Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a machine failure history; ascertaining a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table including a plurality of mappings each mapping between a basic memory depth value and a basic weight range, or a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; ascertaining a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generating initial weights based on the composite initial weight range; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial weights.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for machine failure prediction, the method comprising: identifying, by a first processor in a first computer system, a plurality of basic memory depth values based on a machine failure history; ascertaining, by the first processor, a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table which is stored in a memory and includes a plurality of mappings wherein each mapping relates a basic memory depth value to a basic weight range, or according to a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; ascertaining, by the first processor, a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generating, by the first processor, initial weights based on the composite initial weight range; and predicting, by the first processor, a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial weights. 2. The method according to claim 1, wherein each mapping between a basic memory depth value and a basic weight range in the pre-stored table is ascertained according to the following steps: configuring, by a second processor in a second computer, a Recurrent Neural Network (RNN) with a set of predetermined parameters; selecting, by the second processor, an initial memory depth and an initial weight range based on the basic memory depth value, and ascertaining, by the second processor, the basic weight range for the basic memory depth value by starting with the selected initial weight range and gradually increasing the initial weight range until a minimum number of epochs for convergence is obtained. 3. The method according to claim 2, wherein the selecting an initial memory depth and an initial weight range further comprises: if the basic memory depth value is less than 40, selecting 10 as the initial memory depth value and selecting [−0.5, +0.5] as the initial weight range; and if the basic memory depth value is no less than 40 and not greater than 70, selecting 40 as the initial memory depth value and selecting [−1.8, +1.8] as the initial weight range. 4. The method according to claim 2, wherein configuring the RNN with a set of predetermined parameters comprises: configuring the RNN with the following parameters: Input units: 2 being a first input unit and a second input unit Hidden units: 51 Output units: 1 Number of previous time gaps for back propagation: 30 Activation: Sigmoid for Hidden and Output units Learning Rate: start with 0.2 which is subsequently scaled according to a predetermined scaling rule. 5. The method according to claim 4, wherein the configuring the RNN with a set of predetermined parameters further comprises: if the basic memory depth value is not less than 40 and not greater than 70, configuring the RNN with a third input unit, wherein a value of the third input unit corresponding to a failure indicator in the machine failure history sequence is ascertained based on a count of time stamps since a most recent previous positive failure indicator before the failure indicator. 6. The method according to claim 5, wherein the count of time stamps starts from 0 and the value of the third input unit corresponding to the failure indicator is ascertained by dividing the count of time stamps since a most recent positive failure indicator before the failure indicator by 100. 7. The method according to claim 4, wherein the predetermined scaling rule includes: starting value of learning rate eta=0.2, then if ‘mad’ error, measured as “mean absolute deviation” for each epoch, goes below 0.04, scaling the learning rate to eta I=eta/6; then if ‘mad’ error goes below 0.03, scaling the learning rate further to eta II=eta I/2; if ‘mad’ error goes below 0.025, scaling the learning rate further to eta III=eta II/2; if ‘mad’ error goes below 0.02, scaling the learning rate further to eta IV=eta III/2. 8. The method according to claim 1, wherein the predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value is as follows: Basic Weight Range=0.0002*x2+0.0086*x+2.8797 wherein x refers to the basic memory depth value. 9. The method according to claim 1, wherein the generating initial weights based on the ascertained composite initial weight range, comprises: generating the initial weights using nextDouble( ) method of Java™ based on the ascertained composite initial weight range. 10. The method according to claim 2, wherein the first processor and the second processor are provided as the same processor. 11. A system for machine failure prediction, the system comprising: a first processor and a memory communicably coupled thereto, wherein the first memory is configured to store data to be executed by the first processor, wherein the first processor is configured to identify a plurality of basic memory depth values based on a machine failure history; ascertain a basic weight range for each of the plurality of basic memory depth values according to a pre-stored table which is stored in the memory and includes a plurality of mappings wherein each mapping relates a basic memory depth value to a basic weight range, or according to a predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value; ascertain a composite initial weight range by calculating an average weight range of the ascertained basic weight range for each identified basic memory depth value; generate initial weights based on the composite initial weight range; and predict a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the generated initial weights. 12. The system according to claim 11, further comprising: a second processor, wherein in order to generate each mapping between a basic memory depth value and a basic weight range in the pre-stored table in the memory, the second processor is configured to configure a Recurrent Neural Network (RNN) with a set of predetermined parameters; select an initial memory depth and an initial weight range based on the basic memory depth value, and ascertain the basic weight range for the basic memory depth value by starting with the selected initial weight range and gradually increasing the initial weight range until a minimum number of epochs for convergence is obtained. 13. The system according to claim 12, wherein the second processor is further configured to if the basic memory depth value is less than 40, select 10 as the initial memory depth value and select [−0.5, +0.5] as the initial weight range; and if the basic memory depth value is no less than 40 and not greater than 70, select 40 as the initial memory depth value and select [−1.8, +1.8] as the initial weight range. 14. The system according to claim 12, wherein the set of predetermined parameters comprises: Input units: 2 being a first input unit and a second input unit Hidden units: 51 Output units: 1 Number of previous time gaps for back propagation: 30 Activation: Sigmoid for Hidden and Output units Learning Rate: start with 0.2 which is subsequently scaled according to a predetermined scaling rule. 15. The system according to claim 14, wherein the set of predetermined parameters further comprises a third input unit if the basic memory depth value is not less than 40 and not greater than 70, wherein the second processor is further configured to ascertain a value of the third input unit corresponding to a failure indicator in the machine failure history sequence based on a count of time stamps since a most recent previous positive failure indicator before the failure indicator. 16. The system according to claim 15, wherein the second processor is configured to ascertain the value of the third input unit corresponding to the failure indicator by dividing the count of time stamps since a most recent positive failure indicator before the failure indicator by 100, wherein the count of time stamps between two successive positive failure indicators starts from 0. 17. The system according to claim 14, wherein the predetermined scaling rule includes: starting value of learning rate eta=0.2, then if ‘mad’ error, measured as mean absolute deviation for each epoch, goes below 0.04, scaling the learning rate to eta I=eta/6; then if ‘mad’ error goes below 0.03, scaling the learning rate further to eta II=eta I/2; if ‘mad’ error goes below 0.025, scaling the learning rate further to eta III=eta II/2; if ‘mad’ error goes below 0.02, scaling the learning rate further to eta IV=eta III/2. 18. The system according to claim 11, wherein the predetermined formula for calculating the basic weight range based on the corresponding basic memory depth value is as follows: Basic Weight Range=0.0002*x2+0.0086*x+2.8797 wherein x refers to the basic memory depth value. 19. The system according to claim 11, wherein the first processor is further configured to: generate the initial weights using nextDouble( ) method of Java™ based on the ascertained composite initial weight range. 20. The system according to claim 12, wherein the first processor and the second processor are provided as the same processor. 21. A non-transitory computer readable medium comprising computer program code for machine failure prediction, wherein the computer program code, when executed, is configured to cause a processor in a computer system perform a method according to claim 1.