scholarly journals Statistical Pattern-Based Assessment of Structural Health Monitoring Data

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad S. Islam ◽  
Ashutosh Bagchi
Keyword(s):  
Structural Health Monitoring ◽  
Pattern Matching ◽  
Health Monitoring ◽  
Data Interpretation ◽  
Structural Condition ◽  
Sensor Data ◽  
Structural Health ◽  
Statistical Pattern ◽  
Potential Damage ◽  
Matching Techniques

In structural health monitoring (SHM), various sensors are installed at critical locations of a structure. The signals from sensors are either continuously or periodically analyzed to determine the state and performance of the structure. An objective comparison of the sensor data at different time ranges is essential for assessing the structural condition or excessive load experienced by the structure which leads to potential damage in the structure. The objectives of the current study are to establish a relationship between the data from various sensors to estimate the reliability of the data and potential damage using the statistical pattern matching techniques. In order to achieve these goals, new methodologies based on statistical pattern recognition techniques have been developed. The proposed methodologies have been developed and validated using sensor data obtained from an instrumented bridge and road test data from heavy vehicles. The application of statistical pattern matching techniques are relatively new in SHM data interpretation and current research demonstrates that it has high potential in assessing structural conditions, especially when the data are noisy and susceptible to environmental disturbances.

scholarly journals On sensor optimisation for structural health monitoring robust to environmental variations

2021 ◽  
Vol 6 (5) ◽  
pp. 1107-1116
Author(s):  
Tingna Wang ◽  
David J. Wagg ◽  
Keith Worden ◽  
Robert J. Barthorpe
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health State ◽  
Damage Classification ◽  
Health States ◽  
Structural Health ◽  
Classification Technique ◽  
Statistical Pattern ◽  
Environmental Variations ◽  
Using Data

Abstract. Structural health monitoring (SHM) is often approached from a statistical pattern recognition or machine learning perspective with the aim of inferring the health state of a structure using data derived from a network of sensors placed upon it. In this paper, two SHM sensor placement optimisation (SPO) strategies that offer robustness to environmental effects are developed and evaluated. The two strategies both involve constructing an objective function (OF) based upon an established damage classification technique and an optimisation of sensor locations using a genetic algorithm (GA). The key difference between the two strategies explored here is in whether any sources of benign variation are deemed to be observable or not. The relative performances of both strategies are demonstrated using experimental data gathered from a glider wing tested in an environmental chamber, with the structure tested in different health states across a series of controlled temperatures.

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.

scholarly journals SHMnet: Condition assessment of bolted connection with beyond human-level performance

2019 ◽  
Vol 19 (4) ◽  
pp. 1188-1201 ◽  
Author(s):  
Tong Zhang ◽  
Suryakanta Biswal ◽  
Ying Wang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Steel Frame ◽  
Mode Shapes ◽  
Repeated Impact ◽  
Traditional Methods ◽  
Bolted Connection ◽  
Structural Health ◽  
Sensor Locations

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet .

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 Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring

2006 ◽  
Vol 13 (4-5) ◽  
pp. 519-530 ◽  
Author(s):  
Charles R. Farrar ◽  
David W. Allen ◽  
Gyuhae Park ◽  
Steven Ball ◽  
Michael P. Masquelier
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Software Package ◽  
Model Development ◽  
General Purpose ◽  
Statistical Pattern Recognition ◽  
Structural Health ◽  
Statistical Pattern ◽  
Statistical Model Development ◽  
Processing Platform

The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). The authors' approach is to address the SHM problem in the context of a statistical pattern recognition paradigm. In this paradigm, the process can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. These processes must be implemented through hardware or software and, in general, some combination of these two approaches will be used. This paper will discuss each portion of the SHM process with particular emphasis on the coupling of a general purpose data interrogation software package for structural health monitoring with a modular wireless sensing and processing platform. More specifically, this paper will address the need to take an integrated hardware/software approach to developing SHM solutions.

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.

scholarly journals Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Statistical Decision ◽  
Structural Health ◽  
Statistical Pattern ◽  
Arma Modeling

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

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>

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