Human Error In Maintenance Research Articles

Human error in maintenance: A case study from China

AbstractHuman error is an essential factor that affects the quality and safety of industrial production. To deeply understand the human factors that cause failures in an organization from the perspective of maintenance personnel, we propose an analytical approach combined with Fault Tree Analysis and qualitative analysis and apply this approach to maintenance task failure incidents. These proposed methods are based on human factor classification and the Human Factors Analysis and Classification System. We conduct a case study to prove the effectiveness of applying our approach to the Chinese manufacturing industry. Results enabled the disclosure of the “latent factors” of maintenance incidents, helped improve human error analysis in maintenance incidents and helped to understand the fundamental reasons that affect work reliability and cause maintenance failures. This case study focuses on the impact of critical human factors on organizational effectiveness and operational reliability during maintenance activities.

XGBoost based residual life prediction in the presence of human error in maintenance

XGBoost based residual life prediction in the presence of human error in maintenance

Causes and Impact of Human Error in Maintenance of Mechanical Systems

The concept of minimizing human error in maintenance is progressively gaining attention in various industries. The incorporation of human factors when solving engineering problems, particularly in maintenance, can no longer be ignored where high standards of performance are expected. The journey of improving maintenance performance through the reduction of human error begins with the understanding of causes and impact of human error in maintenance. This paper evaluates previous scholarly writings on human errors, to specifically establish the causes and impact of human error in maintenance. This study relies predominantly on the existing literature on human error in maintenance derived from published and unpublished research. The primary findings emerging from the research exhibit a number of key factors that cause a human error in maintenance such as poor management and supervision, organizational culture, incompetence, poorly written procedures, poor communication, time pressure, plant and environmental conditions, poor work design and many more. The literature review also revealed that human errors have a negative impact on safety, reliability, productivity and efficiency of the equipment. It was further discovered that equipment failures leading to accidents, incidents, loss of life and economic losses are the major effects of human error. Human error in mechanical systems’ maintenance is a serious problem which needs adequate attention in order to develop corrective and preventive measures. This review paper serves as a basis for maintenance practitioners and interested parties to develop corrective and preventive measures for minimizing human error in the maintenance of mechanical systems.

Residual Life Prediction in the Presence of Human Error Using Machine Learning

Accurate maintenance decision making is important for military equipment. Extremely demanding situations like limited time availability for maintenance during the war escalate the possibility of human errors in the maintenance of such equipment. Human errors in maintenance negatively impact the life of the systems. Human Reliability Analysis (HRA) methodologies have evolved to systematically quantify the human error in terms of Human Error Probability (HEP). However, the exact effect of the human error on the life of the component is unknown yet. In the presence of the diverse operating profiles for military equipment, estimating such effects becomes a complex and mathematically challenging problem to be handled by the conventional statistical techniques. This paper presents a machine learning approach to estimate the residual life of a component by incorporating the effect of human error in maintenance. Based on the nature of the maintenance data, a decision tree based boosted ensemble machine learning model is developed which predicts the Remaining Useful Life (RUL) of the component while considering error induced by maintenance personnel during its maintenance. The developed model is illustrated in the decision-making of replacement of a component in a mission critical military system in pre-mission maintenance break.

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

Purpose: Human factors play an inevitable role in maintenance activities, and the occurrence of Human Errors (HEs) affects system reliability and safety, equipment performance and economic results. The high HE rate increased researchers’ attention towards Human Reliability Analysis (HRA) and HE assessment approaches. In these approaches, various environmental and individual factors influence the performance of maintenance operators affecting Human Error Probability (HEP) with a consequent variability in the success of intervention. However, a deep analysis of such factors in the maintenance field, often called Performance Shaping Factors (PSFs), is still missing. This has led the authors to systematically evaluate the literature on Human Error in Maintenance (HEM) and on the PSFs, in order to provide a shared PSF taxonomy.Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify and select peer-reviewed papers that provided evidence on the relationship between maintenance activities and human performance. The obtained results provided a wide overview in the field of interest, shedding light on three main research areas of investigation: methodologies for human error analysis in maintenance, performance shaping factors and maintenance error consequences. In particular, papers belonging to the area of PSFs were analysed in-depth in order to identify and classify the PSFs, with the aim of achieving the PSF taxonomy for maintenance activities. The effects of each PSF on human reliability were defined and detailed.Findings: A total of 63 studies were selected and then analysed through a systematic methodology. 46% of these studies presented a qualitative/quantitative assessment of PSFs through application in different maintenance activities. Starting from the findings of the aforementioned papers, a PSF taxonomy specific for maintenance activities was proposed. This taxonomy represents an important contribution for researchers and practitioners towards the improvement of HRA methods and their applications in industrial maintenance.Originality/value: The analysis outlines the relevance of considering HEM because different error types occur during the maintenance process with non-negligible effects on the system. Despite a growing interest in HE assessment in maintenance, a deep analysis of PSFs in this field and a shared PSF taxonomy are missing. This paper fills the gap in the literature with the creation of a PSF taxonomy in industrial maintenance. The proposed taxonomy is a valuable contribution for growing the awareness of researchers and practitioners about factors influencing maintainers’ performance.

Human error in maintenance: An investigative study for the factories of the future

This paper presents a study of human error in maintenance. Many different aspects of human error in maintenance considered useful for the factories of the future are studied, including facts, figures, and examples; occurrence of maintenance error in equipment life cycle, elements of a maintenance person's time, maintenance environment and the causes for the occurrence of maintenance error, types and typical maintenance errors, common maintainability design errors and useful design guidelines to reduce equipment maintenance errors, maintenance work instructions, and maintenance error analysis methods.

Quantification of human error in maintenance using graph theory and matrix approach

AbstractAssessment of human error in maintenance requires identification of the contributing factors that lead to human error(s). These factors are called human error inducing factors (HEIFs), which take into consideration both the active and latent error contributing aspects related to man, machine and environment. A systems approach of the Graph Theory is applied in this paper for quantifying human error in maintenance activities that models the identified factors and their interactions/interrelationships in terms of human error digraph. The nodes in the digraph represent the HEIFs and the edges represent their interrelationships. The digraph is converted into an equivalent matrix and an expression based on this is developed, which is characteristic of the human error in maintenance. This expression is used to evaluate a human error index by substituting the numerical value of the factors and their interrelations. The index is a measure of the human error potential involved in the maintenance of systems. A higher value of index indicates that the error likelihood is more for the associated tasks, and more efforts are required to make the system less prone to human error. The proposed methodology is illustrated using a case study. The approach is anticipated to play a significant role in identifying sources of human errors and predicting their impact; and will help to integrate human factors during design stage with the objective of reducing human error in maintenance. Copyright © 2011 John Wiley & Sons, Ltd.

A model for estimation of reactor spurious shutdown rate considering maintenance human errors in reactor protection system of nuclear power plants

Modeling of spurious activations in safety instrumented systems has been studied for over a decade. The spurious activation of a plant protection system in nuclear power plants (NPPs) leads to increased electricity generation cost. An in-depth view on spurious activation of digital plant protection systems of NPPs for human errors in maintenance tasks is presented in this paper. A new model which considers human errors in maintenance and periodic tests to predict component failure rates is presented. The model has been applied to OPR-1000 reactor protection system for quantification of spurious trip frequency by fault-tree analysis. The major causes of spurious activation in a nuclear reactor protection system are identified. A set of case studies has been performed with the variation of magnitudes of human errors probability and maintenance strategies, in which, the human errors in maintenance are found to significantly influence reactor spurious trip frequency. This study is expected to provide a useful mean to designers as well as maintainers of the digital reactor protection system to improve plant availability and safety.

Human error in maintenance: a review

PurposeThe aim of the paper is to present the impact of human errors in maintenance as found in the literature in order for practitioners to be aware of their impact and develop actions to mitigate their effect.Design/methodology/approachThe paper systematically categorizes the published literature and then analyzes and reviews it methodically.FindingsHuman error in maintenance is a pressing problem.Practical implicationsA maintenance person plays an important role in the reliability of equipment. It is also a well‐known fact that a significantly large proportion of total human errors occur during the maintenance phase. Human error in maintenance is a subject which in the past has not been given the amount of attention that it deserves. This paper will be useful to people working in the area of maintenance engineering, as it presents a general review of literature published on maintenance errors in various sectors of industry.Originality/valueThe paper contains a comprehensive listing of publications on the field in question and their classification according to industry. The paper will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the importance of human error in maintenance.

Pouzdanost ljudskog faktora u održavanju tehničkih sredstava

This paper presents one approach to define possible human errors and their contributing factors in maintenance of technical systems. Possible human errors in maintenance are estimated on automatic guns. The paper describes a possible way of estimation of human reliability assessment and performance shaping factors by the standard IEC 300-3-8 'Human reliability'.