Browsing by Subject "Extended Kalman filter"

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Fault detection and model-based diagnostics in nonlinear dynamic systems
(2010-12) Nakhaeinejad, Mohsen; Bryant, Michael D.; Driga, Mircea D.; Fahrenthold, Eric P.; Fernandez, Benito; Longoria, Raul G.
Modeling, fault assessment, and diagnostics of rolling element bearings and induction motors were studied. Dynamic model of rolling element bearings with faults were developed using vector bond graphs. The model incorporates gyroscopic and centrifugal effects, contact deflections and forces, contact slip and separations, and localized faults. Dents and pits on inner race, outer race and balls were modeled through surface profile changes. Experiments with healthy and faulty bearings validated the model. Bearing load zones under various radial loads and clearances were simulated. The model was used to study dynamics of faulty bearings. Effects of type, size and shape of faults on the vibration response and on dynamics of contacts in presence of localized faults were studied. A signal processing algorithm, called feature plot, based on variable window averaging and time feature extraction was proposed for diagnostics of rolling element bearings. Conducting experiments, faults such as dents, pits, and rough surfaces on inner race, balls, and outer race were detected and isolated using the feature plot technique. Time features such as shape factor, skewness, Kurtosis, peak value, crest factor, impulse factor and mean absolute deviation were used in feature plots. Performance of feature plots in bearing fault detection when finite numbers of samples are available was shown. Results suggest that the feature plot technique can detect and isolate localized faults and rough surface defects in rolling element bearings. The proposed diagnostic algorithm has the potential for other applications such as gearbox. A model-based diagnostic framework consisting of modeling, non-linear observability analysis, and parameter tuning was developed for three-phase induction motors. A bond graph model was developed and verified with experiments. Nonlinear observability based on Lie derivatives identified the most observable configuration of sensors and parameters. Continuous-discrete Extended Kalman Filter (EKF) technique was used for parameter tuning to detect stator and rotor faults, bearing friction, and mechanical loads from currents and speed signals. A dynamic process noise technique based on the validation index was implemented for EKF. Complex step Jacobian technique improved computational performance of EKF and observability analysis. Results suggest that motor faults, bearing rotational friction, and mechanical load of induction motors can be detected using model-based diagnostics as long as the configuration of sensors and parameters is observable.
Ground-based attitude determination and gyro calibration
(2012-08) Kim, Chang-Su, doctor of aerospace engineering; Schutz, Bob E.; Fowler, Wallace T.; Hull, David G.; Lightsey, E. Glen; Wilson, Clark R.
Some modern spacecraft missions require precise knowledge of the attitude, obtained from the ground processing of on-board attitude sensors. A traditional 6-state attitude determination filter, containing three attitude errors and three gyro bias errors, has been recognized for its robust performance when it is used with high quality measurement data from a star tracker for many past and present missions. However, as higher accuracies are required for attitude knowledge in the missions, systematic errors such as sensor misalignment and scale factor errors, which could often be neglected in previous missions, have become serious, and sometimes, the dominant error sources. The star tracker data have gaps and degradation caused by, for example, the Sun and Moon blocking in the filed of view and data time tag errors. Thus, attitude determination based on the gyro data without using the star tracker data is inevitably required for most missions for the period when the star tracker is unable to provide accurate data. However, any gyro-based attitude errors would eventually grow exponentially because of the uncorrected systematic errors of gyros and the uncorrected gyro random noises. An improved understanding of the gyro random noise characteristics and the estimation of the gyro scale factor errors and gyro misalignments are necessary for precise attitude determination for some present and future missions. The 6-state filters have been extended to 15-state filters to estimate the scale factor and misalignment errors of gyros especially during a high-slew maneuver and the performance of theses filters has been investigated. During a starless period, the inevitable drift of the EKF solutions, which are caused by the uncorrected gyro’s systematic errors and the gyro random noises, can be replaced with the batch solutions, which are less affected by the data gap in the star tracker. Power Spectral Density and the Allan Variance Method are used for analyzing the gyro random noises in both ICESat and simulated gyro data, which provide better information about the process noise covariance in the attitude filter. Both simulated and real data are used for analyzing and evaluating the performances of EKF and batch algorithms.
A method for parameter estimation and system identification for model based diagnostics
(2010-12) Rengarajan, Sankar Bharathi; Bryant, Michael David; Akella, Maruthi R.
Model based fault detection techniques utilize functional redundancies in the static and dynamic relationships among system inputs and outputs for fault detection and isolation. Analytical models based on the underlying physics of the system can capture the dependencies between different measured signals in terms of system states and parameters. These physical models of the system can be used as a tool to detect and isolate system faults. As a machine degrades, system outputs deviate from desired outputs, generating residuals defined by the error between sensor measurements and corresponding model simulated signals. These error residuals contain valuable information to interpret system states and parameters. Setting up the measurements from a faulty system as baseline, the parameters of the idealistic model can be varied to minimize these residuals. This process is called “Parameter Tuning”. A framework to automate this “Parameter Tuning” process is presented with a focus on DC motors and 3-phase induction motors. The parameter tuning module presented is a multi-tier module which is designed to operate on real system models that are highly non-linear. The tuning module combines artificial intelligence techniques like Quasi-Monte Carlo (QMC) sampling (Hammersley sequencing) and Genetic Algorithm (Non Dominated Sorting Genetic Algorithm) with an Extended Kalman filter (EKF), which utilizes the system dynamics information available via the physical models of the system. A tentative Graphical User Interface (GUI) was developed to simplify the interaction between a machine operator and the module. The tuning module was tested with real measurements from a DC motor. A simulation study was performed on a 3-phase induction motor by suitably adjusting parameters in an analytical model. The QMC sampling and genetic algorithm stages worked well even on measurement data with the system operating in steady state condition. But the downside was computational expense and inability to estimate the parameters online – ‘batch estimator’. The EKF module enabled online estimation where update was made based on incoming measurements. But observability of the system based on incoming measurements posed a major challenge while dealing with state estimation filters. Implementation details and results are included with plots comparing real and faulty systems.