  Various methods for designing input features have been proposed for fault
recognition in rotating machines using one-dimensional raw sensor data. The
available methods are complex, rely on empirical approaches, and may differ
depending on the condition monitoring data used. Therefore, this article
proposes a novel algorithm to design input features that unifies the feature
extraction process for different time-series sensor data. This new insight for
designing/extracting input features is obtained through the lens of histogram
theory. The proposed algorithm extracts discriminative input features, which
are suitable for a simple classifier to deep neural network-based classifiers.
The designed input features are given as input to the classifier with
end-to-end training in a single framework for machine conditions recognition.
The proposed scheme has been validated through three real-time datasets: a)
acoustic dataset, b) CWRU vibration dataset, and c) IMS vibration dataset. The
real-time results and comparative study show the effectiveness of the proposed
scheme for the prediction of the machine's health states.
