Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods.

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel.

Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris's law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends.

Analysis of coal face stability of lower coal seam under repeated mining in close coal seams group.

Aiming at the problem of coal face failure of lower coal seam under the influence of repeated mining in close coal seams, with the working face 17,101 as a background, the coal samples mechanics test clarified the strength characteristics of the coal face under repeated mining, through similar simulation experiments, the development of stable roof structure and surrounding rock cracks under repeated mining of close coal seams are further explored. And based on this, establish a coal face failure mechanics model to comprehensively analyze the influence of multiple roof structural instabilities on the stability of the coal face. Finally, numerical simulation is used to further supplement and verify the completeness and rationality of similar simulation experiment and theoretical analysis results. The results show that: affected by repeated mining disturbances, the cracks in the coal face are relatively developed, the strength of the coal body is reduced, and the coal face is more prone to failure under the same roof pressure; During the mining of coal seam 17#, the roofs of different layers above the stope form two kinds of "arch" structures and one kind of "voussoir beam" structure, and there are three different degrees of frequent roof pressure phenomenon, which is easy to cause coal face failure; Under repeated mining of close coal seams, the roof pressure acting on the coal face is not large. The main controlling factor of coal face failure is the strength of the coal body, and the form of coal face failure is mostly the shear failure of soft coal. The research results can provide a theoretical basis for coal face failure under similar conditions.