The FFT transport logistics for the aviation industry use case deals with the maintenance of a complex large mobile asset, the wing upper cover transportation jig. The transportation jig consists of a steel structure and a lightweight aluminium cover. It is used to transport large components of aircraft wings over land, water and in the air, thus it is subject to high environmental stress. The current maintenance is based on manual physical checks with limited time and location windows to perform maintenance. Since it is an important resource of the manufacturing/assembly processes, the high availability of the transportation jigs and optimized maintenance process are expected.
The FFT use case starts with the implementation of UPTIME_SENSE to acquire sensor data. As a prerequisite for mobile field deployment, mobile sensor nodes to implement a large number of sensors are developed and connected to the UPTIME_SENSE. UPTIME_DETECT takes the sensor data as input and detects anomalies, which is passed as event information to the UPTIME_PREDICT, which estimates a prediction of a certain failure that is expected to happen based on the input event(s) and a user configured set of rules. Besides that, FFT provides some historical datasets from their measurement campaigns and some semi-structured data of manual inputs from the maintenance operator, which customized by UPTIME_VISUALIZE to facilitate qualitative assessments relevant to the Jig status. UPTIME_DECIDE receives predictions about forthcoming failures and generates maintenance recommendations and a new maintenance plan.
Because both production and logistics schedules can be significantly affected by the availability and plannability of transportation jig, the benefit of having comprehensive condition information about the jig is potentially very high. FFT maintenance service performance can be improved considerably e.g. by ensuring the availability of resources (equipment, spare parts, materials etc.). UPTIME is expected to enable FFT maintenance service to focus more on the primary maintenance processes by automating secondary processes such as data acquisition, processing and reporting, while continuously providing relevant data to improve all processes: planning, execution, reporting, evaluation, improvement.
This items serves as a filter in support of selecting the case and demonstrators associated to the Digital Transformation Pathway Cases Catalogue (see ConnectedFactories Coordination and Support Action - Information sharing and analysis)
Quantity and quality of data: the available data in the FFT use case mainly consists of legacy data from specific measurement campaigns. The campaigns were mainly targeted to obtain insights about the effect of operational loads on the health of the asset, which is therefore quite suitable to establish the range and type of physical parameters to be monitored by the UPTIME system. UPTIME_SENSE is capable of acquiring data of mobile assets in transit using different modes of transport. While this would have been achievable from a technical point of view, the possibility to perform field trials was limited by the operational requirements of the end-user. Therefore, only one field trial in one transport mode (road transport) was performed, which yielded insufficient data to develop useful state detection capability. Due to the limited availability of the jig, a laboratory demonstrator was designed to enable partially representative testing of UPTIME_SENSE under lab conditions, to allow improvement of data quantity and diversity and to establish a causal relationship between acquired data and observed failures to make maintenance recommendations.