In modern industry, with the expanding of business level and capacity, the maintenance cost and workplace hazards increase dramatically. Condition  monitoring based method has been adopted as a mandatory field of maintenance management that is capable to monitor the equipment‟s operation and determine checking points for maintenance actions to be carried out prior to the occurrence of serious failure, which results in reduced maintenance cost and improved system performance. Coloane Power Station A, as one of CEM‟s subsidary, is equipped with an asynchronous generator, called as Turbo Compound System (TCS), that is typical equipment in power plants and is driven by exhaust gas fed turbine. The objective of this research is to build a predictive condition monitoring based systematic model for machinary like TCS. A TCS prototype system is developed in lab environment, which utilized simulated components and constructed a prototype with the same architecture as the real system. To be able to automatic performing the data analysis and preliminary diagnosis, the research proposed and developed the Fault Detector Program (FDP). The experimental results demonstrated that FDP is a reliable data analysis and interpretation tool for diagnosis typical types of machines faults, such as looseness, misalignment and unbalance. It could determine an immediate maintenance to be undertaken, or suggest next checking point. It is believed the methodology proposed in this research has the potential for expanded applications in other rotating machineries in various industries.