Abstract: The current research on mechanical fault detection of ultrasonic instrument trajectory balls requires dividing multiple categories in feature classification to complete fault diagnosis. Although the multi class pattern can improve diagnostic efficiency, this multi class fault recognition pattern is prone to cyclic iterative comparisons of similar faults between classes, resulting in a decrease in recognition accuracy. Utilizing the feature of fusible spectral features of mechanical faults in trackballs, a diagnostic method using ultrasonic instruments under unified spectral features is designed. By using spectrum analysis technology to perform spectrum analysis on the trajectory ball signal of ultrasonic instruments, high-precision amplitude and phase spectra are obtained. By fusing signal amplitude and phase information, a two-dimensional holographic spectrum is constructed on various monitoring cross-sections of the ultrasonic instrument trajectory ball to extract fault signal features. By randomly assigning labels to each sample based on the extracted features, a sample library is constructed to train a deep neural network (DNN: Deep Neural Network). After DNN training and testing iterations, fault samples with similar features are aggregated into the same class, ultimately achieving fault diagnosis. The experimental results show that the proposed method can accurately diagnose mechanical faults in the trajectory ball of the ultrasound instrument, ensuring the stable operation of the ultrasound instrument. 

Key words: spectral analysis, ultrasonic instruments, trajectory ball, mechanical failure, within the unified category, deep neural networks(DNN)