Multi-level Failure, Causality and Hazard Insights via Knowledge Based Systems

PresentationOver many decades there has been a significant development of knowledge-based, intelligent design tools and their use in the design of process systems. Amongst such tools are “intelligent” piping and instrumentation (P&IDs) design environments, coupled to life cycle design environments. These tools can provide opportunities for the development of new, more efficient and re-usable approaches to hazard identification and diagnostic systems. They leverage modern information technology characteristics of such design environments. These considerations are part of a growing trend in industrial digitalization, as reflected in such initiatives as Industry 4.0 in Europe and driven by the Industrial Internet of Things (IIoT). Within this larger industrial digitalization picture, this work discusses the principles, developments and application of a hazard identification methodology (BLHAZID) that exploits structured representations of the design in the form of ISO15926 data standards. The hazard identification methodology is based in knowledge representations of failure modes of equipment types that are found in many process designs and how those failures subsequently affect the system states and other components. The underlying causal models can be used at various levels of aggregation, model fidelity and component inclusion detail. The aggregation can span across the most detailed view at the smallest component level through subsystem level to plant level perspectives. The ability to represent and then display failure causation and implications at different levels of granularity allows deeper insight into system failures, and the potential for real-time diagnostic deployment. The importance of failure and subsequent propagation prevention through the use of safety instrumented systems and other barrier devices is possible. Outcomes can be visualized in informative ways. The presentation will discuss these intelligent information technology approaches via some a case study, highlighting the advantages and challenges such approaches bring to hazard identification as well as highlighting other application areas such as real-time diagnosis, corporate knowledge capture of failures, operator training and accident investigation

HAZOP: Our Primary Guide in the Land of Process Risks: How can we improve it and do more with its results?

PresentationAll risk management starts in determining what can happen. Reliable predictive analysis is key. So, we perform process hazard analysis, which should result in scenario identification and definition. Apart from material/substance properties, thereby, process conditions and possible deviations and mishaps form inputs. Over the years HAZOP has been the most important tool to identify potential process risks by systematically considering deviations in observables, by determining possible causes and consequences, and, if necessary, suggesting improvements. Drawbacks of HAZOP are known; it is effort-intensive while the results are used only once. The exercise must be repeated at several stages of process build-up, and when the process is operational, it must be re-conducted periodically. There have been many past attempts to semi- automate the HazOp procedure to ease the effort of conducting it, but lately new promising developments have been realized enabling also the use of the results for facilitating operational fault diagnosis. This paper will review the directions in which improved automation of HazOp is progressing and how the results, besides for risk analysis and design of preventive and protective measures, also can be used during operations for early warning of upcoming abnormal process situations

A structural decomposition-based diagnosis method for dynamic process systems using HAZID information

A novel diagnosis method is proposed in this paper that uses the results of the blended HAZID analysis extended to the dynamic case of process systems controlled by operational procedures. The algorithm is capable of finding fault root causes in process systems using nominal and observed possible faulty operational procedure execution traces. The algorithm uses the structural decomposition of the process system and its component-level dynamic HAZID (P-HAZID) tables and executes the diagnosis component-wise by first decomposing the observed execution traces, and then assembling the diagnosis results. The exact structure of the algorithm is also discussed, followed by two case studies on which its operation is demonstrated. © 2014 Elsevier Ltd

Data-Based Semi-Automatic Hazard Identification for More Comprehensive Identification of Hazardous Scenarios

As chemical process plants have become more involved and complex, the likelihood of hazardous incidents has increased simultaneously. That is, the more complex a facility’s systems, the more factors engineers must consider. This results in a higher likelihood of potential hazards being overlooked; thus, the possibility of incidents occurring increases. Many companies and organizations are struggling to identify their weaknesses and reduce hazardous issues by developing hazard identification (HAZID) tools, particularly for large and complex processes. Even though a considerable number of companies merely pursue this objective to conform to government regulations, their efforts play a critical role in improving their reputations and financial profits. Therefore, the advancement of HAZID tools in the process industries has taken significant strides over the last 40 years. Despite the substantial development of HAZID methods, traditional HAZID tools need further development because of their weaknesses in identifying possible hazards. In other words, it is evident that unintended incidents that occasionally occur in the chemical process industry require more enhanced HAZID methodologies. Therefore, this study attempts to ascertain the drawbacks of existing HAZID tools so that a new HAZID methodology, data-based semi-automatic hazard identification (DAHAZID), is proposed. Considering potential HAZID methodologies, this study seeks to identify possible scenarios with a semi-automatic and systemic approach. Based on the two traditional HAZID tools, Hazard Operability study (HAZOP) and Failure Mode, Effects, and Criticality Analysis (FMECA), the DAHAZID method will minimize the limitations of each individual method. Additionally, rather than depending on the HAZID tools to achieve the connectivity of the process system, this study will consider connections with other new technologies in advance. Then, this method can be integrated with proper guidelines regarding process design and safety analysis. To examine its usefulness, the method will be applied to two case studies, and its outcome will be compared to the actual result, performed previously by a traditional HAZOP meeting. Hopefully, this research can contribute to the further development of the process safety field in practice

Visualizing Process Design, Operation and Failure Impacts through State Space Representations

PresentationVisualization can improve insights into choices made in early stages of design, particularly in relation to the impact of system related failures. Improved decision making can lead to higher commitment to inherently safer designs, more fault tolerant systems and increased operational resilience. This paper proposes a means to visualize the function of a design in terms of the state space defined by multiple capabilities possessed by the individual components that constitute the system. Capability is related to the abilities of the component to affect the states of the system, primarily the properties of mass and energy streams that flow through the system. A representation that is constructed from these capability vectors maps out the potential space in which the system can normally operate. It also shows the impact on that space when selected capabilities are degraded or lost. The visualization benefits of the proposed methodology will be displayed with an industrial case study. A typical supply line configuration to a fuel storage facility is investigated to show the fundamental concepts and to assess the utility of the ideas within conceptual process design and operations

A Refreshing Take: Analysing Accident Scenarios through Causal Network Topology Metrics

PresentationAccident causation investigation and even more hazard scenario identification are troubled by the complexity of interactions between three elements in a process facility: People, Plant and Procedures. Interactions are of various nature, such as physical change and information transfer, all influencing the process. To facilitate investigation the digraph network was applied as the most flexible visual aid to describe a causal structure. Such structure consists of nodes and edges representing an event or condition in the accident scenario and a causal link respectively. Attributing the nodes and edges to the type of interaction, numbers of the same type can be counted, and so two metrics are developed: The P3 Interaction Contribution (PIC). This is the proportion of nodes and edges associated with an interaction between People, Plant and Procedures. The Average Edge Weight. This relates to the proportion of events in the scenario that are associated with the logical AND gate conjunction from its causes (incident nodes), where the event requires more than one simultaneous cause. The technique was tried on four CSB accident descriptions. Interesting differences are seen. Also, in view of a paper accepted to be published in Safety Science the approach seems quite helpful in process hazard analysis

Safety Sufficiency for NextGen: Assessment of Selected Existing Safety Methods, Tools, Processes, and Regulations

NextGen is a complex socio-technical system and, in many ways, it is expected to be more complex than the current system. It is vital to assess the safety impact of the NextGen elements (technologies, systems, and procedures) in a rigorous and systematic way and to ensure that they do not compromise safety. In this study, the NextGen elements in the form of Operational Improvements (OIs), Enablers, Research Activities, Development Activities, and Policy Issues were identified. The overall hazard situation in NextGen was outlined; a high-level hazard analysis was conducted with respect to multiple elements in a representative NextGen OI known as OI-0349 (Automation Support for Separation Management); and the hazards resulting from the highly dynamic complexity involved in an OI-0349 scenario were illustrated. A selected but representative set of the existing safety methods, tools, processes, and regulations was then reviewed and analyzed regarding whether they are sufficient to assess safety in the elements of that OI and ensure that safety will not be compromised and whether they might incur intolerably high costs

A structured model based diagnosis method for discrate dynamic process using event sequences

A novel model-based fault detection and diagnosis method is proposed that is based on following event sequences measured in a discrete dynamic process. The model of the nominal and faulty operation modes is given in the form of event sequences, that are decomposed according to the components and sub-components present in the process system. The faulty event sequences are defined using extended procedure HAZID tables. A diagnostic algorithm is also presented that uses a component- wise decomposed form of the event sequences. The operation of the algorithm is illustrated on a simple example of a process system consisting of three similar tanks

Process hazard analysis, hazard identification and scenario definition: are the conventional tools sufficient, or should and can we do much better?

Hazard identification is the first and most crucial step in any risk assessment. Since the late 1960s it has been done in a systematic manner using hazard and operability studies (HAZOP) and failure mode and effect analysis (FMEA). In the area of process safety these methods have been successful in that they have gained global recognition. There still remain numerous and significant challenges when using these methodologies. These relate to the quality of human imagination in eliciting failure events and subsequent causal pathways, the breadth and depth of outcomes, application across operational modes, the repetitive nature of the methods and the substantial effort expended in performing this important step within risk management practice. The present article summarizes the attempts and actual successes that have been made over the last 30 years to deal with many of these challenges. It analyzes what should be done in the case of a full systems approach and describes promising developments in that direction. It shows two examples of how applying experience and historical data with Bayesian network, HAZOP and FMEA can help in addressing issues in operational risk management

Health management design considerations for an all electric aircraft

This paper explains the On-board IVHM system for a State-Of-the-Art “All electric aircraft” and explores implementing practices for analysis based design, illustrations and development of IVHM capabilities. On implementing the system as an on board system will carry out fault detection and isolation, recommend maintenance action, provides prognostic capabilities to highest possible problems before these became critical. The vehicle Condition Based Maintenance (CBM) and adaptive control algorithm development based on an open architecture system which allow “Plug in and Plug off” various systems in a more efficient and flexible way. The scope of the IVHM design included consideration of data collection and communication from the continuous monitoring of aircraft systems, observation of current system states, and processing of this data to support proper maintenance and repair actions. Legacy commercial platforms and HM applications for various subsystems of these aircraft were identified. The list of possible applications was down-selected to a reduced number that offer the highest value using a QFD matrix based on the cost benefit analysis. Requirements, designs and system architectures were developed for these applications. The application areas considered included engine, tires and brakes, pneumatics and air conditioning, generator, and structures. IVHM design program included identification of application sensors, functions and interfaces; IVHM system architecture, descriptions of certification requirements and approaches; the results of a cost/benefit analyses and recommended standards and technology gaps. The work concluded with observations on nature of HM, the technologies, and the approaches and challenges to its integration into the current avionics, support system and business infrastructure. The IVHM design for All Electric Hybrid Wing Body (HWB) Aircraft has a challenging task of addressing and resolving the shortfalls in the legacy IVHM framework. The challenges like sensor battery maintenance, handling big data from SHM, On-Ground Data transfer by light, Extraction of required features at sensor nodes/RDCUs, ECAM/EICAS Interfaces, issues of certification of wireless SHM network has been addressed in this paper. Automatic Deployable Flight Data recorders are used in the design of HWB aircraft in which critical flight parameters are recorded. The component selection of IVHM system including software and hardware have been based on the COTS technology. The design emphasis on high levels of reliability and maintainability. The above systems are employed using IMA and integrated on AFDX data bus. The design activities has to pass through design reviews on systematic basis and the overall approach has been to make system highly lighter, effective “All weather” compatible and modular. It is concluded from the study of advancement in IVHM capabilities and new service offerings that IVHM technology is emerging as well as challenging. With the inclusion of adaptive control, vehicle condition based maintenance and pilot fatigue monitoring, IVHM evolved as a more proactively involved on-board system