Assessing the Technical Specifications of Predictive Maintenance: A Case Study of Centrifugal Compressor  
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of failure. Such analyses were traditionally based on a single parameter, but an Industry 4.0 environment combines several different sensor signals and parameters (i.e., process, health and performance indicators) with enterprise-level data (e.g., production forecasts, spare part management, and equipment information), all the way down to the component level [2]. These novel techniques for data acquisition and analysis require new algorithms that manage the analysis of multivariate sensor signals (i.e., big data) to perform accurate and reliable detection, diagnosis, and prognosis of the equipment or component of interest [3]. The use of predictive maintenance (PdM) in Industry 4.0 is expected to yield many benefits [4], but the extent of the enhancements (i.e., levels of detection, diagnosis, and prognosis) is not yet known. Designing for maintenance and maintenance support is already guided by IEC 60706-2 [5]. In addition, several existing standards provide supportive guidelines for the implementation of different maintenance strategies. For instance, the NORSOK Z-008 standard [6] has guidelines to implement preventive maintenance, while IEC 60812 [7] demonstrates the development process of failure mode, effect, and criticality analysis (FMECA) to support the assessment of component criticality and failures for equipment that is designed-out or frequently maintained. The ISO 13379-1 [8] and ISO 17359 [9] standards stipulate the implementation of condition-based maintenance (CBM), showing how to monitor failure propagation through failure mode and symptom analysis (FMSA). However, no standards are available to design PdM-ready equipment, despite the promise of PdM in an Industry 4.0 environment [4]. Certain technical requirements and specifications must be considered to standardize guidelines to design PdM-ready equipment [10]. First, designing for PdM-ready equipment does not solely consider the process of sensor implementation and data acquisition, as in traditional CBM. However, all the equipment in the physical space (hardware) must be aligned with the cyberspace equipment (software) to perform the required detection, diagnosis, and prognosis. Second, PdM in Industry 4.0 requires combining several different sensor parameters with enterprise-level data of varying quality (i.e., value, resolution, etc.). The technical requirements include determining the specific failure symptom (tracking indicator) of the failure mechanisms associated with the system under study [10]. Then, effective feature extraction procedures and data analysis techniques must be selected to track the symptom as early as possible over the entire fault evolution process to detect, diagnose, and predict under different scenarios (e.g., fluctuating operation, change of loading, multi-faults, and fault interactions). Technical analyses play a decisive role in reaching a detailed understanding of system criticality and risk assessment. FMECA and FMSA are well-known systematic bottom-up approaches. Although they have the same objective of providing a holistic risk picture of a system, they differ to some extent. Briefly, FMECA facilitates examining the effects and consequences of the occurrence of specific failure modes associated with the specific system under study. Meanwhile, FMSA considers the detection of the failure modes associated with the system by connecting the occurrence of failure modes to specific monitorable symptoms using sensor technology. The concept of FMSA was first standardized by ISO-13379-1 [8]. In addition, several big O&G companies developed their own FMSA procedures. The PdM analysis matrix [10] is the most recent update of FMSA, presented in ISO 13379-1 [8]. It adds prediction through prognosis to the traditional focus on detection and diagnosis.