Fault Diagnosis of ZD6 Turnout System Based on Wavelet Transform and GAPSO-FCM

Li, Ziyu; Dai, Shenghua; Zheng, Ziyuan; Yao, Liang

doi:10.1109/itsc48978.2021.9565075

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…S1-S6 represent six distinct stages, each with its unique characteristics. Based on previous research experience 21,22 , fault data has been categorized into F1-F6, with each fault class highlighted using color-coded boxes. As depicted in Figure 2, there are a total of 160 abnormal samples, with 25 in F1, 13 in F2, 5 in F3, 20 in F4, 5 in F5, and 34 in F6.…”

Section: S5 (Pink Section)mentioning

confidence: 99%

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

Fu,

Yang,

et al. 2024

Preprint

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In this work, we tackle the challenge of improving fault diagnosis in railway turnout systems (RTS), a critical component of railway infrastructure. Addressing limitations in existing fault-diagnosis techniques, especially their efficacy with small training datasets, we focus on meticulous identification of RTS failure modes. The limited availability of fault samples and the issue of dataset imbalance further compound this challenge. To this end, we propose an innovative fault-diagnosis approach leveraging the Convolution Transformer (ConvTran) combined with advanced data augmentation techniques. This method is uniquely designed to ensure precise fault detection in situations characterized by restricted and skewed data. Existing augmentation techniques often inadequately capitalize on the specific features of time series data. To counteract this, our study proposes an innovative pseudo-synthetic data generation algorithm, utilizing feature combination to effectively overcome the challenges of sample imbalance and small sample sizes. The effectiveness of our model is rigorously tested and validated through comprehensive experiments using an actual dataset from a railway station monitoring system in China. The experimental outcomes decisively demonstrate that our proposed data augmentation method significantly improves the precision of turnout fault diagnosis, especially in contexts with limited sample sizes and imbalanced data. Impressively, our approach achieves a 92% accuracy rate in fault classification on small datasets, which further escalates to 99.7% following data augmentation. This exceptional performance not only outstrips that of other benchmark models but also underscores the superior efficiency and practical applicability of our proposed methodology in the field of RTS fault diagnosis.

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Section: S5 (Pink Section)mentioning

confidence: 99%

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

Fu,

Yang,

et al. 2024

Preprint

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“…There are abnormal fluctuations in the transition phase when there is poor contact between the carbon brush of the switch machine and the steering gear, which is marked as f 2. In addition, based on previous research [ 16 , 21 ], manual screens, and markers by experts, six failure modes in our historical data set were summarized. Figure 2 shows one health and six fault power curves in our dataset, respectively labeled Health, f 1, f 2, f 3, f 4, f 5, and f 6.…”

Section: Railway Turnout Systemmentioning

confidence: 99%

“…At present, various articles in the literature have put forward the application of artificial intelligence technology to the fault diagnosis of RTS, and the related methods can be summarized as the threshold-based method [ 9 , 10 ], machine learning (ML) based [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], or deep learning (DL) based [ 23 , 24 , 25 , 26 ]. Huang et al [ 9 ] proposed a fault-detection method by using dynamic time warping based on the turnout current curve, which can detect faults by comparing the distance from the template curve.…”

Section: Introductionmentioning

confidence: 99%

“…Unsupervised learning and supervised learning are two types of ML [ 14 , 15 ]. Based on the feature engineering, several classification unsupervised methods, such as fuzzy cognitive map [ 16 ], support vector machine (SVM) [ 17 ], backpropagation [ 18 ], and ensemble classifier [ 19 ], have been presented in order to find the best combination features/classifiers to make intelligent decisions regarding the presence of RTS. In particular, SVM has been widely applied to evaluate condition monitoring as a supervised method.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [ 20 ] proposed a hybrid feature-extraction method, which combines time and frequency domain; then, the SVM is further used for classification purposes. Li et al [ 16 ] constructed a new type of ZD6 turnout system fault-diagnosis model by using a wavelet transform feature extraction and fuzzy cognitive map classification. In [ 22 ], Lee et al employed SVM for early detection and classification of audio data of railway point devices, and the experiment results show that it has a good effect.…”

Section: Introductionmentioning

confidence: 99%

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A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation

Hei

et al. 2022

Sensors

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In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.

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Data-driven technology of fault diagnosis in railway point machines: review and challenges

Cao

Tang

et al. 2022

Transportation Safety and Environment

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Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs’ characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.

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Fault Diagnosis of ZD6 Turnout System Based on Wavelet Transform and GAPSO-FCM

Cited by 5 publications

References 13 publications

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

A fault diagnosis method for railway turnout systems based on Convolution Transformer through data augmentation

A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation

Data-driven technology of fault diagnosis in railway point machines: review and challenges

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