Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence

Abstract At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.

[1]  Xinyang Deng,et al.  An Evidential Axiomatic Design Approach for Decision Making Using the Evaluation of Belief Structure Satisfaction to Uncertain Target Values , 2018, Int. J. Intell. Syst..

[2]  Dongyin Wu,et al.  Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning , 2016 .

[3]  Deyi Li,et al.  A new cognitive model: Cloud model , 2009 .

[4]  H. B. Mitchell.  Data Fusion: Concepts and Ideas , 2012 .

[5]  Syed Mithun Ali,et al.  Supply chain sustainability assessment with Dempster-Shafer evidence theory: Implications in cleaner production , 2019, Journal of Cleaner Production.

[6]  Sankaran Mahadevan,et al.  A new decision-making method by incomplete preferences based on evidence distance , 2014, Knowl. Based Syst..

[7]  Lotfi A. Zadeh,et al.  Review of A Mathematical Theory of Evidence , 1984 .

[8]  Fuyuan Xiao,et al.  A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis , 2017, Sensors.

[9]  Zhou Jianfeng,et al.  Real‐time data‐based risk assessment for hazard installations storing flammable gas , 2008 .

[10]  Tao Wu,et al.  A Comparative Study of Cloud Model and Extended Fuzzy Sets , 2010, RSKT.

[11]  Zhang Lei,et al.  Clustering Methods for Multi-sensor Data Fusion , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.

[12]  Yichao Hu,et al.  Analysis method for causal factors in emergency processes of fire accidents for oil-gas storage and transportation based on ISM and MBN , 2019, Journal of Loss Prevention in the Process Industries.

[13]  Krassimir T. Atanassov,et al.  Answer to D. Dubois, S. Gottwald, P. Hajek, J. Kacprzyk and H. Prade's paper "Terminological difficulties in fuzzy set theory - the case of "Intuitionistic Fuzzy Sets" , 2005, Fuzzy Sets Syst..

[14]  Jose Luis Otegui,et al.  A major leak in a crude oil tank: Predictable and unexpected root causes , 2019, Engineering Failure Analysis.

[15]  Xianguo Wu,et al.  Perceiving safety risk of buildings adjacent to tunneling excavation: An information fusion approach , 2017 .

[16]  Catherine K. Murphy.  Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[17]  Mariarosa Giardina,et al.  Fuzzy environmental analogy index to develop environmental similarity maps for designing air quality monitoring networks on a large-scale , 2019, Stochastic Environmental Research and Risk Assessment.

[18]  Miroslaw J. Skibniewski,et al.  An improved Dempster-Shafer approach to construction safety risk perception , 2017, Knowl. Based Syst..

[19]  Rajat Agrawal,et al.  Assessment of an accidental vapour cloud explosion: Lessons from the Indian Oil Corporation Ltd. accident at Jaipur, India , 2013 .

[20]  Min Huang,et al.  Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion , 2020, Simul. Model. Pract. Theory.

[21]  Faisal Khan,et al.  Precursor-based hierarchical Bayesian approach for rare event frequency estimation: A case of oil spill accidents , 2013 .

[22]  Sankaran Mahadevan,et al.  A new method to determine basic probability assignment from training data , 2013, Knowledge-Based Systems.

[23]  Lotfi A. Zadeh.  Preliminary Draft Notes on a Similarity‐Based Analysis of Time‐Series with Applications to Prediction, Decision and Diagnostics , 2019 .

[24]  Hongyang Yu.  Dynamic risk assessment of complex process operations based on a novel synthesis of soft-sensing and loss function , 2017 .

[25]  Xi Chen,et al.  A risk assessment method based on RBF artificial neural network - cloud model for urban water hazard , 2014, J. Intell. Fuzzy Syst..

[26]  I. R. Goodman,et al.  Mathematics of Data Fusion , 1997 .

[27]  M. Sam Mannan,et al.  Supporting risk management decision making by converting linguistic graded qualitative risk matrices through interval type-2 fuzzy sets , 2020 .

[28]  John D. Lee,et al.  Improving process safety: What roles for Digitalization and Industry 4.0? , 2019 .

[29]  S. Rajakarunakaran,et al.  Application of Fuzzy HEART and expert elicitation for quantifying human error probabilities in LPG refuelling station , 2017 .

[30]  Eric Lefevre,et al.  Belief function combination and conflict management , 2002, Inf. Fusion.

[31]  Jianqiang Wang,et al.  An Uncertain Linguistic Multi-criteria Group Decision-Making Method Based on a Cloud Model , 2014, Group Decision and Negotiation.

[32]  Mariarosa Giardina,et al.  Fuzzy Fault Tree analysis in modern γ-ray industrial irradiator: use of fuzzy version of HEART and CREAM techniques for human error evaluation , 2008 .

[33]  Yaakov Bar-Shalom,et al.  Dimensionless score function for multiple hypothesis tracking , 2007 .

[34]  Tong Li,et al.  Risk Assessment and Online Forewarning of Oil & Gas Storage and Transportation Facilities Based on Data Mining , 2017, KES.

[35]  Yi Liu,et al.  Firefighting Emergency Capability Evaluation on Crude Oil Tank Farm , 2018 .

[36]  Jianfeng Zhou,et al.  SPA-fuzzy method based real-time risk assessment for major hazard installations storing flammable gas. , 2010 .

[37]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Ren C. Luo,et al.  Multisensor fusion and integration: approaches, applications, and future research directions , 2002 .

[39]  Naser Badri,et al.  A multivariable approach for estimation of vapor cloud explosion frequencies for independent congested spaces to be used in occupied building risk assessment , 2013 .

[40]  F Castiglia,et al.  Risk assessment of component failure modes and human errors using a new FMECA approach: application in the safety analysis of HDR brachytherapy , 2014, Journal of radiological protection : official journal of the Society for Radiological Protection.

[41]  Yang Dan,et al.  A robust D-S fusion algorithm for multi-target multi-sensor with higher reliability , 2019, Inf. Fusion.

[42]  Layachi Bentabet,et al.  Automatic determination of mass functions in Dempster-Shafer theory using fuzzy-C-means and spatial neighborhood information for image segmentation , 2002 .

[43]  Zhuang Wu,et al.  Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm , 2020, Reliab. Eng. Syst. Saf..

[44]  Isabelle Bloch,et al.  Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing , 1997, IEEE Trans. Geosci. Remote. Sens..

[45]  H. Kohl,et al.  Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential , 2018, Process Safety and Environmental Protection.

[46]  Liqiong Chen,et al.  Fuzzy fault tree analysis for fire and explosion of crude oil tanks , 2013 .

[47]  Laurence T. Yang,et al.  A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion , 2019, Inf. Fusion.

[48]  F Castiglia,et al.  FUZZY RISK ANALYSIS OF A MODERN &ggr;-RAY INDUSTRIAL IRRADIATOR , 2011, Health physics.

[49]  Mariarosa Giardina,et al.  Fuzzy modelling of HEART methodology: application in safety analyses of accidental exposure in irradiation plants , 2009 .

[50]  Yong Deng,et al.  A new method to measure the divergence in evidential sensor data fusion , 2019, Int. J. Distributed Sens. Networks.

[51]  R. Yager.  A class of fuzzy measures generated from a Dempster–Shafer belief structure , 1999 .

[52]  Dilip Kumar Pratihar,et al.  Multi-sensors data fusion through fuzzy clustering and predictive tools , 2018, Expert Syst. Appl..

[53]  Clément Lenoble,et al.  An international comparison of four quantitative risk assessment approaches—A benchmark study based on a fictitious LPG plant , 2012 .

[54]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[55]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[56]  Kui Xu,et al.  Fuzzy fault tree assessment based on improved AHP for fire and explosion accidents for steel oil storage tanks. , 2014, Journal of hazardous materials.

[57]  Fang Yan,et al.  Methodology and case study of quantitative preliminary hazard analysis based on cloud model , 2019 .

[58]  N. Pinardi,et al.  Towards a common oil spill risk assessment framework – Adapting ISO 31000 and addressing uncertainties. , 2015, Journal of environmental management.

[59]  Liguo Fei,et al.  A new method to identify influential nodes based on relative entropy , 2017 .

[60]  Han De,et al.  Weighted evidence combination based on distance of evidence and uncertainty measure , 2011 .

[61]  Javier Del Ser,et al.  Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0 , 2019, Inf. Fusion.

[62]  Fuyuan Xiao,et al.  Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy , 2019, Inf. Fusion.

[63]  F Castiglia,et al.  Risk analysis using fuzzy set theory of the accidental exposure of medical staff during brachytherapy procedures , 2010, Journal of radiological protection : official journal of the Society for Radiological Protection.