## 1 Introduction
Furthermore, in recent years there have been new emerging technologies in CBM research. For instance, the application of machine learning models in CBM has grabbed the interest of multiple researchers. The comprehensive review by [9] provided a discussion on the application of machine learning models in diagnostic methods. In the research work by [11], the applicability of Shapley Additive explanation (SHAP) machine learning algorithm to identify critical features for fault detection and classification in rotating machinery was investigated. The algorithm demonstrated that, when applied alongside diagnosis models such as Support Vector Machines (SVM) and k-Nearest Neighbour (kNN), prediction accuracies above 98.5% are achieved. Similarly, the work by [12] showcased the application of an artificial neural network (ANN) and Gaussian process regression model to diagnose the transfer robot's multi-component health condition and system performance. Another interesting application of machine learning is in the work by [13],  
Figure 1: CBM detailed framework. where a Generative Adversarial Network (GAN) coupled with a cycle consistency loss function was applied to generate synthetic bearing fault data to substitute its experimental counterpart. Lastly, another emerging area of CBM research is estimation theory, more specifically the application of Kalman filters. For example, the work by [14] proposed the application of an intelligent digital twin coupled with machine learning to identify bearing crack size. They employed the use of a Kalman Filter (KF) in the signal estimation section of their methodology. Similarly, the work by [15] applied an extended KF (EKF) to estimate the bearing end-of-life time. Moreover, ref. [16] conducted a comparative study between the EKF and Unscented KF (UKF) to detect faults in permanent magnet synchronous generators (PMSG). Lastly, the recent published work by [17] merged both a deep belief machine learning network and a minimum UKF to develop a diagnostic model for early bearing fault diagnosis. Their proposed model, when tested on two datasets, achieved accuracy prediction rates above 98% and 99%, respectively, at different operational parameters. Moreover, their model performed significantly higher than other proposed methods, especially at the 300 rpm range. All in all, these various works demonstrate how CBM is a research area filled with new opportunities and emerging technologies. For the presented paper, the focus of the literature review was aimed towards the segmentation of data and misalignment fault detection for linear axis. Detection of faults in a CBM system falls under the diagnosis section. This is a research area filled with multiple opportunities to advance knowledge in the CBM field. However, to reach a proper diagnosis of the system's health state, the collected data must undergo signal processing and analysis. Therefore, to address the challenges of maintenance, a novel vibration signal segmentation technique was developed to detect and localize a horizontal misalignment on the linear guide rail. The paper was structured into five sections. Section 1 offers a concise introduction about the cost of maintenance in industry and research in CBM. Section 2 provides a comprehensive literature review in the application of signal segmentation in CBM systems and discusses how misalignment impacts the linear axis system and why it should be detected promptly. In Section 3, the research methodology and proposed signal segmentation technique are presented. Section 4 discusses the results from the conducted studies. Finally, Section 5 summarizes the conclusion and future work for the presented technique.