Integrated Intelligent Bearing Systems for UHPE Ground Test Demo (I²BS)

Periodic Reporting for period 4 - UHPE (Integrated Intelligent Bearing Systems for UHPE Ground Test Demo (I²BS))

Période du rapport: 2021-01-01 au 2022-06-30

Bearings are one of the most critical components in aero-engines. Faults developing in bearings can lead to unexpected shutdown and damages of the engine. Bearing health monitoring allows maintenance to be scheduled ahead of a fault reaching a critical level, thus avoiding unplanned downtime. Despite noticeable improvements over the past decades, detecting incipient bearing faults based vibration monitoring in noisy environment, such as aero-engines, remains extremely challenging.
The overall objective of the I2BS (integrated intelligent bearing systems) project was to develop innovative smart bearings for an Ultra High Propulsion Efficiency (UHPE) ground test demonstrator that not only meet the demo specifications but also provide significant safety improvement compared to existing standards. Such smart bearings will have significant impact on both the environment and the society by reducing material waste and safer operation. Real-time monitoring of aero-engine bearings enables schedules repairs and overhauls when they are needed, instead of conducting services at fixed intervals determined by operation time or cycles. The project kicked off in July 2016 and was completed in June 2022. It was part of the Clean Sky 2 programme. I²BS pursued an integrated approach comprising the development of sensor technologies, energy harvesting, wireless communication, data management and artificial intelligent algorithms into a smart bearing design to enable automated bearing health monitoring wirelessly in challenging operating conditions (e.g. high temperature, high speed and high thrust). The smart bearings were designed to detect a range of bearing failures at their incipient stages, aiming to enable more efficient part exchanges and optimized maintenance intervals. Thereby, aircrafts will be safer, resources will be economized, and downtime will be reduced or avoided. As part of the UHPE demonstration project, I²BS has designed, developed, evaluated and tested interchangeable smart bearings for the UHPE demonstrator. The bearing design fulfils all requirements and safety standards for aerospace applications. The smart bearings are able to deliver, in real time, information on the bearing’s main functional characteristics and health condition including temperature, axial & radial load, vibration, shaft and cage speed. By using machine learning (ML) based algorithms, a generalised bearing fault detection model has been developed based on the sub-scale and full-scale test data in this project as well as data from literature, which provides a powerful tool that can be used to monitor different bearing designs and operation conditions, such as those on the UHPE ground demonstrator and aircraft engines in the future, without the requirement of further training of the models.

The project has been conducted in three stages, i.e. the initial technology scoping and design stage, the sub-scale testing and initial model development stage and the final full-scale testing and model validation stage. During the first stage, the design of the smart bearings inculding the smart system such as sensors, wireless communication systems, energy harvesters have been designed and preliminarily tested in laboratories before they were implimented on sub-scale smart bearings for testing. All devices are selected to meet the smart bearing in UHPE ground demonstrator requirements such as temperature, speed, load and noise conditions. The following sensors were selected for the smart bearing systems:
- Temperature: Thermocouple
- Load: Resistive strain gauge and fibre bragg grating sensor
- Vibration: Piezoelectric charge mode accelerometer
- Shaft speed: Eddy current probe
- Cage Speed: Inductive sensor

To enable onboard power supply to the sensing system in the smart bearing, thermoelectric generator (TEG) technologies have been explored. Together with ultracapacitors, power management board and microcontrollers, TEGs have been designed and tested in laboratory before it was implemented in the smart bearing systems on the sub-scale test rig.
To enable wireless data transmission and control, a wireless communication system that can be used in the UHPE environment (high temperature and surrounded by metallic housing parts) was designed and tested as part of the smart bearing solutions.
To evaluate the smart bearing design, a sub-scale bearing test rig was designed, developed and manufactured in the second stage of this project. Sub-scale smart bearing testing started in April 2019 and over 50 tests were conducted under a range of bearing conditions. Sensor data collected from the smart bearings have been used to develop bearing fault detection tools and ML-based models for automated health monitoring.

Three data processing and bearing health monitoring software platforms have been established, including
- A bespoke onboard data collection and display platform, which provides real time trending, detection and warning of rig/bearing faults relating to operation, e.g. failure of lubricant supply, and instantaneous sensor response information.
- An automated bearing fault detection toolbox (called “AtoB toolbox”), which enables bearing fault detection and diagnosis by tracking bearing characteristic frequencies through statistical analysis of the ‘big data’.
- A range of artificial intelligence (AI) and machine learning (ML) methods, such as deep neural networks and convolution neural networks have shown a potential to further improve the detection and diagnosis.

During the final stage of this project, the wireless communication system, including high temperature amplifiers that are designed for the UHPE environment, has been tested on full-scale rig.
A small number of full-scale bearing tests were conducted under UHPE flight cycle conditions. Bearing defects were seeded to experiment and initiate spalls and propogate them to failure for ML algorithm development. The results show that, based on the sub-scale and full-scale test data as well as bearing test data from literature, a novel hybrid signal processing method developed in this study can significantly improve the fault detection and diagnosis. By using the hybrid method, a generalised ML model has been developed for generic bearing fault detection and diagnosis. Details of the results have been captured by peer reviewed publications.

This project has developed a state-of-the-art smart bearing systems, incorporating multiple sensors, energy harvesters and wireless communication systems, for the UHPE demonstrator. The smart system has small impact on the existing bearing design (additional space and weight), achieving the interchangeable with conventional bearings aim of the project. At the full-scale stage, a fibre optical sensing system was evaluated in addition to the sensors listed above. So far, no technical solutions are known that are installed directly in the engine environment of the customerwith a self-sufficient energy supply.
The models developed in this project have, for the first time in literature, shown to be able to detect and diagnose faults in a wide range of bearing designs and under different operating conditions. This will provide a powerful tool for future application of smart bearings in aerospace and other industry.

Subscale Test bearing equipped with strain gauges

FullScale bearing

Subscale Test Rig - ready to test

Periodic Reporting for period 4 - UHPE (Integrated Intelligent Bearing Systems for UHPE Ground Test Demo (I²BS))

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