## 4 Conclusions and Future Work
The future scope in this study also involves high-speed measurements, detection of other magnetic track components, quantification of rail defects and developing efficient condition monitoring techniques with the aid of artificial intelligence to detect and predict faults from big data. The work in this study was based on using features obtained from the measurement signals as an input to machine learning algorithms. These features are subject to change when the distance between the sensor and the object varies (i.e., lift-off effect). In this application, lift off can occur due to wheel wear. However, this is a slow-occurring process which can be handled by continuous automatic calibration of the system where healthy signatures are used as a reference. This study will be carried out in future work. Conceptualization, P.C., F.T., J.O., H.L. and M.R. ; methodology, P.C., F.T. and M.R. ; software, P.C., F.T. and J.O. ; validation, P.C. and F.T. ; formal analysis, P.C., F.T., J.O., S.M.F. and M.R. ; investigation, P.C., F.T., resources, P.C., H.L., J.O., S.M.F. and M.R. ; data curation, P.C., F.T., J.O. and S.M.F. ; writing--original draft preparation, P.C. ; writing--review and editing, P.C., F.T., J.O., S.M.F. and M.R. ; supervision, M.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. This research was supported and funded by the Lulea Railway Research Centre (JVTC), Vinnova INFRASWEDEN 2030 and Trafikverket (Swedish Transport Administration), through the European Shift2 Rail project IN2 SMART. The authors would also like to thank [PERSON] of ElOptic i Norden AB, [PERSON], [PERSON] and [PERSON] of Bombardier Transportation Sweden and [PERSON] of Consisthentic AB, for their guidance in this study. The authors declare no conflict of interest. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON] Broken railway fastener detection based on AdaBoost algorithm. In Proceedings of the 2010 International Conference on Optoelectronics and Image Processing, Haikou, China, 11-12 November 2010; Volume 1, pp. 313-316. [CrossRef]
* [PERSON] (2011) [PERSON] Automatic detection of objects of interest from rail track images. Master's Thesis, Faculty of Engineering and Physical Science, University of Manchester, Manchester, UK, 2011. Available online: [[http://studentnet.cs.manchester.ac.uk/resources/library/thesis_abstracts/BkgdReportsMSc11/Rubinsztejn-Yohann-bkgd-rept.pdf](http://studentnet.cs.manchester.ac.uk/resources/library/thesis_abstracts/BkgdReportsMSc11/Rubinsztejn-Yohann-bkgd-rept.pdf)]([http://studentnet.cs.manchester.ac.uk/resources/library/thesis_abstracts/BkgdReportsMSc11/Rubinsztejn-Yohann-bkgd-rept.pdf](http://studentnet.cs.manchester.ac.uk/resources/library/thesis_abstracts/BkgdReportsMSc11/Rubinsztejn-Yohann-bkgd-rept.pdf)) (accessed on 19 February 2021). * [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Component-based track inspection using machine-vision technology. In Proceedings of the 1 st ACM International Conference on Multimedia Retrieval, Trento, Italy, 17-20 April 2011; pp. 1-8. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] High-speed railway fastener detection based on a line local binary pattern. _IEEE Signal Process. Lett._**2018**, 25, 788-792. [CrossRef]
* [PERSON] et al. (2003) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Visual recognition of hexagonal headed bolts by comparing ICA to wavelets. In Proceedings of the 2003 IEEE International Symposium on Intelligent Control, Houston, TX, USA, 8 October 2003; pp. 636-641. [CrossRef]
* [PERSON] et al. (2004) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Filter-based feature selection for rail defect detection. _Mach. Vis. Appl._**2004**, 15, 179-185. [CrossRef]
* [PERSON] et al. (2006) [PERSON]; [PERSON]; [PERSON]; [PERSON] Autonomous rail track inspection using vision based system.