-2000 triaxial hydraulic servo testing machine and a PCI-II acoustic emission monitoring system. Based on the stress–strain curve and IET function, the loading process of the sample is divided into five stages: crack closure, linear elastic deformation, microcrack generation and development, macroscopic fracture generation and energy surge, and post-peak failure. The evolution trend and fracture evolution law of the acoustic emission signal event interval function in different stages are analyzed. In particular, the signals with an amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz are defined as the failure precursor signals before the rock reaches the peak stress. The defined precursor signal conditions agree well with the experimental results. The time–frequency analysis and wavelet packet decomposition of the precursor signal are performed on the extracted characteristic signal of the failure precursor. The results show that the time-domain signal is in the form of a continuous waveform, and the frequency-domain waveform has multi-peak coexistence that is mainly concentrated in the high-frequency region. The energy distribution obtained by the wavelet packet decomposition of the characteristic signal is verified with the frequency-domain waveform. The energy distribution of the signal is mainly concentrated in the 343.75–375 kHz frequency band, followed by the 281.25–312.5 kHz frequency band. The energy proportion of the high-frequency signal increases with the confining pressure.

Practical Challenges of High-Power IGBT’s I-V Curve Measurement and Its Importance in Reliability Analysis

This paper examines the practical challenges of simplified setups aimed at achieving high-power IGBTs’ IC–VCE curve. The slope of this I–V curve (which is defined as on-resistance RCE) and the point where the VCE–VGE curve visibly bends (threshold gate voltage) can be suitable failure precursor parameters to determine an IGBT’s health condition. A simplified/affordable design for these specific measurements can be used for in-situ condition monitoring or field testing of switching devices. First, the possible I–V curve measurement methods are discussed in detail in order to prevent self-heating. The selected design includes two IGBTs in which the high-side IGBT was the device under test (DUT) with a constant gate voltage (VGE) of 15 V. Then, the low-side IGBT was switched by a short pulse (50 μs) to impose a high-current pulse on the DUT. The VCE–VGE curve was also extracted as an important failure-precursor indicator. In the next stage, a power-cycling test was performed, and the impact of degradation on the IGBT was analyzed by these measurement methods. The results show that after 18,000 thermal cycles, a visible shift in I–V curve can be seen. The internal resistance increased by 13%, while the initial collector-emitter voltage and voltage at the knee point in the VCE–VGE curve slightly changed. It is likely that in our case, during the performed power-cycling test and aging process, the bond wires were most affected, but this hypothesis needs further investigation.

Analysis of Mudstone Fracture and Precursory Characteristics after Corrosion of Acidic Solution Based on Dissipative Strain Energy

The deformation and failure of rock materials are closely related to the strain energy characteristics during the loading process. These strain energy characteristics and rock properties are greatly affected when the rock is subjected to the acidic solution. To study the effects of chemical solutions with different pH on the mechanical properties and strain energy mechanism of mudstone, the chemical corrosion mudstone samples are subjected to a uniaxial loading testing machine (CN64 electro-hydraulic servo). The corrosive effects of the acidic solution on the porosity, strain energy characteristics, and failure mode of mudstone samples were thoroughly investigated. The findings of this research indicate that: (1) The rate of change in the porosity and chemical damage coefficient of rock samples after chemical corrosion decreases, which is closely linear with the increase of solution pH; (2) The total strain energy, elastic strain energy, and dissipative strain energy decrease with the increase of pH, and, as a result, it is proposed that the observed turning point of the proportion curve of dissipated strain energy from decline to rise is used as a precursor point of the rock failure; (3) The stress value of the failure precursor point increases and the strain value decreases with the increase in pH value. However, the ratio of the stress value of the failure precursor point to the peak stress hardly changes with pH value, and its value is about 0.883; and (4) Rock samples soaked in a weak acidic chemical solution (pH 7.3 and 5.3) are damaged by tensile crack, while rock samples soaked in a strong acidic chemical solution (pH 3.3 and 1.3) are mainly damaged by the combination of tensile and shear. The findings of this study can be used to provide an experimental and theoretical foundation for monitoring rock engineering disasters such as slope, tunnel, and coal mine failures.

Anomaly Detection and Degradation Prediction of MOSFET

The MOSFET is an important power electronic transistor widely used in electrical systems. Its reliability has an effect on the performance of systems. In this paper, the failure models and mechanisms of MOSFETs are briefly analyzed. The on-resistanceRonis the key failure precursor parameter representing the degree of degradation. Based on the experimental data, a nonlinear dual-exponential degradation model for MOSFETs is obtained. Then, we present an approach for MOSFET degradation state prediction using a strong tract filter based on the obtained degradation model. Lastly, the proposed algorithm is shown to perform effectively on experimental data. Thus, it can provide early warning and enhance the reliability of electrical systems.