Data Set: FD001
Train trjectories: 100
Test trajectories: 100
Conditions: ONE (Sea Level)
Fault Modes: ONE (HPC Degradation)
Data Set: FD002
Train trjectories: 260
Test trajectories: 259
Conditions: SIX
Fault Modes: ONE (HPC Degradation)
Data Set: FD003
Train trjectories: 100
Test trajectories: 100
Conditions: ONE (Sea Level)
Fault Modes: TWO (HPC Degradation, Fan Degradation)
Data Set: FD004
Train trjectories: 248
Test trajectories: 249
Conditions: SIX
Fault Modes: TWO (HPC Degradation, Fan Degradation)
Experimental Scenario
Data sets consists of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different 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 is contaminated with sensor noise.
The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. The objective of the competition is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. Also provided a vector of true Remaining Useful Life (RUL) values for the test data.
The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle, each column is a different variable. The columns correspond to:
1) unit number
2) time, in cycles
3) operational setting 1
4) operational setting 2
5) operational setting 3
6) sensor measurement 1
7) sensor measurement 2
...
26) sensor measurement 26
Reference: A. Saxena, K. Goebel, D. Simon, and N. Eklund, �Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation�, in the Proceedings of the Ist International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.
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数据集C-MAPSS-Data.rar
共20个文件
txt:13个
m:5个
mat:1个
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【数据集】C-MAPSS-Data.rar (20个子文件)
RUL_FD002.txt 1KB
train_FD004.txt 9.87MB
test_FD004.txt 6.64MB
train_FD002.txt 8.66MB
MSE_RMSE_MBE_MAE.m 361B
mainPSOheuristic.m 2KB
RUL_FD001.txt 429B
R_2.m 317B
RUL_FD003.txt 428B
train_FD001.txt 3.35MB
main0.m 1KB
RUL_FD004.txt 1KB
结果.csv 45KB
maydata.mat 3.27MB
train_FD003.txt 4.02MB
test_FD002.txt 5.47MB
main2.m 3KB
test_FD001.txt 2.13MB
test_FD003.txt 2.7MB
readme.txt 2KB
共 20 条
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