Todor P. Petrov University of Minig and Geology “St. Ivan Rilsky” - Sofia

1. APPLICATION OF BAYESIAN BELIEVE NETWORKS FOR CONTINUOUS RISK EVALUATION AND DECISION SUPPORT OF SAFETY MANAGEMENT IN MINING Todor P. Petrov University of Minig and Geology “St. Ivan Rilsky” - Sofia Department of Mine Safety and Ventilation e-mail: tpp@mgu.bg

2. Today the investigation and registering of an accident requires: • more than 60 fields of different data format describing quantitative and qualitative characteristics; • more than 3000 massive of data for description of approximately 50 accidents annually

3. The psychology and cognitive sciences are ascertain the fact that: • the human mind cannot effectively manipulate a large amount of data streams and meet serious difficulties to make an inference when the possible decision have more than three alternatives • the chance of bad decisions runs high, the frequency of wrong actions increasing and the safety become pursuit rather than achieved purpose.

4. Practical decision making • It is well known that taking into account only quantificators of occupational safety risk like coefficients and indexes of frequency and severity of the accidents are not sufficient for characterization of safety state.

5. Important features of safety management • the probability and fuzzy uncertainty; • manipulating of multisource quantitative and qualitative data; • rendering the expert opinion.

6. The inherited “disease” of the typical approach for safety analisys • Analyzing the safety risk by separately studying of isolated factors inevitably relates to loses of information about the mutuality in the examined system; • In the terms of information such a disjoint is irreversible process

7. New synergetic approach should perceive for decision support in occupational safety • A model putting together the dangers, the human factors and the control impacts including their mutual influences is needed.

8. MAR.NET project • Mine Accident Risk dot Net is an expert system for decision support of mine safety management; • providing information fusion of different sources and types of evidence such as history databases, real time control systems and expert opinions.

9. CALCULATION OF RISK LEVEL Risk = Probability x Severity (1)

10. The low threshold of occupational risk can be calculated on • In practice the accident without looses of working days are not registered. • We can thing about Ro as a threshold of sensitivity of the safety monitoring system

11. Calculation of RISK LEVEL • The purpose of risk level is to give one-value quantification of the current state of the safety relative to the acceptable threshold taking into account the sensitivity of the risk measuring. Where: Rc – is the current risk; Ro – is the low threshold of occupational risk.

12. Properties of LR • LR is dimensionless; • LR is always positive; • If the current and the threshold risk are become equal than the safety level is calculated to zero. LR=0 means no risk upper the threshold limit is detected. Natural way of risk representation becausethe human perceptions are determined exactly from logarithmic levels as stated in psychophysical law of Veber-Fehner

13. DRAWING OF INFERENCES FOR OCCUPATIONAL RISK Fig. 1. Annually accident distribution

14. DRAWING OF INFERENCES FOR OCCUPATIONAL RISK Fig. 2. Time row of accident frequencies

15. DRAWING OF INFERENCES FOR OCCUPATIONAL RISK Reconstruction of phase space of the accident frequency per month in 3D Fmonth, Fmonth-1, Fmonth-2

16. DRAWING OF INFERENCES FOR OCCUPATIONAL RISK Time row and reconstructed phase space of 15 minutes beats of a human heart Panchev S. Chaos Theory, Academic Publisher, Sofia 1996

17. Bayesian approach for statistical inference • (1) is a result known as law for complete probability; • (2) is a result known as Bayes Theorem and; • (3) is a result known as chain rule, with significant importance in Bayesian believe networks (BBN)

18. MAR.NET project MAR.NET project – Structure of the network TM Powered by Hugin Lite

19. MAR.NET project Initial probability table of the chance node “10. Job”

20. MAR.NET project Initial conditional probability table P(17.Body|18.Injury)

21. MAR.NET project Learning and adoption of MAR.NET Learning and adoption of MAR.NET Learning and adoption of MAR.NET Posterior probability distribution of node “10.Job” about all given states from A to E

22. Learning and adoption of MAR.NET • Learning of MAR.NET from data cases The machine learning method used in MAR.NET is known as EM-algorithm and it is commonly used in BBN for graphical associated models with missing data.

23. Structure Learning of MAR.NET The algorithms for structure learning of BBN are known as PC-algorithms

24. Structure Learning of MAR.NET As a result of the structure machine learning of MAR.NET with 122 data cases for registered accidents in coal mine of Babino – Bobov dol, the conditional dependency of the following variables was accepted in LC=0.05: • Occupation >> Time of occurrence of the accident; • Length of service >> Human factor; • Education Level >> Day after weekend; • Day after weekend >> Deviation from ordinary actions.

25. Entering Expert Opinions in MAR.NET The algorithm for entering of expert opinion used in MAR.NET allows control of the actuality of learned experience. The control of the actuality uses special data structures for reducing the impact of past called fading tables.

26. Simulation of data cases A way to test the safety system in lack of data and uncertainty Three approaches for obtaining simulated experience are easy applicable in MAR.NET model: • generating of simulated data cases based on variations of the current prior distribution; • generating data cases with simulation model of the object using advanced tools as special languages; • to change structure of the net depending of new knowledge, and to derive conclusions against the direction of the edges

27. MAR.NET example • Example is based on the real data for a Bulgarian coal mining company with underground mining, open pit mining and dress factory. • Structural changes in company are provided in the future time. From the company structure will be ousting the underground mines and the repair shops, but the steam power plant will be incorporated.

28. MAR.NET example • What we need to expect about the risk for different groups of workers and the probabilities of environment causes?

29. Prior distributions The knowledge about the object is extracted from data cases about registered accidents with learning algorithm

30. Posterior distribution • Structural changes in the company are reflected in BBN node structure ; • After Bayesian propagation through the network the posterior distribution is computed.

31. Back propagation.Obtaining inference against the edges of MAR.NET • Let now to propagate the opinion that in future the fatalities will increase twice; • It will change the Bayesian probability in station F. Fatalities of node 3 from 0.08 to 0.16; • Let start the back-propagation of this new prior probability distribution; • The new posterior distribution is achieved.

32. The new posterior distributionis the answer of the question: What we need to expect about the risk for different groups of workers and the probabilities of environment causes? • Using of faulty, unassured machines and facilities; • Using equipment inadequate of working conditions. Will lead to increasing of risk of fatalities in the groups of • Staff at the surface and; • Open pit mine workers.

33. Conclusions • MAR.NET project produced a decision support method with a supporting tool for quantifying safety in complex systems using Bayesian Networks as a core technology. ; • The system can be adopted for different industries; • The well learned MAR.NET models can be used for decision support of safety management, education and training.

34. MAR.NET key benefits • rationally combine different sources and types of evidence in single model; • identify weaknesses in the safety argument such that it can be improved; • specify degrees of confidence associated with prediction; • provide a sound basis for rational discussion and negotiation about the safety system development and deployment.

35. Thank You