Eddy Current Sensor System for Tilting Independent In-Process Measurement of Magnetic Anisotropy

Modern production equipment is based on the results of quality control as well as process parameters. The magnetic anisotropy of materials is closely connected to internal mechanical stress by the Villari effect, and also to hardening effects due to plastic deformations, and could therefore provide an interesting basis for process control. Nevertheless, the analysis of anisotropic properties is extremely sensitive to sensor and workpiece misalignments, such as tilting. In this work, a novel eddy current sensor system is introduced, performing a non-contact measurement of the magnetic anisotropy of a workpiece and realizing a separation and correction of tilting effects. The measurement principle is demonstrated with the example of two samples with different magnetic anisotropy values induced by cold forming. Both samples are analyzed under different tilt angles between the sensor axis and the surface of the workpiece. In this work, digital signal processing is demonstrated on the acquired raw data in order to differentiate the effects of tilt and of anisotropy, with the use of preliminary results as an example of two prepared samples.

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Application of Eddy Current Sensor System and LDV Device for Ultrasonic Vibrations Measurements

Advances in Intelligent Systems and Computing - Mechatronics - Ideas for Industrial Application ◽

10.1007/978-3-319-10990-9_38 ◽

2015 ◽

pp. 407-415 ◽

Cited By ~ 2

Author(s):

Roman Wdowik ◽

Piotr Nazarko ◽

Janusz Porzycki

Keyword(s):

Eddy Current ◽

Sensor System ◽

Current Sensor ◽

Ultrasonic Vibrations ◽

Eddy Current Sensor

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Train-based differential eddy current sensor system for rail fastener detection

Measurement Science and Technology ◽

10.1088/1361-6501/ab2b24 ◽

2019 ◽

Vol 30 (12) ◽

pp. 125105 ◽

Cited By ~ 1

Author(s):

Praneeth Chandran ◽

Matti Rantatalo ◽

Johan Odelius ◽

Håkan Lind ◽

Stephen M Famurewa

Keyword(s):

Eddy Current ◽

Sensor System ◽

Current Sensor ◽

Eddy Current Sensor

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Supervised Machine Learning Approach for Detecting Missing Clamps in Rail Fastening System from Differential Eddy Current Measurements

Applied Sciences ◽

10.3390/app11094018 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4018

Author(s):

Praneeth Chandran ◽

Florian Thierry ◽

Johan Odelius ◽

Stephen M. Famurewa ◽

Håkan Lind ◽

...

Keyword(s):

Machine Learning ◽

Eddy Current ◽

Learning Algorithm ◽

Field Measurements ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Sensor System ◽

Current Sensor ◽

Field Frequency ◽

Eddy Current Sensor

The rail fastening system forms an integral part of rail tracks, as it maintains the rail in a fixed position, upholding the track stability and track gauge. Hence, it becomes necessary to monitor their conditions periodically to ensure safe and reliable operation of the railway. Inspection is normally carried out manually by trained operators or by employing 2-D visual inspection methods. However, these methods have drawbacks when visibility is minimal and are found to be expensive and time consuming. In the previous study, the authors proposed a train-based differential eddy current sensor system that uses the principle of electromagnetic induction for inspecting the railway fastening system that can overcome the above-mentioned challenges. The sensor system includes two individual differential eddy current sensors with a driving field frequency of 18 kHz and 27 kHz respectively. This study analyses the performance of a machine learning algorithm for detecting and analysing missing clamps within the fastening system, measured using a train-based differential eddy current sensor. The data required for the study was collected from field measurements carried out along a heavy haul railway line in the north of Sweden, using the train-based differential eddy current sensor system. Six classification algorithms are tested in this study and the best performing model achieved a precision and recall of 96.64% and 95.52% respectively. The results from the study shows that the performance of the machine learning algorithms improved when features from both the driving channels were used simultaneously to represent the fasteners. The best performing algorithm also maintained a good balance between the precision and recall scores during the test stage.

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