Wolfgang Bisle

Knowledge-based mobile remote engineering for maintenance processes

This paper presents the OMA system (Online Maintenance Assistance) for remote service support. The system is used to support complex maintenance processes by assistance from remote experts in an online video conference with integration to inspection workflow and devices. The system operates on Commercial of the Shelf hardware and in standard Web Browsers. It allows flexible integration into the enterprise IT e.g. in existing role and authentication concepts and is compliant to standard enterprise security regulations. The OMA system is still in development, but will be ready for use in the near future. Current research focuses on creating a model based signal monitoring function and improving the workflow support by using context awareness.

Improved shearography for use on optical non cooperating surfaces under daylight conditions

Recent developments will be presented that make the Phase Stepping Shearography Method fit for use in aircraft maintenance conditions. Outgoing from current commercially available systems we introduced a program to improve the capability of the shearography testing method to deal with metallic (highly reflective) surfaces, completely black parts (e.g. CFRP) as well as with surfaces where surface conditions vary in the inspection area. Another demand was the inspections must be carried out under daylight (with no cover) and in rough maintenance conditions (hangar, working on mobile platforms…) but the non contact character of the method should not be influenced. Several approaches have been investigated; from laser illumination to receiving optics, sensor and fringe processing software nearly nothing was left unchanged. The resulting system is highly portable, fast in use (nearly real-time) and produces clear and easy to read documents. The operating software is portable between almost all usual software p...

Content Adaptive Signal Compression for Remote SHM and NDT Operation

Remote engineering systems are valuable tools to give visual assistance and remote support e.g. in Structural Health Monitoring (SHM) or Non-destructive Testing (NDT). They allow discussing a second opinion with a remote expert and thus reducing the human factor during testing and monitoring. For an optimal impression of the situation, the second person requires both a camera view of the location and the screen view of the SHM/NDT system used. The Online Maintenance Assistance (OMA) system implements this two-view collaboration. Remote partners can see and actively control measuring equipment, while observing details of the location in the camera window. Due to varying screen signals and communication properties, an adaption for optimal visual quality of the camera and screen signal compression is required. The adaption is performed in a Machine Learning System (MLS), which uses an adapted k-nearest neighbors algorithm to adapt signal compression parameters to the measurement task and the communication channel. doi: 10.12783/SHM2015/148