首页|Akita University Researcher Provides Details of New Studies and Findings in the Area of Artificial Intelligence (Enhancing Interpretabilityin Drill Bit Wear An alysis through Explainable Artificial Intelligence: A Grad-CAM Approach)
Akita University Researcher Provides Details of New Studies and Findings in the Area of Artificial Intelligence (Enhancing Interpretabilityin Drill Bit Wear An alysis through Explainable Artificial Intelligence: A Grad-CAM Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting from Akita, Japan, by Ne wsRx journalists, research stated, “This study introduces a novel method for ana lyzing vibration data related to drill bit failure.” Our news journalists obtained a quote from the research from Akita University: “ Our approach combines explainable artificial intelligence (XAI) with convolution al neural networks (CNNs). Conventional signal analysis methods, such as fast Fo urier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are tran sformed into their frequency components and then employed as inputs to a CNN mod el, which is trained to detect patterns indicative of drill bit failure. XAI alg orithms are then employed to generate attention maps, highlighting regions of in terest in the CNN. By scrutinizing these maps, engineers can identify critical f requencies associated with drill bit failure, providing valuable insights for ma intenance and optimization.”
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