O envelhecimento de uma instalação industrial provoca o aumento do número de falhas. A probabilidade de falhar é um indicador do momento em que deve ser feita uma parada para manutenção. É desenvolvido um método estatístico, baseado na teoria não-markoviana, para a determinação da variação da probabilidade de falhar em função do tempo de operação, que resulta num sistema de equações diferenciais parciais de natureza hiperbólica. São apresentadas as soluções por passo-fracionário e Lax-Wendroff com termo fonte. Devido à natureza suave da solução, os dois métodos chegam ao mesmo resultado com erro menor que 10−3. No caso estudado, conclui-se que o colapso do sistema depende principalmente do estado inicial da cadeia de Markov, sendo que os demais estados apresentam pouca influência na probabilidade de falha geral do sistema. / During the useful life of an industrial plant, the failure occurrence follows an exponential distribution. However, the aging process in an industrial plant generates an increase of the failure number. The failure probability is a rating for the maintenance stopping process. In this paper, an statistical method for the assessment of the failure probability as a function of the operational time, based on the non-Markovian theory, is presented. Two maintenance conditions are addressed: In the first one, the old parts are utilized, after the repair this condition being called as good as old; in the second one the old parts are substituted by brand new ones this condition being called as good as new. A non-Markovian system with variable source term is modeled by using hyperbolic partial differential equations. The system of equations is solved using the Lax-Wendroff and fractional-step numerical schemes. The two methods achieve to approximately the same results, due to the smooth behavior of the solution. The main conclusion is that the system collapse depends essentially on the initial state of the Markov chain.

This work is dedicated to faulty induction motors. These motors are often used in industrial applications thanks to their usability and their robustness. However, nowadays optimisation of production becomes so critical that the conceptual reliability of the motor is not sufficient anymore. Motor condition monitoring is expanding to serve maintenance planning and uptime maximisation. Moreover, the use of drive control sensors (namely stator current and voltage) can avoid the installation and maintenance of dedicated sensors for condition monitoring.<p><p>Many authors are working in this field but few approach the diagnosis from a detailed and clear physical understanding of the localised phenomena linked to the faults. Broken bars are known to modulate stator currents but it is shown in this work that it also changes machine saturation level in the neighbourhood of the bar. Furthermore, depending on the voltage level, this change in local saturation affects the amplitude and the phase of the modulation. This is of major importance as most diagnosis techniques use this feature to detect and quantify broken bars. For stator short-circuits, a high current is flowing in the short-circuited coil due to mutual coupling with the other windings and current spikes are flowing in the rotor bars as they pass in front of the short-circuited conductors. In the case of rotor eccentricities, the number of pole-pairs and the connection of these pole-pairs greatly affect the airgap flux density distribution as well as the repartition of the line currents in the different pole-pairs.<p><p>These conclusions are obtained through the use of time-stepping finite element models of the faulty motors. Moreover, circuit models of faulty machines are built based on the conclusions of previously explained fault analysis and on classical Park models. A common mathematical description is used which allows objective comparison of the models for representation of the machine behaviour and computing time.<p><p>The identifiability of the parameters of the models as well as methods for their identification are studied. Focus is set on the representation of the machine behaviour using these parameters more than the precise identification of the parameters. It is shown that some classical parameters can not be uniquely identified using only stator measurements.<p><p>Fault detection and identification using computationally cheap models are compared to advanced detection through motor stator current spectral analysis. This last approach allows faster detection and identification of the fault but leads to incorrect conclusions in low load conditions, in transient situations or in perturbed environments (i.e. fluctuating load torque and unideal supply). Efficient quantification of the fault can be obtained using detection techniques based on the comparison of the process to a model.<p><p>Finally, the work provides guidelines for motor supervision strategies depending on the context of motor utilisation. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge.