Periodic Reporting for period 3 - UHPE (Integrated Intelligent Bearing Systems for UHPE Ground Test Demo (I²BS))
Reporting period: 2019-07-01 to 2020-12-31
All requirements for the sensors, communication Systems, bearings, test rig and testing conditions were defined. First sensors and communication Systems were selected and ordered. University of Southampton performed pre-testing to evaluate sensor behavior at required conditions.
The following sensors were evaluated:
Vibration: Piezoelectric charge mode accelerometer
Load: Resistive strain gauge
Cage Speed: Inductive sensor
Shaft speed: Eddy current probe
For autonomous energy supply, a thermoelectric generator (TEG), ultracapacitors, power management board and microcontroller was tested in laboratory with relevant oil-in and oil-out temperatures. The voltage output of the TEG was connected to the power management board and energy was stored in ultracapacitors. It could be demonstrated that the TEG provides sufficient energy for one sensor to measure for 1s every 3 to 4 minutes. As a conclusion, multiple TEGs or performance optimized TEGs can be used for a main shaft engine bearing to provide the required energy for multiple sensors.
Alternatively, a Switched Reluctance Generator (SRG) can be used to generate power from rotation of the shaft. The rotation of the rotor is achieved by energising and de-energising the phase winding on the stator poles. A protoype has been developed and manufactured and is currently in the test phase on a test rig at SAG. The commissioning of the measuring and control unit took longer than planned. The complete system is currently implemented and the evaluation of the data is still pending.
A subscale bearing test rig was designed, developed and manufactured. The test rig was ready to test in April 2019.
The subscale testing of the sensing system has shown that the selected sensors are useful for the monitoring purposes, where
• Vibration sensors, with the aid of a range of analysis tools, are able to detect and diagnose bearing defects and spall propagation, offline for subscale testing with potential of being automated for the future.
• Strain gauges on the bearing can accurately detect load variation
• Speed sensors enabled slippage detection
Three data processing and bearing health monitoring software platforms have been explored, including
• An onboard platform, which provides real time detection and warning of rig/bearing faults relating to operation, e.g. failure of lubricant supply, and instantaneous sensor response information. It is aimed to develop a ‘traffic light’ system for operators if time allows.
• An AtoB toolbox, which provides ‘automatic’ bearing fault detection and diagnosis. This will be further validated through the full-scale testing.
• An approach based on artificial intelligence and machine learning, where deep neural networks and convolution neural networks have a potential to improve condition monitoring. Further development with more data on different bearings is required to provide a comprehensive tool for general use.
Thermoelectric generators (TEGs) were planned for the energy supply of the electronic brain 2. However, the results from the subscale testing showed that the TEGs could only be used to provide the initial power or ‘bootstrapping’ power during taking off. The system will be further tested in ‘real’ conditions at full-scale. Switch Reluctance Generator (SRG) is being investigated and will be implemented on the full-scale test rig.
High-temperature amplifiers, tested at room temperature in Southampton lab, are functional and ready for system testing on full-scale rig at specified temperature (up to 200 ˚C). The designed processing and wireless communication have been tested in the lab, where the power consumption characterised and compatible with TEG power output in take-off and landing phases.