Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures (2023)

Abstract: This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (The IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).

## introduction daily activities usually face incidents that can significantly affect risk management. in specific industries such as manufacturing, an incident can make a significant consequence that not only reduces the reputation of companies but also breaks the product chain and costs a lot of money. it motivates the introduction of the safety-critical area where ai solutions have been proposed to prevent repeated failures from historical samples @xcite @xcite @xcite @xcite . there still exists a gap in the adoption of ai techniques for actual incident management scenarios due to the lack of high-quality annotated datasets. the main challenges arise from two main reasons. first, data annotation of incidents for ai-related tasks is a labor-expensive and time-consuming task that requires domain experts who have a deep understanding and excellent experience in their daily work. second, the collection of historical incidents is also challenging due to its dependence on the policies of companies. we argue that the growth of the safety-critical area can be leveraged by introducing annotated incident datasets. to fill the gap, this paper takes the high-pressure gas domain, a sector of the gas industry, as a case study. this is because gas and its products are the major industry in the energy market that play an influential role in the global economy @xcite . in addition, a gas incident may cost a lot of money with significant consequences. the detection and analysis of past incidents are crucial for improving safety prevention and avoiding future failures. figure 1 shows the scenario of the detection and analysis. the description of an occurred incident is noted in an incident report. then, important information (named entity and cause-effect extraction) from the incident report is extracted and stored in an incident database. in operation, given the description of an incident, the manager can search historical incidents for potential risk analyses. the system alerts the worker by showing historically relevant incidents and cause-effect information based on assigned tasks. the worker can use suggested information for failure analysis to avoid future incidents. in practice, given an incident report, workers, managers, or analyzers would like to know: (i) which aspects (entities) are relevant to the incident?, (ii) what is the cause and effect of the incident?, (iii) and which are historically relevant incidents of the current incident for risk and failure analyses? to address the aforementioned questions, this paper introduces a new japanese dataset that focuses on high-gas incidents and demonstrates the potential nlp applications in analyzing high-gas incident reports. to do that, we first work closely with business members and domain experts to identify three potential nlp tasks: named entity recognition (ner), cause-effect extraction (ce), and information retrieval (ir) based on actual scenarios. the ner task allows analyzers to extract fundamental units of an incident in the form of entities, e.g., the product or the process of the product. this information is used to visualize statistics concerning key entities from past incidents retrieved through ir steps. the ce task allows analyzers to extract the cause and effect of an incident. the ir task is typically used to examine historical incidents similar to the current one and to develop countermeasures to prevent the recurrence of such incidents. in business scenarios, information from the three tasks is vital for safety-critical and risk management. this paper makes three main contributions as follows. ## related work incident databases there exist industry-specific incident databases in many industries @xcite . the databases contain a wide range of incidents such as cyber-security vulnerabilities and exposures (corporation, 2023), aviation reports that contain accident and incident information of air flights (administration, 2023), or reports of the pharmaceutical industry, healthcare providers, and consumers (food and administration, 2023). recently, an aiid database (ai incident database) was introduced @xcite . it indexes more than 1,000 publicly available incident reports. the existing databases allow the storage of incident information, yet simple ai techniques (simple matching or classification) create a gap to analyze the incidents. we leverage safety operations by introducing a new dataset that includes three main tasks: ner, cause-effect extraction, and ir. it facilitates the adoption of ai to prevent future failures in the context of high-pressure gas incidents. nlp techniques have been applied to analyze incident reports @xcite @xcite @xcite @xcite @xcite . the methods range from indexing for incident databases @xcite @xcite to deeper analyses using machine learning @xcite @xcite . recently, bert @xcite has been adapted to aviation safety @xcite @xcite . the recent survey also shows the role of nlp in aviation safety @xcite . we share the direction of using nlp techniques in the analysis of incident reports. however, instead of focusing on single tasks, we are interested in three different tasks: ner, ce, and ir, that provide critical information for safety prevention. in addition, we provide a japanese dataset to facilitate the creation of ai pipelines in a low-resource language. ## nlp tasks and methodology once the dataset has been created, nlp tasks were designed to establish the baselines of each task. ## conclusion this paper introduces a new japanese dataset for safety prevention by using ai models. the highquality dataset is annotated by domain experts for ner, ce, and ir tasks. the dataset contributes to incidentai in two important points. first, it composes the three nlp tasks in a corpus that facilitates the development of ai pipelines for safety prevention in a low-resource language. second, it benchmarks the results of the three tasks which are beneficial for the next studies of analyzing incident reports. future work will adapt the dataset to create ai pipelines for preventing failures of incidentai.