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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.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.