Image reconstruction for magnetic resonance spectroscopic imaging (MRSI) requires specialized spatial and spectral data processing methods and benefits from the use of several sources of prior information that are not commonly available, including MRI-derived tissue segmentation, morphological analysis and spectral characteristics of the observed metabolites. In addition, incorporating information obtained from MRI data can enhance the display of low-resolution metabolite images and multiparametric and regional statistical analysis methods can improve detection of altered metabolite distributions. As a result, full MRSI processing and analysis can involve multiple processing steps and several different data types. In this paper, a processing environment is described that integrates and automates these data processing and analysis functions for imaging of proton metabolite distributions in the normal human brain. The capabilities include normalization of metabolite signal intensities and transformation into a common spatial reference frame, thereby allowing the formation of a database of MR-measured human metabolite values as a function of acquisition, spatial and subject parameters. This development is carried out under the MIDAS project (Metabolite Imaging and Data Analysis System), which provides an integrated set of MRI and MRSI processing functions. It is anticipated that further development and distribution of these capabilities will facilitate more widespread use of MRSI for diagnostic imaging, encourage the development of standardized MRSI acquisition, processing and analysis methods and enable improved mapping of metabolite distributions in the human brain.

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This paper proposes an approach to detecting bearing faults in electromechanical actuators (EMAs) using features extracted from experimental data. The method of feature extraction proposed uses established parameter estimation techniques based on system identification followed by an orthogonal transformation of estimated parameters to derive the required features. A Bayesian classifier is then used to create health classes from the extracted features. The performance of the approach is tested using both data obtained from simulations of bearing faults in a permanent magnet DC motor system as well as data recorded from a Moog MaxForce EMA. The approach shows a misclassification performance of 10% when tested with 50 different data sets generated via simulations. Marginally inferior performance is observed when using 40 different data sets collected from the Moog MaxForce EMA. The conclusion is that bearing fault detection in EMAs is possible via the proposed approach, although further refinements are required. Nomenclature f cBearing fault frequency reflected in stator current y(t), u(t) Generic discrete-time output and input data e(t) Generic discrete-time white-noise process/Equation-error q −1 , φ Backward shift operator and regression vector v m , i m , ω m Armature voltage, current and angular speed L, T ex Armature inductance and external torque load J, BNet system inertia and net viscous friction coefficient K e , K t