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.
没有合适的资源?快使用搜索试试~ 我知道了~
LSTM代码预测固体颗粒浓度
共30个文件
txt:16个
py:6个
sac:1个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
0 下载量 178 浏览量
2023-04-15
22:41:09
上传
评论
收藏 57.12MB ZIP 举报
温馨提示
附件中是两个文件,压缩包是昨天今天测的数据,190组。每组五列数据,从左向右依次为:温度、压差、湿度、上游静电传感器电压、下游静电传感器电压。 另一个excel文档是对记录数据各种参数的记录。比如第一组数据 2022-3-19 13-50-24-原始 风速、给料(频率,Hz)、温度、湿度、采样频率。 有的数据测了两遍,比如 第三组的 2022-3-19 14-45-29-原始 和 2022-3-20 10-49-23-原始 这种情况建议以后一组数据为准。 这次我只改变了风速和给料。其他的没有变。 可做的事情: ①按照上次那种相关法求每组数据第四列和第五列数据相关性,从而算出两组数据最大相关点间隔的采样点数,以此可以评估颗粒的速度 ②上述附件中那个excel 中右边有个给料频率(Hz)和给料速度(克/秒)对照表。也就是说压缩包中每组数据的“给料”就是这里的给料频率,每个给料频率(Hz)与给料速度(克/秒)相对应。可以计算给料频率或给料速度与风速的比值(这个比值与流体中颗粒的浓度成正比)。 ③可以寻找 浓度、颗粒速度、压差这三个量与上游静电传感器电压的
资源推荐
资源详情
资源评论
收起资源包目录
LSTM代码及数据集.zip (30个子文件)
Preprocess2.R 6KB
corraltion
test2.sac 5KB
test1.ascii 8KB
cmpcor.py 4KB
cmpcor.png 260KB
xiaobo.py 2KB
W_N_A_DCNN.py 3KB
corraltion.zip 387KB
互相关.docx 173KB
ECAnet_1D.py 2KB
CMAPSSData
RUL_FD002.txt 1KB
train_FD004.txt 9.87MB
test_FD004.txt 6.64MB
train_FD002.txt 8.66MB
CMAPSS.xlsx 44.76MB
RUL_FD001.txt 429B
train_FD004_unit243.txt 54KB
Damage Propagation Modeling.pdf 424KB
RUL_FD003.txt 428B
train_FD001.txt 3.35MB
RUL_FD004.txt 1KB
train_FD001_unit2-3.txt 78KB
train_FD003.txt 4.02MB
train_FD001_unit2.txt 48KB
test_FD002.txt 5.47MB
test_FD001.txt 2.13MB
data_preprocess.py 4KB
test_FD003.txt 2.7MB
readme.txt 2KB
W_N_A.py 12KB
共 30 条
- 1
资源评论
AI信仰者
- 粉丝: 1w+
- 资源: 143
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功