Technology developments in structural health monitoring of large-scale bridges

There has been a growing interest in monitoring the ongoing "health" of products and systems in order to predict failures and provide warning to avoid catastrophic failure. Here, health is defined as the extent of degradation or deviation from an expected normal condition. While the application of health monitoring, also referred to as prognostics, is well established for assessment of mechanical systems, this is not the case for electronic systems. However, electronic systems are integral to the functionality of most systems today, and their reliability is often critical for system reliability. This paper presents the state-of-practice and the current state-of-research in the area of electronics prognostics and health management. Four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads. Examples are provided for these different approaches, and the implementation challenges are discussed.

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Prognostics and health management of electronics

Reliability is the ability of a product or system to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life-cycle environment. Commonly used electronics reliability prediction methods (e.g., Mil-HDBK-217, 217-PLUS, PRISM, Telcordia, FIDES) based on handbook methods have been shown to be misleading and provide erroneous life predictions. The use of stress and damage models permits a far superior accounting of the reliability and the physics of failure (PoF); however, sufficient knowledge of the actual operating and environmental application conditions of the product is still required.



This article presents a PoF-based prognostics and health management approach for effective reliability prediction. PoF is an approach that utilizes knowledge of a product's life-cycle loading and failure mechanisms to perform reliability modeling, design, and assessment. This method permits the assessment of the reliability of a system under its actual application conditions. It integrates sensor data with models that enable in situ assessment of the deviation or degradation of a product from an expected normal operating condition and the prediction of the future state of reliability. This article presents a formal implementation procedure, which includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty. Applications of PoF-based prognostics and health management are also discussed.


Keywords:

reliability;
prognostics;
physics of failure;
design-for-reliability;
reliability prediction

Prognostics and Health Management of Electronics

The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.

https://iopscience.iop.org/article/10.1088/0964-1726/25/5/053001/pdf

Guided wave based structural health monitoring: A review

A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

Increased awareness of the economic and social effects of aging, deterioration and extreme events on civil infrastructure has been accompanied by recognition of the need for advanced structural health monitoring and damage detection tools. Today, these tasks are done by visual inspection and very traditional methods such as the tap test. This labor-intensive task is done at a frequency of less than once every two years for bridges, and on an as-needed basis for other infrastructures such as buildings. Structural health monitoring techniques based on changes in dynamic characteristics have been studied for the last three decades. When the damage is substantial, these methods have some success in determining if damage has occurred. At incipient stages of damage, however, the existing methods are not as successful. A number of new research projects have been funded to improve the damage detection methods including the use of innovative signal processing, new sensors, and control theory. This survey paper hig...

S. C. Liu

Papers

Review Paper: Health Monitoring of Civil Infrastructure: