James R. Ottewill

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Variable speed drives are becoming increasingly popular as the source of power for three-phase induction motors. In addition to providing significant energy savings over Direct-On-Line arrangements, drives measure and compute many signals (e.g., electrical currents, estimated speed, torque), which can be used to detect various conditions in the motor or(More)
In this paper a graphical approach for Condition Monitoring Systems (CM) based on Model Driven Architecture is presented; in particular, the software application “Smart Monitoring Agent” or SMA. This graphical approach starts from the idea of modeling, applying and visualizing non-hierarchical relations. The presented method makes use of(More)
This paper presents a method to detect transient disturbances in a multivariate context, and an extension of that method to handle multi-rate systems. Both methods are based on a time series analysis technique known as nearest neighbors, and on multivariate statistics implemented as a singular value decomposition. The motivation for these developments is(More)
The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition(More)
In this paper, a technique of merging typical process data with variables containing fast periodic oscillations is proposed for the purpose of detecting faults in industrial systems working under variable operating conditions. Analysing windows of the fast-oscillating signals allowed key features to be extracted from the data at the same rate at which the(More)
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