Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

Development and Longer Term In Situ Evaluation of Fiber-Optic Sensors for Monitoring of Structural Concrete

In this paper, new solutions to the problem of making measurements, of carbonation and chloride ingress, in particular, in concrete structures are considered. The approach has focused on the design, development, and use of fiber-optic sensors (FOSs), recognizing the need in that conventional devices are often either inaccurate, expensive, or unsuitable for encapsulation in the material. The sensors have been designed to monitor, in situ and nondestructively, relevant physical, and chemical changes in cementitious materials. Three different types of FOS were constructed, tested, and evaluated specifically for this application, these being a temperature sensor (based on the fluorescence decay) and pH and chloride sensors, based on sol-gel (solidified gel) technology with appropriate impregnated indicators. The sensors were all designed to be inserted into the structures and evaluated under the harshest conditions, i.e., being mounted when the mortar is poured and thus tested in situ, with the temperature and pH sensors successfully embedded in mortar. The outcomes of these tests have shown that both the temperature sensor and the pH sensor were able to function correctly for the duration of the work - for over 18 months after placement. The laboratory tests on the chloride sensor showed it was able to make measurements but was not reversible, limiting its potential utility for in situ environments. Research is ongoing to refine the sensor performance and extend the testing.