Unsupervised Deep Characterization of Machine Behavior Through Acoustic Signal Transitions

Abstract

Machine condition monitoring using acoustic signals is essential for ensuring the reliability, safety, and efficiency of industrial machinery. Traditionally, fault detection relied on manual inspections, vibration sensors, and rule-based systems. These conventional methods were limited in capturing subtle audio variations produced by machines such as engines, compressors, and motors. They often required expert interpretation and were prone to errors, particularly in noisy industrial environments. Machine learning models based on handcrafted features, such as Mel-Frequency Cepstral Coefficients (MFCC) with Logistic Regression (LRC), Linear Discriminant Analysis (LDA), offered some automation but struggled with generalization, misclassifying acoustically similar faults like air leaks, idling disturbances, and oil leak variations. To address these limitations, this research proposes a hybrid intelligent framework, Hidden Unit Tree (HUT), which combines Hidden-Unit Bidirectional Encoder Representations from Transformers (HuBERT)-based deep audio embeddings with a Tree Alternating Optimization (TAO) Tree classifier. HuBERT, a transformer-based self-supervised model, captures high-level, contextual acoustic features, while the TAO Tree classifier provides robust non-linear decision-making. The system is implemented in a Tkinter graphical user interface (GUI), enabling end-to-end functionality including dataset upload, MFCC and HuBERT feature extraction, model training, evaluation, and real-time audio fault prediction. This research successfully integrates deep learning and advanced classification techniques to deliver a reliable, automated machine condition monitoring solution. The combination of rich acoustic feature extraction and robust classification ensures accurate fault detection, making the system suitable for predictive maintenance and Industry 4.0 applications, thereby completing a fully functional, real-time intelligent fault diagnosis tool.