Early fault detection with multi-target neural networks

TL;DR

This paper demonstrates that multi-target neural networks can detect wind turbine faults earlier than traditional single-target models, enabling more timely maintenance and cost savings.

Contribution

The study applies multi-target neural networks to wind turbine fault detection, showing improved early detection capabilities over single-target models.

Findings

Multi-target neural networks detect faults earlier than single-target models.

Fault detection can be several days earlier with multi-target approaches.

Multi-target models maintain prediction stability comparable to single-target models.

Abstract

Wind power is seeing a strong growth around the world. At the same time, shrinking profit margins in the energy markets let wind farm managers explore options for cost reductions in the turbine operation and maintenance. Sensor-based condition monitoring facilitates remote diagnostics of turbine subsystems, enabling faster responses when unforeseen maintenance is required. Condition monitoring with data from the turbines' supervisory control and data acquisition (SCADA) systems was proposed and SCADA-based fault detection and diagnosis approaches introduced based on single-task normal operation models of turbine state variables. As the number of SCADA channels has grown strongly, thousands of independent single-target models are in place today for monitoring a single turbine. Multi-target learning was recently proposed to limit the number of models. This study applied multi-target neural networks to the task of early fault detection in drive-train components. The accuracy and delay of detecting gear bearing faults were compared to state-of-the-art single-target approaches. We found that multi-target multi-layer perceptrons (MLPs) detected faults at least as early and in many cases earlier than single-target MLPs. The multi-target MLPs could detect faults up to several days earlier than the single-target models. This can deliver a significant advantage in the planning and performance of maintenance work. At the same time, the multi-target MLPs achieved the same level of prediction stability.