# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 1 Introduction  
In modern manufacturing, machines in factories operate continuously for 24 h a day to fulfill the production requirements. In such cases, continuous production or continuous flow process enables factories to manufacture, produce, or process materials without interruptions [1]. However, massive industrial processes can be detrimental to machines. Various unstable operations, such as abnormal events, failures, or non-active states, can occur in the machines. These non-active states can significantly affect continuous production in the factories, incurring a significant loss to the fabricator and decreasing production rates. Besides, constant interruptions in manufacturing make it difficult for fabricators to fulfill their commitments to the consumers [2]. Therefore, predicting non-active states of machines in advance is essential to reduce machine faults and increase productivity. As industrial processes become more and more complex, there is a need to automate the detection and prediction of unstable operations of machines as maintenance decisions need to be taken quickly to avoid costly interruptions [3]. However, the automation of such tasks was often challenging in the past due to the lack of sensors and data-driven technologies. On the other hand, with the rapid development of data acquisition devices based on sensor technologies, factories now collect a massive amount of data related to the state of the machines. We can apply various data-driven models to analyze the sensor data and obtain meaningful insights that could be helpful in achieving automation in detecting and predicting the unstable operations of machines [4,5]. Thus, data-driven maintenance of machines has become the mainstream solution for solving various manufacturing issues. Most studies on data-driven maintenance of machines in the literature are based on statistical, machine learning, and deep learning methods. Well-known statistical analysis methods, such as Bayesian algorithms [6] and hidden Markov models [3], have been proved to be an effective solution forestimating time-to-failure (TTF) and detection faults in machines. On the other hand, machine learning methods, such as k-nearest neighbors (kNN) [7] and Artificial Neural Networks (ANN) [8], have been actively utilized in predicting unstable operations of machines, especially in the presence of a large amount of data. Deep learning methods, such as a dual-path recurrent neural network (RNN) [9] and Back Propagation Neural Network (BPNN) [10], have recently shown strong performances in detecting the early mechanical fault in various manufacturing processes. Most of the methods mentioned above focus on detecting or predicting large-scale failures in machine operation that can be potentially harmful but not frequent. On the other hand, there could be various non-active states associated with machines that occur more frequently and can be equally damaging. For example, machines may become non-active due to the supply of poor materials, wear, disconnection, burnout, or equipment related malfunctioning in sensors, motors, and control switches. Predicting the non-active status of machines can bring to the benefits efficient production of manufacturing machines, such as reduction of long-term machine failures and maintenance cost, and increasing machine lifetime and production. Thus, in this paper, we focus on predicting non-active states of the machines using statistical feature extraction and machine learning. More specifically, we make the following contributions in the paper:  
* We propose to extract various statistical features from the raw data. We first extract so-called reliability features using Weibull, lognormal, and exponential distributions. These distributions are mainly used for life data analysis to estimate the lifetime of a machine. Further, we propose a method to compute a new feature, i.e., the machine status tracking value (MSTV), which can distinguish the active and non-active patterns of a machine more effectively.