Abrupt fault detection and isolation for gas turbine components based on a 1D convolutional neural network using time series data

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dc.contributor.authorZhao, Junjie
dc.contributor.authorLi, Yiguang
dc.date.accessioned2021-07-05T15:36:34Z
dc.date.available2021-07-05T15:36:34Z
dc.date.issued2020-08-17
dc.description.abstractThe FDI step identifies the presence of a fault, its level, type, and possible location. Gas turbine gas-path fault detection and isolation can improve the availability and economy of gas turbine components. Data-driven FDI methods are studied in this paper. Some notable gas turbine FDI challenges include: insensitivity to operating conditions, robust separation of faults, noisy sensor readings and missing data, reliable fault detection in time-varying conditions, and the influence of performance gradual deterioration. For conventional ML methods, the problem with handling time series data is its volume and the associated computational complexity; therefore, the available information must be appropriately compressed via the transformation of high-dimensional data into a low-dimensional feature space with minimal loss of class separability. In order to improve the detection and isolation sensitivity, this paper develops a method for FDI based on CNNs. Work in this paper includes: (1) Defining the problem and assembling a dataset. (2) Preparing data for training, validation and test: data generation, feature engineering, data pre-processing, data formatting. (3) Building up the model. (4) Training and validating the model (evaluation protocol). (5) Optimizing: a. deciding the model size. b. regularizing the model by getting more training data, reducing the capacity of the network, adding weight regularization or adding dropout. c. tuning hyperparameters. (6) Evaluation.en_UK
dc.identifier.citationZhao J, Li YG. (2020) Abrupt fault detection and isolation for gas turbine components based on a 1D convolutional neural network using time series data. In: AIAA Propulsion and Energy 2020 Forum, 24-28 August 2020, Virtual Eventen_UK
dc.identifier.urihttps://doi.org/10.2514/6.2020-3675
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16844
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFault Detection and Isolationen_UK
dc.subjectGas Turbine Enginesen_UK
dc.subjectBayesian Optimizationen_UK
dc.titleAbrupt fault detection and isolation for gas turbine components based on a 1D convolutional neural network using time series dataen_UK
dc.typeConference paperen_UK

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