A processor-implemented method for health monitoring and fault signature identification, said method comprising: estimating Health Index of a system being monitored, via one or more hardware processors, by a health monitoring and fault signature identification system, wherein the HI of the system represents health status of the system and wherein the HI is estimated as time-series data representing HI of the system for multiple time intervals; characterized in that, processing HI windows of the time-series data to identify one or more low HI windows and one or more high HI windows of the time-series data, via the one or more hardware processors, by the health monitoring and fault signature identification system, wherein the one or more low HI windows of the time-series data comprise a majority of HI values below a dynamically determined threshold value and wherein the one or more high HI windows of the time-series data comprise a majority of HI values above the dynamically determined threshold value of HI and wherein a low HI value is an indicative of abnormal behavior of the system; and detecting at least one sensor associated with a component. of the system carrying faulty signatures as responsible for the abnormal behavior, based on a local Bayesian Network generated based on data from the at least one low HI window and data from the at least one high HI window for the system, via the one or more hardware processors, by the health monitoring and fault signature identification system, wherein the local Bayesian network captures dependencies between estimated HI and signatures from at least one sensor of the system, and wherein the dependencies between the estimated HI and the signatures from the at least one sensor of the system is captured by: generating an Explainability Index for the at least one sensor from which data for the HI estimation is obtained, based on the local Bayesian network, wherein the EI quantifies contribution of the at least one sensor to the low HI values through the change in distribution of the readings the sensor takes over time between predicted high HI and low HI ranges; and identifying dependency between the low HI values and the at least one sensor of the system based on the EI generated for the at least one sensor; determining the component associated with the detected sensor as contributing to the abnormal behavior of the system. estimating Health Index of a system being monitored, via one or more hardware processors, by a health monitoring and fault signature identification system, wherein the HI of the system represents health status of the system and wherein the HI is estimated as time-series data representing HI of the system for multiple time intervals; characterized in that, processing HI windows of the time-series data to identify one or more low HI windows and one or more high HI windows of the time-series data, via the one or more hardware processors, by the health monitoring and fault signature identification system, wherein the one or more low HI windows of the time-series data comprise a majority of HI values below a dynamically determined threshold value and wherein the one or more high HI windows of the time-series data comprise a majority of HI values above the dynamically determined threshold value of HI and wherein a low HI value is an indicative of abnormal behavior of the system; and detecting at least one sensor associated with a component. of the system carrying faulty signatures as responsible for the abnormal behavior, based on a local Bayesian Network generated based on data from the at least one low HI window and data from the at least one high HI window for the system, via the one or more hardware processors, by the health monitoring and fault signature identification system, wherein the local Bayesian network captures dependencies between estimated HI and signatures from at least one sensor of the system, and wherein the dependencies between the estimated HI and the signatures from the at least one sensor of the system is captured by: generating an Explainability Index for the at least one sensor from which data for the HI estimation is obtained, based on the local Bayesian network, wherein the EI quantifies contribution of the at least one sensor to the low HI values through the change in distribution of the readings the sensor takes over time between predicted high HI and low HI ranges; and identifying dependency between the low HI values and the at least one sensor of the system based on the EI generated for the at least one sensor; determining the component associated with the detected sensor as contributing to the abnormal behavior of the system. generating an Explainability Index for the at least one sensor from which data for the HI estimation is obtained, based on the local Bayesian network, wherein the EI quantifies contribution of the at least one sensor to the low HI values through the change in distribution of the readings the sensor takes over time between predicted high HI and low HI ranges; and identifying dependency between the low HI values and the at least one sensor of the system based on the EI generated for the at least one sensor;