Process safety performance indicators

For over 50 years to measure safety performance the Lost Time Incident Rate, LTIR was used. Fortunately, over the years the learning attitude towards accidents changed from a retrospective to a pro-active one. In the 90-s the safety management system was introduced. No management though, without the Deming cycle of Plan, Do, Check, Act, and checking, means the need of indicators. Existing LTIR-values were used not realizing these refl ect personal rather than process safety. In 2005 after the BP Texas City refi nery vapor cloud explosion, awareness of the difference broke through and Process Safety Leading and Lagging Metrics were formulated. In January 2012 an international conference was held in Brussels organized by EPSC and CEFIC. Results will be summarized. The paper will explain briefl y, where we are now, and what still is ahead

Aircraft Damage Classification by using Machine Learning Methods

Safety is the most significant factor that affected incidents (non-fatal) and accidents (fatal) in civil aviation history related to scheduled flights. In the history of scheduled flights, the total incident and accident number until 2022 is 1988. In this study, 677 of them are taken into consideration since 11 September 2001. The purpose of this study is to reveal the factors that can classify type of aircraft damages such as none, minor and substantial in all-time incidents and accidents. ML algorithms with different configurations are applied for the classification process. The RFE and PCA are used to find the most important factors that are effective on the classification. Four components are found with PCA as zone, weather, time, and history. The results of multinomial logistic regression and ANNs showed that the most important 5 features are latitude, wind speed, wind direction, year, and longitude to classify aircraft damage. Then, temperature, total number of injury passenger, and month factors comes with more than 50% importance. The managerial implication of the study shows that as time passes the number of substantial accidents has decreased due to increasing level of safety precautions in civil aviation

Tree-Network Overrun Model Associated with Pilots’ Actions and Flight Operational Procedures

The runway excursions are defined as the exit of an aircraft from the surface of the runway. These excursions can take place at takeoff or at landing and consist of two types of events: veer off and overrun. This last one, which occurs when the aircraft exceeds the limits at the end of the runway, is the event of interest in the current study. This chapter aims to present an accident model with a new approach in aeronautical systems, based on the tasks of the pilots related to the operational procedures necessary for the approach and landing, in order to obtain the chain of events that lead to this type of accident. Thus, the tree-network overrun model (TNO model) was proposed, unlike most traditional models, which consider only the hardware failures or which do not satisfactorily explain the interrelationship between the factors influencing the operator. The proposed model is developed in a fault tree and transformed into a Bayesian network up to the level of the basic elements. The results showed the qualitative model of the main tasks performed by the pilots and their relation to the accident. It has also been suggested how to find and estimate the probability of factors that can impact on each of the tasks

Probabilistic risk analysis in manufacturing situational operation: application of modelling techniques and causal structure to improve safety performance

[EN] The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address the risks. To remedy this problem, the model presented in this paper considers the effects of human, software and calibration reliability in the process. Bayesian Belief Network coupled to a Bow Tie diagram is used to identify potential engine failure scenarios. In this context and to meet this objective, an in depth literature research was conducted to identify the most appropriate modeling techniques and an interview were conducted with experts. As a result of this study, this paper presents a model that combines fault tree analysis, event tree analysis and a Bayesian Belief Networks into a single model that can be used by decision makers to identify critical risk factors in order to allocate resources to improve the safety of the system. The model is delivered in the form of a computer assisted decision tool supported by subject expert estimates.Pereira, JC.; Alves Lima, GB. (2015). Probabilistic risk analysis in manufacturing situational operation: application of modelling techniques and causal structure to improve safety performance. International Journal of Production Management and Engineering. 3(1):33-42. doi:http://dx.doi.org/10.4995/ijpme.2015.3287.SWORD334231Clément, B., Haste, T., Krausmann, E., Dickinson, S., Gyenes, G., Duspiva, J., … Sartmadjiev, A. (2005). Thematic network for a Phebus FPT1 international standard problem (THENPHEBISP). Nuclear Engineering and Design, 235(2-4), 347-357. doi:10.1016/j.nucengdes.2004.08.057Cooke, R.M. (1991). Experts in uncertainty. Opinion and subjective probability in science. Oxford University Press.Kumamoto, H., Henley, E.J. (1996). Probabilistic risk assessment and management for engineers and scientists. IEEE Press.Pereira, J. C., Lima, G. B. A., Sant'Anna, A. P., Peres, L., Pizzolato, N. (2014). Causal Model for Probabilistic Risk Analysis of Jet Engine Failure in Manufacturing Situational Operation (CAPEMO). International Journal of Engineering Science and Innovative Technology, 3(3): 701-717.Petersen, D. (2000). Safety management our strengths & weaknesses. Professional Safety, 16-19

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory

Further development of a causal model for air transport safety (CATS) : building the mathematical heart

The development of the Netherlands international airport Schiphol has been the subject of fierce political debate for several decades. One of the considerations has been the safety of the population living around the airport, the density of which has been and still is growing. In the debate about the acceptability of the risks associated with the air traffic above, The Netherlands extensive use has been made of statistical models relating the movement of airplanes to the risks on the ground. Although these models are adequate for the debate and for physical planning around the airport, the need has arisen to gain a more thorough understanding of the accident genesis in air traffic, with the ultimate aim of improving the safety situation in air traffic in general and around Schiphol in particular. To this aim, a research effort has started to develop causal models for air traffic risks in the expectation that these will ultimately give the insight needed. The concept was described in an earlier paper. In this paper, the backbone of the model and the way event sequence diagrams, fault-trees and Bayesian belief nets are linked to form a homogeneous mathematical model suitable as a tool to analyse causal chains and quantify risks is described

Chapter 2 - Integrated risk and uncertainty assessment of climate change response policies

This framing chapter considers ways in which risk and uncertainty can affect the process and outcome of strategic choices in responding to the threat of climate change

Integrated Risk and Uncertainty Assessment of Climate Change Response Policies

Review of Uncertainty and climate chang

Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks

In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety