Elevator-Assisted Sensor Data Collection for Structural Health Monitoring

2012 ◽  
Vol 11 (10) ◽  
pp. 1555-1568 ◽  
Author(s):  
Tao Zhang ◽  
Dan Wang ◽  
Jiannong Cao ◽  
Yi Qing Ni ◽  
Li-Jun Chen ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Data Collection ◽  
Health Monitoring ◽  
Sensor Data ◽  
Structural Health

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.

Optimal Sensor Configuration for Fatigue Life Prediction in Structural Applications

2019 ◽  
Author(s):  
Mohamed Khalil ◽  
Ioannis Kouroudis ◽  
Roland Wüchner ◽  
Kai-Uwe Bletzinger
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Estimation Error ◽  
Operating Conditions ◽  
Added Value ◽  
Sensor Data ◽  
Damage Prediction ◽  
Structural Health ◽  
Structural Applications ◽  
Prediction Approach

Abstract Structural health monitoring is spreading widely across engineering domains. Its added value is not restricted to observing structural behavior, but crosses over to enabling the assessment of structural integrity under varying operating conditions. Damage prognosis is one vital demand from structural health monitoring solutions. Many methods have been developed to update damage predictions based on sensor data, nonetheless the selection and positioning of sensors to alleviate the prediction errors remains a question under investigation. In this work, an optimal sensor placement method is proposed for fatigue damage prediction in structures. An optimization problem is formulated to minimize the a-posteriori damage estimation error based on a Kalman filter. The derivation of the objective function is presented, along with a discussion of algorithm-related issues. Finally, the mentioned damage prediction approach is applied to two structures to verify the adequacy of the sensor configurations proposed by the method.

Ontology-based sharing of structural health monitoring data

2019 ◽  
Author(s):  
Seppo Törmä ◽  
Markku Kiviniemi ◽  
Mikko Lampi ◽  
Pekka Toivola ◽  
Päivi Puntila ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Service Providers ◽  
Facility Management ◽  
Monitoring Data ◽  
Sensor Data ◽  
Health Monitoring System ◽  
Structural Health ◽  
Monitoring Model ◽  
Monitoring Service

<p>A structural health monitoring system installed in a bridge produces a vast amount of sensor data that is analyzed and periodically reported to a bridge owner at an aggregate level. The data itself typically remains in the monitoring service of a service provider; it may be accessible to clients and third parties through a dedicated user interface and API. This paper presents an ontology to defining the monitoring model based on the Semantic Sensor Network Ontology by W3C. The goal is to enable an asset owner to utilize preferred tools to view and access monitoring data from different service providers, and in longer term, increase the utilization of monitoring data in facility management. The ultimate aim is to use BrIM as a digital twin of a bridge and to link external datasets to improve information management and maintenance over its lifecycle.</p>

scholarly journals Structural Health Monitoring for Advanced Composite Structures: A Review

2020 ◽  
Vol 4 (1) ◽  
pp. 13 ◽  
Author(s):  
Alfredo Güemes ◽  
Antonio Fernandez-Lopez ◽  
Angel Renato Pozo ◽  
Julián Sierra-Pérez
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Optics ◽  
Health Monitoring ◽  
Composite Structures ◽  
Guided Waves ◽  
Probability Of Detection ◽  
Sensor Data ◽  
Structural Health ◽  
Early Fault Detection ◽  
Pod Analysis

Condition-based maintenance refers to the installation of permanent sensors on a structure/system. By means of early fault detection, severe damage can be avoided, allowing efficient timing of maintenance works and avoiding unnecessary inspections at the same time. These are the goals for structural health monitoring (SHM). The changes caused by incipient damage on raw data collected by sensors are quite small, and are usually contaminated by noise and varying environmental factors, so the algorithms used to extract information from sensor data need to focus on sensitive damage features. The developments of SHM techniques over the last 20 years have been more related to algorithm improvements than to sensor progress, which essentially have been maintained without major conceptual changes (with regards to accelerometers, piezoelectric wafers, and fiber optic sensors). The main different SHM systems (vibration methods, strain-based fiber optics methods, guided waves, acoustic emission, and nanoparticle-doped resins) are reviewed, and the main issues to be solved are identified. Reliability is the key question, and can only be demonstrated through a probability of detection (POD) analysis. Attention has only been paid to this issue over the last ten years, but now it is a growing trend. Simulation of the SHM system is needed in order to reduce the number of experiments.

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