TY - JOUR

T1 - Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines

AU - Mao, Wentao

AU - Zhang, Wen

AU - Feng, Ke

AU - Beer, Michael

AU - Yang, Chunsheng

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China [http://dx.doi.org/10.13039/501100001809] [Grant No. U1704158 , 61963026 , and 61963026 ], the the Key Technologies Research Development Joint Foundation of Henan Province [Grant No. 225101610001 ].

PY - 2024/2

Y1 - 2024/2

N2 - In recent years, deep transfer learning techniques have been successfully applied to solve RUL prediction across different working conditions. However, for RUL prediction across different machines in which the data distribution and fault evolution characteristics vary largely, the extraction and transition of prognostic knowledge become more challenging. Even if fault mode information can assist in the knowledge transfer, model bias will inevitably exist on the target machine with mixed or unknown faults. To address this issue from a transferability perspective, this paper proposes a novel selective transfer learning approach for RUL prediction across machines. First, the paper utilizes the tensor representation to construct the meta-degradation trend of each fault mode and evaluates the transferability of source domain data from fault mode and degradation characteristics through a new cross-machine transfer degree indicator (M-TDI). Second, a Long Short-Term Memory (LSTM)-based selective transfer strategy is proposed using the M-TDIs. The paper designs a training algorithm with an alternating optimization scheme to seek the optimal tensor decomposition and knowledge transfer effect. Theoretical analysis proves that the proposed approach significantly reduces the upper bound of prediction error. Furthermore, experimental results on three benchmark datasets prove the effectiveness of the proposed approach.

AB - In recent years, deep transfer learning techniques have been successfully applied to solve RUL prediction across different working conditions. However, for RUL prediction across different machines in which the data distribution and fault evolution characteristics vary largely, the extraction and transition of prognostic knowledge become more challenging. Even if fault mode information can assist in the knowledge transfer, model bias will inevitably exist on the target machine with mixed or unknown faults. To address this issue from a transferability perspective, this paper proposes a novel selective transfer learning approach for RUL prediction across machines. First, the paper utilizes the tensor representation to construct the meta-degradation trend of each fault mode and evaluates the transferability of source domain data from fault mode and degradation characteristics through a new cross-machine transfer degree indicator (M-TDI). Second, a Long Short-Term Memory (LSTM)-based selective transfer strategy is proposed using the M-TDIs. The paper designs a training algorithm with an alternating optimization scheme to seek the optimal tensor decomposition and knowledge transfer effect. Theoretical analysis proves that the proposed approach significantly reduces the upper bound of prediction error. Furthermore, experimental results on three benchmark datasets prove the effectiveness of the proposed approach.

KW - LSTM

KW - Remaining useful life prediction

KW - Tensor decomposition

KW - Transfer learning

KW - Transferability analytics

UR - http://www.scopus.com/inward/record.url?scp=85174835469&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2023.109695

DO - 10.1016/j.ress.2023.109695

M3 - Article

AN - SCOPUS:85174835469

VL - 242

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

M1 - 109695

ER -