Multi-rate Data Fusion Research Articles

Global Displacement Reconstruction of Lattice Tower Using Limited Acceleration and Strain Sensors

Displacement is an important parameter for evaluating structural performance. However, the accurate measurement of global dynamic displacement remains a challenging task. To solve this problem, this paper proposes a global dynamic displacement reconstruction method for lattice tower structures, fusing limited acceleration and strain data. First, the strain-displacement mapping method for cantilever beams with variable cross-sections is presented and then extended to lattice towers. Thereafter, a modified multi-rate data fusion algorithm incorporating Kalman filtering and the proposed strain-displacement mapping methods is developed to fuse the acceleration and strain data to reconstruct the global dynamic displacement of the tower. The numerical simulation, model test and full-scale test demonstrate that the reconstructed dynamic displacements using the proposed method agree well with the reference values in both the time and frequency domains, and the parametric analysis has also been carried out, exhibiting great robustness and high reconstruction accuracy.

A Smart Multi-Rate Data Fusion Method for Displacement Reconstruction of Beam Structures.

Dynamic displacement plays an essential role in structural health monitoring. To overcome the shortcomings of displacement measured directly, such as installation difficulty of monitoring devices, this paper proposes a smart reconstruction method, which can realize real-time intelligent online reconstruction of structural displacement. Unlike the existing approaches, the proposed algorithm combines the improved mode superposition methods that is suitable for complex beam structures with the Kalman filtering approach using acceleration and strain data. The effectiveness of the proposed multi-rate data fusion method for dynamic displacement reconstruction is demonstrated by both numerical simulation and model vibration experiment. Parametric analysis shows that the reconstruction error is only 5% when the noise signal to noise ratio is 5 dB, illustrating that the proposed algorithm has excellent anti-noise performance. The results also indicate that both the high-frequency and low-frequency components of the dynamic displacements can be accurately reconstructed through the proposed method, which has good robustness.

Multi-rate data fusion for dynamic displacement measurement of beam-like supertall structures using acceleration and strain sensors

Accurate measurement of dynamic displacement is important for the structural health monitoring and safety assessment of supertall structures. However, the displacement of a supertall structure is difficult to be accurately measured using the conventional methods because they are either inaccurate or inconvenient to be set up in practice. This study provides an accurate and economical method to measure dynamic displacement of supertall structures accurately by fusing acceleration and strain data, which are generally available in the structural health monitoring system. Dynamic displacement is first derived from the measured longitudinal strains based on geometric deformation without requiring mode shapes. An optimization technique is utilized to optimize the deployment of strain sensors for achieving more accurate strain-derived displacement. The strain-derived displacement is then combined with measured acceleration via a multi-rate Kalman filtering approach. Applications to a numerical supertall structure and a laboratory cantilever beam verify that the proposed method accurately estimates displacement including both high-frequency and pseudo-static components, under different noise cases and sampling rates. A full-scale field test on the 600 m-high Canton Tower is implemented to validate the applicability of the proposed method to real supertall structures. Error analysis demonstrates that the data fusion displacement is more accurate than the global position system-measured displacement in the time and frequency domains.

Data fusion based multi-rate Kalman filtering with unknown input for on-line estimation of dynamic displacements

Structural dynamic displacement is one of the most important measurands that describe the dynamic characteristics of a structure. However, accurate measurement of dynamic displacements of a civil infrastructure is still a challenging task. To solve the difficulties and drawbacks of direct dynamic displacement measurement, the approach of multi-rate Kalman filtering for the data fusion of displacement and acceleration measurement was developed. Recently, an improved technique for dynamic displacement estimation by fusing biased high-sampling rate acceleration and low-sampling rate displacement measurements has been proposed. However, this technique can only take constant acceleration bias into account. In this paper, based on the algorithm of Kalman filter with unknown input recently developed by the authors, dynamic displacement is on line estimated based on multi-rate data fusion of high-sampling rate acceleration with time-varying bias and low-sampling rate displacement measurements. The time history of time-varying acceleration bias is treated as “unknown input” in the algorithm of Kalman filter with unknown input to overcome the limitations of the previous technique. Some numerical examples with linear or polynomial acceleration bias are used to demonstrate the effectiveness of the proposed approach for on line estimation of structural dynamic displacement.

Single camera absolute motion based digital elevation mapping for a next generation planetary lander

Robotic planetary surface exploration missions are becoming much more ambitious in their science goals as they attempt to answer the bigger questions relating to the possibility of life elsewhere in our solar system. Answering these questions will require scientifically rich landing sites. Such sites are unlikely to be located in relatively flat regions that are free from hazards, therefore there is a growing need for next generation entry descent and landing systems to possess highly sophisticated navigation capabilities coupled with active hazard avoidance that can enable a pin-point landing. As a first step towards achieving these goals, a multi-source, multi-rate data fusion algorithm is presented that combines single camera recursive feature-based structure from motion (SfM) estimates with measurements from an inertial measurement unit in order to overcome the scale ambiguity problem by directly estimating the unknown scale factor. This paper focuses on accurate estimation of absolute motion parameters, as well as the estimation of sparse landing site structure to provide a starting point for hazard detection. We assume no prior knowledge of the landing site terrain structure or of the landing craft motion in order to fully assess the capabilities of the proposed algorithm to allow a pin-point landing on distant solar system bodies where accurate knowledge of the desired landing site may be limited. We present results using representative synthetic images of deliberately challenging landing scenarios, which demonstrates that the proposed method has great potential.

A novel calibration approach of soft sensor based on multirate data fusion technology

In multirate chemical processes, soft sensor is commonly used for fast-rate quality estimations. As the kernel of soft sensor, modeling technique has drawn much attention from researchers. But most of the modeling methods have disadvantages of uncertain estimation errors and limited modeling capability. To these issues, soft sensor calibration is considered as an efficient alternative, which however is often ignored by researchers and the current application level is relatively low. A novel soft sensor calibration method is proposed in this paper. Under multirate sampling conditions, data fusion technology based on Kalman filter is introduced into soft sensor maintenance to integrate the soft sensor model estimations with process measurements. The performance of the algorithm is evaluated through simulations and a laboratory scale experiment. The factors that may influence the performance are also discussed in detail. The results demonstrate that the multirate Kalman filter approach is able to improve the accuracy and reliability of soft estimations, when the essential dynamics is included in the Kalman filtering model and the filter parameters are properly tuned.