Short Text Mining for Fault Diagnosis of Railway System Based on Multi-Granularity Topic Model

Wu, Shun

doi:10.1109/liss.2018.8593228

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…Applying Text Mining Methods in Railway Safety from accident and fault analysis reports been conducted [49]. Also, As well as Big Data and Natural Language is an opportunity should be to use for processing for Analysing Railway Safety, NLP framework for analysing accident data been explained using investigation reports of railway accidents [50].Moreover, for Fault Diagnosis in Railway System, classification of maintenance text been proposed using (LDA) algorithm [51] ,and to improve the fault diagnosis performance [52]. In China railway , for prediction passenger capacity, the social network text data have been used with a combination of text mining and deep learning which show a good accuracy rate [53].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations

Alawad

Kaewunruen

2023

IEEE Access

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For both passenger and freight transportation, railroad operations must be dependable, accessible, maintained, and safe (RAMS). In many urban areas, railway stations risk and safety accidents represent an essential safety concern for daily operations. Moreover, the accidents lead to damage to market reputation, including injuries and anxiety among the people and costs. This stations under pressure caused by higher demand which consuming infrastructure and raised the safety administration consideration. To analysing these accidents and utilising the technology such AI methods to enhance safety, it is suggested to use unsupervised topic modelling for better understand the contributors to these extreme accidents. It is conducted to optimise Latent Dirichlet Allocation (LDA) for fatality accidents in the railway stations from textual data gathered RSSB including 1000 accidents in the UK railway station. This research describes using the machine learning topic method for systematic spot accident characteristics to enhance safety and risk management in the stations and provides advanced analysing. The study evaluates the efficacy of text by mining from accident history, gaining information, lesson learned and deeply coherent of the risk caused by assessing fatalities accidents for large and enduring scale. This Intelligent Text Analysis presents predictive accuracy for valuable accident information such as root causes and the hot spots in the railway stations. Further, the big data analytics ' improvement results in an understanding of the accidents' nature in ways not possible if a considerable amount of safety history and not through narrow domain analysis of the accident reports. This technology renders stand with high accuracy and a beneficial and extensive new era of AI applications in railway industry safety and other fields for safety applications.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations

Alawad

Kaewunruen

2023

IEEE Access

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“…To further verify the advantages of the proposed method in this paper, we compare it with the latest methods [13,[15][16][17] of fault diagnosis methods for VOBE. We use the real fault data mentioned in section 5.1 as the benchmark data for the experiment.…”

Section: Benchmark Experimentsmentioning

confidence: 99%

“…The [15] and [16] are both improved LDA, and their experimental results are improved compared with the LDA-SVM in the baseline experiment. The F1-Scores of majority classes are maintained at about 0.8-0.9, such as FF-1, FF-2, FF-3, but still have not overcome the essence of the LDA disadvantagethe generation of topics is random.…”

Section: Benchmark Experimentsmentioning

confidence: 99%

“…In addition, the problem of unbalanced fault class distribution is not taken into account, which leads to low fault diagnosis efficiency, such as FF-5, FF-7. Finally, their accuracy is only about 85% Shang et al [17] injected prior fault feature words into a topic model with preset labels to improve the accuracy of the generated topic, and used PSO to optimize the SVM classifier, its fault diagnosis results are better than those mentioned methods [15,16]. Among them, the F1-Score of FF-2 is 0.8931.…”

Section: Benchmark Experimentsmentioning

confidence: 99%

“…However, it is difficult to determine the number of topics in pLSA and LDA, which affect the accuracy of fault diagnosis. Wu [16] evaluated LDAs with different numbers of topics by using perplexity as an indicator, and proposed a multi-granularity LDA fault diagnosis method by fusing different topic feature space. LDA is an unsupervised model, and the extraction of text features is relatively random, which has an adverse effect on fault text classification.…”

Section: Introductionmentioning

confidence: 99%

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Bi-TLLDA and CSSVM based fault diagnosis of vehicle on-board equipment for high speed railway

Wei¹,

Zhao²

2021

Meas. Sci. Technol.

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Vehicle on-board equipment (VOBE) is a significant component of the control system of high-speed railway train, the fault diagnosis of VOBE mainly depends on maintenance experience, which is inefficiency. The fault data of on-board equipment is described by natural language. Due to its unstructured, high-dimensional and unbalanced fault class distribution, it has become a challenge in fault diagnosis. In this paper, bilevel topic labeled latent Dirichlet allocation for extraction feature of fault text data is proposed. Firstly, label information is set according to prior knowledge of railway field. Then, local topics and global topics are defined for two level fault types of VOBE. The fault feature space generated by Gibbs sampling from local topics and global topics contains two level fault features, which are conducive to fault text classification. Finally, considering the imbalanced distribution of fault class, cost sensitive support vector machine based fault text classification is proposed. By using actual fault data of on-board equipment of China Railway Corporation, the accuracy, precision and F1-score are used as performance indicators to compare the proposed method with other fault diagnosis methods. The results show that the accuracy of the proposed method is 90.3%, which is about 2% higher than that of the suboptimal method, and the average recall, precision and F1-score of various fault classes are 77.9%, 91.8% and 83.4%, respectively, which outperforms other fault diagnosis methods.

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Technical language processing for Prognostics and Health Management: applying text similarity and topic modeling to maintenance work orders

Sundaram,

Zeid

2024

J Intell Manuf

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Modern manufacturing paradigms have incorporated Prognostics and Health Management (PHM) to implement data-driven methods for fault detection, failure prediction, and assessment of system health. The maintenance operation has similarly benefitted from these advancements, and predictive maintenance is now being used across the industry. Despite these developments, most of the approaches in maintenance rely on numerical data from sensors and field devices for any sort of analysis. Text data from Maintenance Work Orders (MWOs) contain some of the most crucial information pertaining to the functioning of systems and components, but are still regarded as ‘black holes’, i.e., they store valuable data without being used in decision-making. The analysis of this data can help save time and costs in maintenance. While Natural Language Processing (NLP) methods have been very successful in understanding and examining text data from non-technical sources, progress in the analysis of technical text data has been limited. Non-technical text data are usually structured and consist of standardized vocabularies allowing the use of out-of-the-box language processing methods in their analysis. On the other hand, records from MWOs are often semi-structured or unstructured; and consist of complicated terminologies, technical jargon, and industry-specific abbreviations. Deploying traditional NLP to such data can result in an imprecise and flawed analysis which can be very costly. Owing to these challenges, we propose a Technical Language Processing (TLP) framework for PHM. To illustrate its capabilities, we use text data from MWOs of aircraft to address two scenarios. First, we predict corrective actions for new maintenance problems by comparing them with existing problems using syntactic and semantic textual similarity matching and evaluate the results with cosine similarity scores. In the second scenario, we identify and extract the most dominant topics and salient terms from the data using Latent Dirichlet Allocation (LDA). Using the results, we are able to successfully link maintenance problems to standardized maintenance codes used in the aviation industry.

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Short Text Mining for Fault Diagnosis of Railway System Based on Multi-Granularity Topic Model

Cited by 7 publications

References 3 publications

Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations

Unsupervised Machine Learning for Managing Safety Accidents in Railway Stations

Bi-TLLDA and CSSVM based fault diagnosis of vehicle on-board equipment for high speed railway

Technical language processing for Prognostics and Health Management: applying text similarity and topic modeling to maintenance work orders

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