Abstract: A system for monitoring the state of a two-part-cowl lock unit, the lock unit comprising a pair hook-keeper, has a rod mechanically connected to the lock unit, a spring mechanically coupled with the rod, and a warning mechanism configured to be activated by the rod, to warn of the state of the two-part-cowl lock unit. An aircraft may also be configured to include at least one of the systems for monitoring the state of a two-part cowl lock.

Abstract: A control system and method of the anti-skid computers of an aircraft are provided. The control system comprises two IMA computers, comprising each one an Avionics Computer Control Device, an Avionics Computer Monitoring Device and at least one dual data acquisition means and at least one dual processing means. Each Avionics Computer Control Device and each Avionics Computer Monitoring Device are connected to the anti-skid computers of both sides.

Abstract: A method and system of monitoring a structure, the method including: a) synchronously acquiring data comprising a plurality of operational parameters and at least one strain data, b) building a significant points dataset from the data acquired in step a), and c) modelling a relationship between the operational parameters and the strain data using the built significant points dataset to train a non-adaptive prediction functional supervised approximation method, wherein the step of building a significant points dataset comprises deletion of redundant information from the acquired data. The resultant models may be used to process structure real operation data in order to estimate the eventual crack initiation and crack growth on a set of predefined locations of the structure.

Abstract: A method and system of monitoring a structure, the method including: a) synchronously acquiring data comprising a plurality of operational parameters and at least one strain data, b) building a significant points dataset from the data acquired in step a), and c) modelling a relationship between the operational parameters and the strain data using the built significant points dataset to train a non-adaptive prediction functional supervised approximation method, wherein the step of building a significant points dataset comprises deletion of redundant information from the acquired data. The resultant models may be used to process structure real operation data in order to estimate the eventual crack initiation and crack growth on a set of predefined locations of the structure.