In the new data intensive world, predictive maintenance has become a central issue for the modern industrial plants. Monitoring of electric machinery is one of the most important challenges in predictive maintenance. Adaptive manufacturing processes/plants may be possible through the monitored conditions. In this respect, several attempts have been made to utilize deep learning algorithms for rotating machinery fault detection and diagnosis. Among them, deep autoencoders are very popular, because of their denoising effect. They are also implemented in electric machinery fault diagnostics in order to obtain lower order representation of signals. However, none of these efforts regard the autoencoders as compression units. Bearing in mind that spectra of vibration and current signals that are collected from electric machinery are critical instruments for detection and diagnosis of their faults, we propose that deep stacked autoencoder can be utilized as spectrum compression units. The performance of the proposed strategy are assessed using a bearing data set in three ways: (1)Rule-based classifiers are implemented on raw and compressed-decompressed spectrum and their performance are compared. (2) It is shown that the several machine learning classifiers such as support vector machines, artificial neural networks and k-nearest neighbour classifiers on compressed-decompressed spectrum achieves the performance of them on raw data. (3) A multi-layer perceptron (MLP) classifier is implemented on the low dimensional representation and it is demonstrated that the strategy of employing the same autoencoder as pretraining of feature extraction module cannot outperform the performance of this MLP classifier.

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