2019/10/30 by Jarno Kansanaho, Kansanaho, Jarno, Tommi Kärkkäinen +1
Computer Science · Engineering · #Actuator #Artificial intelligence #Bearing (navigation) #Component-based software engineering #Computer science #Data mining #Embedded system #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Fault (geology) #Fault Detection and Control Systems #Fault detection and isolation #Hydraulic and Pneumatic Systems #Implementation #Machine Fault Diagnosis Techniques #Operating system #Plug and play #Plug-in #Prognostics #Real-time computing #Reliability (semiconductor) #Reliability engineering #Signal Processing (eess.SP) #Software #Software Engineering (cs.SE) #Software engineering #Software framework #Software system #Systems and Control (eess.SY) #cs.SE #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.13764
arxiv created 2019/10/30 · openalex publication_date 2019/10/30 · arxiv updated 2019/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Increasing the capabilities of sensors and computer algorithms produces a need for structural support that would solve recurring problems. Autonomous tribotronic systems self-regulate based on feedback acquired from interacting surfaces in relative motion. This paper describes a software framework for tribotronic systems. An example of such an application is a rolling element bearing (REB) installation with a vibration sensor. The presented plug-in framework offers functionalities for vibration data management, feature extraction, fault detection, and remaining useful life (RUL) estimation. The framework was tested using bearing vibration data acquired from NASA's prognostics data repository, and the evaluation included a run-through from feature extraction to fault detection to remaining useful life estimation. The plug-in implementations are easy to update and new implementations are easily deployable, even in run-time. The proposed software framework improves the performance, efficiency, and reliability of a tribotronic system. In addition, the framework facilitates the evaluation of the configuration complexity of the plug-in implementation.