Varied System Geometry and Noise Implementation Applied to Nonlinear Model Tracking

2016 ◽  
Author(s):  
Timothy A. Doughty ◽  
Liam J. Cassidy ◽  
Shannon M. Danforth ◽  
Nicholas Pendowski
Keyword(s):  
Nonlinear Model ◽  
Health Monitoring ◽  
Physical System ◽  
Nonlinear Behavior ◽  
Data Sets ◽  
Nonlinear Stiffness ◽  
Near Resonance ◽  
Recorded Data ◽  
Varied System ◽  
System Geometry

The following is a study in nondestructive health monitoring wherein the physical system being studied is excited near resonance and mapped through its transition from health to failure. The system studied is a slender cantilever beam excited near its second natural frequency. For this study, no damage is initiated and so it comes in contrast to the more common techniques where the damage type and location allow for an element of control in instrumentation and analysis. The method implemented allows for health monitoring in situ, so it does not require stopping the event to do system testing, as is the case for many common approaches. Moreover, this method, implements a nonlinear model of the physical system, avoiding false flags that can be problematic for linear-based methods when applied to systems demonstrating healthy nonlinear behavior. The method, known as Nonlinear Model Tracking (NMT) uses a theoretical model of the system that includes a cubic nonlinear stiffness term. Experimentally, stimulus and response data are collected and used in Continuous Time-based system identification to estimate the system’s nonlinear stiffness coefficient. Harmonic fitting to the two recorded data sets allow for robust performance in the presence of noise and variations in the system geometry show that, even in cases where the nonlinear model is not accurate for the system being studied, the method works consistently. In many of the tests the method gives premonition of failure hours in advance, which would in many real world scenarios, gives users time to react safely. This study focusses particularly on varying inputs to the system and attempting to map changes in parameter estimation to stages of damage.

scholarly journals Understanding Power System Behavior through Mining Archived Operational Data

2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Sarasij Das ◽  
Nagendra Rao P S
Keyword(s):  
Data Mining ◽  
Power System ◽  
System Level ◽  
Data Sets ◽  
Total System ◽  
System Behavior ◽  
Average System ◽  
Recorded Data ◽  
Operational Data ◽  
Southern Regional

This paper is the outcome of an attempt in mining recorded power system operational data in order to get new insight to practical power system behavior. Data mining, in general, is essentially finding new relations between data sets by analyzing well known or recorded data. In this effort we make use of the recorded data of the Southern regional grid of India. Some interesting relations at the total system level between frequency, total MW/MVAr generation, and average system voltage have been obtained. The aim of this work is to highlight the potential of data mining for power system applications and also some of the concerns that need to be addressed to make such efforts more useful.

On the Nonlinear Behavior of Rotating Shafts

1991 ◽  
Author(s):  
Tatu Leinonen
Keyword(s):  
Theoretical Model ◽  
General Theory ◽  
Nonlinear Model ◽  
Nonlinear Behavior ◽  
Rotating Shaft ◽  
Rotating Shafts ◽  
Bending Behaviour

Abstract This paper presents a nonlinear model to describe the bending behaviour of a rotating shaft, based on the general theory of a bending bar. Justification for this theoretical model has been provided by tests, the resulting curves more closely fitting observed results than those of other models.

Station Keeping Trials in Ice: Ice Load Monitoring System

2018 ◽  
Author(s):  
Håvard Nyseth ◽  
Anders Hansson ◽  
Johan Johansson Iseskär
Keyword(s):  
Data Sets ◽  
Station Keeping ◽  
Ice Load ◽  
Hull Structure ◽  
Load Monitoring ◽  
Ice Loads ◽  
Recorded Data ◽  
Measurement Concept ◽  
Ice Impacts ◽  
Basic Philosophy

In connection with the Statoil SKT project, DNV GL have developed a method for estimating ice loads on the ship hull structure and mooring tension of the anchor handling tug supply (AHTS) vessel Magne Viking by full scale measurements. In March 2017, the vessel was equipped with an extensive measurement system as a preparation for the dedicated station-keeping trial in drifting ice in the Bay of Bothnia. Data of the ice impacts acting on the hull were collected over the days of testing together with several other parameters from the ship propulsion system. Whilst moored, the tension in the mooring chain was monitored via a load cell and logged simultaneously to the other parameters. This paper presents the processes involved in developing the measurement concept, including the actual installation and execution phases. The basic philosophy behind the system is described, including the methods used to design an effective measurement arrangement, and develop procedures for estimation of ice loads based on strain measurements. The actual installation and the process of obtaining the recorded data sets are also discussed.

Introduction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Physical System ◽  
Weather Prediction ◽  
Climate Prediction ◽  
Lead Times ◽  
Prediction Methods ◽  
Data Sets ◽  
Short Term ◽  
Global Data Sets ◽  
Geophysical System

This is first and foremost a book about short-term climate prediction. The predictions we have in mind are for weather/climate elements, mainly temperature (T) and precipitation (P), at lead times longer than two weeks, beyond the realm of detailed Numerical Weather Prediction (NWP), i.e. predictions for the next month and the next seasons out to at most a few years. call this short-term climate so as to distinguish it from long-term climate change which is not the main subject of this book. A few decades ago “short-term climate prediction” was known as “longrange weather prediction”. In order to understand short-term climate predictions, their skill and what they reveal about the atmosphere, ocean and land, several chapters are devoted to constructing prediction methods. The approach taken is mainly empirical, which means literally that it is based in experience. We will use global data sets to represent the climate and weather humanity experienced (and measured!) in the past several decades. The idea is to use these existing data sets in order to construct prediction methods. In doing so we want to acknowledge that every measurement (with error bars) is a monument about the workings of Nature. We thought about using the word “statistical” instead of “empirical” in the title of the book. These two notions overlap, obviously, but we prefer the word “empirical” because we are driven more by intuition than by a desire to apply existing or developing new statistical theory. While constructing prediction methods we want to discover to the greatest extent possible how the physical system works from observations. While not mentioned in the title, diagnostics of the physical system will thus be an important part of the book as well. We use a variety of classical tools to diagnose the geophysical system. Some of these tools have been developed further and/or old tools are applied in novel ways. We do not intend to cover all diagnostics methods, only those that relate closely to prediction. There will be an emphasis on methods used in operational prediction. It is quite difficult to gain a comprehensive idea from existing literature about methods used in operational short-term climate prediction.

A novel rapid screening method for health monitoring of building structures from earthquake records

2019 ◽  
Vol 22 (16) ◽  
pp. 3544-3557
Author(s):  
Ging-Long Lin ◽  
Jer-Fu Wang ◽  
Chi-Chang Lin ◽  
Jim Lin
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Screening Method ◽  
Nonlinear Behavior ◽  
Relative Displacement ◽  
Rapid Screening ◽  
Building Structures ◽  
Detection Techniques ◽  
Earthquake Records ◽  
Rapid Screening Method

This study presents a rapid screening method for health monitoring of building structures based on earthquake records. Compared with conventional damage detection techniques, the rapid screening system with few sensors is more attractive and cost-effective in assessing the global behaviors of a building structure. Only two tri-axial accelerometers are required for a building. One is mounted at the ground level, and another one is mounted at the top floor. First, the relative displacement of top floor to ground is calculated by on-line integration. Then, the diagram of absolute acceleration versus relative displacement of top floor is used to determine the pseudo stiffness of the whole building by linear regression. The decrease of pseudo stiffness denotes the occurrence and degree of damage in the building. A novel real-time damage technique is also proposed to detect nonlinear behavior of a building. A five-story shear-type building under earthquake excitations was illustrated for sensitivity analysis of pseudo stiffness considering different damage cases. Shaking-table-test data of a three-story benchmark building were used to verify the accuracy of the proposed damage assessment technique. In addition, the proposed method was also applied to evaluate a new eight-story residential building equipped with accelerometers in Taipei, Taiwan. Finally, the acceleration response records of a real building, which experienced moderate damages caused by the main shock of 1999 Taiwan Chi-Chi earthquake ( ML = 7.3), were considered to examine the applicability of the proposed method to generate a real-time damage indicator for a building with nonlinear behavior. All of the results show that the proposed method is reliable and effective for rapid diagnosis of building health.

Past, present, and future of geophysical inversion—A new millennium analysis

Geophysics ◽  
2001 ◽  
Vol 66 (1) ◽  
pp. 21-24 ◽  
Author(s):  
Sven Treitel ◽  
Larry Lines
Keyword(s):  
Inverse Problem ◽  
Review Article ◽  
Data Sets ◽  
Inversion Process ◽  
Geophysical Inversion ◽  
Field Recordings ◽  
Theory To Practice ◽  
Rough Model ◽  
Recorded Data

Geophysicists have been working on solutions to the inverse problem since the dawn of our profession. An interpreter infers subsurface properties on the basis of observed data sets, such as seismograms or potential field recordings. A rough model of the process that produces the recorded data resides within the interpreter’s brain; the interpreter then uses this rough mental model to reconstruct subsurface properties from the observed data. In modern parlance, the inference of subsurface properties from observed data is identified with the solution of a so‐called “inverse problem.” In contrast, the “forward problem” consists of the determination of the data that would be recorded for a given subsurface configuration and under the assumption that given laws of physics hold. Until the early 1960s, geophysical inversion was carried out almost exclusively within the geophysicist’s brain. Since then, we have learned to make the geophysical inversion process much more quantitative and versatile by recourse to a growing body of theory, along with the computer power to reduce this theory to practice. We should point out the obvious, however, namely that no theory and no computer algorithm can presumably replace the ultimate arbiter who decides whether the results of an inversion make sense or nonsense: the geophysical interpreter. Perhaps our descendants writing a future third Millennium review article can report that a machine has been solving the inverse problem without a human arbiter. For the time being, however, what might be called “unsupervised geophysical inversion” remains but a dream.

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