Optimized Manufacturing and Construction Site Machines Maintenance Prediction using Machine Learning and Crayfish Algorithm

Efficient maintenance prediction is critical for ensuring the operational continuity and longevity of industrial machinery. This paper presents a comparative analysis of diverse machine-learning algorithms for the task of machine maintenance prediction. Through rigorous experimentation and evaluation, we assess the performance of algorithms including AdaBoost, Random Forest, Gradient Boosting, and Support Vector Machines (SVM). Additionally, to enhance predictive accuracy, we integrate an optimizer algorithm, Cuckoo Search,into our framework. This optimization technique fine-tunes algorithm parameters, further improving accuracy. Our findings offer valuable insights into optimizing machine maintenance prediction, empowering industries with proactive maintenance strategies to mitigate downtime and enhance productivity.