Fault diagnosis of industrial robot gearbox using joint interclass and intraclass mappings

Yang, Shuai; Kong, Lijie; Bai, Yun; Hou, Ming; Li, Chuan

doi:10.1088/1361-6501/ac7a07

Cited by 3 publications

(4 citation statements)

References 46 publications

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“…On this basis, it needs to compute the (13). Finally, the two-dimensional approximate entropy AE2d is calculated as shown in formula (14).…”

Section: Gearbox Fault Vibration Signal Decomposition Based On Resona...mentioning

confidence: 99%

“…On the other hand, data-driven intelligent diagnosis technology 13 is a new developed subject in recent years. Its advantage is that it integrates the expert experience in many fields, improves the diagnostic accuracy 14 which possesses the engineering advantage. 12 In recent years, artificial intelligence diagnosis technology 15,16 has developed vigorously, such as support vector machine 7,15 and deep learning.…”

Section: Introductionmentioning

confidence: 99%

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Research on gearbox compound fault diagnosis method and system development based on entire gearbox health maintenance

Yan,

Qin-Xiao,

Cheng

et al. 2023

Advances in Mechanical Engineering

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In general, gearbox is prone to occur compound fault due to its harsh working environment and its fault vibration signal contains multi-components which correspond to each gearbox parts. As the multi-components are often coupled with each other and accompanied by strong noise which brings great difficulties to diagnose fault, however, the existing diagnosis methods are mainly applied on single fault rather than the entire gearbox health maintenance, therefore, this paper presents a gearbox compound fault diagnosis method and develops a diagnosis system which has potential value for gearbox health maintenance. In specific, on account of the morphological difference between multi-components, this paper uses resonance sparse signal decomposition (RSSD) to decompose the fault vibration signal into high and low resonance components respectively for achieving gearbox compound fault separation. Furthermore, as for low resonance component containing rolling bearing fault information, a weak fault feature extraction algorithm based on singular value decomposition (SVD) and cepstrum pre-whitening stochastic resonance is proposed, besides, aiming at the high resonance component containing gear fault information, an early gear fault warning algorithm based on local mean decomposition and two-dimensional approximate entropy of chaotic oscillator is also given. Finally, a gearbox fault diagnosis system, which has the ability such as the gearbox vibration signal acquisition, fault indicator warning, health status evaluation, fault signal storage is developed. Simulation validation and comparison prove the effectiveness of proposed method in this paper.

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“…On this basis, it needs to compute the (13). Finally, the two-dimensional approximate entropy AE2d is calculated as shown in formula (14).…”

Section: Gearbox Fault Vibration Signal Decomposition Based On Resona...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on gearbox compound fault diagnosis method and system development based on entire gearbox health maintenance

Yan,

Qin-Xiao,

Cheng

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Because planetary gearboxes benefit from a high transmission ratio, a great load capacity, and a small structure. Gearboxes are vulnerable to fault due to the demanding working conditions of industrial robots, which include load variation, dust abrasion, and chemical corrosion [2,3]. Industrial robot malfunctions can halt production and result in financial losses * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Li,

Liu,

et al. 2024

Meas. Sci. Technol.

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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.

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“…Due to the high loads, stresses and wear, gearboxes are susceptible to failure, resulting in reduced performance, reduced efficiency, increased maintenance costs and potential risks to personnel safety [1,2]. Accurate and efficient diagnosis of gearbox faults is therefore essential [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

Liu,

Wang,

2023

Meas. Sci. Technol.

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Accurate diagnosis of gearbox faults is vital to maintain the stability and dependability of rotating machinery. In practical engineering scenarios, the installation of multiple sensors is often necessary to monitor numerous variables and gather extensive information. To effectively utilize the fault information from multiple sensors and improve fault diagnosis performance, this paper presents a gearbox fault diagnosis method using generalized minimum entropy deconvolution (GMED) and main frequency center extraction (MFCE). Firstly, the introduced GMED enhances the fault signals from multiple sensors through the maximization of standardized moments, offering advantages such as stability, wide applicability, and minimal parameter tuning requirements. Subsequently, the proposed MFCE is employed to extract features. MFCE selects main spectral lines and derives a novel feature metric named main frequency center. This not only further reduces the influence of interfering components, but also requires the extraction of only 2 n features (where n is the number of sensors) to achieve a high fault recognition rate, while remaining compatible with multiple classical classifiers. Finally, the effectiveness and robustness of the proposed method are demonstrated through experimental results on two gearbox fault datasets.

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Fault diagnosis of industrial robot gearbox using joint interclass and intraclass mappings

Cited by 3 publications

References 46 publications

Research on gearbox compound fault diagnosis method and system development based on entire gearbox health maintenance

Research on gearbox compound fault diagnosis method and system development based on entire gearbox health maintenance

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

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