Industry 4.0 is the latest paradigm of industrial production enabling a new level of organizing and controlling the entire value chain within a product life cycle by creating a dynamic and real‐time understanding of cross‐company behaviors. It is expected to have a considerable impact in the oil and gas (O&G) sector by revolutionizing current predictive maintenance and operation optimization. There are several challenges to be overcome before the Industry 4.0 vision is achieved: a standardized reference architecture, a business model, robust services, and products are all lacking. This paper develops a reference architecture for an intelligent maintenance management system that complies with Industry 4.0 visions and requirements. The industrial needs were derived from stakeholders and use case scenarios using a case study methodology. Systems engineering methods were applied to transfer the needs of the existing maintenance management system into a desired functional architecture. The new and upgraded requirements are predominantly related to advanced data analytics, resulting in new and modified functions within the traditional “Reporting” and “Analyses” modules. A more complex maintenance program is created through interfaces between various enabled data categories (historical records, real‐time measurements of performance and health, expert‐just‐in‐time). The study points to the changes required in the classical O&G maintenance management process to comply with Industry 4.0 vision and requirements.
Dependability analyses in the design phase are common in IEC 60300 standards to assess the reliability, risk, maintainability, and maintenance supportability of specific physical assets. Reliability and risk assessment uses well-known methods such as failure modes, effects, and criticality analysis (FMECA), fault tree analysis (FTA), and event tree analysis (ETA)to identify critical components and failure modes based on failure rate, severity, and detectability. Monitoring technology has evolved over time, and a new method of failure mode and symptom analysis (FMSA) was introduced in ISO 13379-1 to identify the critical symptoms and descriptors of failure mechanisms. FMSA is used to estimate monitoring priority, and this helps to determine the critical monitoring specifications. However, 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). The paper proposes a novel predictive maintenance (PdM) assessment matrix to overcome these problems, which is tested using a case study of a centrifugal compressor and validated using empirical data provided by the case study company. The paper also demonstrates the possible enhancements introduced by Industry 4.0 technologies.
The introduction of Industry 4.0 is expected to revolutionize current maintenance practices by reaching new levels of predictive (detection, diagnosis, and prognosis processes) and prescriptive maintenance analytics. In general, the new maintenance paradigms (predictive and prescriptive) are often difficult to justify because of their multiple inherent trade-offs and hidden systems causalities. The prediction models, in the literature, can be considered as a “black box” that is missing the links between input data, analysis, and final predictions, which makes the industrial adaptability to such models almost impossible. It is also missing enable modeling deterioration based on loading, or considering technical specifications related to detection, diagnosis, and prognosis, which are all decisive for intelligent maintenance purposes. The purpose and scientific contribution of this paper is to present a novel simulation model that enables estimating the lifetime benefits of an industrial asset when an intelligent maintenance management system is utilized as mixed maintenance strategies and the predictive maintenance (PdM) is leveraged into opportunistic intervals. The multi-method simulation modeling approach combining agent-based modeling with system dynamics is applied with a purposefully selected case study to conceptualize and validate the simulation model. Three maintenance strategies (preventive, corrective, and intelligent) and five different scenarios (case study data, manipulated case study data, offshore and onshore reliability data handbook (OREDA) database, physics-based data, and hybrid) are modeled and simulated for a time period of 20 years (175,200 h). Intelligent maintenance is defined as PdM leveraged in opportunistic maintenance intervals. The results clearly demonstrate the possible lifetime benefits of implementing an intelligent maintenance system into the case study as it enhanced the operational availability by 0.268% and reduced corrective maintenance workload by 459 h or 11%. The multi-method simulation model leverages and shows the effect of the physics-based data (deterioration curves), loading profiles, and detection and prediction levels. It is concluded that implementing intelligent maintenance without an effective predictive horizon of the associated PdM and effective frequency of opportunistic maintenance intervals, does not guarantee the gain of its lifetime benefits. Moreover, the case study maintenance data shall be collected in a complete (no missing data) and more accurate manner (use hours instead of date only) and used to continuously upgrade the failure rates and maintenance times.
Emerging technologies of Industry 4.0 have introduced novel ways of perceiving maintenance management, which has developed from being perceived as a "necessary evil" to become proactive with a holistic focusing on entire systems rather than single machines from Maintenance 3.0. In this context, the industry has begun to really appreciate the unique opportunities followed by system dynamics and simulation tools capabilities of representing the real world. However, maintenance management and performance are complex aspects of asset's operation that is difficult to justify because of its multiple inherent trade-offs. Although the majority areunanimous when it comes to the expected impact maintenance plays on company profitability, this is in most cases challenging to determine and quantify. Moreover, relevant literature is considered as limited, especially with regards to impact simulation of Maintenance 4.0. Therefore, this paper focuses on the supportive function system dynamics, and modeling and simulation tools can be of help to assess behavior and predicting the future outcome of Maintenance 4.0 in the era of Industry 4.0. This includes developing a conceptualized model that enables simulating the future expected behavior i.e. (un)availability and cost by implementing such a maintenance system. In this context, a centrifugal compressor with the function of exporting gas to Europe is applied as a case study.
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