Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for repair maintenance machinery processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various types severities rotating machinery. First, activation function utilized construct MSAE establish accurate nonlinear mapping between raw nonstationary vibration data different states. Then, nonnegative constraint applied enhance cost improve sparsity performance reconstruction quality. Finally, fruit fly optimization algorithm used determine adjustable parameters flexibly match characteristics analyzed data. The method analyze collected from sun gear unit roller bearing unit. Experimental results show superior other state-of-the-art methods.