Steady-state and transient operation discrimination by Variational Bayesian Gaussian Mixture Models

Bingham, C.M.; Gallimore, Michael

doi:10.1109/mlsp.2013.6661970

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…It is well known that gradual bearing wear leading to failure is often preceded by gradual changes in vibration characteristics. Figure 8 provides an ellipse boundary drawn according to the 1-cluster GMM model (see (13)). In general, the confidence level to identify outliers will be set according to application.…”

Section: Case Study 1: Operation State Discrimination Featurementioning

confidence: 99%

“…For instance, [11] employed ANNs for fault detection on gas turbines during the engine start-up phase, whilst [12] only considers solutions during steady-state operation. Typically, such techniques do not attempt to address the issue by incorporating implicit methods that discriminate between steady-state and transient behaviour as part of the fault detection system, and it is this aspect that is initially considered here [13].…”

Section: Introductionmentioning

confidence: 99%

“…Here then, it is initially shown that GMMs provide a convenient mechanism to effectively discriminate between data relating to steady-state operation and that relating operation with transients and are specifically regarded in this instance as a preprocessing tool for subsequent operational pattern discrimination [13]. An essential part of developing the GMM methodology is parameter fitting, which is often carried out using an expectation-maximization (EM) method [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Zhang

Bingham

Martínez-García

et al. 2017

International Journal of Rotating Machinery

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This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.

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Section: Case Study 1: Operation State Discrimination Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Zhang

Bingham

Martínez-García

et al. 2017

International Journal of Rotating Machinery

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“…In this sense, Bayesian method is a popular solution, when one aims to "take the human out of the loop" [13]. For example, a variational Bayesian Gaussian mixture model (VBGMM) has been applied to discriminate the operational data of IGTs into steady-state and transient responses automatically [14].…”

Section: Introductionmentioning

confidence: 99%

Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling

Zhang

Martínez-García

Latimer

2017

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational Bayesian Gaussian mixture model (VBGMM) techniques. A data-driven compressor model is built using ANFIS, and VBGMM is applied in the setup stage to automatically select the number of input membership functions in the fuzzy system. The efficacy of the proposed BFNM approach is established through experimental trials of a sub-15MW gas turbine, and the results, from the model that is built using test bed data, are shown to be promising for estimating the compressor discharge temperatures on the gas turbine during commissioning.

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“…To address this issue, [3] has proposed an automatic on-line system to distinguish the input daily batch data to two categories-steady-state operation and operation with transients-in order to accomplish the pre-processing scenario of operational pattern discrimination, through the use of Gaussian mixture models (GMMs), especially variational Bayesian GMM (VBGMM) in this case.…”

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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Steady-state and transient operation discrimination by Variational Bayesian Gaussian Mixture Models

Cited by 4 publications

References 11 publications

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling

Novelty detection based on extensions of GMMs for industrial gas turbines

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