Rotating Machine Prognostics Using System-Level Models

Choi

2021

Sensors

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

Section: Algorithms For System-level Prognosticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Challenges and Opportunities of System-Level Prognostics

Choi

2021

Sensors

“…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.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Technical Specifications of Predictive Maintenance: A Case Study of Centrifugal Compressor

Nordal

El‐Thalji

2021

Applied Sciences

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.

“…Traditionally, such analyses were based on a single parameter originating from one sensor; in contrast, the Industry 4.0 environment requires combining several different sensor signals and parameters, ie, process and health, with enterprise level data 28 . Furthermore, the new techniques for data acquisition and analysis require new algorithms that analyze the big data from these multivariate sensor signals to perform accurate and reliable diagnosis and prognosis 29 . This calls for a PdM architecture that integrates enterprise‐level data with monitoring data, ie, performance and health parameters 14 …”

Section: Introductionmentioning

confidence: 99%

Modeling a predictive maintenance management architecture to meet industry 4.0 requirements: A case study

Nordal

El‐Thalji

2020

Systems Engineering

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.