An intelligent fault identification algorithm based on ensemble deep neural networks and correlation coefficients is proposed for rotating machinery fault detection. In this algorithm, three deep neural networks (DNNs) are arranged to initially identify faults in the frequency domain, wavelet domain and envelope spectrum angle, respectively. Then, correlation coefficients are adopted to evaluate the recognition results of the DNNs. The reliabilities of the DNNs’ recognition results are divided into 6 levels according to the outputs of the DNNs and the correlation coefficients. Finally, the evaluation results of the three DNNs are put through fusion processing to generate more reliable recognition results. This algorithm identifies fault signals from multiple angles and combines the correlation coefficients to make a comprehensive judgment, which is beneficial for fault identification. The proposed algorithm is tested with the bearing data provided by the Case Western Reserve University Bearing Data Center. The test results show that the algorithm has a high recognition accuracy for homologous data and a good generalization ability for nonhomologous data.