Browsing by Subject "fault detection"
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Item A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems(Texas A&M University, 2007-04-25) Jaradat, Mohammad Abdel Kareem RasheedIn this study, an efficient new hybrid approach for multiple sensors data fusion and fault detection is presented, addressing the problem with possible multiple faults, which is based on conventional fuzzy soft clustering and artificial immune system (AIS). The proposed hybrid system approach consists of three main phases. In the first phase signal separation is performed using the Fuzzy C-Means (FCM) algorithm. Subsequently a single (fused) signal based on the information provided from the sensor signals is generated by the fusion engine. The information provided from the previous two phases is used for fault detection in the third phase based on the Artificial Immune System (AIS) negative selection mechanism. The simulations and experiments for multiple sensor systems have confirmed the strength of the new approach for online fusing and fault detection. The hybrid system gives a fault tolerance by handling different problems such as noisy sensor signals and multiple faulty sensors. This makes the new hybrid approach attractive for solving such fusion problems and fault detection during real time operations. This hybrid system is extended for early fault detection in complex mechanical systems based on a set of extracted features; these features characterize the collected sensors data. The hybrid system is able to detect the onset of fault conditions which can lead to critical damage or failure. This early detection of failure signs can provide more effective information for any maintenance actions or corrective procedure decisions.Item Biological Pathways Based Approaches to Model and Control Gene Regulatory Networks(2015-01-20) Vakulabaranam Sridharan, SriramThe aim of effective cancer treatment is to prolong the patients? life while offering a reasonable quality of life during and after treatment. The treatments must carry their actions/effects in a manner such that a very large percentage of tumor cells die or shift into a state where they stop proliferating. The fundamental issue in systems biology is to model gene interaction via gene regulatory networks (GRN) and hence provide an informatics environment to study the effects of gene mutation as well as derive newer and effective intervention (via drugs) strategies to alter the cancerous state of the network, thereby eradicating the tumor. In this dissertation, we present two approaches to model gene regulatory networks. These approach are different, albeit having a common structure to them. We develop the GRN under a Boolean formalism with deterministic and stochastic framework. The knowledge used to model these networks are derived from biological pathways, which are partial and incomplete. This work is an attempt towards understanding the dynamics of a proliferating cell and to control this system. Initial part (deterministic) of this work focuses on formulating a deterministic model by assuming the pathway regulations to be complete and accurate. Using these models algorithms were developed to pin-point faults (mutations) in the network and design personalized combination therapy depending on the expression signature of specific output genes. To introduce stochastic nature onto the model due to incompleteness in the prior biological knowledge, an uncertainty class of models was defined over the biological network. Two such uncertainty class of models are modeled- one over the state transitions and the other over the node transitions in the system. This knowledge is transferred to priors, and the existing Bayesian theory is used to update and converge to a good model. The Bayesian control theory for Markovian processes is applied to the problem of intervention in Markovian gene regulatory networks, while simultaneously updating the model. Via a toy example, it is shown that effective prior knowledge quantification can significantly help in converging on to the actual model with limited information from the system and take advantage of the optimality promised by Bayesian intervention. These control methods however, suffer from computational and memory complexity issues- Curse of Dimensionality, to be useful for any network size of biological relevance. To counter these issues associated with Dynamic Programming, suboptimal approximate algorithm known as Q-learning and its Bayesian variation are used to save on computational and memory complexities. These sub-optimal approximate algorithms perform very close (but inferior) to optimal policy, but the computational saving, both in terms of time and memory are significant to extend them to networks of larger size.Item Fault detection of multivariable system using its directional properties(Texas A&M University, 2006-04-12) Pandey, Amit NathA novel algorithm for making the combination of outputs in the output zero direction of the plant always equal to zero was formulated. Using this algorithm and the result of MacFarlane and Karcanias, a fault detection scheme was proposed which utilizes the directional property of the multivariable linear system. The fault detection scheme is applicable to linear multivariable systems. Results were obtained for both continuous and discrete linear multivariable systems. A quadruple tank system was used to illustrate the results. The results were further verified by the steady state analysis of the plant.Item Soft Sensors for Process Monitoring of Complex Processes(2012-10-19) Serpas, Mitchell RoySoft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies. First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions. Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC. Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.