## 4 Discussion  
This study demonstrated the feasibility of using active acoustic sensing for non-invasive fault diagnosis in robots. By analyzing frequency-domain features derived from reflected sound signals, the system could reliably distinguish between different structural configurations. This affirms that acoustic responses offer meaningful indicators of internal mechanical states. However, a key limitation emerged regarding the generalization of model performance. While all classifiers showed perfect accuracy under cross-validation using the training data, their performance dropped significantly when evaluated on an independent test dataset collected under slightly different conditions. Even models like MLP and SVM, which performed best, experienced noticeable declines in accuracy. These discrepancies suggest that environmental noise, slight inconsistencies in joint tension or component attachment, or subtle shifts in joint configuration can lead to instability in spectral features and undermine classification reliability. This limitation invites reflection on the role and scope of dataset size in acoustic fault diagnosis. While deep learning models are known to benefit from large-scale data, the objective of this study was not to maximize accuracy through extensive data collection, but rather to evaluate the feasibility of low-resource, lightweight diagnostic systems that could be deployed on simple or mass-producible robotic platforms. In such contexts, acquiring thousands of labeled samples for each fault condition would be impractical. The experiment was therefore deliberately designed with a minimal but structured dataset, aiming to determine whether basic acoustic features could still enable meaningful fault classification under constrained data regimes. Given this experimental framing, strategies for improving generalization focused on model tuning rather than scaling the dataset. Measures such as hyperparameter optimization, feature normalization, and cross-validation across different data segments were adopted to minimize overfitting. Although these methods offered some mitigation, the results indicate that low-data regimes inherently carry trade-offs in model robustness. Therefore, alternative approaches must be considered for real-world deployment. One promising direction is the integration of moderately complex architectures, such as shallow convolutional neural networks or hybrid models incorporating attention mechanisms, which could extract more invariant patterns even from limited data. Additionally, exploring time-frequency representations like wavelet transforms or phase-based features might yield improved resistance to environmental variation. For settings where small \begin{table}
\begin{tabular}{c c} \hline
**Model** & **Accuracy** \\ \hline SVM & 65.7 \\ Random Forest & 51.4 \\ KNN & 60.0 \\ XGBoost & 57.1 \\ LightGBM & 62.9 \\ MLP & 71.4 \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy of fault detection of six classifiers. scale customization is feasible, on-device learning using a small number of self-recorded examples could enable robots to adapt their models locally without centralized retraining. From a practical perspective, the current setup--relying solely on a speaker and microphone--remains attractive for lightweight deployments. Potential applications include real-time diagnostics in modular robots, swarm systems, or educational kits. Yet to make such systems viable in uncontrolled settings, it is essential to systematically investigate their robustness to environmental shifts and hardware variations. Overall, this study provides a foundational proof of concept while identifying clear paths forward. Through a combination of modest data expansion, improved feature engineering, and task-specific architecture design, it may be possible to bridge the gap between controlled experimental results and robust performance in real-world conditions.