Model refinement and system health monitoring using data from multiple static loads and vibration tests

1994 ◽  
Author(s):  
D. Zimmerman ◽  
T. Simmermacher
Keyword(s):  
Health Monitoring ◽  
Model Refinement ◽  
Static Loads ◽  
System Health Monitoring ◽  
Using Data ◽  
Vibration Tests

scholarly journals Challenges in Bridge Health Monitoring: A Review

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4336
Author(s):  
Piervincenzo Rizzo ◽  
Alireza Enshaeian
Keyword(s):  
United States ◽  
Health Monitoring ◽  
Highway Bridges ◽  
The United States ◽  
Anomalous Behavior ◽  
Existing Structures ◽  
Bridge Health Monitoring ◽  
Using Data ◽  
The Many ◽  
Major Factors

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures.

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.

Informing Design Using Data Mining Methods

2002 ◽  
Author(s):  
M. D. Giess ◽  
S. J. Culley ◽  
A. Shepherd
Keyword(s):  
Test Performance ◽  
Critical Parameters ◽  
Manufacturing Processes ◽  
Forward Prediction ◽  
Complex Engineering ◽  
Mining Methods ◽  
Low Volume ◽  
Using Data ◽  
Vibration Tests ◽  
Design And Manufacture

Manufacturing processes produce a considerable amount of data as dimensions are measured, tests are performed and assembly checks are undertaken. Predominantly these data are used to inform and help improve the associated manufacturing processes and procedures. A variety of Knowledge Discovery techniques [1] have been introduced in the engineering field, most typically in areas with large quantities of data [2]. This paper describes research into the use of such techniques in the manufacture and assembly of large complex engineering products, an area which is characterised by low volume of data and dispersed databases. The developed methodology seeks to incorporate various approaches, with emphasis on using extracted knowledge to inform the implementation of subsequent techniques. This investigation centres on discovering and quantifying relationships between the various balance and vibration tests performed throughout assembly of gas turbine rotors, and to highlight critical parameters. Current assembly practices do not use forward prediction of test performance, and the first stages of this project aim to produce a model to enable this. The scope of this model will then be extended to feed this knowledge back to be used in the design and manufacture of future components.

scholarly journals Social Isolation and Loneliness in Old Age: Review and Model Refinement

1996 ◽  
Vol 16 (3) ◽  
pp. 333-358 ◽  
Author(s):  
G. Clare Wenger ◽  
Richard Davies ◽  
Said Shahtahmasebi ◽  
Anne Scott
Keyword(s):  
Social Isolation ◽  
Statistical Modelling ◽  
Household Composition ◽  
Empirical Literature ◽  
Critical Factors ◽  
Model Refinement ◽  
North Wales ◽  
Wide Range ◽  
Using Data ◽  
Network Type

ABSTRACTThis paper reviews the empirical literature on social isolation and loneliness and identifies a wide range of published correlates. Using data from a study conducted in North Wales, which included many of the same correlated variables, a statistical modelling technique is used to refine models of isolation and loneliness by controlling for co-variance. The resulting models indicate that the critical factors for isolation are: marital status, network type and social class; and, for loneliness: network type, household composition and health.

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