An Ordinal Multi-Dimensional Classification (OMDC) for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances. To deal with this problem, a novel approach, named Ordinal Multi-dimensional Classification (OMDC), is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values. To demonstrate the prediction ability of the proposed approach, eleven different multi-dimensional classification algorithms (traditional Binary Relevance (BR), Classifier Chain (CC), Bayesian Classifier Chain (BCC), Monte Carlo Classifier Chain (MCC), Probabilistic Classifier Chain (PCC), Classifier Dependency Network (CDN), Classifier Trellis (CT), Classifier Dependency Trellis (CDT), Label Powerset (LP), Pruned Sets (PS), and Random k-Labelsets (RAKEL)) were implemented using the Ordinal Class Classifier (OCC) algorithm. Besides, seven different classification algorithms (Multilayer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Decision Tree (C4.5), Bagging, Random Forest (RF), and Adaptive Boosting (AdaBoost)) were chosen as base learners for the OCC algorithm. The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy. Also, it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.     

The probiotic chestnut-based dairy matrix: Influence on the metabolomic formation and the nutritional quality

The aim of this study was to characterise the impact of mixed probiotic strains and fortification with chestnut beverage (CB) on metabolomic formation in a plant-based dairy matrix. Bacterial growth, nutritional composition and metabolomics were investigated during the period of 21-day storage. The amino acid score (AAS), essential amino acid index (EAAI), nutritional index (NI) and biological value (BV) of the samples were determined to characterise their nutritional quality. The fortification with CB increased the nutritional quality of samples due to high EAAI, AAS, NI and BV. Using HPLC-DAD, GC–MS and HS-GC/MS-based instrumental techniques, 48 metabolomics were identified.