Gear Local Fault Diagnosis with Empirical Mode Decomposition and Hilbert Huang Transformation

Abstract

A new method for gear local fault diagnosis based on vibration signal analysis is presented in this paper by using the concept of instantaneous frequency. The data from the physical simulation are used to detect the change in the instantaneous frequency and meshing vibration energy of the gear tooth fault by Empirical Mode Decomposition and Hilbert Huang Transformation (EMD-HHT). It is verified that method is effective by rig testing of geared system.

The EEG Signal Process Based on EEMD

“…Through this process, any signal can be decomposed into a series of Intrinsic Mode Functions (IMF). Each Intrinsic Mode Functions (IMF) must satisfy the following two conditions:1)In the entire data set, the number of extreme points and the number of zero-crossings must either equal or differ by one at most;2)Throughout the signal curve, the mean value of envelope defined by the local minimum and the envelope defined by the local maxima is zero [5].…”

Fine-grained transfer learning based on deep feature decomposition for rotating equipment fault diagnosis

“…Equipment fault diagnosis can be divided into model-based fault diagnosis algorithms [4][5][6] and databased fault diagnosis algorithms [7]. Model-based diagnosis algorithms can be divided into deterministic fault diagnosis methods [8], stochastic fault diagnosis methods [9], fault diagnosis for discrete-events and hybrid systems [10], and fault knowledge of health system symptoms, including the wavelet transform [16], empirical mode decomposition [17], and Hilbert-Huang transform [18]. Machine learning methods analyze faults by manually extracting fault features and then using machine learning algorithms such as support vector machine algorithm [19], K-nearest neighbor algorithm [20], and Markov model [21].…”