Feedback-driven error-corrected single-sensor analytics for real-time condition monitoring

In this paper, a single-sensor based output-only algorithm is proposed for real-time condition monitoring of mechanical vibrating systems. Four key aspects of real-time condition monitoring and maintenance are presented through Recursive singular spectrum analysis (RSSA): (a) Filtering, (b) Enhancification, (c) Fault detection, and (d) Modal identification. Recent prominence of eigen perturbation (EP) solutions for condition monitoring has led to the development of RSSA as the go-to real-time algorithm for single-sensor diagnosis. As single-sensor econometrics has been long sought as a viable option for cases involving instrumentation redundancies, non-optimal sensor placement, and cost considerations, RSSA provides replication, scalability, and transferability for real-time fault detection studies. With the output vibration signals streaming in real-time, the Hankel covariance matrix is formed which filters out the noise subspace in the grouping stage. Online enhancification becomes particularly useful when the signal statistics are masked by time-varying non-stationary excitation. Application examples involving AM–FM signals and operational noise in structural systems demonstrates the versatility of RSSA towards spatio-temporal fault detection in real-time. The efficacy of the proposed algorithm is further validated by experimental investigations of real-time complete modal identification from partial sensor information. With applications extending to real-time passive control and aligned to current infrastructure monitoring demands worldwide, RSSA demonstrates potential to establish as a benchmark algorithm for online condition monitoring.