Hardware Failure Prediction on Imbalanced Times Series Data: Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware.
Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represe...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 1; pp. 182 - 190 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148753845&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148753845 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2021 vid: 34 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148753845 147940994 148753845 148753845 10.1007/s10278-020-00411-4 148753845 ppf: 182 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Hardware Failure Prediction on Imbalanced Times Series Data: Generation of Artificial Data Using Gaussian Process and Applying LSTMFCN to Predict Broken Hardware. aug: au: Rücker, Nadine Pflüger, Lea Maier, Andreas affil: Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany sug: subj: Magnetic Resonance Imaging Computer Hardware Magnetic Fields Equipment Failure Memory Neural Networks (Computer) Machine Learning Statistics Human Superconductivity Equipment Maintenance Technology Descriptive Statistics Head Injuries Radiography Neck Injuries Radiography Diagnostic Imaging Equipment and Supplies Quality of Health Care Time Series ab: Magnetic resonance imaging (MRI) systems and their continuous, failure-free operation is crucial for high-quality diagnostics and seamless workflows. One important hardware component is coils as they detect the magnetic signal. Before every MRI scan, several image features are captured which represent the used coil's condition. These image features recorded over time are used to train machine learning models for classification of coils into normal and broken coils for faster and easier maintenance. The state-of-the-art techniques for classification of time series involve different kinds of neural networks. We leveraged sequential data and trained three models, long short-term memory (LSTM), fully convolutional network (FCN), and the combination of those called LSTMFCN as reported by Karim et al. (IEEE access 6:1662–1669, 2017). We found LSTMFCN to combine the benefits of LSTM and FCN. Thus, we achieved the highest F1-score of 87.45% and the highest accuracy of 99.35% using LSTMFCN. Furthermore, we tackled the high data imbalance of only 2.1% data collected from broken coils by training a Gaussian process (GP) regressor and adding predicted sequences as artificial samples to our broken labelled data. Adding 40 synthetic samples increased the classification results of LSTMFCN to an F1-score of 92.30% and accuracy of 99.83%. Thus, MRI head/neck coils can be classified normal or broken by training a LSTMFCN on image features, successfully. Augmenting the data using GP-generated samples can improve the performance even further. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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