<p>Fault detection in belt conveyor idlers is crucial for minimising downtime and reducing maintenance costs in industrial operations. Traditional methods, like vibration or temperature-based monitoring, face limitations, including challenging sensor installation and restricted data accessibility. Moreover, these approaches often emphasise spatial features, neglecting the temporal dynamics essential for understanding idler performance over time. This study introduces TD-CLNet, a hybrid fault detection framework that leverages acoustic signals captured via contactless microphones processed through a Time-Distributed CNN-LSTM architecture. The model combines the spatial feature extraction capabilities of Convolutional Neural Networks (CNNs) with the temporal sequence modelling strengths of Long Short-Term Memory (LSTM) networks. A key innovation is the use of the Time-Distributed layer, which enables consistent feature extraction across individual log-Mel spectrogram frames while preserving their temporal relationships. This ensures a robust and coordinated learning process, efficiently addressing the challenges of detecting complementary and relevant features. The performance of TD-CLNet is compared to a frame-based feature extraction approach, which treats each log-Mel spectrogram frame as an independent sample, as well as traditional machine learning methods. Results demonstrate that TD-CLNet achieves a test accuracy of 92% on real-world idler data using K-fold cross-validation, significantly outperforming competing methods. This research provides a scalable and effective solution for fault detection in belt conveyor idlers, advancing predictive maintenance strategies, improving operational efficiency, and minimising unplanned downtime in industrial environments.</p>