Evaluation and Comparison of the Principal Component Analysis (PCA) and Isometric Feature Mapping (Isomap) Techniques on Gas Turbine Engine Data

This paper performs a comparative analysis of the results of PCA and ISOMAP for the purpose of reducing or eliminating erratic failure of the Gas Turbine Engine (GTE) system. We employ Nearest-neighbour classification for GTE fault diagnosis and M-fold cross validation to test the performance of our models. Comparison evaluation of performance indicates that, with PCA, 80% of good GTE is classified as good GTE, 77% of the average GTE is classified as average GTE and 67.6% of bad GTE is classified as bad GTE. With ISOMAP, 67% of good GTE is classified as good GTE, 70.8% of the average GTE is classified as average GTE and 81% of bad GTE is classified as bad GTE. PCA produces 26% error rate with nearest neighbour classification and 17% error rate with M-fold cross validation. While ISOMAP produces 35% error rate with nearest neighbour classification, and 26.5% error rate with M-fold cross validation. Results indicate that PCA is more effective in analyzing the GTE data set, giving the best classification for fault diagnosis. This enhances the reliability of the turbine engine during wear out phase, through predictive maintenance strategies

Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries