A new approach for structural health monitoring by applying anomaly detection on strain sensor data

2014 ◽  
Author(s):  
Konstantinos Trichias ◽  
Richard Pijpers ◽  
Erik Meeuwissen
Keyword(s):  
Structural Health Monitoring ◽  
Anomaly Detection ◽  
Health Monitoring ◽  
Strain Sensor ◽  
Sensor Data ◽  
New Approach ◽  
Structural Health

scholarly journals A Wireless Strain Sensor Network for Structural Health Monitoring

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chengyin Liu ◽  
Jun Teng ◽  
Ning Wu
Keyword(s):  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Evaluation System ◽  
Strain Sensor ◽  
Modal Testing ◽  
Dynamic Strain ◽  
Structural Dynamic ◽  
Structural Health ◽  
Strain Monitoring

Structural strain under external environmental loads is one of the main monitoring parameters in structural health monitoring or dynamic tests. This paper presents a wireless strain sensor network (WSSN) design for monitoring structural dynamic strain field. A precision strain sensor board is developed and integrated with the IRIS mote hardware/software platform for multichannel strain gauge signal conditioning and wireless monitoring. Measurement results confirm the sensor’s functionality regarding its static and dynamic characterization. Furthermore, in order to verify the functionality of the designed wireless strain sensor for dynamic strain monitoring, a cluster-star network evaluation system is developed for strain modal testing on an experimental steel truss structure. Test results show very good agreement with the finite element (FE) simulations. This paper demonstrates the feasibility of the proposed WSSN for large structural dynamic strain monitoring.

scholarly journals Numerical Study of a Multi-Layered Strain Sensor for Structural Health Monitoring of Asphalt Pavement

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 41
Author(s):  
Jiayue Shen ◽  
Minghao Geng ◽  
Abby Schultz ◽  
Weiru Chen ◽  
Hao Qiu ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Polyurethane Foam ◽  
Health Monitoring ◽  
Numerical Study ◽  
Asphalt Pavement ◽  
Strain Sensor ◽  
Traffic Load ◽  
Structural Health ◽  
Initiation And Propagation ◽  
Load Monitoring

Crack initiation and propagation vary the mechanical properties of the asphalt pavement and further alter its designate function. As such, this paper describes a numerical study of a multi-layered strain sensor for the structural health monitoring (SHM) of asphalt pavement. The core of the sensor is an H-shaped Araldite GY-6010 epoxy-based structure with a set of polyvinylidene difluoride (PVDF) piezoelectric transducers in its center beam, which serve as a sensing unit, and a polyurethane foam layer at its external surface which serves as a thermal insulation layer. Sensors are coated with a thin layer of urethane casting resin to prevent the sensor from being corroded by moisture. As a proof-of-concept study, a numerical model is created in COMSOL Multiphysics to simulate the sensor-pavement interaction, in order to design the strain sensor for SHM of asphalt pavement. The results reveal that the optimum thickness of the middle polyurethane foam is 11 mm, with a ratio of the center beam/wing length of 3.2. The simulated results not only validate the feasibility of using the strain sensor for SHM (traffic load monitoring and damage detection), but also to optimize design geometry to increase the sensor sensitivity.

scholarly journals Anomaly detection for large span bridges during operational phase using structural health monitoring data

2020 ◽  
Vol 29 (4) ◽  
pp. 045029 ◽  
Author(s):  
Xiang Xu ◽  
Yuan Ren ◽  
Qiao Huang ◽  
Zi-Yuan Fan ◽  
Zhao-Jie Tong ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Anomaly Detection ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Large Span ◽  
Structural Health ◽  
Operational Phase

scholarly journals Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6894
Author(s):  
Nicola-Ann Stevens ◽  
Myra Lydon ◽  
Adele H. Marshall ◽  
Su Taylor
Keyword(s):  
Survival Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Transport Networks ◽  
Data Set ◽  
Structural Health

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

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