Remaining Useful Life Prediction for Rolling Element Bearings

Dear Colleagues,

Industry 4.0 has transformed the business environment, from the increase in sensors and control systems to the development of new maintenance strategies, with data-based decision making being one of the most popular aspects. This type of maintenance aims to optimize the times of activities and is more efficient from an economic point of view compared to traditional methods, based on the times and similarities. Data-based maintenance increases system reliability by improving the useful life of machine components, allowing them to be repaired or replaced before a critical failure occurs that causes severe and costly problems. The most vulnerable components of rotating machines are bearings, and they are widely used in the manufacturing industry; most of the critical failures in industrial machinery occur due to the malfunction of these elements, to such an extent that by guaranteeing the correct operation of the bearings a safe and economical state of operation is generated during the production process. Bearings are one of the main sources of nonlinearity in systems formed by rotating machines, since they significantly affect their operation. This nonlinear behavior has led to the development of a wide range of techniques, both for monitoring and for maintenance, making it possible to guarantee the normal operation of a machine's bearings. In applications such as turbines and aircraft engines, the condition of these elements is paramount because a simple imperfection can cause critical problems and extremely dangerous as well as expensive results. In CNC machine tools, the progressive wear of bearings cannot be avoided and it is important to continuously monitor and diagnose in order to generate an accurate diagnosis of the condition of machinery.

Dr. David Isaac Ibarra-Zarate
Guest Editor

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