Control efficiency improvement of an electro-hydraulic winch

Dutu, Iulian; Fratila, C.; Axinte, Tiberiu; Munteanu, M.G.; Calancea, L.; Diaconu, M.; Drăgan, Crina Violeta

doi:10.47577/technium.v3i9.5093

Cited by 1 publication

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“…The predicting of the remaining useful life time therefore becomes approximate and can have serious consequences. A late prediction implies that the failure has already occurred, which can affect the safety of passengers and crew in flight [6] and an early prediction leads to additional maintenance costs at nonoptimized periods [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling and fault detection of a turbofan engine by deep-learning approach

Offole¹,

Dieudonne²,

Kennedy³

et al. 2022

Technium

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Throughout the world, thousands of passengers travel by air, their quality depends on that of the equipment used. Predictive maintenance is increasingly used to estimate. The remaining useful life of system components and in particular turbofan engines as an essential component. It is used to predict failure before it occurs, optimize component design, extend equipment life, and reduce maintenance costs. However, the algorithms proposed in the literature to date to determine the remaining useful life lack precision with a quadratic error around 20 while the physical models have errors of the order of 0.02. The problem here is how to increase the accuracy of predicting the remaining useful life of a turbofan engine. The objective of this study is to develop a more realistic and accurate algorithm for calculating the remaining useful life of a turbofan engine. To do this, we considered the degradation of the high pressure compressor and the fan as essential organs of the turbojet engine and we used deep learning, known for its high precision linked to a great capacity for extracting information. More specifically, it involved acquiring data on a turbojet engine in operation, pre-processing this data, developing the prediction model, training the model and finally validating the approach in comparison with other diagnostic methods. and to model these defects. We compared two deep learning architectures per application against the CMAPSS dataset to assess their performance. The LSTM architecture we developed prevailed with an RMSE of 13.76, well positioned compared to the literature architecture.

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Section: Introductionmentioning

confidence: 99%