## 2 Related Work  
Segmentation techniques proposed in the literature are a well-established signal processing method that are mainly focused on improving the signal's quality or detecting operational change points in the signal for a more efficient and focused analysis. This can be observed, for instance, in the presented research work by [18]. An automatic time series segmentation technique was proposed to identify and characterize machine tool operational states. Their technique divided the power factor curve to identify the machine's constant (stand-by, operational readiness) and non-constant (acceleration, deceleration, machining, and set-up) operational segments. It was concluded that with their proposed segmentation, signal preparation in an industrial environment can be conducted for possible automatic device number recognition and load disaggregation at a machine perspective. Furthermore, through signal segmentation, operational state-based and self adaptive CBM is possible. Finally, detecting constant and non-constant operational states through their proposed segmentation method can be utilized to determine value and non-value-added operational times for a more efficient use of the machine. Similarly, the research work presented by [19] proposed a vibration signal segmentation technique for condition monitoring of wind turbine gearboxes. The segmentation technique segments the non-stationary signal to match specific speed profiles to the component. An improvement on signal features for fault diagnosis was observed when applying their proposed segmentation technique. Moreover, their technique aimed at removing redundant information that is contained within the non-stationary portion of the evaluated vibration signal. It was concluded that their approach yielded a 97% classification accuracy. Furthermore, their proposed methodology was able to accurately segment the signal to match a component rotation. However, their technique was dependent on an external sensor to synchronize the signal for accurate segmentation. Another example of signal segmentation application was presented by [20]. In their work, the use of signal segmentation to increase the accuracy of a convolutional neural network (CNN) in identifying gear faults was showcased. Since, it was identified that neural networks are tested against the same datasets as validation. However, in practice, faults may develop somewhere else, which may lead to erroneous health assessments. The proposed segmentation technique divided the signal based on the corresponding gear teeth interval. The method demonstrated its performance-enhancing capability for the CNN to detect artificially infused faults accurately. However, they identified, as a future step, the need to test it in actual machining environments. More recently, ref. [21] proposed a ballscrew health monitoring method by evaluating its health through inertial sensor data. In their method, signal segmentation was applied as a pre-processing step to separate the signal based on its steady and transient states. It was concluded that their presented method was capable of detecting faults in the ballscrew system and quantify backlash error. Similarly, ref. [22] proposed a novel acoustic emission signal segmentation framework for bearing fault analysis. The framework utilized signal segmentation as a pre-processing method with the main objective of accurately extracting REB fault impact characteristics, with the aim of maximizing detection rate. Likewise, in the work presented by [23], a signal segmentation procedure was utilized for the analysis of seismic signals. In their work, their method isolated events from the collected seismic signal and evaluated them individually. Finally, ref. [24] applied a signal segmentation technique that primarily divided the signal into sections so they can be assigned to specific machine operational states including idle, machining, and anomalies. Their segmentation utilized an RMS windowing function to detect change points in the signal.