## 3 Literature Survey
### Big Data Approaches for Specific Manufacturing Applications
#### 3.2.1 Fault Detection  
In [40], a MapReduce framework is proposed and applied to the fault diagnosis problem in cloud-based manufacturing under the circumstance of a heavily unbalanced dataset. An unbalanced dataset is one in which a large number of examples but another class is represented by comparatively far fewer [41; 42]. In terms of features for use in model training, each record of input data contains 27 independent variables and one fault type. There is no explicit discussion of reducing the 27 input variables to a smaller subset or what steps might be taken to do so for a scenario with higher dimensionality. A hybridized CloudView framework is proposed in [43] for analyzing large quantities of machine maintenance data in a cloud computing environment. The hybridized framework contrasts with a global or offline approach [44] and a local or online approach [45], providing the advantage of being able to analyze sensor data in real-time while also predicting faults in machines using global information on previous faults from a large number of machines [43]. Feature selection is discussed at a high level, but the illustrative case study employs only three data inputs. The purpose of the case study is simply to illustrate the case-based reasoning applied and not apparently to address a specific situation. In [46], [PERSON] and [PERSON] employ deep belief networks (DBN) for health state classification of manufacturing machines, with IIOT sensor data employed for model inputs. Specifically, signal data from seven different signals out of a possible 21 were selected for model training. Selection of which signals to include for model training was made based on literature and not on a specific methodological approach. Deep belief networks are compared favorably to support vector machines (SVM), back-propagation neural networks (BNN), Mahalanobis distance (MD), and self-organizing maps (SOM) [46]. The deep belief network structure consists of a data layer, a network layer, and some number of hidden layers in between. This particular framework structures its hidden layers as a stacked network of restricted Boltzmann machines (RBMs) [47], with the hidden layer of the nth RBM as the data layer of the (n+1)th RBM. A similar machine learning methodology is employed by [PERSON] et al. (2016) for fault characterization of a rotating machinery in an environment characterized by massive data using deep neural networks (DNNs) [48]. A DNN is similar to the DBN, except that the layers are not constrained to be RBM. For an extensive overview of deep learning in neural networks, see [49]. In a case study in fault diagnosis of rolling element bearings, a total of 2400 features are extracted from 200 signals using fast Fourier transform (FFT); no explicit reduction step is performed or discussed. Rather, the full dataset is input into the DNN. The DNN model achieves impressive results when compared with a back-propagation neural network (BPNN), with correct classification rates over 99% compared to 65-80% for the BPNN [48]. This indicates that the specific algorithm employed can have a non-trivial impact on the results, depending on the problem under study. A framework for fault signal identification is proposed by [PERSON] et al. (2010) in [50] using short term Fourier transforms (STFT) to separate the signal and SVM to classify it, and [PERSON] and [PERSON] (2012) extend the approach in [51]. An explicit discussion on data preparation or feature filtration is absent due to the manageable feature set used for model training. However, the approach to extract features from signal data can lead to an excessive number of potential features, making such a step value-added. Note also that this framework is a hybrid of several techniques, taking sensor data into the SVM after it has already been processed by signal processing and the time-based model. This is in contrast to frameworks relying exclusively on SVM [52; 53] or exclusively on time series analysis [54].