# Machine Failure Prediction Using Survival Analysis  
[PERSON]  
1 Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, 35131 Lamia, Greece  
prompt fault reporting. Previous research has used various techniques to create predictions in scenarios such as the autoregressive model [2], principal component analysis [3], and opposite degree algorithm [4]. Furthermore, a significant amount of research has been carried out in the anomaly detection field, which has a closely correlated relation to machine failure. The research involves a variety of proposed models mostly making use of different machine learning techniques such as ANN [5; 6], RF and SVM [7], and convolutional neural networks (CNNs) and long short-term memory (LSTM) [8]. To accurately predict machine failure and prevent costly downtime, it is essential to utilize a reliable and flexible approach that can account for a wide range of factors influencing machine degradation. While traditional statistical models have been widely used in failure prediction [9], they often rely on strict assumptions about machine degradation patterns that may not accurately reflect the real-world complexity of the problem. In contrast, survival analysis has emerged as a promising approach, offering several advantages that can help improve the accuracy and efficiency of predictions. By incorporating time-to-failure information, handling right-censored data, accounting for covariate effects, and providing flexibility in application, survival analysis represents a superior alternative to traditional models in the context of machine failure prediction. This research aims to address a gap in the literature by exploring the use of survival analysis in combination with various feature selection/analysis and machine learning methods to predict machine failure. While some studies have utilized survival analysis to predict failure, few have examined the effectiveness of different feature selection/analysis methods in conjunction with this technique. Most of the existing research focuses primarily on different machine learning models, such as LSTM networks [10; 11], moving away from the survival analysis approach. While machine learning models are undoubtedly useful in predicting machine failure, survival analysis may be more appropriate when the goal is to predict failure times and identify key factors in failure prediction. In this proposed model, a machine learning survival analysis technique is used, along with feature analysis and selection methods. The machine learning model used is random survival forest (RSF), which is well-suited to time-to-event data, such as machine failure times, and has several advantages over other machine learning methods in the context of survival analysis. Examples include the ability to handle time-dependent covariates, non-linear relationships between covariates and survival, and interactions between covariates [12]. Unlike survival analysis models, standard machine learning algorithms are not equipped to handle the right-censored data that are prevalent in machine failure datasets. RSF is a great way to incorporate machine learning and simultaneously overcome this problem, due to its ability to handle high-dimensional, complex data [13]. RSF can handle both continuous and categorical predictors, as well as complex interactions, nonlinear relationships, and time-varying effects. This makes it particularly useful in survival analysis settings where there may be many potential predictors that interact in complex ways [14]. One of the key benefits of using RSF is its ability to handle missing data, which is common in many real-world datasets [13; 14; 15]. RSF uses a tree-based approach to impute missing values by splitting the data at each node based on the available data, and then using the available data to make a prediction for the missing value [13]. This imputation process is repeated multiple times, resulting in a distribution of imputed datasets that can be used to estimate uncertainty.