Plain bearings are increasingly being used under mixed friction, resulting in a higher risk of wear-induced failure. Therefore, the relevance of condition monitoring is increasing, which can enable the early detection of wear inducing conditions. The presented work focuses on the development of an energy-autonomous, temperature-based condition monitoring system for plain bearings. The components of the condition monitoring system are to be integrated directly into the machine element and operating data to be transmitted wirelessly.The fundamental monitoring approach pursued, is the in-operando calculation of the lubrication gap height via the standardized Gümbel-curve based on the temperature map in the plain bearing. If the gap height undercuts a critical threshold, the plain bearing will operate in mixed friction. Thus, representing a potentially wear-causing operating point. The energy required for operating the system is harvested thermoelectrically within the plain bearing via thermo-generators, which use the temperature gradients in the plain bearing during operation.In this work, the simulative evaluation of the harvestable energy will be presented. The usable temperature difference will be determined for varying positioning of the energy harvester. The results obtained allow the subsequent manufacture of a first Sensor integrating Machine Element (SiME)-prototype. In this context a feasible setup regarding the types and number of sensors as well as a microcontroller for processing the measured data will be defined with respect to the harvestable energy. Furthermore, the principle for temperature-based gap height determination will be introduced, applied for different operating points and compared to simulative results.

Recent years revealed a notable trend for rolling bearings to be increasingly replaced by plain bearings for particular applications. A prominent example is the planetary stage in wind turbine gearboxes. The advantages of plain bearings are higher realizable power density due to smaller installation space compared to rolling bearings as well as the possibility of wear-free operation under ideal operational conditions. In real applications, however, plain bearings are subject to wear, for example due to start-stop processes, which lead to mixed friction conditions. Since the wear of plain bearings strongly depends on the operational conditions, monitoring is essential to detect inadmissible operating conditions. To avoid extra installation space and to achieve a minimal influence on the surrounding system, this work aims to integrate an energy-autarkic sensor system into the bearing volume to monitor the operational status of the plain bearing. To achieve autarky, the resulting data must be transmitted wirelessly and the external power supply must be eliminated. In this work, the software and hardware architecture of a sensor-integrating plain bearing prototype for monitoring the minimum oil gap height and the displacement angle of the shaft based on in-situ temperature measurements is presented. Furthermore, energy harvesting and energy saving measures are implemented and evaluated which intend to enable completely autarkic operation of the sensor integrating machine element in the future. A prototype of the sensor system is in turn used in-the-field to investigate required sampling rates and the spatial resolution of the temperature measurements.

As global wind capacity expands, reducing operations and maintenance costs is critical to lowering the levelized cost of energy. This paper explores the state of the art in condition monitoring and prognostic strategies for wind turbine drivetrains, which are among the most failure-prone and maintenance-intensive subsystems. Current diagnostic methodologies are evaluated, covering supervisory control and data acquisition (SCADA) data, high-frequency vibration and acoustic analysis, machine learning and digital twin frameworks. Finally, practical challenges are identified that limit wide-scale industrial adoption, in order to guide future research and industrial efforts.

Increased usage of hydrodynamic plain bearings is being encountered in applications that require traversing the mixed friction area, such as in wind turbines gearboxes or heavy-duty drivetrains. These operating conditions are not covered by the established design standards. Condition monitoring systems (CMS) can detect such critical operating conditions—e.g. start-up procedures—and enable timely, condition-based maintenance to prevent damage. In hydrodynamic plain bearings, the lubrication gap height and the bearing temperature are key parameters for detecting failure-critical conditions. The research project “Auto-informative Plain Bearings” uses temperature field monitoring to detect critical conditions. Previous work showed that Gümbel-curve’s relation to the temperature field allows the lubrication gap height determination in fluid friction. In this work it will be proven that the developed CMS approach allows online detection of mixed friction. This paper outlines the CMS design process, focusing on testing under transient conditions and lubricant failure. Consequently, the CMS’s ability to identify critical events will be verified. Sensor-integrating Machine Elements (SiME) need to fulfill the characteristic of energy autarky. The energy harvestable in thermal equilibrium by utilizing the waste heat using a plain bearing integrated thermogenerator (TEG) is determined. The harvestable energy budget is in the range of the non-optimized demand of the Sensor-integrating Plain Bearing (SiPB)-prototype. Finally, measures are derived to achieve permanent autarkic operation of the SiPB.

As wind energy scales up to meet global decarbonization and energy security goals, reducing the LCoE has become essential, particularly through improvements in O&M. This positioning paper explores the state-of-the-art in condition monitoring for wind turbines. It focuses on drivetrain components, which are among the most failure-prone and maintenance-intensive subsystems. It examines current diagnostic and prognostic strategies using SCADA data, vibration and acoustic analysis, and digital twin frameworks, alongside emerging techniques in machine learning, signal processing, and hybrid modelling. The paper also identifies key challenges, including data availability, labelling, standardization, and the gap between academic research and industrial adoption. This work aims to guide future research and industrial efforts in making wind energy more reliable, predictable, and cost-efficient.

Hydrodynamic plain bearings are characterized by their high load-carrying capacity, good damping properties and resistance to short-term abrasive wear. Therefore, plain bearings are widely used in heavy-duty applications, e.g., wind turbine drivetrains or ship propulsion systems . However, certain abnormal operating conditions can lead to rapid bearing damage. This may result in failure of the entire drivetrain. Two such abnormal events are mechanical overload causing overheating of the bearing and damage caused by particle contamination. Condition monitoring systems (CMS) exist for hydrodynamic plain bearings and numerous measurement methods currently represent the state of the art. However, most of these systems cannot detect particle contamination and mechanical overloading in a timely manner or are associated with high costs and installation effort that limit their practical applicability. Consequently, there is a need for a CMS approach that enables the timely detection of particle contamination and mechanical overloading. In this paper, temperature field measurement (TFM) is presented as a suitable approach to address this gap. The developed approach is characterized by its low cost and low energy demand. The energy efficiency of the developed Sensor-integrating Plain Bearing (SiPB) allows the CMS to operate autarkically by utilizing the waste heat generated by friction during regular operation within the plain bearing. Timely detection of the aforementioned failure causes requires a correspondingly high sampling rate, which in turn results in relatively high energy consumption. Therefore, the energy requirements of the SiPB prototype are discussed in relation to the measurement strategy and the resulting need to optimize both energy consumption and operating strategy is addressed. Subsequently, the temperature differences required to meet energy demand and ensure autarkic operation of the SiPB are determined. Finally, a measurement strategy for the autarkic operation of the SiPB is defined.