  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
remaining useful life 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.
