Predictive Maintenance using Python with Silhouette Analysis

Abstract: Predictive maintenance (PdM) has emerged as a key application of artificial intelligence, machine learning, and data analytics in modern industry. Unlike preventive maintenance, predictive maintenance utilizes historical and real-time sensor data to identify equipment degradation and estimate potential failures before they occur. Python has become one of the most widely adopted programming languages for predictive maintenance due to its extensive ecosystem of scientific and machine learning libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn. Among clustering validation techniques, silhouette analysis plays a significant role in determining the optimal number of clusters and evaluating the quality of clustering results. This review paper presents a comprehensive overview of predictive maintenance using Python with silhouette analysis. It discusses maintenance strategies, machine learning approaches, clustering techniques, silhouette coefficient interpretation, Python-based implementation frameworks, publicly available datasets, and recent research contributions. The paper also highlights advantages, limitations, and future research directions in predictive maintenance systems employing unsupervised learning and silhouette-based cluster validation.

Keywords: Predictive Maintenance, Python, Silhouette Analysis, Machine Learning, K-Means Clustering, Unsupervised Learning, Industrial Analytics, Condition Monitoring.