## 1 Introduction  
Maintenance is a crucial and necessary activity in the industrial sector to ensure the nominal operation of computer numerical control (CNC) production machines, their systems, and components. When timely and accurate maintenance is conducted, an increase in the competitive edge of an industry due to higher uptime, productivity, and lower unscheduled downtime can be observed. However, conducting maintenance in a system must be properly managed, since it can quickly become very costly. In the survey conducted by [1], it was estimated that the total cost of annual maintenance on average is USD 220 billion. In the report by [2], the goal of maintenance is to use its personnel more efficiently to reduce costs. Nevertheless, maintenance work may fall into three categories including unnecessary, unproductive, and counterproductive work. Where unnecessary work refers to routine equipment checks and equipment maintenance that was never needed. Unproductive work is related to activities such as data entry and retrieval, work-order reporting, and other types of paperwork that reduce the time an expert can spend working on the damaged machine. Lastly, counterproductive work is a maintenance activity that reduces the equipment's reliability, such as incorrect assembly, incorrect tightening, misalignment among other errors. According to [2], 70% of equipment failures can be traced back to improper initial installation or major preventative maintenance. Maintenance becomes more difficult due to the complex nature of the CNC machine. Therefore, separating the CNC into its crucial subsystems simplifies the maintenance required. The linear axis is one of the CNC's most crucial subsystems. Its capability for precise and robust positioning dictates the quality of the manufactured part. The operational and maintenance costs of the linear axis can be significant if no action is taken when faults start to develop. Furthermore, if faults are undetected or untreated in a timely manner, they can lead to part quality issues and complete system breakdown. To put it into perspective, the complete failure in the components of the linear axis, such as the linear guide rail, will warrant the need for immediate action, where a full replacement repair job must take place. This can be very costly due to the immense downtime needed to replace the damaged component and realign the axis. For instance, as reported by [3], the maintenance of linear axis components can cost over USD 10,000 per hour. As an example, it was reported that a production line lost an estimated USD 200,000 per day due to improper maintenance. Moreover, as discussed by a recent report by [4], an hour of unplanned downtime due to failure of machines and their components can cost the automotive sector USD 2 million. With the continuously increasing competition in the manufacturing industry, maintenance for CNCs and their components must be conducted smarter and with more precision. This goal has become more of a reality with the introduction of condition-based monitoring (CBM). CBM utilizes data collected from external sensors and the machine's operation to determine its health condition and that of its components. Through signal processing of the collected data, a CBM system can detect machine faults (diagnosis) and calculate the remaining useful life (RUL) (prognosis). Finally, the CBM system outputs a condition report that can be used to schedule maintenance activities without affecting the critical operational uptime of the machine. The detailed framework of a CBM system is illustrated in Figure 1.  
As previously mentioned, the complexity of the CNC machines and the noisy nature of the industrial environment makes the application of a holistic CBM system difficult, thus separating into its critical subsystems is crucial. In the literature, CBM and its application has a wide variety of research papers and reviews available in this field alone, some recently published review papers are referenced for the reader [5; 6; 7; 8; 9; 10].