## 3 Literature Survey
### Big Data Approaches for Specific Manufacturing Applications
#### 3.2.1 Fault Detection
Probabilistic frameworks for fault diagnosis grounded in Bayesian networks (BN) and the more generalized Dempster-Shafer theory (DST) are examined in [55] and [56], respectively. For background and additional information on DST, see [57]. The challenge explored by [PERSON] et al. (2016) in [56] is that of conflicting evidence, with the observation that, in practice, sensors are often disturbed by various factors. This can result in a conflict in the obtained evidence, specifically in a discrepancy between the observed results and the results obtained by fusion through [PERSON]'s combination rule. This challenge reveals the need to reprocess the evidence using some framework or methodology prior to fusing it. [PERSON] et al. (2016) propose to do so with an information fusion fault diagnosis method based on the static discounting factor, and a combination of K-nearest neighbors (KNN) and dimensionless indicators [56]. Just as in [PERSON] et al. (2016), [PERSON] et al. (2016)'s method is applied to fault diagnosis among rotating machinery in a large-scale petrochemical enterprise. [PERSON] et al. (2017) employ Bayesian inference to mine semiconductor manufacturing data for the purposes of detecting underperforming tool-chamber at a given production time. The authors use [PERSON]'s kappa coefficient to eliminate the influence of extraneous variables [58]. The tool-chamber problem examined in [58] is relevant to this review in that it employs a large number of binary input variables in its model, one for each tool and each step, equal to 1 if the tool-chamber feature was used in a step and equal to 0 if not. The feature filtration approach employed is a two-fold application of [PERSON]'s kappa coefficient, once for pairwise comparison of the features against each other and once for features against the target. Features exhibiting high agreement with each other are wrapped with peers into a group; feature exhibiting low agreement with the target are removed from the model, with 0.20 as the threshold for removal. This method is appropriate when features and the target are both binary; a limitation is the method is not suitable for data in other forms. This required the target to be transformed from a continuous yield percentage to a categorical classification. A second possible limitation is that each variable is tested independently of the others, with no consideration for interaction. It is logically possible that a feature could have a poor [PERSON]'s kappa coefficient but could interact with other features to produce an overall better model. An advantage of the approach, though not specifically discussed in the article, is that [PERSON]'s kappa coefficient scores for each feature may be preserved from one analysis to the next and analyzed to see if they harbor latent relationships that might point to root causes of inadequate tool-chamber and not simply forecast it. The final framework for fault detection that this literature review will explore is a cyber-physical system (CPS) architecture proposed by [PERSON] (2017) for fault detection and classification (FDC) in manufacturing processes for vehicle high intensity discharge (HID) headlight and cable modules [59]. For additional background and exploration of CPS, see [60; 61; 62; 63]. Although much of the article is devoted to material outside the scope of this review, such as network and database architecture, the manufacturing process explored is notable because it involves multiple subprocesses, some of which are performed in-house and some of which are outsourced to external parties. Furthermore, although there is a small set of main defects that may be observed (shorted cable, cable damage, insufficient soldering, and bad marking), those faults are not directly traceable to a single subprocess. Rather, any number of different subprocesses may result in any fault type. The impact, when performing fault detection and classification, is that the cause-effect relationships and the backwards tracing of faults to diagnoses must take place beforehand.