Return to search results
PHM 2008 Challenge
This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008.
Data sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"026:00"
]
|
| contactPoint |
{
"fn": "undefined",
"@type": "vcard:Contact",
"hasEmail": "mailto:sdps@oceancolor.gsfc.nasa.gov"
}
|
| description | This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008. Data sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "View this dataset's algorithm theoretical basis document",
"format": "HTML",
"mediaType": "text/html",
"description": "NASA Ocean Color Web - Algorithm Description Documentation",
"downloadURL": "https://oceancolor.gsfc.nasa.gov/resources/atbd/"
},
{
"@type": "dcat:Distribution",
"title": "View this dataset's data citation policy",
"format": "HTML",
"mediaType": "text/html",
"description": "NASA Ocean Color Web - Data Citation Policy",
"downloadURL": "https://oceancolor.gsfc.nasa.gov/resources/how-to-cite/"
},
{
"@type": "dcat:Distribution",
"title": "View this dataset's processing history",
"format": "HTML",
"mediaType": "text/html",
"description": "NASA Ocean Color Web - Processing History",
"downloadURL": "https://oceancolor.gsfc.nasa.gov/data/reprocessing/"
},
{
"@type": "dcat:Distribution",
"title": "This dataset's landing page",
"format": "HTML",
"mediaType": "text/html",
"description": "OLCI-Sentinel-3B 3B Inland Waters (ILW) Dataset Landing Page",
"downloadURL": "https://oceancolor.gsfc.nasa.gov/data/10.5067/S3B/OLCI/L3B/ILW/4"
},
{
"@type": "dcat:Distribution",
"title": "Google Scholar search results",
"format": "HTML",
"mediaType": "text/html",
"description": "Search results for publications that cite this dataset by its DOI.",
"downloadURL": "https://scholar.google.com/scholar?q=10.5067%2FS3B%2FOLCI%2FL3B%2FILW%2F4"
}
]
|
| identifier | https://data.nasa.gov/api/views/nk8v-ckry |
| issued | 2023-02-16 |
| keyword |
[
"degradation",
"phm",
"prognostics"
]
|
| landingPage | https://data.nasa.gov/dataset/phm-2008-challenge |
| license | https://www.usa.gov/government-works |
| modified | 2025-05-29 |
| programCode |
[
"026:001"
]
|
| publisher |
{
"name": "PCoE",
"@type": "org:Organization"
}
|
| theme |
[
"Raw Data"
]
|
| title | PHM 2008 Challenge |
