Mechanical equipment in modern industries becomes more automatic, precise and efficient. To fully inspect its health conditions, condition monitoring systems are used to collect real-time data from the equipment, and massive data are acquired after the long-time operation, which promotes machinery health monitoring to enter the age of big data. Mechanical big data has the properties of large-volume, diversity and high-velocity. Effectively mining characteristics from such data and accurately identifying the machinery health conditions with advanced theories become new issues in machinery health monitoring. To harness the properties of mechanical big data and the advantages of deep learning theory, a health monitoring and fault diagnosis method for machinery is proposed. In the proposed method, deep neural networks with deep architectures are established to adaptively mine available fault characteristics and automatically identify machinery health conditions. Correspondingly, the proposed method overcomes two deficiencies of the traditional intelligent diagnosis methods: (1) the features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise; (2) the used models have shallow architectures, limiting their capability in fault diagnosis issues. The proposed method is validated using datasets of multi-stage gear transmission systems, which contain massive data involving different health conditions under various operating conditions. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.
