## 1 Introduction
If planned, the machine is periodically inspected, and the identified parts are replaced based on a timetable. (_iii_) **Proactive Reliability Maintenance** (PRM) aims at determining the root failure causes, collecting measures, and performing corrective actions to avoid service stoppage [17]. It aims to anticipate failures or defects before they occur. It is defined in ISO 13306:2010 as an active preventive maintenance that can be carried out while the item is functioning. (_iv_) With a **Condition-Based Maintenance** approach (CBM), anomalies are identified and resolved prior to functional failure based on sensor signal monitoring. Maintenance is performed only when equipment problems have been registered. According to ISO 13306:2010, CBM is a preventive maintenance that includes a combination of condition monitoring and/or inspection and/or testing, analysis, and the ensuing maintenance actions. The condition-based monitoring approach calculates potential degradations and effective program maintenance. CBM points on data in order to get maintenance decisions. Vibration and pressure signals are generally used as the signals in monitoring. CBM in telligently avoids any unnecessary maintenance tasks in predicting interventions. CBM technologies significantly reduce the cost of maintenance and improve operational safety. PdM can be considered a CBM smart release that measures the condition of the equipment, forecasts possible failures and stoppages, and, consequently, proceeds in action to avoid any ramifications of failure. It acts on the role of a reliability centred maintenance (RCM), plant asset management system (PAM), and total productive maintenance (TPM) [18]. As this research area is an active one, a number of other reviews were identified in related fields. [PERSON] reviewed maintenance approaches with applicability in the industrial environment. They found that literature did not inform as to which new AI technology based on ML methods and techniques is influencing and changing the maintenance approaches in the industrial setup [19]. [PERSON] et al. reviewed the use of ML techniques in PdM applications [20]. The goal was to identify if ML was being applied in this field rather than to look at the selection of ML techniques to best serve PdM. Long over this paper, we considered situations where input of learning is collected in multiple data gathered from factory for digital twin applications [21, 22]. This paper reports a systematic mapping [23] of the machine learning technologies for predictive maintenance in smart manufacturing. It quantifies its role in literature to identify trends in research and usage. Its main aim is to assist new researchers in the identification of ML techniques for potential application the PdM field. In recent times, with the consolidating of Industry 4.0 and smart manufacturing [24], different proposals have appeared with predictive maintenance framework and, above all, systematic review analysis. [PERSON] and [PERSON] in 2020 proposed a predictive maintenance framework, developing fault classification based on machine learning, with application for motor conveyors. In this work, a principal component classification was applied to improve the effectiveness of fault diagnosis [25]. [PERSON] et al. in 2020 analysed predictive maintenance initiatives in the production of a high-quality hot rolling process. They applied Bayesian filter and machine learning to estimate machine degradation and plan maintenance initiatives [26]. [PERSON] et al., in 2019, analysed data-driven methods for PdM in the context of automatic washing equipment. They concluded about the importance of data mining and data amount for forecasting error affordability. Accuracy over different ML approaches was discussed [27]. A state-of-the-art analysis about industrial IoT devices and cyber-physical system cases related to maintenance was reported in the work of [PERSON] et al. in 2019. They remarked the importance of automatic fault classification for smart maintenance detection [28].