Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool

Uhlmann, Eckart; Pontes, Rodrigo Pastl; Geisert, Claudio; Hohwieler, Eckhard

doi:10.1016/j.promfg.2018.06.009

Cited by 49 publications

(25 citation statements)

References 4 publications

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“…Their study involved the comparison of SVM and k-NN algorithms and it was concluded that the SVMs outperformed the k-NN algorithm and that ML-based PdM consistently outperformed preventive maintenance approaches. By exploring patterns in the process data sourced from historical database, Uhlmann et al [46] devised an unsupervised k-Means clustering approach to subgroup the health condition of a machine tool into four categories: normal operations, faulty conditions due to pressure systems, faulty conditions due to protection gas, and faulty conditions keeping the machine in a standby mode. In a related study, Amruthnath and Gupta [61] collected the vibrational monitoring data of exhaust fans over a 4 h interval for 12 days and were able to group the exhaust fan as healthy, warning, and faulty state, using three clustering algorithms including k-Means, C-Means, and hierarchical clustering with identical clustering performances.…”

Section: For Pdmmentioning

confidence: 99%

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Suvarna

Büth

Hejny

et al. 2020

Advanced Intelligent Systems

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According to statistics provided by the UN in 2018, the global urban population rose from 0.75 billion in 1950 to 4.2 billion by 2018, a 5.5-fold increase in nearly seven decades. Currently, this 4.2 billion urban population accounts for 55% of the current global population, and given the trend, the global urban population is expected to touch 68% of total population by 2050. [1,2] To keep abreast with this growing shift toward urbanization, the last decade witnessed a multitude of global projects across the USA, EU, Asia Pacific, and Middle East regions to cater to the needs of a futuristic urban population through the concept of "smart cities." [3,4] Some of the popular programs include Smart Cities Initiatives by the USA, [5] EU H2020 Smart Cities and Communities project, [6] Smart Nation Program by Singapore, [7] Global Initiative for Resource Efficient Cities (GI-REC) by UN, [8] etc., to mention a few. Existing literature shows that there is no single or standardized definition to the concept of a smart city. [9] However, the central theme usually revolves around the application of information and digital technologies to provide intelligent and innovative solutions to the demanding requirements of an urban ecosystem including infrastructure, transport, healthcare, governance, and security. [2,10] The overall essence and motivation of a smart city is to facilitate the highest quality of life to its residents, while simultaneously optimizing the resources and energy efficiency, aiding the overall sustainability, social, and economic development of the city. Given the fact that the concept of smart cities is constantly evolving, devising a single template to describe the various characteristic features of a smart city can be a challenging task. [9] However, notable efforts have been made to determine and define a few salient features or indices of a smart city, published by research organizations as well as by consulting firms. [11] Both types of reports present commonalities in terms of indices, which are integral to smart cities and hence enable their acceptance with a degree of confidence. Some of the most notable and cited reports include Cities in Motion Index by IESE, [12] Smart Cities Ranking by Juniper Research, [13] Top 50 Smart City

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Section: For Pdmmentioning

confidence: 99%

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Suvarna

Büth

Hejny

et al. 2020

Advanced Intelligent Systems

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“…In a PdM setting, the main goal of clustering becomes to detect failure and anomalies within data. In a paper by Uhlmann et al [68], k-means is deployed in a supervised setting to see the performance of detecting faults on a Selective Laser Melting machine tool.…”

Section: Clusteringmentioning

confidence: 99%

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Olesen

Shaker

2020

Sensors

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Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.

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“… Knowledge-based models: This category is based on experiences that can be represented by rules, facts or cases collected during years of operation and maintenance of the industrial system. There are three types of models, those based on rules [2] or cases [3], or fuzzy models based on knowledge [4];  Data-driven models: The data collected using the sensors is used to study component degradation, the health of the system in real time or its remaining useful life. These models are divided into three types:…”

Section: A Single-model Approachmentioning

confidence: 99%

“…Three main stochastic processes used, namely the Gaussian processes [7], those of Markov chains [4], and Levy model [8]; -Machine Learning Models (ML): ML is an offshoot of artificial intelligence (AI), which relies on learning algorithms to create models using data. The analysis of the literature carried out by Thyago P & Co. [9], dealing with the themes of predictive maintenance, reveals a preference for certain machine learning methods: Random Forest RF [10], Networks of Artificial Neurons (ANN) [11,12], Support Vector Machines (SVM) [13,14], and Kmeans [2,10].…”

Section: A Single-model Approachmentioning

confidence: 99%

Diagnostic and Prognostic Models for Predictive Maintenance: Multi-Criteria Comparative Analysis

Bouaicha¹,

Adraoui²,

Machkour³

et al. 2021

IJETAE

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Predictive maintenance has evolved considerably over the past two decades making this strategy an effective way to monitor the operation of industrial systems, thereby predicting its future states and remaining lifespan. It is therefore developed through a process that begins with the collection of information from the industrial system, the objective of which is its diagnosis or / and its prognosis. This article presents an analysis of single-model and multi-model approaches to the effect of diagnostic and prognostic tasks. This analysis is based on a multi-criteria comparison of the different models in order to provide a clear vision to choose the appropriate approach for predictive maintenance. The relevance of the comparative study is argued by the development of criteria directly impacting performance, reliability, efficiency and mutual cooperation between models. Conclusions are then drawn, in order to identify the appropriate diagnostic and prognostic approach for predictive maintenance.

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Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool

Cited by 49 publications

References 4 publications

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges

Diagnostic and Prognostic Models for Predictive Maintenance: Multi-Criteria Comparative Analysis

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