About this Research Topic
The rapid growth of industrial systems and the increasing complexity of their operations have created a pressing need for effective anomaly detection techniques. Anomalies in industrial systems can result in significant financial losses, safety hazards, and production inefficiencies. Machine learning-based approaches have shown great potential in addressing this challenge by providing automated, scalable, and accurate anomaly detection solutions.
The problem addressed in this Research Topic is the efficient and accurate detection of anomalies in complex industrial systems. Industrial processes generate vast amounts of data from sensors, machinery, and processes, making it challenging to identify abnormal patterns that could lead to disruptions, failures, or safety risks. Recent advances in machine learning, especially deep learning and ensemble techniques, offer promising solutions to this problem. By leveraging these approaches, researchers can develop models that can automatically learn the normal behavior of industrial systems and identify deviations from it. Additionally, transfer-learning techniques enable the adaptation of anomaly detection models across different industrial domains, reducing the need for extensive domain-specific labeled data. This research topic seeks to explore the application of these recent advances to enhance anomaly detection accuracy, real-time monitoring, and operational efficiency in various industrial sectors.
This Research Topic aims to showcase the latest advancements, methodologies, and applications in anomaly detection, with a specific focus on industrial systems. Topics of interest include, but are not limited to:
• Machine learning and statistical approaches for sensor anomaly detection in industrial systems
• Fault detection and diagnosis of industrial sensors and measurement devices
• Process anomaly detection using sensor data fusion techniques
• Unsupervised and semi-supervised learning methods for process anomaly detection
• Real-time and online anomaly detection algorithms for sensor and process data
• Feature engineering and selection techniques for improving anomaly detection accuracy
• Data-driven anomaly detection models for sensor and process monitoring
• Case studies and practical applications of sensor and process anomaly detection in industrial domains (manufacturing, energy, transportation, etc.)
• Sensor failure prediction and proactive maintenance strategies
• Calibration drift detection and compensation in industrial sensors
• Novel sensor technologies and architectures for enhanced anomaly detection capabilities
• Integration of sensor and process anomaly detection with control systems
• Privacy-preserving techniques for anomaly detection in sensitive industrial data
The problem addressed in this Research Topic is the efficient and accurate detection of anomalies in complex industrial systems. Industrial processes generate vast amounts of data from sensors, machinery, and processes, making it challenging to identify abnormal patterns that could lead to disruptions, failures, or safety risks. Recent advances in machine learning, especially deep learning and ensemble techniques, offer promising solutions to this problem. By leveraging these approaches, researchers can develop models that can automatically learn the normal behavior of industrial systems and identify deviations from it. Additionally, transfer-learning techniques enable the adaptation of anomaly detection models across different industrial domains, reducing the need for extensive domain-specific labeled data. This research topic seeks to explore the application of these recent advances to enhance anomaly detection accuracy, real-time monitoring, and operational efficiency in various industrial sectors.
This Research Topic aims to showcase the latest advancements, methodologies, and applications in anomaly detection, with a specific focus on industrial systems. Topics of interest include, but are not limited to:
• Machine learning and statistical approaches for sensor anomaly detection in industrial systems
• Fault detection and diagnosis of industrial sensors and measurement devices
• Process anomaly detection using sensor data fusion techniques
• Unsupervised and semi-supervised learning methods for process anomaly detection
• Real-time and online anomaly detection algorithms for sensor and process data
• Feature engineering and selection techniques for improving anomaly detection accuracy
• Data-driven anomaly detection models for sensor and process monitoring
• Case studies and practical applications of sensor and process anomaly detection in industrial domains (manufacturing, energy, transportation, etc.)
• Sensor failure prediction and proactive maintenance strategies
• Calibration drift detection and compensation in industrial sensors
• Novel sensor technologies and architectures for enhanced anomaly detection capabilities
• Integration of sensor and process anomaly detection with control systems
• Privacy-preserving techniques for anomaly detection in sensitive industrial data
Keywords: Industrial systems, fault detection, process monitoring, data-driven methods, deep learning
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