Adaptive software fault prediction approach using object-oriented metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

A model of the hydrothermal system at Casa Diablo in Long Valley, California, based on resistivity profiles and soil mercury analyses

A description and model of the near-surface hydrothermal system at Casa Diablo, with its implications for the larger-scale hydrothermal system of Long Valley, California, is presented. The data include resistivity profiles with penetrations to three different depth ranges, and analyses of inorganic mercury concentrations in 144 soil samples taken over a 1.3 by 1.7 km area. Analyses of the data together with the mapping of active surface hydrothermal features (fumaroles, mudpots, etc.), has revealed that the relationship between the hydrothermal system, surface hydrothermal activity, and mercury anomalies is strongly controlled by faults and topography. There are, however, more subtle factors responsible for the location of many active and anomalous zones such as fractures, zones of high permeability, and interactions between hydrothermal and cooler groundwater. In addition, the near-surface location of the upwelling from the deep hydrothermal reservoir, which supplies the geothermal power plants at Casa Diablo and the numerous hot pools in the caldera with hydrothermal water, has been detected. The data indicate that after upwelling the hydrothermal water flows eastward at shallow depth for at least 2 km and probably continues another 10 km to the east, all the way to Lake Crowley.

Physical dynamic simulation of shipboard power system components in a distributed computational environment

Shipboard power systems have different characteristics than the utility power systems. In the Shipboard power system it is crucial that the systems and equipment work at their peak performance levels. One of the most demanding aspects for simulations of the Shipboard Power Systems is to connect the device under test to a real-time simulated dynamic equivalent and in an environment with actual hardware in the Loop (HIL). The real time simulations can be achieved by using multi-distributed modeling concept, in which the global system model is distributed over several processors through a communication link. The advantage of this approach is that it permits the gradual change from pure simulation to actual application. In order to perform system studies in such an environment physical phase variable models of different components of the shipboard power system were developed using operational parameters obtained from finite element (FE) analysis. These models were developed for two types of studies low and high frequency studies. Low frequency studies are used to examine the shipboard power systems behavior under load switching, and faults. High-frequency studies were used to predict abnormal conditions due to overvoltage, and components harmonic behavior. Different experiments were conducted to validate the developed models. The Simulation and experiment results show excellent agreement. The shipboard power systems components behavior under internal faults was investigated using FE analysis. This developed technique is very curial in the Shipboard power systems faults detection due to the lack of comprehensive fault test databases. A wavelet based methodology for feature extraction of the shipboard power systems current signals was developed for harmonic and fault diagnosis studies. This modeling methodology can be utilized to evaluate and predicate the NPS components future behavior in the design stage which will reduce the development cycles, cut overall cost, prevent failures, and test each subsystem exhaustively before integrating it into the system.

Avaliação da influência de cinza da casca de arroz em cimentos para poços de petróleo submetidos à injeção de vapor

Reservoirs that present highly viscous oils require methods to aid in their recovery to the surface. The elev ated oil viscosity hinders its flow through porous media and conventional recovery methods have not obtained significant efficiency. As such, the injection of steam into the reservoir through an injection well has been the most widely used method of therma l recovery, for it allows elevated volumes of recovery due to the viscosity reduction of the oil, facilitating the oil’s mobility within the rock formation and consequently into the production well where it will be exploited. On the other hand, the injecti on of vapor not only affects the fluids found in the rock pores, but the entire structure that composes the well where it is injected due to the high temperatures used in the process. This temperature increment is conducted to the cement, found in the annu lus, responsible for the isolation of the well and the well casing. Temperatures above 110 ̊C create new fazes rich in calcium in the cement matrix, resulting in the reduction of its permeability and the consequential phenomenon of mechanical resistance ret rogression. These alterations generate faults in the cement, reducing the well’s hydraulic isolation, creating insecurity in the operations in which the well will be submitted as well as the reduction of its economic life span. As a way of reducing this re trograde effect, this study has the objective of evaluating the incorporation of rice husk ash as a mineral additive substitute of silica flour , commercially utilized as a source of silica to reduce the CaO/SiO 2 ratio in the cement pastes submitted to high temperatures in thermal recovery. Cement pastes were formulated containing 20 and 30% levels of ash, apart from the basic paste (water + cement) and a reference paste (water + cement + 40% silica flour) for comparison purposes. The tests were executed th rough compression resistance tests, X - Ray diffraction (XRD) techniques, thermogravimetry (TG), scanning electron microscopy (SEM) and chemical anal ysis BY X - ray fluorescence (EDS) on the pastes submitted to cure at low temperatures (45 ̊C) for 28 days following a cure at 280 ̊C and a pressure of 2,000 PSI for 3 days, simulating vapor injection. The results obtained show that the paste containing 30% r ice shell ash is satisfactory, obtaining mechanical resistance desired and equivalent to that of the paste containing 40% silica flour, since the products obtained were hydrated with low CaO/SiO 2 ratio, like the Tobermorita and Xonotlita fases, proving its applicability in well subject to vapor injection.

Método híbrido para detecção e diagnóstico de falhas baseado em resíduos

The detection and diagnosis of faults, ie., find out how , where and why failures occur is an important area of study since man came to be replaced by machines. However, no technique studied to date can solve definitively the problem. Differences in dynamic systems, whether linear, nonlinear, variant or invariant in time, with physical or analytical redundancy, hamper research in order to obtain a unique solution . In this paper, a technique for fault detection and diagnosis (FDD) will be presented in dynamic systems using state observers in conjunction with other tools in order to create a hybrid FDD. A modified state observer is used to create a residue that allows also the detection and diagnosis of faults. A bank of faults signatures will be created using statistical tools and finally an approach using mean squared error ( MSE ) will assist in the study of the behavior of fault diagnosis even in the presence of noise . This methodology is then applied to an educational plant with coupled tanks and other with industrial instrumentation to validate the system.