CMAPSS Jet Engine Simulated Data

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 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) 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 1st International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.

本数据集由多组多变量时间序列构成。每组数据集进一步划分为训练子集与测试子集。每条时间序列均来自一台独立的发动机，换言之，该数据集可视为同型号发动机集群所产生的观测数据。每台发动机在初始阶段均存在不同程度的初始磨损与制造偏差，且此类信息对用户而言是未知的。此类磨损与偏差属于正常工况范畴，并非故障状态。数据集包含三类对发动机性能具有显著影响的运行参数设置，相关数据亦已纳入数据集内。此外，原始数据中混入了传感器噪声。
每条时间序列的初始阶段，发动机均处于正常运行状态，随后在运行过程中的某个时刻逐渐产生故障。在训练集中，故障的严重程度随时间不断加剧直至系统发生故障；而在测试集中，时间序列的采集节点位于系统发生故障前的某个时刻。本次竞赛的目标为预测测试集中各发动机在故障前剩余的运行循环数，即相较于测试集最后一个循环，发动机还可继续运行的循环次数。同时，数据集还提供了测试数据对应的真实剩余使用寿命（Remaining Useful Life, RUL）向量。
本数据集以压缩zip格式的文本文件形式提供，文件内含26列以空格分隔的数值数据。每一行代表单次运行循环下采集的观测快照，每一列则对应一个不同的变量。各列的含义依次为：1）机组编号；2）运行时间（单位：循环次数）；3）运行参数设置1；4）运行参数设置2；5）运行参数设置3；6）传感器测量值1；7）传感器测量值2……26）传感器测量值26。
数据集FD001：训练轨迹数：100；测试轨迹数：100；运行工况：1种（海平面工况）；故障模式：1种（高压压气机（High Pressure Compressor, HPC）退化）
数据集FD002：训练轨迹数：260；测试轨迹数：259；运行工况：6种；故障模式：1种（高压压气机退化）
数据集FD003：训练轨迹数：100；测试轨迹数：100；运行工况：1种（海平面工况）；故障模式：2种（高压压气机退化、风扇退化）
数据集FD004：训练轨迹数：248；测试轨迹数：249；运行工况：6种；故障模式：2种（高压压气机退化、风扇退化）
参考文献：A. Saxena、K. Goebel、D. Simon 与 N. Eklund，《用于航空发动机全生命周期失效模拟的损伤传播建模》，收录于第一届国际预测与健康管理会议（PHM08）论文集，美国科罗拉多州丹佛市，2008年10月。

搜集汇总

背景与挑战

背景概述

该数据集是一个多变量时间序列数据集，用于模拟喷气发动机的故障预测。数据包含训练集和测试集，记录了发动机在不同操作条件下的传感器测量值，目标是预测剩余使用寿命（RUL）。数据集分为四个子集（FD001-FD004），分别对应不同的操作条件和故障模式。