An In-Line, High-Flowrate, and Maintenance Free Ultrasonic Sensor With a High Dynamic Range for Particle Monitoring in Fluids

Johansen, Rasmus R.; Dupont, Sune; Christiansen, Uffe Noe; Horning, Jonas; Catalano, Jacopo; Sorensen, Joan S; Bentien, Anders

doi:10.1109/jsen.2018.2798708

Cited by 6 publications

(2 citation statements)

References 10 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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Section: Predictive Maintenance Approachesmentioning

confidence: 99%

Predictive Maintenance with Linguistic Text Mining

Postiglione,

Monteleone

2024

Mathematics

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“…The technology of oil monitoring sensor has been developed so far, mainly including optical [5,6], acoustic [7], capacitive [8] and inductive [9] sensors. Optical sensors can comprehensively obtain wear debris morphology, size, color and other characteristics, but the image quality is affected by the transparency of the oil.…”

Section: Introductionmentioning

confidence: 99%

Online Detection System of Metal Wear Debris Based on Symmetrical Magnetic Excitation Balance Magnetic Field Model

Bai

et al. 2022

Preprint

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The metal wear debris generated by wear during the operation of the mechanical equipment can reflect the fault status of the equipment, and the online monitoring of the metal wear debris in the oil can timely understand the operation information of the equipment. Aiming at the effective response of metal wear debris under symmetrical magnetic excitation, an online monitoring system for metal wear debris with dual-coil balanced magnetic excitation was developed. Based on the spatial magnetic field distribution of the current-carrying element, an analytical model of the magnetic field intensity distribution of a single-turn current-carrying ring is designed, and it is optimized as a single set of coils made of a current-carrying ring, and the coupling of the magnetic excitation of the symmetrical double coil is deduced. The magnetic field distribution model is simulated and analyzed by numerical method. The flow channel of the system is 8 mm. After the semi-physical simulation test with the wear debris speed of 3.03-5.25 m/s, the sensor can realize the detection of 100 μm ferromagnetic metal and 1000 μm non-ferromagnetic metal. The experiment verifies the correctness of the simulation model of the coupled magnetic field distribution. This research provides technical support for the detection of hydraulic oil contaminants, which is of great significance for preventing hydraulic system failures and extending the working life of hydraulic equipment.

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