Advanced data-driven methods for prognostics and life extension of assets using condition monitoring and sensor data.

A considerable number of engineering assets are fast reaching and operating beyond their orignal design lives. This is the case across various industrial sectors, including oil and gas, wind energy, nuclear energy, etc. Another interesting evolution is the on-going advancement in cyber-physical systems (CPS), where assets within an industrial plant are now interconnected. Consequently, conventional ways of progressing engineering assets beyond their original design lives would need to change. This is the fundamental research gap that this PhD sets out to address. Due to the complexity of CPS assets, modelling their failure cannot be simplistically or analytically achieved as was the case with older assets. This research is a completely novel attempt at using advanced analytics techniques to address the core aspects of asset life extension (LE). The obvious challenge in a system with several pieces of disparate equipment under condition monitoring is how to identify those that need attention and prioritise them. To address this gap, a technique which combined machine learning algorithms and practices from reliability-centered maintenance was developed, along with the use of a novel health condition index called the potential failure interval factor (PFIF). The PFIF was shown to be a good indicator of asset health states, thus enabling the categorisation of equipment as “healthy”, “good ” or “soon-to-fail”. LE strategies were then devoted to the vulnerable group labelled “good – monitor” and “soon-to-fail”. Furthermore, a class of artificial intelligence (AI) algorithms known as Bayesian Neural Networks (BNNs) were used in predicting the remaining useful life (RUL) for the vulnerable assets. The novelty in this was the implicit modelling of the aleatoric and epistemic uncertainties in the RUL prediction, thus yielding interpretable predictions that were useful for LE decision-making. An advanced analytics approach to LE decision-making was then proposed, with the novelty of implementing LE as an on-going series of activities, similar to operation and maintenance (O&M). LE strategies would therefore be implemented at the system, sub-system or component level, meshing seamlessly with O&M, albeit with the clear goal of extending the useful life of the overall asset. The research findings buttress the need for a paradigm shift, from conventional ways of implementing LE in the form of a project at the end of design life, to a more systematic approach based on advanced analytics.Shafiee, Mahmood (Associate)PhD in Energy and Powe

A Bayesian Optimization AdaBN-DCNN Method with Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction

The prediction of remaining useful life (RUL) of mechanical equipment provides a timely understanding of the equipment degradation and is critical for predictive maintenance of the equipment. In recent years, the applications of deep learning (DL) methods to predict equipment RUL have attracted much attention. There are two major challenges when applying the DL methods for RUL prediction: (1) It is difficult to select the prediction model structure and hyperparameters such as network depth, learning rate, batch size, and etc. (2) The developed prediction model is domain dependent, i.e., it can only give good prediction performance in one data domain (one particular type of working conditions and fault modes). In order to meet the challenges, a novel RUL prediction method developed using a deep convolutional neural network (DCNN) combined with Bayesian optimization and adaptive batch normalization (AdaBN) is presented in this paper. The proposed RUL prediction model is validated by the turbofan engine degradation simulation dataset provided by NASA. The prediction results show that the proposed prediction model provides better prediction results than model structures obtained by random search and grid search. The results also show that the domain adaptation capability of the prediction model has been improved