A multi-head neural network with unsymmetrical constraints for remaining useful life prediction. (October 2021)

• Record Type: Journal Article
• Authors: Liu, Zhenyu; Liu, Hui; Jia, Weiqiang; Zhang, Donghao; Tan, Jianrong
• Is Part Of: Advanced engineering informatics. Volume 50(2021)
• Publication Date: 2021-10
• Subjects: Remaining useful life -- Unsymmetrical constraints -- Multi-head neural network -- Deep learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101396
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: This paper proposes a multi-head neural network (MHNN) model with unsymmetrical constraints for remaining useful life (RUL) prediction of industrial equipment. Generally, the existing deep learning methods proposed for RUL prediction utilize symmetrical constraint loss functions such as the mean squared error function to calculate training errors. However, if the predicted RUL is much larger than the actual value in some safety–critical applications, severe damage may occur. To address this issue, an unsymmetrical constraint function is proposed as the loss function in this work that penalizes the late predictions (i.e., the predicted RUL is larger than the actual RUL) more strongly. In addition, an adjustable parameter is added to this function to adjust the model's attention to the late predictions. In MHNN model, the bidirectional gated recurrent units (BGRU) and self-attention mechanism are employed to extract temporal features from the condition monitoring data. In addition, the structure of the multi-head neural network is adopted in the proposed model, helping to capture more degradation information by means of multiple identical and parallel networks. The proposed method is validated against a commonly used turbofan engine dataset. Compared with other latest methods on the same dataset, the proposed method is proven to be superior. Taking the FD004 dataset as an example, the score obtained by MHNN is 24.09% lower than that obtained by the best existing method.