Alok A. Joshi

Modeling and Multivariable Control Design Methodologies for Hexapod-Based Satellite Vibration Isolation

A mathematical model of a six-degree-of-freedom (6-DOF) hexapod system for vibration isolation was derived in the discrete-time domain on the basis of the experimental data obtained from a satellite. Using a Box–Jenkins model structure, the transfer functions between six piezoelectric actuator input voltages and six geophone sensor output voltages were identified empirically. The 6×6 transfer function matrix is symmetric, and its off-diagonal terms indicate the coupling among different input/output channels. Various multi-input multi-output (MIMO) control techniques such as Linear Quadratic Gaussian and H∞ were proposed for active vibration isolation in the broadband up to 100 Hz. The simulation results using these controllers obtain 13 and 8 dB vibration attenuation at 25 and 35 Hz, respectively.

Assessment of Charge-Air Cooler Health in Diesel Engines Using Nonlinear Time Series Analysis of Intake Manifold Temperature

Degradation in the cooling effectiveness of a charge-air cooler (CAC) in a medium-duty turbocharged diesel engine has significant impact on engine performance. This degradation lowers the boost pressure and raises the intake manifold temperature. As a result, the engine provides lower horsepower and higher hydrocarbon levels than the rated values. The objective of this research is to monitor the health of the charge-air cooler by analyzing the intake manifold temperature signal. Experiments were performed on a Cummins ISB series turbocharged diesel engine, a 6-cylinder inline configuration with a 5.9 l displacement volume. Air flowing over the cooler was blocked by varying amounts, while various engine temperatures and pressures were monitored at different torque-speed conditions. Similarly, data were acquired without the introduction of any fault in the engine. For the construction of the manifold temperature trajectory vector, average mutual information estimates and a global false nearest neighbor analysis were used to find the optimal time parameter and embedding dimensions, respectively. The prediction of the healthy temperature vector was done by local linear regression using torque, speed, and their interaction as exogenous variables. Analysis of residuals generated by comparing the predicted healthy temperature vector and the observed temperature vector was successful in detecting the degradation of the charge-air cooler. This degradation was quantified by using box plots and probability density functions of residuals generated by comparing intake manifold temperature of healthy and faulty charge-air coolers. The general applicability of the model was demonstrated by successfully diagnosing a fault in the exhaust gas recirculation cooler of a different engine.

Information theoretic fault detection

In this paper we propose a novel method of fault detection based on a clustering algorithm developed in the information theoretic framework. A mathematical formulation for a multi-input multi-output (MIMO) system is developed to identify the most informative signals for the fault detection using mutual information (MI) as the measure of correlation among various measurements on the system. This is a model-independent approach for the fault detection. The effectiveness of the proposed method is successfully demonstrated by employing MI-based algorithm to isolate various faults in 16-cylinder diesel engine in the form of distinct clusters.

Information-Theoretic Feature Selection for Classification

Feature selection has always been an important aspect of statistical model identification and pattern classification. In this paper we introduce a novel information-theoretic index called the compensated quality factor (CQF) which selects the important features from a large amount of irrelevant data. The proposed index does an exhaustive combinatorial search of the input space and selects the feature that maximizes the information criterion conditioned on the decision rules defined by the compensated quality factor. The effectiveness of the proposed CQF-based algorithm was tested against the results of mallows Cp criterion, Akaike information criterion (AIC), and Bayesian information criterion (BIC) using post liver operation survival data [Neter, J., et al., 1996] (continuous variables) and NIST sonoluminescent light intensity data [Wilcox, E., et al., 1999] (categorical variables). Due to computational time and memory constraints, the CQF-based feature selector is only recommended for an input space with dimension p < 20. The problem of higher dimensional input spaces (20 < p < 50) was solved by proposing an information-theoretic stepwise selection procedure. Though this procedure does not guarantee a globally optimal solution, the computational time- memory requirements are reduced drastically compared to the exhaustive combinatorial search. Using diesel engine data for fault detection (43 variables, 8-classes, 30000 records), the performance of the information-theoretic selection technique was tested by comparing the misclassification rates before and after the dimension reduction using various classifiers.

Diagnosis of clogged charge air cooler faults in a diesel engine using singular spectrum analysis

Abstract Charge air cooler faults in diesel engines are diagnosed following OBD standards. One charge air cooler fault occurs when debris blocks the charge air cooler. This leads to poor engine performance and emissions violations. An experiment has been designed to collect sensor data from a Cummins ISB5.9 liter diesel engine experiencing a charge air cooler fault. A symptom of the fault has been identified by analyzing the dynamics of the sensor signals using a technique called singular spectrum analysis. The resulting diagnostic strategy makes use of the oil pressure signal to generate symptoms of the charge air cooler fault.

A comprehensive physics-based model for medium-duty diesel engine with exhaust gas recirculation

Physics-based models of diesel engines with exhaust gas recirculation and a variable geometry turbine (EGR/VGT) have been developed extensively in the control system design community. However, these models omit the heat transfer effects of the charge-air cooler and the recirculated exhaust gas cooler in order to avoid the added complexity in model order for online implementation. Generally, there is no need to include these effects if the purpose of the model is to control the target variables, such as boost pressure and air-to-fuel ratio. In this paper, after surveying the existing state of physics-based models for the EGR/VGT subsystem, a comprehensive model of the EGR/VGT subsystem is developed. This model includes heat transfer effects in the coolers, pressure drops across air filters and pipes, and mass flow rate calculations for a variable geometry turbine and an exhaust gas recirculation control valve. The purpose and scope of this work is offline modeling-for-diagnostics. Such models, though complex, will assist in the fault sensitivity analysis of a subsystem while avoiding any destructive testing when a major design modification in the EGR/VGT subsystem is proposed. For example, the impact of charge-water or EGR cooler degradation on the boost pressure and the air-to-fuel ratio can be studied with such models to further help in designing diagnostic reasoning strategies. Simulation performed using the proposed physicsbased model demonstrates a dominant failure effect of an EGR cooler coolant leak over a charge-water cooler water leak on the properties of the intake air.Copyright

Data-Dimensionality Reduction Using Information-Theoretic Stepwise Feature Selector

A novel information-theoretic stepwise feature selector (ITSFS) is designed to reduce the dimension of diesel engine data. This data consist of 43 sensor measurements acquired from diesel engines that are either in a healthy state or in one of seven different fault states. Using ITSFS, the minimum number of sensors from a pool of 43 sensors is selected so that eight states of the engine can be classified with reasonable accuracy. Various classifiers are trained and tested for fault classification accuracy using the field data before and after dimension reduction by ITSFS. The process of dimension reduction and classification is repeated using other existing dimension reduction techniques such as simulated annealing and regression subset selection. The classification accuracies from these techniques are compared with those obtained by data reduced by the proposed feature selector.

Information-Theoretic Sensor Subset Selection: Application to Signal-Based Fault Isolation in Diesel Engines

In this paper a stepwise information-theoretic feature selector is designed and implemented to reduce the dimension of a data set without losing pertinent information. The effectiveness of the proposed feature selector is demonstrated by selecting features from forty three variables monitored on a set of heavy duty diesel engines and then using this feature space for classification of faults in these engines. Using a cross-validation technique, the effects of various classification methods (linear regression, quadratic discriminants, probabilistic neural networks, and support vector machines) and feature selection methods (regression subset selection, RV-based selection by simulated annealing, and information-theoretic selection) are compared based on the percentage misclassification. The information-theoretic feature selector combined with the probabilistic neural network achieved an average classification accuracy of 90%, which was the best performance of any combination of classifiers and feature selectors under consideration.Copyright

Multivariable Control for Hexapod-Based Satellite Vibration Isolation

Abstract A mathematical model of a six-degree-of-freedom hexapod system for vibration isolation was derived in the discrete-time domain on the basis of the experimental data obtained from a satellite. Using Box-Jenkins model structure, the transfer functions between six piezoelectric actuator input voltages and six geophone sensor output voltages were identified empirically. The 6×6 transfer function matrix is symmetric, and its off-diagonal terms indicate the coupling among different input/output channels. Using multi-input multi-output control techniques such as Linear Quadratic Gaussian and H , high-order controllers were developed. The simulation results using these controllers obtain 37 dB and 15 dB vibration attenuation at 2.5 and 20 Hz frequencies, respectively.

System Identification and Multivariate Controller Design for a Satellite Ultraquiet Isolation Technology Experiment (SUITE)

A mathematical model of a six-degree-of-freedom hexapod system for vibration isolation was derived in the discrete-time domain on the basis of the experimental data obtained from a satellite. Using Box-Jenkins model structure, the transfer functions between six piezoelectric actuator input voltages and six geophone sensor output voltages are identified empirically. The 6×6 transfer function matrix is symmetric, and its off-diagonal terms indicate the coupling among different input/output channels. Though the coupling was observed among various input/output channels up to 10 Hz, the single-input single-output (SISO) controllers were designed neglecting the effect of coupling. The SISO controllers demonstrated limited performance in vibration attenuation. Using multi-input multi-output (MIMO) control techniques such as Linear Quadratic Gaussian (LQG) and H∞ , high-order controllers were developed. The simulation results using these controllers obtain 33 dB, and 12 dB attenuation at 5, and 25 Hz corner frequencies, respectively.© 2002 ASME