Research on Human Reliability of Coal Mine Underground Work

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459,ISO 9001:2008 Certified Journal, Volume 4, Issue 11, November 2014)

455

Research on Human Reliability of Coal Mine Underground

Work

WANG Lei

1

, LU Gang

2

, CHEN Hong

3

, LI Qing-liu

4

, KONG Qun

5 1,2,4,5

School of mines, China University of Mining and Technology, Daxue Road No.1, Xuzhou, Jiangsu Province, 221116, People’s Republic of China.

3_{School of Management, China University of Mining and Technology, Daxue Road No.1, Xuzhou, Jiangsu Province, 221116,}

People’s Republic of China.

Abstract—Since the unsafe behaviors have become increasingly prominent in underground coal mine accidents, human reliability of undermine workers in coal mining enterprise are studied. The common performance condition (CPC), eco-indicator



and region of controlling pattern in cognition reliability and error analytical method (CREAM) are corrected so as to be more in line with the undermine working environment and conditions. As concluded from empirical results, the failure probability of underground drilling works in the coal mine is 0.025. Reducing the failure model probability of angle and depth measurement may effectively improve the human reliability of underground works in coal mine enterprises.

Keywords—Human Reliability; Common Performance Condition Factor (CPC); CREAM; Eco-indicator; Region of Controlling Pattern

I. INTRODUCTION

The safety intrinsic coal mine construction proposed by the State Administration of Work Safety (SAWS) has effectively reduced the probability of accidents and death roll of underground works during coal production operations [1]. But safety intrinsic coal mine construction intends to reduce the occurrence of coal mine accidents by improving management system, so the evaluation of coal mine accidents remain static state which is unable to evaluate the real-time underground dynamic safety status of coal mines. When underground safety factors of coal mine changes, it is unable to effectively measure the safety conditions and put forward preventive security measures. To this end, the author has put forward a dynamic quantitative analytical evaluation model on the basis of safety intrinsic coal mine construction theory to study the factors for coal mine underground production.

W. H. Heinrich[2]believed that the direct causes of accidents are the unsafe behaviors of human and objects.

With the increasingly refining mechanical equipment and improving safety level, the unsafe behavior of objects are no more the major cause of accidents in coal mine enterprises, while the unsafe behaviors of human beings have become the most important cause for current underground accidents. Studies of many scholars both home and abroad [3-5] found that the achievement of safety intrinsic coal mine construction relies on the human reliability. The application of corrected relevant theories of human reliability research into safety system of coal mine enterprises, so as to study the operation error probability of coal mine underground workers and accident control are of theoretical and practical significance of improving safety intrinsic coal mine construction and ensure safety production of coal enterprises.

II. HUMAN RELIABILITY THEORY

Human reliability theory originated from the weapon system reliability report issued by the American National Laboratory in 1952 [6], which put forward researches on human error in risk analysis of complex system for equipment reliability. Subsequently, human reliability analysis (HRA) disciplines began to form into the first generation, the second generation and the third generation method. Selecting appropriate HRA methods for different situations to analyze the dynamic behavior of the human cognitive status can reflect good effect of display.

A. Human Reliability Method

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International Journal of Emerging Technology and Advanced Engineering

456 It was represented by a complete set of personnel reliability analysis method - Technique for Human Error Rate Prediction (THERP) proposed by Sw ain A. D. , Guttmann H. E. [7] et al. But the adopted performance shaping factor (PSF) in THERP is obtained from the table, of which data is subject to the subjective selection of analyst, and the result reliability is poor; The second generation of HRA is represented by Cognitive Reliability and Error Analysis Method (CREAM) and A Technique for Human Error Analysis (ATHEANA) and so on focusing on studies of human cognitive reliability model and emphasizing on the impact of scene environment on human cognitive reliability. But the second generation of HRA relies mainly on expert judgment in analysis and result determination with incomplete behavior factors determination; the third generation of HRA is represented by cognitive environment simulation (CES) and Cognitive Simulation Model (COSIMO) etc., which has set up a simulation model combined with human reliability databases and the second-generation HRA, so as to simulate the human reliability of operator in the real scene through virtual scenes. Untrue simulation scenes, low matching degree with data in the database and other issues exist in the third generation of HRA.

B. CREAM Method

Coal mining in China is mainly underground mining with high mechanical device packing density and personnel congestion degree. Most underground tunnels are long and confined or semi-confined space with generally high gas content. Once the mine accident (such as gas explosions, seepage, etc.) happens, it will inevitably lead to heavy casualties and severe damages to the machinery and equipment; also, underground works require the team to coordinate together, which therefore requires higher degree on the level of human reliability and team coordination. In the absence of human reliability database in coal mines as well as in the complex situations, selecting appropriate HRA method is an urgent problem to solve at current stage. By quantifying the scenario environment, CREAM reduces dependence of coal mine on human reliability database, which is able to analyze the factors affecting coordination works of underground group, thus more in line with the human reliability analysis for coal mine underground works.

However, to apply CREAM into human reliability analysis methods, it also needs to conduct partial correction on CREAM method: 1. common performance condition factor (CPC) and the human reliability analysis of coal mine underground workers are not fully consistent, which needs to conduct partial factor correction for CPC factors and add; 2. CREAM fails to consider the importance of CPC factors under different scenarios in the adoption of eco-indicator, which needs to correct its value; 3. CREAM method is discrete in the regions of controlling pattern, which is inconsistent with the continuous production and underground coal mining works. Thus the controlling pattern in discrete areas should be corrected as the controlling pattern of continuous area.

III. CORRECTION AND ANALYSIS ON UNDERGROUND HUMAN RELIABILITY METHOD FOR COAL MINING

ENTERPRISES

A. Correction of Common Performance Condition Factor (CPC)

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International Journal of Emerging Technology and Advanced Engineering

457

TABLE I

COMMON PERFORMANCE CONDITION FACTORS AND EXPECTATION

STATES

CPC Factor

Import-ance Degree

Expecta -tion State

Quantifica -tion value

Organizatio n integrity



1

Very effective

Improve

d -1

Effective

Not significa

nt

0

Invalid/poor

effect Reduced 1

Integrity of information

transfer 2



Very complete

Improve

d -1

Basically complete

Not significa

nt

0

Incomplete Reduced 1

Procedures/ program

availability 3



Appropriate Improve

d -1

Acceptable

Not significa

nt

0

Unacceptabl

e Reduced 1

Integrity of MMI and operational

support

4



Support Improve

d -1

Adequate

Not significa

nt

0

Tolerable/ inappropriat

e

Reduced 1

Working

conditions



5

Superior Improve

d -1

Match

Not significa

nt

0

Mismatch Reduced 1

Properties of works

and tasks 6



Easy Improve

d -1

Normal

Not significa

nt

0

Difficult Reduced 1

Adequacy of training

and experience

7



Adequate with rich experience

Improve

d -1

Adequate with limited

experience

Not significa

nt

0

Inadequate Reduced 1

Number of objectives appearing at

the same time

8



Lower than the human handling capacity

Improve

d -1

Equivalent to human

handling capacity

Not significa

nt

0

Higher than the human

handling capacity

Redu

ced 1

Physical states of

workers 9



Very good Improve

d -1

Good

Not significa

nt

0

Bad Reduced 1

Duty time

zone



10

Day

Not significa

nt

0

Night Redu

ced 1

Cooperatio n quality of

group members

11



Very effective

Improve

d -1

Effective

Not significa

nt

0

Invalid/Poor

effect Reduced 1

B. Correction of Eco-indicator



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International Journal of Emerging Technology and Advanced Engineering

458 Also, take the pairwise relationships of scenario environmental states into consideration, the relational matrix is applied to determine the importance of CPC factor [9]. Suppose there are

_{

_a

₁

_,

_a

₂

_,...,

_a

_n

_}

n CPC factors in total, then the relational matrix

M



{

b

_ij

}

can be

established, where

b

_ij indicates the pairwise relationship

between

i

a

and

a

_j (relationship between

i

a

and

a

_jcan be represented by extremely important, very important, obviously important, somewhat important, and equally important, and respectively assign with the value 5, 4, 3, 2,1). Calculate the importance



_i of the

i

CPC via

Eq.(1)-(3) (

S

_C

(

i

)

and

S

_R

(

i

)

refer to the sum of elements of human reliability CPC factors in rows and columns of the relational matrix):



 





_n

j i

ij C R

i

b

i

S

i

S

1 , 1

2 )

(

)

(



(

i



1 ，

2 ，

...

n

)

(1)





n

i ij

R

i

b

S

1

)

(

i



1 ，

2 ，

...

n

)

(2)





n

j ij

C

i

b

S

1

)

(

i



1 ，

2 ，

...

n

)

(3)

Define the Eco-indicator



as:





11

1

11

1

-

_improve

reduce





, according to the importance

degree of CPC factors, the corrected



'

can be obtained

as:





11

1

11

1

'

-'

'



_reduce



_improve



; also calculate the

probability of basic cognitive failure CFP₀ , cognitive failure probability CFP, and the relationship between the coefficient

k

and



'

is shown in Eq.(4）

'

)

/

lg(

CFP

₀



k



(4)

C. Correction of regions with CREAM method controlling pattern

By summarizing the CPC factors



_improved and



reduced with improving and reducing role, the controlling

pattern of workers in the scene can be determined (Fig.I), in which the failure region of strategic controlling pattern (St) is (0.00005, 0.01), the failure region of tactic controlling pattern (Ta) is (0.001,0.1), the failure region of opportunity controlling pattern (Op) is (0.01,0.5) and the failure region of scrambled mode (Sc) is (0.1,1). As is discovered, regions of controlling pattern where workers belong to are rough, and a certain failure probability may belong to both failure modes, or belong to neither failure mode. Also the failure modes are discrete.

To establish regional model for continuous controlling pattern, the following assumptions must be made: 1. the regions of controlling patterns are continuous rather than four separate areas [11]; 2. The failure distribution function exists in each point of the region of controlling pattern; 3. The failure distribution function follows the logarithm distribution function (human behaviors can be reflected through logarithm function by changing external conditions); 4. The mean value of failure distribution function equals to the regional logarithm of controlling pattern (as specified in 3); 5. The improvement of environmental scenarios means



_improved=



_reduced; 6. if



improved =0 or



reduced is at the maximum, then the

mean failure rate (MFR) is at a maximum; 7. if



recuded =0 or



improved is at the maximum, then the

MFR is at the minimum. Based on the above assumptions, it can assume that:

MFR



MFR

0



10

A (A refers to

0

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International Journal of Emerging Technology and Advanced Engineering

459

1

7

6

5

4

3

2

1

9

8

7

6

5

4

3

2

8

9 

improved



reduced

10 11

Strategic

Tactical

Opportunistic

Scrambled

Fig.I Regions of Controlling Pattern That Workers Belong To



improved



reduced improved

N

reduced

N

 

4 1 

Balanced line min

MFR MFR

max

MFR MFR

0

MFR MFR

Maximum arc

Fig.II Region of Continuous Controlling Pattern

Based on assumptions and regional model diagram of continuous control mode, the equation is derived as follows:

A

MFR



₀



10

（

0 







4

） (5)

0 min

max 0

max

log

4

1 log

4

1 MFR

MFR

R

MFR

R

A









































_



_



（

2

4 





_

） (6)

Where:

R





_improved





_reduced ，

reduced

improved

N

R

_max





，







reduced improved 1

tan



Draw the scheme of MFR value in regions of continuous controlling pattern, as shown in Fig.III:



improved



reduced

MFR

max

MFR

0

MFR

0

MFR

min

MFR

Fig.III MFR Value in Regions Of Continuous Controlling Pattern

IV. EMPIRICAL ANALYSIS

Mining industry is a high risk industry, where the probability of accidents in coal mining industry is the highest with the largest number of death poll and most serious economic losses [12]. The author applies the corrected CREAM method into the analysis on probability of cognitive failure of coal mine drilling workers, and forecast the probability of failure.

A. Analysis on behaviors of coal mine drilling workers

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International Journal of Emerging Technology and Advanced Engineering

460

B. Prediction on the failure probability of coal mine drilling workers

The cognitive behaviors of coal mine drilling workers are divided, and their corresponding cognitive function and most likely failure modes and basic values of failure probability are determined according to the names of failure models and basic value of failure probability [10], (as shown in Table II):

TABLE II

COGNITIVE FUNCTION AND FAILURE MODE OF DRILLING WORKERS

No. Cognitive activities

Cogniti ve behavio

r

Cogniti ve function

The most likely failure

mode

Basic failure probab ility

1 Observatio n plan

Recogni ze

Observa tion, identific

ation

Target identifica tion error

0.001

2

Locate drilling position

Perform Perform

Action target

error

0.0005

3

Adjust the angle and position of equipment

Observe, perform

Observe or action error

0.003

4 Trial

drilling Perform Perform

Action target

error

0.0005

5

Adjust equipment

angle

Observe, perform

Observe or action error

0.003

6 Drilling Perform Perform

Action target

error

0.005

7

Observe drilling angle

Observat ion, identific

ation

Observa tion, identific

ation

Observe target

error

0.001

8

Measure drilling angle and

depth

Evaluati on and compari son

Explain, plan

Decision

Failure 0.01

The expectation state of CPC is determined from the first cognitive activity; also based on the scenarios in which drilling workers belong to and Eq.(1) - Eq.(3), the determined CPC weights are shown in Table III:

TABLE III

SATES AND IMPORTANCE OF CPCFACTORS

CPC Factors States Importance

Organization integrity Insignificant 1.10

Integrity of information

transfer Improved 0.95

Procedures/program

availability Insignificant 0.90

Integrity of MMI and

operational support Insignificant 0.84

Working conditions Improved 1.01

Properties of works and

tasks Reduce 1.11

Adequacy of training and

experience Improved 0.99

Number of objectives

appearing at the same time Reduce 0.93

Physical states of workers Improved 0.98

Duty time zone Reduce 0.98

Cooperation quality of

group members Improved 1.12

Reliability of underground observation and drilling calibration work are in 10-2 (1 unreliable behavior occurs in 100 works), assume the error factor (EF) EF = 1, namely:

R

=3,

R

_max =4,



=26.23,

MFR

_max =10-1，

MFR

_min=10

-3_{, through Eq.(5) and (6), it can be calculated that}

MFR

=1.47*10-2. As determined, the cognitive activities of mine drilling workers in the tactic failure zone, which is consistent with the results determined in the controlling pattern region of uncorrected CREAM method.

At the same time, calculate the corrected eco-indicator

修正



=-2.03, and calculate the coefficient

k

=0.127 according to the failure mode of cognitive activity. Substitute



_修正 and

k

into Eq.(4) and calculate the failure probability

CFP

=0.00056. Similarly, the failure probability of other cognitive activities can be calculated, as shown in Table IV:

Then the failure probability

)

1 (

1

8

1 i

i

CFP

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International Journal of Emerging Technology and Advanced Engineering

461 In order to reduce the failure probability of mine drilling works, it is required to reduce the failure probability of drilling angle and depth measurement. Because the most likely failure mode in drilling angle and depth measurement is decision failure, namely the drilling worker ignores the measurement of drilling angle and depth, this requires the manager and team leader to stress the need for measurement with good reward management.

TABLE IV

FAILURE PROBABILITY OF COGNITIVE ACTIVITY FOR MINE DRILLING

WORKERS

Cognitive activity

Failure probability

Cognitive activity

Failure probability

Observation plan 0.00056 Adjust

equipment angle 0.00083

Locate drilling

position 0.00093 Drilling 0.0074

Adjust the angle and position of

equipment

0.0021 Observe drilling

angle 0.00059

Trial drilling 0.00043 Measure drilling

angle and depth 0.012

V. CONCLUSION

The following conclusions should be reached in the research in human reliability in coal mining work adopting the corrected CREAM method:

a. The corrected model of CREAM method for CPC factors reflect that in coal mine underground working conditions and environment, the correction of region of controlling pattern shows the underground continuous production mode in coal mining. While the correction of eco-indicator has taken the importance of CPC in different situation into consideration, the corrected CREAM method is more in line with the research on human reliability of underground drilling works in coal mines.

b. The controlling pattern and CPC factor model built on the basis of corrected CREAM method reflects the reliability of corrected CREAM method application via empirical analysis, thus effectively promotes the accuracy of human reliability on underground production application in coal mine and reduces the human uncertainties involved in, therefore has better operability.

Acknowledgement

This work was financially supported by Central Universities Fundamental Research Funds (Grant No.2010QNA34), Program for New Century Excellent Talents in University(Grant No. NCET-13-1022). These supports are gratefully acknowledged.

REFERENCES

[1] Quan-long Liu. Modeling and evaluation of the safety control capability of coal mine based on system safety[C].Journal of Cleaner Production, In Press, Corrected Proof, Available online 4 December 2013.

[2] W. H. Heinrich. Industrial Accident Prevention[M]. Rarebooksclub.com, 2012.

[3] Quan-long Liu,Xin-chun Li. Modeling and evaluation of the safety control capability of coal mine based on system safety[J]. Journal of Cleaner Production, December 2013,Pages 1-6.

[4] Zheng Kaihuan, Jiang Fuchuan. Research on Intrinsic Safety Method for Open-pit Mining[C]. International Symposium on Safety Science and Engineering in China, 2012, Pages 453-458.

[5] C. Özgen Karacan, Felicia A. Ruiz, Michael Cotè, Sally Phipps. Coal mine methane: A review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction[J]. International Journal of Coal Geology, Volume 86, Issues 2-3, 1 May 2011, Pages 121-156

[6] Alan D. Swain. Human reliability analysis: Need, status, trends and limitations[J]. Reliability Engineering & System Safety, Volume 29, Issue 3, 1990, Pages 301-313.

[7] Swain A D, Guttmann H E. Handbook of Human Reliability Analysis with Emphasis on Nuclear Pover Plant Application [S].NUREG/CR-1278,1983.

[8] hollnagel E. Cognitive Realiability and Error Analysis Method. Elsevier Science Lid,1998.

[9] I. Misztal, A. Legarra, I. Aguilar. Using recursion to compute the inverse of the genomic relationship matrix[J]. Journal of Dairy Science, Volume 97, Issue 6, June 2014, Pages 3943-3952. [10] Alexander J. Macpherson, Peter P. Principe, Megan Mehaffey.

Using Malmquist Indices to evaluate environmental impacts of alternative land development scenarios[J]. Ecological Indicators, Volume 34, November 2013, Pages 296-303.

[11] E. Hollnagel. Modelling the orderliness of human action[M].R. Amalberti, N. Sarter (Eds.), Cognitive engineering in the aviation domain, Erlbaum, Hillsdale, NJ (2000).

[12] Izabela, Jonek, Kowalska. Risk management in the hard coal mining industry: Social and environmental aspects of collieries liquidation[J]. Resources Policy, Volume 41, September 2014, Pages 124-134.

corresponding author: WANG Lei E-mail: