EAGER: A Systems Approach to Predicting and Preventing Accidents During Operations

EAGER：预测和预防运营期间事故的系统方法

• 批准号: 1841231
• 负责人: Nancy Leveson
• 金额: $ 12.45万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841231&HistoricalAwards=false
• 关键词: EAGER; Systems; Approach; Predicting; Preventing

This EArly-concept Grant for Exploratory Research (EAGER) project will contribute to the economic prosperity, safety and wellbeing of the nation by taking the first proof-of-concept steps in investigating a new approach to identifying leading indicators signaling the potential for an accident before it occurs. Despite our best intentions to develop safe products and services, accidents and losses still occur. As the potential for greater losses involved in our advanced technology today increases, it will be harder to design inherently safe systems and operational risk management practices will become more important. There are always warning signs before a major accident, but these signs may only be noticeable and interpretable in hindsight. At the same time, enormous amounts of data can now be collected by computers but most of theses data are not used because of the size and difficulty in creating useful information from the data. This research will take a new and unique approach to analyzing operational data and identifying when risk is increasing. Three international airlines are participating in the research by providing real operational data and their expertise to validate the feasibility and efficacy of the new risk analysis technique. While airline operations are used as the testbed, the research results can be extended to any safety-critical industry. The basic hypothesis to be evaluated in this research is that useful leading indicators can be identified based on the assumptions underlying the safety engineering practices employed and on the vulnerability of those assumptions. This process will require the use of various types of operational data that can be (and often is) collected but needs some process for determining the potential consequences of the collected data. The research will build on a new model of accident causation called STAMP (System-Theoretic Accident Model and Processes) and a new hazard analysis technique (STPA or System-Theoretic Process Analysis) to work on that model. STAMP extends current accident causality to include more complex causes than simply component failures and chains of failure events or deviations from operational expectations (which is the basis for traditional hazard analysis methods). STAMP incorporates basic principles of systems thinking and is based on systems theory rather than traditional reliability theory. A new analysis tool called Active STPA will be created that will use data commonly collected during operations to identify when the assumptions made during design and development of a product or service are violated during operations and thus may be a precursor for an accident and evidence that risk may be increasing. The test bed will be airline operations. While the subject of this research is limited to identifying leading indicators related to safety and accidents, the ideas will apply to leading indicators and risk management for system properties other than safety and could open a new path to evaluating risk during operations for researchers to exploreThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

EARLY概念探索性研究资助（EAGER）项目将通过采取第一个概念验证步骤，调查一种新方法，以确定领先指标，从而为国家的经济繁荣，安全和福祉做出贡献。尽管我们有开发安全产品和服务的最佳意图，但事故和损失仍然发生。随着当今先进技术带来更大损失的可能性增加，设计固有安全的系统将变得更加困难，运营风险管理实践将变得更加重要。在重大事故发生之前总是有警告信号，但这些信号可能只有在事后才能被注意到和解释。与此同时，计算机现在可以收集大量的数据，但由于数据的大小和从数据中创建有用信息的困难，这些数据中的大多数没有被使用。这项研究将采用一种新的独特方法来分析运营数据，并确定风险何时增加。三家国际航空公司参与了这项研究，提供了真实的运营数据及其专业知识，以验证新的风险分析技术的可行性和有效性。虽然航空公司运营被用作测试平台，但研究结果可以扩展到任何安全关键行业。在这项研究中进行评估的基本假设是，有用的领先指标，可以确定的基础上的安全工程实践的假设和这些假设的脆弱性。这个过程将需要使用各种类型的操作数据，这些数据可以（并且经常）收集，但需要一些过程来确定所收集数据的潜在后果。该研究将建立在一个新的模型的事故原因称为STAMP（系统理论事故模型和过程）和一个新的危险分析技术（STPA或系统理论过程分析），该模型的工作。STAMP扩展了当前的事故因果关系，使其包括比简单的部件故障和故障事件链或偏离操作预期（这是传统危险分析方法的基础）更复杂的原因。STAMP融合了系统思想的基本原理，并以系统理论而不是传统的可靠性理论为基础。将创建一个名为Active STPA的新分析工具，该工具将使用在运营过程中通常收集的数据，以确定在产品或服务的设计和开发过程中所做的假设在运营过程中何时被违反，从而可能成为事故的前兆和风险可能增加的证据。试验台将是航空公司的业务。 虽然这项研究的主题仅限于确定与安全和事故有关的主要指标，这个奖项反映了美国国家科学基金会的法定使命，并且被认为是值得通过利用基金会的智力价值和更广泛的知识来进行评估的。影响审查标准。