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The evolution of journal bearings, from fixed profiles to adaptive configurations, signifies a captivating progression in journal bearing technology. A notable innovation in adaptive bearings is Multi-pad Bidirectional Adjustable Bearing (MBAB), which can control film thickness in radial and circumferential directions. This paper introduces a novel data-driven approach, utilizing machine learning to model and optimize the static performance of MBABs with asymmetric bearing element adjustments. The study uses a parameter-independent Jaya algorithm coupled with machine learning models to identify optimal combinations of adjustments. Results highlight the significance of negative radial adjustments and asymmetric profiles for optimal performance. This research contributes to Industry 4.0 by bridging the physical-digital gap, offering a data-driven solution to enhance the performance of these advanced bearings. [ABSTRACT FROM AUTHOR]

The wider availability of low-cost sensing and data acquisition technologies means that real-time sensing of tribological parameters is becoming increasingly viable. Consequently, the potential to use these technologies to monitor in-service tribological components has increased significantly. This paper presents a review of a number of state-of-the-art in sensors for measuring friction, wear and lubricant properties. It also elaborates on the use of sensor coatings as an emerging area for directly probing the tribological interface. It is concluded that sensors will find ever increasing uses in condition monitoring" applications. However, sensing and tribology is beginning to evolve towards "Tribotronics" where combining the sensing of machine elements that have conventionally been passive with computational capability, or even embedded intelligence, along with actuation can create active machine elements, optimised to operate with say minimum power loss in all situations of duty. Additionally, it is noted that by incorporating sensing and responsive capabilities, functional surfaces can also become part of a bigger connected systems particularly in association with Industry 4.0. Increased use of sensors in tribological components alongside machine learning and artificial intelligence, will also support the shift in industrial tribological analytics. [ABSTRACT FROM AUTHOR]