RESEARCH IN STRUCTURAL MECHANICS AND MONITORING OF RENEWABLE ENERGY SYSTEMS
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.
REAL-TIME STRUCTURAL HEALTH MONITORING OF BUILT INFRASTRUCTURE
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.
REAL-TIME DOWNTIME DETECTION OF RENEWABLE ENERGY DEVICES
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.
SINGLE-SENSOR BASED REAL-TIME INFRASTRUCTURE MONITORING FRAMEWORK
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.
ONLINE BRIDGE MONITORING TECHNIQUES - CHALLENGES OF SCOUR, VARIABLE DAMPING, AND REHABILITATION
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:
ElCentro dataset from vibrationdata
SHM Datasets from LANL
Structural Benchmark comparison from the University of Illinois
Nonlinear Benchmarks - Workshop on Nonlinear System Identification Benchmarks
Operational Modal Analysis from Harvard University
SHM lab at the São Paulo State University - UNESP
Experimental Benchmark Problem for Vibration-Based SHM by Prof. Onur Avci
LUMO - Leibniz Universtity Test Structure for Monitoring
Z-24 bridge data from KU Leuven
NASA - Prognostics Center of Excellence
Bearing Data Center - Case Western Reserve University