###### Abstract  
Distillation columns are vital for substance separation and purification in various industries, where malfunctions can lead to equipment damage, compromised product quality, production interruptions, and environmental harm. Early fault detection using AI-driven methods like deep learning can mitigate downtime and safety risks. This study employed a lab-scale distillation column to collect passive acoustic signals under normal conditions and three potential faults: flooding, dry, and leakage. Signal processing techniques were used to extract acoustic features from low signal-to-noise ratios and weak time-domain characteristics. A deep learning-based passive acoustic feature recognition method was then applied, achieving an average accuracy of 99.03% on Mel-frequency cepstral coefficient (MFCC) spectrogram datasets. This method demonstrated robust performance across different fault types and limited data scenarios, effectively predicting and detecting potential faults in distillation columns. chemical equipment; distillation column; deep learning; passive acoustic monitoring; neural network +
Footnote †: journal: Medical  
acoustic signals also change accordingly. This continuous nature of state changes makes it possible to monitor potential faults in the distillation column through acoustic signal analysis. Acoustic signals can capture the initial signs of faults, which are often difficult to detect in advance using traditional methods. By continuously monitoring and analyzing these signals, preventive maintenance measures can be taken before the faults develop into severe problems, significantly reducing the impact of faults on the production process [5]. The passive acoustic signals from a distillation column typically contain rich information about the equipment's operating conditions [6]. In passive acoustic signal analysis, Mel-frequency cepstral coefficients (MFCCs), inspired by the human auditory system, can effectively extract key features. When combined with deep learning models, it enhances the accuracy and efficiency of fault detection and preventive maintenance. [PERSON] et al. [7] proposed a fault diagnosis method for converter transformers based on multi-strategy-improved MFCCs and time convolutional networks (TCNs). This method considers the impact of load factors and employs an improved hunter-prey optimization algorithm (IHPO) and variational modal decomposition (VMD) for denoising acoustic signals. The S-transform is used for time-frequency conversion, and multi-strategy-improved MFCCs are utilized for feature extraction. Combined with an improved time convolutional network (ITCN), this method achieves effective fault diagnosis for converter transformers. In the area of multi-fault diagnosis, [PERSON] et al. [8] introduced a fault diagnosis method based on improved Mel-frequency cepstral coefficients (MFCCs) and their derivatives. This method uses deep learning models, including LSTM, BiLSTM, and CNN, to efficiently classify a total of 30 faults in centrifugal pumps and reciprocating compressors. By leveraging the pseudo-periodicity of fault signals for block-wise classification and applying an efficient data augmentation approach for vibration signals, the method demonstrates good adaptability and reliability in industrial applications. Currently, state monitoring and fault diagnosis methods for chemical equipment based on deep learning have been applied to devices such as pumps [9; 10; 11; 12], heat exchangers [13], fans [14; 15; 16], fluidized beds [17; 18; 19; 20], and pipelines [21; 22; 23; 24], showing considerable potential. Against this backdrop, methods based on deep learning have become a primary choice and trend. Back for the distillation process, [PERSON] et al. [25] employed the support vector machine (SVM) method to establish a soft sensor model for viscosity in vacuum distillation. Utilizing a differential evolution algorithm's search strategy, they constructed a model with high prediction accuracy and excellent trend-tracking performance.