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This paper proposes a different likelihood formulation within the Bayesian paradigm for parameter estimation of reliability models. Moreover, the assessment of the uncertainties associated with parameters, the goodness of fit, and the model prediction of reliability are included in a systematic framework for better aiding the model selection procedure. Two case studies are appraised to highlight the contributions of the proposed method and demonstrate the differences between the proposed Bayesian formulation and an existing Bayesian formulation.

PurposeThe purpose of this article is to compare maintenance policies based on Weibull and q-Weibull models.Design/methodology/approachThis paper uses analytical developments, several figures and tables for graphical and numerical comparison. Previously published hydropower equipment data are used as examples.FindingsModels for optimal maintenance interval determination based on q-Weibull distribution were defined. Closed-form expressions were found, and this allows the application of the method with small computational effort.Practical implicationsThe use of the q-Weibull model to guide the definition of maintenance strategy allows decision-making to be more consistent with sample data. The flexibility of the q-Weibull model is able to produce failure rate modeling with five different formats: decreasing, constant, increasing, unimodal and U-shaped. In this way, the maintenance strategies resulting from this model should be more assertive.Originality/valueExpressions for determining the optimal interval of preventive maintenance were deduced from q-Weibull distribution. Expected costs per maintenance cycle of Brazilian hydropower equipment were calculated with q-Weibull and Weibull distributions. These results were compared in terms of absolute values and trends. Although a large number of works on corrective and preventive maintenance have been proposed, no applications of the q-Weibull distribution were found in literature.

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The increasing use/abuse of psychotropic drugs is an alarming social phenomenon with repercusions in many areas, especially in reliability engineering. The aim of this paper is to present a method developed by a multidisciplinary team composed of ergonomists, psychiatrists, information technologists, and reliability engineers to quantitatively consider the impact of psychotropic drugs on the assessment of human reliability of Operation and Maintenance (O&M) personnel of a hydroelectric plant. To achieve the proposed objective, the first step was the identification of drugs that affect the psychic-cognitive/sensory and motor functions as a side effect and the frequency (probability of occurrence) of the effect. This was done mining public and private drug databases. A qualitative (symbolic) scale later translated into numerical values was used to quantify the impact of each drug on the affected functions. At the same time, O&M tasks were broken down into observable activity sequences, recording the frequency of each activity on each task (Task Analysis). Then, the relationship between each activity and the sensory-cognitive-motor psychic functions was established, based on the knowledge and experience of the team involved. Again a qualitative (symbolic) scale was defined and later transformed into a numerical scale. As a result, the first version of a drug effect-task knowledge database (KDB) was built with the symbolic and numerical values assigned to all relationships between the different model elements. Although the KDB needs to be systematically reviewed and updated by a broader network of ergonomists and psychiatrists, it has served as proof of concept and a starting point for the development of a human risk management tool for hydroelectric power plants. The last step was to calculate a new Performance Shaping Factor (PSF) due to Psychotropic Drugs Use (PDU), for each drug in each of the O&M tasks. In a preliminary assessment of the inclusion of PDU-PSF with three common methods of Human Reliability Assessment (HRA), we found an increased risk of human failure ranging from approximately 15% to 35%, depending on the HRA method. The case study used to illustrate the method considered a routine operator inspection task using an antidepressant drug. The proposed method can be updated for new drugs and can be refined/customized for other high-risk human activities such as oil and gas and petrochemical industries, nuclear power plants, aviation, and surgery, among others. INDEX TERMS Human error analysis, reliability, task analysis, psychotropic drugs, human reliability.