Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis

In an era driven by technological evolution, this paper embarks on an unprecedented journey to revolutionize rail track anomaly detection. Amidst the bustling world of transportation, our innovative approach harnesses the synergy of cloud-based processing, audio analysis, and Mel-frequency cepstral coefficients (MFCC) extraction to unveil the previously hidden secrets of rail track conditions. The hallmark of our method lies in its dynamic fusion of intricate components. It transcends traditional boundaries and converts audio data into spectrograms through the short-time Fourier transform (STFT). This visual tapestry of frequencies unfolds a tale of spectral evolution across time, acting as the precursor to the essence of rail track anomalies. The proposed method commences with the conversion of audio data into spectrograms using short-time Fourier transform (STFT), facilitating the visualization of frequency content changes over time. Through this, the study computes MFCC features by first calculating Mel frequencies and subsequently deriving coefficients using cosine functions. Embodying key spectral characteristics, these features are then stored and standardized within a data frame. Enter cloud computing—a celestial realm of limitless computational prowess. Our approach transcends conventional confines by fusing the cloud's scalable might with the agility of audio analysis. Massive datasets unravel effortlessly, invoking a novel era of real-time rail track surveillance. Machine learning models, nurtured by standardized MFCCs, become vigilant sentinels against anomalies, forging a vigilant shield of safety. With experimental applause, our approach emerges victorious. A parade of metrics—accuracy, precision, recall, the cadence of F-score, and the majestic ROC curve—testify to the unparalleled prowess of our method. Anomalies that once whispered are now boisterously identified, etching a new chapter in rail track safety. In summary, this paper pioneers an innovative approach to rail track anomaly detection by amalgamating audio analysis, cloud-based processing, and MFCC features. The experimental outcomes underscore the effectiveness of the approach in identifying anomalies, thereby presenting a valuable contribution to rail track maintenance and safety protocols.