Paulo Gil

The GINSENG system for wireless monitoring and control: Design and deployment experiences

Todays industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.

A New Algorithm for Detection of S1 and S2 Heart Sounds

This paper presents a new algorithm for segmentation and classification of S1 and S2 heart sounds without ECG reference. The proposed approach is composed of three main stages. In the first stage the fundamental heart sound lobes are identified using a fast wavelet transform and the Shannon energy. Next, these lobes are validated and classified into S1 and S2 classes based on Mel-frequency coefficients and on a non-supervised neural network. Finally, regular heart cycles are identified in a post-processing stage by a heart rhythm criterion. This approach was tested using sound samples collected from prosthetic valve implanted patients. Results are comparable with ECG based approaches

Gain Tuning of Fuzzy PID Controllers for MIMO Systems: A Performance-Driven Approach

A new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral-derivative controllers, by taking explicitly into account the closed-loop system performance is proposed in this study. The solution is obtained by solving a nonlinear constrained optimization problem, considering a set of constraints on the scaling factors of the Mamdani-type fuzzy system, and on the plants inputs and outputs. Two distinct approaches are presented, which are associated with the optimization being carried out offline or in real time. The offline tuning scheme assumes the system dynamics described by a nonlinear model, while for the real-time implementation, the plants dynamics is locally approximated by a linear model, with the underlying parameters recursively updated. In order to cope with rather stringent sampling time requirements, the constrained online optimization problem is implemented based on the grid computing paradigm. Given the adaptive nature of the real-time scheme, time-varying dynamics and unknown disturbances can be accommodated in such a way that the closed-loop performance is effectively maximized, while avoiding wind-up phenomena induced by the integrator term. The proposed tuning methodologies are assessed on a benchmark three-tank system and compared against a conventional-based tuning approach. Results from experiments illustrate the feasibility of the proposed approaches and also all the relevance in optimal control systems based on Mamdani-type fuzzy controllers.

Affine Neural Network-Based Predictive Control Applied to a Distributed Solar Collector Field

This paper presents experimental results concerning the control of a distributed solar collector field, where the main objective concerns the regulation of the outlet oil temperature by suitably manipulating the oil flow rate. This is achieved by means of a constrained nonlinear adaptive model-based predictive control framework where the control action sequence is obtained by solving an open-loop optimization problem, subject to a set of constraints. The plant dynamics is approximated by an affine state-space neural network, whose complexity is specified in terms of the cardinality of dominant singular values associated with a subspace oblique projection of data-driven Hankel matrices. The neural network is first trained offline and subsequently improved through a recursive updating of its weights and biases, based on a dual unscented Kalman filter. The control scheme is implemented on the Acurex field of the Plataforma Solar de Almería, Spain. Results from these experiments demonstrate the feasibility of the proposed framework, and highlight the ability to cope with time-varying and unmodeled dynamics, under the form of disturbances, and its inherent capability for accommodating actuation faults.

WSN evaluation in industrial environments first results and lessons learned

The GINSENG project develops performance-controlled wireless sensor networks that can be used for time-critical applications in hostile environments such as industrial plant automation and control. GINSENG aims at integrating wireless sensor networks with existing enterprise resource management solutions using a middleware. A cornerstone is the evaluation in a challenging industrial environment — an oil refinery in Portugal. In this paper we first present our testbed. Then we introduce our solution to access, debug and flash the sensor nodes remotely from an operations room in the plant or from any location with internet access. We further present our experimental methodology and show some exemplary results from the refinery testbed.

Process control based on PCA models

In this paper an approach to design controllers based on principal components analysis (PCA) models is presented. Closed-loop control can be formulated and implemented within the reduced space defined by a PCA model. This PCA controller, results in an integral controller, which can be used as an inferential controller when a measurement of a primary variable is not available. The main contributions of the paper are: a) the incorporation of an adjustable gain on the classical PCA controller; b) the proposal of a set of tuning rules; c) the performance evaluation of this new controller, in nominal operation conditions and in faulty situations. Some experimental results, obtained with the three tank benchmark (European COSY project), are presented.

Simulation platform for Wireless Sensor Networks based on the TrueTime toolbox

Research on wireless sensor networks (WSN) has received considerable attention in the last few years due to their unique characteristics, including, flexibility, self-organization, easy deployment, which make them an ideal candidate for low-cost monitoring. In order to help WSN planning and to enable the design of new protocols and applications and assess their performance, several existent simulation platforms have been extended to include simulation frameworks for WSN. In this paper, a higher level simulation platform for WSN is proposed based on the truetime toolbox. Relevant features include graphical representation of communication components, wireless communication and battery-driven operation. Special attention was paid to the 3D graphical interface, simulator interactivity and its extendibility. Simulation results demonstrate the applicability and usability of the proposed simulation platform.

A predictive adaptive approach to generic ECG data compression

In the modern hospital, the efficient storage of electronically recorded biomedical signals as well as its transmission over communication networks is becoming more and more important. Although digital storage media is currently almost inexpensive and computational power has exponentially increased in last years, effective electrocardiogram (ECG) compression techniques are still very attractive. In fact, several millions of electrocardiograms are recorded annually and the transfer of electrocardiogram records over communication networks for remote analysis is now done more than ever. Besides the increased storage capacity for archival purposes, ECG compression allows real-time transmission over communication networks, economic off-line transmission to remote interpretation sites and enables efficient HCG rhythm analysis algorithms. In this paper, an adaptive approach to ECG compression is proposed, which provides comparable results with various types of ECG signal, namely normal sinus rhythm, ventricular tachycardia and ventricular fibrillation. Most proposals in the literature approach the problem of ECG compression without considering the possibility of such pathologies and, consequently, their performance deteriorate in cases where such signals are present. Experimental results of the proposed algorithm are promising, although compression ratios are still not yet as good as they can get.

NEURAL OUTPUT REGULATION FOR A SOLAR POWER PLANT

In this paper the modelling capabilities of a recurrent neural network and the effectiveness and stability of the output regulation control theory are combined. The control structure consists in a neural based indirect adaptive control scheme, being the main goal to provide a viable practical control strategy suitable for real-time implementations. This control scheme was applied to the distributed solar collector field at Plataforma Solar de Almeria, Spain. Experimental results obtained at the solar power plant are presented showing the effectiveness of the proposed approach.

Scheduling of PID controllers by means of a neural network with application to a solar power plant

This paper concerns the application of a neural network control strategy to the distributed collector field of a solar power plant The neural network is trained based on measured data from the plant providing a way of scheduling between a set of PID controllers, a priori timed in different operating points by means of Takahashi rules. The present work consists in a hybrid scheme combining the potentialities of neural networks for approximation purposes with the well-know theory and widespread industrial application of BID techniques. Experimental results collected at Plataforma Solar de Almeria (Spain), show the effectiveness of the proposed approach.