PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes

Proto, Stefano; Ventura, Francesco; Apiletti, Daniele; Cerquitelli, Tania; Baralis, Elena; Macii, Enrico; Macii, Alberto

doi:10.1109/bigdatacongress.2019.00032

Cited by 17 publications

(22 citation statements)

References 14 publications

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“…Our results suggest that RW is of importance to performance, but its impact is more nuanced than that of PW. Therefore, in developing prediction models, a much broader range of reading windows should be tested compared to designs proposed in previous research (Proto et al. , 2019; Leahy et al.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

Leukel

González²,

Riekert³

2022

IJQRM

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PurposeMachine learning (ML) models are increasingly being used in industrial maintenance to predict system failures. However, less is known about how the time windows for reading data and making predictions affect performance. Therefore, the purpose of this research is to assess the impact of different sliding windows on prediction performance.Design/methodology/approachThe authors conducted a factorial experiment using high dimensional machine data covering two years of operation, taken from a real industrial case for the production of high-precision milled and turned parts. The impacts of different reading and prediction windows were tested for three ML algorithms (random forest, support vector machines and logistic regression) and four metrics (accuracy, precision, recall and F-score).FindingsThe results reveal (1) the critical role of the prediction window contingent upon the application domain, (2) a non-monotonic relationship between the reading window and performance, and (3) how sliding window selection can systematically be used to improve different facets of performance.Originality/valueThe study's findings advance the knowledge of ML-based failure prediction, by highlighting how systematic variation of two important but yet understudied factors contributes to the development of more useful prediction models.

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Section: Discussionmentioning

confidence: 99%

“…First, only a handful of studies tested different lengths of the reading window, thus considered this parameter in their model development (Kaparthi and Bumblauskas, 2020; Leahy et al. , 2018; Proto et al. , 2019; Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

Leukel

González²,

Riekert³

2022

IJQRM

View full text Add to dashboard Cite

show abstract

“…A general-purpose service suited to fulfill the condition monitoring and maintenance needs of modern companies in the context of industry 4.0 is proposed. The architecture of the analytical service proposed in this research, reported in Figure 2, is flexible and it can be customized for any other use cases of interest (e.g., [22]). The service is based on four main steps: feature engineering, predictive analytics, model validation, self-assessment.…”

Section: The Predictive Analytic Servicementioning

confidence: 99%

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Panicucci¹,

Nikolakis

Cerquitelli

et al. 2020

Electronics

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Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0.

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“…The dataset has been pre-processed according to the following data cleaning procedures, similarly to popular approaches [3,47,48]: (i) a domain-driven threshold-based filter has been applied, and (ii) a data-driven additional filter has been used.…”

Section: Data Cleaningmentioning

confidence: 99%

Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Apiletti¹,

Pastor²

2020

Electronics

Self Cite

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Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

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PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes

Cited by 17 publications

References 14 publications

Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

Machine learning-based failure prediction in industrial maintenance: improving performance by sliding window selection

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

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