###### Abstract
However, the SVM method has a slower learning speed and is prone to getting stuck in local minima. [PERSON] et al. [26] proposed an extreme learning machine method based on a wavelet kernel function, applying it to the soft sensor modeling problem in acetic acid distillation. Simulation results demonstrated that this algorithm's learning speed was 92 times faster than that of the SVM while also improving the method's accuracy and generalization capability. In the field of fault diagnosis for distillation columns, [PERSON] et al. [27] leveraged a dataset comprising photographs capturing instances of liquid flooding processes occurring 35 times over a 17 h period within an extraction column. Employing a convolutional neural network (CNN) coupled with class activation mapping, they identified key regions in the images influencing the network's performance significantly. Their study underscores the practicality and effectiveness of employing deep learning methodologies for predicting and monitoring operational statuses and faults in chemical processing equipment. In the realm of complex reactive distillation processes, [PERSON] et al. [28] devised a method to transform 14 operational variables and temporal information representing 13 types of faults into two-dimensional features. These features were then utilized to train and validate a deep CNN for monitoring and diagnosing the operation status of reactive distillation equipment. The highest accuracy achieved across various CNN architectures during training reached 91.31%. This underscores the efficacy of utilizing both temporal and spatial information as inputs to CNNs for diagnosing and detecting operational states in reactive distillation equipment. The residual network (ResNet) introduces a structural innovation of skip connections on top of the traditional CNN architecture. This design aims to enhance the training process of deep networks, improve overall network performance, and accelerate convergence rates [29]. By incorporating skip connections, ResNet effectively addresses challenges commonly encountered during the training of deep networks within the CNN framework. In this study, ResNet was chosen for acoustic signal classification due to its proven ability to handle the vanishing gradient problem that often plagues deep neural networks. ResNet's architecture allows for the construction of very deep networks without the degradation in performance that typically accompanies increased depth. This capability is particularly advantageous for complex tasks such as fault detection in distillation columns, where subtle differences in acoustic signals must be accurately identified. In this study, acoustic signals from the distillation column under different operating conditions were collected, including normal operation, flooding, dry tray, weeping, and three potential fault states. These acoustic signals were then converted into MFCC feature maps through a series of signal processing and feature extraction techniques, and a corresponding dataset was generated. Finally, a classification method based on a residual network was proposed, trained, and tested on the dataset. The results indicated that the distillation column potential fault detection technology based on deep learning and acoustic signal analysis demonstrated good performance, effectively ensuring the safe and stable operation of the distillation process.