[IMC 2026] Taming Complexity: Advanced Analytics for a Diverse Turbomachinery Fleet

Filippo Guidi, Everllence Schweiz AG As reducing maintenance costs and enhancing operational efficiency become increasingly critical, remote monitoring with automated analytics has emerged as a key strategy for managing turbomachinery installations. While the use of complex physics-based algorithms like thermodynamic models can be beneficial for fleets with machines of similar types and instrumentation, this becomes increasingly difficult for fleets comprising multiple machine types, such as compressors and turbines, operating under diverse conditions, configurations, ages, and service histories. To address this, an end-to-end data processing chain is presented that transforms raw sensor signals into actionable diagnostic insights. This spans from sensor measurements through analytics to decision support. As specialized approaches only have a limited impact on the overall fleet, the first step is to reliably detect anomalies relative to expected behavior. For that, a combination of semi-supervised, ML-based anomaly detection and physicsinformed regression algorithms is employed. The regression algorithms predict important KPIs like efficiency, depending on the operating condition and comparing them to target values. The output of these algorithms is then fed into a fuzzy logic framework that is based on formalized knowledge mostly collected via expert workshops. This enables the integration of insights from root-cause analyses into an automated data flow, creating actionable advice for detected anomalies. The presentation also emphasizes how well-suited different analytics solutions are for diverse machine fleets and how large language models can be leveraged to accelerate the knowledge-collection process by giving suggestions based on documentation and service reports.