Dynamic characteristics of a rod-fastening rotor system with tie rod bolt loosening

Yang, Shixi; Zhou, Chen; He, Jun; Cheng, Qichao; Yu, Peijiong

doi:10.1007/s11071-023-08848-2

Cited by 6 publications

(8 citation statements)

References 59 publications

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“…In the following section, the identification ability of the proposed method to bolt loosening fault will be demonstrated. 8 shows the test bench for tie rod bolt loosening of the RFR system [7]. The RFR system is simulated by the discontinuous rotor system with two discs and four uniformly distributed bolts at the joint.…”

Section: Diagnostic Results and Analysismentioning

confidence: 99%

“…The vibration characteristics of tie rod bolts of a RFR system are greatly affected by the working conditions [7]. Due to the fluctuation of these conditions, the data distribution of the same fault vibration signal will be inconsistent.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…In the pursuit of engine lightness and high thrust-weight ratio, the RFR system has adopted the discontinuous rotor system structure [3,4]. The weakest part of the RFR system is the joint, which prone to loosening the tie rod bolt under complex operating conditions [5][6][7]. In more severe cases, shaft breakage may occur, resulting in a huge safety risk and economic losses.…”

Section: Introductionmentioning

confidence: 99%

“…Bolt loosening is the primary fault of connecting structures, and can be roughly divided into two categories according to the state of connection structures: bolt loosening of fixed structures and bolt loosening of rotating structures [7]. The diagnosis of bolt loosening of fixed structures is the main focus of current research, such as the diagnosis of bolt loosening of threaded fasteners [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The reason may be that abnormal scarce fault data of RFR systems, or data privacy protection leads to difficult access to fault data. However, as the core rotating component of gas turbines, the RFR system has been running under harsh and variable load conditions, and the effect of long-term vibration and fatigue loads will inevitably lead to the preloads of tie rod bolts being detuning, resulting in the loosening of tie rod bolts and aggravating the risk of misalignment and rub-impact of the rotor system [7,[23][24][25]. Some literatures have studied the effect of tie rod bolt loosening on the dynamic behaviors and characteristics of RFR systems [6,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

Zhou,

He,

Yang

et al. 2024

Meas. Sci. Technol.

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The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under variable working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network (CNN) and the graph convolutional network (GCN) to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of local neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under variable working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

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Section: Diagnostic Results and Analysismentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%