Vibration based Fault Diagnosis Techniques for Rotating Mechanical Components: Review Paper

Kumar, Sujesh; Lokesha, M.; Kumar, K. Vijay; Srinivas, K R

doi:10.1088/1757-899x/376/1/012109

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…Indeed, the FFT can be a good approach for predicting faults. However, it is difficult to determine severity using FFT alone [8]; thus, other sources and techniques, such as ANN, can be used for this purpose.…”

Section: Post-processing Approachmentioning

confidence: 99%

“…The monitoring of vibration spectra, specifically, can be considered a pattern recognition task. This is historically a primary application of Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN) [8]. Thus, by using AI methods, emphasis can be given to the fact that when vibrations are used to extract features for FD, the correlation between the measured signal and the current status of the structure of the machine is very important [9].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Finite Element Method–Analytical Model for Classifying the Effects of Cracks on Gear Train Systems Using Artificial Neural Networks

et al. 2022

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Rotating machinery is fundamental in industry, gearboxes especially. However, failures may occur in their transmission components due to regular usage over long periods of time, even when operations are not intense. To avoid such failures, Structural Health Monitoring (SHM) techniques for damage prediction and in-advance detection can be applied. In this regard, correlations between measured signal variations and damage can be inspected using Artificial Intelligence (AI), which demands large numbers of data for training. Since obtaining signal samples of damaged components experimentally is currently unviable for complex systems due to destructive test costs, model-based numerical approaches are to be explored to solve this problem. To address this issue, this work applied an innovative hybrid Finite Element Method (FEM)–analytical approach, reducing computational effort and increasing performance with respect to traditional FEM. With this methodology, a system can be simulated with accuracy and without geometrical simplifications for healthy and damaged cases. Indeed, considering different positions and dimensions of damages (e.g., cracks) on the tooth roots of gears can offer new ways of damage investigation. As a reference to validate healthy systems and damage cases in terms of eigenfrequencies, a back-to-back test rig was used. Numerical simulations were performed for different cases, resulting in vibrational spectra for systems with no damage, with damage, and with damage of different intensities. The vibration spectra were used as data to train an Artificial Neural Network (ANN) to predict the machine state by Condition Monitoring (CM) and Fault Diagnosis (FD). For predicting the health and the intensity of damage to a system, classification and multi-class classification methods were implemented, respectively. Both sets of classification results presented good prediction agreement.

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Section: Post-processing Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Finite Element Method–Analytical Model for Classifying the Effects of Cracks on Gear Train Systems Using Artificial Neural Networks

et al. 2022

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“…As a real-time and noninvasive condition detection method, vibration signal analysis is widely used in engineering fields, mainly for the recognition and diagnosis of the conditions of mechanical equipment [40][41][42][43]. In the field of vibration analysis and monitoring in machining and construction, Fu et al [44] presented an automatic feature construction method for machining state monitoring based on vibration signals, using a deep belief network to reveal the relationship between input signals and output states.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

Pan,

Zhao,

et al. 2023

Applied Sciences

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High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

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“…The time domain, frequency domain, and time-frequency (TF) domain are the three groups in which these techniques are categorised [19][20][21]. Analysis in all three domains has its own advantages in representing the localized vibration signal [22,23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Vashishtha¹,

Chauhan²,

Yadav³

et al. 2022

Eng. Res. Express

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A critical step in fault diagnosis is determining the frequency of faults. Variational mode decomposition (VMD) is extensively employed for this purpose since it can describe the signal in the time-frequency domain. On the other hand, the VMD frequently fails to analyse non-stationary data containing low-frequency disturbances/noises. A multipoint optimal minimal entropy deconvolution adjusted (MOMEDA) is used with VMD in this research to improve defect detection performance in the presence of low-frequency disturbances. The filter length has a strong influence on the output of MOMEDA thus choosing the right one is a critical step in recovering a periodic pulse in the event of a weak defective signal. Improved grey wolf optimization (GWO) adaptively selects the appropriate filter length using the autocorrelation energy as its fitness function. The GWO is improved by introducing a gaussian mutation strategy which maintains the proper balance between the exploration and exploitation process. The proposed method has been applied to investigate the bucket defects of the Pelton wheel. The raw vibration signal is first decomposed into a series of modes using VMD. Second, the MOMEDA model is used to purify each mode by reducing low-frequency noise interference. The modes processed by MOMEDA are reconstructed again into a pure signal. Finally, the Hilbert envelop spectrum of the pure signal is obtained to determine the fault frequency. The same is verified from the theoretical fault feature frequency of the turbine bucket. The performance of the proposed method in extracting fault frequency accurately is also compared with other two models: a) MOMEDA model with ensemble empirical mode decomposition (EEMD) and b) MOMEDA model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The comparison results proved the efficacy and superiority of the proposed method.

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Vibration based Fault Diagnosis Techniques for Rotating Mechanical Components: Review Paper

Cited by 13 publications

References 12 publications

A Hybrid Finite Element Method–Analytical Model for Classifying the Effects of Cracks on Gear Train Systems Using Artificial Neural Networks

A Hybrid Finite Element Method–Analytical Model for Classifying the Effects of Cracks on Gear Train Systems Using Artificial Neural Networks

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

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