# Dual-Domain Impulse Complexity Index-Guided Projection Iterative-Methods-Based Optimizer-Feature Mode Decomposition (DICI-Guided PIMO-FMD): A Robust Approach for Bearing Fault Diagnosis Under Strong Noise Conditions

## TL;DR

This paper introduces a new method for diagnosing bearing faults in noisy environments by combining signal decomposition and adaptive optimization.

## Contribution

The novel DICI-Guided PIMO-FMD method uses dual-domain metrics and adaptive optimization to improve fault diagnosis in strong noise conditions.

## Key findings

- The proposed DICI criterion improves FMD parameter optimization stability.
- PIMO-based optimization enhances fault detection under low SNR conditions.
- Simulation and real-signal tests confirm superior performance over existing methods.

## Abstract

Bearings are core components in many types of industrial equipment, and their operating environment is often accompanied by strong background noise. This results in a low Signal-to-Noise Ratio (SNR) in the collected vibration signals, making it difficult for traditional methods to extract fault information effectively. Given that bearing failures often manifest as periodic impact signals, a Feature Mode Decomposition (FMD) method has been proposed by researchers which optimizes filter design through correlated kurtosis to enhance the ability to capture fault impact components. However, the decomposition performance of FMD is significantly affected by its parameters (mode number and filter length), and relies on manual settings, resulting in insufficient stability of the results. Therefore, this paper proposes a Dual-domain Impulse Complexity Index (DICI) that combines time-domain impulse characteristics and frequency-domain complexity as an evaluation criterion for FMD parameter optimization. Further, the projection-iterative-methods-based optimizer (PIMO) is adopted to achieve adaptive optimization of parameters. Subsequently, sensitive components are selected based on the maximum Fault Frequency Correlation (FFC) criterion, and their envelope spectra are calculated to recognize bearing fault modes. Simulation and real-signal verification show that the proposed method outperforms several established signal-processing approaches under low SNR conditions.