On the Soundness of XAI in Prognostics and Health Management (PHM)
The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effective...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/5/256 |
| _version_ | 1797599774082334720 |
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| author | David Solís-Martín Juan Galán-Páez Joaquín Borrego-Díaz |
| author_facet | David Solís-Martín Juan Galán-Páez Joaquín Borrego-Díaz |
| author_sort | David Solís-Martín |
| collection | DOAJ |
| description | The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as remaining useful life (RUL). Deep learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that these kinds of black box models are opaque decision systems, and it may be hard to explain their outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This paper presents a critical and comparative revision on a number of explainable AI (XAI) methods applied on time series regression models for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. This study addresses three distinct RUL problems using three different datasets, each with its own unique context: gearbox, fast-charging batteries, and turbofan engine. Five XAI methods were reviewed and compared based on a set of nine metrics that quantify desirable properties for any XAI method. One of the metrics introduced in this study is a novel metric. The results show that Grad-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of image processing. |
| first_indexed | 2024-03-11T03:39:04Z |
| format | Article |
| id | doaj.art-ea874c38d012467f9b8700806c0b3ecb |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-11T03:39:04Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ea874c38d012467f9b8700806c0b3ecb2023-11-18T01:47:36ZengMDPI AGInformation2078-24892023-04-0114525610.3390/info14050256On the Soundness of XAI in Prognostics and Health Management (PHM)David Solís-Martín0Juan Galán-Páez1Joaquín Borrego-Díaz2Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, 41012 Sevilla, SpainDepartamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, 41012 Sevilla, SpainDepartamento de Ciencias de la Computación e Inteligencia Artificial, Universidad de Sevilla, 41012 Sevilla, SpainThe aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as remaining useful life (RUL). Deep learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that these kinds of black box models are opaque decision systems, and it may be hard to explain their outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This paper presents a critical and comparative revision on a number of explainable AI (XAI) methods applied on time series regression models for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. This study addresses three distinct RUL problems using three different datasets, each with its own unique context: gearbox, fast-charging batteries, and turbofan engine. Five XAI methods were reviewed and compared based on a set of nine metrics that quantify desirable properties for any XAI method. One of the metrics introduced in this study is a novel metric. The results show that Grad-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of image processing.https://www.mdpi.com/2078-2489/14/5/256XAIinterpretabilitypredictive maintenanceprognostics and health managementremaining useful lifedeep learning |
| spellingShingle | David Solís-Martín Juan Galán-Páez Joaquín Borrego-Díaz On the Soundness of XAI in Prognostics and Health Management (PHM) Information XAI interpretability predictive maintenance prognostics and health management remaining useful life deep learning |
| title | On the Soundness of XAI in Prognostics and Health Management (PHM) |
| title_full | On the Soundness of XAI in Prognostics and Health Management (PHM) |
| title_fullStr | On the Soundness of XAI in Prognostics and Health Management (PHM) |
| title_full_unstemmed | On the Soundness of XAI in Prognostics and Health Management (PHM) |
| title_short | On the Soundness of XAI in Prognostics and Health Management (PHM) |
| title_sort | on the soundness of xai in prognostics and health management phm |
| topic | XAI interpretability predictive maintenance prognostics and health management remaining useful life deep learning |
| url | https://www.mdpi.com/2078-2489/14/5/256 |
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