An improved chaotic recognition method for weak signal frequency and its application to fault diagnosis of planetary gearboxes

The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.

Section: Fault Detection Of the Inner Racementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes

Yang,

Liu,

Wei

et al. 2024

“…Industrial robot malfunctions can halt production and result in financial losses * Author to whom any correspondence should be addressed. by causing downtime in the manufacturing process [4,5]. Therefore, in order to minimize the financial losses brought on by downtime, gearbox fault identification is crucial to robot operation and maintenance.…”

Section: Introductionmentioning

confidence: 99%

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Li,

Liu,

et al. 2024

Motor Current Signal Analysis (MCSA) provides a non-intrusive approach to fault diagnosis. However, the fault impact reacting in the current is reduced due to the presence of flexible structures in the transmission path from the fault source to the motor. Therefore, this paper proposes a method to enhance the frequency domain of the current signal of a single mechanical fault through a transfer model between motor torque and link vibration. First, the joint system dynamics model was developed based on a three-inertia simplified model. The transfer model of motor torque and link vibration was defined based on the system dynamics. The link vibration is then estimated based on the transfer model and electromagnetic torque. Link vibration signal is considered as an enhancement of the torque signal. Finally, the link vibration signature analysis is performed instead of MCSA. The experimental results show that the method is effective in enhancing the features of individual mechanical faults and improving the fault diagnosis performance.

“…The above research shows a broad application of chaotic oscillators in weak signal detection, which is still in the empirical exploratory stage [26][27][28], and practical models have rarely been reported thus far. In addition, the detection model is mainly an improved Duffing system, such as Rössler, Lorenz, Chua and their extended models [29][30][31][32], which have rarely been reported thus far. At present, coupled Duffing oscillators have some disadvantages, such as that the system greatly depends on the initial state, has limited anti-noise ability and is insensitive to weak pulse signals with narrow bandwidths.…”

Section: Introductionmentioning

confidence: 99%

Identification of low-SNR Coulter signals based on strongly coupled Lorenz-like oscillators

Zhao¹,

Yang

Wang³

2023

A Coulter prototype experiment is established and innovatively combined with coupled Lorenz-like oscillators to identify low-SNR Coulter signals. Coulter signals are collected by a Wheatstone bridge and fitted by Gaussian pulse signals. Using numerical simulation, low-SNR Gaussian pulses are added to the oscillators, and the synchronization mutation phenomena in the chaotic oscillators performed by the time scale transformation are analysed. The maxima in the phenomena are used to detect the low-SNR pulses and construct an identification formula for different particle sizes. The nonlinear circuit of the oscillators is designed and fabricated. Then, it is applied to process low-SNR Coulter signals in the experiment. By selecting the maxima in the output signals from the circuit, according to the identification formula containing the correction coefficient, the particle size distribution can be obtained. The nonlinear circuit can effectively process low-SNR Coulter signals, which provides a good foundation for reducing the lower limit of detection in Coulter signals.