Alberto Prieto-Moreno

A New Approach for Fault Diagnosis of Industrial Processes During Transitions

This paper presents a new approach for fault diagnosis of industrial processes during transitions. The proposed diagnosis strategy is based on the combination of the nearest-neighbor classification rule and the multivariate Dynamic Time Warping time series similarity measure. The proposal is compared with four different classification methods: Bayes Classifier, Multi-Layer Perceptron Neural Network, Support Vector Machines and Long Short-Term Memory Network which have high performance in the specialized scientific bibliography. The continuous stirred tank heater benchmark is used under scenarios of faults occurring at different moments of a transition and scarce fault data. The proposed approach achieves a classification performance approximately 20% superior compared to the best results of the four instance-based classifiers.

A novel index for the robustness comparison of classifiers in fault diagnosis

Abstract The design of robust data-based fault diagnostic systems can be formulated in terms of classification tasks. A diagnostic classifier designed to effectively minimize the false and missing alarm rates resulting from noise, uncertainty, and unknown disturbances while maintaining a relatively high performance can be defined as robust. This paper presents a novel criterion to compare off-line the robustness of classifiers. The proposed index allows to complement the estimated misclassification rate and to quantify the quality of any data-based diagnostic system more rigorously. In order to evaluate the effectiveness of the proposed index, both Artificial Neural Networks and Support Vector Machines are used as diagnostic classifiers for the Continuous Stirred-Tank Reactor benchmark.

An Unsupervised Approach to Leak Detection and Location in Water Distribution Networks

Abstract The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising results mainly because they can deal with uncertain data and the variability of models better than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location in water distribution networks. This approach is based on a zone division of the network, and it only requires data from a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than 2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure sensors.

Leaks' detection in water distribution networks with demand patterns

A novel approach for the continuous detection of leaks in water distribution networks (WDNs) assuming uncertain demand patterns is presented. The proposal is based on a demand pattern construction with repetitive multiple statistic models which allow detection through residual generation for each corresponding model. The key of the method is the time division of the pattern such that the demand satisfies the stationarity condition during each time interval. Thus, the sequential set of demand models allows the use of traditional multivariate statistical tool such as principal component analysis (PCA) for the monitoring of each interval in a sequential manner. Simulations with an academic network are used to test the performance of the proposal, and the results show a lower false alarm rate than the standard PCA.

Uncertainty analysis in mass transfer parameter estimations for chromatographic separation of glucose and fructose

In this paper, a statistical approach for the analysis of the propagation of uncertainty is shown, in the estimate of the kinetic parameters of mass transference used to model a chromatographic column in Simulated Moving Bed. The modeling of the chromatography column was accomplished intervening the new approach front velocity. The analysis of how it is propagated the operational factors uncertainty involved in the process of chromatography toward the estimated parameters was carried out by the use of response surface methodology. Furthermore, chromatographic regions where factors cause bigger variation in the output and their respective patterns were determined. The analysis was applied to the separation process of glucose and fructose.