Condition Monitoring of Machines Using Fused Features From EMD-Based Local Energy With DNN

Several data-driven methods such as signal processing and machine learning exist separately to analyze non-linear and non-stationary data but their performance degrades due to insufficient information in the real-time application. In order to improve the performance, this paper proposes a novel feature extraction method using fusion of hand-crafted (low-level) features and high-level features, followed by feature extraction/selection on fused features. Local energy-based hand-crafted features have been derived from empirical mode decomposition, and high-level features have been extracted from the deep neural network. A method is also proposed for reduction of massive data points in the samples. The proposed scheme has studied the effect of variation in the number of extracted/selected features. The effectiveness of the proposed scheme is validated through three case studies: a) on acoustic dataset collected from the reciprocating type air compressor, b) on vibration dataset collected from deep groove ball bearing, and c) on steel plate faults dataset. The classification accuracy on acoustic dataset are obtained as high as 100.0%, 99.78%, and 99.78% using the random forest, linear support vector machine, and radial basis function support vector machine, respectively, with 5-fold cross-validation. Similarly, on vibration dataset obtained accuracies are 100.0%. The proposed scheme has been compared with ten conventional methods on five-fold cross-validation. These experimental results show considerable improvement in the prediction performance of machine conditions using the proposed scheme.