Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas-turbine and combined-cycle models able to undertake appropriate diagnostics and life-cycle costing. These are complex processes as they include the simulation of such issues as performance and assessment of degraded gas-turbines, life usage and risk analysis. This report describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Network (ANN) and Genetic Algorithm (GA) based techniques have provided new opportunities in the field of engine-fault diagnostics. It also discusses the potential of advanced engine-diagnostics, employing such features as ANN and GA for contributing to the management of availability of industrial gas-turbines.