André Laurindo Maitelli

Real time leak detection system applied to oil pipelines using sonic technology and neural networks

This work proposes a leak detection system using sonic technology, wavelet transform and neural networks to decompose and analyze pressure signals from oil pipelines in real time. The similarity between pressure and sound signals makes it possible to treat the first through digital filtering and wavelet decomposition together with a neural network to characterize and classify leak profiles. The leak detection system logic is embedded on 32 bit/150 MHz floating point DSPs. This system uses piezoresistive sensors, converters to the communication interface (Ethernet) and GPS devices, which are responsible for synchronizing reports and leak alarms. The DSPs code was written using ANSI C language.

A new bilinear generalized predictive control approach: Algorithm and results

Abstract In this paper a new approach of bilinear predictive control is presented. The approach is based in the Bilinear Generalized Predictive Control (BGPC), strategy that uses a time-step quasi-linearised NARIMAX model. In that approach, due to the used model, a prediction error exist, which increases with the prediction horizon, degrading the performance of that controller. Thus, in the present approach, a compensated model is used, whose compensation term depends of the prediction horizon. The algorithm and the results obtained in a example simulation are shown, evidencing that a new approach presents a better performance than the controller based on the quasi-linear model.

A two-stage dual suboptimal controller for stochastic systems using approximate moments

Abstract This work concerns the development of a dual suboptimal controller for discrete time systems with stochastic parameters. The control signal is computed in such a way as to minimize the variance of the output around a reference value two steps ahead in time. The higher-order statistical moments are approximated using optimal predictions of the output. The behavior of the controller is illustrated by two examples.

Magnetic Sensor Used to Detect Contamination of Insulating Oil in Motors Applied to Electrical Submersible Pump

This paper presents a solution for detecting contamination of insulating oil used in the artificial lift method of the oil-type electrical submersible pump (ESP), which indirectly protects the induction motor associated with that system. The objective of this sensor is to generate an alarm signal at just the moment when the contamination in the isolated oil is present. The prototype was designed to work in harsh conditions to reach a depth of 2000 m and temperatures up to 200°C. It used simulator software to define the mechanical and electromagnetic variables. Results of field experiments were performed to validate the prototype. The final results performed in an ESP system with a 60-HP motor showed a good reliability and fast response of the prototype.

GENERALIZED PREDICTIVE CONTROL BASED IN MULTIVARIABLE BILINEAR MULTI- MODEL

Abstract This paper shows a new approach of Model Predictive Control (MPC). A multivariable bilinear multi-model is presented. A set of local bilinear models is identified and the proposed algorithm implements the timestep quasilinearization in trajectory. A metric based in norms is presented to measure the distance from the current operation point to a designed controller in other operation point. Application results are showed in a case study.

A Wavelet Approach to Pipeline Leak Detection by Pressure Analysis

One of the most challenging tasks in an oil field is implementation of a software-based leak detection system on a multi-phase flow pipeline. When a leak occurs in a multi-phase flow pipeline, the flow cannot be measured with accuracy. So, none of the various pipeline leak detection methodologies can offer good performance on a multi-phase flow pipeline. This paper will discuss implementation of a leak detection system in a particular oil field using state-of-the-art signal processing techniques to apply to the data collected in a oil pipeline. This leak detection system is still in development and uses a more practical approach to the problem than traditional methods and was implemented on a PC under the Windows operating system. Windowing, joint time-frequency analysis and wavelets were considered to develop methods of detecting leaks by watching for the wavefront. The idea behind these techniques is to cut the signal of interest into several parts and then analyze the parts separately. It is impossible to know the exact frequency and the exact time of occurrence of the leak frequency in a signal. In other words, a leak signal can simply not be represented as a point in the time-frequency space. It is very important how one cuts the signal to implement the analysis. The wavelet transform or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier transform. The wavelet transforms are used to perform atomic decompositions of the pressure signal that comes from a single point of a pipeline. A number of time-frequency decompositions are attempted. What is expected of this decomposition is that it fits the perceptible changes in the pressure and then an Artificial intelligent System (AIS) decides if the variations in the signal are inherent (common-cause variations) or external to the process (failed instrument, occurrence of a leak, causes that are not part of the process). The AIS learns about continual changes in the pipeline. This is useful as pipeline operation always changes and instrument drift could occur over a long time period.Copyright

A multistage suboptimal dual controller using optimal predictors

This work concerns the control of stochastic systems with unknown and randomly time-varying parameters. Cost functions which consider the sum of output variances up to M steps ahead in time are adopted in the optimization of the control performance. Optimal predictors are used to replace the future outputs which are needed in the solution of the optimization problem. The consequences of this simplification are investigated. A formula is obtained for the computation of the control signal in the case of M-steps-ahead optimization. The relationships between the controller presented here and other classical suboptimal dual controllers are analyzed. Simulation results illustrate the actual performance of the new controller.

AngelCare mobile system: Homecare patient monitoring using Bluetooth and GPRS.

The quick progress in technology has brought new paradigms to the computing area, bringing with them many benefits to society. The paradigm of ubiquitous computing brings innovations applying computing in peoples daily life without being noticed. For this, it has used the combination of several existing technologies like wireless communications and sensors. Several of the benefits have reached the medical area, bringing new methods of surgery, appointments and examinations. This work presents telemedicine software that adds the idea of ubiquity to the medical area, innovating the relation between doctor and patient. It also brings security and confidence to a patient being monitored in homecare.

A Modified Matricial PSO Algorithm Applied to System Identification with Convergence Analysis

Recently, several evolutionary computation techniques have been used in research areas such as parameter estimation of linear and nonlinear dynamic processes. This motivates the use of algorithms such as the particle swarm optimization (PSO) in the aforementioned fields of knowledge. However, little is known about the convergence of this algorithm, and mainly the analyses and studies have focused on experimental results. Therefore, the objective of this work is to propose a structure for the PSO that better analyze the convergence of the algorithm analytically. For this, the PSO is restructured to assume a matrix form, reformulated as a piecewise linear system. There was a convergence analysis of the algorithm as a whole, using an almost sure convergence criterion applicable to switched systems. Subsequently, traditional parameter identification algorithms were combined with the matricial PSO (MPSO), so as to make the identification results as good as or better than identifying only using the PSO or only the traditional algorithms. The obtained functions, after the identification, using the MPSO algorithm combined with the conventional identification algorithms, presented a better generalization and proper identification. The conclusions reached were that the hybridization permits a minimum performance and also contributes to improve the results obtained with the traditional algorithms, allowing the system representation in a higher range of frequencies.

Model Reference Adaptive Control with inverse compensation applied to a pH plant

In this paper, the Model Reference Adaptive Control (MRAC) with dead-zone compensation is proposed to improve two MRAC variations: MRAC with fixed adaptive gain (γ) and MRAC with variable γ based on η-adaptive optimization algorithm. The proposed MRAC is often used to compensate the dead-zone effect on valve that is not taken into consideration in the usual MRAC. It was also implemented a compensation in inverse dynamic on dead-zone. The nonlinearity inverse model is estimated by method of Recursive Least Squares (RLS). All comparisons and validations were made based on results collected from a simulation and developed in accordance with two models: a simplified Hammerstein model and a phenomenological model of Wiener. To verify the proposed controllers with inverse compensation performance we applied to a pH neutralization process. Simulations have been presented to confirm the effectiveness of the technique.