ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY

The changes caused by Industry 4.0 determine the decisions taken by manufacturing companies. Their activities are aimed at adapting processes and products to dynamic market requirements. Additive manufacturing technologies (AM) are the answer to the needs of enterprises. The implementation of AM technology brings many benefits, although for most 3D printing techniques it is also relatively expensive. Therefore, the implementation process should be preceded by an appropriate analysis, in order, finally, to assess the solution. This article presents the concept of using the Bayesian network when planning the implementation of AM technology. The use of the presented model allows the level of the success of the implementation of selected AM technology, to be estimated under given environmental conditions

Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling

The prediction of loss of position in the offshore industry would allow optimization of dynamic positioning drilling operations, reducing the number and severity of potential accidents. In this paper, the probability of an excursion is determined by developing binary logistic regression models based on a database of 42 incidents which took place between 2011 and 2015. For each case, variables describing the configuration of the dynamic positioning system, weather conditions, and water depth are considered. We demonstrate that loss of position is significantly more likely to occur when there is a higher usage of generators, and the drilling takes place in shallower waters along with adverse weather conditions; this model has very good results when applied to the sample. The same method is then applied for obtaining a binary regression model for incidents not attributable to human error, showing that it is a function of the percentage of generators in use, wind force, and wave height. Applying these results to the risk management of drilling operations may help focus our attention on the factors that most strongly affect loss of position, thereby improving safety during these operations

An integrated approach for real-time hazard mitigation in complex industrial processes

Modern engineering systems give paramount importance to safety in order to avoid or mitigate hazardous accidents which can lead to huge economic losses, environmental contamination, and human injuries. This paper proposes an integrated approach that uses both Hidden Markov Model and Bayesian Network to estimate an optimum safety-threshold for complex industrial processes. In order to estimate the safety threshold, the proposed approach considers different cost factors and the joint probabilities of multiple process variables leading to an accident. In addition to the system level threshold, it also estimates the safety-threshold for components. This helps in identifying the component that needs maintenance to enhance system performance and safety. Furthermore, it proposes a dynamic risk assessment methodology based on multiple real-time process variables. The optimum safety-thresholds are estimated using Genetic Algorithm which aims at minimizing the system running cost over a finite time horizon. A case study on Tennessee Eastman Chemical Process is presented to demonstrate the proposed methodology for optimizing process safety-threshold.</p

Modelos de regresión logística aplicados a incidentes de posicionamiento dinámico ocurridos durante operaciones de perforación mar adentro.

156 p.La prevención de incidentes en la industria offshore es una parte crucial del proceso de análisis y gestiónde riesgos, ya que permite optimizar las operaciones de perforación de posicionamiento dinámico, y asíreducir las consecuencias de un posible incidente.En esta disertación, aplicaremos modelos de regresión logística binaria sobre una base de datos de 42incidentes ocurridos durante el período 2011-2015. Para cada caso, se consideran las variables quedescriben las diferentes configuraciones del sistema de posicionamiento dinámico, las condicionesclimáticas y la profundidad del agua.El primer objetivo trata de determinar la probabilidad de tener una excursión durante un incidente. Enesta investigación se comprueba que las pérdidas de posición tienen mayor probabilidad de ocurrircuando hay un mayor uso de generadores, y la perforación se realiza en aguas menos profundas,obteniendo este modelo muy buenos resultados cuando se aplica a la muestra. Las variablesclimatológicas también son consideradas, obteniendo un modelo que combina las variables antesmencionadas con la fuerza del viento.Los incidentes causados por factores humanos son cada vez más numerosos e importantes. Laprobabilidad de que ocurra un incidente de origen humano durante las operaciones de perforación deposicionamiento dinámico se determina utilizando modelos de regresión logística binaria sobre la mismabase de datos. Los resultados obtenidos mostraron que es mucho más probable que ocurran incidentes deorigen humano cuando hay un menor uso de los propulsores.Estos resultados, aplicados a la gestión de riesgos de las operaciones de perforación, pueden ayudar acentrar la atención en los elementos que afectan más fuertemente las pérdidas de posición, mejorando asíla seguridad durante estas operaciones. Asimismo, estos resultados son útiles para enfocar nuestraatención en variables que pueden estar asociadas a incidentes atribuibles a error humano, así como paraestablecer límites operacionales que podrían ayudar a prevenir estos incidentes y mejorar la seguridaddurante estas operaciones