成人直播

Condition monitoring (CM) of rolling element bearings, hereafter called bearings, has been the main point of attention for many decades in the industry for maintenance. This is because bearings play a very vital role in the operation and availability of rotating machinery. Despite successful implementations in industrial applications, CM of bearings still poses many challenges. One of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong noise from other mechanical components such as gears, screws, etc., fault diagnosis using such signals is not an easy task. Having a robust and reliable CM system for low-speed bearings will have significant impacts on reducing machine downtime in many industrial applications, including steel casting, paper mill, food industry, wind energy, etc., which leads to reductions in operation and maintenance expenditure (OPEX).

Various technologies that can be used for CM of low-speed bearings are available on the market, which can be divided into vibration- and ultrasound/acoustic emission (AE)-based technologies. Despite some success stories of the use of ultrasound/AE-based technologies for CM of low-speed bearings, high investment cost for hardware and software is the main bottleneck in adopting these technologies in many industrial applications. In the case of vibration-based technologies, where conventional vibration sensors are used, the effectiveness of fault diagnosis for low-speed bearing applications is hindered due to the low signal-to-noise ratio. Hence, low-cost alternative solutions for robust and reliable CM of low-speed bearings are still highly demanded by the industry to ensure that the availability of their machines is maximised, or machine downtime is minimised.

The aim is to develop a smart sensor prototype and demonstrator for condition monitoring of low-speed bearings. The following objectives are defined to achieve the aim: 

1. To perform a market search and review existing solutions to understand market requirements for smart sensors for condition monitoring of low-speed bearings. 
2. To specify, design and develop a prototype/demonstrator of a low-cost smart sensor. 
3. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. 
4. To validate the sensor prototype on existing test rigs at 成人直播 and benchmark with high-end commercial solutions

This project has a high impact on the industry as it can lower the hardware installation and operation costs significantly. Besides, there is an opportunity to explore the commercialisation paths of the developed smart sensor prototype.

You will gain from the experience in numerous ways, whether it be transferable skills in the technical area of optimisation, industrial exposure, or soft skills including presentation skills, project management, and communication skills. There are also numerous employability opportunities that the PhD will offer whether it be in Industry or Academia.