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With the world aircraft fleets growing exponentially, the maintenance burden on airlines is also becoming overwhelming. One way to counter this is by making systems “smarter” so that they can self-diagnose themselves, help with troubleshooting, and estimate remaining useful life. Prognostics and Health Management (PHM) is the engineering discipline that forms the basis for developing such smart systems. In this course, the basic tenets of PHM will be taught with an emphasis on the practical application of PHM to aerospace systems.

In today's industrial sphere, machines are the key supporting various sectors and their operations. Over time, due to extensive usage, these machines undergo wear and tear, introducing subtle yet consequential faults that may go unnoticed. Given the pervasive dependence on machinery, the early and precise detection of these faults becomes a critical necessity. Detecting faults at an early stage not only prevents expensive downtimes but also significantly improves operational efficiency and safety standards. This research focuses on addressing this crucial need by proposing an effective system for condition monitoring and fault detection, leveraging the capabilities of advanced deep learning techniques. The study delves into the application of five diverse deep learning models—LSTM, Deep LSTM, Bi LSTM, GRU, and 1DCNN—in the context of fault detection in bearings using accelerometer data. Accelerometer data is instrumental in capturing vital vibrations within the machinery.

The purpose of the Air Generation System is to provide a constant supply of conditioned fresh air to meet the necessary oxygen availability and to prevent carbon dioxide (CO2) concentrations for the occupants in an aircraft. The engine bleed energy or electrical load energy consumed towards this circumstance accounts to be approx. 5% of total fuel burn and in turn, contributes to the global emissions of greenhouse gases. This paper studies the improvement areas of the present conventional system such as fuel burn consumption associated with an aircraft environmental control system (ECS) depending on, the amount of bleed and ram air usage, electric power consumption. Improved systems for propulsion, power generation, sustainability, hybridization, and environmental control can be desirable for an aircraft.

The present study discusses about the determination of the Seal drag force in the application where elastomeric seal is used with metallic interface in the presence of different fluids. An analytical model was constructed to predict the seal drag force and experimental test was performed to check the fidelity of the analytical model. A Design of Experiment (DoE) was utilized to perform experimental test considering different factors affecting the Seal drag force. Statistical tools such as Test for Equal Variances and One way Analysis of Variance (ANOVA) were used to draw inferences for population based on samples tested in the DoE test. It was observed that Glycol based fluids lead to lubricant wash off resulting into increased seal drag force. Additionally, non-lubricated seals tend to show higher seal drag force as compared to lubricated seals. Keywords: Seal Drag, DoE, ANOVA

Aviation industry is striving to leverage the technological advancements in connectivity, computation and data analytics. Scalable and robust connectivity enables futuristic applications like smart cabins, prognostic health management (PHM) and AI/ML based analytics for effective decision making leading to flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Wireless Sensor Networks (WSN) are gaining prominence on the aircraft for providing large scale connectivity solution that are essential for implementing various health monitoring applications like Structural Health Monitoring (SHM), Prognostic Health Management (PHM), etc. and control applications like smart lighting, smart seats, smart lavatory, etc. These applications help in improving passenger experience, flight operational efficiency, optimized maintenance planning and aircraft downtime reduction.