Data Utilization in Applied Reliability Engineering

Khalid A. Al-Jabr
and
Rabi M. Hakeem
and
Osama Nadhir
26 June

Introduction

In today’s technological era, data science is playing a critical role for making decisions and supporting management and engineers to achieve the optimal solutions based on technical and financial analysis. The data can be defined as facts or numbers that is usually collected, analyzed and utilized to make useful decisions. On the other hand, information is knowledge that is obtained from data through analysis and studies. With newly developed high tech and IT solutions such as IR4.0, IoT and other applications, data is coming more important and useful in reliability engineering applications and science.

Data Importance & Challenges

In the Reliability Engineering discipline, data is vast; however, information is not as well developed as data collection and tracking. It is worth emphasizing that, in a sense, information is the mature level of data quality and reliability. Data, its structure, quality, validity, continuity and consistency are important factors to ensure data quality is overall acceptable to be used for reliability analysis. Although, information is used to derive business decisions, if the data is not well treated: incorrect, or unreliable, it shall not be trusted and the information will not be concluded. The results will be dangerous for organizations due to make the decisions based on wrong or faulty information.

Enhance Data Quality & Reliability

Enhance the data reliability by improving the data collection practices and guidelines. For example; Performing random auditing of the data can improve the quality and the study’s outcomes. Furthermore, organizing the database and its systems is another method to enhance our decision-making tools. Thus, we frequently need to clean the data, which can be defined as the process of fixing faulty data points, for example: incomplete forms and duplicated line items. Enhancing the database to reflect correct, updated and accurate data is one of the main objectives of data integrity. These are also used to maintain data security, data quality, and regulatory compliance.

Data Utilization for Applied Reliability Engineering

Reliability analysis is an effective way to help management and engineers making important technical and financial decisions. Thus, the reliable data is required to be utilized for all reliability applications and studies such as:

Failure Mode Effects and Criticality Analysis (FMECA)

It is an extension of failure mode and effects analysis (FMEA), a standard procedure for analyzing each potential failure mode in a product to determine the results or effects thereof on the product. When the analysis is extended to classify each potential failure mode according to its severity and probability of occurrence, it is called a Failure Mode, Effects, and Criticality Analysis (FMECA). This analysis needs extensive works, long processing time and several efforts from multi-discipline team members. Thus, wrong or inaccurate date will loss all these efforts. The main reason to perform this analysis is to determine the potential failure modes within a system and its assets. This is in order to determine the effect of the assets and the system performance and possible failures and production effects. The objective here will not be achieved if the input data is incorrect. As part of a proactive reliability improvement initiative, a cross-functional team conducted a Failure Mode, Effects, and Criticality Analysis (FMECA) on a critical centrifugal pump used in a hydrocarbon processing facility. During the analysis, the team identified several potential failure modes, including seal leakage and bearing wear, and assigned severity, occurrence, and detection scores to calculate the Failure Criticality Index (FCI). One of the key findings was that the occurrence ranking for bearing wear was initially underestimated due to reliance on an incorrect Mean Time Between Failures (MTBF) value sourced from generic industry databases rather than site-specific historical data. This led to a lower FCI score than warranted, causing the failure mode to be deprioritized in mitigation planning. Subsequent field failures prompted a re-evaluation of the data inputs, revealing that site-specific MTBF was significantly lower—only 40 months compared to the assumed 90 months. This triggered a revision of the FMECA table, reclassification of the failure mode as high criticality, and the implementation of enhanced monitoring and lubrication control measures. The case underscores the importance of accurate data inputs in FMECA studies to ensure that critical failure modes are properly prioritized and addressed.

Root Cause Analysis (RCA)