Elliptical novelty grouping for on-line short-turn detection of excited running rotors

Guttormsson, S.E.; Marks, Robert J.; El-Sharkawi, M.A.; Kerszenbaum, Isidor

doi:10.1109/60.749142

Cited by 56 publications

(26 citation statements)

References 9 publications

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“…The objective is to learn a function f : X → Y that can label unlabeled instances. This general setting is applicable to different monitoring and event detection problems, such as the ones described in [23, 7, 9, 16]. …”

Section: Problem Definitionmentioning

confidence: 99%

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Mining recent temporal patterns for event detection in multivariate time series data

Batal

Fradkin

Harrison

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

133

101

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Improving the performance of classifiers using pattern mining techniques has been an active topic of data mining research. In this work we introduce the recent temporal pattern mining framework for finding predictive patterns for monitoring and event detection problems in complex multivariate time series data. This framework first converts time series into time-interval sequences of temporal abstractions. It then constructs more complex temporal patterns backwards in time using temporal operators. We apply our framework to health care data of 13,558 diabetic patients and show its benefits by efficiently finding useful patterns for detecting and diagnosing adverse medical conditions that are associated with diabetes.

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Section: Problem Definitionmentioning

confidence: 99%

“…Examples of such problems are the detection of adverse medical events (e.g. drug toxicity) in clinical data [10], detection of the equipment malfunction [9], fraud detection [23], environmental monitoring [16], intrusion detection [7] and others.…”

Section: Introductionmentioning

confidence: 99%

Mining recent temporal patterns for event detection in multivariate time series data

Batal

Fradkin

Harrison

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

133

101

View full text Add to dashboard Cite

show abstract

“…Methods to detect anomalies without using abnormal signals have also been proposed for various machines such as space crafts [4] [5], aircrafts [6], space shuttles [7] [8], bearings and couplings of rotating machines [9], and turbine rotors [10]. These methods learn rules that capture the normal behavior or a stochastic model of the normal signals.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of motors with feature emphasis using only normal sounds

Ono

Onishi

Koshinaka

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper proposes an anomaly detection method for sound signals observed from motors in operation without using abnormal signals. It is based on feature emphasis and effectively detects anomalies that appear in a small subset of features. To emphasize the features, the method optimally estimates the contribution rates of various features to the dissimilarity score between an observed signal and the distribution of normal signals. We report here our evaluation of the method using sound data observed from PCs and fans in operation. The evaluation demonstrates that the proposed method emphasizes a small subset of narrow frequency ranges of sounds and that it achieves an error reduction rate of up to 76%.

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“…The technique for the ISCFW detection of generators through the twin-signal sensing method is described in detail in [9]. The reflected signals can produce a signature signal that contains information about the rotor's state.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Amplitude and Frequency Characteristics of the Rotor Unbalanced Magnetic Pull of a Multi-Pole Synchronous Generator with Inter-Turn Short Circuit of Field Windings

Guangtao

2018

Energies

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Inter-turn short circuit of field windings (ISCFW) is a common generator fault which can result in serious safety accidents for power systems, if the fault is not eliminated in time. Taking advantage of the electrical and mechanical characteristics of the generator after a fault as a fault criterion is a new idea for fault monitoring, so finding out the frequency and amplitude frequency characteristics of rotor unbalanced magnetic pull (UMP)-the vibration excitation source of the fault-is the basis and key of the research. Taking a six-pole generator as an example, the effects of harmonic magnetic motive force (MMF) interaction on rotor UMP, as well as the frequency characteristics of rotor UMP after generator faults in different stator windings, are obtained based on the analysis of the air-gap MMF of the generator after a fault, and the results of theoretical analysis are verified by simulation. Based on the above results, the simulation calculation on rotor UMP of generators with three stator winding forms under different operating conditions has been achieved, to get the relation between rotor UMP amplitude and active power and field current, and to find out the mechanism of rotor UMP amplitude change along with operating conditions and effect of stator winding forms on UMP amplitude by theoretical analysis. The conclusions are of important significance for studying fault mechanical characteristics of generators and lay a foundation for online monitoring on ISCFW by integrating mechanical and electrical information.

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Elliptical novelty grouping for on-line short-turn detection of excited running rotors

Cited by 56 publications

References 9 publications

Mining recent temporal patterns for event detection in multivariate time series data

Mining recent temporal patterns for event detection in multivariate time series data

Anomaly detection of motors with feature emphasis using only normal sounds

Analysis on the Amplitude and Frequency Characteristics of the Rotor Unbalanced Magnetic Pull of a Multi-Pole Synchronous Generator with Inter-Turn Short Circuit of Field Windings

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