A 3×3 wear debris sensor array for real time lubricant oil conditioning monitoring using synchronized sampling. (15th January 2017)

• Record Type: Journal Article
• Authors: Zhu, Xiaoliang; Du, Li; Zhe, Jiang
• Is Part Of: Mechanical systems and signal processing. Volume 83(2017)
• Page Start: 296
• Page End: 304
• Publication Date: 2017-01-15
• Subjects: Oil condition monitoring -- Wear debris -- Inductive sensor -- Crosstalk -- Synchronized sampling -- Time division multiplexing; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.06.014
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: A high throughput wear debris sensor consisting of 3×3 sensing channels is presented for real time online lubricant oil conditioning monitoring. Time division multiplexing was applied to the sensing channels for measuring responses of multiple channels using one set of measurement electronics. Crosstalk among the 3×3 sensing channels was eliminated by diodes that are connected in series with each channel. Parallel L-C-R resonance was also applied to each sensing coil to increase the sensitivity. Furthermore, a unique synchronized sampling method was used to reduce the date size 50 times. Finally, we demonstrated that the sensor is capable of real time detection of wear debris as small as 50 µm in SAE 0W-5 at a flow rate of 460 ml/min; the measured debris concentration is in good agreement with the estimated actual concentration. The design can be extended to a N×N sensor array for an extremely high throughput without sacrificing the sensitivity, and can potentially be used for real time wear debris monitoring for health condition of rotating or reciprocating machineries. Highlights: The wear debris sensor array enables a high throughput (i.e., 4.1 L/min). Time division multiplexing combined with series diodes removes the crosstalk effect. Synchronized sampling significantly reduces the data size and data processing time.