Intelligent Data Analysis Using Machine Learning for Predictive Maintenance in Big Data Environment

Predictive Maintenance (PdM) in industrial systems is crucial for minimizing downtime and reducing operational costs. Additionally, the model’s computational complexity could hinder its scalability in large, real-time data environments, limiting its applicability in some industrial settings. This study aims to enhance PdM through the integration of the Stochastic Fractal Search algorithm with Enriched Support Vector Regression (SFS-ESVR) to improve model performance in predicting machine failures in a big data environment. The dataset includes real-time industrial sensor data, including equipment performance metrics, failure records, and environmental factors. Data preprocessing involves cleaning the raw sensor data by removing outliers and filling missing values. [Formula: see text]-score normalization is employed to standardize the data, ensuring consistency and improving the performance of machine learning simulations in PdM tasks. Linear Discriminant Analysis (LDA) is used for feature extraction, reducing dimensionality and enhancing the model’s ability to differentiate between normal and failure events. SFS optimization algorithm is used to optimize the hyperparameters of the ESVR model, improving its performance in predicting machine failures. The SFS-ESVR model was implemented in Python. Evaluation metrics include accuracy ([Formula: see text]%), precision ([Formula: see text]%), recall ([Formula: see text]%), and [Formula: see text] score ([Formula: see text]%), demonstrating the model’s effectiveness in predicting failures and optimizing maintenance schedules for industrial systems. A comparative analysis is performed with other existing algorithms, and the outcome demonstrates the efficacy of the proposed model for PdM in a big data environment.