Machine Failure Prediction Using Survival Analysis

With the rapid growth of cloud computing and the creation of large-scale systems such as IoT environments, the failure of machines/devices and, by extension, the systems that rely on them is a major risk to their performance, usability, and the security systems that support them. The need to predict such anomalies in combination with the creation of fault-tolerant systems to manage them is a key factor for the development of safer and more stable systems. In this work, a model consisting of survival analysis, feature analysis/selection, and machine learning was created, in order to predict machine failure. The approach is based on the random survival forest model and an architecture that aims to filter the features that are of major importance to the cause of machine failure. The objectives of this paper are to (1) Create an efficient feature filtering mechanism, by combining different methods of feature importance ranking, that can remove the “noise” from the data and leave only the relevant information. The filtering mechanism uses the RadViz, COX, Rank2D, random survival forest feature ranking, and recursive feature elimination, with each of the methods used to achieve a different understanding of the data. (2) Predict the machine failure with a high degree of accuracy using the RSF model, which is trained with optimal features. The proposed method yields superior performance compared to other similar models, with an impressive C-index accuracy rate of approximately 97%. The consistency of the model’s predictions makes it viable in large-scale systems, where it can be used to improve the performance and security of these systems while also lowering their overall cost and longevity.