Normal accidents. Living with high-risk technologies

Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

Hydrogen storage and delivery: Review of the state of the art technologies and risk and reliability analysis

Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Bayesian Networks in Fault Diagnosis

https://hal.archives-ouvertes.fr/hal-00546144/document

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Diagnosis is a critical activity in the PHM domain (Prognostics and Health Management) due to its impact on the downtime and on the global performances of a system. This activity becomes complex when dealing with large systems such as power plants, ships, aircrafts, which are composed of multiple systems, sub-systems and components of different technologies, different usages, and different ages. In order to ease diagnosis activities, this paper proposes to use a fleet-wide approach based on ontologies in order to capitalize knowledge and data to help decision makers to identify the causes of abnormal operations. In that sense, taking advantage of a fleet dimension implies to provide managers and engineers more knowledge as well as relevant and synthetized information about the system behavior. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. This paper presents a knowledge structuring scheme of fleets in the marine domain based on ontologies for diagnostic purposes. The semantic knowledge model formalized with an ontology allowed to retrieve data from a set of heterogeneous units through the identification of common and pertinent points of similarity. Hence, it allows to reuse past feedback experiences to build fleet-wide statistics and to search "deeper" causes producing an operation drift.

Predictive diagnosis based on a fleet-wide ontology approach

PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment

Industrial system knowledge formalization to aid decision making in maintenance strategies assessment

Large complex systems, such as power plants, ships and aircraft, are composed of multiple systems, subsystems and components. When they are considered as embedded in operating systems such as a fleet, mission readiness and maintenance management issues are raised. PHM (Prognostics and Health Management) plays a key role in controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. Moreover considering a fleet implies to provide managers and engineers a relevant synthesis of information and to keep this information updated in terms of the global health of the fleet as well as the current status of their maintenance efforts. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme based on ontologies for fleet PHM management applied to marine domain, with emphasis on prognostics modeling.

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Gabriela Medina-Oliva

Papers

Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas

Predictive diagnosis based on a fleet-wide ontology approach

PRM-based patterns for knowledge formalisation of industrial systems to support maintenance strategies assessment

Industrial system knowledge formalization to aid decision making in maintenance strategies assessment

Prognostics Assessment Using Fleet-wide Ontology