Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method
Wang and Shao Chin. J. Mech. Eng.
Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method
Li‑Ming Wang 0
Yi‑Min Shao 0
0 State Key Laboratory of Mechanical Transmission, Chongqing University , Chongqing 400044 , China
During the condition monitoring of a planetary gearbox, features are extracted from raw data for a fault diagnosis. However, different features have different sensitivity for identifying different fault types, and thus, the selection of a sensitive feature subset from an entire feature set and retaining as much of the class discriminatory information as possible has a directly effect on the accuracy of the classification results. In this paper, an improved hybrid feature selection technique (IHFST) that combines a distance evaluation technique (DET), Pearson's correlation analysis, and an ad hoc technique is proposed. In IHFST, a temporary feature subset without irrelevant features is first selected according to the distance evaluation criterion of DET, and the Pearson's correlation analysis and ad hoc technique are then employed to find and remove redundant features in the temporary feature subset, respectively, and hence, a sensitive feature subset without irrelevant or redundant features is selected from the entire feature set. Further, the k‑ means clustering method is applied to classify the different kinds of health conditions. The effectiveness of the proposed method was validated through several experiments carried out on a planetary gearbox with incipient cracks seeded in the tooth root of the sun gear, planet gear, and ring gear. The results show that the proposed method can successfully distinguish the different health conditions of a planetary gearbox, and achieves a better classification performance than other methods. This study proposes a sensitive feature subset selection method that achieves an obvious improvement in terms of the accuracy of the fault classification.
Planetary gearbox; Gear crack; Feature selection technique; K‑ means classification
1 Introduction
Owing to its advantages of a compact structure, large
transmission ratio, and high load capacity, a planetary
gear transmission system is widely used in large-scale
and complex mechanical equipment [
1, 2
], e.g., wind
turbines, helicopters, and automobiles.
A planetary gearbox typically consists of some key
components: a sun gear, planet gear, ring gear, carrier,
and bearing, and faults may occur in these components
owing to fatigue or tough working conditions.
According to a condition-monitoring report on wind turbines, a
gearbox failure is the leading contributor to all wind
turbine failures [
3
]. A vibration-based method was proven
to be one of the most popular techniques in the fault
diagnosis of rotating machinery, and it has been
determined that certain changes to the vibration signals can be
seen when a fault occurs, e.g., crack or spalling [
4–6
]. The
commonly used vibration signal processing methods can
be divided into three categories: time domain methods,
frequency domain methods, and time–frequency domain
methods. Time domain methods refer to the analysis of
a signal with respect to time, and are relatively easy and
direct compared to both frequency and time–frequency
domain methods. Statistical indicators, the time
synchronous averaging (TSA) method, and an autoregressive
(AR) model are typically used in the fault diagnosis of
rotating machinery [
4, 7–9
]. Frequency domain methods
refer to an analysis of the signals with respect to the
frequency, and a periodic signal in the time domain can be
converted into a frequency component through a Fourier
transformation. In this way, researchers can identify the
difference between the spectrum of a normal vibration
signal and a fault vibration signal with commonly used
methods that include a spectrum-based analysis,
resonance demodulation technique, and cepstrum analysis
[
10–12
]. In contrast, time–frequency domain methods
are used to study a signal in both the time and frequency
domains simultaneously, allowing both the constituent
frequency components and their time variation features
to be revealed and analyzed. Researchers have
developed various time–frequency domain methods
including a short-time Fourier transformation, Wigner–Ville
distribution, continuous wavelet transform, and
HilbertHuang transformation [
13–16
]. Although
vibrationbased methods have been successfully used in the fault
diagnosis and condition monitoring of rotating
machinery, the appearance of faults in the analysis results has to
be identified artificially, e.g., the identification of a fault
characteristic frequency in the spectrum, the
determination of a filter sub-band in a demodulation analysis, or the
determination of a wavelet type, all of which require
considerable of experience and expertise [
17–19
]. Therefore,
it is necessary to develop some intelligent techniques that
can automatically det (...truncated)