Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing

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. (C) 2020 Elsevier Ltd. All rights reserved.