A review: multiplicative faults and model-based condition monitoring strategies for fault diagnosis in rotary machines

Kumar, Prabhat; Tiwari, Rajiv

doi:10.1007/s40430-023-04203-z

Cited by 18 publications

(9 citation statements)

References 132 publications

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“…Manufacturing systems consist of various machine tools, robots, and conveyor systems, all of which need to serve their purpose correctly to ensure efficient, high-quality production. In the literature, failure represents deterioration in operating performance and plant or automation equipment disruptions [14][15][16].…”

Section: Methods Of Fault Detectionmentioning

confidence: 99%

“…According to the scientific paper by Kumar P. et al and cited in the publication [16], it has been determined that multiplicative faults can emerge due to the influence of product terms of process variables on the overall process. This means that these faults arise due to the interdependent nature of multiple variables that simultaneously affect the process.…”

Section: Methods Of Fault Detectionmentioning

confidence: 99%

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Application of the Residue Method in Steam Superheater Fault Detection

Maican,

Rădulescu,

Pană

2023

Applied Sciences

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Steam superheaters experience various defects that can affect the system’s operation differently. Maintaining a constant temperature and pressure of the steam at the turbine input is crucial for optimal steam turbine running conditions. This is achieved by regulating the steam temperature in the superheater, which is divided into three parts with devices mounted at each connection point to allow for the injection of condensates to cool the steam. The steam pressure is controlled by adjusting the fuel flow command. However, controlling the output temperature can be challenging because of the transfer time delay between the points where the water is sprayed and the points where the steam temperature is measured. To address this challenge, a temperature control system was developed and tested in three different environments using an electronic simulator, the block-oriented simulation hardware BORIS, data acquisition board dSpace, and a MATLAB R2020a Simulink with a PI controller model. These simulations allowed for the study of the superheater’s function in both normal and fault conditions, with the possibility of controlling the system structure and detecting faults through the proper implementation of weighting matrices.

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Section: Methods Of Fault Detectionmentioning

confidence: 99%

Section: Methods Of Fault Detectionmentioning

confidence: 99%

Application of the Residue Method in Steam Superheater Fault Detection

Maican,

Rădulescu,

Pană

2023

Applied Sciences

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“…The latter uses artificial intelligence diagnostic methods to achieve the feature extraction and classification of bolt loosening. For rotor system, the research mainly focuses on the diagnosis of pedestal loosening [18][19][20][21][22][23]. Meanwhile, there are few literatures on the intelligent diagnosis of tie rod bolt loosening in a RFR system at present.…”

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%

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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“…A better understanding and, when it is possible, controlling vibrations is a longstanding challenge [1] and then it is currently an important research field.This is because it is necessary to ensure structural integrity, predicting and preventing failures in a system [2,3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Vibration Analysis with Singular Spectrum Analysis in a System with Passive Damper Attached

Silva,

Cayres,

Silva

2024

Preprint

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Vibration analysis is a process of monitoring and searching for anomalies or changes in the vibrational behavior of a system. In this context, the Fourier Transform has been widely employed to study systems whose responses to external disturbances are analyzed through vibrations. However, considering that vibration signals are often represented as time series, the application of Singular Spectrum Analysis (SSA) has been a promising methodology in this type of analysis. In this work, the SSA technique was used to investigate the vibrational responses of a single degree of freedom system with an attached tuned liquid column damper (TLCD). The system responses were captured by an accelerometer and processed by a data acquisition module, followed by analysis using the R programming language. The experimental results demonstrated the efficiency of the damper in attenuating the system vibration; it was also possible to identify the dominant frequencies through the Fourier Transform, specifically its computational version, the Fast Fourier Transform. In this work, the SSA methodology was applied which allowed the identification of distinct signals, including possible signals from the vibrations of aluminum rods coupled to the system, not detected by frequency domain analysis. Thus, the application of the SSA technique enabled the identification and quantification of different signals in the system with an attached passive damper and proved to be an excellent methodology to be added to the classic techniques of vibration analysis.

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A review: multiplicative faults and model-based condition monitoring strategies for fault diagnosis in rotary machines

Cited by 18 publications

References 132 publications

Application of the Residue Method in Steam Superheater Fault Detection

Application of the Residue Method in Steam Superheater Fault Detection

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

Enhancing Vibration Analysis with Singular Spectrum Analysis in a System with Passive Damper Attached

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