Abstract:When analyzing heterogeneous samples using spectroscopy, the light scattering effect introduces non-linearity into the measurements and deteriorates the prediction accuracy of conventional linear models. This paper compares the prediction performance of two categories of chemometric methods: pre-processing techniques to remove the non-linearity, and non-linear calibration techniques to directly model the non-linearity. A rigorous statistical procedure is adopted to ensure reliable comparison. The results suggest that optical path length estimation and correction (OPLEC) and Gaussian process (GP) regression are the most promising among the investigated methods. Furthermore, the combination of pre-processing and non-linear models is explored with limited success being achieved.
In this paper, a novel approach of sensor placement is proposed for the purpose of maximizing fault detectability and isolability. This new approach rests on the basic fact that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will change the consistency of the corresponding ARRs. Based on these basic facts, the minimal isolating (MI) set is introduced to formulate the full/maximal isolability which is the constraint for sensor placement. Consequently, the optimization problem for sensor placement is reformulated as searching an MI set which is related to the least number of candidate sensors. To find the optimal MI set, a low complexity dynamic programming (LCDP) algorithm is developed on the fault set that consists of system faults and sensor faults. However, sensor faults are varied as different candidate sensors are used. Therefore, another dedicated procedure is proposed to handle this issue. A case study shows that the proposed approach outperforms an existing sensor placement approach in terms of efficiency.
Note to Practitioners-This paper is motivated by the problem of placing the minimum number of sensors for achieving max-imal isolability in a system. Some existing approaches to search the optimal set of candidate sensors generally have performed relatively low efficiency when a system has many more candidate sensors and system faults. This paper suggests a new approach integrated with dynamic programming (DP). DP inherently qualifies an out-performance on searching solutions from large data. In this research, the approach is further improved by reducing the computational and space complexities of DP, resulting in higher efficiency. The case study results demonstrate that this approach has an out-performance in terms of efficiency. In future research, the sensor placement problem takes more requirements such as sensor weight, sensor volume, and sensor reliability, under consideration besides detectability and isolability.Index Terms-Analytical redundancy relations, dynamic programming, fault detectability and isolability, sensor placement.
This paper proposes a novel sensor placement approach based on the bond graph (BG) for isolability. It is investigated in the linear differential-algebraic equations (DAEs) model regarding a BG. Causal paths are employed to capture the cause-effect relationships of model equations. The case study shows that this novel approach is independent of causality assignment on BGs and performs well. 0018-9286 (c)
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