In today's highly competitive industrial environment, machine health management systems become a crucial factor for sustainability and success. The traditional feature extraction methods to reveal the health condition of the machine are labor-extensive. They usually depend on engineered design features, which require an expert knowledge level. Inspired by the successful results of deep learning approaches that redefine representation learning from raw data, we propose moving averaged features-based on Long-Short Term Memory (MaF-LSTM) networks. It is a hybrid approach that combines engineered features design with self-feature learning for the purpose of machine condition monitoring. First, features from overlapped sliding windows of the input time-series signals are extracted. Then, a moving average filter is applied on the top of the generated features to enhance the feature's condition indicter's content. Next, a bidirectional LSTM is applied to learn the features representation from the moving averaged features. Two experiments, namely, bearing fault diagnosis and hydraulic accumulator fault detection, are implemented to verify the effectiveness of the proposed MaF-LSTM. The experimental results demonstrated that the proposed method outperforms all traditional condition monitoring methods in both use cases.
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