A Predictive Maintenance System Based on Vibration Analysis for Rotating Machinery Using Wireless Sensor Network (WSN)

Boughardini, Imane El; Mechkouri, Meriem Hayani; Reklaoui, Kamal

doi:10.1007/978-3-031-35251-5_9

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Further methods for predictive maintenance are much more hardware driven, as they base their analysis predominantly on the acquisition of some parameters related to the machine's hardware, analyzing the cumulative damage and degradation of the components. These methods include vibration analysis systems [29][30][31], infrared thermography systems [32], oil analysis systems [33], ultrasonic monitoring systems [34], and prognostics and health management (PHM) systems [35][36][37], which integrate data analysis with physics-based models to predict equipment health and estimate the remaining useful life. These methods detect machine deterioration when it is already occurring, albeit at an early stage.…”

Section: Predictive Maintenance Approachesmentioning

confidence: 99%

Predictive Maintenance with Linguistic Text Mining

Postiglione,

Monteleone

2024

Mathematics

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The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

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