## 4 Conclusions  
PdM needs to be incorporated with opportunistic maintenance to avoid shutdowns due to failures, unintended maintenance events, and high levels of service (repair or replacement due to damage spread from the faulty unit to the entire system). Knowing the failure symptoms, measuring techniques, and diagnostic analytics is not sufficient to determine the required technical specifications, even though monitoring technology has evolved over time, and a new method of FMSA was introduced by ISO 13379-1 [8] to identify both the critical symptoms and descriptors of failure mechanisms. FMSA is used to estimate the monitoring priority, and this helps to determine the critical monitoring specification. However, current FMSA cannot determine the effectiveness of technical specifications that are essential for predictive maintenance such as detection techniques (capability and coverage), diagnosis (fault type, location, and severity), or prognosis (precision and predictive horizon). This paper proposes a novel PdM assessment matrix to cover these gaps. This includes priority indexes that enable the efficiency of detection, diagnosis, and prognosis techniques to be compared using qualitative descriptions with quantitative values. The PdM assessment matrix enables (1) identification of the most suitable PdM solution for the generic system under study, (2) assessment of the levels of the existing monitoring solution, and (3) determination of how an existing monitoring solution can achieve higher levels of PdM. The proposed PdM assessment matrix is demonstrated in the case study of a centrifugal compressor and validated using empirical data provided by the case study company. The example highlights the entire process from the identification of critical components and failure modes to the incorporation of PdM to detect, diagnose, and predict the failure modes under study. It also highlights gaps in sensor coverage, potential superfluous sensors, and the optimal monitoring solution (i.e., sensor combinations and techniques for detection, diagnosis, and prognosis). The results clearly demonstrate the limitations of simple data analysis and the trending of single parameters, especially considering the levels of detection, diagnosis, and prognosis of failure mechanisms and failure modes. As a supplement, the paper develops a PdM assessment matrix based on the literature and considering Industry 4.0 technologies. It reinforces industrial expectations about the potential to improve current levels of detection, diagnosis, and prognosis and shows improvements may occur with the application of multivariate analysis. Several advanced diagnosis and prognosis techniques are not yet utilized in this industrial case, which shows a gap between what is available in literature and what is applied in industry, specifically, in this case study. Therefore, more case studies shall be conducted to study if the gap is case-specific or exists at the sector level. However, the proposed PdM assessment matrix can clearly help maintenance engineers at the design phase to determine and assess their selected technical solutions (combination of sensors, detection, and diagnosis and prognosis techniques). The developed PdM assessment matrix is generalizable due to several considerations: (1) standardized and industrially well-known definitions are used, e.g., failure modes, mechanisms, and symptoms, (2) it adopts the maintenance data definitions of ISO 14224, (3) it involves well-known stakeholders and their opinion in deciding about monitoring technique effectiveness, and (4) it includes well-known techniques presented in literature. However, it is important to highlight that since humans are involved in the PdM assessment matrix process (e.g., the categorization matrices in Tables 3-5), the assigned values are somewhat subjective, case-specific, and based on human experience.