Monitoring aircraft performance in a fleet is fundamental to ensure optimal operation and promptly detect anomalies that can increase fuel consumption or compromise flight safety. Accurate failure detection and life prediction methods also result in reduced maintenance costs. The major challenges in fleet monitoring are the great amount of collected data that need to be processed and the variability between engines of the fleet, which requires adaptive models. In this paper, a framework for monitoring, diagnostics, and health management of a fleet of aircrafts is proposed. The framework consists of a multi-level approach: starting from thresholds exceedance monitoring, problematic engines are isolated, on which a fault detection system is then applied. Different methods for fault isolation, identification, and quantification are presented and compared, and the related challenges and opportunities are discussed. This conceptual strategy is tested on fleet data generated through a performance model of a turbofan engine, considering engine-to-engine and flight-to-flight variations and uncertainties in sensor measurements. Limitations of physics-based methods and machine learning techniques are investigated and the needs for fleet diagnostics are highlighted.

In this paper two different advanced control approaches for a pressurized SOFC hybrid system are investigated and compared against traditional proportional–integral–derivative (PID). Both advanced control methods use model predictive control (MPC): in the first case, the MPC has direct access to the plant manipulated variables, in the second case the MPC operates on the setpoints of PIDs which control the plant. In the second approach the idea is to use MPC at the highest level of the plant control system to optimize the performance of bottoming PIDs, retaining system stability and operator confidence. Two MIMO (multi-input multi-output) controllers were obtained: fuel cell power and cathode inlet temperature are the controlled variables; fuel cell by-pass flow, current and fuel mass flow rate (the utilization factor kept constant) are the manipulated variables. The two advanced control methods were tested and compared against the conventional PID approach using a SOFC hybrid system model. Then, the MPC controller was implemented in the hybrid system emulator test rig developed by the Thermochemical Power Group (TPG) at the University of Genoa. Experimental tests were carried out to compare MPC against classic PID method: load following tests were carried out. Ramping the fuel cell load from 100% to 80% and back, keeping constant the target of the cathode inlet temperature, the MPC controller was able to reduce the mismatch between the actual and the target values of the cathode inlet temperature from 7 K maximum of the PID controller to 3 K maximum, showing more stable behavior in general.

Despite the high efficiency and flexibility of fuel cells, which make them an attractive technology for the future energy generation, their economic competitiveness is still penalized by their short lifetime, due to multiple degradation phenomena. As a matter of fact, electrochemical performance of solid oxide fuel cells (SOFCs) is reduced because of different degradation mechanisms, which depend on operating conditions, fuel and air contaminants, impurities in materials, and others. In this work, a real-time, one dimensional (1D) model of a SOFC is used to simulate the effects of voltage degradation in the cell. Different mechanisms are summarized in a simple empirical expression that relates degradation rate to cell operating parameters (current density, fuel utilization and temperature), on a localized basis. Profile distributions of different variables during cell degradation are analyzed. In particular, the effect of degradation on current density, temperature, and total resistance of the cell are investigated. An analysis of localized degradation effects shows how different parts of the cell degrade at a different time rate, and how the various profiles are redistributed along the cell as consequence of different degradation rates.

The hybridization of solid oxide fuel cell (SOFC) and gas turbine technologies provides an increase in system efficiency and economic performance. The latter aspect is significantly affected by fuel cell degradation, due to several mechanisms. However, hybrid systems allow different control strategies to minimize degradation effects on system performance and their impact on economic feasibility. A real-time distributed model of a SOFC was used to simulate fuel cell degradation in the cases of a standalone stack and a hybrid configuration, in the latter of which the numerical model is normally coupled with the hybrid system hardware components of the National Energy Technology Laboratory (NETL) Hyper facility. The results showed how in a hybrid system it is possible, with an appropriate strategy, to maintain constant voltage even if the cell is degrading, reducing degradation rate during time. At constant power demand, fuel cell life could be significantly extended using the operating strategies allowed by coupling with a turbine (an order of magnitude longer than a standalone fuel cell), maintaining high system efficiency despite fuel cell degradation.

The market for the small-scale micro gas turbine is expected to grow rapidly in the coming years. Especially, utilization of commercial off-the-shelf components is rapidly reducing the cost of ownership and maintenance, which is paving the way for vast adoption of such units. However, to meet the high-reliability requirements of power generators, there is an acute need of a real-time monitoring system that will be able to detect faults and performance degradation, and thus allow preventive maintenance of these units to decrease downtime. In this paper, a micro gas turbine based combined heat and power system is modelled and used for development of physics-based diagnostic approaches. Different diagnostic schemes for performance monitoring of micro gas turbines are investigated.