Health indicator construction for system health assessment in smart manufacturing

Moncef Soualhi (1) , Thi Phuong Khanh Nguyen (1) , Kamal Medjaher (1) , Denis Lebel (2) , David Cazaban-Loustaunau (2)

1 LGP - Laboratoire Génie de Production
2 Metallicadour (FRANCE)

Smart manufacturing is a part of the fourth industry revolution (Industry 4.0), which offers promising perspectives for high reliability, availability, maintainability, and safety production process. Indeed, smart monitoring methods, that are implemented in this kind of manufacturing process, allow efficient tracking of a system degradation in real time through appropriate sensors. Then, the sensor data are analyzed and processed to extract effective health indicators for fault detection, diagnostic and prognostics. This paper aims to develop a practical methodology for constructing a new health indicator based on heterogeneous sensor measurements to efficiently monitor system states. The proposed methodology is applied to extract the health indicator of a robot cutting tool (i.e. end-flat mill). This indicator is then used to diagnose the different fault types of the tool by an adaptive neuro-fuzzy inference system model.

Mots clés: Smart manufacturing; Smart monitoring; Industry 4.0; Health indicator construction; Signal processing; Tool condition monitoring

Domaines: Intelligence artificielle [cs.AI]; Robotique [cs.RO]

Moncef Soualhi, Thi Phuong Khanh Nguyen, Kamal Medjaher, Denis Lebel, David Cazaban-Loustaunau. Health indicator construction for system health assessment in smart manufacturing. 2019 Prognostics and System Health Management Conference, May 2019, Paris, France. pp.1-6. ⟨hal-02487078⟩