## Appendix A
In Proceedings of the 2015 IEEE 10 th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Guarda, Portugal, 1-4 September 2015; pp. 359-365. * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach. In Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 1-24 September 2016; pp. 1839-1842. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] [PERSON] Using LSTM networks to predict engine condition on large scale data processing framework. In Proceedings of the 2017 4 th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, Turkey, 8-10 April 2017; pp. 281-285. * [PERSON] (2018) [PERSON] A System Approach to Implementation of Predictive Maintenance with Machine Learning. Massachusetts Institute of Technology. PhD. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2018. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Learning for Improved System Remaining Life Prediction. _Procedia Cirp_**2018**, _72_, 1033-1038. [CrossRef]* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Unsupervised real-time anomaly detection for streaming data. _Neurocomputing_**2017**, _262_, 134-147. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] [PERSON] A Research Study on Unsupervised Machine Learning Algorithms for Fault Detection in Predictive Maintenance. In Proceedings of the 2018 5 th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, 26-28 April 2018; pp. 355-361. * [PERSON] (2015) [PERSON] Machine Learning Approaches for Failure Type Detection and Predictive Maintenance. _Tech. Univ. Darmstadt_**2015**, _19_, 83. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Machine learning in manufacturing: Advantages, challenges, and applications. _Prod. Mamuf. Res._**2016**, \(4\), 23-45. [CrossRef]
* [PERSON] (2016) [PERSON]; [PERSON] Machine Learning Algorithms: A Review. _Int. J. Comput. Sci. Inf. Technol._**2016**, \(7\), 1174-1179. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] A Review on Machine Learning Algorithms, Tasks and Applications Laptop security system View project Handwritten Gurmukhi Character Recognition System. _Int. J. Adv. Res. Comput. Eng. Technol._**2017**, \(6\), 1323-2278. * [PERSON] (2018) [PERSON] Introduction to Machine Learning. _J. Manuf. Syst._**2018**, _101_, 1-6. * [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Adaboost algorithm in the frame of predictive maintenance tasks. In Proceedings of the 2018 23 rd International Scientific-Professional Conference on Information Technology (IT), Zabljak, Montenegro, 19-24 February 2018; pp. 1-4. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning. In Proceedings of the 2017 IEEE International Conference on Circuits and Systems (ICCS), Thiruvananthapuram, India, 20-21 December 2017; pp. 306-311. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line. _Reliab. Eng. Syst. Saf._**2018**, _184_, 202-212. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Fault Class Prediction in Unsupervised Learning using Model-Based Clustering Approach. In Proceedings of the 2018 International Conference on Information and Computer Technologies (ICICT), DeKalb, IL, USA, 23-25 March 2018; pp. 5-12. * [PERSON] (2017) [PERSON] Remaining Useful Life Prediction of Bearings Using Fuzzy Multimodal Extreme Learning Regression. _Int. Conf. Sens. Diagn. Prog. Control_**2017**, \(1\), 499-503. * [PERSON] et al.