Multidimensional Sensing and Big Data Aided Intelligent Maintenance (MS-BDAIM) is a research field focusing on the theory and applications of multi-sensing, signal processing, and data mining in industrial scenarios. It aims to improve the efficiency and reliability of various industrial products and equipment.

The core of MS-BDAIM is to access hidden condition and quality clues using state-of-the-art sensing and big data techniques. Theories and applications for multidimensional sensing and internet of things (IoT) are encompassed. Sensing techniques include but are not limited to vibration, images, videos, electrical parameters, and operating condition configurations. Feature extraction, feature selection and feature fusion for decision making in industrial scenarios are within the scope of this Issue. Approaches that aim to perform denoising, preprocessing, sensitive feature identification, and operating condition isolation in industrial scenarios are also welcome. Intelligent maintenance approaches denote using intelligent learning frameworks and algorithms (such as machine learning, deep learning, transfer learning, reinforce learning, etc.) to benefit industrial applications (fault diagnosis, remaining life prediction, quality evaluation, operation parameter optimization, etc.).

The focus of the Special Issue will be on a broad range of multidimensional sensing, IoT, feature extraction, data mining in industrial scenarios, prognostic and health management (PHM), and intelligent maintenance involving novel theories, algorithms, and applications. Original research and review articles on these topics are welcome.

Potential topics include but are not limited to the following:

• Multidimensional sensing
• IoT
• Data mining in industrial scenarios
• Prognostic and health management (PHM)
• Intelligent maintenance
• Machine learning: theory, algorithms and applications
• Life prediction and reliability assessment
• Vibration and noise control
• Feature extraction, feature selection and feature fusion