Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox

Heyns, Theo; Heyns, P.S.; Villiers, J. P. de

doi:10.1016/j.ymssp.2012.05.008

Cited by 45 publications

(42 citation statements)

References 14 publications

(17 reference statements)

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“…Discrepancy analysis is a powerful novelty detection technique that allows gearbox diagnostics to be performed under varying operating conditions [35][36][37][38][39]. In discrepancy analysis, a localised novelty score or discrepancy measure is assigned to the extracted localised features with a model optimised on the features of a healthy gearbox.…”

Section: Introductionmentioning

confidence: 99%

“…Different inputs to the model e.g. windowed vibration data [36], features extracted from the wavelet packet transform [39], features extracted from the continuous wavelet transform [37,38] as well as different models e.g. Gaussian mixture models [36], Gaussian models [39], hidden Markov models [38] can be used in the process.…”

Section: Introductionmentioning

confidence: 99%

“…windowed vibration data [36], features extracted from the wavelet packet transform [39], features extracted from the continuous wavelet transform [37,38] as well as different models e.g. Gaussian mixture models [36], Gaussian models [39], hidden Markov models [38] can be used in the process. The synchronous average of the discrepancy signal is a very efficient technique for gear diagnostics under varying operating conditions [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

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An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

Schmidt

Heyns

2019

Mechanical Systems and Signal Processing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

Schmidt

Heyns

2019

Mechanical Systems and Signal Processing

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“…For this reason several models of novelty detection have been proposed performing well on different types of data [14][15][16][17][18][19][20]. On the other hand it is clearly evident that there is no single best model for novelty detection and that the success depends not only on the type of the method used but also on the statistical properties of the data handled.…”

Section: Introductionmentioning

confidence: 99%

An on-line SAX and HMM-based anomaly detection and visualization tool for early disturbance discovery in a dynamic industrial process

Yiakopoulos

Gryllias

Chioua³

et al. 2016

Journal of Process Control

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a b s t r a c tIn order to achieve an optimum and successful operation of an industrial process, it is important firstly to detect upsets, equipment malfunctions or other abnormal events as early as possible and secondly to identify and remove the cause of those events. Univariate and multivariate statistical process control methods have been widely applied in process industries for early fault detection and localization.The primary objective of the proposed research is the design of an anomaly detection and visualization tool that is able to present to the shift operator -and to the various levels of plant operation and company management -an early, global, accurate and consolidated presentation of the operation of major subgroups or of the whole plant, aided by a graphical form.Piecewise Aggregate Approximation (PAA) and Symbolic Aggregate Approximation (SAX) are considered as two of the most popular representations for time series data mining, including clustering, classification, pattern discovery and visualization in time series datasets. However SAX is preferred since it is able to transform a time series into a set of discrete symbols, e.g. into alphabet letters, being thus far more appropriate for a graphical representation of the corresponding information, especially for the shift operator. The methods are applied on individual time records of each process variable, as well as on entire groups of time records of process variables in combination with Hidden Markov Models. In this way, the proposed visualization tool is not only associated with a process defect, but it allows also identifying which specific abnormal situation occurred and if this has also occurred in the past. Case studies based on the benchmark Tennessee Eastman process demonstrate the effectiveness of the proposed approach. The results indicate that the proposed visualization tool captures meaningful information hidden in the observations and shows superior monitoring performance.

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“…Specially, in [4], GMMs have been applied to the detection of 'novel' vibration signatures in gearboxes, with experimental results showing good fault detection properties with known classifications. In this paper, outlier components are considered as background 'noise' for the GMM in the final mixture model [5,6], i.e.…”

Section: Introductionmentioning

confidence: 99%

Novelty detection based on extensions of GMMs for industrial gas turbines

Bingham

Gallimore

Cox

2015

2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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Abstract-The paper applies the application of Gaussian mixture models (GMMs) for operational pattern discrimination and novelty/fault detection for an industrial gas turbine (IGT). Variational Bayesian GMM (VBGMM) is used to automatically cluster operational data into steady-state and transient responses, where extraction of steady-state data is an important preprocessing scenario for fault detection. Important features are extracted from steady-state data, which are then fingerprinted to show any anomalies of patterns which may be due to machine faults. Field data measurements from vibration sensors are used to show that the extensions of GMMs provide a useful tool for machine condition monitoring, fault detection and diagnostics in the field. Through the use of experimental trials on IGTs, it is shown that GMM is particularly useful for the detection of emerging faults especially where there is a lack of knowledge of machine fault patterns.

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Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox

Cited by 45 publications

References 14 publications

An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

An open set recognition methodology utilising discrepancy analysis for gear diagnostics under varying operating conditions

An on-line SAX and HMM-based anomaly detection and visualization tool for early disturbance discovery in a dynamic industrial process

Novelty detection based on extensions of GMMs for industrial gas turbines

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