  A single unit (head) is the conventional input feature extractor in deep
learning architectures trained on multivariate time series signals. The
importance of the fixed-dimensional vector representation generated by the
single-head network has been demonstrated for industrial machinery condition
monitoring and predictive maintenance. However, processing heterogeneous sensor
signals with a single-head may result in a model that cannot explicitly account
for the diversity in time-varying multivariate inputs. This work extends the
conventional single-head deep learning models to a more robust form by
developing context-specific heads to independently capture the inherent pattern
in each sensor reading. Using the turbofan aircraft engine benchmark dataset
(CMAPSS), an extensive experiment is performed to verify the effectiveness and
benefits of multi-head multilayer perceptron, recurrent networks, convolution
network, the transformer-style stand-alone attention network, and their
variants for remaining useful life estimation. Moreover, the effect of
different attention mechanisms on the multi-head models is also evaluated. In
addition, each architecture's relative advantage and computational overhead are
analyzed. Results show that utilizing the attention layer is task-sensitive and
model dependent, as it does not provide consistent improvement across the
models investigated. The best model is further compared with five
state-of-the-art models, and the comparison shows that a relatively simple
multi-head architecture performs better than the state-of-the-art models. The
results presented in this study demonstrate the importance of multi-head models
and attention mechanisms to an improved understanding of the remaining useful
life of industrial assets.
