Abstract Smart manufacturing is one of the key parts of the fourth industry revolution (Industry 4.0). It offers promising perspectives for high reliability, availability, maintainability and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system health state. In this paper, a practical and effective method that can be applied for fault detection and diagnostics of a given system is developed. The proposed method relies on a pattern recognition technique based on the construction of a new health indicator. This health indicator, which can be applied to different types of sensor measurements, is fed to an Adaptive Neuro-Fuzzy Inference System (ANFIS) to detect the health states of the system and diagnose the causes. Furthermore, the performance and the robustness of the proposed method are highlighted by considering various case studies under numerous operating conditions.

中文翻译：

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基于新健康指标的智能制造故障诊断模式识别方法

摘要 智能制造是第四次工业革命（工业4.0）的关键组成部分之一。它为高可靠性、可用性、可维护性和安全生产过程提供了有希望的前景，但也使系统在健康评估方面更加复杂和具有挑战性。为了应对这些挑战，人们需要开发一种强大的方法来监控和评估系统健康状态。在本文中，开发了一种实用且有效的方法，可用于给定系统的故障检测和诊断。所提出的方法依赖于基于新健康指标构建的模式识别技术。此健康指标可应用于不同类型的传感器测量，被馈送到自适应神经模糊推理系统 (ANFIS) 以检测系统的健康状态并诊断原因。此外，通过考虑多种操作条件下的各种案例研究，突出了所提出方法的性能和稳健性。