# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 3 State-of-the-Art
### Data Science Applications in Sensor Analysis
#### 3.1.5 Predictive Maintenance  
Predictive maintenance [40, 41], is the latest trend in Industry 4.0 to provide superior customer experience by using an automated and highly interconnected network of sensors to predict faults at the early stage and minimize industrial downtime. Predictive maintenance is a wider approach and mostly includes anomaly detection by detecting faults at an early stage in addition to estimating the remaining useful life and time-to-failure of equipment in industrial processes. A general framework of designing predictive maintenance solution is described in [42] with a case study from steel industries to estimate the degradation of coiler drums, using the discrete Bayesian filter. [PERSON] et al. in [43] used the wavelet decomposition model to analyze and predict faults in Cartesian robots based on the motor current signal, while [PERSON] et al. in [44] presented an ARIMA-based time series prediction approach to predict the remaining useful life of the target device in the factory settings based on aging features. [PERSON] et al. [45] used clustering techniques to assess the health of the wind turbines based on sensor data and introduced some post-processing techniques to interpret the meaning of the clusters and the associated healthy state they represent.  
Deep learning techniques are also frequently used in predictive maintenance. In [46], autoencoders and deep belief networks were proposed for early fault detection in digital manufacturing. In [47], dynamic predictive maintenance framework was proposed for the turbofan engine by using deep learning. In [48], deep learning was used on IoT for predictive maintenance of a _Porsche_ car based on the sound recorded on different parts of the engine.  
[PERSON] et al. [49] concluded that multi-core CPU machines do not scale sufficiently well for training deep neural networks and still require a dedicated GPU. This is a a serious limitation for those Industry 4.0 applications with machines embedded with computation-intensive hardware, such as 3D printers, as adding a GPU implies increasing the printing time of jobs. This also adds extra cost to the total value of the machine. Additionally, deep learning models are generally assumed to be black-box in nature and cannot explain the proposed predictions, which is critical in a decision-making context, as predictive maintenance often is.  
Thus, there is no existing method known to be effective in analyzing and controlling the manufacturing process in the context of multi-jet large-scale 3D printers [50]. A machine like a multi-jet 3D printer, running non-stop and generating tons of data, would thus require appropriate statistical techniques to ingest data and process them rather rapidly. Hence, for a suitable insertion in real production systems, near-real-time and non-supervised approaches are required; classic deep learning approaches for predictive maintenance might not be suitable.  
In this paper, the authors are attempting to establish telemetry monitoring and analysis, using an unsupervised learning approach (hierarchical clustering in particular) with a focus on explainable artificial intelligence methods.