A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Cheng, Chao; Wang, Jiuhe; Chen, Hongtian; Chen, Zhiwen; Luo, Hao; Xie, Pu

doi:10.3390/e23010001

Cited by 48 publications

(21 citation statements)

References 139 publications

(128 reference statements)

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“…The way decisions are nowadays made in the industry is changing thanks to increasing data availability [19] and the development of intelligent tools for condition and fault assessment [20]. In commercial industrial gas turbines, data are captured by several sensors located along the machine and are then sent back to the engineering office where they are analyzed and processed to diagnose any unexpected event.…”

Section: Machine Learning Diagnosismentioning

confidence: 99%

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder

Castro-Cros

Rosso

Bahilo

et al. 2021

Sensors

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Maintenance is the process of preserving the good condition of a system to ensure its reliability and availability to perform specific operations. The way maintenance is nowadays performed in industry is changing thanks to the increasing availability of data and condition assessment methods. Soft sensors have been widely used over last years to monitor industrial processes and to predict process variables that are difficult to measured. The main objective of this study is to monitor and evaluate the condition of the compressor in a particular industrial gas turbine by developing a soft sensor following an autoencoder architecture. The data used to monitor and analyze its condition were captured by several sensors located along the compressor for around five years. The condition assessment of an industrial gas turbine compressor reveals significant changes over time, as well as a drift in its performance. These results lead to a qualitative indicator of the compressor behavior in long-term performance.

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Section: Machine Learning Diagnosismentioning

confidence: 99%

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder

Castro-Cros

Rosso

Bahilo

et al. 2021

Sensors

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“…This algorithm is superior to the traditional VMD method and further extends the application of VMD for compound fault diagnosis. Many scholars have also applied neural networks for fault diagnosis [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and the improved algorithms have shown good results. In addition, there are also many decomposition methods based on signal processing, including local mean decomposition [ 36 , 37 , 38 , 39 , 40 ], Fourier decomposition [ 41 ], and other methods, which are widely used in gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

Yang

Yao

et al. 2021

Entropy

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In the signal processing of real subway vehicles, impacts between wheelsets and rail joint gaps have significant negative effects on the spectrum. This introduces great difficulties for the fault diagnosis of gearboxes. To solve this problem, this paper proposes an adaptive time-domain signal segmentation method that envelopes the original signal using a cubic spline interpolation. The peak values of the rail joint gap impacts are extracted to realize the adaptive segmentation of gearbox fault signals when the vehicle was moving at a uniform speed. A long-time and unsteady signal affected by wheel–rail impacts is segmented into multiple short-term, steady-state signals, which can suppress the high amplitude of the shock response signal. Finally, on this basis, multiple short-term sample signals are analyzed by time- and frequency-domain analyses and compared with the nonfaulty results. The results showed that the method can efficiently suppress the high-amplitude components of subway gearbox vibration signals and effectively extract the characteristics of weak faults due to uniform wear of the gearbox in the time and frequency domains. This provides reference value for the gearbox fault diagnosis in engineering practice.

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“…Several relevant proposals were recently published to design increasingly reliable monitoring systems [21] thanks to new signal-processing techniques such as linear prediction coefficients (LPC), mel-frequency cepstral coefficients (MFCC), and machine learning. Meiying et al [16] proposed to combine a CNN and LSTM for health assessment and failure diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

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A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Cited by 48 publications

References 139 publications

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder

Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based in Autoencoder

Subway Gearbox Fault Diagnosis Algorithm Based on Adaptive Spline Impact Suppression

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

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