A method for detecting faults associated with a gas chromatograph device in a process plant by at least one server operationally coupled with the gas chromatograph device , the process plant including at least one piece of equipment operating with and ejecting a gaseous mixture comprising a plurality of compounds, the gas chromatograph device being coupled with the piece of equipment, wherein the gas chromatograph device is further associated with at least one database configured to store at least one real-time gas chromatogram and at least one historic gas chromatogram obtained from the gas chromatograph device by performing one or more gas chromatography of a gaseous mixture, the method comprising: receiving a real-time gas chromatogram for a real-time gas chromatography performed for the gaseous mixture, from the at least one database , wherein the real-time gas chromatogram is a graph representing values of composition of each of the plurality of compounds against retention time associated with a corresponding compound in a predefined sequence; detecting at least one real-time symptom for the real-time gas chromatogram using predetermined data relating to the real-time gas chromatography, the predetermined data indicating accurate values of composition of each of the plurality of compounds against the retention time, wherein the at least one real-time symptom indicates variation in the graph with respect to the predetermined data; and determining one or more faults from a plurality of faults associated with the gas chromatograph device using the at least one real-time symptom and a fault signature data, wherein the fault signature data is generated using a machine learning model trained by providing the plurality of faults and historic gas chromatogram received from the at least one database , wherein the historic gas chromatogram is obtained for one or more previous gas chromatography of the gas chromatograph device , wherein the fault signature data indicates association of each of the plurality of faults with a plurality of symptoms. receiving a real-time gas chromatogram for a real-time gas chromatography performed for the gaseous mixture, from the at least one database , wherein the real-time gas chromatogram is a graph representing values of composition of each of the plurality of compounds against retention time associated with a corresponding compound in a predefined sequence; detecting at least one real-time symptom for the real-time gas chromatogram using predetermined data relating to the real-time gas chromatography, the predetermined data indicating accurate values of composition of each of the plurality of compounds against the retention time, wherein the at least one real-time symptom indicates variation in the graph with respect to the predetermined data; and determining one or more faults from a plurality of faults associated with the gas chromatograph device using the at least one real-time symptom and a fault signature data, wherein the fault signature data is generated using a machine learning model trained by providing the plurality of faults and historic gas chromatogram received from the at least one database , wherein the historic gas chromatogram is obtained for one or more previous gas chromatography of the gas chromatograph device , wherein the fault signature data indicates association of each of the plurality of faults with a plurality of symptoms. <EOS>