Vibration-based anomaly detection using LSTM/SVM approaches

Vos, Kilian; Peng, Zhongxiao; Jenkins, Christopher; Shahriar, Md Rifat; Borghesani, Pietro; Wang, Wenyi

doi:10.1016/j.ymssp.2021.108752

Cited by 86 publications

(31 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent decades, several machine learning approaches have been explored and incorporated into machine fault diagnosis, as they offer the possibility to adaptively learn the diagnosis knowledge of machinery from previously collected data [5][6][7][8][9][10][11]. Key features can be extracted from a variety of transducer signals and correlated to different machine health states to perform online diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Key features can be extracted from a variety of transducer signals and correlated to different machine health states to perform online diagnosis. This strategy can be used with classification algorithms, for anomaly detection and to identify certain types of faults [8,9], or with regression algorithms, to model and predict dynamic behavior [6,10,11,12]. Physics-informed machine learning refers to the integration of real world data and mathematical physics models even in partially understood, uncertain contexts [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Peyrano

Juan

Mayer

et al. 2022

JDMD

View full text Add to dashboard Cite

Multiple-stage steam turbine generators, like those found in nuclear power plants, pose special challenges with regards to mechanical unbalance diagnosis. Several factors contribute to a complex vibrational response, which can lead to incorrect assessments if traditional condition monitoring strategies are used without considering the mechanical system as a whole. This, in turn, can lead to prolonged machinery downtime. Several machine learning techniques can be used to integrally correlate mechanical unbalance along the shaft with transducer signals from rotor bearings. Unfortunately, this type of machinery has scarce data regarding faulty behavior. However, a variety of fault conditions can be simulated in order to generate these data using computational models to simulate the dynamic response of individual machines. In the present work, a multibody model of a 640 MW steam turbine flexible rotor is employed to simulate mechanical unbalance in several positions along the shaft. Synchronous components of the resulting vibration signals at each bearing are obtained and utilized as training data for two regression models designed for mechanical unbalance diagnosis. The first approach uses an artificial neural network and the second one utilizes a support vector regression algorithm. In order to test their performance, the stiffness of each bearing in the multibody simulation was altered between 50% and 150% of the training model values, random noise was added to the signal and several dynamic unbalance conditions were simulated. Results show that both approaches can reliably diagnose dynamic rotor unbalance even when there is a typical degree of uncertainty in bearing stiffness values.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Peyrano

Juan

Mayer

et al. 2022

JDMD

View full text Add to dashboard Cite

show abstract

“…The results of simulation experiments showed that the accuracy of this method is higher than that of Continuous Wavelet Transform (CWT), Discrete Wavelet Transformation (DWT), Wavelet Packet Transform (WPT), Dual-Tree Complex Wavelet Transform (DTCWT) and Tunable Q-factor Wavelet Transform (TQWT). Vos et al [ 14 ] combined LSTM and SVM for diagnosing gear faults. Two-step LSTM and SVM were used for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Huo

Kang

Wang

et al. 2022

Entropy

View full text Add to dashboard Cite

The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

show abstract

“…Hence, it is significant to achieve accurate health diagnostics for rotary machinery (Lei et al, 2020; Kumar et al, 2021). To this end, several non-invasive techniques like thermal imaging (Glowacz, 2021a), acoustic analysis (Glowacz et al, 2021b), and vibration monitoring (Vos et al, 2022) have been explored for machinery health diagnostics. Owing to the advantage of good responsiveness to component damages, reliable acquisition, and wide measurable frequency range, vibration-based health diagnostics have been recognized as one of the most effective means (Babu Rao and Mallikarjuna Reddy 2021; Kong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Sparsity assisted intelligent recognition method for vibration-based machinery health diagnostics

Kong

Han

Chu

2022

Journal of Vibration and Control

View full text Add to dashboard Cite

Vibration-based health diagnostics technique has shown great potentials to enhance the safety and reliability for many industrial rotary machinery. The emerging sparse representation classification (SRC) paradigm provides a promising tool for intelligent machinery health diagnostics. However, traditional SRC approaches neglect the useful priori information in rotary machinery vibration data, limiting the reconstruction ability and thus restricting their diagnostic accuracy. To address this issue, we present a novel sparsity assisted intelligent recognition (SAIR) methodology for vibration-based machinery health diagnostics. SAIR is constituted by two stages for dictionary design and intelligent recognition. In the dictionary design stage, SAIR exploits the useful priori information of the prediction shift-invariance property, to design class-specific dictionaries via an overlapping segmentation strategy. Additionally, this dictionary design strategy can leverage those local and nonlocal features within data segments. In the intelligent recognition stage, SAIR implements health state recognition by means of the sparsity-based health diagnostic strategy according to minimal sparse approximation errors. Finally, the feasibility and advantage of SAIR have been comprehensively evaluated for planetary gearbox health diagnostics, indicating that SAIR yields an overall diagnostic accuracy of 99.72%. Besides, comparative studies demonstrate that SAIR outperforms the advanced mainstream methods with better diagnostic accuracy and lower computation costs for vibration-based machinery health diagnostics.

show abstract

Vibration-based anomaly detection using LSTM/SVM approaches

Cited by 86 publications

References 26 publications

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Online Unbalance Detection and Diagnosis on Large Flexible Rotors by SVR and ANN trained by Dynamic Multibody Simulations

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Sparsity assisted intelligent recognition method for vibration-based machinery health diagnostics

Contact Info

Product

Resources

About