A novel method based on health index similarity in multiple time scales with autoencoder (AE MTS-HI) was proposed aiming at the shortage of the traditional similarity-based method in extracting health index and similarity matching. Autoencoder was applied to construct the health index based on monitoring data, which can minimize the loss of nonlinear information. The health index in multiple time scales was developed for similarity matching by considering the fluctuation of the length of test degradation trajectories. The method can remove the accuracy limitation caused by fixed time scales and enhance the prediction robustness. Performance of the proposed method was evaluated on public turbofan engines datasets. Results demonstrate that the method can improve the remaining useful life (RUL) prediction accuracy and provide stable support for predictive maintenance.
Yu-hui XU,Jun-qing SHU,Ya SONG,Yu ZHENG,Tang-bin XIA. Remaining useful life prediction of turbofan engine based on similarity in multiple time scales. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1937-1947.
Fig.1Schematic diagram of condition monitoring parameters
Fig.2Structure of autoencoder
Fig.3Framework of AE MTS-HI
层
神经元数
激活函数
输入层
$ {d}_{\mathrm{i}\mathrm{n}} $
—
隐含层1
$ {d}_{\mathrm{i}\mathrm{n}}/2 $
ReLU函数
隐含层2
$ {d}_{\mathrm{o}\mathrm{u}\mathrm{t}} $
线性激活函数
隐含层3
$ {d}_{\mathrm{i}\mathrm{n}}/2 $
ReLU函数
输出层
$ {d}_{\mathrm{i}\mathrm{n}} $
tanh函数
Tab.1Parameters setting of autoencoder
Fig.4Schematic diagram of similarity matching and RUL prediction
数据集
样本个数
数据总量
最小运行周期数
最大运行周期数
训练集
100
20631
128
362
测试集
100
13096
31
303
Tab.2Detailed descriptions of FD001
Fig.5Distributions of running cycles in FD001
Fig.6Classification based on tendency of signals
分类
传感器编号
增长型
2，3，4，8，9，11，13，14，15，17
衰减型
7，12，20，21
固定型
1，5，6，10，16，18，19
Tab.3Classification of sensor signals
Fig.7Schematic diagram of RUL rectification
测试样本运行长度
测试样本数量
选用时间尺度
$30\leqslant {t}_{i} < 50$
7
30
$50\leqslant {t}_{i} < 70$
7
30,50
$70\leqslant {t}_{i}$
86
30,50,70
Tab.4Time scales for similarity matching
Fig.8Experimental results of parameter tuning of autoencoder
Fig.9Degradation model reference library
Fig.10RUL prediction results
方法
RMSE
Score
SVM[24]
29.822
—
CNN[9]
18.448
1 286.7
LSTM[10]
16.14
338
Similarity-based[16]
19.87
—
Similarity-based with SVR[25]
—
388
Similarity-based with KTST[15]
16.87
377.08
AE MTS-HI
14.07
291.67
Tab.5Comparison results with methods proposed by other researchers
降维方法
RMSE
Score
多元线性回归
16.32
486.50
等距特征映射
14.50
339.95
主成分分析
14.29
302.95
自编码器
14.07
291.67
Tab.6Comparison results with other dimensionality reduction methods
隐含层个数
隐含层神经元数
RMSE
1
1
14.31
3
4，1，4
14.17
3
7，1，7
14.07
3
10，1，10
14.12
5
10，6，1，6，10
14.13
5
7，7，1，7，7
14.09
Tab.7Comparison results with other autoencoder structures and parameters
样本长度
样本数量
Score
L=30
L=50
L=70
多时间尺度
$30\leqslant {t}_{i} < 50$
7
26.30
—
—
26.30
$50\leqslant {t}_{i} < 70$
7
8.41
8.86
—
6.56
$70\leqslant {t}_{i}$
86
337.70
276.23
284.35
258.81
所有样本
100
372.41
—
—
291.67
Tab.8Effects of multiple time scales on predicting results
Fig.11Effects of single time scale size on predicting results
样本编号
样本长度
RUL
e
L=30
L=50
L=70
多时间尺度
23
130
113
4.40
4.99
?2.77
2.21
60
147
100
4.81
1.61
?7.17
?0.25
61
159
21
2.32
5.88
4.69
4.30
76
205
10
0.12
?1.80
?1.83
?1.17
49
303
21
?2.36
?3.82
?4.08
?3.42
Tab.9Description and prediction error of typical instances
Fig.12Health indexes of medium-length instances
Fig.13Health indexes of end-of-life instances
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