Integration of multidimensional fault diagnostic indicators on the example of rolling element bearings

Diagnostics of rotating machinery relies on examining of many dozens of fault indicators that enable recognition of malfunction symptoms at the earliest stage possible. Unfortunately, in many industrial applications and especially in large machinery parks, the number of diagnostic features to monitor goes beyond the perception capabilities of responsible maintenance personnel. Therefore, there is need for a data reduction techniques that simplify and provide the most important information within the condition monitoring system, starting from a single kinematic element. In this paper it is proposed to employ a simple Euclidean distance that relates the object’s condition to the difference between the vibration-based indicators and the initial state. As an example, the authors examine the integration of diagnostic features used to identify localized and extended fault of rolling element bearings for simulated data and real industrial event that occurred at wind turbine’s generator bearing

The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings

Modern condition monitoring systems (CMS) collect and process enormous amount of data in order to provide the earliest and most dependable information of fault development within any of the machine components and their operation combined. According to numerous studies one of the most fault susceptible mechanical elements in rotating machinery are rolling element bearings. Although reliable techniques for their diagnostics are already proposed, the new investigation is needed. According to authors experience in many industrial applications the operators are obligated to simultaneously track hundreds of diagnostic estimates, such as signals energy, its peakedness or narrowband characteristics for localized faults. As mentioned, for a vibration-based CMS of single wind turbine there are nearly 150 of them. Therefore, the authors employ a fuzzy logic approach for integration of bearing diagnostic features. A new estimate that carry most relevant information about bearing condition is discussed. The reasoning is presented on simulated data that mimics real rotating machine