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Conventional methods used for fault detection of planetary gears are usually targeted for use with gears under constant speed conditions. Recently, some methods have been developed using an order-tracking method, or wavelet transform (WT), to detect planetary gear faults under variable-speed conditions. However, these previously studied techniques are computationally inefficient. Inefficiencies in these methods exist because 1) the order-tracking method of these techniques requires additional signal processing procedures, along with angle measuring devices, and/or 2) the WT process requires a lengthy computation time. Therefore, this study proposes a more efficient method, specifically a variance of energy residual (VER) method, for planetary gear fault detection under variable-speed conditions. The proposed VER method does not need angular information, and offers the potential to reduce computation time by using short-time Fourier transform (STFT) instead of WT. To demonstrate the performance of the proposed VER method, we calculated the fault sensitivity and computation time of the VER method using both simulation and experimental signals. From the results, it can be concluded that the VER method shows better fault sensitivity – with less computation time – than the previous method that uses WT. Image 103 • A new fault detection method is proposed for variable-speed planetary gears. • The method could minimize the effect from variable-speed conditions. • The method could reduce computation time from time-efficient time-frequency analysis. • The method is validated using planetary gear data from the simulation and experiment. • The method shows better performance than the previous methods. [ABSTRACT FROM AUTHOR]

• A novel method is proposed to diagnose faults of rotating machinery under variable speed conditions. • The method can extract fault-related features from time-frequency image data. • The proposed method is validated by experiment data from the planetary and spur gearboxes. • The proposed method shows better sensitivity than the previous methods, and consistent behaviors under different phases of speed profiles. This paper proposes a new feature extraction method using time–frequency image data for fault diagnosis of variable-speed rotating machinery. Time-frequency representation (TFR) is widely used to analyze time-varying behaviors of rotating machinery. Recently, methods have been developed to extract fault-related features from TFR image data. However, these methods can be only applied to in-phase TFR image data, or have limited sensitivity because they cannot utilize the characteristics of faults in rotating machinery. Therefore, the research outlined in this paper proposes a new fault feature for rotating machinery under variable-speed conditions. The proposed feature enhances sensitivity by exploiting faulty behaviors in the TFR image data. Two experimental case studies are presented to demonstrate the performance of the proposed method: a planetary gearbox and a spur gearbox. From the results, we conclude that the proposed method shows higher fault sensitivity than the previous image-based features, while showing consistent behavior under different phases of TFR image data. [ABSTRACT FROM AUTHOR]

• A cepstrum-assisted empirical wavelet transform (CEWT) is proposed for fault diagnostics. • Cepstrum is adopted to identify the modulation sideband clusters around gear mesh harmonics. • Envelope averaging is proposed to improve demodulation analysis for fault diagnostics. • Simulation and experimental results demonstrate the superiority of the proposed method for fault diagnostics. Demodulation analysis is a widely used approach for fault diagnostics of planetary gearboxes by identifying the fault-induced modulation effect buried in noise with complicated characteristics. To enhance the performance of demodulation analysis, previous studies have employed signal decomposition, including empirical wavelet transform (EWT), to decompose a signal with a clear modulation effect. However, EWT requires a physical understanding of the modulation effect to isolate the fault-related signals. To solve this challenge, we propose a cepstrum-assisted empirical wavelet transform (CEWT). In the proposed method, the vibration signal is decomposed using empirical wavelet filters designed based on the smoothed spectrum from cepstrum analysis. To further enhance the fault-related signal, the proposed method employs averaging for the envelopes of the decomposed signals for the demodulation analysis. The proposed method is validated by examining numerical simulation and experiment. The results show that the proposed method improves fault diagnostic performance, as compared to existing methods. [ABSTRACT FROM AUTHOR]

• We propose a new phase-based time domain averaging (PTDA) method. • A systematic approach is proposed to detect fault of a gearbox in an industrial robot. • The proposed method is demonstrated by the experimental data from a six-degree-of-freedom (6-DOF) industrial robot test-bed. • The proposed method shows better performance compared to the previous method. This paper proposes a fault detection method that uses vibration signals in the gearboxes of industrial robots. The vibration signals from gearboxes consist of both deterministic signals and residual signals; fault-related signals usually exist in the residual signals. Previously, time domain averaging (TDA) has been studied to derive the deterministic signals. However, the performance of TDA method is limited when the signals are poorly synchronized. Therefore, we propose a new phase-based time domain averaging (PTDA) method. The proposed PTDA method can estimate deterministic signals that are more synchronized by considering the phase angle of the vibration signals. Then, the residual signals can be calculated by subtracting the estimated deterministic signals from the measured vibration signals using the PTDA method. We use two health features, root-mean-square (RMS) and power spectrum entropy, to quantify the fault severity in the residual signals. To demonstrate the proposed method, we use vibration signals measured from a six-degree-of-freedom (6-DOF) industrial robot test-bed under 1) a simple one-joint rotating motion, 2) a complicated arc welding motion, and 3) a spot welding motion. The results show that the proposed PTDA method can improve the performance of fault detection for gearboxes in industrial robots. [ABSTRACT FROM AUTHOR]

Highlights • A novel method is proposed to detect faults of a planetary gear under variable speed conditions. • The method can be applied to large fluctuations of speeds, and it does not need angular information. • The proposed method is validated by the simulation and experiment data. • The proposed method shows better performance compared to the previous methods. Abstract Most existing studies on vibration-based fault detection for planetary gears were developed and tested under constant speed conditions. Recently, some methods were developed to consider the variability of the rotating speed; however, these methods have limitations. Specifically, these methods are applicable only for small fluctuations of speed, or the methods require additional angular information as an input. This paper thus proposes a new method, the positive energy residual (PER) method, for fault detection of planetary gears. PER does not require the assumption of only small fluctuations of speed, nor does it need angular information. The proposed PER algorithm is based on two techniques, the wavelet transform (WT) and the Gaussian process (GP), which are used to remove the variability of the signals while extracting the faulty signals. Further, a fault feature is presented that is able to effectively quantify the characteristics of faulty signals. The performance of the proposed method is demonstrated using two case studies: vibration signals from a simulation model and vibration signals from a real test-bed. A comparison study with other methods, WT and energy residual (ER), is also presented to clarify the performance of the proposed PER algorithm. From the results, we conclude that the proposed PER method is capable of detecting faults of a planetary gear under variable speed conditions, while showing better performance than the two other methods. [ABSTRACT FROM AUTHOR]