Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method

Chinese Journal of Mechanical Engineering, Feb 2018

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.

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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)


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Li-Ming Wang, Yi-Min Shao. Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method, Chinese Journal of Mechanical Engineering, 2018, pp. 4, Volume 31, Issue 1, DOI: 10.1186/s10033-018-0202-0