A method of fault detection on diesel engine based on EMD-fractal dimension and fuzzy C-mean clustering algorithm

Zeng, Ruili; Zhang, Shuai; Zeng, Rong; Shen, Hong; Zhang, Lingling

doi:10.1109/ccdc.2017.7978582

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…In this topic, compared to, e.g., topic 1, it is addressed without focusing on impulsive signals that can be extracted from vibration measurements. In this sub-area, while still using vibration analysis, clustering takes on a more diagnostic role, used as a tool to identify the nature of the fault [54]. Similar analyses, using the same algorithm but with different parameters, specifically the energy ratios of the intrinsic mode functions of the bearings, have also been carried out in other research projects [55].…”

Section: Topic 3-general Power Generation and Transmissionmentioning

confidence: 99%

Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation

et al. 2022

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In the field of industrial process monitoring, scholars and practitioners are increasing interest in time-varying processes , where different phases are implemented within an unknown time frame. The measurement of process parameters could inform about the health state of the production assets, or products, but only if the measured parameters are coupled with the specific phase identification. A combination of values could be common for one phase and uncommon for another phase; thus, the same combination of values shows a high or low probability depending on the specific phase. The automatic identification of the production phase usually relies on clustering techniques. This is largely due to the difficulty of finding training fault data for supervised models. With these two considerations in mind, this contribution proposes the Latent Dirichlet Allocation as a natural language-processing technique for reviewing the topic of clustering applied in time-varying contexts, in the maintenance field. Thus, the paper presents this innovative methodology to analyze this specific research fields, presenting the step-by-step application and its results, with an overview of the theme.

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Section: Topic 3-general Power Generation and Transmissionmentioning

confidence: 99%

Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation

et al. 2022

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“…This gives insight into how this algorithm is currently performing [57]. Analyzing the contributions from the point of view of the techniques, the authors notice in the keywords that the technique of fuzzy c-means is highlighted, mainly applied for the bearings monitoring [54,55], but even in different contexts [58]. The same contributions, and some more [56,59] show a fruitful application of the decomposition of monitored signals into intrinsic mode functions, in order to use some extracted information as a discriminative feature within the analysis.…”

Section: Topicmentioning

confidence: 99%

“…The same contributions, and some more [56,59] show a fruitful application of the decomposition of monitored signals into intrinsic mode functions, in order to use some extracted information as a discriminative feature within the analysis. Another important technique in this topic is the empirical mode decomposition, for the decomposition of monitored signals [54][55][56]59,60]. The temperature theme, on the other hand, highlighted among the keywords, relates to the topic of temperature controller monitoring [51,57].…”

Section: Topicmentioning

confidence: 99%

Clustering Application for Condition-based Maintenance in Time-varying Processes: A Review

Quatrini¹,

Colabianchi²,

Costantino³

et al. 2021

Preprint

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In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

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“…Bai et al [14] proposed a fault diagnosis method based on empirical wavelet transform and FCM clustering algorithm. In [15], [16], the empirical mode decomposition and FCM clustering algorithm are combined and applied to fault diagnosis. Ramos et al [17] designed a fault diagnosis system of steam generator using FCM clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

Wan

Zhang

et al. 2021

IEEE Access

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K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing. Therefore, a novel fault diagnosis method of rolling bearing using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based three-layer wavelet packet decomposition approach is developed to efficiently preprocess the running-state monitoring data to obtain eigenvectors, which are stored in Hadoop Distributed File System (HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis is proposed to improve the diagnosis accuracy. ACO algorithm is adopted to obtain the global optimal initial clustering centers of K-Means from all eigenvectors, and the K-Means clustering algorithm based on weighted Euclidean distance is used to perform clustering analysis on all eigenvectors to obtain a rolling bearing fault diagnosis model. Thirdly, the efficient parallelization of ACO-K-Means clustering algorithm is implemented on a Spark platform, which can make full use of the computing resources of a cluster to efficiently process large-scale rolling bearing datasets in parallel. Extensive experiments are conducted to verify the effectiveness of the proposed fault diagnosis method. Experimental results show that the proposed method can not only achieve good fault diagnosis accuracy but also provide high model training efficiency and fault diagnosis efficiency in a big data environment.

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A method of fault detection on diesel engine based on EMD-fractal dimension and fuzzy C-mean clustering algorithm

Cited by 6 publications

References 1 publication

Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation

Clustering Application for Condition-Based Maintenance in Time-Varying Processes: A Review Using Latent Dirichlet Allocation

Clustering Application for Condition-based Maintenance in Time-varying Processes: A Review

A Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm

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