Online and Real-Time Condition Prediction for Transmissions based on CAN-Signals

Stephan Rinderknecht; Rafael Fietzek; Stéphane Foulard

doi:10.4271/2017-01-1627

An online and real-time Condition Prediction system, so-called lifetime monitoring system, was developed at the Institute for Mechatronic Systems in Mechanical Engineering (IMS) of the TU Darmstadt, which is intended for implementation in standard control units of series production cars. Without additional hardware and only based on sensors and signals already available in a standard car, the lifetime monitoring system aims at recording the load/usage profiles of transmission components in aggregated form and at estimating continuously their remaining useful life. For this purpose, the dynamic transmission input and output torques are acquired realistically through sensor fusion.

In a further step, the lifetime monitoring system is used as an input-module for the introduction of innovative procedures to more load appropriate dimensioning, cost-efficient lightweight design, failure-free operation and predictive maintenance of transmissions. This is based on damage-oriented operating strategies (so-called eLIFE) and a paradigm shift in the design philosophy relying on a smart big data approach (so-called ecoLIFE3 design procedure).

The paper will present the lifetime monitoring system by the example of two concrete application cases, namely a manual and a dual clutch transmission (DCT). Furthermore, the concepts of eLIFE and ecoLIFE3 will be introduced and the economic and ecologic potentials of the approach will be discussed and quantified on basis of a DCT.

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Online and Real-Time Condition Prediction for Transmissions based on CAN-Signals 2017-01-1627

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