Data-driven prognostics and health management solutions play an important role for condition monitoring of industrial systems. They offer promising perspectives for high reliability, availability, maintainability, and safety production, especially in Industry 4.0 where model-based approaches are not suitable for complex systems. To build an efficient diagnostic and prognostic model, the collected sensor measurements are injected into processing algorithms to extract features and construct health indicators. The performance of this procedure strictly depends on the data quality. However, the availability of reliable and exploitable data remains one of the most challenging issues in condition monitoring. This paper presents advanced data analytics to construct prognostic health indicator. This method aims at obtaining reliable and exploitable indicators that represent multiple degradations evolution of the system. It consists of an automated processing algorithm that detect and isolate trajectories representing the system health state from its nominal condition to its critical level. The performance of the proposed method is investigated through a real data of pulp and paper manufacturing where the degradation process is an evolution of fouling in a black liquor heat exchanger.
How to Cite
Smart manufacturing, Condition monitoring, Prognostics and health management, Health indicator construction, Signal processing, Pulp and paper, Heat exchangers, Black liquor, Fouling
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