Fault Detection for Systems with Multiple Unknown Modes and Similar Units

This dissertation considers fault detection for large-scale practical systems with many nearly identical units operating in a shared environment. A special class of hybrid system model is introduced to describe such multi-unit systems, and a general approach for estimation and change detection is proposed. A novel fault detection algorithm is developed based on estimating a common Gaussian-mixture distribution for unit parameters whereby observations are mapped into a common parameter-space and clusters are then identified corresponding to different modes of operation via the Expectation- Maximization algorithm. The estimated common distribution incorporates and generalizes information from all units and is utilized for fault detection in each individual unit. The proposed algorithm takes into account unit mode switching, parameter drift, and can handle sudden, incipient, and preexisting faults. It can be applied to fault detection in various industrial, chemical, or manufacturing processes, sensor networks, and others. Several illustrative examples are presented, and a discussion on the pros and cons of the proposed methodology is provided. The proposed algorithm is applied specifically to fault detection in Heating Ventilation and Air Conditioning (HVAC) systems. Reliable and timely fault detection is a significant (and still open) practical problem in the HVAC industry { commercial buildings waste an estimated 15% to 30% ($20.8B -$41.61B annually) of their energy due to degraded, improperly controlled, or poorly maintained equipment. Results are presented from an extensive performance study based on both Monte Carlo simulations as well as real data collected from three operational large HVAC systems. The results demonstrate the capabilities of the new methodology in a more realistic setting and provide insights that can facilitate the design and implementation of practical fault detection for systems of similar type in other industrial applications

Determination of Head Kinematics from Impact Acceleration Test Data Using Neural Networks

This paper presents a study of feed-forward neural network (NN) systems developed to determine the head kinematics of subjects who are exposed to impact accelerations. The neural networks process accelerometer data collected during short-duration impact acceleration tests conducted at the National Biodynamcis Laboratory of the University of New Orleans. During an impact acceleration experiment, the subject sits on the sled chair and a piston gives impetus to the sled to travel down a track. Head data is gathered by an array of nine accelerometers. Two more accelerometers are mounted on the sled. The neural processing systems produce the history of the rotational and translational position, velocity, and acceleration of the origin of the accelerometer array mounted on the mouth. Output produced by a least squares algorithm that uses both photographic and accelerometer raw data are used as a baseline and to provide training data for the neural networks. The main disadvantages of the NNs are their speed, and that statistical information and accurate modeling of the testing system are not required. Results show that the neural networks provide accurate information about the kinematics of the subject even when no photographic data are used

Determination of Head Kinematics from Impact Acceleration Test Data Using Neural Networks

This paper presents a study of feed-forward neural network (NN) systems developed to determine the head kinematics of subjects who are exposed to impact accelerations. The neural networks process accelerometer data collected during short-duration impact acceleration tests conducted at the National Biodynamcis Laboratory of the University of New Orleans. During an impact acceleration experiment, the subject sits on the sled chair and a piston gives impetus to the sled to travel down a track. Head data is gathered by an array of nine accelerometers. Two more accelerometers are mounted on the sled. The neural processing systems produce the history of the rotational and translational position, velocity, and acceleration of the origin of the accelerometer array mounted on the mouth. Output produced by a least squares algorithm that uses both photographic and accelerometer raw data are used as a baseline and to provide training data for the neural networks. The main disadvantages of the NNs are their speed, and that statistical information and accurate modeling of the testing system are not required. Results show that the neural networks provide accurate information about the kinematics of the subject even when no photographic data are used

A Comparison Between Linear Quadratic Control and Sliding Mode Control - Paper Source Missing / PDF Mathematics Font Missing

Sliding mode control is quickly becoming a popular research field due to several favorable qualities, including robustness. Linear quadratic control has been one of the more popular and more traditional control techniques, partially due to the ease of implementation and its optimality quality.\ud \ud This paper does not display correctly due to the lack of one of the fonts used by the Latex-to-PDF compiler (and the fact that the original source of the document is no longer available to recompile). This paper should have been removed from CiteSeerX, but there is no simple way to do so

Development of a hybrid estimation toolbox for rapid prototyping

The authors have developed a toolbox for hybrid estimator evaluation which allows rapid comparison of algorithms in different scenarios. The toolbox is flexible in implementing, simulating, and evaluating various algorithms, particularly those for hybrid estimation - state estimation under parametrical and/or structural uncertainties. While the toolbox is extensible, numerous models, filters, estimators, and error measures are provided by default. In this paper, examples are given of short programs written in Matlab that illustrate some of the benefits that such a toolbox can bring to researchers

Performance Comparison of Target Maneuver Onset Detection Algorithms

This paper compares six di#erent algorithms for target maneuver detection in a number of typical maneuvering target tracking scenarios. Measurement residual based chi-square test, input estimate based chi-square test, input estimate based significance test, generalized likelihood ratio, cumulative sum, and marginalized likelihood ratio detectors are examined. Maneuver onset detection times and ROC curves are presented and performance measures are discussed through simulations. Further, the e#ect of di#erent window sizes on detection performance is evaluated

Distributed Implementations of Particle Filters

Particle filtering has a great potential for solving highly nonlinear and non-Gaussian estimation problems, generally intractable within a standard linear Kalman filtering based framework. However, the implementation of particle filters (PFs) is rather computationally involved, which nowadays prevents them from practical real-world application. A natural idea to make PFs feasible for "real-time" data processing is to implement them on distributed multiprocessor computer systems. This paper presents three schemes for distributing the computations of generic particle filters, including resampling and, optionally, a Metropolis-Hastings (MH) step. Simulation results based on a maneuvering target tracking scenario show that distributed implementations can provide a promising solution to the steep computational burden incurred when using a large number of particles

Expected-Mode Augmentation Algorithms for Variable-Structure Multiple-Model Estimation

This paper presents a new class of variable-structure algorithms, referred to as expected-mode augmentation (EMA), for multiple-model estimation. In this approach, the original model set is augmented by a variable set of models intended to match the expected value of the unknown true mode. These models are generated adaptively in real time as (globally or locally) probabilistically weighted sums of modal states over the model set. This makes it possible to cover a large continuous mode space by a relatively small number of models at a given accuracy level. Performance of the proposed EMA algorithms is evaluated via a simulated example of a maneuvering target tracking problem

Temporal Difference Learning

Reinforcement learning, in general, has not been totally successful at solving complex realworld problems which can be described by nonlinear functions. However, temporal difference learning is a type of reinforcement learning algorithm that has been researched and applied to various prediction problems with promising results. This paper discusses the application of temporal-difference learning in the training of a neural network to play a scaled-down version of the board game Chinese Chess. Preliminary results show that this technique is favorable for producing desired results. In test cases where minimal factors of the game are presented, the network responds favorably. However, when introducing more complexity, the network does not function as well, but generally produces reasonable results. These results indicate that temporal difference learning has the potential to solve real-world problems of equal or greater complexity. Continuing research in the application of neural networks to complex strategic games will most likely lead to more responsive and accurate systems in the future