Automatic calculation of thresholds for load dependent condition indicators by modelling of probability distribution functions – maintenance of gearboxes used in mining conveying system

Wodecki, Jacek; Stefaniak, Paweł; Michalak, Anna; Wyłomańska, Agnieszka

doi:10.21595/vp.2017.19036

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…In this topic, the analyses of gearboxes are closely linked to those of conveyor belts. Clustering is here applied considering not only the most common vibration analyses [64], but also some temperature analyses [65]. Taking up one of the terms highlighted as representative of this topic, namely the concept of decision making, this is explicitly stressed in the context of conveyor belts [64,65].…”

Section: Topic 4-intelligent Diagnosismentioning

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 4-intelligent Diagnosismentioning

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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“…Another interesting evidence is that in this topic the analyses of gearboxes are closely linked to those of conveyor belts. Clustering is here applied starting not only from the most common vibration analyses [64], but also from temperature analyses [65]. Taking up one of the terms highlighted as representative of this topic, namely the concept of decision making, this is explicitly stressed in the context of conveyor belts [64,65].…”

Section: Topicmentioning

confidence: 99%

“…Clustering is here applied starting not only from the most common vibration analyses [64], but also from temperature analyses [65]. Taking up one of the terms highlighted as representative of this topic, namely the concept of decision making, this is explicitly stressed in the context of conveyor belts [64,65]. Another recurring theme in topic 4 is bearings.…”

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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“…Model-based diagnostics (MBD) is a widely used approach in the field of maintenance management of industrial machinery, where it is important to distinguish dynamics-related models [ 3 , 12 , 13 ] as well as signal models [ 14 , 15 , 16 , 17 , 18 , 19 ]. Krot et al described diagnostics of springs from the point of view of a dynamic model [ 3 ], and in this paper authors attempt an alternative approach using data-driven signal model.…”

Section: Introductionmentioning

confidence: 99%

Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes

Michalak

Wodecki

Drozda

et al. 2020

Sensors

Self Cite

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Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for its diagnostics. In this paper, we present a model of the signal, which describes vibrations of the sieving screen. This particular type is used in the mining industry for the classification of ore pieces in the material stream by size. The model describes the real vibration signal measured on the spring set being the suspension of this machine. This way, it is expected to help in better understanding how the overall motion of the machine can impact the efforts of diagnostics. The analysis of real vibration signals measured on the screen allowed to identify and parameterize the key signal components, which carry valuable information for the following stages of diagnostic process of that machine. In the proposed model we take into consideration deterministic components related to shaft rotation, stochastic Gaussian component related to external noise, stochastic α-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations.

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Automatic calculation of thresholds for load dependent condition indicators by modelling of probability distribution functions – maintenance of gearboxes used in mining conveying system

Cited by 4 publications

References 12 publications

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

Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes

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