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Hydraulic systems faults have the characteristics of being highly concealed and unclear. Due to the characteristics of the complex vibration transmission mechanism and strong nonlinear time-varying signals in hydraulic systems, it is extremely difficult to achieve fault diagnosis for hydraulic systems. Different components of the system can fail individually or simultaneously. Signal processing faces the problem of coupling between multi-component faults, which makes it more difficult to realise multi-component fault diagnosis. On the one hand, existing techniques rely on hand-designed features and only use a traditional single shallow machine model as the base classifier, and these do not have the ability to self-learn meaningful features. On the other hand, the diagnostic performance of a single base classifier sometimes does not meet engineering requirements. To handle the above problems, a bagging strategy based heterogeneous ensemble deep neural networks (DNNs) approach is proposed for the multiple components fault diagnosis of hydraulic systems. First, Pearson correlation coefficient and neighbourhood component analysis are developed for data channel selection and feature dimensionality reduction. Second, two distinct DNNs are constructed as base learners: a stacked sparse autoencoder and a deep hierarchical extreme-learning machine. Finally, a bagging strategy is adopted to integrate different DNNs to obtain robust diagnostic results. The results from this experiment demonstrate that the proposed method can precisely diagnose hydraulic system faults compared with comparative methods.