The OSCAR research group is interested in combining machine-learning methods with contemporary numerical approaches for real-time diagnosis and prognosis of our aging infrastructure. These methods have the potential to expand toward renewable energy system monitoring – especially downtime detection – with a reduced order model framework. Experimental testbeds and practical field studies will be summarily conducted to validate the numerical models and arrive at a real-life computational expense study, cost-benefit analysis, and document guidelines around the process.


With growing demands for accurate identification of damage, asset managers rely on an online fault identification framework. Assessment of a vibrating system in relation to certain performance indices requires knowledge of the current system state considering aging and steady deterioration due to environmental factors. This necessitates the need for tracking in situ performance or health of the system by measuring data and interpreting them using application-specific knowledge. Resorting to efficient real-time vibration-based damage detection techniques, structural performance, condition, and reliability can be quantified objectively. The outcome of such analysis also furnishes the exact instant of occurrence of damage, and its localization, additionally evaluating the intensity of the damage, and providing statistics regarding the remaining service life of the vibrating system.

Interested researchers are referred to the following for more details:

A) Bhowmik, B., Tripura, T., Hazra, B., & Pakrashi, V. (2019). First-order eigen-perturbation techniques for real-time damage detection of vibrating systems: Theory and applications. Applied Mechanics Reviews, 71(6),

B) Bhowmik, B., Tripura, T., Hazra, B., & Pakrashi, V. (2020). Robust linear and nonlinear structural damage detection using recursive canonical correlation analysis. Mechanical Systems and Signal Processing, 136, 106499,


Complex systems are susceptible to many types of anomalies, faults, and abnormal behavior caused by a variety of off-nominal conditions that may ultimately result in major failures or catastrophic events. Early and accurate detection of these anomalies using system inputs and outputs collected from sensors and smart devices has become a challenging problem and an active area of research in many application domains. This is succeeded using critical instrumentation that is characterized by increased flexibility, modularity, and ease of use, and may consequently be exploited beyond completion of the project on further critical infrastructure components, including bridges, buildings, dams, etc.

Present approaches for downtime detection for wind turbines include identification of classifiers that belong to either fault, excess of wind (or its lack thereof), and maintenance schedules. Considering the trade-off between misclassification errors and detection rates, detection studies are performed using wind power and speed - calibrated against available alarm classifiers. Using a kNN structure integrated within a real-time framework, applications for spatially distributed wind farms have been made and the results published in the following links:

A) Mucchielli, P., Bhowmik, B., Ghosh, B., & Pakrashi, V. (2021). Real-time accurate detection of wind turbine downtime-an Irish perspective. Renewable Energy, 179, 1969-1989.

B) Mucchielli, P., Bhowmik, B., Hazra, B., & Pakrashi, V. (2020). Higher-order stabilized perturbation for recursive eigen-decomposition estimation. Journal of Vibration and Acoustics, 142(6).


Four key aspects of real-time condition monitoring and maintenance have been developed using the exclusive Recursive singular spectrum analysis (RSSA): (a) Filtering, (b) Enhancification, (c) Fault detection, and (d) Modal identification. 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. With applications extending to real-time passive control and aligned to current infrastructure monitoring demands worldwide, RSSA demonstrates the potential to establish as a benchmark algorithm for online condition monitoring.

The relevant publications are as follows:

A) Bhowmik, B., Panda, S., Hazra, B., & Pakrashi, V. (2022). Feedback-driven error-corrected single-sensor analytics for real-time condition monitoring. International Journal of Mechanical Sciences, 214, 106898.

B) Bhowmik, B., Krishnan, M., Hazra, B., & Pakrashi, V. (2019). Real-time unified single-and multi-channel structural damage detection using recursive singular spectrum analysis. Structural Health Monitoring, 18(2), 563-589.


Scour in railway bridges is often an important problem, especially for old bridges. Typically, scour weakens the bridge structure by modifying its boundary conditions. Such changes, when significant enough, can lead to changes in modal properties and vibrations measurements with respect to the ideal baseline. On the other hand, a repair attempts to restore the release in boundary conditions. Consequently, a repair also changes the modal properties and the dynamic responses. This indicates that significant and consistent changes in bridges before and after repair can indicate the adequacy and efficiency of scour repair.

As a part of the group’s research on bridge monitoring, the group has worked in instrumenting various bridges across Ireland – ranging from pedestrian bridges to train load-carrying ones – which will be continued in various parts of India as well. The data obtained from the sensors allow us to better assess the condition of existing bridges, as well as their capacity to withstand extreme loads and weather conditions. Additionally, vibration-based scour detection procedures using piezoelectric energy harvesting devices (EHD) have been conducted that promise to serve as a future benchmark in the research to come. Using one EHD attached to the central bridge pier, both scour at the pier of installation and scour at another bridge pier can be detected from the EHD voltage generated during the bridge free-vibration stage, while the harvester is attached to a healthy pier. Frequency components corresponding to harmonic loading and electrical interference arising from experiments are removed using the filter bank property of singular spectrum analysis (SSA). These frequencies can then be monitored by using harvested voltage from the energy harvesting device and successfully utilized towards SHM of a model bridge affected by scour.

Relevant publication list:

A) Bhowmik, B., Hazra, B., O’Byrne, M., Ghosh, B., & Pakrashi, V. (2021). Damping estimation of a pedestrian footbridge–an enhanced frequency-domain automated approach. Journal of Vibroengineering, 23(1), 14-25.

B) Micu, E. A., Khan, M. A., Bhowmik, B., Florez, M. C., Obrien, E., Bowe, C., & Pakrashi, V. (2021, August). Scour Repair of Bridges Through Vibration Monitoring and Related Challenges. In International Conference of the European Association on Quality Control of Bridges and Structures (pp. 499-508). Springer, Cham.

C) Bhowmik, B., Quqa, S., Sause, M.G.R., Pakrashi, V., Droubi, M.G. (2021). Data Reduction Strategies. In: Sause, M.G.R., Jasiūnienė, E. (eds) Structural Health Monitoring Damage Detection Systems for Aerospace. Springer Aerospace Technology. Springer, Cham.

Research Datasets

  1. ElCentro dataset from vibrationdata

  2. SHM Datasets from LANL

  3. Structural Control and Monitoring Benchmarks from NEES

  4. Structural Benchmark comparison from the University of Illinois

  5. Nonlinear Benchmarks - Workshop on Nonlinear System Identification Benchmarks

  6. Operational Modal Analysis from Harvard University

  7. SHM lab at the São Paulo State University - UNESP

  8. Experimental Benchmark Problem for Vibration-Based SHM by Prof. Onur Avci

  9. LUMO - Leibniz Universtity Test Structure for Monitoring

  10. Z-24 bridge data from KU Leuven

  11. NASA - Prognostics Center of Excellence

  12. Bearing Data Center - Case Western Reserve University