Industrial use of safety-related artificial neural networks
Industrial use of safety-related artificial neural networks
复制标题
安全相关人工神经网络的工业应用
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Paulo J G Lisboa
中科院分区：
文献类型：
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作者：
Paulo J G Lisboa
本研究的总体目标是调查神经网络在安全相关应用中的使用程度，以及在不久的将来可能会使用的程度。神经网络产品正在积极上市，其中一些产品经常用于安全相关领域，包括癌症筛查和办公楼火灾探测。有些是已经获得美国食品和药物管理局 (FDA) 认证的医疗器械。从行业主导的研究范围来看，这项技术的商业潜力是显而易见的，而且安全效益也会随之提高。例如，在流程工业中，确实有可能进行更密切的工厂监控，从而实现生产维护，包括延长工厂寿命。从所审查的应用中可以清楚地看出，神经网络成功转移到市场的关键是与常规实践的成功集成，而不是针对当前大部分开发工作发生的理想化环境进行优化。这需要能够使用结构化领域知识以及性能测试来评估他们根据经验得出的响应。在控制器设计中，生产模型解决方案的可扩展性，以及在工厂磨损下保持安全高效运行的需要，导致了线性设计方法与神经网络架构的集成。有必要在两个方向进行进一步的研究，首先将当前设计各种截然不同的神经计算软件模型和硬件系统的最佳实践系统化，然后制定安全相关应用中高复杂性计算的统一视角。有必要制定良好实践指南，教育非专业用户并告知已经广泛的从业者基础。与安全意识举措相结合，这对于这项具有重要商业意义的新技术的开发以及在安全相关应用中的安全使用都有同样的好处。本报告及其描述的工作由健康与安全执行局 (HSE) 资助。其内容（包括所表达的任何意见和/或结论）仅代表作者个人观点，并不一定反映 HSE 政策。
The overall objective of this study is to investigate to what extent neural networks are used, and are likely to be used in the near future, in safety-related applications. Neural network products are actively being marketed and some are routinely used in safety-related areas, including cancer screening and fire detection in office blocks. Some are medical devices already certified by the Food and Drug Administration (FDA). The commercial potential for this technology is evident from the extent of industry-led research, and safety benefits will arise. In the process industries, for instance, there is real potential for closer plant surveillance and consequently productive maintenance, including plant life extension. It is clear from the applications reviewed that the key to successful transfer of neural networks to the marketplace is successful integration with routine practice, rather than optimisation for the idealised environments where much of the current development effort takes place. This requires the ability to evaluate their empirically derived response using structured domain knowledge, as well as performance testing. In controller design, the scalability of solutions to production models, and the need to maintain safe and efficient operation under plant wear, have led to the integration of linear design methods with neural network architectures. Further research is necessary in two directions, first to systematise current best practice in the design of a wide range of quite different neural computing software models and hardware systems, then to formulate a unified perspective of high-complexity computation in safety-related applications. There is a need to develop guidelines for good practice, to educate non-specialist users and inform what is already a wide base of practitioners. Combined with a safety awareness initiative, this would be of as much of benefit to the development of this commercially important new technology, as to its safe use in safety-related applications. This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the author alone and do not necessarily reflect HSE policy.