ArticleMaterials (Basel, Switzerland)2023
Assessment of Condition Diagnosis System for Axles with Ferrous Particle Sensor.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Numerical Analysis for Appropriate Positioning of Ferrous Wear Debris Sensors with Permanent Magnet in Gearbox Systems.Sensors (Basel, Switzerland) · 2024Article
- Numerical Approach and Verification Method for Improving the Sensitivity of Ferrous Particle Sensors with a Permanent Magnet.Sensors (Basel, Switzerland) · 2023Article
- Varnish Formation and Removal in Lubrication Systems: A Review.Materials (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a condition diagnosis system based on a ferrous particle sensor to estimate the durability of axles in construction equipment. Axles are mechanical devices that play the role of the differential gear in construction equipment that move with wheels and require high reliability. In the durability testing of new axles, failure identification and real-time diagnosis are required. One of the typical failure modes of an axle is increased ferrous-wear particles due to metal-to-metal contact. Therefore, a condition diagnostic program based on the ferrous particle sensor is developed and applied in the bench tests of axles. This program provides information on the amount of wear with respect to ferrous particles using a simple diagnostic algorithm. Additionally, it allows separation and storage of measured data that exceed the reference values; the system provides warnings using color, sound, and pop-up windows to facilitate diagnosis. In the two tests, the first case detected a failure, but in the other case, the sensor did not detect it even though a failure occurred. From the results of bench tests, it is confirmed that the sensor location is a critical factor. Therefore, a multi-physics-based analysis method is suggested for positioning the ferrous particle sensor.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.