# Scaling-basis chirplet extracting transform and its application in bearing fault diagnosis

## TL;DR

This paper introduces a new time-frequency analysis method for accurately diagnosing bearing faults in machinery.

## Contribution

The novel 'extraction operator' in the scaling-basis chirplet extracting transform (SBCET) improves time-frequency energy representation for non-stationary signals.

## Key findings

- SBCET provides high-resolution time-frequency representations for signals with close frequency intervals.
- The method performs well in intense background noise environments.
- Numerical and experimental results confirm the effectiveness of SBCET.

## Abstract

In this paper, we propose a new time-frequency analysis (TFA) method, namely scaling-basis chirplet extracting transform (SBCET). Based on the time-frequency representation (TFR) results obtained by scaling-basis chirplet transform (SBCT), the method introduces a new “extraction operator” to extract the time-frequency (TF) energy associated with the signal to portray the TF energy distribution information of the signal with high accuracy. SBCET can also obtain a TFR with concentrated energy and high resolution for non-stationary signals with close frequency intervals and intense background noise. The effectiveness and superiority are proved by numerical signal processing and experimental verification.