# Compound Fault Diagnosis of Wind Turbine Gearbox via Modified Signal Quality Coefficient and Versatile Residual Shrinkage Network

## TL;DR

This paper introduces a new wind turbine gearbox fault diagnosis method using modified signal processing and a specialized neural network to improve accuracy in complex fault detection.

## Contribution

A novel compound fault diagnosis method combining modified signal quality coefficient and a versatile residual shrinkage network is proposed.

## Key findings

- The MSQC effectively balances noise removal and signal fidelity for wind turbine operation status analysis.
- The VRSN accurately identifies the number and type of compound faults in wind turbine gearboxes.
- Experimental results show the proposed method achieves high compound fault diagnosis accuracy.

## Abstract

Wind turbine gearbox fault diagnosis is critical to guarantee working efficiency and operational safety. However, the current diagnostic methods face enormous restrictions in handling nonlinear noise signals and intricate compound fault patterns. Herein, a compound fault diagnosis method based on modified signal quality coefficient (MSQC) and versatile residual shrinkage network (VRSN) is proposed to resolve these issues. In detail, the MSQC is designed to remove the noise components irrelevant to wind turbine operation status, and it has the ability to balance the denoised effect and signal fidelity. The VRSN is constructed for compound fault diagnosis, and it consists of two heterogeneous residual shrinkage networks. The former is designed to count the number of faults, and the latter is adopted to identify the single or compound fault pattern. Finally, a self-built wind turbine gearbox compound fault test rig is adopted to verify the proposed method’s effectiveness. The results demonstrate that the proposed method is competitive in terms of compound fault diagnosis accuracy.