Sistema híbrido para detecção e diagnóstico de falhas em sistemas dinâmicos

The industries are getting more and more rigorous, when security is in question, no matter is to avoid financial damages due to accidents and low productivity, or when it s related to the environment protection. It was thinking about great world accidents around the world involving aircrafts and industrial process (nuclear, petrochemical and so on) that we decided to invest in systems that could detect fault and diagnosis (FDD) them. The FDD systems can avoid eventual fault helping man on the maintenance and exchange of defective equipments. Nowadays, the issues that involve detection, isolation, diagnose and the controlling of tolerance fault are gathering strength in the academic and industrial environment. It is based on this fact, in this work, we discuss the importance of techniques that can assist in the development of systems for Fault Detection and Diagnosis (FDD) and propose a hybrid method for FDD in dynamic systems. We present a brief history to contextualize the techniques used in working environments. The detection of fault in the proposed system is based on state observers in conjunction with other statistical techniques. The principal idea is to use the observer himself, in addition to serving as an analytical redundancy, in allowing the creation of a residue. This residue is used in FDD. A signature database assists in the identification of system faults, which based on the signatures derived from trend analysis of the residue signal and its difference, performs the classification of the faults based purely on a decision tree. This FDD system is tested and validated in two plants: a simulated plant with coupled tanks and didactic plant with industrial instrumentation. All collected results of those tests will be discussed

Utilização de redes neurais artificiais para detecção e diagnóstico de falhas

In a real process, all used resources, whether physical or developed in software, are subject to interruptions or operational commitments. However, in situations in which operate critical systems, any kind of problem may bring big consequences. Knowing this, this paper aims to develop a system capable to detect the presence and indicate the types of failures that may occur in a process. For implementing and testing the proposed methodology, a coupled tank system was used as a study model case. The system should be developed to generate a set of signals that notify the process operator and that may be post-processed, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and amplifiers of the real plant, the data set of the faults will be computationally generated and the results collected from numerical simulations of the process model. The system will be composed by structures with Artificial Neural Networks, trained in offline mode using Matlab®

Sistema para detecção e diagnóstico de falhas em motores elétricos de indução utilizando lógica fuzzy

Induction motors are one of the most important equipment of modern industry. However, in many situations, are subject to inadequate conditions as high temperatures and pressures, load variations and constant vibrations, for example. Such conditions, leaving them more susceptible to failures, either external or internal in nature, unwanted in the industrial process. In this context, predictive maintenance plays an important role, where the detection and diagnosis of faults in a timely manner enables the increase of time of the engine and the possibiity of reducing costs, caused mainly by stopping the production and corrective maintenance the motor itself. In this juncture, this work proposes the design of a system that is able to detect and diagnose faults in induction motors, from the collection of electrical line voltage and current, and also the measurement of engine speed. This information will use as input to a fuzzy inference system based on rules that find and classify a failure from the variation of thess quantities

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

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.

Sistema de detecção e isolamento de falhas em sistemas dinâmicos baseado em identificação paramétrica

The present research aims at contributing to the area of detection and diagnosis of failure through the proposal of a new system architecture of detection and isolation of failures (FDI, Fault Detection and Isolation). The proposed architecture presents innovations related to the way the physical values monitored are linked to the FDI system and, as a consequence, the way the failures are detected, isolated and classified. A search for mathematical tools able to satisfy the objectives of the proposed architecture has pointed at the use of the Kalman Filter and its derivatives EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter). The use of the first one is efficient when the monitored process presents a linear relation among its physical values to be monitored and its out-put. The other two are proficient in case this dynamics is no-linear. After that, a short comparative of features and abilities in the context of failure detection concludes that the UFK system is a better alternative than the EKF one to compose the architecture of the FDI system proposed in case of processes of no-linear dynamics. The results shown in the end of the research refer to the linear and no-linear industrial processes. The efficiency of the proposed architecture may be observed since it has been applied to simulated and real processes. To conclude, the contributions of this thesis are found in the end of the text

O direito à saúde e a política nacional de atenção oncológica: uma análise a partir da crescente judicialização dos medicamentos antineoplásicos

The 1988 Federal Constitution of Brazil by presenting the catalog of fundamental rights and guarantees (Title II) provides expressly that such rights reach the social, economic and cultural rights (art. 6 of CF/88) as a means not only to ratify the civil and political rights, but also to make them effective and practical in the life of the Brazilian people, particularly in the prediction of immediate application of those rights and guarantees. In this sense, health goes through condition of universal right and duty of the State, which should be guaranteed by social and economic policies aimed at reducing the risk of disease and other hazards, in addition to ensuring universal and equal access to actions and services for its promotion, protection and recovery (Article 196 by CF/88). Achieving the purposes aimed by the constituent to the area of health is the great challenge that requires the Health System and its managers. To this end, several policies have been structured in an attempt to establish actions and services for the promotion, protection and rehabilitation of diseases and disorders to health. In the mid-90s, in order to meet the guidelines and principles established by the SUS, it was established the Política Nacional de Atenção Oncológica PNAO, in an attempt to sketch out a public policy that sought to achieve maximum efficiency and to be able to give answers integral to effective care for patients with cancer, with emphasis on prevention, early detection, diagnosis, treatment, rehabilitation and palliative care. However, many lawsuits have been proposed with applications for anticancer drugs. These actions have become very complex, both in the procedural aspects and in all material ones, especially due to the highcost drugs more requested these demands, as well as need to be buoyed by the scientific evidence of these drugs in relation to proposed treatments. The jurisprudence in this area, although the orientations as outlined by the Parliament of Supreme Court is still in the process of construction, this study is thus placed in the perspective of contributing to the effective and efficient adjudication in these actions, with focus on achieving the fundamental social rights. Given this scenario and using research explanatory literature and documents were examined 108 lawsuits pending in the Federal Court in Rio Grande do Norte, trying to identify the organs of the Judiciary behave in the face of lawsuits that seeking oncology drugs (or antineoplastic), seeking to reconcile the principles and constitutional laws and infra constitutional involving the theme in an attempt to contribute to a rationalization of this judicial practice. Finally, considering the Rational Use of health demands and the idea of belonging to the Brazilian people SUS, it is concluded that the judicial power requires ballast parameters of their decisions on evidence-based medicine, aligning these decisions housing constitutional principles that the right to health and the scientific conclusions of efficacy, effectiveness and efficiency in oncology drugs, when compared to the treatments offered by SUS

Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

Método Fuzzy para auxílio ao diagnóstico de câncer de mama em ambiente inteligente de telediagnóstico colaborativo para apoio à tomada de decisão

Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Sistema de teleoftalmologia para auxílio ao pré-diagnóstico de disfunções oculomotoras

The vision is one of the five senses of the human body and, in children is responsible for up to 80% of the perception of world around. Studies show that 50% of children with multiple disabilities have some visual impairment, and 4% of all children are diagnosed with strabismus. The strabismus is an eye disability associated with handling capacity of the eye, defined as any deviation from perfect ocular alignment. Besides of aesthetic aspect, the child may report blurred or double vision . Ophthalmological cases not diagnosed correctly are reasons for many school abandonments. The Ministry of Education of Brazil points to the visually impaired as a challenge to the educators of children, particularly in literacy process. The traditional eye examination for diagnosis of strabismus can be accomplished by inducing the eye movements through the doctor s instructions to the patient. This procedure can be played through the computer aided analysis of images captured on video. This paper presents a proposal for distributed system to assist health professionals in remote diagnosis of visual impairment associated with motor abilities of the eye, such as strabismus. It is hoped through this proposal to contribute improving the rates of school learning for children, allowing better diagnosis and, consequently, the student accompaniment

Detecção de bactérias periodontopatogênicas cultiváveis e não cultiváveis em placas ateromatosas

Periodontal infections consist of a group of inflammatory conditions caused by microorganisms that colonize the tooth surface through the formation of dental biofilm. Chronic infections such as periodontitis have been associated to the development and progression of atherosclerosis. AIM: Detect cultivatable and non-cultivatable periodontopathogenic bacteria in atheromatous plaques; search for factors associated to the presence of these bacteria in the atheromatous plaques and characterize the presence of cultivatable and non-cultivatable bacteria in these plaques. METHODOLOGY: A cross-sectional study was performed with a sample of 30 patients diagnosed with atherosclerosis in the carotid, coronary or femoral arteries and surgically treated with angioplasty and stent implant, bypass or endarterectomy. The plaques were collected during surgery and analyzed using the PCR molecular technique for the presence of the DNA of the cultivatable bacteria Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis and Treponema denticola and of the non-cultivatable Synergistes phylotypes. The patients were examined in the infirmary, after the surgery, where they also responded to a questionnaire aimed at determining factors associated to the presence of periodontopathogenic bacteria in the atheromatous plaques. RESULTS: All patients with tooth (66,7%) possessed disease periodontal, being 95% severe and 65% widespread. No periodontopathogenic bacteria were found in the atheromatous plaques. However, four samples (13.3%) were positive for the presence of bacteria. Of these, three participants were dentate, being two carriers of widespread severe chronic periodontite and one of located severe chronic periodontitis. None of them told the accomplishment of procedures associated to possible bacteremia episodes, as treatment endodontic, extraction the last six months or some procedure surgical dental. CONCLUSION: The periodontopathogenic bacteria studied were not found in the atheromatous plaques, making it impossible to establish the prevalence of these pathogens or the factors associated to their presence in plaques, the detection of positive samples for bacteria suggests that other periodontal and non-periodontal pathogens be studied in an attempt at discovering the association or not between periodontal disease and/or others infections and atherosclerosis, from the presence of these bacteria in atheromas

Diagnóstico ambiental das águas do estuário Jundiaí-Potengi pela determinação dos índices de qualidade da água e toxidez

This study is an environmental diagnosis of the Jundiaí-Potengi/RN estuarine system waters, using calculations of pollution indicator indices such as the Water Quality Index (WQI) and the Toxicity Index (TI). The samples were collected at twelve points on the estuary, at high and low tide, between August and November 2007, over four campaigns. The study area, located in a high impact region, has various activities on its banks such as: discharge of untreated or undertreated domestic and industrial sewage, shrimp farming, immunizer stabilization lakes, riverside communities, etc. All the parameters analyzed were compared to the limits of CONAMA Resolution No. 357 of 2005 for healthy and saline Class 1 waters. The results found prove the impact caused by various activities, mainly the parameters related to the presence of organic material, such as DQO, DBO, COT and thermotolerant colliforms. The IQA for most of the collection points was of medium quality. For the metals, although values above the Resolution limits were found, most of them were lower than the detection limits of ICP-OES used, indicating that they tend to be transported by the dynamic of the tides or rainfall and are deposited in bottom sediments, resulting in a TI of 1.0 in this water, when they are absent, which occurs in most cases, or 0.0, when heavy metals are found in these waters

Um sistema inteligente de teleoftalmologia para o auxílio no diagnóstico de catarata

The increasing in world population, with higher proportion of elderly, leads to an increase in the number of individuals with vision loss and cataracts are one of the leading causes of blindness worldwide. Cataract is an eye disease that is the partial or total opacity of the crystalline lens (natural lens of the eye) or its capsule. It can be triggered by several factors such as trauma, age, diabetes mellitus, and medications, among others. It is known that the attendance by ophthalmologists in rural and poor areas in Brazil is less than needed and many patients with treatable diseases such as cataracts are undiagnosed and therefore untreated. In this context, this project presents the development of OPTICA, a system of teleophthalmology using smartphones for ophthalmic emergencies detection, providing a diagnostic aid for cataract using specialists systems and image processing techniques. The images are captured by a cellphone camera and along with a questionnaire filled with patient information are transmitted securely via the platform Mobile SANA to a online server that has an intelligent system available to assist in the diagnosis of cataract and provides ophthalmologists who analyze the information and write back the patient’s report. Thus, the OPTICA provides eye care to the poorest and least favored population, improving the screening of critically ill patients and increasing access to diagnosis and treatment.