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

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.publishedVersio

The Impact of Patient Infection Rate on Emergency Department Patient Flow: Hybrid Simulation Study in a Norwegian Case

The COVID-19 pandemic put emergency departments all over the world under severe and unprecedented distress. Previous methods of evaluating patient flow impact, such as in-situ simulation, tabletop studies, etc., in a rapidly evolving pandemic are prohibitively impractical, time-consuming, costly, and inflexible. For instance, it is challenging to study the patient flow in the emergency department under different infection rates and get insights using in-situ simulation and tabletop studies. Despite circumventing many of these challenges, the simulation modeling approach and hybrid agent-based modeling stand underutilized. This study investigates the impact of increased patient infection rate on the emergency department patient flow by using a developed hybrid agent-based simulation model. This study reports findings on the patient infection rate in different emergency department patient flow configurations. This study’s results quantify and demonstrate that an increase in patient infection rate will lead to an incremental deterioration of the patient flow metrics average length of stay and crowding within the emergency department, especially if the waiting functions are introduced. Along with other findings, it is concluded that waiting functions, including the waiting zone, make the single average length of stay an ineffective measure as it creates a multinomial distribution of several tendencies.publishedVersio

A summary of adapting Industry 4.0 vision into engineering education in Azerbaijan

Industry 4.0 vision and associated technologies are rapidly adopted in several industrial sectors to gain the benefits of creating smart cyber-physical systems and operations. Some sectors, e.g. manufacturing, oil and gas, offshore wind energy, have progressed in developing digitization strategies, executing pilot projects and progressing toward mature implementation of industry 4.0 vision. Offshore Oil and Gas industry highly believes in the potential industrial and societal impacts of digital transformation, due to the need for stochastic and remote operations. Azerbaijan as one of the countries that heavily depend on the Oil and Gas industry is developing more projects in the Caspian Sea. There are several worldwide challenges, mainly, lack of standards, business models, ready products/services and competent and skilled employees. Fortunately, specific developed countries are working hard to standardize industry 4.0 architecture. Moreover, large-scale companies are creating alliances to create a trustful and long-term business model. Furthermore, large-scale companies of information and operational technology are creating robust products and services to be commercially available off the shelf. In terms of education and training, many worldwide universities are upgrading their programs, curriculums, teaching approaches with the goal to support the industry with competent future employees and entrepreneurs. Therefore, the purpose of this paper is, to present the status of engineering education programs in adapting the industry 4.0 vision in Azerbaijan and address the skills that are required for future employment. In order to present the targeted status, the curriculums of all engineering education programs at the master level were collected and analyzed. However, five of them were directly adapting industry 4.0 vision and relevant for industry 4.0. Moreover, a semi-structured interview with industrial managers was applied to extract the future required skills. This study can be considered as a first step in developing a roadmap for engineering education, particularly industrial engineering, to adopt industry 4.0 vision at the national level.acceptedVersio

Problems with using Fast Fourier Transform for rotating equipment: Is it time for an update?

Condition-Based Maintenance (CBM) is widely used to manage the condition of rotating machinery. They require a number of CBM tools to detect acous-tic and vibration signals. One such method is the Fast Fourier Transform (FFT). FFT converts a vibration signal from time domain to its equivalent fre-quency domain representation. Unfortunately, there are dramatic assumptions made related to the proper use of FFT. The paper will provide evidence that this approach may not be the perfect tool for fault detection and diagnosis. The paper celebrates with the limitations of FFT and does not muffle the culpa-bilities of our developed diagnosis culture. The aim is to challenge researchers to come up with something more developed to eventually take use of the processing power we have today

Characterization of acoustic emission signals under 3-point bending test

This paper summarizes a master’s thesis project which explored whether the characteristics of Acoustic Emission Testing (AET) signals can be used to detect yielding in steel samples undergoing a three-point bending test. A subset of existing data from a three-point bending test was exported and used as input. Data was processed by utilizing and developing tools to visualize and analyse the signal characteristics, primarily through a parameter-based approach. Signals were visualized, and parameters were optimized to identify and classify signal types. According to the obtained results, some limitations on classification were experienced due to the length of the hit data recorded. Though the work reported in this article lead to a reliable method for detecting yielding, the developed algorithms were not successful in identifying characteristics that could be used to detect yielding.publishedVersio

Context analysis of Offshore Fish Farming

The in-land and nearshore fish farming is facing capacity limitation and onshore push-out regulations. Huge technological innovations are rapidly evolving toward developing competitive Offshore fish farming. These technological innovations are mainly targeting to innovate new farming concepts that dynamically stable, reliable and compatible with offshore environmental loads and conditions. The dynamic operational behaviour of each farming concept is quite complicated. It is a combination of reinforcing behaviours (Loads, cage deformations, welfare issues, e.g. escaping, stress-related disease) and leveraging behaviours (Biofouling-cleaning, Deterioration-maintenance) and all influenced by fluctuating and harsh environmental loads and conditions. Therefore, the purpose of this paper is to analyse the context of offshore fish farming and explore quantitative descriptions of its reinforcement and leveraging behaviours. The context analysis is a well-known method within systems engineering methodology to illustrate and extract critical interfaces. The context analysis is considered as the first step in building simulation model to quantify the impact of systems interfaces on the entire farming economics, i.e. income and cost.publishedVersio

Predictive maintenance (PdM) analysis matrix: A tool to determine technical specifications for PdM ready-equipment

Predictive maintenance (PdM) and operations optimisation are expected to generate the highest industrial and societal impact within the oil and gas industry. Such an optimistic expectation requires several changes in asset design and maintenance management. Nowadays, design for maintenance and maintenance support needs are mainly guided by the IEC 60706-2 standard. However, Designing for PdM ready-equipment is not yet part of that standard. To design PdM ready-equipment a specific analysis method shall be performed to evaluate the technical requirements and specifications of designed equipment to be PdM ready. Therefore, the purpose of this paper is to propose and demonstrate a PdM analysis method that helps to specify the technical specifications to monitor and predict the health of a specific physical asset. The proposed matrix is an evolution of further development of failure Mode, effect and criticality analysis (FMECA) and failure mode symptoms analysis (FMSA) rather than a revolutionary analysis. The case study method is used to extract stakeholders needs of what they expect from PdM analysis (PdMA) and how practical such type of analysis shall be. The developed PdMA matrix shows a simple relation between failure (their modes/levels) and measured abnormal symptoms and tracking and prediction indicators. The electric generator is used to demonstrate the use of the matrix. The PdMA matrix can be developed further to be more quantitative by including the probability of detection and probability of prediction.publishedVersio

Lifetime Benefit Analysis of Intelligent Maintenance: Simulation Modeling Approach and Industrial Case Study

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.publishedVersio