About the Project
This project aims to develop an integrated production and maintenance planning model by use of big data. The researcher is expected to develop a collaborative learning of the sensor data and production information to improve the production and maintenance process.
The researcher is expected to achieve the following objectives:
1. Develop a predictive maintenance tool with knowledge of product quality - Sensors are the machine’s gateway to sense its status and surrounding physical environment. Taking advantage of the sensor measurements, the predictive maintenance model is established to timely intervene the machine. In addition, matching the data from machine sensor and product quality enables to identify the influence of machine degradation on product quality.
2. Establish the evaluation of machine degradation by use of sensor measurements and product quality as the indicators - In traditional studies, the degradation process of the machine is assessed only by the dedicated sensor measurements. However, sensor failure and degradation may pass inaccurate readings to the decision-making algorithms, which leads to suboptimal decisions. Since the product quality somewhat reveals the degradation status of the machine, combination of the product quality and sensor measurements contributes to improving the estimation accuracy of the machine degradation process. A hybrid approach will be developed to evaluate the machine degradation, where machine learning tools will also be employed to enable handling of fast moving and big volumes of data.
3. Design and develop an integrated production and maintenance planning system - The production process and maintenance activities are mutually interactive in such a way that production on various items accelerates (or decelerates) the machine degradation process, which will advance (or postpone) the maintenance activities, while maintenance activities exert impacts on the product quality and thereby the profit. By balancing and compensating the work load and stress for each machine according to their individual health condition, production and machine performance can be maximized. An integrated production and maintenance planning model will be developed aiming to achieve the maximum profit.
Candidates are required to have
- 1st class honours/undergraduate degree (essential) and an excellent Masters-level qualification or equivalent (highly desirable), in a closely relevant subject such as computer science, operations research, mathematics and statistics, management science, and industrial engineering, from a recognised academic institution.
- If English is not your first language, you will also be required to provide evidence such as a recent UKVI recognised English language test (such as IELTS, minimum overall band score of 6.5 with no individual test score below 5.5) or a university degree completed in a recognized English speaking country.
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