Sensor set expansion for modal vibration testing

Kammer, Daniel C.

doi:10.1016/j.ymssp.2004.06.003

Cited by 99 publications

(63 citation statements)

References 10 publications

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“…It works well with the sensor set expansion method proposed in [3], and it does not need any special consideration of the sensor orientation, but can compare any sensor to any other sensor in the candidate set.…”

Section: Discussionmentioning

confidence: 97%

“…The EfI method seeks to maximise the Fisher information matrix (FIM), Q c , determinant, shown by Kammer in [3] to be formed as…”

Section: Methods Of Effective Independencementioning

confidence: 99%

“…The target mode shapes partitioned to three translations for each triaxial location is denoted Φ 3c ∈ R m×n m where n m is the number of modes and [3] describes the EfI method with triaxial sensors where sensors are placed in the final set from a candidate set formed from the full FE model. Two ranking methods exist, one for a full rank FIM and one for a rank deficient FIM.…”

Section: Methods Of Effective Independencementioning

confidence: 99%

“…The rank deficient case is more complicated and a full derivation and expression is given by Kammer in [3].…”

Section: Methods Of Effective Independencementioning

confidence: 99%

“…The method places sensors so that the mode shapes linear independence and signal strength are maximised. It was later updated by Kammer and Tinker [2] to account for triaxial sensors and by Kammer [3] to allow for a user selected initial set of positions, and increased computational efficiency by iteratively adding sensors to a final set from a candidate set instead of removing sensors from the candidate set until the amount of sensors required is satisfied. The method has also been made more computationally effective by Li, Li and Fritzen in [4,5] by utilising the relationship between the EfI and modal kinetic energy methods, and a downdating algorithm of the QR decomposed reduced modal matrix.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Redundant Information Rejection in Sensor Localisation Using System Gramians

Gibanica

Abrahamsson

Kammer

2016

Topics in Modal Analysis &Amp; Testing, Volume 10

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Sensors placement is important in vibration testing. The method of effective independence, recently extended to account for triaxial sensors, is widely used for this purpose in case a finite element model of the structure is available. In this paper a criteria is added to reject redundant information that usually arises in symmetric structures or finite element models with high candidate sensor density. A sensor placement strategy is proposed in which, initially, the method of effective independence is used to select the best sensors from a candidate set by maximising the Fisher information matrix determinant. Next, the gramians of a balanced realisation is used to compare the information between systems consisting of only previously added sensors to the final set with systems of the previous and candidate sensors. Sensors with redundant information will have negligible effect on the gramian and can be rejected. The method is fast, as gramians of systems with only one or two outputs are evaluated. It is sub-optimal in the sense that all possible sensor placement combinations are not evaluated for optimality. A test case, consisting of a rectangular plate, is presented, but the method has been used on a large scale industrial model with good results.

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Section: Discussionmentioning

confidence: 97%

“…The EfI method seeks to maximise the Fisher information matrix (FIM), Q c , determinant, shown by Kammer in [3] to be formed as…”

Section: Methods Of Effective Independencementioning

confidence: 99%

Section: Methods Of Effective Independencementioning

confidence: 99%

“…The rank deficient case is more complicated and a full derivation and expression is given by Kammer in [3].…”

Section: Methods Of Effective Independencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Redundant Information Rejection in Sensor Localisation Using System Gramians

Gibanica

Abrahamsson

Kammer

2016

Topics in Modal Analysis &Amp; Testing, Volume 10

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Sensor Placement Optimization

Barthorpe

Worden

2008

Encyclopedia of Structural Health Monitoring

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Structural health monitoring (SHM) systems depend critically on using the correct number of sensors and on positioning them appropriately on the structure. A rigorous approach to this problem requires an optimization methodology as described in this article. The article begins with a definition of the problem and provides a literature review covering relevant methodologies. The basic theories of the main approaches are then discussed in more detail. Finally, the article provides an illustrative case study of a combinatorial optimization method based on the problem of impact location on a composite panel.

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Fault Diagnosis Improvement Using Dynamic Fault Model in Optimal Sensor Placement: A Case Study of Steam Turbine

Salehpour-Oskouei

Pourgol-Mohammad

2016

Quality & Reliability Eng

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Health data are collected dominantly through sensors mounted on different locations in the system. Optimization of sensor network has a significant influence on the reliability of system health prognostics process. In this research, the effect of sensors reliability is studied on their placement optimization. Sensors are considered in this study as components in system failure model. This study is aimed to use ‘Priority AND’ gate for evaluating the effect of time dependencies of sensors as well as components failure on the optimal sensor placement. Because of that, PAND gate is added to the fault tree between all sensors and their corresponding components to develop the failure model of each sensor placement scenario. For calculating the probability of top event, a Monte Carlo‐based algebraic approach is proposed. In algebraic approach, temporal operator ‘BEFORE’ is proposed for calculating the probability of ‘PAND’ gate. The result of using ‘BEFORE’ operator is an analytical solution for probability of each cut sequence. Because of the complexity of analytical solution in practical problems, a Monte Carlo simulation is utilized on the solution in this research. Then the probability of each cut sequence is calculated. Consequently, the probability of top event for each scenario is obtained. Finally, all scenarios are ranked based on top event probabilities. As a case study, optimization of sensor placement has been demonstrated on steam turbine and results are discussed. Copyright © 2016 John Wiley & Sons, Ltd.

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Sensor set expansion for modal vibration testing

Cited by 99 publications

References 10 publications

Redundant Information Rejection in Sensor Localisation Using System Gramians

Redundant Information Rejection in Sensor Localisation Using System Gramians

Sensor Placement Optimization

Fault Diagnosis Improvement Using Dynamic Fault Model in Optimal Sensor Placement: A Case Study of Steam Turbine

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