A system for monitoring a rotating equipment, the system comprising: a sensor, positioned in proximity of the rotating equipment, configured to: sense vibrations of the rotating equipment; generate digital signals corresponding to the vibrations at a first time instance and a second time instance; and transmit the digital signals; and a server in communication with the sensor via a communication network, configured to: receive, from the sensor over the communication network, the digital signals; determine a plurality of intrinsic mode functions based on the digital signals; select a set of intrinsic mode functions from the plurality of intrinsic mode functions; extract a set of features from the set of intrinsic mode functions, wherein the set of features comprise at least first peak values of the set of intrinsic mode functions in a time domain at the first time instance and second peak values of the set of intrinsic mode functions in the time domain at the second time instance; apply the set of features including at least the first peak values and the second peak values and time values at the first time instance and the second time instance, as input values, to a wavelet neural network, wherein the wavelet neural network comprises at least a wavelet basis function configured to be used as an activation function for the first peak values at the first time instance and the second peak values at the second time instance; and predict life percentage of the rotating equipment using an output generated by the wavelet neural network. a sensor, positioned in proximity of the rotating equipment, configured to: sense vibrations of the rotating equipment; generate digital signals corresponding to the vibrations at a first time instance and a second time instance; and transmit the digital signals; and sense vibrations of the rotating equipment; generate digital signals corresponding to the vibrations at a first time instance and a second time instance; and transmit the digital signals; and a server in communication with the sensor via a communication network, configured to: receive, from the sensor over the communication network, the digital signals; determine a plurality of intrinsic mode functions based on the digital signals; select a set of intrinsic mode functions from the plurality of intrinsic mode functions; extract a set of features from the set of intrinsic mode functions, wherein the set of features comprise at least first peak values of the set of intrinsic mode functions in a time domain at the first time instance and second peak values of the set of intrinsic mode functions in the time domain at the second time instance; apply the set of features including at least the first peak values and the second peak values and time values at the first time instance and the second time instance, as input values, to a wavelet neural network, wherein the wavelet neural network comprises at least a wavelet basis function configured to be used as an activation function for the first peak values at the first time instance and the second peak values at the second time instance; and predict life percentage of the rotating equipment using an output generated by the wavelet neural network. receive, from the sensor over the communication network, the digital signals; determine a plurality of intrinsic mode functions based on the digital signals; select a set of intrinsic mode functions from the plurality of intrinsic mode functions; extract a set of features from the set of intrinsic mode functions, wherein the set of features comprise at least first peak values of the set of intrinsic mode functions in a time domain at the first time instance and second peak values of the set of intrinsic mode functions in the time domain at the second time instance; apply the set of features including at least the first peak values and the second peak values and time values at the first time instance and the second time instance, as input values, to a wavelet neural network, wherein the wavelet neural network comprises at least a wavelet basis function configured to be used as an activation function for the first peak values at the first time instance and the second peak values at the second time instance; and predict life percentage of the rotating equipment using an output generated by the wavelet neural network.