A system and method for providing health indication of a mechanical system, includes receiving signals indicative of vibration data of the mechanical system; pre-training features in the signals with a model; determining information related to vibration signatures in the signals; associating the vibration signatures with historical vibration data of the mechanical system; and building a multi-layer Deep Neural Network (DNN) from the vibration signatures and the historical vibration data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for providing health indication of a mechanical system, comprising: receiving, with a processor, signals indicative of vibration data of the mechanical system; pre-training, with the processor, features in the signals with a model; determining, with the processor, information related to vibration signatures in the signals; associating, with the processor, the vibration signatures with historical vibration data of the mechanical system; and building, with the processor, a multi-layer Deep Neural Network (DNN) from the vibration signatures and the historical vibration data. 2. The method of claim 1, wherein the associating of the vibration signatures further comprises associating the vibration signatures with known fault types from the historical data. 3. The method of claim 1, wherein the pre-training further comprises building an initial two-layer Deep Belief Net (DBN) from the signals. 4. The method of claim 3, further comprising building a DBN from a stack of Restricted Boltzmann Machines (RBM) comprising hidden variables and observed variables. 5. The method of claim 4, further comprising determining a non-linearity in the hidden variables using stepped sigmoid units, sigmoid units, or rectified linear units. 6. The method of claim 4, further comprising building an additional two-layer DBN from the initial two-layer DBN. 7. The method of claim 1, further comprising associating the vibration signatures with ground truth labels representing known fault types from the historical vibration data. 8. The method of claim 1, further comprising building the DNN with identical data from the model. 9. A system to provide health indication of a mechanical system, comprising: a moving machinery associated with the mechanical system; a sensor associated with the moving machinery; a processor; and memory having instructions stored thereon that, when executed by the processor, cause the system to: receive signals indicative of vibration data of the mechanical system; pre-train features in the signals with a model; determine information related to vibration signatures in the signals; associate the vibration signatures with historical vibration data of the mechanical system; and build a multi-layer Deep Neural Network (DNN) from the vibration signatures and the historical vibration data. 10. The system of claim 9, wherein the processor is configured to associate the vibration signatures with known fault types from the historical data. 11. The system of claim 9, wherein the processor is configured to build an initial two-layer Deep Belief Net (DBN) from the signals. 12. The system of claim 11, wherein the processor is configured to build a DBN from a stack of Restricted Boltzmann Machines (RBM) comprising hidden variables and observed variables. 13. The system of claim 12, wherein the processor is configured to determine a non-linearity in the hidden variables using stepped sigmoid units, sigmoid units, or rectified linear units. 14. The system of claim 12, wherein the processor is configured to build an additional two-layer DBNs from the initial two-layer DBN. 15. The system of claim 9, wherein the processor is configured to associate the vibration signatures with ground truth labels representing known fault types from the historical vibration data.