Real-time cable tension estimation from acceleration measurements using wireless sensors with packet data losses: analytics with compressive sensing and sparse component analysis

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Department of Mechanical Engineering-Engineering Mechanics


Stay-cables in the cable-stayed bridge are the most vital components as they carry the bridge deck’s load and transmit the force to the bridge pylons. However, dynamics loads due to vortex-induced vibration, ambient wind excitation, and even vehicular vibration cause fatigue in the stay-cable. Hence continuous real-time performance monitoring of such cables is necessary for maintenance to avoid any kind of damage to the cable. Wireless sensors are contact-based sensor that provide accurate measurement, and it does not involve any wiring cost like conventional wired sensors. Monitoring cable health using such wireless sensors is a good choice provided packet loss (which occurs while transmitting the measured data to the base station) that invariably occurs is addressed by data processing. Such discontinuity in data (due to packet loss) may interrupt the real-time/online cable health monitoring process - depending on the window length of the data loss. In general, online health monitoring using multiple sensors reduces the estimation errors. In this paper, we propose a framework that takes the wireless sensor data as the input, then reconstructs the packet lost samples (if any), and finally, provides a real-time tension estimation as an output. The novel framework, first adopts compressive sensing algorithm to reconstruct the data due to packet loss. Subsequently, we synthesize the reconstructed responses from multiple sensors to estimate the real-time frequency variation using Blind Source Separation (BSS) Technique. As the cable response due to ambient vibration contains a large number of modes, the dominant modal response or the corresponding dominant frequency is estimated from very few measurements using a variant of the BSS technique named Sparse Component Analysis (SCA). Finally, real-time cable tension is estimated from the frequency variation using the taut-string theory. The proposed technique is applied to a real full-scale cable-stayed bridge. The mean tension obtained from the framework is comparable with the cable’s actual design tension. The accurate estimation of real-time stay-cable tension by the proposed algorithm shows great potential in the field of structural health monitoring.

Publication Title

Journal of Civil Structural Health Monitoring