Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions

Abstract

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.

Keywords

• prognostics and health management
• condition monitoring
• open data science
• data processing
• health indicator
• fault detection and diagnostics
• eelectrical machines
• electrical machines
• rotating machines
• mechanical faults
• electrical faults