Fault diagnosis for electromechanical drivetrains using a joint distribution optimal deep domain adaptation approach

Robust and reliable drivetrain is important for preventing electromechanical (e.g., wind turbine) downtime. In recent years, advanced machine learning (ML) techniques including deep learning have been introduced to improve fault diagnosis performance for electromechanical systems. However, electromechanical systems (e.g., wind turbine) operate in varying working conditions, meaning that the distribution of the test data (in the target domain) is different from the training data used for model training, and the diagnosis performance of an ML method may become downgraded for practical applications. This paper proposes a joint distribution optimal deep domain adaptation approach (called JDDA) based auto-encoder deep classifier for fault diagnosis of electromechanical drivetrains under the varying working conditions. First, the representative features are extracted by the deep auto-encoder. Then, the joint distribution adaptation is used to implement the domain adaptation, so the classifier trained with the source domain features can be used to classify the target domain data. Lastly, the classification performance of the proposed JDDA is tested using two test-rig datasets, compared with three traditional machine learning methods and two domain adaptation approaches. Experimental results show that the JDDA can achieve better performance compared with the reference machine learning, deep learning and domain adaptation approaches

Weakly-supervised Multi-output Regression via Correlated Gaussian Processes

Multi-output regression seeks to infer multiple latent functions using data from multiple groups/sources while accounting for potential between-group similarities. In this paper, we consider multi-output regression under a weakly-supervised setting where a subset of data points from multiple groups are unlabeled. We use dependent Gaussian processes for multiple outputs constructed by convolutions with shared latent processes. We introduce hyperpriors for the multinomial probabilities of the unobserved labels and optimize the hyperparameters which we show improves estimation. We derive two variational bounds: (i) a modified variational bound for fast and stable convergence in model inference, (ii) a scalable variational bound that is amenable to stochastic optimization. We use experiments on synthetic and real-world data to show that the proposed model outperforms state-of-the-art models with more accurate estimation of multiple latent functions and unobserved labels

Fuzzy Transfer Learning

The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data