A Hybrid Deep Learning Model-Based Remaining Useful Life Estimation for Reed Relay With Degradation Pattern Clustering

Reed relay serves as the fundamental component of functional testing, which closely relates to the successful quality inspection of electronics. To provide accurate remaining useful life (RUL) estimation for reed relay, a hybrid deep learning network with degradation pattern clustering is proposed based on the following three considerations. First, multiple degradation behaviors are observed for reed relay, and hence, a dynamic time wrapping-based K-means clustering is offered to distinguish degradation patterns from each other. Second, although proper selections of features are of great significance, few studies are available to guide the selection. The proposed method recommends operational rules for easy implementation purposes. Third, a neural network for RUL estimation (RULNet) is proposed to address the weakness of the convolutional neural network (CNN) in capturing temporal information of sequential data, which incorporates temporal correlation ability after high-level feature representation of convolutional operation. In this way, three variants of RULNet are constructed with health indicators, features with self-organizing map, or features with curve fitting. Ultimately, the proposed hybrid model is compared with the typical baseline models, including CNN and long short-term memory network (LSTM), through a practical reed relay dataset with two distinct degradation manners. The results from both degradation cases demonstrate that the proposed method outperforms CNN and LSTM regarding the index root-mean-squared error.

Automated summary

In this paper, a hybrid deep learning network with degradation pattern clustering is proposed for accurate remaining useful life (RUL) estimation of reed relay. Multiple degradation behaviors are distinguished using dynamic time wrapping-based K-means clustering. The proposed method also provides operational rules for feature selection and combines a neural network with temporal correlation ability to address the weakness of CNN in capturing sequential data.