## 3 Literature Survey
### Big Data Approaches for Specific Manufacturing Applications
#### 3.2.2 Fault Prediction  
In [64], [PERSON] et al. (2017) present a manufacturing big data approach to the active preventive maintenance problem, which includes a proposed system architecture, analysis of data collection methods, and cloud-level data processing. The paper mainly focuses on data processing in the cloud, with pseudocode provided for a real-time processing algorithm. Two types of active maintenance are proposed as necessary: a real-time component to facilitate immediate responses to alarms and an offline component to analyze historic data to predict failures of equipment, workshops or factories. Of interest to this review is to note that the aforementioned approach is in the context of an organization's ability to perform active preventive maintenance and not in the context of how a data scientist goes about performing his or her analysis. For example, 'data collection' in the context that [PERSON] et al. describe refers to the required service-oriented architecture to integrate data from diverse sources. To the data scientist, 'data collection' is the employment of that architecture in identifying and obtaining specific data elements for model inclusion. [PERSON] and [PERSON] (2014) apply big data predictive analytics to proactive semiconductor production equipment maintenance. Beginning with a review of maintenance strategies, the researchers present advantages and disadvantages for each of four different maintenance strategies: run to failure (R2F), preventive, predictive, and condition-based. Following this background, an approach for predictive maintenance is presented as follows [65]:  
* Collect raw FDC, equipment tracking (ET), and metrology data
* Perform data reduction using a combination of principal component analysis (PCA) and subject matter expertise. This step, in the semiconductor case study, reduces the set of possible parameters from over 1000 to precisely 16
* Train model
* Display output to dashboard with a Maintenance/No Maintenance status  
Two immediate observations are apparent when considering the data reduction step employed in this model. First, the use of PCA is effective but it carries with it the loss of interpretability after the fact. This limits the options associated with the dashboards created for visualization of model results. If there were an alternative to PCA that retains interpretability, it may be possible to identify specific thresholds in the input data that are triggers for required maintenance and then track proximity to those thresholds in a dashboard. A second observation is that PCA requires linearity among the parameters because it relies on Pearson correlation coefficients. It also assumes that a feature'scontribution to variance relates directly to its predictive power [66]. It is not clear that this is always an appropriate assumption. [PERSON] and [PERSON] (2017) present a big-data analytics-based fault prediction approach for shop floor scheduling. This application of the big data problem focuses less on the availability of machining resources and more on the problem of potential errors after scheduling [67]. Specifically, it is observed that task scheduling using traditional techniques considers currently available equipment, with time and cost saving as the main objectives. Missing from consideration is the condition prediction of the machines and their states. In other words, scheduling is made absent of any information on the expected condition of the machines during the production process. In the proposed framework, tasks are represented by a set of data attributes, which are then compared to fault patterns mined through big data analytics. This information is then used to assign a risk category to tasks based on generated probabilities. The model provides the opportunity for prediction of potential machine-related faults such as machine error, machine fault, or maintenance states based on scheduling patterns. This knowledge can lead to better machine utilization.