A novel hybrid predictive maintenance model based on clustering, smote and multi-layer perceptron neural network optimised with grey wolf algorithm

Abstract

Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset by first, extracting and selecting the most significant statistical time-domain features. The selected features were then grouped into distinct clusters allowing for reduced computational complexity through a comparative study of four different and frequently used categories of unsupervised clustering algorithms in fault classification. The Synthetic Minority Over Sampling Technique (SMOTE) was then employed to balance the classes of the training samples from the various clusters which then served as inputs for training the supervised GWO-MLP. To validate the proposed hybrid technique (CLUST-SMOTE-GWO-MLP), it was compared with its distinct modifications (variants). The superiority of CLUST-SMOTE-GWO-MLP is demonstrated by outperforming all the distinct modifications in terms of test accuracy and seven other statistical performance evaluation metrics (error rate, sensitivity, specificity, precision, F score, Mathews Correlation Coefficient and geometric mean). The overall analysis indicates that the proposed CLUST-SMOTE-GWO-MLP is efficient and can be used to classify multiclass and imbalanced fault conditions. Article Highlights The issue of multiclass and imbalanced class outputs is addressed for improving predictive maintenance. A multiclass fault classifier based on clustering and optimised multi-layer perceptron with grey wolf is proposed. The robustness and feasibility of the proposed technique is validated on a complex hydraulic system dataset.

Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model

“…Artificial neural network is a technical copy of biological neural network in a specific simplified sense. The corresponding learning algorithm simulates certain intelligent activities of the human brain and is technically copied to solve practical problems [ 15 , 16 ]. Artificial neural network is composed of many basic neurons in processing equipment.…”

Predictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification

“…Hence, different statistical time-domain features were extracted based on the variance, median, mean, standard deviation, kurtosis, skewness, and position of maximum values from various time intervals partitions. Details of the time-domain feature extraction can be found in the prior works of Buabeng et al [45,46]. e feature extraction drastically reduced the dimension of the hydraulic dataset to 1806 features, thus to some extent reducing the computational cost.…”

“…e BPFRS algorithm as proposed by Salido and Murakami [74] uses the concept of β-precision aggregation as a generalisation to Zarinko's Variable Precision Rough Set (VPRS) [76] for addressing uncertainty in huge datasets. In its implementation, the β-precision quasi-T-norm and β-precision quasi-T-conorm are utilised in defining the β-precision versions of fuzzy lower and upper approximations of a fuzzy set X in U expressed as (44) and (45), respectively:…”

“…is manuscript is an extension of prior studies by Buabeng et al [45] and a preprint available at Research Square, Buabeng et al [46] which can currently be accessed via https://www.researchsquare.com/article/rs-600110/v1.pdf? c�1631887023000.…”

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

“…It discovers hidden patterns within complex and multivariate data where the traditional methods (e.g., statistical inference methods) remain inadequate. In most of the ML-based PdM approaches, Multilayer Perceptron (MLP) [3,4], Convolutional Neural Network (CNN) [5][6][7], Long Short-term Memory Network (LSTM) [8][9][10], Support Vector Machine (SVM) [11,12], k-Nearest Neighbour (kNN) [13], Random Forest (RF) [14][15][16][17], and Decision Tree [18] were used. For example, Kiangala et al [5] proposed a novel PdM framework that combines time-series imaging and CNN models to detect a conveyor system's impairments and reduce the risk of incorrect faults.…”