Survey paper on Anomaly Detection in Industrial Machine using Machine Learning Technique

Abstract

 The rapid advancement of Industry 4.0 and Industrial Internet of Things (IIoT) technologies has led to the widespread deployment of sensors and smart monitoring systems in industrial environments. These systems continuously generate large volumes of operational data, creating opportunities for intelligent condition monitoring and predictive maintenance. Anomaly detection plays a crucial role in identifying abnormal machine behavior, equipment faults, and potential failures before they result in costly downtime or safety hazards. Traditional rule-based and statistical approaches often struggle to handle complex and high-dimensional industrial data, leading to the adoption of Machine Learning (ML) techniques for more accurate and adaptive anomaly detection.

This survey paper presents a comprehensive review of anomaly detection methods in industrial machines using machine learning techniques. Various supervised, unsupervised, and semi-supervised learning approaches are analyzed, including One-Class Support Vector Machine (OC-SVM), Isolation Forest, Local Outlier Factor (LOF), K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Neural Networks, and Deep Learning-based methods such as Autoencoders and Long Short-Term Memory (LSTM) networks. The study examines their working principles, advantages, limitations, and applicability to industrial fault diagnosis and predictive maintenance. Furthermore, the survey discusses commonly used industrial datasets, performance evaluation metrics, and recent research trends in anomaly detection.

The findings indicate that machine learning-based anomaly detection significantly improves fault identification accuracy, reduces maintenance costs, and enhances operational reliability. Among the reviewed techniques, unsupervised methods such as Isolation Forest and LOF demonstrate effectiveness in scenarios with limited labeled data, while deep learning models provide superior performance for complex and high-dimensional sensor datasets. The paper concludes by highlighting current challenges and future research directions, including explainable artificial intelligence, real-time edge deployment, federated learning, and hybrid anomaly detection frameworks for smart industrial systems.