5 research outputs found
Diagnóstico de fallas con redes neuronales. parte ii: reconocimiento de flujos
En el presente trabajo el sistema de diagnóstico presentado en la parte I es modificado para supervisar procesos que evolucionan en forma compleja ante la presencia de fallas. Al igual que en la Parte I, se considera que cuando una falla afecta a un proceso, cada variable evoluciona siguiendo una trayectoria. Sin embargo, esta vez dicha trayectoria no es única, sino que pertenece a un conjunto de infinitas trayectorias posibles denominado flujo. Cada falla tiene asociado un flujo particular para cada variable. Entonces, en un proceso afectado por una falla, el problema del diagnóstico de fallas se traduce a reconocer, para todas las variables, a cuál flujo pertenece la trayectoria que está siendo observada. Al identificar los flujos se habrá identificado la falla que los provoca. Modelado el diagnóstico de fallas como un problema de reconocimiento de flujos, se realizó un desarrollo teórico que culminó con la definición tanto de la estructura como del método de entrenamiento de las redes neuronales empleadas por el nuevo sistema de diagnóstico. En las pruebas hechas, el nuevo sistema de diagnóstico presentó muy buen comportamiento, siendo el diagnóstico exacto, de alta resolución y estable frente al ruido. Finalmente, la teorÃa desarrollada también indica cómo deben ser escaladas las redes para supervisar procesos de mayor complejidad.The diagnostic system introduced in Part I is modified in this work for supervising complex processes when faults present themselves. As in Part I, it is supposed that when a fault affects a process, then each variable evolves fo-llowing a trajectory. However, this time the aforementioned trajectory is not unique but belongs to a set of infinite possible trajectories named flow. Each fault in a particular flow is associated with each variable. Faults affecting a process can then be diagnosed by recognising which flow the trajectory being observed belongs to for every variable in turn. Once flows have been identified, then the fault causing them is also identified. Theory was de-veloped after modelling fault diagnosis as being a flow recognition problem, definitions being yielded for both structure and training method for the artificial neural networks used by the new diagnostic system. The diagnostic system performed well in tests, diagnosis being exact, having high, stable resolution in the presence of noise. The theory so developed recommends networks being scaled-up for supervising more complex processes
Modeling and fault detection of an industrial copper electrowinning process
Copper electrowinning plants are where high purity copper (Cu) product is obtained through electrochemical reduction of copper from the leaching solution. The presence selenium (Se) and tellurium (Te) in copper sulphide minerals may result in contamination of the leach solution and, eventually of the copper cathode. Unfortunately, hydrometallurgical processes are often difficult to monitor and control due to day-to-day fluctuations in the process as well as limitations in capturing the data at high frequencies. The purpose of this work is to model key variables in the copper electrowinning tank and to apply statistical fault detection to the
selenium/tellurium removal and copper electrowinning process operations.
First principle modeling was applied to the copper electrowinning tank and partial
differential equation models were derived to describe the process dynamics. Industrial data were used to estimate the model parameters and validate the resulting models. Comparison with industrial model shows that the models fit reasonably well with industrial operation.
Simulations of the models were run to explore the dynamics under varying operating
conditions. The derived models provide a useful tool for future process modification and
control development. Using the collected industrial operating data, dynamic principal component analysis (DPCA) based fault detection was applied to Se/Te removal and copper electrowinning processes at Vale’s Electrowinning Plant in Copper Cliff, ON. The fault detection results from the DPCA based approach were consistent with the industrial product quality test. After faults were detected, fault diagnosis was then applied to determine the causes of faults. The fault detection and diagnosis system helps define causes of upset conditions that lead to coppercathode contamination.Master of Applied Science (M.A.Sc.) in Natural Resources Engineerin
Model based fault detection for two-dimensional systems
Fault detection and isolation (FDI) are essential in ensuring safe and reliable operations in industrial
systems. Extensive research has been carried out on FDI for one dimensional (1-D)
systems, where variables vary only with time. The existing FDI strategies are mainly focussed
on 1-D systems and can generally be classified as model based and process history data based
methods. In many industrial systems, the state variables change with space and time (e.g., sheet
forming, fixed bed reactors, and furnaces). These systems are termed as distributed parameter
systems (DPS) or two dimensional (2-D) systems. 2-D systems have been commonly represented
by the Roesser Model and the F-M model. Fault detection and isolation for 2-D systems
represent a great challenge in both theoretical development and applications and only limited
research results are available.
In this thesis, model based fault detection strategies for 2-D systems have been investigated
based on the F-M and the Roesser models. A dead-beat observer based fault detection has been
available for the F-M model. In this work, an observer based fault detection strategy is investigated
for systems modelled by the Roesser model. Using the 2-D polynomial matrix technique,
a dead-beat observer is developed and the state estimate from the observer is then input to a
residual generator to monitor occurrence of faults. An enhanced realization technique is combined
to achieve efficient fault detection with reduced computations. Simulation results indicate
that the proposed method is effective in detecting faults for systems without disturbances as well
as those affected by unknown disturbances.The dead-beat observer based fault detection has been shown to be effective for 2-D systems
but strict conditions are required in order for an observer and a residual generator to exist. These
strict conditions may not be satisfied for some systems. The effect of process noises are also not
considered in the observer based fault detection approaches for 2-D systems. To overcome the
disadvantages, 2-D Kalman filter based fault detection algorithms are proposed in the thesis. A recursive 2-D Kalman filter is applied to obtain state estimate minimizing the estimation
error variances. Based on the state estimate from the Kalman filter, a residual is generated
reflecting fault information. A model is formulated for the relation of the residual with faults
over a moving evaluation window. Simulations are performed on two F-M models and results
indicate that faults can be detected effectively and efficiently using the Kalman filter based fault
detection.
In the observer based and Kalman filter based fault detection approaches, the residual signals
are used to determine whether a fault occurs. For systems with complicated fault information
and/or noises, it is necessary to evaluate the residual signals using statistical techniques. Fault
detection of 2-D systems is proposed with the residuals evaluated using dynamic principal component
analysis (DPCA). Based on historical data, the reference residuals are first generated using
either the observer or the Kalman filter based approach. Based on the residual time-lagged
data matrices for the reference data, the principal components are calculated and the threshold
value obtained. In online applications, the T2 value of the residual signals are compared with
the threshold value to determine fault occurrence. Simulation results show that applying DPCA
to evaluation of 2-D residuals is effective.Doctoral These
Contribution au pronostic de défaut dans les systèmes complexes par les techniques intelligentes
Nous avons présenté une nouvelle approche basée sur l'utilisation d'une méthode guidée par les données pour le pronostic des défauts. Cette méthode requiert des données décrivant le processus de dégradation. Lorsque les données sont insuffisantes, la prédiction des états devient difficile avec les modèles profonds de type mémoire à long terme (LSTM), qui nécessitent une quantité importante de données d'apprentissage. Pour résoudre ce problème de rareté des données dans la prédiction de la durée de vie restante (RUL), nous proposons d'adopter une stratégie d'augmentation des données.
Les résultats obtenus sont démontrent que l'application d'une stratégie d'augmentation des données, peut améliorer les performances de prédiction de la RUL en utilisant les techniques LSTM.
Nous avons validé cette approche en utilisant les données de la NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPPS)
A two-step supervisory fault diagnosis framework
Techniques enabling early detection and diagnosis of faults are important in the processing industries. This paper emphasises a technique for early fault detection and diagnosis based on dynamic fault data and a two-step fault detection and diagnosis framework. The approach shows various advantages over alternative methods including prompt fault detection and localisation, applicability to large-scale systems without the need for excessive computing resources, and a modular architecture that allows plant sections to be treated individually. In the proposed method, the large-scale plant is broken up into sections and a Petri net based on real time data is used to locate the particular section of the plant in which the fault originates. This Petri net then activates secondary neural networks, which diagnose the exact location of the fault in that particular plant section. Applicability of the proposed technique is demonstrated through a pilot plant case study