# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 2 Literature Review  
Various studies have been conducted on the prediction of anomalies in manufacturing processes. We classify these studies into the following three categories: (1) studies that determine process capability and performance, (2) studies that detect abnormal situations, and (3) studies that targeted other issues in the smart manufacturing applications. Recall from Section 1 that there are several processes condition variables, such as Cpk and Ppk for process capability and performance [8; 9; 10; 11; 12], X-chart and R-chart for statistical process control [13; 14; 15], and production cycle time [16], which can be used collectively to determine the state of the manufacturing processes. Even though a unifying approach was proposed to determine Ppk by Spiring [9], the proposed methodology does not fully reflect the repeating characteristics and manufacturing flow of products. On the other hand, [PERSON] and [PERSON] [16] proposed determining production cycle time for analyzing the periodic iterative production processes in automated manufacturing systems. The authors decided that work patterns with the same production cycle time are exquisite. Although the proposed study considers the repeatability characteristic, it does not provide the stability characteristic of the data. Therefore, there is still a tradeoff between these studies. Most of the recent work on smart manufacturing focused on finding failures and abnormalities in the data by examining the process condition variables over time and finding patterns. For example, [PERSON] et al. [18] proposed real-time anomaly engine fault detection and prediction of the next sequence of engine conditions. They applied their approach to a real-world dataset of a company and used an auto-regressive integrated moving average (ARIMA) to predict the future value of a univariate time series. In addition, the authors developed a concept to improve their prediction model to obtain higher accuracy by using fault and warranty information to forecast. However, because their proposed approach does not achieve higher accuracy, their current results are still in the testing and accuracy improvement phase. Apart from that, [PERSON] et al. [19] proposed a hierarchical temporal memory (HTM), which is a novel anomaly detection algorithm. Their proposed method can detect unusual, anomalous behaviors using real-time streaming analytics in an unsupervised fashion. Similarly, [PERSON] et al. [20] proposed an anomaly detection and a predictive model based on LSTM to detect and monitor faults in multivariate industrial time series with sensors and control signals. They validate their approach by examining real-world data sets from several industries to detect normal and abnormal behavior. [PERSON] et al. [21] argued that predicting large-scale failures in machine operation could be harmful but not frequent. The authors define various non-active states associated with machines that occur more frequently and can be equally damaging. Predicting the non-active status of machines using a well-known machine-learning algorithm, linear support vector machines (SVM), demonstrated that the proposed method can predict the non-active status of machines with an accuracy of 98%. Several researchers tried to predict working condition and working pattern prediction in smart manufacturing applications. For example, [PERSON] et al. [22] proposed a method to predict power station working conditions based on the LSTM model. They collected data for three months from thirty-three sensors in the main pump of a power station. Then, they built an LSTM neural network model to predict equipment-monitoring data. Lastly, they improved the LSTM prediction model by optimal hypermeter searching and feature engineering. To optimize the prediction parameters, they used orthogonal experiment design and root mean square error (RMSE) to evaluate the LSTM model prediction accuracy. On the other hand, [PERSON] et al.