# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 2 Related Studies  
In this section, we discuss the related studies that focus on the detection and prediction of unstable operations in machines. We can classify these studies into three categories: (1) Studies based on statistical analysis, (2) studies based on machine learning, and (3) studies based on deep learning. There have been several studies that used statistical analysis for the detection and prediction of unstable operations in machines. For example, [PERSON] et al. [6] proposed a three-step degradation TTF prognostic method for rolling element bearings (REBs) in an electrical machine. The three-step degradation approach includes degradation feature extraction, degradation feature reduction, and TTF prediction. To detect degradations during degradation feature extraction, multiple degradation features, including statistical, intrinsic energy, and fault frequency features, were extracted. In the degradation feature reduction step, the authors performed a feature fusion using dynamic principle component analysis (DPCA) and Mahalanobis distance. In the final step, the authors utilized the exponential regression algorithm to compute the local degradation model and the empirical Bayesian algorithm to calculate the global TTF prediction. The experimental results demonstrated that the proposed method achieves a good performance of the TTF predictions compared with the existing methods. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed a method for detecting and diagnosing mechanical faults in machining processes and rotating machinery using hidden Markov models. The experiment results with cutting tool and bearing monitoring cases demonstrate that the fault severity classification was greater than 95%. The authors also analyzed fault localization using a new concept called location index. The results of the analysis demonstrated that the proposed method classifies the various fault location in bearing monitoring with an accuracy of 96%. Machine learning techniques are efficient in analyzing a large amount of data and derive various useful patterns. Considering that factories collect more and more data related to the machine state, we can efficiently utilize machine learning techniques in detecting and predicting unstable operations of machines. A similar study was carried out by [PERSON] et al. [PERSON] et al. (2018). In this study, the authors introduced an approach for fault detection using random projections and the RP-kNN rule for semiconductor manufacturing processes. The authors utilized random projection, which is a type of dimension reduction method, to maintain the pairwise distances between samples in a random subspace and established a detection model with the kNNs algorithm. Besides, the authors illustrated PC-kNN ([PERSON] et al., 2015) methods in a principal component subspace (PCS) to prove that the RP-kNN was more effective at preserving pairwise distances in the PCS. In the experiment, it was discovered that the detection model constructed using RP-kNN performed better, with approximately 82% accuracy compared with 77% of the PC-kNN model. [PERSON] et al. [PERSON] et al. (2018) proposed to estimate the remaining life of used components in consumer products with Weibull distribution and ANN. The proposed model was constructed in two stages. In the first stage, the authors used Weibull distribution to analyze the behavior of components for reuse. Specifically, the Weibull distribution was applied to the TTF data to assess the mean life of the component. In the second phase, the authors developed an ANN model, a multilayer feedforward backpropagation neural network, to analyze the degradation and condition monitoring data. The performance evaluation with life cycle data from a washing machine demonstrates that the proposed model achieved an accuracy of approximately 86.6%. With the recent advances in deep learning, many researchers have proposed various data-driven methods to detect abnormal events in a machine or provide fault prognostics for factory machines. For example, [PERSON] et al.