Joint time-frequency analysis of time-varying signals for condition monitoring.

Joint Time-Frequency Analysis (JTFA) is a well-known concept for studying time-varying signals in engineering and scientific applications. Short-Time Fourier Transform (STFT) is a widely used JTFA technique due to its superior response for real-time applications. However, the apriori's fixed window size has limited time and frequency resolution. There are several advanced techniques proposed in the literature to overcome the limitations of STFT, such as Wigner-Ville Transform (WVT), Continuous Wavelet Transform (CWT), S-Transform (ST), Generalized S-transform (GST), Complex S-Transform (CST), Bi-Gaussian S-Transform (BGST), and Hyperbolic S-Transform (HST). These advanced (JTFA) techniques demonstrated good time and frequency resolution of time-varying signals, resulting in better fault detection and classification in many engineering and scientific fields, especially for condition monitoring. In addition, the performance of these JTFA algorithms under noisy conditions is widely available in the literature. In this paper, the authors investigate the performance of S-Transform-based JTFA techniques for time-varying signals under noisy conditions and highlight their superior performance in selecting a suitable method for specific real-time industrial applications. In this study, several non-stationary test signals proposed in the literature were simulated using Matlab. The joint time-frequency analysis of these unique non-stationary signals was conducted under both normal and noisy conditions for the performance evaluation of various transforms/algorithms mentioned earlier. The results obtained are suitable for developing real-time monitoring and JTFA tools for condition monitoring and diagnostic studies with the help of IoT and Machine Learning for Smart Health Monitoring and Asset Management. [ABSTRACT FROM AUTHOR]