Stacking deep learning models to analyse vibration and current signatures in rotating machinery

Purpose We explore the enhancement of deep learning (DL) algorithms from time series data analysis for rotating machinery monitoring. Design/methodology/approach Our proposed method combines the strengths of three distinct neural network architectures: a Multilayer Perceptron (MLP), a Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). The skills of each architecture allow us to tune them to process data from time, frequency and time-frequency domains, respectively. The multi-domain stacking approach has proven effective, confirming its versatility and potential for applicability in several diagnostic scenarios on industry applications. Findings It is challenging to work with high-quality and extensive datasets, remarkably like electrical currents. Electrical current can be interpreted in time, frequency and time-frequency domains. Multi-domain stacking DL models can be tuned to be more suitable for electrical current analysis. Multi-domain stacking approach has applicability in several diagnostic scenarios in industry applications. Originality/value We introduce a novel approach to fault detection by exploiting machine learning algorithms and signal processing techniques across time, frequency and time-frequency domains. Our method integrates data from these domains using three specialized neural network architectures: MLP for structured data from the Fast Fourier Transform (FFT), CNN for matrix-like data from spectrograms and LSTM networks for sequential data with dependencies over time. We stack these specialized architectures to form a neural network framework that enhances data utility, improving classification accuracy rate and noise robustness. It is applicable not only to electrical signal analysis but also to vibration data, offering broad potential for preventive maintenance and operational safety across various industry applications.