Criticality Analysis Techniques Applied to Optimize Maintenance Management Processes

The techniques of criticality analysis are tools that allow identifying and hierarchy for their importance the assets of an installation on which it is worth directing resources (human, economic, and technological). In other words, the process of criticality analysis helps determine the importance and consequences of potential failure events of production systems within the operational context in which they perform. Taking as reference the maintenance management model (MMM) of the eight phases, this chapter related to techniques of prioritization and criticality is part of Phase 2 of the MMM. In the following chapter, the most important theoretical aspects of equipment hierarchical analysis techniques are explained, based on the qualitative and quantitative risk model (failures frequencies and consequences). Finally, two case studies in the oil refining industry are developed; the first case uses the tool qualitative risk matrix (QRM), and the second case uses the tool risk analytic hierarchy process (RAHP).

Integrity Assessment of Pipelines Failures

The proactive maintenance of pipelines through condition based monitoring, plays an essential role in improving their overall reliability and availability. Their criticality can also be assessed by conducting reliability analysis such as FMECA, which helps in identifying relevant failure modes and averting catastrophic failures to sustain economic growth. This paper will focus on an integrity assessment set up for pipelines and the potential failure modes associated are evaluated and mitigated by determining the risk triggers. The outcome of this research has shown the various threats associated with pipelines, having an effective integrity assessment program will help mitigate such threats. Keywords: Risk Assessment, Corrosion, FMECA, Risk Priority, Failure, Inspection, Regulations, Pipelines, Monitoring

An Extended FMEA Model for Exploring the Potential Failure Modes: A Case Study of a Steam Turbine for a Nuclear Power Plant

Critical types of infrastructure are provided by the state to maintain the people’s livelihood, ensure economic development, and systematic government operations. Given the development of ever more complicated critical infrastructure systems, increasing importance is being attached to the protection of the components of this infrastructure to reduce the risk of failure. Power facilities are one of the most important kinds of critical infrastructure. Developing an effective risk detection system to identify potential failure modes (FMs) of power supply equipment is crucial. This study seeks to improve upon prior approaches for risk assessment by proposing a hybrid risk-assessment model using the concepts of failure mode and effect analysis (FMEA) and multiple-criteria decision-making (MCDM). The proposed model includes a cost-based factor for decision-makers. The subjectivity and uncertainty in FM assessment are adjusted through the rough number method. The original risk priority number (RPN) can be expanded by including the entropy weights in the risk index. Furthermore, to rank the risk priorities in a rational manner, a modified technique for order preference by similarity to ideal solution (modified TOPSIS) is adopted. The applicability and effectiveness of the proposed method were demonstrated by considering an example of a turbine steam engine in a nuclear power plant.

APPLICATION OF FUZZY FMEA TO PERFORM AN EXTENSIVE RISK ANALYSIS IN MARITIME TRANSPORTATION ENGINEERING

The nature of maritime transportation involves numerous hazards, which can lead to serious consequences for human life, marine environment and ship. Therefore, achieving a high level of safety is recognised as paramount in maritime industry (Akyuz, 2016). In order to achieve this purpose, this paper prompts a fuzzy based Failure Mode and Effects analysis (FMEA) to perform an extensive risk analysis in the maritime transportation industry. The method has capable of identifying potential failures and calculating risk priority number (RPN) by capturing nonlinear casual relationships between the failures. The proposed method is applied to hatch cover failures in operational aspects in bulk carrier ships since potential failures of hydraulic hatch covers have serious concerns for ship owners. Besides its theoretical insight, the paper has practical benefits to ship owners, superintendents as well as safety professionals by identifying potential failure and offering early corrective actions.

Well Integrity Catastrophe Avoided Through Advanced Well Integrity and Reservoir Monitoring Analysis, a Case Study

Well integrity is one of the main challenges that are facing operators, finding the source of the well problem and isolating it before a catastrophic event occurs. This study demonstrates the power of integrating different reservoir monitoring and well integrity logs to evaluate well integrity, identify the underlying cause of the potential failure, and providing a potential corrective solution. Recently, some Injector/producer wells reported migration of injection fluids/gas into shallower sections, charging these formations and increasing the risk of compromised well integrity. Characterization of the well issues required integration of multi-detector pulsed-neutron, well integrity (multi finger caliper, multi-barrier corrosion, cement evaluation, and casing thickness measurements), high precision temperature logs and spectral noise logs. After data integration, detailed analysis was performed to specifically find the unique issues in each well and assess possible corrective actions. The integrated well integrity logs clearly showed different 9.625-inch and 13.375-inch casings leak points. The reservoir monitoring logs showed lateral and vertical gas and water movements across Wara, Tayarat, Rus, and Radhuma formations. Cement evaluation loges showed no primary cement behind the first barrier casing which was the root cause of the problem. Therefore, the proposed solution, was a cement squeeze. Post squeeze, re-logging occurred, validating zonal isolation and a return of a standard geothermal gradient across the Tayarat formation. Most importantly, the cement evaluation identified good bond from the squeeze point clear to surface, isolating all formations. All these wells were returned to service (injector/producer), daily annular pressure monitoring confirmed that no further pressure build up was seen. Kuwait Oil Company managed to avoid a catastrophic well integrity event on these wells and utilized the approach presented to take the proper corrective actions, and validate that the action taken resolved the initial well integrity issues. Consequently, the wells were returned to service, and the company avoided a costly high probability blowout.

Innovation in Times of Uncertainty: Clinical Trials in Nursing Homes During SARs-CoV-2

In March 2020 the Centers for Medicare & Medicaid Services (CMS) announced restrictions on visitors and nonessential personnel in nursing homes to protect residents and facilities from SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreaks. At the time, these measures were “temporary” but they continued well into 2021 resulting in a prolonged pause on in-person study activities in a palliative care clinical trial in 12 nursing homes. This session will address the impact of this pause and decisions made to overcome the potential failure of the trial. Of utmost importance was respecting nursing homes rapidly changing context, continued communication with the site leadership, transitioning to phone and video-conference study activities, and designing a retrospective study using existing data to answer a different but similar research question. As clinical researchers move forward implementing trials and complex interventions in nursing homes, we must use the lessons learned to design flexible trial protocols.

A Combined Metrics Approach to Cloud Service Reliability using Artificial Intelligence

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

Mechanical analysis and failure modes prediction of composite rock under uniaxial compression

Composite rocks are easily encountered in a wide range of geotechnical construction projects. Understanding their mechanical properties and failure modes is very important to ensure project quality and safety. This study conducted a mechanical analysis to assess the stress distribution in composite rock with a horizontal interlayer and predicted the possible failure modes. Uniaxial compression tests were carried out on the composite rock samples to reveal their mechanical properties. It was concluded that a composite rock with a thick interlayer failed more easily than a composite rock with a thin interlayer. Four potential failure modes were related to the internal stress distribution under compression and the differences in deformation capacity and strength among the constituent components. The stress distribution derived from the mechanical analysis could explain the failure mechanism very well. These results verified the validity of the mechanical analysis results and improved understanding of the mechanical properties of composite rock with a horizontal interlayer.