Feedback-driven error-corrected single-sensor analytics for real-time condition monitoring. (15th January 2022)

• Record Type: Journal Article
• Authors: Bhowmik, Basuraj; Panda, Satyam; Hazra, Budhaditya; Pakrashi, Vikram
• Is Part Of: International journal of mechanical sciences. Volume 214(2022)
• Publication Date: 2022-01-15
• Subjects: Single-sensor -- Online filtering -- Enhancification -- Damage detection -- Modal identification -- Error-adaptation; Mechanical engineering -- Periodicals; Génie mécanique -- Périodiques; Mechanical engineering; Maschinenbau; Mechanik; Zeitschrift; Periodicals; 621.05
• DOI: 10.1016/j.ijmecsci.2021.106898
• Languages: English
• ISSNs: 0020-7403

Abstract

Abstract: 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. Graphical abstract: Highlights: A feedback -driven enhanced version of Real-time Singular Spectrum Analysis (RSSA) is proposed. An error-adaptive real-time filtering algorithm for the monitoring of continuously operational vibrating system is presented. Application to mechanical vibrating systems in a unified framework is demonstrated through numerical simulations, NASA milling dataset, and experimental trials. Effective real-time filtering of both constant and modulated frequency signals is illustrated. Simulated case study of bearing fault is presented with real time filtering, enveloping and fault diagnosis.