## 1 Introduction  
In a machinery system, diagnostics and prognostics usually involve two kinds of problems, a fault-type classification and a remaining useful life (RUL) prediction problem. In particular, prognostics has been applied to the field of machinery maintenance as it allows industries to better plan logistics, as well as save cost by conducting maintenance only when needed [1]. Various approaches have been proposed in each problem and they can be divided into three categories: Physics-based, data-driven, and hybrid-based approaches. Physics-based approaches incorporate prior system-specific knowledge from an expert, as shown in previous studies, of fault-type classification [2; 3; 4; 5] and RUL prediction [6; 7; 8; 9] problems. Alternatively, data-driven approaches are based on statistical-machine-learning techniques using the historical data (see example studies about fault-type classification [10; 11; 12; 13; 14] and RUL estimation [15; 16; 17; 18]). Hybrid-based approaches attempt to utilize the strengths of both approaches, if applicable, by combining knowledge related to the physical process and information obtained from the observed data (see example studies about fault-type classification [19; 20; 21; 22] and RUL prediction [23; 24; 25; 26]). However, physics-based and hybrid-based approaches are limited in practice because the underlying physical models are not available in most real systems. Therefore, data-driven approaches have become increasingly popular along with recent advancements in sensor systems and data storage/analysis techniques. From a review of the data-driven approaches, we noticed that many different types of learning methods and data pre-processing algorithms have been employed. For example, the fisher discriminative analysis and the support vector machine were used for feature extraction and classification, respectively, to diagnose seven failure modes of three different polymer electrolyte membrane fuel cell systems in a previous study [10]. Another study applied a single hidden-layer feedforward neural network combined with an extreme learning machine technology to identify the offset, stuck, and noise faults in induction motor drive systems [11]. A multi-classification model based on the recurrent neural network was established to classify ten different fault-types of a wind power generation system [27]. A deep convolutional neural network and a random forest ensemble were employed to diagnose faults of a reliance electric motor and a rolling mill [28]. In [29], a genetic algorithm-based optimal feature subset selection and a _K_-nearest-neighbor classifier were applied to distinguish between normal and crack conditions of a spherical tank. Various approaches have also been tried for the RUL prediction. A previous study presented a new deep feature learning method for the RUL estimation of a bearing through a multiscale convolutional neural network [18]. A support vector machine was applied to predict the RUL of a Li-ion battery [30] and a microwave component [31]. Another study investigated the applicability of the Kalman filter to fuse the estimates of the RUL from five learning methods such as generalized linear models, neural networks, _K_-nearest neighbors, random forests, and support vector machines, using the field data of an aircraft bleed valve [15]. This literature review indicated that the process of developing data-driven diagnostics and prognostics methods involved some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine-learning in the fault-type and/or RUL prediction problems. In addition, the best performing algorithm in each subtask was varied across the characteristics of the given dataset. Moreover, each algorithm required appropriate specification of a number of hyper-parameters. Therefore, it is always challenging to develop a general diagnostics/ prognostics framework that can automatically identify the best subtask algorithms and optimize the involved parameters for a given dataset.